From d395ed2b99597b78e93de62fccfba0be1bc01767 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juli=C3=A1n=20D=2E=20Ot=C3=A1lvaro?= Date: Tue, 21 Jul 2026 21:30:12 +0100 Subject: [PATCH 1/5] SAEM v1.0 --- .gitignore | 102 +- CHANGELOG.md | 134 +- Cargo.toml | 67 +- README.md | 82 +- benches/saem.rs | 95 + docs/nonmem-comparison.md | 227 + docs/saem-convergence.md | 179 + docs/saem-support.md | 144 + examples/bimodal_ke/prior.csv | 48 - examples/bimodal_ke_backend_compare.rs | 15 +- examples/bimodal_ke_saem.rs | 58 + examples/drusano/main-old.rs | 477 - examples/meta/main.rs | 4 +- examples/meta_saem/main.rs | 168 + examples/new_iov/subjects.csv | 101 - examples/vanco.rs | 4 +- examples/vanco_sde/data.csv | 2726 ---- iiv.md | 195 + plans/saem-validation-roadmap.md | 84 + src/algorithms/mod.rs | 36 +- src/algorithms/nonparametric/controller.rs | 4 +- src/algorithms/nonparametric/error_optim.rs | 6 +- src/algorithms/nonparametric/ncnpag.rs | 6 +- src/algorithms/nonparametric/npag.rs | 7 +- src/algorithms/nonparametric/npmap.rs | 6 +- src/algorithms/nonparametric/npod.rs | 19 +- src/algorithms/parametric/controller.rs | 562 + src/algorithms/parametric/mod.rs | 148 +- src/algorithms/parametric/saem.rs | 10584 ++++++++++++++++ src/algorithms/parametric/saem_config.rs | 996 +- src/bestdose/cost.rs | 13 +- src/bestdose/mod.rs | 2 +- src/estimation/assay_error.rs | 2044 +++ src/estimation/error_models.rs | 214 +- src/estimation/likelihood/batch.rs | 171 + src/estimation/likelihood/distributions.rs | 140 + src/estimation/likelihood/matrix.rs | 193 + src/estimation/likelihood/mod.rs | 14 + src/estimation/likelihood/objective.rs | 139 + src/estimation/likelihood/observation.rs | 102 + src/estimation/likelihood/particle.rs | 228 + src/estimation/likelihood/residual.rs | 208 + src/estimation/mod.rs | 37 +- src/estimation/nonparametric/cycles.rs | 4 +- src/estimation/nonparametric/mod.rs | 2 + .../nonparametric/parameter_optimizer.rs | 119 + src/estimation/nonparametric/predictions.rs | 3 +- src/estimation/nonparametric/psi.rs | 13 +- src/estimation/nonparametric/result.rs | 7 +- src/estimation/nonparametric/summaries.rs | 27 +- .../parametric/conditional_uncertainty.rs | 956 ++ src/estimation/parametric/covariance.rs | 618 + src/estimation/parametric/covariates.rs | 1063 ++ src/estimation/parametric/individual.rs | 279 + src/estimation/parametric/information.rs | 2267 ++++ .../parametric/marginal_likelihood.rs | 1383 ++ src/estimation/parametric/markov_variance.rs | 338 + src/estimation/parametric/mod.rs | 46 + src/estimation/parametric/posterior.rs | 95 + src/estimation/parametric/posthoc.rs | 158 + src/estimation/parametric/prior.rs | 2736 ++++ src/estimation/parametric/rank_diagnostics.rs | 976 ++ src/estimation/parametric/residual.rs | 1079 ++ src/estimation/parametric/shrinkage.rs | 946 ++ src/estimation/parametric/sufficient.rs | 320 + src/estimation/parametric/transforms.rs | 130 + src/estimation/problem.rs | 699 +- src/estimation/residual_error.rs | 399 + src/estimation/sde_particle.rs | 277 + src/iov/mod.rs | 366 +- src/iov/optimizer.rs | 327 +- src/lib.rs | 100 +- src/model/parameter_space.rs | 56 +- src/results/fit_result.rs | 1618 ++- src/results/information_criteria.rs | 665 + src/results/mod.rs | 33 +- src/results/parametric_output.rs | 7322 +++++++++++ src/results/summary.rs | 25 +- tests/fixtures/combined_residual.csv | 401 + tests/fixtures/conditional_modes.csv | 33 + tests/fixtures/constant_sigma.csv | 337 + tests/fixtures/correlated_iiv.csv | 321 + tests/fixtures/exponential_residual.csv | 401 + tests/fixtures/proportional_residual.csv | 321 + tests/fixtures/sparse_iiv.csv | 9 + tests/fixtures/two_occasion_iov.csv | 385 + tests/iov_diffusion_optimizer.rs | 311 + tests/ode_scoring_parity.rs | 179 + tests/ode_solver_profile.rs | 240 + tests/onecomp.rs | 12 +- tests/particle_filter_scientific.rs | 74 + tests/results_summary_tests.rs | 13 +- tests/saem_correlated_residual.rs | 321 + tests/saem_covariates.rs | 296 + tests/saem_information_criteria.rs | 840 ++ tests/saem_kernel.rs | 204 + tests/saem_lifecycle.rs | 326 + tests/saem_marginal_likelihood.rs | 968 ++ tests/saem_no_iiv.rs | 309 + tests/saem_operational_convergence.rs | 379 + tests/saem_outputs.rs | 1498 +++ tests/saem_prediction_parity.rs | 341 + tests/saem_regressions.rs | 1742 +++ tests/saem_tracing.rs | 97 + tests/saem_uncertainty.rs | 320 + tests/saem_warm_start.rs | 745 ++ tests/sde_particle_filter.rs | 325 + 107 files changed, 53992 insertions(+), 3997 deletions(-) create mode 100644 benches/saem.rs create mode 100644 docs/nonmem-comparison.md create mode 100644 docs/saem-convergence.md create mode 100644 docs/saem-support.md delete mode 100644 examples/bimodal_ke/prior.csv create mode 100644 examples/bimodal_ke_saem.rs delete mode 100644 examples/drusano/main-old.rs create mode 100644 examples/meta_saem/main.rs delete mode 100644 examples/new_iov/subjects.csv delete mode 100644 examples/vanco_sde/data.csv create mode 100644 iiv.md create mode 100644 plans/saem-validation-roadmap.md create mode 100644 src/algorithms/parametric/controller.rs create mode 100644 src/algorithms/parametric/saem.rs create mode 100644 src/estimation/assay_error.rs create mode 100644 src/estimation/likelihood/batch.rs create mode 100644 src/estimation/likelihood/distributions.rs create mode 100644 src/estimation/likelihood/matrix.rs create mode 100644 src/estimation/likelihood/mod.rs create mode 100644 src/estimation/likelihood/objective.rs create mode 100644 src/estimation/likelihood/observation.rs create mode 100644 src/estimation/likelihood/particle.rs create mode 100644 src/estimation/likelihood/residual.rs create mode 100644 src/estimation/nonparametric/parameter_optimizer.rs create mode 100644 src/estimation/parametric/conditional_uncertainty.rs create mode 100644 src/estimation/parametric/covariance.rs create mode 100644 src/estimation/parametric/covariates.rs create mode 100644 src/estimation/parametric/individual.rs create mode 100644 src/estimation/parametric/information.rs create mode 100644 src/estimation/parametric/marginal_likelihood.rs create mode 100644 src/estimation/parametric/markov_variance.rs create mode 100644 src/estimation/parametric/mod.rs create mode 100644 src/estimation/parametric/posterior.rs create mode 100644 src/estimation/parametric/posthoc.rs create mode 100644 src/estimation/parametric/prior.rs create mode 100644 src/estimation/parametric/rank_diagnostics.rs create mode 100644 src/estimation/parametric/residual.rs create mode 100644 src/estimation/parametric/shrinkage.rs create mode 100644 src/estimation/parametric/sufficient.rs create mode 100644 src/estimation/parametric/transforms.rs create mode 100644 src/estimation/residual_error.rs create mode 100644 src/estimation/sde_particle.rs create mode 100644 src/results/information_criteria.rs create mode 100644 src/results/parametric_output.rs create mode 100644 tests/fixtures/combined_residual.csv create mode 100644 tests/fixtures/conditional_modes.csv create mode 100644 tests/fixtures/constant_sigma.csv create mode 100644 tests/fixtures/correlated_iiv.csv create mode 100644 tests/fixtures/exponential_residual.csv create mode 100644 tests/fixtures/proportional_residual.csv create mode 100644 tests/fixtures/sparse_iiv.csv create mode 100644 tests/fixtures/two_occasion_iov.csv create mode 100644 tests/iov_diffusion_optimizer.rs create mode 100644 tests/ode_scoring_parity.rs create mode 100644 tests/ode_solver_profile.rs create mode 100644 tests/particle_filter_scientific.rs create mode 100644 tests/saem_correlated_residual.rs create mode 100644 tests/saem_covariates.rs create mode 100644 tests/saem_information_criteria.rs create mode 100644 tests/saem_kernel.rs create mode 100644 tests/saem_lifecycle.rs create mode 100644 tests/saem_marginal_likelihood.rs create mode 100644 tests/saem_no_iiv.rs create mode 100644 tests/saem_operational_convergence.rs create mode 100644 tests/saem_outputs.rs create mode 100644 tests/saem_prediction_parity.rs create mode 100644 tests/saem_regressions.rs create mode 100644 tests/saem_tracing.rs create mode 100644 tests/saem_uncertainty.rs create mode 100644 tests/saem_warm_start.rs create mode 100644 tests/sde_particle_filter.rs diff --git a/.gitignore b/.gitignore index a3aa28889..d446c9237 100644 --- a/.gitignore +++ b/.gitignore @@ -1,52 +1,62 @@ -/target +# Rust build output +/target/ /Cargo.lock +*.profraw +lcov.info + +# Editor and operating-system files +/.idea/ +/.vscode/ +.DS_Store +settings.json + +# Local runtime outputs +/outputs/ +**/outputs/ +/examples/bestdose/theta.csv +stop *.log -theta.csv -obs.csv -time.csv -n_psi.csv -psi.csv -r.csv -correlation.csv -/docs -diagnostics.json -predictions.csv -summary.csv -summary.json -iterations.csv -population.csv -shrinkage.csv -statistics.csv -posterior.csv -simulation_output.csv -/examples/rosuva/* -/examples/iohexol/* -/examples/vori/* +log.txt +op.csv +*results.txt +/diagnostics.json +/predictions.csv +/summary.csv +/summary.json +/iterations.csv +/population.csv +/shrinkage.csv +/statistics.csv +/posterior.csv +/simulation_output.csv +/covariates.csv +/individual_effects.csv +/individual_parameters.csv +/residual_error.csv +/error_theta.csv +/obs.csv +/time.csv +/n_psi.csv +/psi.csv +/r.csv +/correlation.csv + +# Local research and validation artifacts +/validation/ +/docs/roadmap/ +/docs/saem-ode-solver-validation*.md +/tests/reference/ +/examples/_validation_*.rs +/examples/paper_benchmarks/ +/examples/iov_synthetic/ +/examples/iov_*.rs +/examples/rosuva/ +/examples/iohexol/ +/examples/vori/ /examples/data/iohexol* /examples/data/rosuva* /examples/data/vori* -/examples/paper_benchmarks -/examples/*/output -/.idea -stop -.vscode -*.f90 -settings.json -.DS_Store -/outputs/* /benches/*.json -log.txt -op.csv -*results.txt -covariates.csv -individual_effects.csv -individual_parameters.csv -residual_error.csv -error_theta.csv -lcov.info -Fortran/ -paper/ -docs/ -examples/**/outputs/ -examples/iov_synthetic/ -examples/iov_*.rs \ No newline at end of file +/Fortran/ +/paper/ +*.f90 diff --git a/CHANGELOG.md b/CHANGELOG.md index b6c8b53b9..37b655add 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,138 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +### Added + +- SAEM for deterministic analytical and ODE models with IIV, IOV, and supported + residual-error models. +- Assay and residual likelihood scoring for estimation objectives. +- Explicit SDE particle filtering and bounded diffusion optimization. +- Deterministic one-compartment analytical/ODE prediction and normalized + conditional-objective parity coverage. +- Typed parametric numerical-failure termination that returns no partial fit result. +- SAEM lifecycle, cycle diagnostics, guardrail, and truthful termination tracing. +- Owned live parametric fit snapshots, clamped cycle progress, post-cycle + observers, reason-preserving user/stop-file termination, and stale stop-file cleanup. +- SAEM results retain the original equation, data, and ordered parameter names + for prediction and follow-up-run APIs. +- Parametric results generate population and conditional predictions on demand. +- Parametric results retain requested SAEM configuration, effective chain count, + ordered parameter metadata, and covariance masks. +- Parametric results provide structured population, covariance, residual, + individual, iteration, statistic, and prediction tables with table-owned CSV + writers, plus a versioned equation-free JSON result and output manifest. +- Population parameter summaries distinguish the primary estimate from optional + distribution and uncertainty statistics. Natural-scale standard deviations + and coefficients of variation are present only for free population parameters + with available strict observed-information uncertainty; unsupported and fixed + coordinates remain absent. +- Deterministic free-coordinate metadata, analytic complete-data score/Hessian + recursion, and immutable observed-information diagnostics. Residual derivatives + exactly follow the active likelihood scale floor, canonical proportional + coordinates join persisted residual rows, and every long-form information row + carries its availability status. These remain diagnostic rather than standard + errors. +- An opt-in `AveragedIterates { alpha }` SAEM policy with compatible smoothing + gain, direct phi/raw-covariance/raw-SD Cesaro averaging, eta rebasing, canonical + post-average result recomputation and result metadata. Terminal-iterate + trajectories remain the default and unchanged. +- An opt-in frozen-kernel Markov simulation-variance and mixing diagnostic with + explicit chain/draw/memory budgets and independently seeded prior draws at the + frozen averaged Omega/Omega_IOV. The same retained transitions provide full + complete-score and eta/kappa traces, per-chain multivariate lugsail batch + means, diagnostic-mean and fit-operational LRV scales, rank-normalized and + folded split-Rhat, and bulk ESS. Checked trace allocation fails before model + execution; invalid coordinates retain typed statuses without hiding valid + coordinates. A separate explicit operational policy applies Vehtari rank + thresholds, Gong/Flegal relative fixed width, and caller-supplied PMcore + stationarity thresholds at deterministic checkpoints. A joint pass yields + `Converged`; the default and every failed/ineligible finite schedule yield + `MaxCycles`. Stationarity, mixing, Poisson-equation, and controlled-Markov CLT + assumptions remain unverified, and no uncertainty claim is made. Derived + normal-quantile/implied-ESS values now fail validation before fitting when + unusable; lifecycle warnings distinguish unevaluated, failed, ineligible, and + passed checks; and operational CSV rows retain explicit status for every + criterion, trace statistic, LRV, and information-mapped matrix. Accepted + machine-roundoff asymmetry is canonicalized only after the finite- + symmetry tolerance succeeds, preventing exact-symmetry factorization from + misclassifying a positive-definite matrix without jitter or repair. +- Caller-declared covariance-stability diagnostics record a scale-invariant + generalized SPD margin for Omega/Omega_IOV, emit typed warnings after a + complete consecutive near-boundary rejection window, and make operational + convergence fail closed after such a run. Thresholds and windows have no + PMcore default and do not alter fixed-schedule trajectories. +- Explicit opt-in post-fit population marginal likelihood jointly integrates + eta and each actual-occasion kappa with normalized Student-t importance + sampling around retained conditional modes. Exact no-latent evaluation, + independent subject streams, ESS, zero-weight counts, delta-method N2LL MCSE, + typed unavailable/nonconverged-mode statuses, immutable result accessors, + warm-start recomputation semantics, and complete CSV/JSON output are included. + The compatibility objective and all conditional APIs remain unchanged. +- Pure post-fit AIC and independent-subject BIC derived only from available + population marginal N2LL, with deterministic free-coordinate counts, exact + N2LL-MCSE propagation, typed availability, result/summary accessors, and + status-bearing CSV/statistics output. Criteria never use the conditional + compatibility objective. +- Parametric result and manifest schema 9 retains typed marginal-likelihood, + information-criteria, uncertainty, correlated-residual, shrinkage, + information, and Markov diagnostics. Schema versions 1-8 are intentionally + rejected; derived rows are recomputed and checked against retained inputs. +- Masked Louis observed information supplies strict unregularized population + covariance and exact identity/log/logit/probit delta-method standard errors. + One joint eta/kappa central-difference curvature supplies conditional + covariance and standard errors plus an opt-in Student-t proposal. Raw Omega + blocks remain the default proposal. Shrinkage uses unclamped `N-1` sample + variance for separately named posterior-mean and MAP eta/kappa. +- A scalar within-observation correlated additive/proportional residual family + implements `Var(Y|f) = a² + 2 rho a b f + b²f²`, independent fixed/free + controls, log-SD/Fisher-rho optimization, analytic Louis derivatives, IOV + coexistence, and schema-9 lifecycle support. It does not imply serial, + cross-time, cross-output, or general block-sigma residual correlation. +- Support for numerically estimating population and covariate effects + without IIV from the current latent-trace observation likelihood, using the + ordinary SAEM gain. Observed-information uncertainty remains unsupported. +- Explicit variance- and SD-based diagonal constructors for `Omega` and `Iov`; + legacy `diagonal` remains variance-based and SD overflow fails closed. + +### Changed + +- Exponential residual likelihood scoring now uses the same machine-scale floor + as the canonical residual scale and observed-information derivatives. +- Integrate with pharmsol prediction and simulation APIs. +- Generic SDE fitting is unsupported; use `SdeParticleFilter` for + observation-conditioned filtering. +- Define the deterministic analytical/ODE SAEM support matrix and fail closed on + invalid parameter, data, residual, and operational configuration values. + +### Fixed + +- Update coupled covariate raw first/second moments with one common SAEM gain so + the Gaussian moment history remains realizable. Apply exploration robustness + as an objective-checked, mask-preserving under-relaxation of the accepted + Omega/GEM displacement, with no second smoothing gain. Capped exploration + floors the solved target before interpolation; uncapped covariate smoothing + and non-covariate IIV/IOV preserve legacy floor-after-interpolation + backtracking. Reject estimated initial Omega/Omega_IOV diagonals below their + configured floors while exempting fixed diagonals. +- Keep operational covariance-rejection criteria unavailable until a complete + active-cycle window exists; a short healthy prefix no longer satisfies a + longer configured consecutive window. +- Correct Vehtari/Geyer bulk ESS pair indexing and tau assembly, retain separate + rank-Rhat/folded-Rhat/ESS statuses, preserve failed LRV chain indices, and + report checked required versus actually allocated peak trace memory. +- Update IIV covariance from a dedicated eta second-moment statistic after + population recentering instead of mixing differently stepped phi moments. +- Apply annealing, exploration replacement, and smoothing steps independently to + estimated combined-residual components while preserving fixed components. +- Assemble default MAP-enabled results for models without eta or kappa dimensions. +- Reconstruct IOV individual-parameter output separately for every subject and + occasion from the matching eta and kappa source. +- Reject rank-deficient covariance updates with scale-stable factorization while + retaining finite high-scale positive-definite candidates. +- Preserve sparse residual-output indices and valid fixed-zero combined-error + components when reconstructing parametric warm starts and when accumulating, + validating, and installing averaged SAEM estimates. + ## [0.26.2](https://github.com/LAPKB/PMcore/compare/v0.26.1...v0.26.2) - 2026-07-28 ### Other @@ -108,7 +240,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Other -- Remove unsed dependency (argmin-math) ([#225](https://github.com/LAPKB/PMcore/pull/225)) +- Remove unused dependency (argmin-math) ([#225](https://github.com/LAPKB/PMcore/pull/225)) ## [0.21.1](https://github.com/LAPKB/PMcore/compare/v0.21.0...v0.21.1) - 2025-11-12 diff --git a/Cargo.toml b/Cargo.toml index ed8285808..8c451270c 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -3,35 +3,76 @@ name = "pmcore" version = "0.26.2" edition = "2021" authors = [ - "Julián D. Otálvaro ", - "Markus Hovd", - "Michael Neely", - "Walter Yamada", + "Julián D. Otálvaro ", + "Markus Hovd", + "Michael Neely", + "Walter Yamada", ] -description = "Rust library with the building blocks needed to create new Non-Parametric algorithms and its integration with Pmetrics." +description = "Population pharmacokinetic estimation with nonparametric algorithms, SAEM, and SDE particle filtering." license = "GPL-3.0" documentation = "https://lapkb.github.io/PMcore/pmcore/" repository = "https://github.com/LAPKB/PMcore" -exclude = [".github/*", ".vscode/*"] +include = [ + "/Cargo.toml", + "/README.md", + "/CHANGELOG.md", + "/LICENSE", + "/iiv.md", + "/docs/saem-support.md", + "/docs/saem-convergence.md", + "/docs/nonmem-comparison.md", + "/src/**", + "/tests/*.rs", + "/tests/fixtures/**", + "/benches/*.rs", + "/examples/bestdose/main.rs", + "/examples/bimodal_ke/main.rs", + "/examples/bimodal_ke/bimodal_ke.csv", + "/examples/bimodal_ke_backend_compare.rs", + "/examples/bimodal_ke_saem.rs", + "/examples/chain_algorithms.rs", + "/examples/drusano/main.rs", + "/examples/drusano/data.csv", + "/examples/iov/main.rs", + "/examples/iov/test.csv", + "/examples/meta/main.rs", + "/examples/meta/meta.csv", + "/examples/meta_saem/main.rs", + "/examples/neely/main.rs", + "/examples/neely/data.csv", + "/examples/new_iov/main.rs", + "/examples/new_iov/data.csv", + "/examples/theophylline/main.rs", + "/examples/theophylline/theophylline.csv", + "/examples/two_eq_lag/main.rs", + "/examples/two_eq_lag/two_eq_lag.csv", + "/examples/vanco.rs", + "/examples/vanco_sde/main.rs", + "/examples/vanco_sde/vanco_clean.csv", +] [dependencies] csv = "1.3.1" -ndarray = { version = "0.17.2", features = ["rayon"] } +ndarray = { version = "0.17.2", features = ["rayon", "serde"] } serde = "1.0.188" serde_json = "1.0.66" sobol_burley = "0.5.0" argmin = "0.11.0" tracing = "0.1.41" tracing-subscriber = { version = "0.3.19", features = [ - "env-filter", - "fmt", - "time", + "env-filter", + "fmt", + "time", ] } faer = "0.24.0" -pharmsol = { version = "=0.28.3" } +pharmsol = { path = "../pharmsol-likelihood-ownership", version = "=0.28.2" } anyhow = "1.0.100" +statrs = "0.18.0" rayon = "1.10.0" rand = "0.10.1" +rand_distr = "0.6.0" +ahash = "0.8.12" +thiserror = "2.0.18" [features] default = [] @@ -59,6 +100,10 @@ faer = "0.24.0" name = "bimodal_ke" harness = false +[[bench]] +name = "saem" +harness = false + # The BestDose example defines its model from a DSL text string, which requires # a runtime DSL backend. Build/run it with: `--features dsl-jit`. [[example]] diff --git a/README.md b/README.md index 36ab1ef11..8f5a8eb45 100644 --- a/README.md +++ b/README.md @@ -5,42 +5,76 @@ [![Security Audit](https://github.com/LAPKB/PMcore/actions/workflows/security_audit.yml/badge.svg)](https://github.com/LAPKB/PMcore/actions/workflows/security_audit.yml) [![crates.io](https://img.shields.io/crates/v/pmcore.svg)](https://crates.io/crates/pmcore) -Rust library with the building blocks to create and implement new non-parametric algorithms for population pharmacokinetic modelling and their integration with [Pmetrics](https://github.com/LAPKB/Pmetrics). +PMcore provides population pharmacokinetic estimation algorithms and result +handling for models executed by `pharmsol`. -## Implemented functionality +## Algorithms -- Solver for ODE-based population pharmacokinetic models -- Supports the Pmetrics data format for seamless integration -- Covariate support, carry-forward or linear interpolation -- Option to cache results for improved speed -- Powerful simulation engine -- Bestdose module for dose optimization +### Nonparametric -## Available algorithms +- Nonparametric adaptive grid (NPAG) +- Nonparametric optimal design (NPOD) +- Nonparametric maximum a posteriori estimation (NPMAP) +- Non-collapsing NPAG (NCNPAG) -This project aims to implement several algorithms for non-parametric population pharmacokinetic modelling. +### Parametric SAEM -- [x] Non Parametric Adaptive Grid (NPAG) - - [Yamada et al (2021)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823953/) - - [Neely et al (2012)](https://pubmed.ncbi.nlm.nih.gov/22722776/) -- [x] Non Parametric Optimal Design (NPOD) - - [Otalvaro et al (2023)](https://pubmed.ncbi.nlm.nih.gov/36478350/) - - [Leary et al (2003)](https://www.page-meeting.org/default.asp?abstract=421) -- [ ] Non Parametric Simulated Annealing (NPSA) - - [Chen et al (2023)](https://arxiv.org/abs/2301.12656) +PMcore supports deterministic analytical and ODE models with: -In the future we also aim to support parametric algorithms, such as the Iterative 2-Stage Bayesian (IT2B) +- identity, log, logit, and probit parameter scales; +- estimated or fixed population parameters; +- inter-individual and inter-occasion variability; +- subject-static continuous and categorical covariate effects; +- parameters and covariate effects with or without IIV; +- explicit fixed, free, and structural-zero covariance entries; +- additive, proportional, combined, correlated-combined, and exponential + residual models; and +- multiple independently scored outputs. -## Examples +Eta and kappa are additive in transformed parameter space. Model execution uses +natural parameter values. Covariance declarations use transformed-space +variances and covariances; undeclared covariances are structural zeros. -Look at the examples in the `examples` folder to see how to use this library. The examples cover a variety of scenarios. +The default finite schedule ends with `MaxCycles`. `Converged` is available only +through an explicit operational policy whose information, movement, rank, +precision, stationarity, and covariance-stability checks all pass. See +[SAEM convergence](docs/saem-convergence.md). -You may run them with the following command, e.g. +`FitResult::objf()` and cycle objectives are conditional N2LL values. An +independent opt-in calculation provides population marginal likelihood by +integrating eta and actual-occasion kappa. AIC and BIC are available only when +that marginal N2LL is available; they never fall back to the conditional +objective. -``` +Observed-information covariance and standard errors require an unmodified +strict positive-definite information matrix. They are unavailable when +estimated structural effects omit IIV because structural observation +sensitivities are not implemented. PMcore reports unavailable diagnostics +instead of repairing matrices or fabricating partial uncertainty. + +See the [SAEM support matrix](docs/saem-support.md), +[IIV parameterization guide](iiv.md), and +[NONMEM comparison](docs/nonmem-comparison.md) for detailed behavior and syntax. + +### Stochastic differential equations + +PMcore exposes observation-conditioned particle filtering and bounded diffusion +optimization. Generic SDE fitting through `EstimationProblem` is unsupported; +use `SdeParticleFilter` for filtering. + +## Examples + +The `examples` directory contains maintained model-fitting and dose-optimization +programs. For example: + +```sh cargo run --example bimodal_ke --release +cargo run --example bimodal_ke_saem --release ``` +The SAEM example summarizes the deliberately bimodal data with one Gaussian +random-effects population; it does not reproduce a nonparametric mixture. + ## Documentation -For more information on how to use this crate, please review the [documentation](https://lapkb.github.io/PMcore/) +API documentation is published at . diff --git a/benches/saem.rs b/benches/saem.rs new file mode 100644 index 000000000..78743f2e2 --- /dev/null +++ b/benches/saem.rs @@ -0,0 +1,95 @@ +use criterion::{criterion_group, criterion_main, Criterion}; +use pmcore::prelude::*; + +fn model() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_v01_benchmark", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + }, + } +} + +fn data() -> Data { + Data::new( + [ + ("s1", 100.0, [4.70, 4.15, 3.15, 1.75]), + ("s2", 120.0, [4.65, 4.25, 3.45, 2.15]), + ("s3", 80.0, [4.45, 3.75, 2.65, 1.20]), + ("s4", 110.0, [4.55, 4.10, 3.25, 1.95]), + ] + .into_iter() + .map(|(id, dose, observations)| { + Subject::builder(id) + .infusion(0.0, dose, "iv", 0.5) + .observation(0.5, observations[0], "cp") + .observation(1.0, observations[1], "cp") + .observation(2.0, observations[2], "cp") + .observation(4.0, observations[3], "cp") + .build() + }) + .collect(), + ) +} + +fn problem() -> Result> { + EstimationProblem::parametric(model(), data()) + .parameter(Parameter::log("ke").with_initial(0.30)) + .parameter(Parameter::log("v").with_initial(20.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() +} + +fn config(compute_map: bool) -> SaemConfig { + SaemConfig::new() + .seed(20_260_710) + .n_chains(3) + .mcmc_iterations(2) + .burn_in(2) + .k1_iterations(8) + .k2_iterations(4) + .compute_map(compute_map) + .map_max_iterations(100) +} + +fn benchmark(c: &mut Criterion) { + let mut group = c.benchmark_group("saem_v01"); + group.sample_size(20); + group.bench_function("core_12_cycles", |b| { + b.iter(|| { + let problem = match problem() { + Ok(problem) => problem, + Err(error) => panic!("V01 benchmark problem failed: {error}"), + }; + match problem.fit_with(config(false)) { + Ok(result) => result, + Err(error) => panic!("V01 core fit failed: {error}"), + } + }); + }); + group.bench_function("core_plus_conditional_modes", |b| { + b.iter(|| { + let problem = match problem() { + Ok(problem) => problem, + Err(error) => panic!("V01 benchmark problem failed: {error}"), + }; + match problem.fit_with(config(true)) { + Ok(result) => result, + Err(error) => panic!("V01 fit with modes failed: {error}"), + } + }); + }); + group.finish(); +} + +criterion_group!(benches, benchmark); +criterion_main!(benches); diff --git a/docs/nonmem-comparison.md b/docs/nonmem-comparison.md new file mode 100644 index 000000000..bd3b5d81c --- /dev/null +++ b/docs/nonmem-comparison.md @@ -0,0 +1,227 @@ +# NONMEM and PMcore model declarations + +This guide maps common NONMEM declarations to PMcore's parametric API. It is a +syntax and parameterization comparison, not a control-stream converter. + +## Concepts + +| NONMEM | PMcore | +| --- | --- | +| `$THETA` | `Parameter` declarations and `with_initial` | +| `$OMEGA` | `Omega` for IIV and `Iov` for IOV | +| `ETA(n)` | named eta associated with a parameter | +| occasion-specific ETA terms | named kappa generated from `Iov` | +| `$SIGMA` and `$ERROR` | explicit `ParametricErrorModel` per output | +| `$PK` covariate equations | `CovariateEffect` in transformed phi space | +| `$ESTIMATION` | `SaemConfig` and `fit_with` | + +PMcore uses names rather than numeric ETA, kappa, output, and covariance +positions. Model macros supply equation metadata used to validate those names. + +## Population values and IIV + +NONMEM log-normal clearance: + +```text +$THETA +(0, 5) + +$OMEGA +0.09 + +$PK +CL = THETA(1) * EXP(ETA(1)) +``` + +PMcore: + +```rust +.parameter(Parameter::log("cl").with_initial(5.0)) +.omega(Omega::diagonal_variances([("cl", 0.09)])) +``` + +`Parameter::log` means eta is additive on the log scale. The model receives the +natural value `5 * exp(eta)`. `Omega` values are variances, not standard +deviations. + +For additive IIV, use `Parameter::real`. Bounded logit and probit declarations +store their bounds and transformations directly instead of requiring a manual +inverse transformation in the model equation. + +## Correlated IIV + +NONMEM: + +```text +$OMEGA BLOCK(2) +0.09 +0.01 0.04 +``` + +PMcore: + +```rust +.omega( + Omega::diagonal_variances([ + ("cl", 0.09), + ("v", 0.04), + ]) + .covariance("cl", "v", 0.01) +) +``` + +Undeclared PMcore covariances are structural zeros. Use `fixed_variance` and +`fixed_covariance` for fixed entries. PMcore rejects a declaration that is not +finite, symmetric, and strictly positive definite. + +## Inter-occasion variability + +A NONMEM model often selects a distinct ETA by occasion: + +```text +$OMEGA +0.09 ; IIV variance for CL +0.04 ; occasion 1 variance +0.04 ; occasion 2 variance + +$PK +KAPPA = 0 +IF (OCC.EQ.1) KAPPA = ETA(2) +IF (OCC.EQ.2) KAPPA = ETA(3) +CL = THETA(1) * EXP(ETA(1) + KAPPA) +``` + +PMcore declares one kappa distribution and creates one draw for every actual +occasion: + +```rust +.parameter(Parameter::log("cl").with_initial(5.0)) +.omega(Omega::diagonal_variances([("cl", 0.09)])) +.iov(Iov::diagonal_variances([("cl", 0.04)])) +``` + +The individual occasion value is + +```text +CL_i,k = TVCL * exp(eta_i + kappa_i,k). +``` + +In builder-created data, `SubjectBuilderExt::reset()` starts the next occasion: + +```rust +let subject = Subject::builder("1") + .bolus(0.0, 100.0, "iv") + .observation(1.0, 2.1, "cp") + .reset() + .bolus(0.0, 100.0, "iv") + .observation(1.0, 1.9, "cp") + .build(); +``` + +Imported data retains its `Subject -> Occasion -> Event` hierarchy. Kappa is +indexed by subject, actual occasion, and named IOV effect. `Iov` supports the +same fixed/free entries, structural zeros, and variance/SD constructors as +`Omega`. + +## Parameters without IIV + +NONMEM omits ETA from the parameter expression: + +```text +$PK +BASE = THETA(1) +``` + +PMcore makes the choice explicit: + +```rust +.parameter( + Parameter::real("baseline") + .with_initial(1.0) + .without_random_effect() +) +``` + +The population value may still be estimated. PMcore estimates structural no-IIV +population and covariate effects from the observation likelihood. Their +observed-information covariance is currently unavailable. + +## Covariates + +A PMcore continuous effect is linear in transformed phi space. For a log +parameter, + +```rust +.covariate_effect( + CovariateEffect::continuous("cl", "wt", 70.0) + .with_initial(0.01) +) +``` + +defines + +```text +log(CL_i) = log(TVCL) + 0.01 * (WT_i - 70) + eta_i. +``` + +The comparable NONMEM expression is: + +```text +$PK +CL = THETA(1) * EXP(THETA(2) * (WT - 70) + ETA(1)) +``` + +Categorical PMcore effects name the parameter, covariate, reference level, and +active level. Values must be finite, present, and constant within a subject. +PMcore does not infer or rewrite nonlinear covariate equations. + +## Residual error + +NONMEM proportional error with `$SIGMA 0.01`: + +```text +$ERROR +Y = F + F * EPS(1) +``` + +uses an EPS standard deviation of `0.1`. The PMcore declaration receives that +coefficient directly: + +```rust +.error_model("cp", ResidualErrorModel::proportional(0.1)) +``` + +Combined PMcore error uses additive SD `a` and proportional coefficient `b`: + +```rust +.error_model("cp", ResidualErrorModel::combined(0.2, 0.1)) +``` + +Correlated combined error additionally declares `rho` and has + +```text +Var(Y | f) = a^2 + 2 rho a b f + b^2 f^2. +``` + +Each measured output requires its own explicit declaration. PMcore does not use +the data `ErrorPoly` values to select parametric residual scoring. + +## Estimation + +A typical PMcore fit ends with an explicit configuration: + +```rust +let result = problem.fit_with( + SaemConfig::new() + .burn_in(100) + .k1_iterations(300) + .k2_iterations(200) + .n_chains(4) + .seed(42), +)?; +``` + +The default finite schedule reports `MaxCycles`. Operational `Converged` +termination requires an additional explicit policy. Conditional objectives, +population marginal likelihood, information criteria, and uncertainty retain +distinct result fields and availability statuses. diff --git a/docs/saem-convergence.md b/docs/saem-convergence.md new file mode 100644 index 000000000..aee71bece --- /dev/null +++ b/docs/saem-convergence.md @@ -0,0 +1,179 @@ +# SAEM convergence and information diagnostics + +PMcore distinguishes finite schedule completion, operational stopping, +information diagnostics, and statistical uncertainty. These are separate +results with separate assumptions. + +## Estimator policies + +`SaemEstimatorPolicy::TerminalIterate` is the default. A finite schedule that +does not satisfy an enabled operational policy returns `MaxCycles`. + +`SaemEstimatorPolicy::AveragedIterates { alpha }` requires `0.5 < alpha < 1`. +During smoothing it uses gain `s^-alpha` and installs the unweighted average of +completed smoothing M-step iterates. Population values are averaged in phi +space, covariance matrices as accepted raw iterates, residual components on +their reported scales, and correlated-combined rho on its raw correlation +scale. Eta is rebased after installation. + +An `OperationalConvergenceConfig` is opt-in. It evaluates immutable averaged +candidates at scheduled checkpoints and may stop with `Converged` only when all +configured checks are eligible and satisfied. A failed or ineligible final +check returns `MaxCycles`. This policy does not prove model correctness or +mathematical convergence. + +## Free-coordinate order + +Information and simulation-variance matrices use this deterministic order: + +1. estimated population parameters in declaration order, in phi space; +2. estimated structural lower-triangle Omega entries; +3. estimated structural lower-triangle Omega_IOV entries; +4. estimated residual components by output index: additive, proportional, then + within-observation correlation. + +Fixed values and structural zeros are excluded. Covariance coordinates are raw +covariances. Residual SDs and rho use their reported raw coordinates. + +## Observed information + +After burn-in, PMcore forms one complete-data replicate per chain by aggregating +all subjects and occasions. Score and Hessian terms are evaluated at the same +pre-M-step parameters and sampled cycle-end latent values used by the SA update. +The current SA gain updates: + +```text +Delta <- E[complete score] +C <- E[complete Hessian] +G <- E[complete Hessian + score score'] +H = G - Delta Delta' +Iobs = -H +``` + +The implementation averages `score score'` across complete chain replicates; it +does not use the outer product of the mean score. Burn-in gain zero leaves the +recursion unchanged. + +Derivatives are analytic for Gaussian eta/kappa priors, free raw +Omega/Omega_IOV entries, and every supported residual family. Missing and +non-observation events contribute nothing. Unsupported censoring, invalid +dimensions, non-finite terms, likelihood-floor boundaries, or covariance +failures make information unavailable. + +Finite symmetry is accepted only within `64 * f64::EPSILON`. Accepted roundoff +is pairwise averaged before strict Cholesky factorization. PMcore does not add +jitter, ridge, clipping, projection, eigenvalue repair, SVD, or a pseudoinverse. +An indefinite information matrix is retained and labeled rather than repaired. + +Population covariance and standard errors are produced only from an unchanged +strict positive-definite observed-information matrix. Identity, log, logit, and +probit coordinates use their exact delta-method transformations. These values +remain unavailable for estimated structural no-IIV effects. + +## Frozen-kernel simulation variance + +`MarkovSimulationVarianceConfig` explicitly sets the diagnostic seed, chain +count, warmup, retained draws, batch size, lugsail parameters, and trace-memory +limit. No budget is inferred. + +After installing the averaged estimate, PMcore starts independent diagnostic +chains from `Normal(0, Omega)` and `Normal(0, Omega_IOV)` draws. Population, +covariance, residual, proposal-scale, and compound-kernel settings remain +fixed. Diagnostic streams do not consume the fit RNG. Each transition runs eta +block attempts, eta component sweeps, and occasion-kappa sweeps without +adaptation or M-steps. + +Before allocation or model execution, checked arithmetic calculates a +conservative upper bound for trace storage and workspaces. Exceeding the limit +returns `TraceByteCapExceeded`; arithmetic overflow returns +`TraceMemoryAccountingOverflow`. Raw traces are temporary and are not persisted. + +For `n = a b`, nonoverlapping multivariate batch means are + +```text +BM_b = b/(a-1) sum_j (mean_j - overall)(mean_j - overall)' +``` + +and the retained lugsail long-run variance is + +```text +Lambda_c = (BM_b - c BM_(b/r)) / (1-c). +``` + +Chains are never concatenated. PMcore retains both the diagnostic-chain mean LRV +and the fit-operational LRV. With strict observed information, + +```text +Xi = Iobs^-1 Lambda_operational Iobs^-T +simulation covariance of the average = Xi / n_avg. +``` + +Every chain and matrix retains its own typed status. Failed or indefinite chains +remain visible and make the aggregate ineligible. No matrix is projected or +repaired. With no latent dimensions, usable information yields exact zero +simulation-variance matrices without additional model execution. + +## Rank and precision checks + +PMcore reports rank-normalized split-R-hat, folded split-R-hat, and bulk ESS for +each retained eta and kappa coordinate. Ties use average ranks and Blom scores. +Bulk ESS uses split-chain autocovariances and the initial positive, monotone pair +sequence. Constant traces, odd draw counts, non-finite values, invalid +variances, and insufficient chains receive typed ineligible statuses. + +Operational stopping requires every configured information, covariance, +movement, score, eta, and kappa check to be eligible. The supplied rank policy +requires at least four diagnostic chains, maximum R-hat below `1.01`, total bulk +ESS above `400`, and average bulk ESS per split chain of at least `50`. + +Relative fixed width is + +```text +2 z_(delta/2) * worst_simulation_sd_fraction <= epsilon. +``` + +Newton displacement is `sqrt(g' Iobs^-1 g)`. Its Monte Carlo SD uses the +diagnostic-mean LRV divided by retained draws. The caller supplies checkpoint +scheduling, confidence, precision, covariance, rejection-window, and +stationarity thresholds. + +## Covariance-boundary guardrail + +Operational convergence requires an explicit `CovarianceStabilityConfig`. For a +current covariance `Omega` and declared initial covariance `Omega0 = L0 L0'`, +PMcore records + +```text +m(Omega; Omega0) = lambda_min(L0^-1 Omega L0^-T). +``` + +The margin is dimensionless and approaches zero near the positive-definite +boundary. A cycle qualifies only when the margin is at or below the caller's +threshold and the matching covariance update was rejected. Once the declared +consecutive window occurs, operational convergence remains blocked. Recording +this diagnostic does not change the fit trajectory or RNG stream. + +## Interpretation + +Passing the operational policy means only that the configured numerical and +sampling checks passed for that fit. Stationarity, adequate mixing, the Markov +Poisson equation, and controlled-Markov stochastic-approximation assumptions +remain unverified. Information and simulation-variance matrices are not by +themselves proof of convergence. For consequential use, compare an independent +fit with larger MCMC and schedule budgets. + +Population marginal likelihood and AIC/BIC are separate post-fit calculations. +They do not change operational stopping and do not turn a conditional objective +into population evidence. + +## References + +- Delyon, B., Lavielle, M., and Moulines, E. (1999). Convergence of a stochastic + approximation version of the EM algorithm. *Annals of Statistics* 27(1), + 94-128. +- Kuhn, E., and Lavielle, M. (2004). Coupling a stochastic approximation version + of EM with an MCMC procedure. *ESAIM: Probability and Statistics* 8, 115-131. +- Vehtari, A. et al. (2021). Rank-normalization, folding, and localization. + *Bayesian Analysis* 16(2), 667-718. +- Vats, D., and Flegal, J. M. (2022). Lugsail lag windows for estimating + time-average covariance matrices. *Biometrika* 109(3), 735-750. diff --git a/docs/saem-support.md b/docs/saem-support.md new file mode 100644 index 000000000..b891a12d1 --- /dev/null +++ b/docs/saem-support.md @@ -0,0 +1,144 @@ +# SAEM support + +PMcore validates the model, data, parameter, covariance, residual, and runtime +configuration before fitting. Unsupported combinations fail with an error +instead of selecting a fallback. + +## Models and data + +| Area | Supported behavior | +| --- | --- | +| Equations | Deterministic analytical and ODE equations with complete metadata | +| Subjects | One or more subjects and at least one measured observation | +| Outputs | Explicit metadata names; numeric `N` requires a declared `outeq_N` | +| Missing values | Retained in the event stream and omitted from scoring | +| Censoring | Not supported for parametric estimation | +| Covariates | Finite subject-static continuous and categorical values | +| Assay metadata | `ErrorPoly` C0-C3 values are transported but do not select parametric scoring | + +Every measured output requires an explicit `ParametricErrorModel`. Population +covariate effects are linear in transformed parameter space. Covariate values +must be present, finite, and constant within each subject. + +PMcore executes each ODE with its configured solver and tolerances. Solver +choice is a scientific model input; PMcore does not replace it during fitting. +Stiff models generally require an implicit solver and tolerances selected for +the model's scale. Completion of a finite SAEM schedule does not establish ODE +accuracy. + +## Parameters and variability + +| Area | Supported behavior | +| --- | --- | +| Scales | Identity, Log, Logit, Probit | +| Population values | Independently fixed or estimated, with or without IIV | +| IIV | Named parameter subsets or zero-dimensional | +| IOV | Named parameter subsets, independently of IIV | +| Covariance | Fixed/free entries, structural zeros, strict positive definiteness | + +Eta and kappa are additive in transformed parameter space. Model execution uses +natural parameter values. `Parameter::with_initial` is the natural-scale value +at zero eta, kappa, and covariate offsets. + +`Omega::diagonal_variances` and `Iov::diagonal_variances` accept variances. +`diagonal_standard_deviations` accepts finite positive SDs and checks overflow +before squaring. Legacy `diagonal` remains variance-based. Undeclared +covariances are structural zeros. + +Covariance updates preserve fixed entries and structural zeros. Invalid, +non-finite, non-symmetric, or non-positive-definite matrices are rejected; no +jitter, ridge, clipping, projection, eigendecomposition repair, or pseudoinverse +is used. + +Estimated no-IIV population and covariate coordinates use the observation +likelihood. Population observed-information covariance and standard errors are +reported as unsupported while those coordinates are estimated. + +## Residual models + +| Family | Parameters | +| --- | --- | +| Constant | fixed or estimated SD | +| Proportional | fixed or estimated coefficient | +| Combined | independently fixed or estimated additive and proportional components | +| Correlated combined | additive SD, proportional coefficient, and within-observation correlation | +| Exponential | fixed or estimated log-scale SD | + +For correlated combined error, + +```text +Var(Y | f) = a^2 + 2 rho a b f + b^2 f^2 +``` + +with finite `a,b > 0` and `-1 < rho < 1`. Correlation is scalar and applies only +to the additive and proportional components of one observation. Serial, +cross-time, cross-output, dense, and general block residual covariance are not +supported. Multiple outputs use independent named residual declarations. + +## Schedule and MCMC + +A valid schedule has `k1 + k2 > 0`, `burn_in <= k1`, and no integer overflow. +Burn-in performs MCMC without parameter updates. Exploration uses gain one. +Smoothing uses a decreasing gain. The default estimator returns the terminal +iterate. `AveragedIterates { alpha }` requires `k2 > 0` and `0.5 < alpha < 1`. + +MCMC chain counts, iteration counts, adaptation intervals, and proposal scales +must be positive. `eta_block_iterations = 0` disables eta block proposals. Raw +Omega blocks are the default block scale. Conditional-curvature scaling is +opt-in and fails with a typed status when strict curvature is unavailable. + +Covariate raw first and second moments always use the same SA gain. PMcore forms +a centered covariance target before applying masks and the constrained local +GEM update. Exploration may under-relax the accepted displacement; smoothing +applies no second covariance gain. + +## Objectives and uncertainty + +`FitResult::objf()`, cycle records, and compatibility summaries contain +conditional N2LL. They are diagnostics and never select a fit or substitute for +population evidence. + +Population marginal likelihood is an explicit post-fit calculation. It jointly +integrates eta and actual-occasion kappa with normalized Student-t importance +sampling, or evaluates the observation likelihood exactly when there are no +latent dimensions. Results include ESS, zero-weight counts, and delta-method +N2LL Monte Carlo error. AIC and BIC are derived only from available marginal +N2LL and retain that MC error. + +Observed information uses analytic complete-data derivatives in a deterministic +free-coordinate order. Population covariance and standard errors require an +unmodified strict-Cholesky inverse. Conditional eta/kappa uncertainty uses one +joint central-difference curvature in `[eta, kappa_1, ..., kappa_K]` order. +Unavailable curvature or information remains unavailable without fallback. + +Shrinkage is reported separately for posterior-mean and MAP eta/kappa sources. +It uses `100 * (1 - sample_variance / population_variance)` with `N-1` sample +variance and is not clamped. + +## Results and lifecycle + +Results retain the equation, data, ordered parameter metadata, covariance masks, +requested configuration, effective chain count, cycle diagnostics, predictions, +conditional modes, uncertainty statuses, and optional marginal likelihood. +Schema 9 is current; older schemas are rejected. + +The controller supports cycle stepping, post-cycle observers, owned snapshots, +user abort, stop-file termination, and truthful terminal reasons. A stale +current-directory `stop` file is removed before a new run. + +The default finite schedule reports `MaxCycles`. An opt-in operational policy +may report `Converged` only when all configured information, movement, +rank-normalized R-hat, bulk ESS, relative fixed-width, stationarity, and +covariance-stability checks pass. See [SAEM convergence](saem-convergence.md). + +## Unsupported + +- Generic SDE fitting through `EstimationProblem`; use `SdeParticleFilter` or + bounded diffusion optimization. +- Parametric BLOQ or ALOQ censoring. +- Time-varying population covariate effects. +- Arbitrary nonlinear parameter constraints. +- Serial or multivariate residual covariance. +- Observed-information covariance for estimated structural no-IIV effects. +- Automatic theorem-level convergence claims. +- FO, FOCE, and FOCE-I. diff --git a/examples/bimodal_ke/prior.csv b/examples/bimodal_ke/prior.csv deleted file mode 100644 index 3dcea0b70..000000000 --- a/examples/bimodal_ke/prior.csv +++ /dev/null @@ -1,48 +0,0 @@ -ke,v,prob -0.086518359375,103.1787109375,0.06604378868514989 -0.1304490234375,97.24609375,0.03921568624623767 -1.02282724609375,194.51904296875,0.019607843142661945 -0.1058478515625,139.169921875,0.019607843160936878 -0.32169384765625003,68.35205078125,0.019607843063932227 -0.043173437500000016,76.416015625,0.0196073013792577 -0.33223720703125,92.21435546875,0.01960807990514778 -0.34922373046875,75.73486328125,0.01960670003045904 -0.0953044921875,70.0439453125,0.019606956508021193 -0.33282294921875,117.35107421875,0.01964585748382621 -0.30822177734375,92.69775390625,0.01975670895609882 -0.11697695312500002,78.3935546875,0.019619208417896015 -0.06279580078125,101.79443359375,0.019666917705669012 -0.06894609375,73.779296875,0.019699184590165703 -0.10379775390625,91.33544921875,0.019647919855277668 -0.06279580078125,90.85205078125,0.01955998873859117 -0.016522167968749984,115.98876953125,0.019793705140549383 -0.0712890625,65.25390625,0.01951690846832004 -0.34102333984375,99.59716796875,0.019551229003115674 -0.018279394531249986,104.03564453125,0.019405565279144433 -0.08710410156250001,126.25,0.019899255227995273 -0.045809277343749996,88.56689453125,0.02066523951433268 -0.281716943359375,140.872802734375,0.019969683446714893 -0.282888427734375,126.063232421875,0.02127479495876752 -0.29006376953125,94.45556640625,0.02065863312983064 -0.29226030273437503,128.656005859375,0.01917390219361373 -0.09149716796875,91.64306640625,0.01943770284493975 -0.301632177734375,127.864990234375,0.019078962756467307 -0.08271103515625,94.45556640625,0.0241816111400729 -0.03555878906250001,94.1259765625,0.018566761532708417 -0.098233203125,110.2099609375,0.023038596893006538 -0.28479208984375,92.60986328125,0.018533734166784076 -0.29387109375,103.7939453125,0.01822422576005151 -0.09208291015624999,112.73681640625,0.015898902638926116 -0.29064951171875,112.91259765625,0.018513912166075022 -0.35449541015624997,86.50146484375,0.002760163376971263 -0.31261484375,116.3623046875,0.03156130684380014 -0.07626787109375001,110.80322265625,0.02748830588911653 -0.08681123046875,85.97412109375,0.030053057517925558 -0.056059765625,121.85546875,0.0351791922472714 -0.056645507812499996,122.822265625,0.017357447100567722 -0.08681123046875,86.01806640625,0.0035995120779823866 -0.07626787109375001,110.84716796875,0.005626398604392106 -0.29064951171875,112.86865234375,0.039672866438526334 -0.35449541015624997,86.54541015625,0.03647494807053948 -0.31261484375,116.40625,0.008483311033275254 -0.056645507812499996,122.7783203125,0.006252336668887009 diff --git a/examples/bimodal_ke_backend_compare.rs b/examples/bimodal_ke_backend_compare.rs index 81cf95807..88210b404 100644 --- a/examples/bimodal_ke_backend_compare.rs +++ b/examples/bimodal_ke_backend_compare.rs @@ -66,9 +66,18 @@ struct ComparisonResult { } fn main() -> Result<()> { - let mut results = Vec::new(); - results.push(run_legacy()?); - results.push(run_macro()?); + #[cfg(any( + feature = "dsl-jit", + all(feature = "dsl-aot", feature = "dsl-aot-load"), + feature = "dsl-wasm" + ))] + let mut results = vec![run_legacy()?, run_macro()?]; + #[cfg(not(any( + feature = "dsl-jit", + all(feature = "dsl-aot", feature = "dsl-aot-load"), + feature = "dsl-wasm" + )))] + let results = vec![run_legacy()?, run_macro()?]; #[cfg(feature = "dsl-jit")] results.push(run_runtime_jit()?); diff --git a/examples/bimodal_ke_saem.rs b/examples/bimodal_ke_saem.rs new file mode 100644 index 000000000..4fb53d005 --- /dev/null +++ b/examples/bimodal_ke_saem.rs @@ -0,0 +1,58 @@ +//! Fit the `bimodal_ke` model and dataset with SAEM. +//! +//! SAEM assumes one Gaussian random-effects population, so this fit summarizes +//! the deliberately bimodal elimination-rate distribution. + +use anyhow::Result; +use pmcore::prelude::*; + +fn main() -> Result<()> { + Logger::new().stdout(true).init()?; + + let eq = ode! { + name: "bimodal_ke_saem", + params: [ke, v], + states: [central], + outputs: [outeq_1], + routes: [ + infusion(input_1) -> central, + ], + diffeq: |x, _t, dx| { + dx[central] = -ke * x[central]; + }, + out: |x, _t, y| { + y[outeq_1] = x[central] / v; + }, + } + .with_solver(OdeSolver::ExplicitRk(ExplicitRkTableau::Tsit45)); + + let data = data::read_pmetrics("examples/bimodal_ke/bimodal_ke.csv")?; + + let problem = EstimationProblem::parametric(eq, data) + .parameter(Parameter::log("ke").with_initial(0.15)) + .parameter(Parameter::log("v").with_initial(120.0)) + .omega(Omega::diagonal([("ke", 0.5), ("v", 0.1)])) + .error_model("outeq_1", ResidualErrorModel::proportional(0.15)) + .build()?; + + let config = SaemConfig::new() + .seed(20_260_714) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(100) + .k1_iterations(180) + .k2_iterations(80); + + let result = problem.fit_with(config)?; + result.write_outputs("outputs/bimodal_ke_saem", 0.0, 0.0)?; + + println!("population ke: {:.6}", result.population_parameters()[0]); + println!("population v: {:.6}", result.population_parameters()[1]); + println!("omega ke: {:.6}", result.omega()[[0, 0]]); + println!("omega v: {:.6}", result.omega()[[1, 1]]); + println!("proportional sigma: {:.6}", result.residual_sigmas()[0]); + println!("conditional N2LL: {:.6}", result.conditional_n2ll()); + println!("termination: {:?}", result.termination_reason()); + + Ok(()) +} diff --git a/examples/drusano/main-old.rs b/examples/drusano/main-old.rs deleted file mode 100644 index b3ca6d530..000000000 --- a/examples/drusano/main-old.rs +++ /dev/null @@ -1,477 +0,0 @@ -#![allow(dead_code)] -#![allow(unused_variables)] -use argmin::core::observers::{ObserverMode, SlogLogger}; -use argmin::core::{CostFunction, Error, Executor, TerminationReason, TerminationStatus}; -use argmin::solver::neldermead::NelderMead; -use eyre::Result; -use pmcore::prelude::{ - datafile::{CovLine, Infusion, Scenario}, - predict::{Engine, Predict}, - start, -}; - -const ATOL: f64 = 1e-4; -const RTOL: f64 = 1e-4; -use ode_solvers::*; -use std::{collections::HashMap, process::exit}; -#[derive(Debug, Clone)] -struct Model<'a> { - v1: f64, - cl1: f64, - v2: f64, - cl2: f64, - popmax: f64, - kgs: f64, - kks: f64, - e50_1s: f64, - e50_2s: f64, - alpha_s: f64, - kgr1: f64, - kkr1: f64, - e50_1r1: f64, - alpha_r1: f64, - kgr2: f64, - kkr2: f64, - e50_2r2: f64, - alpha_r2: f64, - init_3: f64, - init_4: f64, - init_5: f64, - h1s: f64, - h2s: f64, - h1r1: f64, - h2r2: f64, - _scenario: &'a Scenario, - infusions: Vec, - cov: Option<&'a HashMap>, -} - -type State = Vector5; -type Time = f64; - -impl ode_solvers::System for Model<'_> { - fn system(&self, t: Time, x: &State, dx: &mut State) { - let mut rateiv = [0.0, 0.0]; - for infusion in &self.infusions { - if t >= infusion.time && t <= (infusion.dur + infusion.time) { - rateiv[infusion.compartment] += infusion.amount / infusion.dur; - } - } - // Sec - let e50_2r1 = self.e50_2s; - let e50_1r2 = self.e50_1s; - let h2r1 = self.h2s; - let h1r2 = self.h1s; - let mut xm0best = 0.0; - - ///////////////////// USER DEFINED /////////////// - - // if x[0] < 0.0 { - // x[0] = 0.0; - // } - // if x[1] < 0.0 { - // x[1] = 0.0; - // } - dx[0] = rateiv[0] - self.cl1 * x[0] / self.v1; - dx[1] = rateiv[1] - self.cl2 * x[1] / self.v2; - - let xns = x[2]; - let xnr1 = x[3]; - let xnr2 = x[4]; - let e = 1.0 - (xns + xnr1 + xnr2) / self.popmax; - let mut d1 = x[0] / self.v1; - let mut d2 = x[1] / self.v2; - let mut u = d1 / self.e50_1s; - let mut v = d2 / self.e50_2s; - let mut w = self.alpha_s * d1 * d2 / (self.e50_1s * self.e50_2s); - let mut h1 = 1.0_f64 / self.h1s; - let mut h2 = 1.0_f64 / self.h2s; - let mut xx = (h1 + h2) / 2.0; - if u < 1.0E-5 && v < 1.0E-5 { - xm0best = 0.0; - } else { - if v < 0.0 { - xm0best = u.powf(1.0 / h1); - } - if u < 0.0 { - xm0best = v.powf(1.0 / h2); - } - - if v > 0.0 && u > 0.0 { - let start = 0.00001; - let tol = 1.0e-10; - let step = -2.0 * start; - // CALL ELDERY(1,START,XM0BEST1,VALMIN1,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT) - let bm0 = BESTM0 { - u, - v, - w, - h1, - h2, - xx, - }; - let (xm0best1, valmin1, iconv) = bm0.get_best(start, step); - if iconv == false { - // Output a message indicating no convergence on the selection of best M0 for s - println!(" NO CONVERGENCE ON SELECTION OF BEST M0 FOR s."); - - // Output a message indicating the XP(3) EQ... - println!(" FOR THE XP(3) EQ.... "); - - // Output the values of XM0BEST1 and VALMIN1 with formatting - println!(" THE EST. FOR M0 FROM ELDERY WAS {:>20.12}", xm0best1); - println!(" AND THIS GAVE A VALMIN OF {:>20.12}", valmin1); - - // Output the values of D1, D2, U, V, W, ALPHA_S, H1, and H2 with formatting - println!(" NOTE THAT D1,D2 = {:>20.12} {:>20.12}", d1, d2); - println!(" U,V = {:>20.12} {:>20.12}", u, v); - println!(" W,ALPHA_S = {:>20.12} {:>20.12}", w, self.alpha_s); - println!(" H1,H2 = {:>20.12} {:>20.12}", h1, h2); - - exit(-1); - } - if valmin1 < 1.0e-10 { - xm0best = xm0best1; - } else { - // CALL FINDM0(U,V,alpha_s,H1,H2,XM0EST) - let xm0est = find_m0(u, v, self.alpha_s, h1, h2); - if xm0est < 0.0 { - xm0best = xm0best1; - } else { - // START(1) = XM0EST - // STEP(1)= -.2D0*START(1) - // CALL ELDERY(1,START,XM0BEST2,VALMIN2,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT) - let bm0 = BESTM0 { - u, - v, - w, - h1, - h2, - xx, - }; - let (xm0best2, valmin2, iconv) = bm0.get_best(xm0est, -2.0 * xm0est); - xm0best = xm0best1; - if valmin2 < valmin1 { - xm0best = xm0best2; - } - if iconv == false { - panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR s."); - } //235 - } //237 - } //240 - } //243 - } - let xms = xm0best / (xm0best + 1.0); - dx[2] = xns * (self.kgs * e - self.kks * xms); - - d1 = x[0] / self.v1; - d2 = x[1] / self.v2; - u = d1 / self.e50_1r1; - v = d2 / e50_2r1; - w = self.alpha_r1 * d1 * d2 / (self.e50_1r1 * e50_2r1); - h1 = 1.0_f64 / self.h1r1; - h2 = 1.0_f64 / h2r1; - xx = (h1 + h2) / 2.0; - if u < 1.0e-5 && v < 1.0e-5 { - xm0best = 0.0; - } else { - if v < 0.0 { - xm0best = u.powf(1.0 / h1); - } - if u < 0.0 { - xm0best = v.powf(1.0 / h2); - } - if v > 0.0 && u > 0.0 { - //START(1) = .00001 - let tol = 1.0e-10; - // STEP(1)= -.2D0*START(1) - // CALL ELDERY(1,START,XM0BEST1,VALMIN1,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT) - let bm0 = BESTM0 { - u, - v, - w, - h1, - h2, - xx, - }; - let (xm0best1, valmin1, iconv) = bm0.get_best(0.00001, -2.0 * 0.00001); - if iconv == false { - panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR r1."); - } - if valmin1 < 1.0e-10 { - xm0best = xm0best1; - } else { - // CALL FINDM0(U,V,alpha_r1,H1,H2,XM0EST) - let xm0est = find_m0(u, v, self.alpha_s, h1, h2); - if xm0est < 0.0 { - xm0best = xm0best1; - } else { - // START(1) = XM0EST - // STEP(1)= -.2D0*START(1) - // CALL ELDERY(1,START,XM0BEST2,VALMIN2,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT) - let bm0 = BESTM0 { - u, - v, - w, - h1, - h2, - xx, - }; - let (xm0best2, valmin2, iconv) = bm0.get_best(xm0est, -2.0 * xm0est); - xm0best = xm0best1; - if valmin2 < valmin1 { - xm0best = xm0best2; - } - if iconv == false { - panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR r1."); - } //235 - } //237 - } //240 - } - } - let xmr1 = xm0best / (xm0best + 1.0); - dx[3] = xnr1 * (self.kgr1 * e - self.kkr1 * xmr1); - - d1 = x[0] / self.v1; - d2 = x[1] / self.v2; - u = d1 / e50_1r2; - v = d2 / self.e50_2r2; - w = self.alpha_r2 * d1 * d2 / (e50_1r2 * self.e50_2r2); - h1 = 1.0_f64 / h1r2; - h2 = 1.0_f64 / self.h2r2; - xx = (h1 + h2) / 2.0; - if u < 1.0e-5 && v < 1.0e-5 { - xm0best = 0.0; - } else { - if v < 0.0 { - xm0best = u.powf(1.0 / h1); - } - if u < 0.0 { - xm0best = v.powf(1.0 / h2); - } - - if v > 0.0 && u > 0.0 { - //START(1) = .00001 - let tol = 1.0e-10; - // STEP(1)= -.2D0*START(1) - // CALL ELDERY(1,START,XM0BEST1,VALMIN1,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT) - let xm0best1 = 0.0; - let valmin1 = 0.0; - let iconv = 0.0; - if iconv == 0.0 { - panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR r1."); - } - if valmin1 < 1.0e-10 { - xm0best = xm0best1; - } else { - // CALL FINDM0(U,V,alpha_s,H1,H2,XM0EST) - let xm0est = find_m0(u, v, self.alpha_s, h1, h2); - if xm0est < 0.0 { - xm0best = xm0best1; - } else { - // START(1) = XM0EST - // STEP(1)= -.2D0*START(1) - // CALL ELDERY(1,START,XM0BEST2,VALMIN2,TOL,STEP,1000,BESTM0,0,ICONV,NITER,ICNT) - let xm0best2 = 0.0; - let valmin2 = 0.0; - let iconv = 0.0; - xm0best = xm0best1; - if valmin2 < valmin1 { - xm0best = xm0best2; - } - if iconv == 0.0 { - panic!("NO CONVERGENCE ON SELECTION OF BEST M0 FOR s."); - } //235 - } //237 - } //240 - } //243 - } - let xmr2 = xm0best / (xm0best + 1.0); - dx[4] = xnr2 * (self.kgr2 * e - self.kkr2 * xmr2); - - //////////////// END USER DEFINED //////////////// - } -} - -#[derive(Debug, Clone)] -struct Ode {} - -impl Predict for Ode { - fn predict(&self, params: Vec, scenario: &Scenario) -> Vec { - let mut system = Model { - v1: params[0], - cl1: params[1], - v2: params[2], - cl2: params[3], - popmax: params[4], - kgs: params[5], - kks: params[6], - e50_1s: params[7], - e50_2s: params[8], - alpha_s: params[9], - kgr1: params[10], - kkr1: params[11], - e50_1r1: params[12], - alpha_r1: params[13], - kgr2: params[14], - kkr2: params[15], - e50_2r2: params[16], - alpha_r2: params[17], - init_3: params[18], - init_4: params[19], - init_5: params[20], - h1s: params[21], - h2s: params[22], - h1r1: params[23], - h2r2: params[24], - _scenario: scenario, - infusions: vec![], - cov: None, - }; - let mut yout = vec![]; - let mut x = State::new( - 0.0, - 0.0, - 10.0_f64.powf(1.0), - 10.0_f64.powf(system.init_4), - 10.0_f64.powf(system.init_5), - ); - let mut index: usize = 0; - for block in &scenario.blocks { - system.cov = Some(&block.covs); - for event in &block.events { - if event.evid == 1 { - if event.dur.unwrap_or(0.0) > 0.0 { - //infusion - system.infusions.push(Infusion { - time: event.time, - dur: event.dur.unwrap(), - amount: event.dose.unwrap(), - compartment: event.input.unwrap() - 1, - }); - } else { - //dose - x[event.input.unwrap() - 1] += event.dose.unwrap(); - } - } else if event.evid == 0 { - //obs - let v1 = params[0]; - let v2 = params[2]; - let out = match event.outeq.unwrap() { - 1 => x[0] / v1, - 2 => x[1] / v2, - 3 => (x[2] + x[3] + x[4]).log10(), - 4 => x[3].log10(), - 5 => x[4].log10(), - _ => { - log::error!("Invalid output equation"); - exit(1) - } - }; - yout.push(out); - } - if let Some(next_time) = scenario.times.get(index + 1) { - // let mut stepper = Rk4::new(system.clone(), lag_time, x, *next_time, 0.1); - if event.time < *next_time { - let mut stepper = Dopri5::new( - system.clone(), - event.time, - *next_time, - 1e-3, - x, - RTOL, - ATOL, - ); - let _res = stepper.integrate(); - let y = stepper.y_out(); - x = *y.last().unwrap(); - } else if event.time > *next_time { - log::error!("next time is in the past!"); - log::error!("event_time: {}\nnext_time: {}", event.time, *next_time); - } - } - index += 1; - } - } - yout - } -} - -struct BESTM0 { - u: f64, - v: f64, - w: f64, - h1: f64, - h2: f64, - xx: f64, -} -impl CostFunction for BESTM0 { - type Param = f64; - type Output = f64; - fn cost(&self, xm0: &Self::Param) -> Result { - let t1 = self.u / xm0.powf(self.h1); - let t2 = self.v / xm0.powf(self.h2); - let t3 = self.w / xm0.powf(self.xx); - - Ok((1.0 - t1 - t2 - t3).powi(2)) - } -} - -impl BESTM0 { - fn get_best(self, start: f64, step: f64) -> (f64, f64, bool) { - let other_point = start + step; - let solver = NelderMead::new(vec![start, other_point]) - .with_sd_tolerance(0.0001) - .unwrap(); - let res = Executor::new(self, solver) - .configure(|state| state.max_iters(1000)) - // .add_observer(SlogLogger::term(), ObserverMode::Always) - .run() - .unwrap(); - let converged = match res.state.termination_status { - TerminationStatus::Terminated(reason) => match reason { - TerminationReason::SolverConverged => true, - _ => false, - }, - _ => false, - }; - - ( - res.state.best_param.unwrap(), - res.state.best_cost, - converged, - ) - } -} -fn find_m0(ufinal: f64, v: f64, alpha: f64, h1: f64, h2: f64) -> f64 { - let noint = 1000; - let delu = ufinal / (noint as f64); - let mut xm = v.powf(1.0 / h2); - let mut u = 0.0; - let hh = (h1 + h2) / 2.0; - - for int in 1..=noint { - let top = 1.0 / xm.powf(h1) + alpha * v / xm.powf(hh); - let b1 = u * h1 / xm.powf(h1 + 1.0); - let b2 = v * h2 / xm.powf(h2 + 1.0); - let b3 = alpha * v * u * hh / xm.powf(hh + 1.0); - let xmp = top / (b1 + b2 + b3); - - xm = xm + xmp * delu; - - if xm <= 0.0 { - return -1.0; // Greco equation is not solvable - } - - u = delu * (int as f64); - } - - xm // Return the calculated xm0est -} -fn main() -> Result<()> { - fit( - Engine::new(Ode {}), - "examples/drusano/config.toml".to_string(), - )?; - Ok(()) -} diff --git a/examples/meta/main.rs b/examples/meta/main.rs index 01d7edb64..9cb98c21a 100644 --- a/examples/meta/main.rs +++ b/examples/meta/main.rs @@ -29,7 +29,9 @@ fn main() -> Result<()> { y[outeq_1] = x[central] / v; y[outeq_2] = x[metabolite] / v2; }, - }; + } + .with_tolerances(1e-10, 1e-12) + .with_solver(OdeSolver::ExplicitRk(ExplicitRkTableau::Tsit45)); let data = data::read_pmetrics("examples/meta/meta.csv")?; let parameters = ParameterSpace::bounded() diff --git a/examples/meta_saem/main.rs b/examples/meta_saem/main.rs new file mode 100644 index 000000000..c5cbc39c7 --- /dev/null +++ b/examples/meta_saem/main.rs @@ -0,0 +1,168 @@ +//! Fit the parent/metabolite model with SAEM. +//! +//! PMcore NONMEM Monolix +//! ------------------------------------------------------------------------- +//! Parameter initial Initial $THETA Initial typical value +//! Parameter scale ETA equation in $PK Parameter distribution +//! Omega $OMEGA Random-effect SD/correlation +//! Error model $ERROR / $SIGMA Observation error model +//! +//! `with_initial(...)`, `Omega::diagonal(...)`, and the error-model values +//! are initial estimates unless explicitly fixed. + +use pmcore::prelude::*; + +fn main() -> Result<()> { + Logger::new().stdout(true).init()?; + + // Structural model: approximately NONMEM $DES or Monolix [LONGITUDINAL]. + let eq = ode! { + name: "meta_saem", + params: [cls, fm, k20, relv, theta1, theta2, vs], + covariates: [wt, pkvisit], + states: [central, metabolite], + outputs: [outeq_1, outeq_2], + routes: [ + infusion(input_1) -> central, + ], + diffeq: |x, _t, dx| { + let cl = + cls * ((pkvisit - 1.0) * theta1).exp() * (wt / 70.0).powf(0.75); + + let v = + vs * ((pkvisit - 1.0) * theta2).exp() * (wt / 70.0); + + let ke = cl / v; + + dx[central] = -ke * x[central] * (1.0 - fm) - fm * x[central]; + dx[metabolite] = fm * x[central] - k20 * x[metabolite]; + }, + out: |x, _t, y| { + let cl = + cls * ((pkvisit - 1.0) * theta1).exp() * (wt / 70.0).powf(0.75); + + let v = + vs * ((pkvisit - 1.0) * theta2).exp() * (wt / 70.0); + + let v2 = relv * v; + let _ke = cl / v; + + y[outeq_1] = x[central] / v; + y[outeq_2] = x[metabolite] / v2; + }, + } + .with_solver(OdeSolver::Bdf) + .with_tolerances(1e-8, 1e-10); + + let data = data::read_pmetrics("examples/meta/meta.csv")?; + + // Parametric NLME model: estimates typical values, IIV, and residual error. + let problem = EstimationProblem::parametric(eq, data) + // Log-normal IIV: + // CLS_i = TVCLS * exp(ETA_CLS,i) + // NONMEM: CLS = THETA(1) * EXP(ETA(1)) + // Monolix: distribution=logNormal, typical=TVCLS, sd=omega_CLS + .parameter(Parameter::log("cls").with_initial(1.0)) + // Logit-normal IIV bounded to (0, 1): + // logit(FM_i) = logit(TVFM) + ETA_FM,i + // NONMEM: explicit logit/inverse-logit transformation in $PK + // Monolix: distribution=logitNormal, typical=TVFM, sd=omega_FM + .parameter(Parameter::logit("fm", 0.0, 1.0).with_initial(0.20)) + // Log-normal IIV: K20_i = TVK20 * exp(ETA_K20,i) + .parameter(Parameter::log("k20").with_initial(0.10)) + // Logit-normal IIV bounded to (0, 1). + .parameter(Parameter::logit("relv", 0.0, 1.0).with_initial(0.50)) + // Fixed population coefficients without ETA. + // + // `pkvisit` is currently a covariate, not an occasion-level random + // effect. Therefore, this model does not define IOV. + // + // NONMEM: fixed THETA values with no corresponding ETA + // Monolix: fixed effects with no variability + .parameter( + Parameter::real("theta1") + .with_initial(0.0) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::real("theta2") + .with_initial(0.0) + .fixed() + .without_random_effect(), + ) + // Log-normal IIV: VS_i = TVVS * exp(ETA_VS,i) + .parameter(Parameter::log("vs").with_initial(2.0)) + // Initial IIV covariance matrix: + // + // NONMEM: + // $OMEGA DIAGONAL(5) + // 0.10 + // 0.10 + // 0.10 + // 0.10 + // 0.10 + // + // PMcore and NONMEM values are ETA variances. + // Monolix uses ETA SDs, so 0.10 variance corresponds to: + // omega = sqrt(0.10) ≈ 0.316 + // + // Undeclared covariances are fixed to zero. + .omega(Omega::diagonal([ + ("cls", 0.10), + ("fm", 0.10), + ("k20", 0.10), + ("relv", 0.10), + ("vs", 0.10), + ])) + // Combined additive/proportional residual error. + // + // NONMEM: observation model in $ERROR with residual terms in $SIGMA + // Monolix: combined observation error with initial a=0.50, b=0.10 + // + // Each output has its own independently estimated error parameters. + .error_model("outeq_1", ResidualErrorModel::combined(0.50, 0.10)) + .error_model("outeq_2", ResidualErrorModel::combined(0.50, 0.10)) + .build()?; + + // SAEM algorithm settings + let config = SaemConfig::new() + .seed(20_260_717) + .n_chains(4) + .mcmc_iterations(4) + .eta_block_iterations(1) + .burn_in(100) + .k1_iterations(220) + .k2_iterations(180) + .averaged_iterates(0.75); + + let result = problem.fit_with(config)?; + result.write_outputs("outputs/meta_saem", 0.0, 0.0)?; + + println!("termination: {:?}", result.termination_reason()); + println!("conditional N2LL: {:.6}", result.conditional_n2ll()); + + // Final typical population values: NONMEM THETA / Monolix typical values. + println!("population parameters:"); + for (name, value) in result + .parameter_names() + .iter() + .zip(result.population_parameters()) + { + println!(" {name}: {value:.6}"); + } + + // Final diagonal $OMEGA values, reported by PMcore as ETA variances. + println!("IIV variances:"); + for (index, name) in result.random_effect_names().iter().enumerate() { + println!(" {name}: {:.6}", result.omega()[[index, index]]); + } + + // Final $SIGMA / Monolix observation-error parameter estimates. + println!("residual models:"); + for estimate in result.residual_error_estimates() { + println!(" {}: {:?}", estimate.output, estimate.model); + } + + Ok(()) +} diff --git a/examples/new_iov/subjects.csv b/examples/new_iov/subjects.csv deleted file mode 100644 index c5a094596..000000000 --- a/examples/new_iov/subjects.csv +++ /dev/null @@ -1,101 +0,0 @@ -sub,Ke,sKe,Vol -0, 1.1537057285734011, 0.10289541640270668, 50 -1, 1.4055612966624533, 0.09639474422747848, 50 -2, 1.1710303971859157, 0.08639424528661005, 50 -3, 1.3460198695803256, 0.08570562300129098, 50 -4, 1.1554824575854128, 0.0995401334124625, 50 -5, 1.2143588043852875, 0.09452739681289624, 50 -6, 1.2278932130799713, 0.10167991593451817, 50 -7, 1.098898765793177, 0.0979097597171234, 50 -8, 1.2676471420496378, 0.10998042907759475, 50 -9, 1.2396981758367795, 0.10112948002412282, 50 -10, 1.117977631957013, 0.09509903677831381, 50 -11, 1.1218128017472697, 0.11258246839062266, 50 -12, 1.0570770700535563, 0.10901940784275149, 50 -13, 1.3086459805837078, 0.10199380624314083, 50 -14, 1.224909824636831, 0.09176339607666309, 50 -15, 1.2364421542805297, 0.10195378693392695, 50 -16, 1.2447022980721614, 0.09816450833528423, 50 -17, 1.0066430977249738, 0.0937567623723915, 50 -18, 1.0248748122975728, 0.09697434986643566, 50 -19, 1.1738535522369364, 0.09860182726274573, 50 -20, 1.2876552425478005, 0.10579657258010625, 50 -21, 1.2486211245416499, 0.0931927131998619, 50 -22, 1.0112097287516961, 0.1154175328721026, 50 -23, 1.1767184172339717, 0.12118419195337941, 50 -24, 1.2351478381804541, 0.12062354460327637, 50 -25, 1.1787961570706984, 0.09555639445693169, 50 -26, 1.3458841273883968, 0.09905409014148021, 50 -27, 1.0630331359257796, 0.0933021368257885, 50 -28, 1.108062308587545, 0.09529187016455509, 50 -29, 1.340623771830854, 0.10486979259352251, 50 -30, 1.1869593362388007, 0.12413765264759334, 50 -31, 1.2813169033370082, 0.0948188595877337, 50 -32, 1.064347711058793, 0.09510807250779679, 50 -33, 1.0524279818040063, 0.09801471704249785, 50 -34, 1.2533274970403327, 0.10653972217518849, 50 -35, 1.2013492131344725, 0.08857802962888102, 50 -36, 1.3004905392045623, 0.10210248288307533, 50 -37, 1.1076842014795856, 0.08059643688007641, 50 -38, 1.2688132137947212, 0.11900907616490888, 50 -39, 1.3221373140196022, 0.08923677354687104, 50 -40, 1.1866381500162704, 0.1041857802145374, 50 -41, 1.1112976938814996, 0.09336801473753686, 50 -42, 1.2295540033914845, 0.12053241093171055, 50 -43, 1.3510846402579197, 0.09090671121805115, 50 -44, 1.2285269381332489, 0.09060328343782482, 50 -45, 1.2615316236096836, 0.08569954123287596, 50 -46, 1.355040886379684, 0.0981102526027017, 50 -47, 1.209599693647232, 0.0895166768928232, 50 -48, 1.0179441848057886, 0.06849061740076876, 50 -49, 1.0696351866993108, 0.10267688640425526, 50 -50, 1.278859849997356, 0.0986897719878365, 50 -51, 1.1268211653925553, 0.08556789523774115, 50 -52, 1.1797201873016918, 0.08986372996336187, 50 -53, 1.248185906357412, 0.10247472618104703, 50 -54, 1.0675677605376521, 0.11564596558466642, 50 -55, 0.9697137996356358, 0.11183105566166397, 50 -56, 1.0921249127084762, 0.11264559252006313, 50 -57, 1.2931213068145355, 0.09733962543048513, 50 -58, 0.886820731374182, 0.09596054209213793, 50 -59, 1.1972305791891324, 0.10091692382494825, 50 -60, 1.1438810654208762, 0.095634978222107, 50 -61, 1.2105942190436731, 0.1091189235433658, 50 -62, 1.1218180302299432, 0.11732029332477402, 50 -63, 1.2667711119801168, 0.09598261537724273, 50 -64, 1.225642941806333, 0.08616025858594184, 50 -65, 0.9987592457582799, 0.1013981877042509, 50 -66, 1.2138946485453377, 0.10062881241060935, 50 -67, 1.3995467472337397, 0.0837534197032114, 50 -68, 1.1503861962448882, 0.08970545547459753, 50 -69, 1.2917968488378215, 0.10198981216193112, 50 -70, 1.2778069149863, 0.09606849753661166, 50 -71, 1.080351648006885, 0.11385621954234637, 50 -72, 1.1972642754581033, 0.09820646537237751, 50 -73, 1.2699424429054966, 0.11132956173515586, 50 -74, 1.0382734580265272, 0.10695713528078032, 50 -75, 1.215294466880341, 0.10824664157811359, 50 -76, 1.0750734337834869, 0.09334672510188377, 50 -77, 1.1491816816250375, 0.09105906901035939, 50 -78, 1.243505049274979, 0.08460268066036611, 50 -79, 1.035306988798836, 0.09087617946948215, 50 -80, 1.0582060793991683, 0.10094839455166812, 50 -81, 1.1357471297990474, 0.10358493403627932, 50 -82, 1.1586266841792847, 0.1232733758135109, 50 -83, 1.1692415618123773, 0.10369932924182489, 50 -84, 1.089704993345683, 0.09485483949272429, 50 -85, 1.1426673589726786, 0.08286971279699235, 50 -86, 1.3071602622738743, 0.10378982443294256, 50 -87, 1.1658696086370257, 0.08730846319176624, 50 -88, 0.9167324903824648, 0.10579852144930121, 50 -89, 1.1508404841198825, 0.09245940690596459, 50 -90, 1.2280184498451163, 0.08582550001397626, 50 -91, 1.09848817578252, 0.10798970381960545, 50 -92, 1.2400275782782906, 0.07333044137551628, 50 -93, 1.1436910224831092, 0.09234258577097995, 50 -94, 1.1428469515924335, 0.10880822347600295, 50 -95, 1.0731800787050982, 0.10477870068503345, 50 -96, 1.1532202352777614, 0.10481938224654984, 50 -97, 1.0398723117148394, 0.10566419670499176, 50 -98, 1.1001955107995218, 0.10577401199277633, 50 -99, 0.9456245532389306, 0.0960663151430105, 50 diff --git a/examples/vanco.rs b/examples/vanco.rs index 469d20bca..ddde94240 100644 --- a/examples/vanco.rs +++ b/examples/vanco.rs @@ -40,9 +40,7 @@ fn main() -> Result<()> { .repeat(1000, 0.01) .build(); - let op = eq - .simulate_subject_dense(&subject, &[0.3, 0.2, 0.5], None)? - .0; + let op = eq.estimate_predictions_dense(&subject, &[0.3, 0.2, 0.5])?; let times = op.flat_times(); let pred = op.flat_predictions(); diff --git a/examples/vanco_sde/data.csv b/examples/vanco_sde/data.csv deleted file mode 100644 index 6b6f25f4f..000000000 --- a/examples/vanco_sde/data.csv +++ /dev/null @@ -1,2726 +0,0 @@ -ID,EVID,TIME,DUR,DOSE,ADDL,II,INPUT,OUT,OUTEQ,C0,C1,C2,C3,SCR,WT,HT,MALE -3,1,0,0,0,.,.,1,.,.,.,.,.,.,0.4,9.3,76,1 -3,1,66.3,0,150,.,.,1,.,.,.,.,.,.,0.2,9.3,76,1 -3,1,71.9,0,150,.,.,1,.,.,.,.,.,.,0.2,9.3,76,1 -3,1,77.2,0,150,.,.,1,.,.,.,.,.,.,0.2,9.3,76,1 -3,0,83,.,.,.,.,.,17.7,1,.,.,.,.,0.3,9.3,76,1 -3,1,83.1,0,150,.,.,1,.,.,.,.,.,.,0.3,9.3,76,1 -3,1,89.5,0,150,.,.,1,.,.,.,.,.,.,0.2,9.3,76,1 -3,1,95.7,0,150,.,.,1,.,.,.,.,.,.,0.2,9.3,76,1 -3,1,100.6,0,150,.,.,1,.,.,.,.,.,.,0.2,9.3,76,1 -3,1,107,0,150,.,.,1,.,.,.,.,.,.,0.3,9.3,76,1 -3,1,115.5,0,150,.,.,1,.,.,.,.,.,.,0.3,9.3,76,1 -3,1,123.3,0,150,.,.,1,.,.,.,.,.,.,0.3,9.3,76,1 -3,1,131,0,150,.,.,1,.,.,.,.,.,.,0.3,9.3,76,1 -3,0,139.2,.,.,.,.,.,10.9,1,.,.,.,.,0.3,9.3,76,1 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+PMcore defines inter-individual variability (IIV) in transformed parameter +space: + +```text +phi(P_i) = phi(TVP_i) + eta_i +eta_i ~ Normal(0, Omega) +``` + +`phi` is selected by the parameter declaration. `TVP_i` includes the population +value and subject-static covariate offsets. Model execution converts the result +back to natural parameter space. + +| PMcore declaration | Individual parameter | +| --- | --- | +| `Parameter::real("p")` | `P_i = TVP_i + eta_i` | +| `Parameter::log("p")` | `P_i = TVP_i * exp(eta_i)` | +| `Parameter::logit("p", lower, upper)` | additive eta on the bounded logit scale | +| `Parameter::probit("p", lower, upper)` | additive eta on the bounded probit scale | + +`Parameter::with_initial` always receives the natural-scale typical value at +zero eta, kappa, and covariate offsets. + +## Additive IIV + +NONMEM: + +```text +$THETA +(0, 10) ; initial TVP + +$OMEGA +4.0 ; variance + +$PK +P = THETA(1) + ETA(1) +``` + +PMcore: + +```rust +use pmcore::prelude::*; + +let parameter = Parameter::real("p").with_initial(10.0); +let omega = Omega::diagonal_variances([("p", 4.0)]); +``` + +Both declarations define `P_i = TVP + eta_i` with `eta_i ~ Normal(0, 4)`. +The initial random-effect standard deviation is `2`. + +## Log-normal IIV + +NONMEM: + +```text +$THETA +(0, 5) ; initial TVCL + +$OMEGA +0.09 ; variance on the log scale + +$PK +CL = THETA(1) * EXP(ETA(1)) +``` + +PMcore: + +```rust +let parameter = Parameter::log("cl").with_initial(5.0); +let omega = Omega::diagonal_variances([("cl", 0.09)]); +``` + +Both define `CL_i = TVCL * exp(eta_CL,i)`. The transformed-space SD is `0.3`. +The corresponding natural-scale coefficient of variation is +`sqrt(exp(0.09) - 1)`, approximately `0.307`. + +## Correlated random effects + +NONMEM: + +```text +$OMEGA BLOCK(2) +0.09 +0.01 0.04 +``` + +PMcore: + +```rust +let omega = Omega::diagonal_variances([ + ("cl", 0.09), + ("v", 0.04), +]) +.covariance("cl", "v", 0.01); +``` + +Both initialize + +```text +Omega = [[0.09, 0.01], + [0.01, 0.04]] +``` + +In PMcore, undeclared covariances are structural zeros. Declare every covariance +that may be estimated. + +`Omega::diagonal_standard_deviations` accepts finite positive SDs and squares +them after checking overflow. Legacy `Omega::diagonal` remains variance-based. + +## Fixed population values and covariance entries + +NONMEM fixes values with `FIX`: + +```text +$THETA +(0, 5 FIX) + +$OMEGA +0.09 FIX +``` + +PMcore fixes the population value and variance independently: + +```rust +let parameter = Parameter::log("cl") + .with_initial(5.0) + .fixed(); + +let omega = Omega::new().fixed_variance("cl", 0.09); +``` + +A fixed population value may retain estimated IIV by using `.fixed()` on the +parameter and an estimated `variance` entry in `Omega`. + +## Parameters without IIV + +A NONMEM parameter has no IIV when its `$PK` expression contains no `ETA` term: + +```text +$THETA +(0, 1) + +$PK +BASE = THETA(1) +``` + +The PMcore equivalent is explicit: + +```rust +let parameter = Parameter::real("baseline") + .with_initial(1.0) + .without_random_effect(); +``` + +The population value may be fixed or estimated. Estimated no-IIV population and +covariate effects use the observation likelihood directly. Their +observed-information covariance and standard errors remain unsupported until +structural observation sensitivities are available. + +## Bounded parameters + +PMcore can put eta on a bounded logit or probit scale directly: + +```rust +let parameter = Parameter::logit("fm", 0.0, 1.0) + .with_initial(0.20); +let omega = Omega::diagonal_variances([("fm", 0.10)]); +``` + +This guarantees `0 < FM_i < 1`. A NONMEM model typically writes the inverse +logit transformation explicitly in `$PK`; PMcore stores the bounds and +transformation in the parameter declaration. + +## Inter-occasion variability + +PMcore `Iov` uses the same variance, covariance, fixedness, and diagonal +constructor semantics as `Omega`. Kappa is additive in transformed parameter +space and indexed by subject and actual occasion. A parameter may have IIV, +IOV, both, or neither. + +See [NONMEM and PMcore model declarations](docs/nonmem-comparison.md) for an IOV +example and a broader syntax comparison. + +## Numerical safeguards + +PMcore requires finite, symmetric, strictly positive-definite covariance +matrices. Updates preserve fixed entries and structural zeros and must not +increase the covariance objective. No jitter, clipping, projection, or matrix +repair is applied. + +Covariate raw first and second moments use the same stochastic-approximation +gain. PMcore forms a coherent centered covariance target before applying masks, +local GEM constraints, and any exploration-only displacement cap. Smoothing +does not apply a second covariance gain. diff --git a/plans/saem-validation-roadmap.md b/plans/saem-validation-roadmap.md new file mode 100644 index 000000000..12686799d --- /dev/null +++ b/plans/saem-validation-roadmap.md @@ -0,0 +1,84 @@ +# SAEM current status and future work + +## Current implementation + +PMcore provides a production SAEM path for deterministic analytical and ODE +models. The implementation includes: + +- transformed-space population parameters with identity, log, logit, and probit + scales; +- IIV and IOV with named fixed/free covariance masks and structural zeros; +- subject-static continuous and categorical covariate effects; +- estimated population and covariate effects with or without IIV; +- additive, proportional, combined, correlated-combined, and exponential + residual models; +- persistent eta and kappa MCMC with component and opt-in block proposals; +- burn-in, exploration, and decreasing-gain smoothing phases; +- terminal-iterate and opt-in averaged estimators; +- strict observed-information and conditional-curvature diagnostics; +- eta and kappa posterior-mean/MAP shrinkage; +- population and conditional predictions; +- post-fit population marginal likelihood, AIC, and BIC; +- cycle-by-cycle controllers, observers, snapshots, and typed termination; +- schema-9 persistence, structured outputs, and warm starts; and +- explicit particle filtering and bounded diffusion optimization for SDE use. + +Covariate raw first and second moments use one common SA gain. PMcore forms the +centered covariance target before applying masks, local GEM constraints, strict +positive-definiteness checks, and any exploration-only displacement cap. +Smoothing does not apply a second covariance gain. + +The default finite SAEM schedule reports `MaxCycles`. `Converged` is available +only through an explicit operational policy. Conditional N2LL remains a +diagnostic; it never substitutes for population marginal likelihood. + +The support matrix and failure semantics are maintained in +[`docs/saem-support.md`](../docs/saem-support.md). Convergence and information +semantics are maintained in +[`docs/saem-convergence.md`](../docs/saem-convergence.md). + +## Deferred post-release work + +There is no active implementation slice. The following work is deferred until +after release: + +### Reference-model coverage + +- Add one maintained large-model regression that exercises the public model, + covariate, residual, persistence, and result APIs without creating a separate + validation framework. +- Expand replicated analytical and ODE coverage only when each fixture protects + a concrete supported behavior. +- Add broader cross-engine comparisons only as bounded development work; keep + external run products outside the product repository. + +### Statistical maturity + +- Evaluate convergence and coverage over larger replicated datasets. +- Improve marginal-likelihood proposal diagnostics and ambiguity handling. +- Extend uncertainty reporting where structural observation sensitivities are + available. +- Add shrinkage and information summaries for new supported coordinate types. + +### Lifecycle maturity + +- Bring nonparametric persistence and lifecycle APIs to the same level as the + parametric controller. +- Review result-schema evolution before adding new persisted diagnostics. +- Keep package examples small, self-contained, and runnable. + +## Optional research + +These are not release commitments: + +- shared-random-stream studies and alternative MCMC kernels; +- Hamiltonian Monte Carlo; +- automatic differentiation and shared sensitivity infrastructure; +- FO, FOCE, and FOCE-I; +- broader dense residual covariance models; +- generic SDE estimation after the explicit particle-session boundary can + support it without moving likelihood ownership out of PMcore. + +New work should default to post-release unless a focused regression demonstrates +incorrect behavior, silent fallback, or misleading output inside the supported +matrix. diff --git a/src/algorithms/mod.rs b/src/algorithms/mod.rs index 30a6c8d3d..deceee8c1 100644 --- a/src/algorithms/mod.rs +++ b/src/algorithms/mod.rs @@ -1,6 +1,7 @@ use std::fs; use std::path::Path; +use crate::estimation::likelihood::observation::assay_error_model_log_likelihoods; use crate::estimation::nonparametric::{NonParametricResult, Psi, Theta}; use crate::estimation::{EstimationProblem, Framework}; use crate::results::FitResult; @@ -118,10 +119,11 @@ pub trait NonParametricRunner: Sync + Send + 'stat .into_par_iter() .map(|(i, spp)| { let support_point: Vec = spp.iter().copied().collect(); - let (pred, ll) = self + let pred = self .equation() - .simulate_subject_dense(subject, &support_point, Some(&error_model)) + .estimate_predictions_dense(subject, &support_point) .unwrap(); //TODO: Handle error + let ll = assay_error_model_log_likelihoods(&pred, &error_model).unwrap_or(f64::NAN); (i, support_point, pred.get_predictions(), ll) }) .collect(); @@ -133,13 +135,16 @@ pub trait NonParametricRunner: Sync + Send + 'stat let mut zero = 0; let mut valid = 0; for (_, _, _, ll) in &results { - match ll { - Some(v) if v.is_nan() => nan += 1, - Some(v) if v.is_infinite() && v.is_sign_positive() => pos_inf += 1, - Some(v) if v.is_infinite() => neg_inf += 1, - Some(v) if *v == 0.0 => zero += 1, - Some(_) => valid += 1, - None => nan += 1, + if ll.is_nan() { + nan += 1; + } else if ll.is_infinite() && ll.is_sign_positive() { + pos_inf += 1; + } else if ll.is_infinite() { + neg_inf += 1; + } else if *ll == 0.0 { + zero += 1; + } else { + valid += 1; } } @@ -157,11 +162,7 @@ pub trait NonParametricRunner: Sync + Send + 'stat tracing::debug!("\tValid likelihoods: {} ({:.1}%)", valid, pct(valid)); // Show the most likely support points to aid debugging. - results.sort_by(|a, b| { - b.3.unwrap_or(f64::NEG_INFINITY) - .partial_cmp(&a.3.unwrap_or(f64::NEG_INFINITY)) - .unwrap_or(std::cmp::Ordering::Equal) - }); + results.sort_by(|a, b| b.3.partial_cmp(&a.3).unwrap_or(std::cmp::Ordering::Equal)); const TAKE: usize = 3; tracing::debug!("Top {} most likely support points:", TAKE); @@ -198,7 +199,7 @@ pub trait NonParametricRunner: Sync + Send + 'stat tracing::debug!("====================="); } - fn error_models(&self) -> &pharmsol::prelude::data::AssayErrorModels; + fn error_models(&self) -> &crate::AssayErrorModels; /// Get the equation used in the algorithm fn equation(&self) -> &E; /// Get the data used in the algorithm @@ -249,7 +250,7 @@ pub trait NonParametricRunner: Sync + Send + 'stat /// It is typically performed after the estimation step in each cycle of the algorithm. fn condensation(&mut self) -> Result<()>; - /// Performs optimizations on the current `AssayErrorModels` and updates [Psi] accordingly + /// Optimizes the current assay error models and updates [`Psi`] accordingly. /// /// This step refines the error model parameters to better fit the data, /// and subsequently updates the [Psi] matrix to reflect these changes. @@ -349,6 +350,8 @@ pub enum StopReason { /// Stopped from code — [`request_stop`](crate::algorithms::nonparametric::FitController::request_stop) /// or an observer returning [`CycleFlow::Stop`](crate::algorithms::nonparametric::CycleFlow::Stop). Aborted, + /// A runtime numerical operation failed. + NumericalFailure, } impl std::fmt::Display for StopReason { @@ -358,6 +361,7 @@ impl std::fmt::Display for StopReason { StopReason::MaxCycles => "maximum cycles reached", StopReason::StopFile => "stop file detected", StopReason::Aborted => "aborted", + StopReason::NumericalFailure => "numerical failure", }; f.write_str(reason) } diff --git a/src/algorithms/nonparametric/controller.rs b/src/algorithms/nonparametric/controller.rs index 22a714290..81a6b1a83 100644 --- a/src/algorithms/nonparametric/controller.rs +++ b/src/algorithms/nonparametric/controller.rs @@ -11,11 +11,11 @@ //! Both drive the same underlying runner and finish at the same result. use anyhow::Result; -use pharmsol::prelude::{data::AssayErrorModels, simulator::Equation}; +use pharmsol::prelude::simulator::Equation; use crate::algorithms::{NonParametricRunner, Status, StopReason}; use crate::estimation::nonparametric::{NonParametricResult, Psi, Theta}; -use crate::estimation::{EstimationProblem, NonParametric}; +use crate::estimation::{AssayErrorModels, EstimationProblem, NonParametric}; use super::NonParametricAlgorithm; diff --git a/src/algorithms/nonparametric/error_optim.rs b/src/algorithms/nonparametric/error_optim.rs index 33e8a5b26..8c291e8f8 100644 --- a/src/algorithms/nonparametric/error_optim.rs +++ b/src/algorithms/nonparametric/error_optim.rs @@ -1,13 +1,11 @@ //! Error-model factor optimization used by non-parametric algorithms. use anyhow::Result; -use pharmsol::prelude::{ - data::{AssayErrorModels, Data}, - simulator::Equation, -}; +use pharmsol::prelude::{data::Data, simulator::Equation}; use serde::{Deserialize, Serialize}; use crate::estimation::nonparametric::{calculate_psi, ipm::burke, Psi, Theta, Weights}; +use crate::AssayErrorModels; /// Configuration for the error-model factor (gamma/lambda) optimization. #[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] diff --git a/src/algorithms/nonparametric/ncnpag.rs b/src/algorithms/nonparametric/ncnpag.rs index 860968ce7..774e03ec7 100644 --- a/src/algorithms/nonparametric/ncnpag.rs +++ b/src/algorithms/nonparametric/ncnpag.rs @@ -6,14 +6,12 @@ use crate::{ estimation::nonparametric::{ calculate_psi, CycleLog, NPCycle, NonParametricResult, Psi, Theta, Weights, }, + AssayErrorModels, }; use anyhow::Result; use faer::Mat; -use pharmsol::prelude::{ - data::{AssayErrorModels, Data}, - simulator::Equation, -}; +use pharmsol::prelude::{data::Data, simulator::Equation}; use serde::{Deserialize, Serialize}; diff --git a/src/algorithms/nonparametric/npag.rs b/src/algorithms/nonparametric/npag.rs index 097b9e2d9..112e0da36 100644 --- a/src/algorithms/nonparametric/npag.rs +++ b/src/algorithms/nonparametric/npag.rs @@ -8,12 +8,9 @@ pub(crate) use crate::estimation::nonparametric::qr; use anyhow::bail; use anyhow::Result; -use pharmsol::prelude::{ - data::{AssayErrorModels, Data}, - simulator::Equation, -}; +use pharmsol::prelude::{data::Data, simulator::Equation}; -use pharmsol::prelude::AssayErrorModel; +use crate::{AssayErrorModel, AssayErrorModels}; use crate::estimation::nonparametric::adaptative_grid; diff --git a/src/algorithms/nonparametric/npmap.rs b/src/algorithms/nonparametric/npmap.rs index c5765a6a1..75bfbaa3f 100644 --- a/src/algorithms/nonparametric/npmap.rs +++ b/src/algorithms/nonparametric/npmap.rs @@ -5,11 +5,9 @@ use crate::{ }, }; +use crate::AssayErrorModels; use anyhow::{Context, Result}; -use pharmsol::prelude::{ - data::{AssayErrorModels, Data}, - simulator::Equation, -}; +use pharmsol::prelude::{data::Data, simulator::Equation}; use crate::estimation::nonparametric::ipm::burke; use serde::{Deserialize, Serialize}; diff --git a/src/algorithms/nonparametric/npod.rs b/src/algorithms/nonparametric/npod.rs index 32aaa3372..e90d96a3c 100644 --- a/src/algorithms/nonparametric/npod.rs +++ b/src/algorithms/nonparametric/npod.rs @@ -1,15 +1,15 @@ use crate::{ algorithms::{NonParametricRunner, Status, StopReason}, estimation::nonparametric::{ - calculate_psi, ipm::burke, qr, CycleLog, NPCycle, NonParametricResult, Psi, Theta, Weights, + calculate_psi, ipm::burke, qr, CycleLog, NPCycle, NonParametricResult, ParameterOptimizer, + Psi, Theta, Weights, }, }; -use pharmsol::ParameterOptimizer; +use crate::{AssayErrorModel, AssayErrorModels}; use anyhow::bail; use anyhow::Result; use pharmsol::prelude::{data::Data, simulator::Equation}; -use pharmsol::{prelude::AssayErrorModel, AssayErrorModels}; use ndarray::Array1; use rayon::prelude::{IntoParallelRefMutIterator, ParallelIterator}; @@ -353,11 +353,14 @@ impl NonParametricRunner for NPOD { let spp = Array1::from(candidate); candididate_points.push(spp.to_owned()); } - candididate_points.par_iter_mut().for_each(|spp| { - let optimizer = ParameterOptimizer::new(&self.equation, &self.data, &error_model, &pyl); - let candidate_point = optimizer.optimize_point(spp.to_owned()).unwrap(); - *spp = candidate_point; - }); + candididate_points + .par_iter_mut() + .try_for_each(|spp| -> Result<()> { + let optimizer = + ParameterOptimizer::new(&self.equation, &self.data, &error_model, &pyl); + *spp = optimizer.optimize_point(spp.to_owned())?; + Ok(()) + })?; for cp in candididate_points { self.theta.suggest_point(cp.to_vec().as_slice(), THETA_D)?; } diff --git a/src/algorithms/parametric/controller.rs b/src/algorithms/parametric/controller.rs new file mode 100644 index 000000000..7fceda62f --- /dev/null +++ b/src/algorithms/parametric/controller.rs @@ -0,0 +1,562 @@ +//! Step through a parametric fit cycle by cycle using the same controller API +//! as nonparametric fits. + +use std::{fs, path::Path}; + +use anyhow::{Context, Result}; +use ndarray::Array2; +use pharmsol::prelude::simulator::Equation; + +use crate::algorithms::{Status, StopReason}; +use crate::estimation::{EstimationProblem, Parametric}; +use crate::results::{ParametricResult, SaemCycleDiagnostics}; + +use super::{ParametricAlgorithm, ParametricRunner}; + +/// A self-contained view of a live parametric fit. +#[derive(Debug, Clone, PartialEq)] +pub struct ParametricFitSnapshot { + /// Number of completed cycles. + pub cycle: usize, + /// Number of cycles in the configured schedule. + pub total_cycles: usize, + /// Current lifecycle status. + pub status: Status, + /// Current conditional `-2 log likelihood` diagnostic. + pub conditional_n2ll: f64, + /// Current population parameter estimates in model-space ψ. + pub population_parameters: Vec, + /// Current covariate coefficients in canonical declaration order. + pub covariate_betas: Option>, + /// Current random-effect covariance matrix. + pub omega: Option>, + /// Current inter-occasion covariance matrix. + pub omega_iov: Option>, + /// Current primary residual-error sigma parameters. + pub residual_sigmas: Vec, + /// Most recent completed-cycle diagnostics, if any. + pub latest_cycle_diagnostics: Option, +} + +impl ParametricFitSnapshot { + /// Completed fraction of the configured schedule, clamped to `0.0..=1.0`. + pub fn progress(&self) -> f64 { + if self.total_cycles == 0 { + 0.0 + } else { + (self.cycle as f64 / self.total_cycles as f64).clamp(0.0, 1.0) + } + } + + /// Whether the fit has reached any terminal status. + pub fn is_terminal(&self) -> bool { + self.status.is_stop() + } +} + +/// A running parametric fit that can be advanced one cycle at a time. +/// +/// The controller is algorithm-neutral and drives whichever parametric runner +/// the selected algorithm provides. +pub struct FitController { + runner: Box>, + termination_logged: bool, +} + +impl FitController { + pub(crate) fn new( + algorithm: ParametricAlgorithm, + problem: EstimationProblem, + ) -> Result { + let stop_path = Path::new("stop"); + if stop_path.exists() { + tracing::info!("Removing existing stop file prior to parametric run"); + fs::remove_file(stop_path).context("Unable to remove previous stop file")?; + } + + let runner = algorithm.into_runner(problem)?; + tracing::info!("Starting SAEM fit"); + tracing::debug!( + scheduled_cycles = runner.total_iterations(), + chains = ?runner.n_chains(), + random_effects = ?runner.random_effect_names(), + initial_population = ?runner.population_parameters(), + "SAEM configuration and initial state" + ); + if let Some(iov_effects) = runner.iov_effect_names() { + tracing::debug!(iov_effects = ?iov_effects, "SAEM IOV configuration"); + } + Ok(Self { + runner, + termination_logged: false, + }) + } + + /// Advance one complete parametric estimation cycle and return its status. + pub fn step(&mut self) -> Result { + if self.runner.status().is_stop() { + return Ok(self.runner.status().clone()); + } + + let next_cycle = self.runner.cycle() + 1; + let span = tracing::info_span!("", "{}", format!("Cycle {next_cycle}")); + let _entered = span.enter(); + let previous_cycle = self.runner.cycle(); + match self.runner.step() { + Ok(_) => { + if self.runner.cycle() > previous_cycle { + self.log_completed_cycle(); + if self.runner.status().is_continue() && Path::new("stop").exists() { + self.runner.request_stop(StopReason::StopFile); + } + } + let status = self.runner.status().clone(); + if status.is_stop() { + self.log_termination(&status, None); + } + Ok(status) + } + Err(error) => { + let status = self.runner.status().clone(); + self.log_termination(&status, Some(&error)); + Err(error) + } + } + } + + /// Flag the fit as aborted. Call before [`into_result`](Self::into_result) + /// when stopping from a debugger or observer. + pub fn request_stop(&mut self) { + let was_running = self.runner.status().is_continue(); + self.runner.request_stop(StopReason::Aborted); + if was_running { + let status = self.runner.status().clone(); + self.log_termination(&status, None); + } + } + + /// Current cycle number, 0 before the first [`step`](Self::step). + pub fn cycle(&self) -> usize { + self.runner.cycle() + } + + /// Current controller status. + pub fn status(&self) -> &Status { + self.runner.status() + } + + /// Self-contained snapshot of the current live fit state. + pub fn snapshot(&self) -> ParametricFitSnapshot { + ParametricFitSnapshot { + cycle: self.runner.cycle(), + total_cycles: self.runner.total_iterations(), + status: self.runner.status().clone(), + conditional_n2ll: self.runner.n2ll(), + population_parameters: self.runner.population_parameters().to_vec(), + covariate_betas: self.runner.covariate_betas(), + omega: self.runner.omega().cloned(), + omega_iov: self.runner.omega_iov().cloned(), + residual_sigmas: self.runner.residual_sigmas().to_vec(), + latest_cycle_diagnostics: self.runner.cycle_diagnostics().last().cloned(), + } + } + + /// Number of cycles in the configured schedule. + pub fn total_cycles(&self) -> usize { + self.runner.total_iterations() + } + + /// Completed fraction of the configured schedule, clamped to `0.0..=1.0`. + pub fn progress(&self) -> f64 { + let total_cycles = self.runner.total_iterations(); + if total_cycles == 0 { + 0.0 + } else { + (self.runner.cycle() as f64 / total_cycles as f64).clamp(0.0, 1.0) + } + } + + /// Completed SAEM cycle records, suitable for observers and debuggers. + pub fn cycle_diagnostics(&self) -> &[SaemCycleDiagnostics] { + self.runner.cycle_diagnostics() + } + + /// Current log-likelihood from residual scoring. + pub fn likelihood(&self) -> f64 { + self.runner.log_likelihood() + } + + /// Current population parameter estimates in model-space ψ. + pub fn population_parameters(&self) -> &[f64] { + self.runner.population_parameters() + } + + /// Current covariate coefficients in canonical declaration order. + pub fn covariate_betas(&self) -> Option> { + self.runner.covariate_betas() + } + + /// Names of parameters with IIV random effects, in η/Ω order. + pub fn random_effect_names(&self) -> &[String] { + self.runner.random_effect_names() + } + + /// Names of parameters with IOV random effects, in κ/Ω_IOV order. + pub fn iov_effect_names(&self) -> Option<&[String]> { + self.runner.iov_effect_names() + } + + /// Current η log-prior under Ω. + pub fn eta_log_prior(&self) -> f64 { + self.runner.eta_log_prior() + } + + /// Current κ log-prior under Ω_IOV. + pub fn kappa_log_prior(&self) -> f64 { + self.runner.kappa_log_prior() + } + + /// Current log posterior, up to the current parameterization. + pub fn log_posterior(&self) -> f64 { + self.runner.log_posterior() + } + + /// Last MCMC proposal acceptance rate, when at least one E-step has run. + pub fn acceptance_rate(&self) -> Option { + self.runner.acceptance_rate() + } + + /// Ω-scaled η block acceptance rate in the most recent E-step. + /// Returns `None` when the block kernel is disabled. + pub fn eta_block_acceptance_rate(&self) -> Option { + self.runner.eta_block_acceptance_rate() + } + + /// κ component proposal acceptance rate in the most recent E-step. + /// Returns `None` when IOV is not configured. + pub fn kappa_acceptance_rate(&self) -> Option { + self.runner.kappa_acceptance_rate() + } + + /// Number of rejected proposals in the most recent E-step. + pub fn rejected_proposals(&self) -> Option { + self.runner.rejected_proposals() + } + + /// Number of proposals rejected for non-finite posterior scores in the + /// most recent E-step. + pub fn non_finite_proposals(&self) -> Option { + self.runner.non_finite_proposals() + } + + /// Last MCMC acceptance rates in [`random_effect_names`](Self::random_effect_names) order. + pub fn parameter_acceptance_rates(&self) -> Option<&[f64]> { + self.runner.parameter_acceptance_rates() + } + + /// Current component-wise proposal scales in random-effect order. + pub fn proposal_step_sizes(&self) -> Option<&[f64]> { + self.runner.proposal_step_sizes() + } + + /// Current per-subject Ω-scaled η block multipliers. + /// Returns `None` when the block kernel is disabled. + pub fn eta_block_step_sizes(&self) -> Option<&[f64]> { + self.runner.eta_block_step_sizes() + } + + /// Last per-subject log acceptance ratios from the proposal-scoring pass. + pub fn log_acceptance_ratios(&self) -> Option<&[f64]> { + self.runner.log_acceptance_ratios() + } + + /// Current negative log-likelihood from residual scoring. + pub fn negative_log_likelihood(&self) -> f64 { + self.runner.negative_log_likelihood() + } + + /// Current `-2 log likelihood`, matching the non-parametric controller name. + pub fn n2ll(&self) -> f64 { + self.runner.n2ll() + } + + /// Current stochastic-approximation step size. + pub fn step_size(&self) -> f64 { + self.runner.step_size() + } + + /// Current random-effect covariance matrix in + /// [`random_effect_names`](Self::random_effect_names) order. + pub fn omega(&self) -> Option<&Array2> { + self.runner.omega() + } + + /// Current inter-occasion covariance matrix in + /// [`iov_effect_names`](Self::iov_effect_names) order. + pub fn omega_iov(&self) -> Option<&Array2> { + self.runner.omega_iov() + } + + /// Current diagonal of the random-effect covariance matrix, when available. + pub fn omega_diagonal(&self) -> Option> { + self.runner.omega_diagonal() + } + + /// Current primary residual-error sigma parameters. + pub fn residual_sigmas(&self) -> &[f64] { + self.runner.residual_sigmas() + } + + /// Number of individual MCMC chains currently scheduled per subject. + pub fn n_chains(&self) -> Option { + self.runner.n_chains() + } + + /// Number of scheduled SAEM iterations. + pub fn total_iterations(&self) -> usize { + self.runner.total_iterations() + } + + /// Run to completion and return the final parametric result. + pub fn finish(mut self) -> Result> { + while self.status().is_continue() { + self.step()?; + } + self.into_result() + } + + /// Convert the current state into a parametric result. + pub fn into_result(mut self) -> Result> { + let status = self.runner.status().clone(); + let failure_already_logged = + self.termination_logged && status.stop_reason() == Some(&StopReason::NumericalFailure); + if status.is_stop() { + self.log_termination(&status, None); + } + match self.runner.into_result() { + Ok(result) => { + tracing::info!("SAEM result assembly complete"); + Ok(result) + } + Err(error) => { + if !failure_already_logged { + tracing::error!("{error}"); + } + Err(error) + } + } + } + + fn log_completed_cycle(&self) { + let Some(diagnostics) = self.runner.cycle_diagnostics().last() else { + return; + }; + tracing::info!( + "Conditional N2LL = {:.4}", + 2.0 * diagnostics.conditional_negative_log_likelihood + ); + tracing::debug!( + phase = ?diagnostics.phase, + stochastic_approximation_step = diagnostics.stochastic_approximation_step, + covariance_step = diagnostics.covariance_step, + population_parameters = ?diagnostics.population_parameters, + omega = ?diagnostics.omega, + residual_estimates = ?diagnostics.residual_error_estimates, + eta_accepted = diagnostics.eta_accepted, + eta_proposals = diagnostics.eta_proposals, + eta_acceptance_rate = acceptance_rate(diagnostics.eta_accepted, diagnostics.eta_proposals), + eta_rejected = diagnostics.eta_rejected, + eta_non_finite = diagnostics.eta_non_finite, + kappa_accepted = diagnostics.kappa_accepted, + kappa_proposals = diagnostics.kappa_proposals, + kappa_acceptance_rate = acceptance_rate(diagnostics.kappa_accepted, diagnostics.kappa_proposals), + kappa_rejected = diagnostics.kappa_rejected, + kappa_non_finite = diagnostics.kappa_non_finite, + "SAEM cycle state" + ); + if let Some(omega_iov) = diagnostics.omega_iov.as_ref() { + tracing::debug!(omega_iov = ?omega_iov, "SAEM IOV cycle state"); + } + if diagnostics.eta_block_proposals > 0 { + tracing::debug!( + eta_block_accepted = diagnostics.eta_block_accepted, + eta_block_proposals = diagnostics.eta_block_proposals, + eta_block_acceptance_rate = acceptance_rate( + diagnostics.eta_block_accepted, + diagnostics.eta_block_proposals, + ), + eta_block_rejected = diagnostics.eta_block_rejected, + eta_block_non_finite = diagnostics.eta_block_non_finite, + "SAEM block-kernel state" + ); + } + self.log_cycle_guardrails(diagnostics); + } + + fn log_cycle_guardrails(&self, diagnostics: &SaemCycleDiagnostics) { + if diagnostics.omega_update_rejected { + tracing::warn!("Omega update rejected"); + } + if diagnostics.omega_iov_update_rejected { + tracing::warn!("Omega_IOV update rejected"); + } + if diagnostics.eta_non_finite > 0 { + tracing::warn!( + count = diagnostics.eta_non_finite, + "Non-finite eta proposals" + ); + } + if diagnostics.eta_block_non_finite > 0 { + tracing::warn!( + count = diagnostics.eta_block_non_finite, + "Non-finite eta block proposals" + ); + } + if diagnostics.kappa_non_finite > 0 { + tracing::warn!( + count = diagnostics.kappa_non_finite, + "Non-finite kappa proposals" + ); + } + for residual in &diagnostics.residual_diagnostics { + if residual.update_rejected { + tracing::warn!(output = %residual.output, "Residual update rejected"); + } + if residual.non_finite_prediction_count > 0 { + tracing::warn!( + output = %residual.output, + count = residual.non_finite_prediction_count, + "Non-finite residual predictions" + ); + } + if residual.exponential_domain_violation_count > 0 { + tracing::warn!( + output = %residual.output, + count = residual.exponential_domain_violation_count, + "Residual domain violations" + ); + } + if residual.proportional_floor_count > 0 { + tracing::debug!( + output = %residual.output, + count = residual.proportional_floor_count, + "Residual prediction floor applied" + ); + } + if residual.combined_additive_collapse_warning { + tracing::warn!(output = %residual.output, "Combined-family additive residual collapse"); + } + if residual.optimizer_converged == Some(false) { + tracing::warn!(output = %residual.output, "Residual optimizer did not converge"); + } + } + } + + fn log_termination(&mut self, status: &Status, error: Option<&anyhow::Error>) { + if self.termination_logged { + return; + } + match status.stop_reason() { + Some(StopReason::Converged) => tracing::info!( + "PMcore operational convergence criteria passed; this does not prove mathematical convergence" + ), + Some(StopReason::MaxCycles) => { + tracing::warn!("Maximum SAEM cycles reached; this is not statistical convergence") + } + Some(StopReason::StopFile) => tracing::warn!("SAEM stopped: stop file detected"), + Some(StopReason::Aborted) => tracing::warn!("SAEM aborted"), + Some(StopReason::NumericalFailure) => { + if let Some(error) = error { + tracing::error!("{error}"); + } else { + tracing::error!("SAEM stopped: numerical failure"); + } + } + None => { + if let Some(error) = error { + tracing::error!("{error}"); + } else { + return; + } + } + } + self.termination_logged = true; + } +} + +fn acceptance_rate(accepted: usize, proposed: usize) -> f64 { + if proposed == 0 { + 0.0 + } else { + accepted as f64 / proposed as f64 + } +} + +/// An observer's verdict after a parametric cycle. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum CycleFlow { + Continue, + Stop, +} + +/// Callback run after every parametric cycle. +pub trait FitObserver { + fn on_cycle(&mut self, controller: &FitController) -> CycleFlow; +} + +impl FitObserver for F +where + E: Equation + Send + 'static, + F: FnMut(&FitController) -> CycleFlow, +{ + fn on_cycle(&mut self, controller: &FitController) -> CycleFlow { + self(controller) + } +} + +impl EstimationProblem { + /// Start a parametric fit that can be driven one cycle at a time. + pub fn fit_controller( + self, + algorithm: impl Into, + ) -> Result> { + FitController::new(algorithm.into(), self) + } + + /// Run a parametric fit with an observer callback after every cycle. + /// + /// The observer sees every completed cycle, including the final cycle. Use + /// [`FitController::snapshot`] when the callback needs an owned progress + /// record: + /// + /// ```no_run + /// use pmcore::prelude::*; + /// # fn run(problem: EstimationProblem) -> Result<()> { + /// let result = problem.fit_with_observer(SaemConfig::new(), |controller: &ParametricFitController<_>| { + /// let snapshot = controller.snapshot(); + /// println!("cycle {} | {:.0}%", snapshot.cycle, 100.0 * snapshot.progress()); + /// ParametricCycleFlow::Continue + /// })?; + /// # let _ = result; Ok(()) + /// # } + /// ``` + pub fn fit_with_observer>( + self, + algorithm: impl Into, + mut observer: O, + ) -> Result> { + let mut controller = self.fit_controller(algorithm)?; + loop { + let status = controller.step()?; + let flow = observer.on_cycle(&controller); + if status.is_stop() { + break; + } + if flow == CycleFlow::Stop { + controller.request_stop(); + break; + } + } + controller.into_result() + } +} diff --git a/src/algorithms/parametric/mod.rs b/src/algorithms/parametric/mod.rs index 9951345f1..75d6acb68 100644 --- a/src/algorithms/parametric/mod.rs +++ b/src/algorithms/parametric/mod.rs @@ -8,21 +8,89 @@ //! Use the [`ParametricAlgorithm`] enum to select and configure an algorithm. Each variant //! wraps its algorithm-specific configuration struct (e.g. [`SaemConfig`]). //! -//! Note: the parametric fitting machinery is not yet implemented. Constructing a problem and -//! calling [`fit_with`](crate::estimation::EstimationProblem::fit_with) with a -//! [`ParametricAlgorithm`] type-checks today, but running it will panic until the SAEM solver -//! is implemented. +//! SAEM runs through the same [`Algorithm`] and +//! [`fit_with`](crate::estimation::EstimationProblem::fit_with) lifecycle as the +//! nonparametric algorithms. The cycle controller and result interfaces are +//! algorithm-neutral. +pub mod controller; +mod saem; pub mod saem_config; -pub use saem_config::SaemConfig; +pub use controller::{CycleFlow, FitController, FitObserver, ParametricFitSnapshot}; +use saem::SaemState; +pub use saem_config::{ + CovarianceStabilityConfig, LugsailConfig, MarkovSimulationVarianceConfig, + OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, +}; -use crate::algorithms::Algorithm; +use crate::algorithms::{Algorithm, Status, StopReason}; use crate::estimation::{EstimationProblem, Parametric}; -use crate::results::ParametricResult; +use crate::results::{ParametricResult, SaemCycleDiagnostics}; use anyhow::Result; +use ndarray::Array2; use pharmsol::prelude::simulator::Equation; +/// The SAEM operation that encountered a numerical failure. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum NumericalFailurePhase { + /// The expectation step. + Expectation, + /// The maximization step. + Maximization, + /// Post-fit conditional-mode or result assembly. + ResultAssembly, +} + +impl std::fmt::Display for NumericalFailurePhase { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + let phase = match self { + Self::Expectation => "expectation", + Self::Maximization => "maximization", + Self::ResultAssembly => "result assembly", + }; + f.write_str(phase) + } +} + +/// A numerical error that terminated a parametric fit. +#[derive(Debug, Clone, PartialEq, Eq, thiserror::Error)] +#[error("numerical failure during {phase} at cycle {attempted_cycle}: {source_message}")] +pub struct NumericalFailure { + attempted_cycle: usize, + phase: NumericalFailurePhase, + source_message: String, +} + +impl NumericalFailure { + pub(crate) fn new( + attempted_cycle: usize, + phase: NumericalFailurePhase, + source_message: String, + ) -> Self { + Self { + attempted_cycle, + phase, + source_message, + } + } + + /// The cycle being attempted when the failure occurred. + pub fn attempted_cycle(&self) -> usize { + self.attempted_cycle + } + + /// The operation that encountered the failure. + pub fn phase(&self) -> NumericalFailurePhase { + self.phase + } + + /// The original error message. + pub fn source_message(&self) -> &str { + &self.source_message + } +} + /// The parametric algorithms supported by PMcore. /// /// Use the constructors to select an algorithm with its default configuration: @@ -75,17 +143,71 @@ impl ParametricAlgorithm { pub fn saem() -> Self { Self::Saem(SaemConfig::default()) } + + pub(crate) fn into_runner( + self, + problem: EstimationProblem, + ) -> Result>> { + let runner: Box> = match self { + Self::Saem(config) => Box::new(SaemState::from_problem(problem, &config)?), + }; + Ok(runner) + } +} + +pub(crate) trait ParametricRunner: Send { + fn step(&mut self) -> Result; + fn request_stop(&mut self, reason: StopReason); + fn cycle(&self) -> usize; + fn status(&self) -> &Status; + fn cycle_diagnostics(&self) -> &[SaemCycleDiagnostics]; + fn log_likelihood(&self) -> f64; + fn population_parameters(&self) -> &[f64]; + fn covariate_betas(&self) -> Option>; + fn random_effect_names(&self) -> &[String]; + fn iov_effect_names(&self) -> Option<&[String]>; + fn eta_log_prior(&self) -> f64; + fn kappa_log_prior(&self) -> f64; + fn acceptance_rate(&self) -> Option; + fn eta_block_acceptance_rate(&self) -> Option; + fn kappa_acceptance_rate(&self) -> Option; + fn rejected_proposals(&self) -> Option; + fn non_finite_proposals(&self) -> Option; + fn parameter_acceptance_rates(&self) -> Option<&[f64]>; + fn proposal_step_sizes(&self) -> Option<&[f64]>; + fn eta_block_step_sizes(&self) -> Option<&[f64]>; + fn log_acceptance_ratios(&self) -> Option<&[f64]>; + fn negative_log_likelihood(&self) -> f64; + fn n_chains(&self) -> Option; + fn omega(&self) -> Option<&Array2>; + fn omega_iov(&self) -> Option<&Array2>; + fn residual_sigmas(&self) -> &[f64]; + fn step_size(&self) -> f64; + fn total_iterations(&self) -> usize; + fn into_result(self: Box) -> Result>; + + fn log_posterior(&self) -> f64 { + self.log_likelihood() + self.eta_log_prior() + self.kappa_log_prior() + } + + fn n2ll(&self) -> f64 { + 2.0 * self.negative_log_likelihood() + } + + fn omega_diagonal(&self) -> Option> { + self.omega().map(|omega| { + (0..omega.nrows()) + .map(|index| omega[[index, index]]) + .collect() + }) + } } impl Algorithm for ParametricAlgorithm { type Output = ParametricResult; - fn fit(self, _problem: EstimationProblem) -> Result { - match self { - Self::Saem(_config) => { - unimplemented!("SAEM fitting is not yet implemented") - } - } + fn fit(self, problem: EstimationProblem) -> Result { + FitController::new(self, problem)?.finish() } } diff --git a/src/algorithms/parametric/saem.rs b/src/algorithms/parametric/saem.rs new file mode 100644 index 000000000..ac5ad87e9 --- /dev/null +++ b/src/algorithms/parametric/saem.rs @@ -0,0 +1,10584 @@ +use std::collections::BTreeMap; + +use anyhow::{anyhow, Result}; +use argmin::{ + core::{CostFunction, Error as ArgminError, Executor}, + solver::neldermead::NelderMead, +}; +use ndarray::Array2; +use pharmsol::{Data, Equation, Event, Subject}; +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; + +use crate::algorithms::{Status, StopReason}; +use crate::estimation::likelihood::batch::{ + parametric_occasion_log_likelihood, parametric_subject_log_likelihood, +}; +use crate::estimation::likelihood::objective::parametric_subject_log_likelihoods; +use crate::estimation::parametric::conditional_uncertainty::{ + conditional_mode_curvature, ConditionalModeMetadata, JointLatentCoordinate, + JointLatentCoordinateKind, +}; +use crate::estimation::parametric::covariance::{ + cholesky_lower, relative_spd_margin, worst_contrast, +}; +use crate::estimation::parametric::covariates::{ + rebase_eta, solve_covariate_gls, subject_centered_omega, CovariateGlsProblem, CovariateModel, +}; +use crate::estimation::parametric::individual::{ + individual_phi, individual_phi_from_subject_mean, individual_psi, + individual_psi_from_subject_mean, occasion_psi, occasion_psi_from_subject_mean, population_phi, + population_psi, +}; +use crate::estimation::parametric::information::{ + derive_population_uncertainty, CompleteDerivative, InformationLayout, InformationRecursion, +}; +use crate::estimation::parametric::marginal_likelihood::{ + calculate_population_marginal_likelihood, unavailable_population_marginal_likelihood, + MarginalLikelihoodDiagnostics, MarginalLikelihoodFailureReason, MarginalLikelihoodStatus, + MarginalSubject, +}; +use crate::estimation::parametric::markov_variance::{ + classify_psd, lugsail_batch_means, rows, scale_lrv_sum, transform_simulation_variance, + MatrixClassification, +}; +use crate::estimation::parametric::posterior::{ + eta_log_prior_from_omega, eta_log_priors, SubjectPosteriorScore, +}; +use crate::estimation::parametric::posthoc::optimize_conditional_mode; +use crate::estimation::parametric::prior::CovarianceUpdateResult; +use crate::estimation::parametric::rank_diagnostics::{ + bulk_ess, folded_split_rhat, rank_normalized_split_rhat, RankDiagnosticError, +}; +use crate::estimation::parametric::residual::{ + combined_additive_sigma_collapsed, optimize_combined_residual, + optimize_correlated_combined_residual, primary_sigma_parameter, primary_sigma_parameters, + residual_statistics_for_subject, update_estimated_combined_residual_model, + update_estimated_correlated_combined_residual_model, + update_estimated_simple_residual_model_with_sigma, ResidualSufficientStatistics, +}; +use crate::estimation::parametric::shrinkage::{ + derive_eta_map_shrinkage, derive_eta_posterior_mean_shrinkage, derive_kappa_map_shrinkage, + derive_kappa_posterior_mean_shrinkage, ShrinkageDiagnostics, +}; +use crate::estimation::parametric::sufficient::{ + CovariateSufficientStatistics, PhiSufficientStatistics, +}; +use crate::estimation::parametric::{CovarianceUpdateStatus, ResolvedOmega}; +use crate::estimation::{EstimationProblem, Parametric, ParametricErrorModels}; +use crate::model::{ParameterScale, UnboundedParameter}; +use crate::ResidualErrorModel; + +use crate::results::{ + derive_information_criteria, CovarianceCycleUpdateDiagnostics, CovarianceCycleUpdateOutcome, + CovarianceUpdateNotAttemptedReason, DiagnosticTraceCoordinate, InformationCoordinateKind, + InformationDiagnostics, InformationStatus, MarkovSimulationVarianceChainDiagnostics, + MarkovSimulationVarianceDiagnostics, MarkovSimulationVarianceStatus, OccasionKappaEstimate, + OperationalConvergenceCheck, OperationalConvergenceCriterion, + OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, + OperationalConvergenceOutcome, ParametricResult, ParametricWarning, RankDiagnosticStatus, + RankMixingDiagnostic, RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, + SaemCycleDiagnostics, SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, + SubjectEtaEstimate, +}; + +use super::{ + CovarianceStabilityConfig, NumericalFailure, NumericalFailurePhase, + OperationalConvergenceConfig, ParametricRunner, SaemConfig, SaemEstimatorPolicy, +}; + +fn pending_covariance_update_diagnostics( + phase: SaemPhase, + configured: bool, + has_estimated_entries: bool, +) -> CovarianceCycleUpdateDiagnostics { + let reason = if !configured { + CovarianceUpdateNotAttemptedReason::NotConfigured + } else if !has_estimated_entries { + CovarianceUpdateNotAttemptedReason::NoEstimatedEntries + } else if phase == SaemPhase::BurnIn { + CovarianceUpdateNotAttemptedReason::BurnIn + } else { + CovarianceUpdateNotAttemptedReason::UpdateInactive + }; + CovarianceCycleUpdateDiagnostics::not_attempted(reason) +} + +fn completed_covariance_update_diagnostics( + proposal: &Array2, + update: &CovarianceUpdateResult, +) -> Result { + let outcome = match update.status { + CovarianceUpdateStatus::Accepted => CovarianceCycleUpdateOutcome::Accepted, + CovarianceUpdateStatus::NoOp => CovarianceCycleUpdateOutcome::NoOp, + CovarianceUpdateStatus::Rejected => CovarianceCycleUpdateOutcome::Rejected { + reason: update.rejection_reason.ok_or_else(|| { + anyhow!("rejected covariance update lacks a typed diagnostic reason") + })?, + }, + }; + Ok(CovarianceCycleUpdateDiagnostics { + proposal: Some(proposal.clone()), + solved_target: update.solved_target.clone(), + outcome, + accepted_fraction: update.accepted_fraction, + attempted_fractions: update.attempted_fractions.clone(), + trial_rejections: update.trial_rejections.clone(), + }) +} + +const COMPONENT_TARGET_ACCEPTANCE: f64 = 0.44; +const ETA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; +const KAPPA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; +const PROPOSAL_SCALE_INCREASE: f64 = 1.1; +const MARKOV_VARIANCE_ASSUMPTIONS: &str = concat!( + "diagnostic only: prior draws at frozen averaged Omega/Omega_IOV; ", + "per-chain seed = config.seed.wrapping_add(i).wrapping_mul(0x9E3779B97F4A7C15); ", + "frozen-kernel stationarity, adequate mixing, the Poisson equation, and the ", + "controlled-Markov averaged-SA CLT are unverified; lugsail batch means alone is not a ", + "mixing diagnostic; failure detection (non-finite, ", + "constant, stuck, byte overflow, non-positive tau) is not a convergence claim; ", + "literature recommendations for R̂ and ESS are referenced but no threshold " +); + +#[derive(Clone)] +struct FrozenDiagnosticState { + etas: Vec>>, + kappas: Vec>>>, +} + +struct DiagnosticCandidate { + population_parameters: Vec, + covariate_model: Option, + omega: Array2, + omega_iov: Option>, + error_models: ParametricErrorModels, +} + +#[derive(Debug, Clone)] +struct NonIivCoordinateLayout { + population_indices: Vec, + covariate_indices: Vec, +} + +impl NonIivCoordinateLayout { + fn len(&self) -> usize { + self.population_indices.len() + self.covariate_indices.len() + } + + fn is_empty(&self) -> bool { + self.len() == 0 + } +} + +type NonIivCandidateComponents = (Vec, Option, Option>>); + +fn parameters_are_strictly_in_domain(values: &[f64], scales: &[ParameterScale]) -> bool { + values.len() == scales.len() + && values.iter().zip(scales).all(|(value, scale)| { + value.is_finite() + && match scale { + ParameterScale::Identity => true, + ParameterScale::Log => *value > 0.0, + ParameterScale::Logit { lower, upper } + | ParameterScale::Probit { lower, upper } => *value > *lower && *value < *upper, + } + }) +} + +fn non_iiv_candidate_improves(current: f64, candidate: f64) -> bool { + candidate.is_finite() && candidate < current +} + +struct NonIivPopulationCost<'a, E: Equation> { + state: &'a SaemState, + layout: &'a NonIivCoordinateLayout, +} + +impl CostFunction for NonIivPopulationCost<'_, E> { + type Param = Vec; + type Output = f64; + + fn cost(&self, coordinates: &Self::Param) -> std::result::Result { + Ok(self + .state + .non_iiv_observation_nll(self.layout, coordinates) + .unwrap_or(NON_IIV_OPTIMIZER_PENALTY)) + } +} + +const NON_IIV_OPTIMIZER_MAX_ITERATIONS: u64 = 100; +const NON_IIV_OPTIMIZER_PENALTY: f64 = 1e100; +const NON_IIV_OPTIMIZER_SD_TOLERANCE: f64 = 1e-8; +const PROPOSAL_SCALE_DECREASE: f64 = 0.9; +const MIN_PROPOSAL_SCALE: f64 = 1e-6; +const MAX_PROPOSAL_SCALE: f64 = 5.0; + +/// SAEM iteration schedule derived from [`SaemConfig`]. +/// +/// This uses the established high-level split: a pure burn-in +/// region, an exploration region with full stochastic approximation updates, +/// then a smoothing region with decreasing step size. +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct SaemSchedule { + pub(crate) pure_burn_in: usize, + pub(crate) exploration_iterations: usize, + pub(crate) smoothing_iterations: usize, + pub(crate) total_iterations: usize, + pub(crate) variance_floor_iterations: usize, + pub(crate) annealing_alpha: f64, + pub(crate) omega_sa_max_step: f64, + pub(crate) minimum_variance: f64, + pub(crate) minimum_iov_variance: f64, + pub(crate) minimum_residual_sigma: f64, + pub(crate) averaging_alpha: Option, +} + +impl SaemSchedule { + pub(crate) fn from_config(config: &SaemConfig) -> Self { + let pure_burn_in = config.burn_in; + let exploration_iterations = config.k1_iterations.saturating_sub(pure_burn_in); + let smoothing_iterations = config.k2_iterations; + let total_iterations = config.k1_iterations + config.k2_iterations; + let variance_floor_iterations = if config.sa_iterations > 0 { + config.sa_iterations + } else { + config.k1_iterations / 2 + }; + + Self { + pure_burn_in, + exploration_iterations, + smoothing_iterations, + total_iterations, + variance_floor_iterations, + annealing_alpha: config.sa_cooling_factor, + omega_sa_max_step: config.omega_sa_max_step, + minimum_variance: config.omega_min_variance, + minimum_iov_variance: config.omega_iov_min_variance, + minimum_residual_sigma: config.residual_min_sigma, + averaging_alpha: match config.estimator_policy { + SaemEstimatorPolicy::TerminalIterate => None, + SaemEstimatorPolicy::AveragedIterates { alpha } => Some(alpha), + }, + } + } + + pub(crate) fn stochastic_approximation_step(&self, iteration: usize) -> f64 { + if iteration <= self.pure_burn_in { + 0.0 + } else if iteration <= self.pure_burn_in + self.exploration_iterations { + 1.0 + } else { + let smoothing_iteration = iteration + .saturating_sub(self.pure_burn_in + self.exploration_iterations) + .max(1); + match self.averaging_alpha { + Some(alpha) => (smoothing_iteration as f64).powf(-alpha), + None => 1.0 / smoothing_iteration as f64, + } + } + } + + /// Stochastic-approximation step for Ω/Ω_IOV sufficient statistics. + /// + /// Covariance learning is damped during both pure chain + /// warm-up and exploration so one un-equilibrated draw cannot overwrite a + /// correlated covariance. The cap is lifted in smoothing. + pub(crate) fn covariance_step(&self, iteration: usize) -> f64 { + if iteration <= self.pure_burn_in + self.exploration_iterations { + self.omega_sa_max_step.min(1.0) + } else { + self.stochastic_approximation_step(iteration) + } + } + + pub(crate) fn covariance_update_active(&self, iteration: usize) -> bool { + iteration > self.pure_burn_in + } + + pub(crate) fn phase(&self, iteration: usize) -> SaemPhase { + if iteration <= self.pure_burn_in { + SaemPhase::BurnIn + } else if iteration <= self.pure_burn_in + self.exploration_iterations { + SaemPhase::Exploration + } else { + SaemPhase::Smoothing + } + } + + /// Guard an estimated residual SD against early collapse. + /// + /// During simulated annealing, PMcore cools the previous residual SD by + /// `alpha.sa` and takes the larger of that value and the M-step candidate. + /// The configured residual floor always applies. Fixed residual models are + /// left untouched. + pub(crate) fn guarded_residual_sigma( + &self, + iteration: usize, + previous: f64, + candidate: f64, + ) -> f64 { + let mut guarded = candidate.max(self.minimum_residual_sigma); + if iteration <= self.variance_floor_iterations { + guarded = guarded.max(previous * self.annealing_alpha); + } + guarded + } +} + +fn covariate_omega_update_maximum_fraction( + has_covariates: bool, + phase: SaemPhase, + covariance_step: f64, +) -> f64 { + if has_covariates && phase == SaemPhase::Exploration { + covariance_step + } else { + 1.0 + } +} + +fn applied_combined_residual_component( + schedule: &SaemSchedule, + iteration: usize, + previous: f64, + candidate: f64, + estimated: bool, +) -> f64 { + if !estimated { + return previous; + } + let guarded_candidate = candidate.max(schedule.minimum_residual_sigma); + if iteration <= schedule.variance_floor_iterations { + return guarded_candidate.max(previous * schedule.annealing_alpha); + } + if schedule.phase(iteration) != SaemPhase::Smoothing { + return guarded_candidate; + } + let gamma = schedule.stochastic_approximation_step(iteration); + previous + gamma * (guarded_candidate - previous) +} + +/// Immutable SAEM setup computed once before the iterations begin. +/// +/// Parameter metadata, random/IOV effect indices, the resolved omega +/// specification, and initial subject-conditioned log-likelihoods are all +/// resolved here so the runner state only carries mutable estimation state. +#[derive(Debug, Clone)] +pub(crate) struct SaemInitialization { + pub(crate) schedule: SaemSchedule, + pub(crate) n_chains: usize, + pub(crate) parameter_names: Vec, + pub(crate) parameter_scales: Vec, + pub(crate) estimated_parameters: Vec, + pub(crate) random_effect_indices: Vec, + pub(crate) random_effect_names: Vec, + pub(crate) omega: ResolvedOmega, + pub(crate) iov_effect_indices: Vec, + pub(crate) iov_effect_names: Vec, + pub(crate) omega_iov: Option, + pub(crate) occasion_counts: Vec, + pub(crate) subject_ids: Vec, + pub(crate) observation_count: usize, + pub(crate) initial_population_parameters: Vec, + pub(crate) initial_subject_log_likelihoods: Vec, + pub(crate) initial_negative_log_likelihood: f64, + pub(crate) covariate_model: Option, + pub(crate) initial_subject_mu_phi: Option>>, + pub(crate) initial_residual_values: Vec>, + pub(crate) initial_residual_estimated: Vec>, +} + +fn applied_correlated_residual_correlation( + schedule: &SaemSchedule, + iteration: usize, + previous: f64, + candidate: f64, + estimated: bool, +) -> f64 { + if !estimated { + return previous; + } + if schedule.phase(iteration) != SaemPhase::Smoothing { + return candidate; + } + let gamma = schedule.stochastic_approximation_step(iteration); + previous + gamma * (candidate - previous) +} + +fn validate_initial_estimated_variance_floor( + covariance_name: &str, + floor_name: &str, + omega: &ResolvedOmega, + minimum_variance: f64, +) -> Result<()> { + for (index, effect_name) in omega.names().iter().enumerate() { + let initial_variance = omega.initial()[[index, index]]; + if omega.estimated_mask()[[index, index]] && initial_variance < minimum_variance { + anyhow::bail!( + "SAEM initial {covariance_name} variance for estimated effect '{effect_name}' ({initial_variance}) is below configured {floor_name} ({minimum_variance})" + ); + } + } + Ok(()) +} + +impl SaemInitialization { + pub(crate) fn create( + problem: &EstimationProblem, + config: &SaemConfig, + ) -> Result + where + E: Equation, + { + config.validate()?; + let omega = problem.prior.resolved_omega().clone(); + let n_subjects = problem.data.subjects().len(); + let initial_row = initial_parameter_row(problem.parameters().iter()); + let random_effect_indices = problem + .parameters() + .iter() + .enumerate() + .filter_map(|(index, parameter)| parameter.random_effect.then_some(index)) + .collect::>(); + let random_effect_names = random_effect_indices + .iter() + .map(|index| problem.parameters().items[*index].name.clone()) + .collect(); + let (iov_effect_indices, iov_effect_names, omega_iov) = problem + .prior + .resolved_iov() + .map(|iov| { + ( + iov.parameter_indices().to_vec(), + iov.omega().names().to_vec(), + Some(iov.omega().clone()), + ) + }) + .unwrap_or_else(|| (Vec::new(), Vec::new(), None)); + validate_initial_estimated_variance_floor( + "Omega", + "omega_min_variance", + &omega, + config.omega_min_variance, + )?; + if let Some(omega_iov) = omega_iov.as_ref() { + validate_initial_estimated_variance_floor( + "Omega_IOV", + "omega_iov_min_variance", + omega_iov, + config.omega_iov_min_variance, + )?; + } + if config.marginal_likelihood.is_some() + && (!random_effect_indices.is_empty() || !iov_effect_indices.is_empty()) + && !config.compute_map + { + anyhow::bail!( + "N2 with latent dimensions requires compute_map=true; conditional modes are not enabled" + ); + } + let covariate_model = problem.covariates().cloned(); + let initial_population_phi = population_phi( + &initial_row, + &problem + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect::>(), + )?; + let initial_subject_population = covariate_model + .as_ref() + .map(|model| { + model.subject_population_parameters( + &initial_population_phi, + &problem + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect::>(), + ) + }) + .transpose()?; + let initial_subject_mu_phi = initial_subject_population.as_ref().map(|rows| { + rows.iter() + .map(|row| row.phi().to_vec()) + .collect::>() + }); + let initial_individual_parameters = match initial_subject_population.as_ref() { + Some(rows) => { + Array2::from_shape_fn((n_subjects, initial_row.len()), |(i, j)| rows[i].psi()[j]) + } + None => Array2::from_shape_fn((n_subjects, initial_row.len()), |(_, j)| initial_row[j]), + }; + let initial_subject_log_likelihoods = + parametric_subject_log_likelihoods(problem, &initial_individual_parameters)?; + if let Some((subject_index, _)) = initial_subject_log_likelihoods + .iter() + .enumerate() + .find(|(_, value)| !value.is_finite()) + { + let subject = problem.data.subjects()[subject_index]; + if let Ok(statistics) = residual_statistics_for_subject( + &problem.model.equation, + subject, + &initial_row, + problem.error_models.models(), + ) { + for (output_index, _) in problem.error_models.models().iter() { + let Some(statistic) = statistics.output(output_index) else { + continue; + }; + if statistic.exponential_domain_violation_count > 0 { + let output = problem + .error_models + .output_name(output_index) + .map(str::to_owned) + .unwrap_or_else(|| format!("output_{output_index}")); + anyhow::bail!( + "initial conditional likelihood is non-finite for subject '{}' because exponential residual model output '{}' has {} non-positive or non-finite observation/prediction pair(s); exponential errors require positive finite observations and predictions", + subject.id(), + output, + statistic.exponential_domain_violation_count + ); + } + } + } + anyhow::bail!( + "initial conditional likelihood is non-finite for subject '{}'; verify parameter values, predictions, observations, and residual-model domain", + subject.id() + ); + } + let initial_negative_log_likelihood = + negative_log_likelihood(&initial_subject_log_likelihoods); + Ok(Self { + schedule: SaemSchedule::from_config(config), + n_chains: n_chains(config, n_subjects), + parameter_names: problem.parameters().names(), + parameter_scales: problem + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect(), + estimated_parameters: problem + .parameters() + .iter() + .map(|parameter| parameter.estimate) + .collect(), + random_effect_indices, + random_effect_names, + omega, + iov_effect_indices, + iov_effect_names, + omega_iov, + occasion_counts: problem + .data + .subjects() + .iter() + .map(|subject| subject.occasions().len()) + .collect(), + subject_ids: problem + .data + .subjects() + .iter() + .map(|subject| subject.id().clone()) + .collect(), + observation_count: count_observations(&problem.data), + initial_population_parameters: initial_row, + initial_subject_log_likelihoods, + initial_negative_log_likelihood, + covariate_model, + initial_subject_mu_phi, + initial_residual_values: Vec::new(), + initial_residual_estimated: Vec::new(), + }) + } +} + +#[derive(Debug, Clone)] +struct SaemIterateAverage { + population_phi: Vec, + covariate_betas: Option>, + omega: Array2, + omega_iov: Option>, + residual_model_width: usize, + residual_models: Vec<(usize, ResidualErrorModel)>, + start_cycle: usize, + count: usize, +} + +// ─── Operational convergence lifecycle ──────────────────────────────────── +// +// Result types live in `crate::results::fit_result`. +// `OperationalConvergenceConfig` is the source of truth for settings. + +/// Domain-separation constant for deterministic per-checkpoint seeds. +/// +/// Its fixed bytes are combined with the SAEM seed via wrapping addition. +const OPERATIONAL_CHECKPOINT_SEED_DOMAIN: u64 = 0x4E31_4F50_4352_4954; + +/// Per-cycle SAEM estimation state. +/// +/// MCMC chains, stochastic-approximation sufficient statistics, and the +/// current population / omega / sigma estimates are updated in-place. +#[derive(Debug)] +pub(crate) struct SaemState { + equation: E, + data: Data, + error_models: ParametricErrorModels, + config: SaemConfig, + pub(crate) initialization: SaemInitialization, + cycle: usize, + status: Status, + numerical_failure: Option, + etas: Vec>>, + kappas: Vec>>>, + population_parameters: Vec, + omega: Array2, + omega_iov: Option>, + iiv_second_moment: Array2, + iov_second_moment: Option>, + sufficient_statistics: PhiSufficientStatistics, + covariate_statistics: Option, + subject_mu_phi: Option>>, + covariate_model: Option, + residual_statistics: ResidualSufficientStatistics, + residual_sigmas: Vec, + information: InformationRecursion, + proposal_step_sizes: Vec, + eta_block_step_sizes: Vec, + kappa_proposal_step_sizes: Vec, + mcmc_iterations: usize, + eta_block_iterations: usize, + adapt_interval: usize, + residual_optimizer_max_iterations: usize, + compute_map: bool, + map_max_iterations: usize, + map_sd_tolerance: f64, + map_initial_step: f64, + steps_since_adapt: usize, + adaptation_accept_counts: Vec, + adaptation_proposal_counts: Vec, + eta_block_adaptation_accept_counts: Vec, + eta_block_adaptation_proposal_counts: Vec, + kappa_adaptation_accept_counts: Vec, + kappa_adaptation_proposal_counts: Vec, + rng: StdRng, + subject_log_likelihoods: Vec, + subject_log_priors: Vec, + subject_kappa_log_priors: Vec, + last_log_acceptance_ratios: Vec, + last_acceptance_rate: Option, + last_eta_block_acceptance_rate: Option, + last_kappa_acceptance_rate: Option, + last_rejected_proposals: Option, + last_non_finite_proposals: Option, + last_parameter_acceptance_rates: Vec, + cycle_diagnostics: Vec, + negative_log_likelihood: f64, + iterate_average: Option, + operational_settings: Option, + operational_diagnostics: OperationalConvergenceDiagnostics, +} + +impl SaemState { + pub(crate) fn from_problem( + problem: EstimationProblem, + config: &SaemConfig, + ) -> Result { + let mut initialization = SaemInitialization::create(&problem, config)?; + let EstimationProblem { + model, + data, + error_models, + .. + } = problem; + // Capture immutable initial residual values and estimated masks before + // any SAEM cycle modifies them. + let mut initial_residual_values = Vec::new(); + let mut initial_residual_estimated = Vec::new(); + for (outeq, model) in error_models.models().iter() { + let estimate = error_models.is_estimated(outeq); + let combined = error_models.combined_component_estimated(outeq); + let correlated = error_models.correlated_combined_component_estimated(outeq); + let (additive, proportional, correlation) = + if matches!(model, ResidualErrorModel::CorrelatedCombined { .. }) { + (correlated[0], correlated[1], Some(correlated[2])) + } else { + (combined[0], combined[1], None) + }; + let components = crate::results::parametric_output::residual_components( + *model, + estimate, + Some(additive), + Some(proportional), + correlation, + ); + initial_residual_values.push(components.iter().map(|c| c.1).collect()); + initial_residual_estimated.push(components.iter().map(|c| c.2).collect()); + } + initialization.initial_residual_values = initial_residual_values; + initialization.initial_residual_estimated = initial_residual_estimated; + Ok(Self::new( + model.equation, + data, + error_models, + initialization, + config, + )) + } + + pub(crate) fn new( + equation: E, + data: Data, + error_models: ParametricErrorModels, + initialization: SaemInitialization, + config: &SaemConfig, + ) -> Self { + let n_random_effects = initialization.random_effect_indices.len(); + let etas = zero_etas( + initialization.subject_ids.len(), + initialization.n_chains, + n_random_effects, + ); + let kappas = zero_kappas( + &initialization.occasion_counts, + initialization.n_chains, + initialization.iov_effect_indices.len(), + ); + let population_parameters = initialization.initial_population_parameters.clone(); + let omega = initialization.omega.initial().clone(); + let iiv_second_moment = omega.clone(); + let omega_iov = initialization + .omega_iov + .as_ref() + .map(|omega| omega.initial().clone()); + let iov_second_moment = omega_iov.clone(); + let initial_subject_phi = zero_eta_subject_phi(&population_parameters, &initialization) + .expect("initial population parameters should produce valid phi statistics"); + let mut sufficient_statistics = + PhiSufficientStatistics::from_subject_phi(&initial_subject_phi) + .expect("initial phi statistics should be valid"); + for (eta_row, parameter_row) in initialization.random_effect_indices.iter().enumerate() { + for (eta_col, parameter_col) in initialization.random_effect_indices.iter().enumerate() + { + sufficient_statistics.second_moment[[*parameter_row, *parameter_col]] += + omega[[eta_row, eta_col]]; + } + } + let subject_mu_phi = initialization.initial_subject_mu_phi.clone(); + let covariate_model = initialization.covariate_model.clone(); + let covariate_statistics = subject_mu_phi.as_ref().map(|means| { + let expected_phi = means + .iter() + .map(|mean| { + initialization + .random_effect_indices + .iter() + .map(|index| mean[*index]) + .collect::>() + }) + .collect::>(); + let mut global_second_moment = Array2::zeros((n_random_effects, n_random_effects)); + for mean in &expected_phi { + for row in 0..n_random_effects { + for column in 0..n_random_effects { + global_second_moment[[row, column]] += + mean[row] * mean[column] / expected_phi.len() as f64; + } + } + } + global_second_moment += ω + CovariateSufficientStatistics { + expected_phi, + global_second_moment, + } + }); + let subject_log_priors = eta_log_priors(&etas, &omega, 0) + .expect("validated initial omega should produce finite eta priors"); + let subject_kappa_log_priors = omega_iov + .as_ref() + .map(|omega| { + kappas + .iter() + .map(|subject_chains| { + subject_chains[0] + .iter() + .map(|kappa| eta_log_prior_from_omega(kappa, omega)) + .collect::>>() + .map(|priors| priors.into_iter().sum()) + }) + .collect::>>() + .expect("validated initial omega_iov should produce finite kappa priors") + }) + .unwrap_or_else(|| vec![0.0; initialization.subject_ids.len()]); + let residual_statistics = ResidualSufficientStatistics::zero(error_models.models().len()); + let residual_sigmas = primary_sigma_parameters(error_models.models()); + let proposal_step_sizes = initial_proposal_step_sizes(&omega, config.rw_init); + let eta_block_step_sizes = if config.eta_block_iterations > 0 { + vec![config.rw_init; initialization.subject_ids.len()] + } else { + Vec::new() + }; + let kappa_proposal_step_sizes = omega_iov + .as_ref() + .map(|_| vec![config.rw_init; initialization.subject_ids.len()]) + .unwrap_or_default(); + let mcmc_iterations = config.mcmc_iterations; + let eta_block_iterations = config.eta_block_iterations; + let adapt_interval = config.adapt_interval; + let steps_since_adapt = 0; + let adaptation_accept_counts = vec![0; n_random_effects]; + let adaptation_proposal_counts = vec![0; n_random_effects]; + let eta_block_adaptation_accept_counts = vec![0; eta_block_step_sizes.len()]; + let eta_block_adaptation_proposal_counts = vec![0; eta_block_step_sizes.len()]; + let kappa_adaptation_accept_counts = vec![0; initialization.subject_ids.len()]; + let kappa_adaptation_proposal_counts = vec![0; initialization.subject_ids.len()]; + let rng = StdRng::seed_from_u64(config.seed); + let last_log_acceptance_ratios = vec![0.0; initialization.subject_ids.len()]; + let last_acceptance_rate = None; + let last_parameter_acceptance_rates = vec![0.0; n_random_effects]; + let covariate_effect_names = covariate_model + .as_ref() + .map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.name().to_string()) + .collect::>() + }) + .unwrap_or_default(); + let covariate_estimated = covariate_model + .as_ref() + .map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimated()) + .collect::>() + }) + .unwrap_or_default(); + let information_layout = InformationLayout::new( + &initialization.parameter_names, + &initialization.estimated_parameters, + &covariate_effect_names, + &covariate_estimated, + &initialization.random_effect_names, + initialization.omega.structural_mask(), + initialization.omega.estimated_mask(), + &initialization.iov_effect_names, + initialization + .omega_iov + .as_ref() + .map(|omega| omega.structural_mask()), + initialization + .omega_iov + .as_ref() + .map(|omega| omega.estimated_mask()), + &error_models, + ) + .expect("validated SAEM metadata must produce an information layout"); + let mut information = InformationRecursion::new(information_layout); + let has_non_iiv_population = + initialization + .estimated_parameters + .iter() + .enumerate() + .any(|(index, estimated)| { + *estimated && !initialization.random_effect_indices.contains(&index) + }); + let has_non_iiv_covariate = covariate_model.as_ref().is_some_and(|model| { + model + .estimates() + .iter() + .enumerate() + .any(|(index, estimate)| { + estimate.estimated() + && !initialization + .random_effect_indices + .contains(&model.parameter_indices()[index]) + }) + }); + if has_non_iiv_population || has_non_iiv_covariate { + information.mark_unavailable(InformationStatus::Unsupported( + "structural observation sensitivities are unavailable for estimated non-IIV population or covariate coordinates" + .to_string(), + )); + } + + Self { + equation, + data, + error_models, + config: config.clone(), + etas, + kappas, + population_parameters, + omega, + omega_iov, + iiv_second_moment, + iov_second_moment, + sufficient_statistics, + covariate_statistics, + subject_mu_phi, + covariate_model, + residual_statistics, + residual_sigmas, + information, + proposal_step_sizes, + eta_block_step_sizes, + kappa_proposal_step_sizes, + mcmc_iterations, + eta_block_iterations, + adapt_interval, + residual_optimizer_max_iterations: config.residual_optimizer_max_iterations, + compute_map: config.compute_map, + map_max_iterations: config.map_max_iterations, + map_sd_tolerance: config.map_sd_tolerance, + map_initial_step: config.map_initial_step, + steps_since_adapt, + adaptation_accept_counts, + adaptation_proposal_counts, + eta_block_adaptation_accept_counts, + eta_block_adaptation_proposal_counts, + kappa_adaptation_accept_counts, + kappa_adaptation_proposal_counts, + rng, + subject_log_likelihoods: initialization.initial_subject_log_likelihoods.clone(), + subject_log_priors, + subject_kappa_log_priors, + last_log_acceptance_ratios, + last_acceptance_rate, + last_eta_block_acceptance_rate: None, + last_kappa_acceptance_rate: None, + last_rejected_proposals: None, + last_non_finite_proposals: None, + last_parameter_acceptance_rates, + cycle_diagnostics: Vec::with_capacity(initialization.schedule.total_iterations), + negative_log_likelihood: initialization.initial_negative_log_likelihood, + iterate_average: None, + operational_settings: config.operational_convergence, + operational_diagnostics: OperationalConvergenceDiagnostics { + config: config.operational_convergence, + ..OperationalConvergenceDiagnostics::default() + }, + initialization, + cycle: 0, + status: Status::Continue, + numerical_failure: None, + } + } + + fn e_step(&mut self) -> Result<()> { + let mut eta_accepted = 0usize; + let mut eta_rejected = 0usize; + let mut eta_non_finite = 0usize; + let mut eta_proposed = 0usize; + let mut eta_block_accepted = 0usize; + let mut eta_block_rejected = 0usize; + let mut eta_block_non_finite = 0usize; + let mut eta_block_proposed = 0usize; + let mut kappa_accepted = 0usize; + let mut kappa_rejected = 0usize; + let mut kappa_non_finite = 0usize; + let mut kappa_proposed = 0usize; + let eta_step_sizes_before = self.proposal_step_sizes.clone(); + let eta_block_step_sizes_before = self.eta_block_step_sizes.clone(); + let kappa_step_sizes_before = self.kappa_proposal_step_sizes.clone(); + let kappa_subject_count = if self.omega_iov.is_some() { + self.initialization.subject_ids.len() + } else { + 0 + }; + let mut kappa_subject_accept_counts = vec![0usize; kappa_subject_count]; + let mut kappa_subject_proposal_counts = vec![0usize; kappa_subject_count]; + let eta_block_subject_count = if self.eta_block_iterations > 0 { + self.initialization.subject_ids.len() + } else { + 0 + }; + let mut eta_block_subject_accept_counts = vec![0usize; eta_block_subject_count]; + let mut eta_block_subject_proposal_counts = vec![0usize; eta_block_subject_count]; + let n_parameters = self.initialization.random_effect_indices.len(); + let mut subject_log_acceptance_sums = vec![0.0; self.initialization.subject_ids.len()]; + let mut subject_proposal_counts = vec![0usize; self.initialization.subject_ids.len()]; + let mut parameter_accept_counts = vec![0usize; n_parameters]; + let mut parameter_proposal_counts = vec![0usize; n_parameters]; + + // Compound-kernel order: Omega-scaled eta blocks first, followed by + // component eta walks and occasion-level kappa blocks. Eta blocks are + // opt-in. + for _ in 0..self.eta_block_iterations { + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + let current_eta = self.etas[subject_index][chain_index].clone(); + let proposed_eta = self.block_random_walk_eta(¤t_eta, subject_index)?; + let log_acceptance_ratio = self.proposal_log_acceptance_ratio( + subject_index, + chain_index, + &proposed_eta, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + eta_block_subject_proposal_counts[subject_index] += 1; + self.eta_block_adaptation_proposal_counts[subject_index] += 1; + eta_block_proposed += 1; + eta_proposed += 1; + if !log_acceptance_ratio.is_finite() { + eta_block_non_finite += 1; + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + eta_block_subject_accept_counts[subject_index] += 1; + self.eta_block_adaptation_accept_counts[subject_index] += 1; + eta_block_accepted += 1; + eta_accepted += 1; + } else { + eta_block_rejected += 1; + eta_rejected += 1; + } + } + } + } + + for _ in 0..self.mcmc_iterations { + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + for parameter_index in 0..n_parameters { + let current_eta = self.etas[subject_index][chain_index].clone(); + let proposed_eta = + self.component_random_walk_eta(¤t_eta, parameter_index); + let log_acceptance_ratio = self.proposal_log_acceptance_ratio( + subject_index, + chain_index, + &proposed_eta, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + parameter_proposal_counts[parameter_index] += 1; + eta_proposed += 1; + if !log_acceptance_ratio.is_finite() { + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + parameter_accept_counts[parameter_index] += 1; + eta_accepted += 1; + } else { + eta_rejected += 1; + } + } + + // Gibbs sweep over occasion-specific κ blocks. Every + // proposal is evaluated against the full subject posterior, + // keeping η and all other occasions fixed. + if self.omega_iov.is_some() { + for occasion_index in 0..self.kappas[subject_index][chain_index].len() { + let current_kappa = + self.kappas[subject_index][chain_index][occasion_index].clone(); + let proposed_kappa = + self.block_random_walk_kappa(¤t_kappa, subject_index)?; + let log_acceptance_ratio = self.kappa_proposal_log_acceptance_ratio( + subject_index, + chain_index, + occasion_index, + &proposed_kappa, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + kappa_proposed += 1; + kappa_subject_proposal_counts[subject_index] += 1; + self.kappa_adaptation_proposal_counts[subject_index] += 1; + if !log_acceptance_ratio.is_finite() { + kappa_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.kappas[subject_index][chain_index][occasion_index] = + proposed_kappa; + kappa_accepted += 1; + kappa_subject_accept_counts[subject_index] += 1; + self.kappa_adaptation_accept_counts[subject_index] += 1; + } else { + kappa_rejected += 1; + } + } + } + } + } + } + + self.refresh_subject_scores_from_chains()?; + self.last_log_acceptance_ratios = subject_log_acceptance_sums + .into_iter() + .zip(subject_proposal_counts) + .map(|(sum, count)| if count > 0 { sum / count as f64 } else { 0.0 }) + .collect(); + let proposed = eta_proposed + kappa_proposed; + let accepted = eta_accepted + kappa_accepted; + self.last_acceptance_rate = if proposed > 0 { + Some(accepted as f64 / proposed as f64) + } else { + None + }; + self.last_eta_block_acceptance_rate = if self.eta_block_iterations > 0 { + Some(eta_block_accepted as f64 / eta_block_proposed.max(1) as f64) + } else { + None + }; + self.last_kappa_acceptance_rate = if self.omega_iov.is_some() { + Some(kappa_accepted as f64 / kappa_proposed.max(1) as f64) + } else { + None + }; + self.last_rejected_proposals = Some(eta_rejected + kappa_rejected); + self.last_non_finite_proposals = Some(eta_non_finite + kappa_non_finite); + self.last_parameter_acceptance_rates = parameter_accept_counts + .iter() + .zip(parameter_proposal_counts.iter()) + .map(|(accepted, proposed)| { + if *proposed > 0 { + *accepted as f64 / *proposed as f64 + } else { + 0.0 + } + }) + .collect(); + for parameter_index in 0..n_parameters { + self.adaptation_accept_counts[parameter_index] += + parameter_accept_counts[parameter_index]; + self.adaptation_proposal_counts[parameter_index] += + parameter_proposal_counts[parameter_index]; + } + self.steps_since_adapt += 1; + self.adapt_proposal_step_sizes(); + let phase = self.initialization.schedule.phase(self.cycle); + let omega_update = pending_covariance_update_diagnostics( + phase, + true, + self.initialization.omega.has_estimated_entries(), + ); + let omega_iov_update = pending_covariance_update_diagnostics( + phase, + self.initialization.omega_iov.is_some(), + self.initialization + .omega_iov + .as_ref() + .is_some_and(ResolvedOmega::has_estimated_entries), + ); + self.cycle_diagnostics.push(SaemCycleDiagnostics { + iteration: self.cycle, + phase, + stochastic_approximation_step: self + .initialization + .schedule + .stochastic_approximation_step(self.cycle), + covariance_step: self.initialization.schedule.covariance_step(self.cycle), + eta_proposals: eta_proposed, + eta_accepted, + eta_rejected, + eta_non_finite, + eta_parameter_acceptance_rates: self.last_parameter_acceptance_rates.clone(), + eta_proposal_step_sizes_before_adaptation: eta_step_sizes_before, + eta_proposal_step_sizes_after_adaptation: self.proposal_step_sizes.clone(), + eta_block_proposals: eta_block_proposed, + eta_block_accepted, + eta_block_rejected, + eta_block_non_finite, + eta_block_subject_acceptance_rates: eta_block_subject_accept_counts + .iter() + .zip(eta_block_subject_proposal_counts.iter()) + .map(|(accepted, proposed)| { + if *proposed > 0 { + *accepted as f64 / *proposed as f64 + } else { + 0.0 + } + }) + .collect(), + eta_block_step_sizes_before_adaptation: eta_block_step_sizes_before, + eta_block_step_sizes_after_adaptation: self.eta_block_step_sizes.clone(), + kappa_proposals: kappa_proposed, + kappa_accepted, + kappa_rejected, + kappa_non_finite, + kappa_subject_acceptance_rates: kappa_subject_accept_counts + .iter() + .zip(kappa_subject_proposal_counts.iter()) + .map(|(accepted, proposed)| { + if *proposed > 0 { + *accepted as f64 / *proposed as f64 + } else { + 0.0 + } + }) + .collect(), + kappa_proposal_step_sizes_before_adaptation: kappa_step_sizes_before, + kappa_proposal_step_sizes_after_adaptation: self.kappa_proposal_step_sizes.clone(), + simulated_annealing_active: self.cycle + <= self.initialization.schedule.variance_floor_iterations, + population_parameters: self.population_parameters.clone(), + omega: self.omega.clone(), + omega_iov: self.omega_iov.clone(), + residual_error_estimates: self.residual_error_estimates(), + residual_diagnostics: Vec::new(), + conditional_negative_log_likelihood: self.negative_log_likelihood, + eta_log_prior: self.subject_log_priors.iter().sum(), + kappa_log_prior: self.subject_kappa_log_priors.iter().sum(), + omega_update_rejected: false, + omega_iov_update_rejected: false, + omega_update, + omega_iov_update, + omega_relative_spd_margin: None, + omega_iov_relative_spd_margin: None, + covariate_betas: self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }), + covariate_beta_estimated: self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimated()) + .collect() + }), + }); + self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); + Ok(()) + } + + fn m_step(&mut self) -> Result<()> { + let parameter_step = self + .initialization + .schedule + .stochastic_approximation_step(self.cycle); + let covariance_step = self.initialization.schedule.covariance_step(self.cycle); + if self.covariate_model.is_some() { + let observed = self.current_covariate_statistics()?; + self.covariate_statistics + .as_mut() + .expect("covariate model has initialized statistics") + .stochastic_update(&observed, parameter_step)?; + } else { + let observed_statistics = self.current_phi_statistics()?; + self.sufficient_statistics.stochastic_update_with_steps( + &observed_statistics, + parameter_step, + covariance_step, + )?; + } + + if let Some(second_moment) = self.iov_second_moment.as_mut() { + let observed_second_moment = covariance_from_kappas(&self.kappas)?; + *second_moment = + &*second_moment + &((&observed_second_moment - &*second_moment) * covariance_step); + } + + // Pure burn-in warms the latent chains and their centered covariance + // statistics while theta, Omega, Omega_IOV, and sigma remain fixed. Raw + // covariate phi moments remain unchanged, matching their zero SA gain. + if parameter_step == 0.0 { + let observed_second_moment = second_moment_from_etas(&self.etas)?; + self.iiv_second_moment = &self.iiv_second_moment + + &((&observed_second_moment - &self.iiv_second_moment) * covariance_step); + self.finalize_cycle_diagnostics()?; + return Ok(()); + } + + let pre_update_residual_evidence = self.current_residual_statistics_and_information()?; + if self.covariate_model.is_some() { + // The raw first and second phi moments already share the SAEM gain. + // Keep their centered covariance candidate coherent; exploration + // robustness is applied later to the accepted Omega iterate rather + // than introducing a second sufficient-statistic recursion. + self.iiv_second_moment = self.update_covariate_population_and_recenter_etas()?; + } else { + self.update_population_and_recenter_etas()?; + let observed_second_moment = second_moment_from_etas(&self.etas)?; + self.iiv_second_moment = &self.iiv_second_moment + + &((&observed_second_moment - &self.iiv_second_moment) * covariance_step); + } + + self.update_non_iiv_population(parameter_step)?; + let (observed_residual_statistics, information_replicates) = pre_update_residual_evidence; + match information_replicates { + Ok(replicates) => self.information.update(&replicates, parameter_step), + Err(reason) => self + .information + .mark_unavailable(information_failure_status(reason)), + } + let mut residual_diagnostics = self + .error_models + .models() + .iter() + .map(|(output_index, _)| { + let statistic = observed_residual_statistics + .output(output_index) + .unwrap_or_default(); + ResidualCycleDiagnostics { + output: self + .error_models + .output_name(output_index) + .map(str::to_owned) + .unwrap_or_else(|| format!("output_{output_index}")), + output_index, + prediction_evaluation_count: statistic.observation_count, + proportional_floor_count: statistic.proportional_floor_count, + non_finite_prediction_count: statistic.non_finite_prediction_count, + exponential_domain_violation_count: statistic + .exponential_domain_violation_count, + update_rejected: false, + optimizer_objective: None, + optimizer_converged: None, + optimizer_iterations: None, + optimizer_termination: None, + combined_additive_collapse_warning: false, + } + }) + .collect::>(); + let residual_observations = (0..self.error_models.len()) + .map(|output_index| { + observed_residual_statistics + .observations(output_index) + .unwrap_or_default() + .to_vec() + }) + .collect::>(); + self.residual_statistics = self + .residual_statistics + .stochastic_update(observed_residual_statistics, parameter_step); + + if self + .initialization + .schedule + .covariance_update_active(self.cycle) + { + if self.initialization.omega.has_estimated_entries() { + let phase = self.initialization.schedule.phase(self.cycle); + let update = if self.covariate_model.is_some() && phase == SaemPhase::Exploration { + self.initialization + .omega + .update_with_status_and_max_fraction( + &self.omega, + &self.iiv_second_moment, + self.initialization.schedule.minimum_variance, + covariate_omega_update_maximum_fraction(true, phase, covariance_step), + )? + } else { + // Preserve the established floor-after-interpolation path + // for non-covariate IIV and for uncapped covariate smoothing. + self.initialization.omega.update_with_status( + &self.omega, + &self.iiv_second_moment, + self.initialization.schedule.minimum_variance, + )? + }; + let status = update.status; + let update_diagnostics = + completed_covariance_update_diagnostics(&self.iiv_second_moment, &update)?; + self.omega = update.matrix; + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.omega_update_rejected = status == CovarianceUpdateStatus::Rejected; + diagnostics.omega_update = update_diagnostics; + } + } + if let (Some(specification), Some(omega_iov), Some(second_moment)) = ( + self.initialization.omega_iov.as_ref(), + self.omega_iov.as_mut(), + self.iov_second_moment.as_ref(), + ) { + if specification.has_estimated_entries() { + let update = specification.update_with_status( + omega_iov, + second_moment, + self.initialization.schedule.minimum_iov_variance, + )?; + let status = update.status; + let update_diagnostics = + completed_covariance_update_diagnostics(second_moment, &update)?; + *omega_iov = update.matrix; + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.omega_iov_update_rejected = + status == CovarianceUpdateStatus::Rejected; + diagnostics.omega_iov_update = update_diagnostics; + } + } + } + } + for residual_diagnostic in &mut residual_diagnostics { + let outeq = residual_diagnostic.output_index; + if !self.error_models.is_estimated(outeq) { + continue; + } + let Some(model) = self.error_models.models().get(outeq).copied() else { + residual_diagnostic.update_rejected = true; + continue; + }; + if let ResidualErrorModel::Combined { a, b } = model { + match optimize_combined_residual( + &residual_observations[outeq], + a, + b, + self.error_models.combined_component_estimated(outeq), + self.initialization.schedule.minimum_residual_sigma, + self.residual_optimizer_max_iterations as u64, + ) { + Ok(solution) => { + let component_estimated = + self.error_models.combined_component_estimated(outeq); + let additive_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + a, + solution.additive_sd, + component_estimated[0], + ); + let proportional_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + b, + solution.proportional_sd, + component_estimated[1], + ); + residual_diagnostic.combined_additive_collapse_warning = + combined_additive_sigma_collapsed(additive_sd, component_estimated[0]); + update_estimated_combined_residual_model( + &mut self.error_models, + outeq, + additive_sd, + proportional_sd, + ); + residual_diagnostic.optimizer_objective = Some(solution.objective); + residual_diagnostic.optimizer_converged = Some(solution.converged); + residual_diagnostic.optimizer_iterations = Some(solution.iterations); + residual_diagnostic.optimizer_termination = Some(solution.termination); + } + Err(error) => { + residual_diagnostic.update_rejected = true; + residual_diagnostic.optimizer_termination = Some(error.to_string()); + } + } + continue; + } + if let ResidualErrorModel::CorrelatedCombined { a, b, rho } = model { + match optimize_correlated_combined_residual( + &residual_observations[outeq], + a, + b, + rho, + self.error_models + .correlated_combined_component_estimated(outeq), + self.initialization.schedule.minimum_residual_sigma, + self.residual_optimizer_max_iterations as u64, + ) { + Ok(solution) => { + let component_estimated = self + .error_models + .correlated_combined_component_estimated(outeq); + let additive_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + a, + solution.additive_sd, + component_estimated[0], + ); + let proportional_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + b, + solution.proportional_sd, + component_estimated[1], + ); + let correlation = applied_correlated_residual_correlation( + &self.initialization.schedule, + self.cycle, + rho, + solution.correlation, + component_estimated[2], + ); + if !correlation.is_finite() || correlation <= -1.0 || correlation >= 1.0 { + residual_diagnostic.update_rejected = true; + residual_diagnostic.optimizer_termination = Some( + "correlated-combined residual update left (-1, 1)".to_string(), + ); + continue; + } + residual_diagnostic.combined_additive_collapse_warning = + combined_additive_sigma_collapsed(additive_sd, component_estimated[0]); + update_estimated_correlated_combined_residual_model( + &mut self.error_models, + outeq, + additive_sd, + proportional_sd, + correlation, + ); + residual_diagnostic.optimizer_objective = Some(solution.objective); + residual_diagnostic.optimizer_converged = Some(solution.converged); + residual_diagnostic.optimizer_iterations = Some(solution.iterations); + residual_diagnostic.optimizer_termination = Some(solution.termination); + } + Err(error) => { + residual_diagnostic.update_rejected = true; + residual_diagnostic.optimizer_termination = Some(error.to_string()); + } + } + continue; + } + let Some(candidate_sigma) = self + .residual_statistics + .output(outeq) + .and_then(|statistic| statistic.sigma()) + else { + residual_diagnostic.update_rejected = true; + continue; + }; + let previous_sigma = primary_sigma_parameter(&model); + let sigma = self.initialization.schedule.guarded_residual_sigma( + self.cycle, + previous_sigma, + candidate_sigma, + ); + update_estimated_simple_residual_model_with_sigma(&mut self.error_models, outeq, sigma); + } + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.residual_diagnostics = residual_diagnostics; + } + self.residual_sigmas = primary_sigma_parameters(self.error_models.models()); + self.refresh_subject_scores_from_chains()?; + self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); + self.update_iterate_average()?; + self.finalize_cycle_diagnostics()?; + Ok(()) + } + + fn update_iterate_average(&mut self) -> Result<()> { + if self.initialization.schedule.phase(self.cycle) != SaemPhase::Smoothing + || !matches!( + self.config.estimator_policy, + SaemEstimatorPolicy::AveragedIterates { .. } + ) + { + return Ok(()); + } + let population_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let residual_models = self + .error_models + .models() + .iter() + .map(|(output_index, model)| (output_index, *model)) + .collect::>(); + let residual_model_width = self.error_models.models().len(); + let Some(average) = self.iterate_average.as_mut() else { + self.iterate_average = Some(SaemIterateAverage { + population_phi, + covariate_betas: self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }), + omega: self.omega.clone(), + omega_iov: self.omega_iov.clone(), + residual_model_width, + residual_models, + start_cycle: self.cycle, + count: 1, + }); + return Ok(()); + }; + let next_count = average.count + 1; + for (index, value) in population_phi.iter().copied().enumerate() { + if self.initialization.estimated_parameters[index] { + average.population_phi[index] = + incremental_average(average.population_phi[index], value, next_count); + } + } + if let (Some(average_betas), Some(model)) = ( + average.covariate_betas.as_mut(), + self.covariate_model.as_ref(), + ) { + for (index, estimate) in model.estimates().iter().enumerate() { + if estimate.estimated() { + average_betas[index] = + incremental_average(average_betas[index], estimate.estimate(), next_count); + } + } + } + average_covariance( + &mut average.omega, + &self.omega, + self.initialization.omega.estimated_mask(), + next_count, + ); + if let (Some(average_iov), Some(current_iov), Some(specification)) = ( + average.omega_iov.as_mut(), + self.omega_iov.as_ref(), + self.initialization.omega_iov.as_ref(), + ) { + average_covariance( + average_iov, + current_iov, + specification.estimated_mask(), + next_count, + ); + } + if residual_model_width != average.residual_model_width + || residual_models.len() != average.residual_models.len() + { + anyhow::bail!("residual output declarations changed while accumulating SAEM averages"); + } + for ((average_output_index, previous), (output_index, current)) in + average.residual_models.iter_mut().zip(residual_models) + { + if *average_output_index != output_index { + anyhow::bail!( + "residual output declarations changed while accumulating SAEM averages" + ); + } + let estimated = self.error_models.is_estimated(output_index); + let components = self.error_models.combined_component_estimated(output_index); + let correlated_components = self + .error_models + .correlated_combined_component_estimated(output_index); + *previous = average_residual_model( + *previous, + current, + estimated, + components, + correlated_components, + next_count, + )?; + } + average.count = next_count; + Ok(()) + } + + fn install_iterate_average(&mut self) -> Result { + let policy = self.config.estimator_policy; + let Some(average) = self.iterate_average.clone() else { + tracing::info!("averaged SAEM estimate was not available; retaining terminal iterate"); + return Ok(SaemEstimatorMetadata { + policy, + ..SaemEstimatorMetadata::default() + }); + }; + let terminal_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + validate_average_population(&average.population_phi, &self.initialization)?; + validate_average_covariance(&average.omega, &self.initialization.omega, "Omega")?; + if let (Some(matrix), Some(specification)) = ( + average.omega_iov.as_ref(), + self.initialization.omega_iov.as_ref(), + ) { + validate_average_covariance(matrix, specification, "Omega_IOV")?; + } + validate_average_residuals( + average.residual_model_width, + &average.residual_models, + &self.error_models, + )?; + + self.population_parameters = population_psi( + &average.population_phi, + &self.initialization.parameter_scales, + )?; + if let (Some(model), Some(beta_values), Some(old_means)) = ( + self.covariate_model.as_ref(), + average.covariate_betas.as_ref(), + self.subject_mu_phi.as_ref(), + ) { + let averaged_model = model.with_estimates(beta_values)?; + let new_rows = averaged_model.subject_population_parameters( + &average.population_phi, + &self.initialization.parameter_scales, + )?; + let new_means = new_rows + .iter() + .map(|row| row.phi().to_vec()) + .collect::>(); + for (subject_index, chains) in self.etas.iter_mut().enumerate() { + let old_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| old_means[subject_index][*index]) + .collect::>(); + let new_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| new_means[subject_index][*index]) + .collect::>(); + for eta in chains { + rebase_eta(eta, &old_random, &new_random)?; + } + } + self.covariate_model = Some(averaged_model); + self.subject_mu_phi = Some(new_means); + } else { + for (eta_index, parameter_index) in self + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + let shift = terminal_phi[parameter_index] - average.population_phi[parameter_index]; + for subject_chains in &mut self.etas { + for eta in subject_chains { + eta[eta_index] += shift; + } + } + } + } + self.omega = average.omega; + self.omega_iov = average.omega_iov; + for (output_index, model) in average.residual_models { + match model { + ResidualErrorModel::Combined { a, b } => update_estimated_combined_residual_model( + &mut self.error_models, + output_index, + a, + b, + ), + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + update_estimated_correlated_combined_residual_model( + &mut self.error_models, + output_index, + a, + b, + rho, + ) + } + ResidualErrorModel::Constant { .. } + | ResidualErrorModel::Proportional { .. } + | ResidualErrorModel::Exponential { .. } => { + update_estimated_simple_residual_model_with_sigma( + &mut self.error_models, + output_index, + primary_sigma_parameter(&model), + ) + } + } + } + self.residual_sigmas = primary_sigma_parameters(self.error_models.models()); + self.refresh_subject_scores_from_chains()?; + self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); + tracing::info!( + start_cycle = average.start_cycle, + averaged_iterations = average.count, + "installed averaged SAEM estimate" + ); + Ok(SaemEstimatorMetadata { + policy, + average_applied: true, + averaging_start_cycle: Some(average.start_cycle), + averaged_iterations: average.count, + }) + } + + fn residual_error_estimates(&self) -> Vec { + self.error_models + .models() + .iter() + .map(|(output_index, model)| { + let model = *model; + let combined_components = + self.error_models.combined_component_estimated(output_index); + let correlated_components = self + .error_models + .correlated_combined_component_estimated(output_index); + let is_combined = matches!(model, ResidualErrorModel::Combined { .. }); + let is_correlated = matches!(model, ResidualErrorModel::CorrelatedCombined { .. }); + ResidualErrorEstimate { + output: self + .error_models + .output_name(output_index) + .map(str::to_owned) + .expect("declared residual models have output names"), + output_index, + model, + estimated: self.error_models.is_estimated(output_index), + combined_additive_estimated: if is_combined { + Some(combined_components[0]) + } else { + is_correlated.then_some(correlated_components[0]) + }, + combined_proportional_estimated: if is_combined { + Some(combined_components[1]) + } else { + is_correlated.then_some(correlated_components[1]) + }, + correlation_estimated: is_correlated.then_some(correlated_components[2]), + } + }) + .collect() + } + + fn finalize_cycle_diagnostics(&mut self) -> Result<()> { + let population_parameters = self.population_parameters.clone(); + let omega = self.omega.clone(); + let omega_iov = self.omega_iov.clone(); + let residual_error_estimates = self.residual_error_estimates(); + let conditional_negative_log_likelihood = self.negative_log_likelihood; + let eta_log_prior = self.subject_log_priors.iter().sum(); + let kappa_log_prior = self.subject_kappa_log_priors.iter().sum(); + let (omega_relative_spd_margin, omega_iov_relative_spd_margin) = + if self.config.covariance_stability.is_some() { + let initial_omega = self.initialization.omega.initial(); + let omega_margin = (initial_omega.nrows() > 0) + .then(|| relative_spd_margin(&omega, initial_omega)) + .transpose()?; + let omega_iov_margin = + match (self.initialization.omega_iov.as_ref(), omega_iov.as_ref()) { + (Some(specification), Some(matrix)) + if specification.initial().nrows() > 0 => + { + Some(relative_spd_margin(matrix, specification.initial())?) + } + _ => None, + }; + (omega_margin, omega_iov_margin) + } else { + (None, None) + }; + let covariate_betas = self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }); + let covariate_beta_estimated = self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimated()) + .collect() + }); + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.population_parameters = population_parameters; + diagnostics.omega = omega; + diagnostics.omega_iov = omega_iov; + diagnostics.omega_relative_spd_margin = omega_relative_spd_margin; + diagnostics.omega_iov_relative_spd_margin = omega_iov_relative_spd_margin; + diagnostics.residual_error_estimates = residual_error_estimates; + diagnostics.conditional_negative_log_likelihood = conditional_negative_log_likelihood; + diagnostics.eta_log_prior = eta_log_prior; + diagnostics.kappa_log_prior = kappa_log_prior; + diagnostics.covariate_betas = covariate_betas; + diagnostics.covariate_beta_estimated = covariate_beta_estimated; + } + Ok(()) + } + + fn update_population_and_recenter_etas(&mut self) -> Result> { + let old_population_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let mut new_population_phi = old_population_phi.clone(); + for (parameter_index, parameter_phi) in new_population_phi.iter_mut().enumerate() { + if self.initialization.estimated_parameters[parameter_index] + && self + .initialization + .random_effect_indices + .contains(¶meter_index) + { + *parameter_phi = self.sufficient_statistics.mean_phi[parameter_index]; + } + } + + for (eta_index, parameter_index) in self + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + let realized_shift = + new_population_phi[parameter_index] - old_population_phi[parameter_index]; + for subject_chains in &mut self.etas { + for eta in subject_chains { + eta[eta_index] -= realized_shift; + } + } + } + self.population_parameters = + population_psi(&new_population_phi, &self.initialization.parameter_scales)?; + Ok(new_population_phi) + } + + fn update_covariate_population_and_recenter_etas(&mut self) -> Result> { + let model = self + .covariate_model + .as_ref() + .expect("covariate update requires a resolved model") + .clone(); + let statistics = self + .covariate_statistics + .as_ref() + .expect("covariate update requires sufficient statistics") + .clone(); + let q = self.initialization.random_effect_indices.len(); + let old_population_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let old_subject_mu = self + .subject_mu_phi + .as_ref() + .expect("covariate update requires subject means") + .clone(); + + let free_intercepts = self + .initialization + .random_effect_indices + .iter() + .copied() + .filter(|index| self.initialization.estimated_parameters[*index]) + .collect::>(); + let free_effects = model + .estimates() + .iter() + .enumerate() + .filter_map(|(index, estimate)| { + (estimate.estimated() + && self + .initialization + .random_effect_indices + .contains(&model.parameter_indices()[index])) + .then_some(index) + }) + .collect::>(); + let width = free_intercepts.len() + free_effects.len(); + let random_row = self + .initialization + .random_effect_indices + .iter() + .enumerate() + .map(|(row, parameter)| (*parameter, row)) + .collect::>(); + let mut designs = Vec::with_capacity(model.subject_design().len()); + let mut offsets = Vec::with_capacity(model.subject_design().len()); + for subject in model.subject_design() { + let mut design = Array2::zeros((q, width)); + let mut offset = vec![0.0; q]; + for (row, parameter_index) in self + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + if let Some(column) = free_intercepts + .iter() + .position(|index| *index == parameter_index) + { + design[[row, column]] = 1.0; + } else { + offset[row] = old_population_phi[parameter_index]; + } + } + for (effect_index, value) in subject.values().iter().copied().enumerate() { + let parameter_index = model.parameter_indices()[effect_index]; + let Some(&row) = random_row.get(¶meter_index) else { + continue; + }; + if let Some(effect_column) = + free_effects.iter().position(|index| *index == effect_index) + { + design[[row, free_intercepts.len() + effect_column]] = value; + } else { + offset[row] += value * model.estimates()[effect_index].estimate(); + } + } + designs.push(design); + offsets.push(offset); + } + + let solution = if width == 0 { + Vec::new() + } else { + solve_covariate_gls(CovariateGlsProblem { + design: &designs, + expected_phi: &statistics.expected_phi, + offset: &offsets, + omega: &self.omega, + })? + }; + let mut new_population_phi = old_population_phi; + for (column, parameter_index) in free_intercepts.iter().copied().enumerate() { + new_population_phi[parameter_index] = solution[column]; + } + let mut beta_values = model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + for (column, effect_index) in free_effects.iter().copied().enumerate() { + beta_values[effect_index] = solution[free_intercepts.len() + column]; + } + let updated_model = model.with_estimates(&beta_values)?; + let subject_population = updated_model.subject_population_parameters( + &new_population_phi, + &self.initialization.parameter_scales, + )?; + let new_subject_mu = subject_population + .iter() + .map(|row| row.phi().to_vec()) + .collect::>(); + for (subject_index, subject_chains) in self.etas.iter_mut().enumerate() { + let old_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| old_subject_mu[subject_index][*index]) + .collect::>(); + let new_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| new_subject_mu[subject_index][*index]) + .collect::>(); + for eta in subject_chains { + rebase_eta(eta, &old_random, &new_random)?; + } + } + let subject_mu_random = new_subject_mu + .iter() + .map(|mean| { + self.initialization + .random_effect_indices + .iter() + .map(|index| mean[*index]) + .collect::>() + }) + .collect::>(); + let candidate = if q == 0 { + Array2::zeros((0, 0)) + } else { + subject_centered_omega( + &statistics.global_second_moment, + &statistics.expected_phi, + &subject_mu_random, + )? + }; + self.population_parameters = + population_psi(&new_population_phi, &self.initialization.parameter_scales)?; + self.subject_mu_phi = Some(new_subject_mu); + self.covariate_model = Some(updated_model); + Ok(candidate) + } + + fn adapt_proposal_step_sizes(&mut self) { + if self.steps_since_adapt < self.adapt_interval { + return; + } + + for parameter_index in 0..self.proposal_step_sizes.len() { + let proposed = self.adaptation_proposal_counts[parameter_index].max(1); + let acceptance_rate = + self.adaptation_accept_counts[parameter_index] as f64 / proposed as f64; + self.proposal_step_sizes[parameter_index] = adapt_component_step_size( + self.proposal_step_sizes[parameter_index], + acceptance_rate, + ); + self.adaptation_accept_counts[parameter_index] = 0; + self.adaptation_proposal_counts[parameter_index] = 0; + } + for subject_index in 0..self.eta_block_step_sizes.len() { + let proposed = self.eta_block_adaptation_proposal_counts[subject_index].max(1); + let acceptance_rate = + self.eta_block_adaptation_accept_counts[subject_index] as f64 / proposed as f64; + self.eta_block_step_sizes[subject_index] = adapt_block_step_size( + self.eta_block_step_sizes[subject_index], + acceptance_rate, + ETA_BLOCK_TARGET_ACCEPTANCE, + ); + self.eta_block_adaptation_accept_counts[subject_index] = 0; + self.eta_block_adaptation_proposal_counts[subject_index] = 0; + } + for subject_index in 0..self.kappa_proposal_step_sizes.len() { + let proposed = self.kappa_adaptation_proposal_counts[subject_index].max(1); + let acceptance_rate = + self.kappa_adaptation_accept_counts[subject_index] as f64 / proposed as f64; + self.kappa_proposal_step_sizes[subject_index] = adapt_block_step_size( + self.kappa_proposal_step_sizes[subject_index], + acceptance_rate, + KAPPA_BLOCK_TARGET_ACCEPTANCE, + ); + self.kappa_adaptation_accept_counts[subject_index] = 0; + self.kappa_adaptation_proposal_counts[subject_index] = 0; + } + self.steps_since_adapt = 0; + } + + fn component_random_walk_eta( + &mut self, + current_eta: &[f64], + parameter_index: usize, + ) -> Vec { + let mut proposed_eta = current_eta.to_vec(); + proposed_eta[parameter_index] += + self.proposal_step_sizes[parameter_index] * self.standard_normal(); + proposed_eta + } + + fn block_random_walk_eta( + &mut self, + current_eta: &[f64], + subject_index: usize, + ) -> Result> { + let lower = cholesky_lower(&self.omega)?; + let standard_normals = (0..current_eta.len()) + .map(|_| self.standard_normal()) + .collect::>(); + correlated_random_walk( + current_eta, + &lower, + &standard_normals, + self.eta_block_step_sizes[subject_index], + ) + } + + fn block_random_walk_kappa( + &mut self, + current_kappa: &[f64], + subject_index: usize, + ) -> Result> { + let omega_iov = self + .omega_iov + .as_ref() + .ok_or_else(|| anyhow::anyhow!("kappa proposal requires configured omega_iov"))?; + let lower = cholesky_lower(omega_iov)?; + let standard_normals = (0..current_kappa.len()) + .map(|_| self.standard_normal()) + .collect::>(); + correlated_random_walk( + current_kappa, + &lower, + &standard_normals, + self.kappa_proposal_step_sizes[subject_index], + ) + } + + fn standard_normal(&mut self) -> f64 { + let u1 = self.rng.random::().max(f64::MIN_POSITIVE); + let u2 = self.rng.random::(); + (-2.0_f64 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() + } + + fn accept_proposal(&mut self, log_acceptance_ratio: f64) -> bool { + if !log_acceptance_ratio.is_finite() { + return false; + } + if log_acceptance_ratio >= 0.0 { + return true; + } + self.rng.random::().max(f64::MIN_POSITIVE).ln() < log_acceptance_ratio + } + + fn individual_parameters(&self, subject_index: usize, chain_index: usize) -> Vec { + self.individual_parameters_from_eta(subject_index, &self.etas[subject_index][chain_index]) + .expect("stored eta should match parameter dimensions") + } + + fn individual_parameters_from_eta( + &self, + subject_index: usize, + eta: &[f64], + ) -> Result> { + match self.subject_mu_phi.as_ref() { + Some(means) => individual_psi_from_subject_mean( + &means[subject_index], + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + None => individual_psi( + &self.population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + } + } + + fn individual_phi(&self, subject_index: usize, chain_index: usize) -> Result> { + match self.subject_mu_phi.as_ref() { + Some(means) => individual_phi_from_subject_mean( + &means[subject_index], + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + ), + None => individual_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + ), + } + } + + fn current_phi_statistics(&self) -> Result { + let mut subject_phi = Vec::with_capacity( + self.initialization.subject_ids.len() * self.initialization.n_chains, + ); + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + subject_phi.push(self.individual_phi(subject_index, chain_index)?); + } + } + PhiSufficientStatistics::from_subject_phi(&subject_phi) + } + + fn current_covariate_statistics(&self) -> Result { + let mut subjects = Vec::with_capacity(self.initialization.subject_ids.len()); + for subject_index in 0..self.initialization.subject_ids.len() { + let mut chains = Vec::with_capacity(self.initialization.n_chains); + for chain_index in 0..self.initialization.n_chains { + let phi = self.individual_phi(subject_index, chain_index)?; + chains.push( + self.initialization + .random_effect_indices + .iter() + .map(|index| phi[*index]) + .collect(), + ); + } + subjects.push(chains); + } + CovariateSufficientStatistics::from_subject_chains(&subjects) + } + + fn current_residual_statistics_and_information( + &self, + ) -> Result<( + ResidualSufficientStatistics, + std::result::Result, String>, + )> { + let mut total = ResidualSufficientStatistics::zero(self.error_models.len()); + let layout = self.information.layout(); + let mut replicates = (0..self.initialization.n_chains) + .map(|_| CompleteDerivative::zero(layout.len())) + .collect::>(); + let mut information_error = None; + // Preserve the established subject-major/chain-minor prediction and + // accumulation order so this diagnostic cannot alter fit trajectories. + for subject_index in 0..self.initialization.subject_ids.len() { + let subject = self.data.subjects()[subject_index]; + for (chain_index, derivative) in replicates.iter_mut().enumerate() { + if information_error.is_none() { + let derivative_result = match self.covariate_model.as_ref() { + Some(model) => derivative.add_covariate_population_prior( + &self.etas[subject_index][chain_index], + &self.omega, + &self.initialization.random_effect_indices, + model.parameter_indices(), + model.subject_design()[subject_index].values(), + layout, + ), + None => derivative.add_population_prior( + &self.etas[subject_index][chain_index], + &self.omega, + &self.initialization.random_effect_indices, + layout, + ), + }; + if let Err(error) = derivative_result { + information_error = Some(error.to_string()); + } + } + if self.omega_iov.is_none() { + let parameters = self.individual_parameters(subject_index, chain_index); + let predictions = self + .equation + .estimate_predictions_dense(subject, ¶meters)?; + total.add_assign(&ResidualSufficientStatistics::from_predictions( + &predictions, + self.error_models.models(), + )); + if information_error.is_none() { + if let Err(error) = + derivative.add_predictions(&predictions, &self.error_models, layout) + { + information_error = Some(error.to_string()); + } + } + continue; + } + for (occasion, kappa) in subject + .occasions() + .iter() + .zip(&self.kappas[subject_index][chain_index]) + { + if information_error.is_none() { + if let Some(omega_iov) = self.omega_iov.as_ref() { + if let Err(error) = derivative.add_iov_prior(kappa, omega_iov, layout) { + information_error = Some(error.to_string()); + } + } + } + let parameters = match self.subject_mu_phi.as_ref() { + Some(means) => occasion_psi_from_subject_mean( + &means[subject_index], + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + &self.population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + &self.initialization.iov_effect_indices, + kappa, + ), + }?; + let occasion_subject = + Subject::from_occasions(subject.id().to_owned(), vec![occasion.clone()]); + let predictions = self + .equation + .estimate_predictions_dense(&occasion_subject, ¶meters)?; + total.add_assign(&ResidualSufficientStatistics::from_predictions( + &predictions, + self.error_models.models(), + )); + if information_error.is_none() { + if let Err(error) = + derivative.add_predictions(&predictions, &self.error_models, layout) + { + information_error = Some(error.to_string()); + } + } + } + } + } + Ok(( + total, + match information_error { + Some(error) => Err(error), + None => Ok(replicates), + }, + )) + } + + #[cfg(test)] + fn current_residual_statistics(&self) -> Result { + self.current_residual_statistics_and_information() + .map(|(statistics, _)| statistics) + } + + fn refresh_subject_scores_from_chains(&mut self) -> Result<()> { + let n_chains = self.initialization.n_chains as f64; + let mut subject_log_likelihoods = vec![0.0; self.initialization.subject_ids.len()]; + let mut subject_log_priors = vec![0.0; self.initialization.subject_ids.len()]; + let mut subject_kappa_log_priors = vec![0.0; self.initialization.subject_ids.len()]; + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + let score = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + &self.kappas[subject_index][chain_index], + )?; + subject_log_likelihoods[subject_index] += score.log_likelihood / n_chains; + subject_log_priors[subject_index] += score.eta_log_prior / n_chains; + subject_kappa_log_priors[subject_index] += score.kappa_log_prior / n_chains; + } + } + self.subject_log_likelihoods = subject_log_likelihoods; + self.subject_log_priors = subject_log_priors; + self.subject_kappa_log_priors = subject_kappa_log_priors; + Ok(()) + } + + fn score_subject_latents( + &self, + subject_index: usize, + eta: &[f64], + kappas: &[Vec], + ) -> Result { + self.score_subject_latents_at(subject_index, eta, kappas, None) + } + + fn non_iiv_coordinate_layout(&self) -> NonIivCoordinateLayout { + let population_indices = self + .initialization + .estimated_parameters + .iter() + .enumerate() + .filter_map(|(index, estimated)| { + (*estimated && !self.initialization.random_effect_indices.contains(&index)) + .then_some(index) + }) + .collect(); + let covariate_indices = self + .covariate_model + .as_ref() + .map(|model| { + model + .estimates() + .iter() + .enumerate() + .filter_map(|(index, estimate)| { + (estimate.estimated() + && !self + .initialization + .random_effect_indices + .contains(&model.parameter_indices()[index])) + .then_some(index) + }) + .collect() + }) + .unwrap_or_default(); + NonIivCoordinateLayout { + population_indices, + covariate_indices, + } + } + + fn non_iiv_population_update_active(&self, parameter_step: f64) -> bool { + let Some(post_burn_start) = self.initialization.schedule.pure_burn_in.checked_add(1) else { + return false; + }; + let first_active_cycle = self + .initialization + .schedule + .variance_floor_iterations + .max(post_burn_start); + parameter_step.is_finite() && parameter_step > 0.0 && self.cycle >= first_active_cycle + } + + fn pack_non_iiv_coordinates(&self, layout: &NonIivCoordinateLayout) -> Result> { + let population = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let mut coordinates = layout + .population_indices + .iter() + .map(|index| population[*index]) + .collect::>(); + if let Some(model) = self.covariate_model.as_ref() { + coordinates.extend( + layout + .covariate_indices + .iter() + .map(|index| model.estimates()[*index].estimate()), + ); + } + Ok(coordinates) + } + + fn non_iiv_candidate_components( + &self, + layout: &NonIivCoordinateLayout, + coordinates: &[f64], + ) -> Result { + if coordinates.len() != layout.len() || coordinates.iter().any(|value| !value.is_finite()) { + anyhow::bail!("non-IIV population coordinate width or value is invalid"); + } + let mut population = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + for (coordinate, parameter_index) in coordinates + .iter() + .copied() + .zip(layout.population_indices.iter().copied()) + { + population[parameter_index] = coordinate; + } + let population_parameters = + population_psi(&population, &self.initialization.parameter_scales)?; + if !parameters_are_strictly_in_domain( + &population_parameters, + &self.initialization.parameter_scales, + ) { + anyhow::bail!("non-IIV population candidate violates its declared parameter domain"); + } + + let covariate_model = match self.covariate_model.as_ref() { + Some(model) => { + let mut values = model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + for (coordinate, effect_index) in coordinates[layout.population_indices.len()..] + .iter() + .copied() + .zip(layout.covariate_indices.iter().copied()) + { + values[effect_index] = coordinate; + } + Some(model.with_estimates(&values)?) + } + None if layout.covariate_indices.is_empty() => None, + None => anyhow::bail!("non-IIV covariate coordinates lack a covariate model"), + }; + let subject_rows = covariate_model + .as_ref() + .map(|model| { + model.subject_population_parameters( + &population, + &self.initialization.parameter_scales, + ) + }) + .transpose()?; + if subject_rows.as_ref().is_some_and(|rows| { + rows.iter().any(|row| { + !parameters_are_strictly_in_domain(row.psi(), &self.initialization.parameter_scales) + }) + }) { + anyhow::bail!("non-IIV covariate candidate violates a declared parameter domain"); + } + let subject_means = subject_rows.map(|rows| { + rows.into_iter() + .map(|row| row.phi().to_vec()) + .collect::>() + }); + Ok((population_parameters, covariate_model, subject_means)) + } + + fn non_iiv_observation_nll( + &self, + layout: &NonIivCoordinateLayout, + coordinates: &[f64], + ) -> Result { + let (population_parameters, _covariate_model, subject_means) = + self.non_iiv_candidate_components(layout, coordinates)?; + let chain_count = self.initialization.n_chains; + if chain_count == 0 { + anyhow::bail!("non-IIV observation objective requires at least one chain"); + } + let mut objective = 0.0; + for subject_index in 0..self.initialization.subject_ids.len() { + let subject = self.data.subjects()[subject_index]; + let subject_mean = subject_means + .as_ref() + .map(|means| means[subject_index].as_slice()); + for chain_index in 0..chain_count { + let eta = &self.etas[subject_index][chain_index]; + let log_likelihood = if self.omega_iov.is_none() { + let parameters = match subject_mean { + Some(mean) => individual_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + None => individual_psi( + &population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + }?; + parametric_subject_log_likelihood( + &self.equation, + subject, + ¶meters, + self.error_models.models(), + ) + } else { + let kappas = &self.kappas[subject_index][chain_index]; + if kappas.len() != subject.occasions().len() { + anyhow::bail!("non-IIV objective kappa/occasion dimension mismatch"); + } + let mut value = 0.0; + for (occasion, kappa) in subject.occasions().iter().zip(kappas) { + let parameters = match subject_mean { + Some(mean) => occasion_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + &population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + }?; + let occasion_value = parametric_occasion_log_likelihood( + &self.equation, + subject.id(), + occasion, + ¶meters, + self.error_models.models(), + ); + if !occasion_value.is_finite() { + anyhow::bail!("non-IIV observation objective is non-finite"); + } + value += occasion_value; + } + value + }; + if !log_likelihood.is_finite() { + anyhow::bail!("non-IIV observation objective is non-finite"); + } + objective -= log_likelihood / chain_count as f64; + } + } + if !objective.is_finite() { + anyhow::bail!("non-IIV observation objective is non-finite"); + } + Ok(objective) + } + + fn update_non_iiv_population(&mut self, parameter_step: f64) -> Result { + let layout = self.non_iiv_coordinate_layout(); + if layout.is_empty() || !self.non_iiv_population_update_active(parameter_step) { + return Ok(false); + } + + let initial = self.pack_non_iiv_coordinates(&layout)?; + let initial_objective = self.non_iiv_observation_nll(&layout, &initial)?; + if !initial_objective.is_finite() { + anyhow::bail!("current non-IIV observation objective is non-finite"); + } + + let mut simplex = Vec::with_capacity(initial.len() + 1); + simplex.push(initial.clone()); + for coordinate in 0..initial.len() { + let mut point = initial.clone(); + point[coordinate] += 0.1 * initial[coordinate].abs().max(1.0); + simplex.push(point); + } + let solver = NelderMead::new(simplex).with_sd_tolerance(NON_IIV_OPTIMIZER_SD_TOLERANCE)?; + let execution = Executor::new( + NonIivPopulationCost { + state: self, + layout: &layout, + }, + solver, + ) + .configure(|state| state.max_iters(NON_IIV_OPTIMIZER_MAX_ITERATIONS)) + .run(); + let result = match execution { + Ok(result) => result, + Err(error) => { + tracing::warn!( + error = %error, + "Non-IIV population optimizer failed; retaining current state" + ); + return Ok(false); + } + }; + let Some(candidate) = result.state.best_param.as_ref() else { + return Ok(false); + }; + let candidate_objective = match self.non_iiv_observation_nll(&layout, candidate) { + Ok(value) if value.is_finite() => value, + _ => return Ok(false), + }; + if !non_iiv_candidate_improves(initial_objective, candidate_objective) { + return Ok(false); + } + + let applied = initial + .iter() + .zip(candidate) + .map(|(current, target)| current + parameter_step * (target - current)) + .collect::>(); + match self.non_iiv_observation_nll(&layout, &applied) { + Ok(value) if value.is_finite() => {} + _ => return Ok(false), + } + + let (population_parameters, covariate_model, subject_means) = + self.non_iiv_candidate_components(&layout, &applied)?; + self.population_parameters = population_parameters; + self.covariate_model = covariate_model; + self.subject_mu_phi = subject_means; + Ok(true) + } + + fn score_subject_latents_at( + &self, + subject_index: usize, + eta: &[f64], + kappas: &[Vec], + candidate: Option<&DiagnosticCandidate>, + ) -> Result { + if eta.len() != self.initialization.random_effect_indices.len() { + anyhow::bail!( + "eta has {} values but there are {} random effects", + eta.len(), + self.initialization.random_effect_indices.len() + ); + } + + let subject = self.data.subjects()[subject_index]; + let population_parameters = candidate + .map_or(self.population_parameters.as_slice(), |value| { + value.population_parameters.as_slice() + }); + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + let error_models = candidate.map_or(&self.error_models, |value| &value.error_models); + let candidate_covariates = candidate + .and_then(|value| value.covariate_model.as_ref()) + .or(self.covariate_model.as_ref()); + let calculated_subject_mu = if candidate.is_some() { + candidate_covariates + .map(|model| { + let phi = population_phi( + population_parameters, + &self.initialization.parameter_scales, + )?; + Ok::<_, anyhow::Error>( + model.subject_population_parameters( + &phi, + &self.initialization.parameter_scales, + )?[subject_index] + .phi() + .to_vec(), + ) + }) + .transpose()? + } else { + None + }; + let subject_mu = calculated_subject_mu.as_deref().or_else(|| { + self.subject_mu_phi + .as_ref() + .map(|means| means[subject_index].as_slice()) + }); + let eta_log_prior = eta_log_prior_from_omega(eta, omega)?; + if omega_iov.is_none() { + let parameters = match subject_mu { + Some(mean) => individual_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + None => individual_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + }?; + return Ok(SubjectPosteriorScore { + log_likelihood: parametric_subject_log_likelihood( + &self.equation, + subject, + ¶meters, + error_models.models(), + ), + eta_log_prior, + kappa_log_prior: 0.0, + }); + } + + if kappas.len() != subject.occasions().len() { + anyhow::bail!( + "subject '{}' has {} occasions but {} kappa states", + subject.id(), + subject.occasions().len(), + kappas.len() + ); + } + let omega_iov = omega_iov.expect("checked above"); + let mut log_likelihood = 0.0; + let mut kappa_log_prior = 0.0; + for (occasion, kappa) in subject.occasions().iter().zip(kappas) { + let parameters = match subject_mu { + Some(mean) => occasion_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + }?; + let occasion_log_likelihood = parametric_occasion_log_likelihood( + &self.equation, + subject.id(), + occasion, + ¶meters, + error_models.models(), + ); + if !occasion_log_likelihood.is_finite() { + log_likelihood = f64::NEG_INFINITY; + } else if log_likelihood.is_finite() { + log_likelihood += occasion_log_likelihood; + } + kappa_log_prior += eta_log_prior_from_omega(kappa, omega_iov)?; + } + + Ok(SubjectPosteriorScore { + log_likelihood, + eta_log_prior, + kappa_log_prior, + }) + } + + fn proposal_log_acceptance_ratio( + &self, + subject_index: usize, + chain_index: usize, + proposed_eta: &[f64], + ) -> Result { + let current = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + &self.kappas[subject_index][chain_index], + )?; + let proposed = self.score_subject_latents( + subject_index, + proposed_eta, + &self.kappas[subject_index][chain_index], + )?; + Ok(current.log_acceptance_ratio(proposed)) + } + + fn kappa_proposal_log_acceptance_ratio( + &self, + subject_index: usize, + chain_index: usize, + occasion_index: usize, + proposed_kappa: &[f64], + ) -> Result { + let current_kappas = &self.kappas[subject_index][chain_index]; + let current = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + current_kappas, + )?; + let mut proposed_kappas = current_kappas.clone(); + proposed_kappas[occasion_index] = proposed_kappa.to_vec(); + let proposed = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + &proposed_kappas, + )?; + Ok(current.log_acceptance_ratio(proposed)) + } + + fn markov_variance_diagnostics( + &self, + estimator: &SaemEstimatorMetadata, + information: &InformationDiagnostics, + ) -> MarkovSimulationVarianceDiagnostics { + self.markov_variance_diagnostics_with_seed(estimator, information, None, None) + } + + /// Frozen-kernel diagnostic with an optional deterministic seed override. + /// + /// The override gives each operational checkpoint its own deterministic + /// stream; `None` preserves the exact + /// post-fit path seeded by the diagnostic configuration. + fn markov_variance_diagnostics_with_seed( + &self, + estimator: &SaemEstimatorMetadata, + information: &InformationDiagnostics, + seed_override: Option, + candidate: Option<&DiagnosticCandidate>, + ) -> MarkovSimulationVarianceDiagnostics { + let Some(config) = self.config.markov_simulation_variance else { + return MarkovSimulationVarianceDiagnostics::disabled(); + }; + let diagnostic_seed = seed_override.unwrap_or(config.seed); + let cd = config.diagnostic_chains; + let cf = self.initialization.n_chains; + let mut diagnostic = MarkovSimulationVarianceDiagnostics { + config: Some(config), + coordinates: information.coordinates.clone(), + chain_count: cd, + n_avg: estimator.averaged_iterations, + chains: Vec::new(), + grand_score_mean: Vec::new(), + lambda: Vec::new(), + lambda_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + xi: Vec::new(), + xi_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + simulation_covariance: Vec::new(), + simulation_covariance_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + assumptions: MARKOV_VARIANCE_ASSUMPTIONS.into(), + rank_diagnostics: RankMixingDiagnostics { + diagnostic_chains: cd, + draws_per_chain: config.draws_per_chain, + original_chains: cf, + traces: Vec::new(), + lrv_per_chain: Vec::new(), + lrv_chain_statuses: Vec::new(), + diagnostic_mean_lrv: None, + operational_lrv: None, + max_trace_bytes: 0, + accounted_peak_trace_bytes_required: 0, + accounted_peak_trace_bytes_used: 0, + worst_rhat: None, + min_bulk_ess: None, + min_avg_ess_per_split_chain: None, + assumptions: MARKOV_VARIANCE_ASSUMPTIONS.into(), + status: RankDiagnosticStatus::Disabled, + }, + }; + let width = information.coordinates.len(); + let information_eligible = estimator.average_applied + && matches!(information.status, InformationStatus::Available) + && width > 0; + let observed_information = if information_eligible { + match matrix_from_rows(&information.observed_information, width) { + Ok(matrix) => Some(matrix), + Err(_) => { + diagnostic.xi_status = MarkovSimulationVarianceStatus::CoordinateMismatch; + None + } + } + } else { + None + }; + if self.initialization.random_effect_indices.is_empty() + && self.initialization.iov_effect_indices.is_empty() + { + let zero = Array2::zeros((width, width)); + diagnostic.lambda = rows(&zero); + diagnostic.lambda_status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.xi = rows(&zero); + diagnostic.xi_status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.simulation_covariance = rows(&zero); + diagnostic.simulation_covariance_status = + MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.rank_diagnostics.status = RankDiagnosticStatus::NoLatent; + diagnostic + .rank_diagnostics + .lrv_chain_statuses + .fill(RankDiagnosticStatus::NoLatent); + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + return diagnostic; + } + + // ── Pre-execution byte-cap check (checked) ─────────────────────── + let trace_shape = self + .initialization + .random_effect_indices + .len() + .checked_mul(self.initialization.subject_ids.len()) + .and_then(|n_eta| { + self.initialization + .occasion_counts + .iter() + .try_fold(0usize, |total, count| total.checked_add(*count)) + .and_then(|occasions| { + occasions + .checked_mul(self.initialization.iov_effect_indices.len()) + .and_then(|n_kappa| width.checked_add(n_eta)?.checked_add(n_kappa)) + }) + }); + let Some(n_traces) = trace_shape else { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::TraceMemoryAccountingOverflow, + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, + ); + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + return diagnostic; + }; + // Deterministic requested-capacity accounting. `traces` is nested + // coordinate-major storage, so its heap-resident Vec headers count in + // addition to every f64 leaf payload. The peak adds the larger of: + // (a) one nested draw-major score view, or (b) a conservative upper + // bound for the live rank/folding/ESS workspaces. The latter is eight + // payload-widths per retained draw (including the 24-byte ranked tuple) + // plus sixteen Vec headers per chain. This upper-bounds all capacities + // explicitly requested by the current rank helpers; allocator metadata + // and allocator size-class rounding are intentionally not claimed. + let vec_header = std::mem::size_of::>(); + let f64_bytes = std::mem::size_of::(); + let accounted = cd + .checked_mul(config.draws_per_chain) + .and_then(|samples_per_coordinate| { + samples_per_coordinate + .checked_mul(n_traces) + .and_then(|values| values.checked_mul(f64_bytes)) + .and_then(|leaf_payload| { + n_traces + .checked_mul(cd) + .and_then(|headers| headers.checked_mul(vec_header)) + .and_then(|leaf_headers| leaf_payload.checked_add(leaf_headers)) + }) + .and_then(|bytes| { + n_traces + .checked_mul(vec_header) + .and_then(|middle_headers| bytes.checked_add(middle_headers)) + }) + .and_then(|persistent_bytes| { + config + .draws_per_chain + .checked_mul(width) + .and_then(|values| values.checked_mul(f64_bytes)) + .and_then(|payload| { + config + .draws_per_chain + .checked_mul(vec_header) + .and_then(|headers| payload.checked_add(headers)) + }) + .and_then(|score_transient_bytes| { + samples_per_coordinate + .checked_mul(8 * f64_bytes) + .and_then(|payload| { + cd.checked_mul(16) + .and_then(|headers| headers.checked_mul(vec_header)) + .and_then(|headers| payload.checked_add(headers)) + }) + .and_then(|rank_transient_bytes| { + persistent_bytes + .checked_add( + score_transient_bytes.max(rank_transient_bytes), + ) + .map(|required_bytes| { + ( + persistent_bytes, + score_transient_bytes, + required_bytes, + ) + }) + }) + }) + }) + }); + let Some((persistent_bytes, score_transient_bytes, required_bytes)) = accounted else { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::TraceMemoryAccountingOverflow, + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, + ); + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + return diagnostic; + }; + diagnostic + .rank_diagnostics + .accounted_peak_trace_bytes_required = required_bytes; + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + if required_bytes > config.max_trace_bytes { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::TraceByteCapExceeded, + MarkovSimulationVarianceStatus::InvalidConfiguration(format!( + "diagnostic trace accounted peak requires {required_bytes} bytes, exceeding cap {}", + config.max_trace_bytes + )), + ); + return diagnostic; + } + diagnostic.rank_diagnostics.lrv_per_chain = vec![None; cd]; + diagnostic.rank_diagnostics.lrv_chain_statuses = + vec![RankDiagnosticStatus::Unavailable; cd]; + + // ── Trace coordinate metadata ───────────────────────────────────── + let mut trace_coords: Vec = Vec::with_capacity(n_traces); + for coord in &information.coordinates { + trace_coords.push(DiagnosticTraceCoordinate::Score { + index: coord.index, + name: coord.name.clone(), + kind: coord.kind.clone(), + }); + } + for subject_id in &self.initialization.subject_ids { + for (eff_idx, name) in self.initialization.random_effect_names.iter().enumerate() { + trace_coords.push(DiagnosticTraceCoordinate::Eta { + subject: subject_id.clone(), + effect_index: eff_idx, + effect_name: name.clone(), + }); + } + } + if !self.initialization.iov_effect_indices.is_empty() { + for (subject_idx, subject_id) in self.initialization.subject_ids.iter().enumerate() { + for occasion in self.data.subjects()[subject_idx].occasions() { + for (eff_idx, name) in self.initialization.iov_effect_names.iter().enumerate() { + trace_coords.push(DiagnosticTraceCoordinate::Kappa { + subject: subject_id.clone(), + occasion_index: occasion.index(), + effect_index: eff_idx, + effect_name: name.clone(), + }); + } + } + } + } + + // ── Cd < 2 → still execute frozen chains, LRV, and Xi ──────────── + // Only per-trace rank diagnostics are unavailable (TooFewChains). + let rank_possible = cd >= 2; + + // ── Fresh prior-drawn chains ────────────────────────────────────── + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + let omega_lower = match cholesky_lower(omega) { + Ok(lower) => lower, + Err(_) => { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::InvalidVariance, + MarkovSimulationVarianceStatus::Indefinite, + ); + return diagnostic; + } + }; + let iov_lower = if self.initialization.iov_effect_indices.is_empty() { + None + } else { + match omega_iov.map(cholesky_lower) { + Some(Ok(lower)) => Some(lower), + Some(Err(_)) | None => { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::InvalidVariance, + MarkovSimulationVarianceStatus::Indefinite, + ); + return diagnostic; + } + } + }; + + // Canonical storage: [score_0..score_{w-1}, eta_0.., kappa_0..]. + // A draw-major score view is created one chain at a time for LRV and + // released before the next chain. + let mut traces: Vec>> = (0..n_traces) + .map(|_| vec![Vec::with_capacity(config.draws_per_chain); cd]) + .collect(); + diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = persistent_bytes; + let score_eligible = + width > 0 && matches!(information.status, InformationStatus::Available); + + // Initialize Cd independent diagnostic chains with domain-separated seeds. + // Seed derivation: per-chain seed = base.wrapping_add(i).wrapping_mul(GOLDEN_RATIO) + // where GOLDEN_RATIO = 0x9E3779B97F4A7C15 (2^64 / φ) and base is the + // configured diagnostic seed or the deterministic checkpoint override. + let mut chain_states: Vec = (0..cd) + .map(|chain| { + let chain_seed = diagnostic_seed + .wrapping_add(chain as u64) + .wrapping_mul(0x9E3779B97F4A7C15); + let mut chain_rng = StdRng::seed_from_u64(chain_seed); + FrozenDiagnosticState { + etas: self.draw_prior_etas(&omega_lower, &mut chain_rng), + kappas: self.draw_prior_kappas(iov_lower.as_deref(), &mut chain_rng), + } + }) + .collect(); + // Independent RNG streams for transitions (offset by +1 to separate + // from prior-initialization streams). + let mut chain_rngs: Vec = (0..cd) + .map(|chain| { + let chain_seed = diagnostic_seed + .wrapping_add(chain as u64) + .wrapping_mul(0x9E3779B97F4A7C15) + .wrapping_add(1); + StdRng::seed_from_u64(chain_seed) + }) + .collect(); + let mut chain_counts = vec![(0usize, 0usize, 0usize); cd]; + + // ── Warmup ──────────────────────────────────────────────────────── + for _ in 0..config.warmup_transitions { + for chain in 0..cd { + let mut single = [chain_counts[chain]]; + if self + .frozen_diagnostic_transition( + &mut chain_states[chain], + &mut chain_rngs[chain], + &mut single, + candidate, + ) + .is_err() + { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::Unavailable, + MarkovSimulationVarianceStatus::UnsupportedScore( + "frozen diagnostic warmup transition failed".into(), + ), + ); + return diagnostic; + } + chain_counts[chain] = single[0]; + } + } + begin_retained_transition_accounting(&mut chain_counts); + + // ── Single retained-draw pass: transition → collect traces ────── + for _ in 0..config.draws_per_chain { + for chain in 0..cd { + let mut single = [chain_counts[chain]]; + if self + .frozen_diagnostic_transition( + &mut chain_states[chain], + &mut chain_rngs[chain], + &mut single, + candidate, + ) + .is_err() + { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::Unavailable, + MarkovSimulationVarianceStatus::UnsupportedScore( + "frozen retained diagnostic transition failed".into(), + ), + ); + return diagnostic; + } + chain_counts[chain] = single[0]; + + // Score failure never discards independently valid latent draws. + let score = if score_eligible { + match self.frozen_complete_score(&chain_states[chain], 0, candidate) { + Ok(values) if values.len() == width => Some(values), + Ok(_) | Err(_) => None, + } + } else { + None + }; + for coord_idx in 0..width { + traces[coord_idx][chain] + .push(score.as_ref().map_or(f64::NAN, |values| values[coord_idx])); + } + + // Collect eta coordinates: subject-major, coordinate-major. + let mut trace_idx = width; + for subject_etas in &chain_states[chain].etas { + for eta_coord in &subject_etas[0] { + traces[trace_idx][chain].push(*eta_coord); + trace_idx += 1; + } + } + + // Collect kappa coordinates. + for subject_kappas in &chain_states[chain].kappas { + for kappa_vec in &subject_kappas[0] { + for kappa_coord in kappa_vec { + traces[trace_idx][chain].push(*kappa_coord); + trace_idx += 1; + } + } + } + } + } + + // Preserve the raw grand complete-score mean used by the invariant + // stationarity diagnostic. Any non-finite score leaves it unavailable. + if score_eligible { + let denominator = (cd * config.draws_per_chain) as f64; + let means = (0..width) + .map(|coordinate| { + traces[coordinate].iter().flatten().copied().sum::() / denominator + }) + .collect::>(); + if means.iter().all(|value| value.is_finite()) { + diagnostic.grand_score_mean = means; + } + } + + // ── Per-chain score LRV from transient draw-major views ───────── + let mut lrv_matrices: Vec>> = Vec::with_capacity(cd); + for chain in 0..cd { + let (proposals, accepts, state_changes) = chain_counts[chain]; + let score_view = (0..config.draws_per_chain) + .map(|draw| (0..width).map(|coord| traces[coord][chain][draw]).collect()) + .collect::>>(); + diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = persistent_bytes + .checked_add(score_transient_bytes) + .unwrap_or(required_bytes); + let lrv_result = if score_eligible { + match lugsail_batch_means(&score_view, config.batch_size, config.lugsail) { + Ok(value) => Some(value), + Err(_) => { + diagnostic.xi_status = MarkovSimulationVarianceStatus::UnsupportedScore( + "per-chain score LRV failed".into(), + ); + None + } + } + } else { + None + }; + if let Some((coarse, fine, lrv)) = lrv_result { + let classification = classify_psd(&lrv); + let lrv_status = markov_matrix_status(classification); + diagnostic + .chains + .push(MarkovSimulationVarianceChainDiagnostics { + chain, + bm_batch: rows(&coarse), + bm_batch_over_r: rows(&fine), + lugsail_lrv: rows(&lrv), + status: lrv_status, + proposals, + accepts, + state_changes, + }); + diagnostic.rank_diagnostics.lrv_per_chain[chain] = Some(rows(&lrv)); + diagnostic.rank_diagnostics.lrv_chain_statuses[chain] = match classification { + MatrixClassification::EligiblePsd => RankDiagnosticStatus::Available, + MatrixClassification::NonFinite => RankDiagnosticStatus::NonFiniteDraws, + MatrixClassification::NonSymmetric | MatrixClassification::Indefinite => { + RankDiagnosticStatus::InvalidVariance + } + }; + lrv_matrices.push(Some(lrv)); + } else { + diagnostic + .chains + .push(MarkovSimulationVarianceChainDiagnostics { + chain, + bm_batch: Vec::new(), + bm_batch_over_r: Vec::new(), + lugsail_lrv: Vec::new(), + status: MarkovSimulationVarianceStatus::UnsupportedScore( + "complete-score trace or information unavailable".into(), + ), + proposals, + accepts, + state_changes, + }); + diagnostic.rank_diagnostics.lrv_per_chain[chain] = None; + diagnostic.rank_diagnostics.lrv_chain_statuses[chain] = + RankDiagnosticStatus::ScoreUnavailable; + lrv_matrices.push(None); + } + } + + let stuck_chain = chain_counts + .iter() + .enumerate() + .find(|(_, count)| count.2 == 0) + .map(|(chain, _)| chain); + + // Aggregate only when every chain has an eligible score LRV. + let all_lrvs_available = lrv_matrices.len() == cd + && lrv_matrices.iter().all(Option::is_some) + && diagnostic + .rank_diagnostics + .lrv_chain_statuses + .iter() + .all(|status| matches!(status, RankDiagnosticStatus::Available)); + if all_lrvs_available { + let mut lrv_sum = Array2::zeros((width, width)); + for lrv in &lrv_matrices { + lrv_sum += lrv + .as_ref() + .expect("all per-chain LRV matrices were checked available"); + } + let (diag_mean, operational) = scale_lrv_sum(&lrv_sum, cd, cf); + diagnostic.rank_diagnostics.diagnostic_mean_lrv = Some(rows(&diag_mean)); + diagnostic.lambda = rows(&diag_mean); + diagnostic.lambda_status = markov_matrix_status(classify_psd(&diag_mean)); + + // Cd != Cf is intentional: operational scale is Σ/(Cd*Cf). + diagnostic.rank_diagnostics.operational_lrv = Some(rows(&operational)); + if let Some(observed_information) = observed_information.as_ref() { + match transform_simulation_variance( + observed_information, + &operational, + estimator.averaged_iterations, + ) { + Ok((xi, covariance)) => { + diagnostic.xi = rows(&xi); + diagnostic.xi_status = markov_matrix_status(classify_psd(&xi)); + diagnostic.simulation_covariance = rows(&covariance); + diagnostic.simulation_covariance_status = + markov_matrix_status(classify_psd(&covariance)); + } + Err(_) => { + diagnostic.xi_status = MarkovSimulationVarianceStatus::NonFinite; + diagnostic.simulation_covariance_status = + MarkovSimulationVarianceStatus::NonFinite; + } + } + } else { + diagnostic.xi_status = MarkovSimulationVarianceStatus::InformationUnavailable( + format!("{:?}", information.status), + ); + diagnostic.simulation_covariance_status = diagnostic.xi_status.clone(); + } + } else { + let failure = if !score_eligible { + MarkovSimulationVarianceStatus::InformationUnavailable(format!( + "{:?}", + information.status + )) + } else { + diagnostic + .chains + .iter() + .map(|chain| &chain.status) + .find(|status| { + !matches!( + status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + }) + .cloned() + .unwrap_or_else(|| { + MarkovSimulationVarianceStatus::UnsupportedScore( + "one or more configured diagnostic-chain score LRVs failed".into(), + ) + }) + }; + diagnostic.lambda_status = failure.clone(); + diagnostic.xi_status = failure.clone(); + diagnostic.simulation_covariance_status = failure; + } + + // ── Rank/mixing diagnostics from traces ───────────────────────── + // The prechecked rank workspace is the accounted peak whenever rank + // diagnostics execute; no allocator-specific byte claim is made. + if rank_possible { + diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = required_bytes; + diagnostic.rank_diagnostics.traces = + self.rank_diagnostics_from_traces(cd, &traces, &trace_coords); + } else { + diagnostic.rank_diagnostics.traces = trace_coords + .iter() + .map(|coord| RankMixingDiagnostic { + trace: coord.clone(), + rank_rhat: None, + rank_rhat_status: RankDiagnosticStatus::TooFewChains, + folded_rhat: None, + folded_rhat_status: RankDiagnosticStatus::TooFewChains, + max_rhat: None, + max_rhat_status: RankDiagnosticStatus::TooFewChains, + bulk_ess: None, + bulk_ess_status: RankDiagnosticStatus::TooFewChains, + avg_ess_per_split_chain: None, + tau: None, + status: RankDiagnosticStatus::TooFewChains, + }) + .collect(); + } + + // ── Aggregate per-coordinate worst/min across traces ──────────── + diagnostic.rank_diagnostics.worst_rhat = + worst_valid_max_rhat(&diagnostic.rank_diagnostics.traces); + diagnostic.rank_diagnostics.min_bulk_ess = diagnostic + .rank_diagnostics + .traces + .iter() + .filter_map(|t| t.bulk_ess) + .min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + diagnostic.rank_diagnostics.min_avg_ess_per_split_chain = diagnostic + .rank_diagnostics + .traces + .iter() + .filter_map(|t| t.avg_ess_per_split_chain) + .min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + + // Aggregate status: ineligible if any coordinate or LRV is non-available. + let any_coord_non_available = diagnostic + .rank_diagnostics + .traces + .iter() + .any(|t| !matches!(t.status, RankDiagnosticStatus::Available)); + let any_lrv_non_available = diagnostic + .rank_diagnostics + .lrv_chain_statuses + .iter() + .any(|s| !matches!(s, RankDiagnosticStatus::Available)); + if rank_possible + && (any_coord_non_available || any_lrv_non_available || stuck_chain.is_some()) + { + diagnostic.rank_diagnostics.status = if diagnostic + .rank_diagnostics + .traces + .iter() + .any(|trace| matches!(trace.status, RankDiagnosticStatus::Available)) + { + RankDiagnosticStatus::PartialAvailability + } else { + RankDiagnosticStatus::Unavailable + }; + } else if !rank_possible { + diagnostic.rank_diagnostics.status = RankDiagnosticStatus::TooFewChains; + } else { + diagnostic.rank_diagnostics.status = RankDiagnosticStatus::Available; + } + + // ── Final aggregate markov status ─────────────────────────────── + diagnostic.status = if let Some(chain) = stuck_chain { + MarkovSimulationVarianceStatus::StuckChain { chain } + } else if !estimator.average_applied { + MarkovSimulationVarianceStatus::AverageNotApplied + } else if !matches!(information.status, InformationStatus::Available) { + MarkovSimulationVarianceStatus::InformationUnavailable(format!( + "{:?}", + information.status + )) + } else { + diagnostic + .chains + .iter() + .map(|chain| &chain.status) + .chain([ + &diagnostic.lambda_status, + &diagnostic.xi_status, + &diagnostic.simulation_covariance_status, + ]) + .find(|status| { + !matches!( + status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + }) + .cloned() + .unwrap_or(MarkovSimulationVarianceStatus::AssumptionsUnverified) + }; + diagnostic + } + + /// Build per-coordinate rank/mixing diagnostics from collected trace chains. + fn rank_diagnostics_from_traces( + &self, + cd: usize, + traces: &[Vec>], + trace_coords: &[DiagnosticTraceCoordinate], + ) -> Vec { + trace_coords + .iter() + .enumerate() + .map(|(idx, coord)| { + let chains = &traces[idx]; + let rank_result = rank_normalized_split_rhat(chains); + let folded_result = folded_split_rhat(chains); + let ess_result = bulk_ess(chains); + + let rank_rhat = match rank_result.as_ref() { + Ok(value) => Some(*value), + Err(_) => None, + }; + let folded_rhat = match folded_result.as_ref() { + Ok(value) => Some(*value), + Err(_) => None, + }; + let (bulk_ess, tau) = match ess_result.as_ref() { + Ok((ess, tau)) => (Some(*ess), Some(*tau)), + Err(_) => (None, None), + }; + let avg_ess_per_split_chain = bulk_ess.map(|ess| ess / (2.0 * cd as f64)); + + let score_unavailable = matches!(coord, DiagnosticTraceCoordinate::Score { .. }) + && chains.iter().flatten().any(|draw| !draw.is_finite()); + let statistic_status = |result: Result<(), &RankDiagnosticError>| { + if score_unavailable { + RankDiagnosticStatus::ScoreUnavailable + } else { + result + .map(|()| RankDiagnosticStatus::Available) + .unwrap_or_else(rank_diagnostic_error_status) + } + }; + let rank_rhat_status = statistic_status(rank_result.as_ref().map(|_| ())); + let folded_rhat_status = statistic_status(folded_result.as_ref().map(|_| ())); + let max_rhat = match (rank_rhat, folded_rhat) { + (Some(rank), Some(folded)) => Some(rank.max(folded)), + _ => None, + }; + let max_rhat_status = if matches!(rank_rhat_status, RankDiagnosticStatus::Available) + && matches!(folded_rhat_status, RankDiagnosticStatus::Available) + { + RankDiagnosticStatus::Available + } else if !matches!(rank_rhat_status, RankDiagnosticStatus::Available) { + rank_rhat_status.clone() + } else { + folded_rhat_status.clone() + }; + let bulk_ess_status = statistic_status(ess_result.as_ref().map(|_| ())); + let statuses = [&rank_rhat_status, &folded_rhat_status, &bulk_ess_status]; + let available = statuses + .iter() + .filter(|status| matches!(status, RankDiagnosticStatus::Available)) + .count(); + let status = if available == statuses.len() { + RankDiagnosticStatus::Available + } else if available > 0 { + RankDiagnosticStatus::PartialAvailability + } else if statuses.iter().all(|status| *status == statuses[0]) { + statuses[0].clone() + } else { + RankDiagnosticStatus::Unavailable + }; + + RankMixingDiagnostic { + trace: coord.clone(), + rank_rhat, + rank_rhat_status, + folded_rhat, + folded_rhat_status, + max_rhat, + max_rhat_status, + bulk_ess, + bulk_ess_status, + avg_ess_per_split_chain, + tau, + status, + } + }) + .collect() + } + + /// Draw initial η vectors from N(0, Omega) for fresh diagnostic chains. + fn draw_prior_etas(&self, omega_lower: &[Vec], rng: &mut StdRng) -> Vec>> { + let n_eta = self.initialization.random_effect_indices.len(); + if n_eta == 0 { + return vec![vec![Vec::new(); 1]; self.initialization.subject_ids.len()]; + } + self.initialization + .subject_ids + .iter() + .map(|_| { + let normals: Vec = (0..n_eta) + .map(|_| diagnostic_standard_normal(rng)) + .collect(); + let eta = (0..n_eta) + .map(|row| { + (0..=row) + .map(|col| omega_lower[row][col] * normals[col]) + .sum() + }) + .collect::>(); + vec![eta] + }) + .collect() + } + + /// Draw initial κ vectors from N(0, Omega_IOV) for fresh diagnostic chains. + fn draw_prior_kappas( + &self, + iov_lower: Option<&[Vec]>, + rng: &mut StdRng, + ) -> Vec>>> { + let Some(iov_lower) = iov_lower else { + return vec![vec![Vec::new(); 1]; self.initialization.subject_ids.len()]; + }; + let n_kappa = self.initialization.iov_effect_indices.len(); + self.initialization + .occasion_counts + .iter() + .map(|&n_occasions| { + let kappas: Vec> = (0..n_occasions) + .map(|_| { + let normals: Vec = (0..n_kappa) + .map(|_| diagnostic_standard_normal(rng)) + .collect(); + (0..n_kappa) + .map(|row| { + (0..=row) + .map(|col| iov_lower[row][col] * normals[col]) + .sum() + }) + .collect() + }) + .collect(); + vec![kappas] + }) + .collect() + } + + fn frozen_diagnostic_transition( + &self, + state: &mut FrozenDiagnosticState, + rng: &mut StdRng, + counts: &mut [(usize, usize, usize)], + candidate: Option<&DiagnosticCandidate>, + ) -> std::result::Result<(), String> { + for _ in 0..self.eta_block_iterations { + for subject in 0..self.initialization.subject_ids.len() { + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let lower = cholesky_lower(omega).map_err(|error| error.to_string())?; + for (chain, count) in counts.iter_mut().enumerate() { + let current = state.etas[subject][chain].clone(); + let normals = (0..current.len()) + .map(|_| diagnostic_standard_normal(rng)) + .collect::>(); + let proposed = correlated_random_walk( + ¤t, + &lower, + &normals, + self.eta_block_step_sizes[subject], + ) + .map_err(|error| error.to_string())?; + let current_score = self + .score_subject_latents_at( + subject, + ¤t, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + let proposed_score = self + .score_subject_latents_at( + subject, + &proposed, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + count.0 += 1; + if diagnostic_accept(rng, current_score.log_acceptance_ratio(proposed_score)) { + count.1 += 1; + if proposed != current { + count.2 += 1; + } + state.etas[subject][chain] = proposed; + } + } + } + } + for _ in 0..self.mcmc_iterations { + for subject in 0..self.initialization.subject_ids.len() { + for (chain, count) in counts.iter_mut().enumerate() { + for parameter in 0..self.initialization.random_effect_indices.len() { + let current = state.etas[subject][chain].clone(); + let mut proposed = current.clone(); + proposed[parameter] += + self.proposal_step_sizes[parameter] * diagnostic_standard_normal(rng); + let current_score = self + .score_subject_latents_at( + subject, + ¤t, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + let proposed_score = self + .score_subject_latents_at( + subject, + &proposed, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + count.0 += 1; + if diagnostic_accept( + rng, + current_score.log_acceptance_ratio(proposed_score), + ) { + count.1 += 1; + if proposed != current { + count.2 += 1; + } + state.etas[subject][chain] = proposed; + } + } + let omega_iov = + candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + if let Some(omega_iov) = omega_iov { + let lower = cholesky_lower(omega_iov).map_err(|error| error.to_string())?; + for occasion in 0..state.kappas[subject][chain].len() { + let current = state.kappas[subject][chain][occasion].clone(); + let normals = (0..current.len()) + .map(|_| diagnostic_standard_normal(rng)) + .collect::>(); + let proposed = correlated_random_walk( + ¤t, + &lower, + &normals, + self.kappa_proposal_step_sizes[subject], + ) + .map_err(|error| error.to_string())?; + let current_score = self + .score_subject_latents_at( + subject, + &state.etas[subject][chain], + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + let mut proposed_kappas = state.kappas[subject][chain].clone(); + proposed_kappas[occasion] = proposed.clone(); + let proposed_score = self + .score_subject_latents_at( + subject, + &state.etas[subject][chain], + &proposed_kappas, + candidate, + ) + .map_err(|error| error.to_string())?; + count.0 += 1; + if diagnostic_accept( + rng, + current_score.log_acceptance_ratio(proposed_score), + ) { + count.1 += 1; + if proposed != current { + count.2 += 1; + } + state.kappas[subject][chain][occasion] = proposed; + } + } + } + } + } + } + Ok(()) + } + + // ─── Operational convergence ───────────────────────────────────────── + + /// Evaluate an operational convergence checkpoint if one is due. + fn evaluate_operational_convergence( + &mut self, + iteration: usize, + scheduled: bool, + mandatory_final: bool, + ) -> Result<()> { + let Some(settings) = self.operational_settings else { + return Ok(()); + }; + // Only check during smoothing, unless this is a mandatory final check. + if !mandatory_final && self.initialization.schedule.phase(iteration) != SaemPhase::Smoothing + { + return Ok(()); + } + let Some(ref average) = self.iterate_average else { + return Ok(()); + }; + let n_averaged = average.count; + if n_averaged < settings.first_eligible_averaged_iteration { + return Ok(()); + } + + // Cadence: periodic checkpoints are evaluated every check_interval + // iterations starting from first_eligible_averaged_iteration. + if scheduled && !mandatory_final { + let smoothing_start = self.initialization.schedule.pure_burn_in + + self.initialization.schedule.exploration_iterations + + 1; + let smoothing_offset = iteration.saturating_sub(smoothing_start) + 1; + if smoothing_offset < settings.first_eligible_averaged_iteration + || !(smoothing_offset - settings.first_eligible_averaged_iteration) + .is_multiple_of(settings.check_interval) + { + return Ok(()); + } + } + + // Defensive caching: if this is a mandatory final check and we already + // evaluated at this iteration, reuse instead of rerunning. + if mandatory_final { + if let Some(last) = self.operational_diagnostics.checks.last() { + if last.iteration == iteration { + self.operational_diagnostics.final_check_reused = true; + return Ok(()); + } + } + } + + // Build the deterministic per-checkpoint seed. + let checkpoint_seed = self + .config + .markov_simulation_variance + .expect("operational policy validation requires Markov diagnostics") + .seed + .wrapping_add(OPERATIONAL_CHECKPOINT_SEED_DOMAIN) + .wrapping_add(iteration as u64); + + // Two-sided standard normal quantile. + let z_quantile = normal_two_sided_z(settings.confidence_level); + + let implied_averaged_iterations = + Some(4.0 * z_quantile * z_quantile / settings.relative_fixed_width_epsilon.powi(2)); + + let info = self.information.diagnostics(); + let avg_psi = match population_psi( + &average.population_phi, + &self.initialization.parameter_scales, + ) { + Ok(psi) => psi, + Err(_) => { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + Vec::new(), + "averaged population psi conversion failed".to_string(), + ); + return Ok(()); + } + }; + let mut candidate_error_models = self.error_models.clone(); + for (output_index, model) in &average.residual_models { + match *model { + ResidualErrorModel::Combined { a, b } => update_estimated_combined_residual_model( + &mut candidate_error_models, + *output_index, + a, + b, + ), + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + update_estimated_correlated_combined_residual_model( + &mut candidate_error_models, + *output_index, + a, + b, + rho, + ) + } + ResidualErrorModel::Constant { .. } + | ResidualErrorModel::Proportional { .. } + | ResidualErrorModel::Exponential { .. } => { + update_estimated_simple_residual_model_with_sigma( + &mut candidate_error_models, + *output_index, + primary_sigma_parameter(model), + ) + } + } + } + let candidate_covariate_model = match ( + self.covariate_model.as_ref(), + average.covariate_betas.as_ref(), + ) { + (Some(model), Some(values)) => Some(model.with_estimates(values)?), + (None, None) => None, + _ => anyhow::bail!("averaged covariate metadata dimension mismatch"), + }; + let candidate = DiagnosticCandidate { + population_parameters: avg_psi, + covariate_model: candidate_covariate_model, + omega: average.omega.clone(), + omega_iov: average.omega_iov.clone(), + error_models: candidate_error_models, + }; + let candidate_free_coordinates = match operational_free_coordinates(&info, average) { + Ok(values) if !values.is_empty() => values, + Ok(_) => { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + Vec::new(), + "no free coordinates".to_string(), + ); + return Ok(()); + } + Err(error) => { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + Vec::new(), + error.to_string(), + ); + return Ok(()); + } + }; + if self.initialization.random_effect_indices.is_empty() + && self.initialization.iov_effect_indices.is_empty() + { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + candidate_free_coordinates, + "no latent coordinates".to_string(), + ); + return Ok(()); + } + + let diagnostic_metadata = SaemEstimatorMetadata { + policy: self.config.estimator_policy, + average_applied: true, + averaging_start_cycle: Some(average.start_cycle), + averaged_iterations: n_averaged, + }; + + let markov = self.markov_variance_diagnostics_with_seed( + &diagnostic_metadata, + &info, + Some(checkpoint_seed), + Some(&candidate), + ); + + let rank = &markov.rank_diagnostics; + let simulation_sd_fraction = operational_simulation_sd_fraction(&info, &markov); + let fixed_width = simulation_sd_fraction.map(|fraction| 2.0 * z_quantile * fraction); + let fixed_width_ratio = + fixed_width.map(|width| width / settings.relative_fixed_width_epsilon); + let newton_value = newton_displacement(&info, &markov).filter(|value| value.is_finite()); + let newton_mc_sd = + newton_displacement_mc_sd(&info, &markov).filter(|value| value.is_finite()); + let matrix_valid = matches!(info.status, InformationStatus::Available) + && matches!( + markov.lambda_status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + && matches!( + markov.xi_status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + && matches!( + markov.simulation_covariance_status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ); + let every_chain_moved = + !markov.chains.is_empty() && markov.chains.iter().all(|chain| chain.state_changes > 0); + let every_trace_valid = !rank.traces.is_empty() + && rank.traces.iter().all(|trace| { + trace.rank_rhat.is_some() + && trace.folded_rhat.is_some() + && trace.max_rhat.is_some() + && trace.bulk_ess.is_some() + && matches!(trace.rank_rhat_status, RankDiagnosticStatus::Available) + && matches!(trace.folded_rhat_status, RankDiagnosticStatus::Available) + && matches!(trace.max_rhat_status, RankDiagnosticStatus::Available) + && matches!(trace.bulk_ess_status, RankDiagnosticStatus::Available) + }); + let covariance_policy = self + .config + .covariance_stability + .expect("operational policy validation requires covariance stability"); + let omega_boundary = covariance_boundary_rejection_summary( + &self.cycle_diagnostics, + covariance_policy, + false, + ); + let omega_iov_boundary = + covariance_boundary_rejection_summary(&self.cycle_diagnostics, covariance_policy, true); + let covariance_active_cycles = + iteration.saturating_sub(self.initialization.schedule.pure_burn_in); + let covariance_window_available = + covariance_active_cycles >= covariance_policy.rejection_window; + let boundary_criterion = |name: &str, longest_run: usize| { + if covariance_window_available { + evaluate_criterion( + name, + Some(longest_run as f64), + covariance_policy.rejection_window as f64, + |observed| observed < covariance_policy.rejection_window as f64, + ) + } else { + OperationalConvergenceCriterion { + name: name.to_string(), + observed: Some(longest_run as f64), + threshold: covariance_policy.rejection_window as f64, + status: OperationalConvergenceCriterionStatus::Unavailable(format!( + "covariance-stability window requires {} active cycles; {covariance_active_cycles} completed", + covariance_policy.rejection_window + )), + } + } + }; + let criteria: Vec = vec![ + evaluate_criterion( + "valid_information_and_matrices", + Some(matrix_valid as u8 as f64), + 1.0, + |value| value == 1.0, + ), + evaluate_criterion( + "every_diagnostic_chain_moved", + Some(every_chain_moved as u8 as f64), + 1.0, + |value| value == 1.0, + ), + evaluate_criterion( + "every_rank_diagnostic_valid", + Some(every_trace_valid as u8 as f64), + 1.0, + |value| value == 1.0, + ), + evaluate_criterion("max_rhat", rank.worst_rhat, settings.max_rhat, |observed| { + observed < settings.max_rhat + }), + evaluate_criterion( + "min_bulk_ess", + rank.min_bulk_ess, + settings.min_bulk_ess, + |observed| observed > settings.min_bulk_ess, + ), + evaluate_criterion( + "min_average_bulk_ess_per_split_chain", + rank.min_avg_ess_per_split_chain, + settings.min_average_bulk_ess_per_split_chain, + |observed| observed >= settings.min_average_bulk_ess_per_split_chain, + ), + evaluate_criterion( + "worst_simulation_sd_fraction", + simulation_sd_fraction, + settings.relative_fixed_width_epsilon / (2.0 * z_quantile), + |observed| 2.0 * z_quantile * observed <= settings.relative_fixed_width_epsilon, + ), + evaluate_criterion( + "relative_fixed_width", + fixed_width, + settings.relative_fixed_width_epsilon, + |observed| observed <= settings.relative_fixed_width_epsilon, + ), + evaluate_criterion( + "newton_displacement", + newton_value, + settings.max_newton_displacement, + |observed| observed <= settings.max_newton_displacement, + ), + evaluate_criterion( + "newton_displacement_mc_sd", + newton_mc_sd, + settings.max_newton_displacement_mc_sd, + |observed| observed <= settings.max_newton_displacement_mc_sd, + ), + boundary_criterion("omega_boundary_rejection_run", omega_boundary.longest_run), + boundary_criterion( + "omega_iov_boundary_rejection_run", + omega_iov_boundary.longest_run, + ), + ]; + + let mut ineligible_reasons = criteria + .iter() + .filter_map(|criterion| match &criterion.status { + OperationalConvergenceCriterionStatus::Unavailable(reason) => { + Some(format!("{}: {reason}", criterion.name)) + } + _ => None, + }) + .collect::>(); + if !matrix_valid { + ineligible_reasons.push("information or matrix validation failed".to_string()); + } + if !every_trace_valid { + ineligible_reasons.push("one or more rank diagnostics unavailable".to_string()); + } + if !every_chain_moved { + ineligible_reasons + .push("one or more retained diagnostic chains did not move".to_string()); + } + let failed_criteria = criteria + .iter() + .filter(|criterion| { + matches!( + criterion.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ) + }) + .map(|criterion| criterion.name.clone()) + .collect::>(); + let outcome = if !ineligible_reasons.is_empty() { + OperationalConvergenceOutcome::Ineligible { + reasons: ineligible_reasons, + } + } else if !failed_criteria.is_empty() { + OperationalConvergenceOutcome::Failed { + criteria: failed_criteria, + } + } else { + OperationalConvergenceOutcome::Passed + }; + + let passed = matches!(outcome, OperationalConvergenceOutcome::Passed); + self.operational_diagnostics.final_status = Some(outcome.clone()); + self.operational_diagnostics.worst_rhat = rank.worst_rhat; + self.operational_diagnostics.min_bulk_ess = rank.min_bulk_ess; + self.operational_diagnostics.fixed_width_ratio = fixed_width_ratio; + self.operational_diagnostics.fixed_width_epsilon = + Some(settings.relative_fixed_width_epsilon); + self.operational_diagnostics.implied_minimum_ess = implied_averaged_iterations; + self.operational_diagnostics.newton_displacement = newton_value; + self.operational_diagnostics.newton_displacement_mc_sd = newton_mc_sd; + let checkpoint = OperationalConvergenceCheck { + iteration, + averaged_iterations: n_averaged, + scheduled, + mandatory_final, + checkpoint_seed: Some(checkpoint_seed), + z_quantile: Some(z_quantile), + implied_minimum_ess: implied_averaged_iterations, + candidate_free_coordinates, + information: Some(info), + criteria, + outcome, + markov: Some(markov), + }; + + self.operational_diagnostics.checks.push(checkpoint); + + // Terminate early if converged and this was a scheduled check. + if passed { + self.operational_diagnostics.used_for_termination = true; + self.status = Status::Stop(StopReason::Converged); + } + + Ok(()) + } + + /// Record an ineligible checkpoint (candidate unavailable). + #[allow(clippy::too_many_arguments)] + fn record_ineligible_checkpoint( + &mut self, + iteration: usize, + averaged_iterations: usize, + scheduled: bool, + mandatory_final: bool, + checkpoint_seed: u64, + z_quantile: f64, + implied_averaged_iterations: Option, + candidate_free_coordinates: Vec, + reason: String, + ) { + let settings = self + .operational_settings + .expect("ineligible operational checkpoint requires configured settings"); + let unavailable = |name: &str, threshold: f64| OperationalConvergenceCriterion { + name: name.to_string(), + observed: None, + threshold, + status: OperationalConvergenceCriterionStatus::Unavailable(reason.clone()), + }; + let criteria = vec![ + unavailable("candidate_available", 1.0), + unavailable("valid_information_and_matrices", 1.0), + unavailable("every_diagnostic_chain_moved", 1.0), + unavailable("every_rank_diagnostic_valid", 1.0), + unavailable("max_rhat", settings.max_rhat), + unavailable("min_bulk_ess", settings.min_bulk_ess), + unavailable( + "min_average_bulk_ess_per_split_chain", + settings.min_average_bulk_ess_per_split_chain, + ), + unavailable( + "worst_simulation_sd_fraction", + settings.relative_fixed_width_epsilon / (2.0 * z_quantile), + ), + unavailable( + "relative_fixed_width", + settings.relative_fixed_width_epsilon, + ), + unavailable("newton_displacement", settings.max_newton_displacement), + unavailable( + "newton_displacement_mc_sd", + settings.max_newton_displacement_mc_sd, + ), + ]; + let outcome = OperationalConvergenceOutcome::Ineligible { + reasons: vec![reason], + }; + self.operational_diagnostics.final_status = Some(outcome.clone()); + self.operational_diagnostics + .checks + .push(OperationalConvergenceCheck { + iteration, + averaged_iterations, + scheduled, + mandatory_final, + checkpoint_seed: Some(checkpoint_seed), + z_quantile: Some(z_quantile), + implied_minimum_ess: implied_averaged_iterations, + candidate_free_coordinates, + information: None, + criteria, + outcome, + markov: None, + }); + } + + fn frozen_complete_score( + &self, + state: &FrozenDiagnosticState, + chain: usize, + candidate: Option<&DiagnosticCandidate>, + ) -> std::result::Result, String> { + let layout = self.information.layout(); + let population_parameters = candidate + .map_or(self.population_parameters.as_slice(), |value| { + value.population_parameters.as_slice() + }); + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + let error_models = candidate.map_or(&self.error_models, |value| &value.error_models); + let mut derivative = CompleteDerivative::zero(layout.len()); + for subject_index in 0..self.initialization.subject_ids.len() { + let covariate_model = candidate + .and_then(|value| value.covariate_model.as_ref()) + .or(self.covariate_model.as_ref()); + match covariate_model { + Some(model) => derivative.add_covariate_population_prior( + &state.etas[subject_index][chain], + omega, + &self.initialization.random_effect_indices, + model.parameter_indices(), + model.subject_design()[subject_index].values(), + layout, + ), + None => derivative.add_population_prior( + &state.etas[subject_index][chain], + omega, + &self.initialization.random_effect_indices, + layout, + ), + } + .map_err(|error| error.to_string())?; + let calculated_mu = if candidate.is_some() { + covariate_model + .map(|model| { + let phi = population_phi( + population_parameters, + &self.initialization.parameter_scales, + )?; + Ok::<_, anyhow::Error>( + model.subject_population_parameters( + &phi, + &self.initialization.parameter_scales, + )?[subject_index] + .phi() + .to_vec(), + ) + }) + .transpose() + .map_err(|error| error.to_string())? + } else { + None + }; + let subject_mu = calculated_mu.as_deref().or_else(|| { + self.subject_mu_phi + .as_ref() + .map(|means| means[subject_index].as_slice()) + }); + let subject = self.data.subjects()[subject_index]; + if let Some(omega_iov) = omega_iov { + let occasions = subject.occasions(); + let kappas = &state.kappas[subject_index][chain]; + if occasions.len() != kappas.len() { + return Err(format!( + "subject {} has {} occasions but {} diagnostic kappa states", + subject.id(), + occasions.len(), + kappas.len() + )); + } + for (occasion, kappa) in occasions.iter().zip(kappas) { + derivative + .add_iov_prior(kappa, omega_iov, layout) + .map_err(|error| error.to_string())?; + let parameters = match subject_mu { + Some(mean) => occasion_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + &self.initialization.iov_effect_indices, + kappa, + ), + } + .map_err(|error| error.to_string())?; + let occasion_subject = + Subject::from_occasions(subject.id().to_owned(), vec![occasion.clone()]); + let predictions = self + .equation + .estimate_predictions_dense(&occasion_subject, ¶meters) + .map_err(|error| error.to_string())?; + derivative + .add_predictions_strict(&predictions, error_models, layout) + .map_err(|error| error.to_string())?; + } + } else { + let parameters = match subject_mu { + Some(mean) => individual_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + ), + None => individual_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + ), + } + .map_err(|error| error.to_string())?; + let predictions = self + .equation + .estimate_predictions_dense(subject, ¶meters) + .map_err(|error| error.to_string())?; + derivative + .add_predictions_strict(&predictions, error_models, layout) + .map_err(|error| error.to_string())?; + } + } + Ok(derivative.score) + } +} + +fn diagnostic_standard_normal(rng: &mut StdRng) -> f64 { + let u1 = rng.random::().max(f64::MIN_POSITIVE); + let u2 = rng.random::(); + (-2.0 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() +} + +fn diagnostic_accept(rng: &mut StdRng, ratio: f64) -> bool { + ratio.is_finite() && (ratio >= 0.0 || rng.random::().max(f64::MIN_POSITIVE).ln() < ratio) +} + +fn begin_retained_transition_accounting(counts: &mut [(usize, usize, usize)]) { + counts.fill((0, 0, 0)); +} + +fn mark_diagnostic_failure( + diagnostic: &mut MarkovSimulationVarianceDiagnostics, + rank_status: RankDiagnosticStatus, + markov_status: MarkovSimulationVarianceStatus, +) { + diagnostic.rank_diagnostics.status = rank_status.clone(); + diagnostic + .rank_diagnostics + .lrv_chain_statuses + .fill(rank_status); + diagnostic.lambda_status = markov_status.clone(); + diagnostic.xi_status = markov_status.clone(); + diagnostic.simulation_covariance_status = markov_status.clone(); + diagnostic.status = markov_status; +} + +fn markov_matrix_status(classification: MatrixClassification) -> MarkovSimulationVarianceStatus { + match classification { + MatrixClassification::EligiblePsd => MarkovSimulationVarianceStatus::AssumptionsUnverified, + MatrixClassification::NonFinite => MarkovSimulationVarianceStatus::NonFinite, + MatrixClassification::NonSymmetric => MarkovSimulationVarianceStatus::NonSymmetric, + MatrixClassification::Indefinite => MarkovSimulationVarianceStatus::Indefinite, + } +} + +fn worst_valid_max_rhat(traces: &[RankMixingDiagnostic]) -> Option { + traces + .iter() + .filter(|trace| matches!(trace.max_rhat_status, RankDiagnosticStatus::Available)) + .filter_map(|trace| trace.max_rhat) + .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) +} + +fn rank_diagnostic_error_status(error: &RankDiagnosticError) -> RankDiagnosticStatus { + match error { + RankDiagnosticError::NoChains => RankDiagnosticStatus::NoChains, + RankDiagnosticError::TooFewChains { .. } => RankDiagnosticStatus::TooFewChains, + RankDiagnosticError::UnequalChainLengths { .. } => { + RankDiagnosticStatus::UnequalChainLengths + } + RankDiagnosticError::OddChainLength { .. } => RankDiagnosticStatus::OddDraws, + RankDiagnosticError::NonFiniteDraw => RankDiagnosticStatus::NonFiniteDraws, + RankDiagnosticError::TooFewDraws { .. } => RankDiagnosticStatus::TooFewDraws, + RankDiagnosticError::ConstantDraws => RankDiagnosticStatus::ConstantDraws, + RankDiagnosticError::InvalidVariance => RankDiagnosticStatus::InvalidVariance, + RankDiagnosticError::NonPositiveTau { .. } => RankDiagnosticStatus::NonPositiveTau, + } +} + +fn matrix_from_rows(values: &[Vec], width: usize) -> Result> { + if values.len() != width || values.iter().any(|row| row.len() != width) { + anyhow::bail!("matrix coordinate width mismatch"); + } + Ok(Array2::from_shape_vec( + (width, width), + values.iter().flatten().copied().collect(), + )?) +} + +impl ParametricRunner for SaemState { + fn step(&mut self) -> Result { + if self.status.is_stop() { + return Ok(self.status.clone()); + } + + if self.cycle >= self.initialization.schedule.total_iterations { + self.status = Status::Stop(StopReason::MaxCycles); + return Ok(self.status.clone()); + } + + self.cycle += 1; + if let Err(error) = self.e_step() { + let failure = NumericalFailure::new( + self.cycle, + NumericalFailurePhase::Expectation, + format!("{error:#}"), + ); + self.status = Status::Stop(StopReason::NumericalFailure); + self.numerical_failure = Some(failure.clone()); + return Err(failure.into()); + } + // m_step also accumulates damped covariance sufficient statistics + // during pure burn-in while leaving theta, Omega, Omega_IOV, and sigma + // unchanged, so it must run in every schedule phase. + if let Err(error) = self.m_step() { + let failure = NumericalFailure::new( + self.cycle, + NumericalFailurePhase::Maximization, + format!("{error:#}"), + ); + self.status = Status::Stop(StopReason::NumericalFailure); + self.numerical_failure = Some(failure.clone()); + return Err(failure.into()); + } + + if self.cycle >= self.initialization.schedule.total_iterations { + self.status = Status::Stop(StopReason::MaxCycles); + let scheduled = self + .operational_settings + .zip(self.iterate_average.as_ref()) + .is_some_and(|(policy, average)| { + average.count >= policy.first_eligible_averaged_iteration + && (average.count - policy.first_eligible_averaged_iteration) + .is_multiple_of(policy.check_interval) + }); + self.evaluate_operational_convergence(self.cycle, scheduled, true)?; + } else { + self.evaluate_operational_convergence(self.cycle, true, false)?; + } + + Ok(self.status.clone()) + } + + fn request_stop(&mut self, reason: StopReason) { + if self.status.is_continue() && self.numerical_failure.is_none() { + self.status = Status::Stop(reason); + } + } + + fn cycle(&self) -> usize { + self.cycle + } + + fn status(&self) -> &Status { + &self.status + } + + fn cycle_diagnostics(&self) -> &[SaemCycleDiagnostics] { + &self.cycle_diagnostics + } + + fn log_likelihood(&self) -> f64 { + self.subject_log_likelihoods.iter().sum() + } + + fn population_parameters(&self) -> &[f64] { + &self.population_parameters + } + + fn covariate_betas(&self) -> Option> { + self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }) + } + + fn random_effect_names(&self) -> &[String] { + &self.initialization.random_effect_names + } + + fn iov_effect_names(&self) -> Option<&[String]> { + (!self.initialization.iov_effect_names.is_empty()) + .then_some(&self.initialization.iov_effect_names) + } + + fn eta_log_prior(&self) -> f64 { + self.subject_log_priors.iter().sum() + } + + fn kappa_log_prior(&self) -> f64 { + self.subject_kappa_log_priors.iter().sum() + } + + fn acceptance_rate(&self) -> Option { + self.last_acceptance_rate + } + + fn eta_block_acceptance_rate(&self) -> Option { + self.last_eta_block_acceptance_rate + } + + fn kappa_acceptance_rate(&self) -> Option { + self.last_kappa_acceptance_rate + } + + fn rejected_proposals(&self) -> Option { + self.last_rejected_proposals + } + + fn non_finite_proposals(&self) -> Option { + self.last_non_finite_proposals + } + + fn parameter_acceptance_rates(&self) -> Option<&[f64]> { + self.last_acceptance_rate + .map(|_| self.last_parameter_acceptance_rates.as_slice()) + } + + fn proposal_step_sizes(&self) -> Option<&[f64]> { + Some(&self.proposal_step_sizes) + } + + fn eta_block_step_sizes(&self) -> Option<&[f64]> { + (self.eta_block_iterations > 0).then_some(self.eta_block_step_sizes.as_slice()) + } + + fn log_acceptance_ratios(&self) -> Option<&[f64]> { + Some(&self.last_log_acceptance_ratios) + } + + fn negative_log_likelihood(&self) -> f64 { + self.negative_log_likelihood + } + + fn n_chains(&self) -> Option { + self.etas + .first() + .map(|subject_chains| subject_chains.len()) + .or(Some(self.initialization.n_chains)) + } + + fn omega(&self) -> Option<&Array2> { + Some(&self.omega) + } + + fn omega_iov(&self) -> Option<&Array2> { + self.omega_iov.as_ref() + } + + fn residual_sigmas(&self) -> &[f64] { + &self.residual_sigmas + } + + fn step_size(&self) -> f64 { + self.initialization + .schedule + .stochastic_approximation_step(self.cycle) + } + + fn total_iterations(&self) -> usize { + self.initialization.schedule.total_iterations + } + + fn into_result(mut self: Box) -> Result> { + if let Some(failure) = self.numerical_failure.as_ref() { + return Err(failure.clone().into()); + } + + let result_cycle = self.cycle; + let estimator_metadata = match self.config.estimator_policy { + SaemEstimatorPolicy::TerminalIterate => SaemEstimatorMetadata::default(), + SaemEstimatorPolicy::AveragedIterates { .. } => { + self.install_iterate_average().map_err(|error| { + NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + })? + } + }; + let information_diagnostics = self.information.diagnostics(); + let population_uncertainty = derive_population_uncertainty(&information_diagnostics); + let markov_simulation_variance = if self.operational_settings.is_some() { + self.operational_diagnostics + .checks + .last() + .and_then(|check| check.markov.clone()) + .unwrap_or_else(MarkovSimulationVarianceDiagnostics::disabled) + } else { + self.markov_variance_diagnostics(&estimator_metadata, &information_diagnostics) + }; + let (conditional_modes, conditional_mode_error) = match conditional_modes(&self) { + Ok(modes) => (modes, None), + Err(error) if self.config.marginal_likelihood.is_some() => { + (Vec::new(), Some(format!("{error:#}"))) + } + Err(error) => { + return Err(NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + .into()) + } + }; + let marginal_likelihood = calculate_result_marginal_likelihood( + &self, + &conditional_modes, + conditional_mode_error.as_deref(), + ); + let information_criteria = derive_information_criteria( + marginal_likelihood.as_ref(), + &information_diagnostics.coordinates, + self.initialization.subject_ids.len(), + ); + let eta_chain_means = self + .initialization + .subject_ids + .iter() + .enumerate() + .map(|(subject_index, subject_id)| { + Ok(SubjectEtaEstimate { + subject_id: subject_id.clone(), + values: mean_vectors( + self.etas[subject_index].iter().map(|eta| eta.as_slice()), + )?, + }) + }) + .collect::>>() + .map_err(|error| { + NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + })?; + let mut kappa_chain_means = Vec::new(); + if self.omega_iov.is_some() { + for (subject_index, subject_id) in self.initialization.subject_ids.iter().enumerate() { + for (occasion_position, occasion) in self.data.subjects()[subject_index] + .occasions() + .iter() + .enumerate() + { + kappa_chain_means.push(OccasionKappaEstimate { + subject_id: subject_id.clone(), + occasion_index: occasion.index(), + values: mean_vectors( + self.kappas[subject_index] + .iter() + .map(|chain| chain[occasion_position].as_slice()), + ) + .map_err(|error| { + NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + })?, + }); + } + } + } + let eta_variances = (0..self.omega.nrows()) + .map(|index| self.omega[[index, index]]) + .collect::>(); + let eta_posterior_rows = eta_chain_means + .iter() + .map(|estimate| estimate.values.clone()) + .collect::>(); + let eta_map_rows = (!conditional_modes.is_empty()).then(|| { + conditional_modes + .iter() + .map(|mode| mode.eta.clone()) + .collect::>() + }); + let kappa_variances = self + .omega_iov + .as_ref() + .map(|omega| { + (0..omega.nrows()) + .map(|index| omega[[index, index]]) + .collect::>() + }) + .unwrap_or_default(); + let kappa_posterior_rows = kappa_chain_means + .iter() + .map(|estimate| estimate.values.clone()) + .collect::>(); + let kappa_map_rows = (!conditional_modes.is_empty()).then(|| { + conditional_modes + .iter() + .flat_map(|mode| mode.kappas.iter().map(|kappa| kappa.values.clone())) + .collect::>() + }); + let shrinkage = ShrinkageDiagnostics { + eta_posterior_mean: derive_eta_posterior_mean_shrinkage( + &self.initialization.random_effect_names, + &eta_variances, + &eta_posterior_rows, + ), + eta_map: derive_eta_map_shrinkage( + &self.initialization.random_effect_names, + &eta_variances, + eta_map_rows.as_deref(), + ), + kappa_posterior_mean: derive_kappa_posterior_mean_shrinkage( + &self.initialization.iov_effect_names, + &kappa_variances, + &kappa_posterior_rows, + ), + kappa_map: derive_kappa_map_shrinkage( + &self.initialization.iov_effect_names, + &kappa_variances, + kappa_map_rows.as_deref(), + ), + }; + let residual_error_estimates = self.residual_error_estimates(); + let mut warnings = + parametric_warnings(&self.cycle_diagnostics, self.config.covariance_stability); + if let Some(diagnostics) = marginal_likelihood.as_ref() { + match &diagnostics.status { + MarginalLikelihoodStatus::Unavailable { failures } => { + warnings.push(ParametricWarning::MarginalLikelihoodUnavailable { + subjects: failures + .iter() + .map(|failure| failure.subject_id.clone()) + .collect(), + }); + } + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects } => { + warnings.push(ParametricWarning::MarginalLikelihoodNonconvergedModes { + subjects: subjects.clone(), + }); + } + MarginalLikelihoodStatus::Available => {} + } + } + let omega_structural_mask = self.initialization.omega.structural_mask().clone(); + let omega_estimated_mask = self.initialization.omega.estimated_mask().clone(); + let omega_iov_structural_mask = self + .initialization + .omega_iov + .as_ref() + .map(|omega| omega.structural_mask().clone()); + let omega_iov_estimated_mask = self + .initialization + .omega_iov + .as_ref() + .map(|omega| omega.estimated_mask().clone()); + let individual_estimates = if conditional_modes.is_empty() { + self.initialization + .subject_ids + .iter() + .enumerate() + .map(|(subject_index, subject_id)| { + ( + subject_id.clone(), + self.individual_parameters(subject_index, 0), + ) + }) + .collect() + } else { + conditional_modes + .iter() + .map(|mode| (mode.subject_id.clone(), mode.parameters.clone())) + .collect() + }; + + let SaemState { + equation, + data, + config, + negative_log_likelihood, + initialization, + cycle, + status, + population_parameters, + omega, + omega_iov, + residual_sigmas, + cycle_diagnostics, + operational_diagnostics, + covariate_model, + .. + } = *self; + + Ok(ParametricResult { + equation, + data, + config, + effective_n_chains: initialization.n_chains, + objective_function: 2.0 * negative_log_likelihood, + converged: status.converged(), + termination_reason: status.stop_reason().cloned(), + iterations: cycle, + subject_count: initialization.subject_ids.len(), + observation_count: initialization.observation_count, + parameter_names: initialization.parameter_names, + parameter_scales: initialization.parameter_scales, + estimated_parameters: initialization.estimated_parameters, + population_initial: initialization.initial_population_parameters.clone(), + population_estimates: population_parameters, + random_effect_indices: initialization.random_effect_indices, + random_effect_names: initialization.random_effect_names, + omega, + omega_structural_mask, + omega_estimated_mask, + omega_initial: initialization.omega.initial().clone(), + iov_effect_indices: initialization.iov_effect_indices, + iov_effect_names: initialization.iov_effect_names, + omega_iov, + omega_iov_structural_mask, + omega_iov_estimated_mask, + omega_iov_initial: initialization + .omega_iov + .as_ref() + .map(|omega| omega.initial().clone()), + residual_sigmas, + residual_error_estimates, + residual_initial_values: initialization.initial_residual_values.clone(), + residual_initial_estimated: initialization.initial_residual_estimated.clone(), + eta_chain_means, + kappa_chain_means, + conditional_modes, + shrinkage, + cycle_diagnostics, + warnings, + information_diagnostics, + population_uncertainty, + markov_simulation_variance, + operational_diagnostics, + marginal_likelihood, + information_criteria, + estimator_metadata, + individual_estimates, + covariate_model, + }) + } +} + +// ─── Operational convergence helpers ──────────────────────────────────── + +/// Two-sided standard normal quantile for confidence level `p` ∈ (0, 1). +/// +/// Returns z such that P(|Z| ≤ z) = p, i.e. z = Φ⁻¹(p + (1-p)/2). +fn normal_two_sided_z(p: f64) -> f64 { + use statrs::distribution::{ContinuousCDF, Normal}; + let norm = Normal::new(0.0, 1.0).expect("standard normal parameters are valid"); + let one_sided = p + (1.0 - p) / 2.0; + norm.inverse_cdf(one_sided) +} + +/// Evaluate one operational convergence criterion. +fn evaluate_criterion( + name: &str, + observed: Option, + threshold: f64, + predicate: impl FnOnce(f64) -> bool, +) -> OperationalConvergenceCriterion { + let status = match observed { + Some(value) if value.is_finite() && predicate(value) => { + OperationalConvergenceCriterionStatus::Satisfied + } + Some(value) if value.is_finite() => OperationalConvergenceCriterionStatus::NotSatisfied, + Some(_) => OperationalConvergenceCriterionStatus::Unavailable( + "observed value is non-finite".to_string(), + ), + None => OperationalConvergenceCriterionStatus::Unavailable( + "criterion could not be evaluated".to_string(), + ), + }; + OperationalConvergenceCriterion { + name: name.to_string(), + observed, + threshold, + status, + } +} + +fn operational_free_coordinates( + information: &InformationDiagnostics, + average: &SaemIterateAverage, +) -> Result> { + information + .coordinates + .iter() + .map(|coordinate| match &coordinate.kind { + InformationCoordinateKind::Population { parameter_index } => average + .population_phi + .get(*parameter_index) + .copied() + .ok_or_else(|| anyhow::anyhow!("population coordinate out of range")), + InformationCoordinateKind::CovariateEffect { effect_index } => average + .covariate_betas + .as_ref() + .and_then(|values| values.get(*effect_index)) + .copied() + .ok_or_else(|| anyhow::anyhow!("covariate coordinate out of range")), + InformationCoordinateKind::Omega { row, column } => average + .omega + .get((*row, *column)) + .copied() + .ok_or_else(|| anyhow::anyhow!("Omega coordinate out of range")), + InformationCoordinateKind::OmegaIov { row, column } => average + .omega_iov + .as_ref() + .and_then(|matrix| matrix.get((*row, *column))) + .copied() + .ok_or_else(|| anyhow::anyhow!("Omega_IOV coordinate out of range")), + InformationCoordinateKind::Residual { + output_index, + component, + } => { + let model = average + .residual_models + .iter() + .find(|(index, _)| index == output_index) + .map(|(_, model)| model) + .ok_or_else(|| anyhow::anyhow!("residual coordinate output unavailable"))?; + match (model, component.as_str()) { + (ResidualErrorModel::Constant { a }, "sigma") => Ok(*a), + (ResidualErrorModel::Exponential { sigma }, "sigma") => Ok(*sigma), + (ResidualErrorModel::Proportional { b }, "proportional") => Ok(*b), + (ResidualErrorModel::Combined { a, .. }, "additive") + | (ResidualErrorModel::CorrelatedCombined { a, .. }, "additive") => Ok(*a), + (ResidualErrorModel::Combined { b, .. }, "proportional") + | (ResidualErrorModel::CorrelatedCombined { b, .. }, "proportional") => Ok(*b), + (ResidualErrorModel::CorrelatedCombined { rho, .. }, "correlation") => Ok(*rho), + _ => anyhow::bail!("residual coordinate component mismatch"), + } + } + }) + .collect() +} + +fn operational_simulation_sd_fraction( + information: &InformationDiagnostics, + markov: &MarkovSimulationVarianceDiagnostics, +) -> Option { + let width = information.coordinates.len(); + let observed = matrix_from_rows(&information.observed_information, width).ok()?; + let covariance = matrix_from_rows(&markov.simulation_covariance, width).ok()?; + worst_contrast(&observed, &covariance).ok() +} + +fn solve_spd(matrix: &Array2, rhs: &[f64]) -> Option> { + if matrix.nrows() != matrix.ncols() || matrix.nrows() != rhs.len() { + return None; + } + let lower = cholesky_lower(matrix).ok()?; + let n = rhs.len(); + let mut y = vec![0.0; n]; + for row in 0..n { + let subtotal = (0..row) + .map(|column| lower[row][column] * y[column]) + .sum::(); + y[row] = (rhs[row] - subtotal) / lower[row][row]; + } + let mut result = vec![0.0; n]; + for row in (0..n).rev() { + let subtotal = ((row + 1)..n) + .map(|column| lower[column][row] * result[column]) + .sum::(); + result[row] = (y[row] - subtotal) / lower[row][row]; + } + result + .iter() + .all(|value| value.is_finite()) + .then_some(result) +} + +/// Invariant Newton displacement `sqrt(g^T Iobs^-1 g)`. +fn newton_displacement( + info: &InformationDiagnostics, + markov: &MarkovSimulationVarianceDiagnostics, +) -> Option { + let width = info.coordinates.len(); + if width == 0 || markov.grand_score_mean.len() != width { + return None; + } + let observed = matrix_from_rows(&info.observed_information, width).ok()?; + let displacement = solve_spd(&observed, &markov.grand_score_mean)?; + let squared = markov + .grand_score_mean + .iter() + .zip(&displacement) + .map(|(score, step)| score * step) + .sum::(); + (squared.is_finite() && squared >= 0.0).then(|| squared.sqrt()) +} + +/// Worst-direction Newton-step MC SD from diagnostic-mean LRV/draws. +fn newton_displacement_mc_sd( + info: &InformationDiagnostics, + markov: &MarkovSimulationVarianceDiagnostics, +) -> Option { + let width = info.coordinates.len(); + let draws = markov.config?.draws_per_chain; + if width == 0 || draws == 0 { + return None; + } + let observed = matrix_from_rows(&info.observed_information, width).ok()?; + let mut score_covariance = + matrix_from_rows(markov.rank_diagnostics.diagnostic_mean_lrv.as_ref()?, width).ok()?; + score_covariance /= draws as f64; + let mut inverse = Array2::zeros((width, width)); + for column in 0..width { + let mut unit = vec![0.0; width]; + unit[column] = 1.0; + let solved = solve_spd(&observed, &unit)?; + for row in 0..width { + inverse[[row, column]] = solved[row]; + } + } + let mut mapped = Array2::zeros((width, width)); + for row in 0..width { + for column in 0..=row { + let mut value = 0.0; + for left in 0..width { + for right in 0..width { + value += inverse[[row, left]] + * score_covariance[[left, right]] + * inverse[[column, right]]; + } + } + mapped[[row, column]] = value; + mapped[[column, row]] = value; + } + } + worst_contrast(&observed, &mapped).ok() +} + +fn incremental_average(previous: f64, current: f64, count: usize) -> f64 { + previous + (current - previous) / count as f64 +} + +fn average_covariance( + average: &mut Array2, + current: &Array2, + estimated_mask: &Array2, + count: usize, +) { + for row in 0..average.nrows() { + for col in 0..=row { + if estimated_mask[[row, col]] { + let value = incremental_average(average[[row, col]], current[[row, col]], count); + average[[row, col]] = value; + average[[col, row]] = value; + } + } + } +} + +fn average_residual_model( + previous: ResidualErrorModel, + current: ResidualErrorModel, + estimated: bool, + components: [bool; 2], + correlated_components: [bool; 3], + count: usize, +) -> Result { + let averaged = match (previous, current) { + (ResidualErrorModel::Constant { a }, ResidualErrorModel::Constant { a: current }) => { + ResidualErrorModel::Constant { + a: if estimated { + incremental_average(a, current, count) + } else { + a + }, + } + } + ( + ResidualErrorModel::Proportional { b }, + ResidualErrorModel::Proportional { b: current }, + ) => ResidualErrorModel::Proportional { + b: if estimated { + incremental_average(b, current, count) + } else { + b + }, + }, + ( + ResidualErrorModel::Exponential { sigma }, + ResidualErrorModel::Exponential { sigma: current }, + ) => ResidualErrorModel::Exponential { + sigma: if estimated { + incremental_average(sigma, current, count) + } else { + sigma + }, + }, + ( + ResidualErrorModel::Combined { a, b }, + ResidualErrorModel::Combined { + a: current_a, + b: current_b, + }, + ) => ResidualErrorModel::Combined { + a: if components[0] { + incremental_average(a, current_a, count) + } else { + a + }, + b: if components[1] { + incremental_average(b, current_b, count) + } else { + b + }, + }, + ( + ResidualErrorModel::CorrelatedCombined { a, b, rho }, + ResidualErrorModel::CorrelatedCombined { + a: current_a, + b: current_b, + rho: current_rho, + }, + ) => ResidualErrorModel::CorrelatedCombined { + a: if correlated_components[0] { + incremental_average(a, current_a, count) + } else { + a + }, + b: if correlated_components[1] { + incremental_average(b, current_b, count) + } else { + b + }, + rho: if correlated_components[2] { + incremental_average(rho, current_rho, count) + } else { + rho + }, + }, + _ => anyhow::bail!("residual family changed while accumulating SAEM averages"), + }; + Ok(averaged) +} + +fn validate_average_population(values: &[f64], initialization: &SaemInitialization) -> Result<()> { + let initial = population_phi( + &initialization.initial_population_parameters, + &initialization.parameter_scales, + )?; + if values.len() != initial.len() || values.iter().any(|value| !value.is_finite()) { + anyhow::bail!("averaged population phi values must be finite and retain their width"); + } + for index in 0..values.len() { + if !initialization.estimated_parameters[index] && values[index] != initial[index] { + anyhow::bail!("averaged population phi changed fixed coordinate {index}"); + } + } + Ok(()) +} + +fn validate_average_covariance( + matrix: &Array2, + specification: &ResolvedOmega, + label: &str, +) -> Result<()> { + if matrix.raw_dim() != specification.initial().raw_dim() { + anyhow::bail!("averaged {label} has an invalid shape"); + } + for row in 0..matrix.nrows() { + for col in 0..matrix.ncols() { + let value = matrix[[row, col]]; + if !value.is_finite() || value != matrix[[col, row]] { + anyhow::bail!("averaged {label} must be finite and symmetric"); + } + if !specification.structural_mask()[[row, col]] && value != 0.0 { + anyhow::bail!("averaged {label} changed a structural zero"); + } + if !specification.estimated_mask()[[row, col]] + && value != specification.initial()[[row, col]] + { + anyhow::bail!("averaged {label} changed a fixed entry"); + } + } + } + cholesky_lower(matrix) + .map(|_| ()) + .map_err(|error| anyhow::anyhow!("averaged {label} is not positive definite: {error}")) +} + +fn validate_average_residuals( + original_width: usize, + models: &[(usize, ResidualErrorModel)], + declarations: &ParametricErrorModels, +) -> Result<()> { + if original_width != declarations.models().len() + || models.len() != declarations.models().iter().count() + { + anyhow::bail!("averaged residual output collection changed"); + } + for ((output, model), (declared_output, terminal)) in models.iter().copied().zip( + declarations + .models() + .iter() + .map(|(index, model)| (index, *model)), + ) { + if output != declared_output || output >= original_width { + anyhow::bail!("averaged residual output indices changed"); + } + let output_name = declarations + .output_name(output) + .ok_or_else(|| anyhow::anyhow!("averaged residual output {output} has no name"))?; + let components = declarations.combined_component_estimated(output); + if !declarations.is_estimated(output) && model != terminal { + anyhow::bail!( + "averaged residual model changed fixed output '{output_name}' at index {output}" + ); + } + if let ( + ResidualErrorModel::Combined { a, b }, + ResidualErrorModel::Combined { + a: terminal_a, + b: terminal_b, + }, + ) = (model, terminal) + { + if (!components[0] && a != terminal_a) || (!components[1] && b != terminal_b) { + anyhow::bail!( + "averaged residual model changed a fixed component for output '{output_name}' at index {output}" + ); + } + } + let correlated_components = declarations.correlated_combined_component_estimated(output); + if let ( + ResidualErrorModel::CorrelatedCombined { a, b, rho }, + ResidualErrorModel::CorrelatedCombined { + a: terminal_a, + b: terminal_b, + rho: terminal_rho, + }, + ) = (model, terminal) + { + if (!correlated_components[0] && a != terminal_a) + || (!correlated_components[1] && b != terminal_b) + || (!correlated_components[2] && rho != terminal_rho) + { + anyhow::bail!( + "averaged correlated-combined model changed a fixed component for output '{output_name}' at index {output}" + ); + } + } + let valid = match model { + ResidualErrorModel::Constant { a } => a.is_finite() && a > 0.0, + ResidualErrorModel::Proportional { b } => b.is_finite() && b > 0.0, + ResidualErrorModel::Exponential { sigma } => sigma.is_finite() && sigma > 0.0, + ResidualErrorModel::Combined { a, b } => { + a.is_finite() + && b.is_finite() + && a >= 0.0 + && b >= 0.0 + && (!components[0] || a > 0.0) + && (!components[1] || b > 0.0) + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + a.is_finite() + && a > 0.0 + && b.is_finite() + && b > 0.0 + && rho.is_finite() + && rho > -1.0 + && rho < 1.0 + } + }; + if !valid { + anyhow::bail!( + "averaged residual model for output '{output_name}' at index {output} is outside its domain" + ); + } + } + Ok(()) +} + +#[derive(Debug, Default)] +struct WarningCount { + first_iteration: Option, + cycles: usize, + count: usize, +} + +impl WarningCount { + fn record_cycle(&mut self, iteration: usize) { + self.first_iteration.get_or_insert(iteration); + self.cycles += 1; + } + + fn record_count(&mut self, iteration: usize, count: usize) { + if count == 0 { + return; + } + self.first_iteration.get_or_insert(iteration); + self.count += count; + } +} + +#[derive(Debug, Default, PartialEq, Eq)] +struct CovarianceBoundaryRejectionSummary { + first_iteration: Option, + longest_run: usize, +} + +fn covariance_boundary_rejection_summary( + cycles: &[SaemCycleDiagnostics], + policy: CovarianceStabilityConfig, + iov: bool, +) -> CovarianceBoundaryRejectionSummary { + let mut summary = CovarianceBoundaryRejectionSummary::default(); + let mut current_run = 0usize; + let mut current_start = None; + for cycle in cycles { + let (rejected, margin) = if iov { + ( + cycle.omega_iov_update_rejected, + cycle.omega_iov_relative_spd_margin, + ) + } else { + (cycle.omega_update_rejected, cycle.omega_relative_spd_margin) + }; + if rejected && margin.is_some_and(|value| value <= policy.minimum_relative_spd_margin) { + if current_run == 0 { + current_start = Some(cycle.iteration); + } + current_run += 1; + summary.longest_run = summary.longest_run.max(current_run); + if current_run >= policy.rejection_window && summary.first_iteration.is_none() { + summary.first_iteration = current_start; + } + } else { + current_run = 0; + current_start = None; + } + } + summary +} + +fn parametric_warnings( + cycles: &[SaemCycleDiagnostics], + covariance_stability: Option, +) -> Vec { + let mut omega = WarningCount::default(); + let mut omega_iov = WarningCount::default(); + let mut eta_non_finite = WarningCount::default(); + let mut eta_block_non_finite = WarningCount::default(); + let mut kappa_non_finite = WarningCount::default(); + let mut residual_rejected = BTreeMap::::new(); + let mut proportional_floor = BTreeMap::::new(); + let mut residual_non_finite = BTreeMap::::new(); + let mut exponential_domain = BTreeMap::::new(); + let mut additive_collapse = BTreeMap::::new(); + let mut optimizer_not_converged = BTreeMap::::new(); + + for cycle in cycles { + if cycle.omega_update_rejected { + omega.record_cycle(cycle.iteration); + } + if cycle.omega_iov_update_rejected { + omega_iov.record_cycle(cycle.iteration); + } + eta_non_finite.record_count(cycle.iteration, cycle.eta_non_finite); + eta_block_non_finite.record_count(cycle.iteration, cycle.eta_block_non_finite); + kappa_non_finite.record_count(cycle.iteration, cycle.kappa_non_finite); + for residual in &cycle.residual_diagnostics { + if residual.update_rejected { + residual_rejected + .entry(residual.output.clone()) + .or_default() + .record_cycle(cycle.iteration); + } + proportional_floor + .entry(residual.output.clone()) + .or_default() + .record_count(cycle.iteration, residual.proportional_floor_count); + residual_non_finite + .entry(residual.output.clone()) + .or_default() + .record_count(cycle.iteration, residual.non_finite_prediction_count); + exponential_domain + .entry(residual.output.clone()) + .or_default() + .record_count(cycle.iteration, residual.exponential_domain_violation_count); + if residual.combined_additive_collapse_warning { + additive_collapse + .entry(residual.output.clone()) + .or_default() + .record_cycle(cycle.iteration); + } + if residual.optimizer_converged == Some(false) { + optimizer_not_converged + .entry(residual.output.clone()) + .or_default() + .record_cycle(cycle.iteration); + } + } + } + + let mut warnings = Vec::new(); + if let Some(first_iteration) = omega.first_iteration { + warnings.push(ParametricWarning::OmegaUpdateRejected { + first_iteration, + cycles: omega.cycles, + }); + } + if let Some(first_iteration) = omega_iov.first_iteration { + warnings.push(ParametricWarning::OmegaIovUpdateRejected { + first_iteration, + cycles: omega_iov.cycles, + }); + } + if let Some(policy) = covariance_stability { + let omega_boundary = covariance_boundary_rejection_summary(cycles, policy, false); + if let Some(first_iteration) = omega_boundary.first_iteration { + warnings.push(ParametricWarning::OmegaBoundaryRejection { + first_iteration, + longest_run: omega_boundary.longest_run, + }); + } + let omega_iov_boundary = covariance_boundary_rejection_summary(cycles, policy, true); + if let Some(first_iteration) = omega_iov_boundary.first_iteration { + warnings.push(ParametricWarning::OmegaIovBoundaryRejection { + first_iteration, + longest_run: omega_iov_boundary.longest_run, + }); + } + } + if let Some(first_iteration) = eta_non_finite.first_iteration { + warnings.push(ParametricWarning::EtaNonFiniteProposals { + first_iteration, + count: eta_non_finite.count, + }); + } + if let Some(first_iteration) = eta_block_non_finite.first_iteration { + warnings.push(ParametricWarning::EtaBlockNonFiniteProposals { + first_iteration, + count: eta_block_non_finite.count, + }); + } + if let Some(first_iteration) = kappa_non_finite.first_iteration { + warnings.push(ParametricWarning::KappaNonFiniteProposals { + first_iteration, + count: kappa_non_finite.count, + }); + } + for (output, warning) in residual_rejected { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ResidualUpdateRejected { + output, + first_iteration, + cycles: warning.cycles, + }); + } + } + for (output, warning) in proportional_floor { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ProportionalPredictionFloor { + output, + first_iteration, + count: warning.count, + }); + } + } + for (output, warning) in residual_non_finite { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::NonFiniteResidualPrediction { + output, + first_iteration, + count: warning.count, + }); + } + } + for (output, warning) in exponential_domain { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ExponentialDomainViolation { + output, + first_iteration, + count: warning.count, + }); + } + } + for (output, warning) in additive_collapse { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::CombinedAdditiveCollapse { + output, + first_iteration, + cycles: warning.cycles, + }); + } + } + for (output, warning) in optimizer_not_converged { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ResidualOptimizerNotConverged { + output, + first_iteration, + cycles: warning.cycles, + }); + } + } + warnings +} + +fn calculate_result_marginal_likelihood( + state: &SaemState, + conditional_modes: &[SubjectConditionalMode], + conditional_mode_error: Option<&str>, +) -> Option { + let config = state.config.marginal_likelihood?; + let n_eta = state.initialization.random_effect_indices.len(); + let n_kappa = state.initialization.iov_effect_indices.len(); + let latent = n_eta > 0 || n_kappa > 0; + let occasion_indices = state + .data + .subjects() + .iter() + .map(|subject| { + if n_kappa == 0 { + Vec::new() + } else { + subject + .occasions() + .iter() + .map(|occasion| occasion.index()) + .collect() + } + }) + .collect::>>(); + let mut flattened_modes = Vec::with_capacity(state.initialization.subject_ids.len()); + let mut converged = Vec::with_capacity(state.initialization.subject_ids.len()); + let mut validation_failures = Vec::with_capacity(state.initialization.subject_ids.len()); + + for (subject_index, subject_id) in state.initialization.subject_ids.iter().enumerate() { + if !latent { + flattened_modes.push(Vec::new()); + converged.push(None); + validation_failures.push(None); + continue; + } + let Some(mode) = conditional_modes.get(subject_index) else { + flattened_modes.push(Vec::new()); + converged.push(None); + validation_failures.push(Some( + MarginalLikelihoodFailureReason::MissingConditionalMode, + )); + continue; + }; + let mut validation_failure = None; + if mode.subject_id != *subject_id { + validation_failure.get_or_insert(MarginalLikelihoodFailureReason::SubjectIdMismatch { + expected: subject_id.clone(), + actual: mode.subject_id.clone(), + }); + } + if mode.eta.len() != n_eta { + validation_failure.get_or_insert(MarginalLikelihoodFailureReason::EtaWidthMismatch { + expected: n_eta, + actual: mode.eta.len(), + }); + } + if mode.kappas.len() != occasion_indices[subject_index].len() { + validation_failure.get_or_insert(MarginalLikelihoodFailureReason::KappaCountMismatch { + expected: occasion_indices[subject_index].len(), + actual: mode.kappas.len(), + }); + } + for (position, kappa) in mode.kappas.iter().enumerate() { + if let Some(expected) = occasion_indices[subject_index].get(position) { + if kappa.occasion_index != *expected { + validation_failure.get_or_insert( + MarginalLikelihoodFailureReason::KappaOccasionMismatch { + position, + expected: *expected, + actual: kappa.occasion_index, + }, + ); + } + } + if kappa.values.len() != n_kappa { + validation_failure.get_or_insert( + MarginalLikelihoodFailureReason::KappaWidthMismatch { + position, + expected: n_kappa, + actual: kappa.values.len(), + }, + ); + } + } + let mut flattened = mode.eta.clone(); + for kappa in &mode.kappas { + flattened.extend_from_slice(&kappa.values); + } + if flattened.iter().any(|value| !value.is_finite()) { + validation_failure + .get_or_insert(MarginalLikelihoodFailureReason::NonFiniteModeCoordinate); + } + flattened_modes.push(flattened); + converged.push(Some(mode.converged)); + validation_failures.push(validation_failure); + } + + let curvature_covariances = conditional_modes + .iter() + .map(|mode| { + mode.uncertainty + .latent_covariance + .as_ref() + .and_then(|rows| matrix_from_rows(rows, rows.len()).ok()) + }) + .collect::>(); + let subjects = state + .initialization + .subject_ids + .iter() + .enumerate() + .map(|(index, subject_id)| MarginalSubject { + subject_id, + occasion_indices: &occasion_indices[index], + mode: &flattened_modes[index], + mode_converged: converged[index], + eta_dimension: n_eta, + kappa_dimension: n_kappa, + validation_failure: validation_failures[index].clone(), + curvature_availability: conditional_modes + .get(index) + .map(|mode| &mode.uncertainty.status), + curvature_covariance: curvature_covariances.get(index).and_then(Option::as_ref), + }) + .collect::>(); + if let Some(error) = conditional_mode_error { + return Some(unavailable_population_marginal_likelihood( + config, + &subjects, + MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed(format!( + "global conditional mode calculation failed: {error}" + )), + )); + } + Some(calculate_population_marginal_likelihood( + config, + &subjects, + &state.omega, + state.omega_iov.as_ref(), + |subject_index, eta, kappas| { + state + .score_subject_latents(subject_index, eta, kappas) + .map(SubjectPosteriorScore::log_posterior) + }, + )) +} + +fn conditional_modes(state: &SaemState) -> Result> { + if !state.compute_map { + return Ok(Vec::new()); + } + + let n_eta = state.initialization.random_effect_indices.len(); + let n_kappa = state.initialization.iov_effect_indices.len(); + if n_eta == 0 && n_kappa == 0 { + return Ok(Vec::new()); + } + let mut modes = Vec::with_capacity(state.initialization.subject_ids.len()); + for (subject_index, subject_id) in state.initialization.subject_ids.iter().enumerate() { + let eta_start = mean_vectors(state.etas[subject_index].iter().map(|eta| eta.as_slice()))?; + let occasion_count = if state.omega_iov.is_some() { + state.data.subjects()[subject_index].occasions().len() + } else { + 0 + }; + let mut kappa_start = Vec::with_capacity(occasion_count); + for occasion_position in 0..occasion_count { + kappa_start.push(mean_vectors( + state.kappas[subject_index] + .iter() + .map(|chain| chain[occasion_position].as_slice()), + )?); + } + let mut initial = eta_start; + for kappa in &kappa_start { + initial.extend_from_slice(kappa); + } + + let step_fraction = state.map_initial_step; + let mut scales = (0..n_eta) + .map(|index| state.omega[[index, index]].sqrt() * step_fraction) + .collect::>(); + if let Some(omega_iov) = state.omega_iov.as_ref() { + for _ in 0..occasion_count { + scales.extend( + (0..n_kappa).map(|index| omega_iov[[index, index]].sqrt() * step_fraction), + ); + } + } + for scale in &mut scales { + *scale = scale.max(1e-6); + } + + let solution = optimize_conditional_mode( + initial, + &scales, + state.map_max_iterations as u64, + state.map_sd_tolerance, + |coordinates| { + let (eta, kappas) = unflatten_latents(coordinates, n_eta, occasion_count, n_kappa); + match state.score_subject_latents(subject_index, eta, &kappas) { + Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), + _ => f64::INFINITY, + } + }, + )?; + let mut coordinates = (0..n_eta) + .map(|index| JointLatentCoordinate { + index, + name: format!("eta:{}", state.initialization.random_effect_names[index]), + kind: JointLatentCoordinateKind::Eta { + parameter_index: state.initialization.random_effect_indices[index], + }, + prior_sd: state.omega[[index, index]].sqrt(), + }) + .collect::>(); + if let Some(omega_iov) = state.omega_iov.as_ref() { + for occasion_position in 0..occasion_count { + let occasion_index = + state.data.subjects()[subject_index].occasions()[occasion_position].index(); + for effect_index in 0..n_kappa { + coordinates.push(JointLatentCoordinate { + index: n_eta + occasion_position * n_kappa + effect_index, + name: format!( + "kappa:{occasion_index}:{}", + state.initialization.iov_effect_names[effect_index] + ), + kind: JointLatentCoordinateKind::Kappa { + occasion_index, + effect_index, + parameter_index: state.initialization.iov_effect_indices[effect_index], + }, + prior_sd: omega_iov[[effect_index, effect_index]].sqrt(), + }); + } + } + } + let prior_sds = coordinates + .iter() + .map(|coordinate| coordinate.prior_sd) + .collect::>(); + let mode_metadata = ConditionalModeMetadata { + converged: solution.converged, + iterations: solution.iterations, + objective_value: solution.objective, + termination_message: solution.termination.clone(), + }; + let uncertainty = conditional_mode_curvature( + &solution.coordinates, + &prior_sds, + &coordinates, + &mode_metadata, + |coordinates| { + let (eta, kappas) = unflatten_latents(coordinates, n_eta, occasion_count, n_kappa); + match state.score_subject_latents(subject_index, eta, &kappas) { + Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), + _ => f64::INFINITY, + } + }, + ); + let (eta, kappas) = + unflatten_latents(&solution.coordinates, n_eta, occasion_count, n_kappa); + let parameters = state.individual_parameters_from_eta(subject_index, eta)?; + let kappa_estimates = kappas + .into_iter() + .enumerate() + .map(|(occasion_position, values)| OccasionKappaEstimate { + subject_id: subject_id.clone(), + occasion_index: state.data.subjects()[subject_index].occasions()[occasion_position] + .index(), + values, + }) + .collect(); + modes.push(SubjectConditionalMode { + subject_id: subject_id.clone(), + eta: eta.to_vec(), + kappas: kappa_estimates, + parameters, + objective: solution.objective, + converged: solution.converged, + iterations: solution.iterations, + termination: solution.termination, + uncertainty, + }); + } + Ok(modes) +} + +fn unflatten_latents( + coordinates: &[f64], + n_eta: usize, + occasion_count: usize, + n_kappa: usize, +) -> (&[f64], Vec>) { + let eta = &coordinates[..n_eta]; + let kappas = (0..occasion_count) + .map(|occasion| { + let start = n_eta + occasion * n_kappa; + coordinates[start..start + n_kappa].to_vec() + }) + .collect(); + (eta, kappas) +} + +fn mean_vectors<'a>(vectors: impl IntoIterator) -> Result> { + let mut vectors = vectors.into_iter(); + let Some(first) = vectors.next() else { + anyhow::bail!("cannot summarize random effects without chains"); + }; + let mut mean = first.to_vec(); + let mut count = 1usize; + for vector in vectors { + if vector.len() != mean.len() { + anyhow::bail!("random-effect chains have inconsistent dimensions"); + } + for (sum, value) in mean.iter_mut().zip(vector) { + *sum += value; + } + count += 1; + } + for value in &mut mean { + *value /= count as f64; + } + Ok(mean) +} + +fn zero_etas(n_subjects: usize, n_chains: usize, n_parameters: usize) -> Vec>> { + vec![vec![vec![0.0; n_parameters]; n_chains]; n_subjects] +} + +fn zero_kappas( + occasion_counts: &[usize], + n_chains: usize, + n_kappa: usize, +) -> Vec>>> { + occasion_counts + .iter() + .map(|&n_occasions| vec![vec![vec![0.0; n_kappa]; n_occasions]; n_chains]) + .collect() +} + +fn second_moment_from_etas(etas: &[Vec>]) -> Result> { + let mut samples = etas.iter().flat_map(|subject_chains| subject_chains.iter()); + let Some(first) = samples.next() else { + anyhow::bail!("cannot update omega without subject-chain samples"); + }; + let dimension = first.len(); + let mut second_moment = Array2::zeros((dimension, dimension)); + let mut count = 0usize; + for eta in std::iter::once(first).chain(samples) { + if eta.len() != dimension { + anyhow::bail!("eta samples have inconsistent dimensions"); + } + for row in 0..dimension { + for col in 0..dimension { + second_moment[[row, col]] += eta[row] * eta[col]; + } + } + count += 1; + } + second_moment.mapv_inplace(|value| value / count as f64); + Ok(second_moment) +} + +fn covariance_from_kappas(kappas: &[Vec>>]) -> Result> { + let mut samples = kappas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .flat_map(|chains| chains.iter()); + let Some(first) = samples.next() else { + anyhow::bail!("cannot update omega_iov without occasion samples"); + }; + let dimension = first.len(); + let mut covariance = Array2::zeros((dimension, dimension)); + let mut count = 0usize; + for kappa in std::iter::once(first).chain(samples) { + if kappa.len() != dimension { + anyhow::bail!("kappa samples have inconsistent dimensions"); + } + for row in 0..dimension { + for col in 0..dimension { + covariance[[row, col]] += kappa[row] * kappa[col]; + } + } + count += 1; + } + covariance.mapv_inplace(|value| value / count as f64); + Ok(covariance) +} + +fn correlated_random_walk( + current: &[f64], + lower: &[Vec], + standard_normals: &[f64], + scale: f64, +) -> Result> { + anyhow::ensure!( + lower.len() == current.len() + && standard_normals.len() == current.len() + && lower + .iter() + .enumerate() + .all(|(row, values)| values.len() > row), + "correlated random-walk dimensions do not match" + ); + Ok((0..current.len()) + .map(|row| { + let perturbation = (0..=row) + .map(|column| lower[row][column] * standard_normals[column]) + .sum::(); + current[row] + scale * perturbation + }) + .collect()) +} + +fn initial_proposal_step_sizes(omega: &Array2, rw_init: f64) -> Vec { + (0..omega.nrows()) + .map(|index| omega[[index, index]].max(f64::EPSILON).sqrt() * rw_init) + .collect() +} + +fn adapt_component_step_size(current: f64, acceptance_rate: f64) -> f64 { + adapt_block_step_size(current, acceptance_rate, COMPONENT_TARGET_ACCEPTANCE) +} + +fn adapt_block_step_size(current: f64, acceptance_rate: f64, target: f64) -> f64 { + if acceptance_rate > target { + (current * PROPOSAL_SCALE_INCREASE).min(MAX_PROPOSAL_SCALE) + } else { + (current * PROPOSAL_SCALE_DECREASE).max(MIN_PROPOSAL_SCALE) + } +} + +fn zero_eta_subject_phi( + population_parameters: &[f64], + initialization: &SaemInitialization, +) -> Result>> { + let phi = population_phi(population_parameters, &initialization.parameter_scales)?; + Ok(vec![phi; initialization.subject_ids.len()]) +} + +fn negative_log_likelihood(subject_log_likelihoods: &[f64]) -> f64 { + if subject_log_likelihoods.iter().any(|ll| !ll.is_finite()) { + f64::INFINITY + } else { + -subject_log_likelihoods.iter().sum::() + } +} + +fn count_observations(data: &Data) -> usize { + data.subjects() + .iter() + .flat_map(|subject| subject.occasions()) + .flat_map(|occasion| occasion.events()) + .filter(|event| matches!(event, Event::Observation(_))) + .count() +} + +fn n_chains(config: &SaemConfig, n_subjects: usize) -> usize { + if n_subjects > 0 && n_subjects < 50 && config.n_chains == 1 { + ((50.0 / n_subjects as f64).ceil() as usize).max(1) + } else { + config.n_chains + } +} + +fn initial_parameter_row<'a>( + parameters: impl IntoIterator, +) -> Vec { + parameters + .into_iter() + .map(initial_parameter_value) + .collect() +} + +fn initial_parameter_value(parameter: &UnboundedParameter) -> f64 { + if let Some(initial) = parameter.initial { + return initial; + } + + match parameter.scale { + ParameterScale::Identity | ParameterScale::Log => 1.0, + ParameterScale::Logit { lower, upper } | ParameterScale::Probit { lower, upper } => { + 0.5 * (lower + upper) + } + } +} + +fn information_failure_status(reason: String) -> InformationStatus { + if reason.contains("censored") { + InformationStatus::Unsupported(reason) + } else if reason.contains("non-finite") { + InformationStatus::NonFinite + } else { + InformationStatus::Ineligible(reason) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::parametric::transforms::{phi_to_psi, psi_to_phi}; + use crate::estimation::parametric::ParametricPrior; + use crate::estimation::{EstimationProblem, Iov, Omega, ParametricErrorModel}; + use crate::model::Parameter; + use crate::results::{ + FitResult, PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, + PopulationUncertaintyStatus, + }; + use pharmsol::prelude::*; + use pharmsol::SubjectBuilderExt; + + #[test] + fn finite_improvement_is_eligible_without_a_convergence_flag() { + assert!(non_iiv_candidate_improves(10.0, 9.0)); + assert!(!non_iiv_candidate_improves(10.0, 10.0)); + assert!(!non_iiv_candidate_improves(10.0, f64::NAN)); + } + + #[test] + fn censored_information_failure_has_explicit_unsupported_status() { + let reason = "analytic information is unsupported for censored observations".to_string(); + assert_eq!( + information_failure_status(reason.clone()), + InformationStatus::Unsupported(reason) + ); + } + + fn one_compartment_metadata() -> pharmsol::equation::ModelMetadata { + equation::metadata::new("one_compartment_saem") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["0"]) + .route(equation::Route::bolus("0").to_state("central")) + } + + fn one_compartment() -> pharmsol::ODE { + equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata(one_compartment_metadata()) + .unwrap() + } + + fn sparse_second_output_problem() -> EstimationProblem { + let equation = equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0]; + y[1] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + equation::metadata::new("sparse_second_output") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["unmeasured", "measured"]) + .route(equation::Route::bolus("dose").to_state("central")), + ) + .unwrap(); + let data = Data::new(vec![Subject::builder("sparse") + .bolus(0.0, 100.0, "dose") + .observation(1.0, 8.0, "measured") + .observation(2.0, 6.0, "measured") + .build()]); + + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("measured", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn mixed_residual_output_problem() -> EstimationProblem { + let equation = equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + y[1] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + equation::metadata::new("mixed_residual_outputs") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["fixed", "mixed"]) + .route(equation::Route::bolus("dose").to_state("central")), + ) + .expect("mixed residual equation metadata should validate"); + let data = Data::new(vec![Subject::builder("mixed") + .bolus(0.0, 100.0, "dose") + .observation(1.0, 8.5, "fixed") + .observation(2.0, 6.5, "fixed") + .observation(1.0, 8.0, "mixed") + .observation(2.0, 6.0, "mixed") + .build()]); + + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "fixed", + ParametricErrorModel::new(ResidualErrorModel::constant(0.5)).fixed(), + ) + .error_model( + "mixed", + ParametricErrorModel::new(ResidualErrorModel::combined(0.0, 0.1)) + .fixed_combined_additive(), + ) + .build() + .expect("mixed residual output problem should validate") + } + + fn data() -> Data { + Data::new(vec![ + Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .observation(4.0, 4.0, "0") + .build(), + Subject::builder("s2") + .bolus(0.0, 80.0, "0") + .observation(0.5, 9.0, "0") + .observation(3.0, 2.5, "0") + .build(), + ]) + } + + fn covariate_problem() -> EstimationProblem { + let subjects = [-1.0, 0.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + Subject::builder(format!("cov{index}")) + .covariate("wt", 0.0, wt) + .covariate("sex", 0.0, if index == 2 { 1.0 } else { 0.0 }) + .bolus(0.0, 100.0, "0") + .observation(1.0, 8.0 + index as f64, "0") + .build() + }) + .collect(); + EstimationProblem::parametric(one_compartment(), Data::new(subjects)) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.0), + ) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::categorical("v", "sex", 0.0, 1.0) + .with_initial(0.0), + ) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .unwrap() + } + + fn fixed_covariate_iiv_problem() -> EstimationProblem { + let subjects = [-1.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + Subject::builder(format!("fixed-cov-iiv-{index}")) + .covariate("wt", 0.0, wt) + .bolus(0.0, 100.0, "0") + .observation(1.0, 5.0 + index as f64, "0") + .build() + }) + .collect(); + EstimationProblem::parametric(one_compartment(), Data::new(subjects)) + .parameter(Parameter::log("ke").with_initial(0.2).fixed()) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::diagonal([("ke", 1.0)])) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.0) + .fixed(), + ) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .unwrap() + } + + fn fixed_covariate_without_iiv_problem() -> EstimationProblem { + let subjects = [0.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + Subject::builder(format!("fixed-cov-{index}")) + .covariate("wt", 0.0, wt) + .bolus(0.0, 100.0, "0") + .observation(1.0, 5.0 + index as f64, "0") + .build() + }) + .collect(); + EstimationProblem::parametric(one_compartment(), Data::new(subjects)) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.2) + .fixed(), + ) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .unwrap() + } + + fn problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::combined(0.5, 0.1)).fixed(), + ) + .build() + .unwrap() + } + + fn constant_error_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn partial_iiv_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn iov_data() -> Data { + Data::new(vec![Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 10.0, "0") + .build()]) + } + + fn iov_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .iov(Iov::diagonal([("ke", 0.1)])) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn markov_iov_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::log("ke").with_initial(0.2).fixed()) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.1)) + .iov(Iov::new().fixed_variance("ke", 0.1)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn uneven_iov_problem() -> EstimationProblem { + let data = Data::new(vec![ + Subject::builder("one") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .build(), + Subject::builder("two") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 10.0, "0") + .build(), + Subject::builder("three") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 10.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 11.0, "0") + .build(), + ]); + EstimationProblem::parametric(one_compartment(), data) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .iov(Iov::diagonal([("ke", 0.1)])) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn configured_iov_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .iov( + Iov::diagonal([("ke", 0.10)]) + .fixed_variance("v", 0.20) + .fixed_covariance("ke", "v", 0.05), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn ordered_metadata_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::real("ke").with_initial(0.2)) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .iov(Iov::diagonal([("v", 0.20)])) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn configured_omega_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25)]).fixed_variance("v", 0.5)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn correlated_omega_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25), ("v", 0.25)]).covariance("ke", "v", 0.20)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn fixed_population_iiv_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2).fixed()) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + fn fixed_no_iiv_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() + } + + #[test] + fn initialization_builds_initial_objective() { + let initialization = + SaemInitialization::create(&problem(), &SaemConfig::default()).unwrap(); + + assert_eq!( + initialization.initial_population_parameters, + vec![0.2, 10.0] + ); + assert_eq!(initialization.initial_subject_log_likelihoods.len(), 2); + assert!(initialization.initial_negative_log_likelihood.is_finite()); + } + + #[test] + fn initialization_rejects_estimated_iiv_variance_below_floor() { + let mut config = SaemConfig::new(); + config.omega_min_variance = 0.3; + + let error = SaemInitialization::create(&configured_omega_problem(), &config) + .unwrap_err() + .to_string(); + + assert!(error.contains( + "initial Omega variance for estimated effect 'ke' (0.25) is below configured omega_min_variance (0.3)" + )); + } + + #[test] + fn initialization_rejects_estimated_iov_variance_below_floor() { + let config = SaemConfig::new().omega_iov_min_variance(0.11); + + let error = SaemInitialization::create(&configured_iov_problem(), &config) + .unwrap_err() + .to_string(); + + assert!(error.contains( + "initial Omega_IOV variance for estimated effect 'ke' (0.1) is below configured omega_iov_min_variance (0.11)" + )); + } + + #[test] + fn initialization_floor_exempts_fixed_covariance_diagonals() { + let problem = EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25)]).fixed_variance("v", 0.01)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap(); + let mut config = SaemConfig::new(); + config.omega_min_variance = 0.1; + + let initialization = SaemInitialization::create(&problem, &config).unwrap(); + + assert_eq!(initialization.omega.initial()[[0, 0]], 0.25); + assert_eq!(initialization.omega.initial()[[1, 1]], 0.01); + assert!(!initialization.omega.estimated_mask()[[1, 1]]); + } + + #[test] + fn schedule_counts_real_internal_phases() { + let config = SaemConfig::new() + .burn_in(100) + .k1_iterations(400) + .k2_iterations(700); + let schedule = SaemSchedule::from_config(&config); + let counts = (1..=schedule.total_iterations).fold([0_usize; 3], |mut counts, cycle| { + match schedule.phase(cycle) { + SaemPhase::BurnIn => counts[0] += 1, + SaemPhase::Exploration => counts[1] += 1, + SaemPhase::Smoothing => counts[2] += 1, + } + counts + }); + + assert_eq!(counts, [100, 300, 700]); + assert_eq!(schedule.total_iterations, 1100); + } + + #[test] + fn covariate_omega_cap_applies_only_during_exploration() { + assert_eq!( + covariate_omega_update_maximum_fraction(true, SaemPhase::BurnIn, 0.1), + 1.0 + ); + assert_eq!( + covariate_omega_update_maximum_fraction(true, SaemPhase::Exploration, 0.1), + 0.1 + ); + assert_eq!( + covariate_omega_update_maximum_fraction(true, SaemPhase::Smoothing, 0.1), + 1.0 + ); + assert_eq!( + covariate_omega_update_maximum_fraction(false, SaemPhase::Exploration, 0.1), + 1.0 + ); + } + + #[derive(Debug)] + struct CommonMomentCycle { + expected_phi: Vec>, + global_second_moment: Array2, + beta: Vec, + subject_means: Vec>, + covariance_target: Array2, + omega: Array2, + } + + fn common_moment_cycle( + statistics: &mut CovariateSufficientStatistics, + observed: &CovariateSufficientStatistics, + gain: f64, + designs: &[Array2], + current_omega: &Array2, + omega_specification: &ResolvedOmega, + ) -> Result { + statistics.stochastic_update(observed, gain)?; + let offsets = vec![vec![0.0]; designs.len()]; + let beta = solve_covariate_gls(CovariateGlsProblem { + design: designs, + expected_phi: &statistics.expected_phi, + offset: &offsets, + omega: current_omega, + })?; + let subject_means = designs + .iter() + .map(|design| vec![design[[0, 0]] * beta[0] + design[[0, 1]] * beta[1]]) + .collect::>(); + let covariance_target = subject_centered_omega( + &statistics.global_second_moment, + &statistics.expected_phi, + &subject_means, + )?; + let omega = omega_specification + .update_with_status(current_omega, &covariance_target, 1e-6)? + .matrix; + Ok(CommonMomentCycle { + expected_phi: statistics.expected_phi.clone(), + global_second_moment: statistics.global_second_moment.clone(), + beta, + subject_means, + covariance_target, + omega, + }) + } + + fn assert_nested_close(actual: &[Vec], expected: &[Vec]) { + assert_eq!(actual.len(), expected.len()); + for (actual_row, expected_row) in actual.iter().zip(expected) { + assert_eq!(actual_row.len(), expected_row.len()); + for (actual_value, expected_value) in actual_row.iter().zip(expected_row) { + assert!((actual_value - expected_value).abs() <= 1e-12); + } + } + } + + #[test] + fn common_gain_raw_moments_are_coherent_cycle_by_cycle() { + let designs = [-1.0, 0.0, 1.0] + .into_iter() + .map(|covariate| ndarray::array![[1.0, covariate]]) + .collect::>(); + let parameters = [Parameter::log("x")].into_iter().collect(); + let prior = + ParametricPrior::new(parameters, Some(Omega::diagonal([("x", 1.0)])), None).unwrap(); + let mut current_omega = prior.omega().clone(); + let mut statistics = CovariateSufficientStatistics { + expected_phi: vec![vec![0.0]; 3], + global_second_moment: ndarray::array![[1.0]], + }; + let exploration_observed = CovariateSufficientStatistics::from_subject_chains(&[ + vec![vec![-1.4], vec![-0.6]], + vec![vec![-0.4], vec![0.4]], + vec![vec![0.6], vec![1.4]], + ]) + .unwrap(); + let first_smoothing_observed = CovariateSufficientStatistics::from_subject_chains(&[ + vec![vec![-1.5], vec![-0.5]], + vec![vec![0.5], vec![1.5]], + vec![vec![2.5], vec![3.5]], + ]) + .unwrap(); + let second_smoothing_observed = CovariateSufficientStatistics::from_subject_chains(&[ + vec![vec![-3.0], vec![-1.0]], + vec![vec![-1.0], vec![1.0]], + vec![vec![1.0], vec![3.0]], + ]) + .unwrap(); + + let burn = common_moment_cycle( + &mut statistics, + &exploration_observed, + 0.0, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_eq!(burn.expected_phi, vec![vec![0.0]; 3]); + assert_eq!(burn.global_second_moment, ndarray::array![[1.0]]); + assert_eq!(burn.beta, vec![0.0, 0.0]); + assert_eq!(burn.subject_means, vec![vec![0.0]; 3]); + assert_eq!(burn.covariance_target, ndarray::array![[1.0]]); + assert_eq!(burn.omega, ndarray::array![[1.0]]); + + let exploration = common_moment_cycle( + &mut statistics, + &exploration_observed, + 1.0, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_nested_close( + &exploration.expected_phi, + &[vec![-1.0], vec![0.0], vec![1.0]], + ); + assert!((exploration.global_second_moment[[0, 0]] - 62.0 / 75.0).abs() <= 1e-12); + assert!((exploration.beta[0] - 0.0).abs() <= 1e-12); + assert!((exploration.beta[1] - 1.0).abs() <= 1e-12); + assert_nested_close(&exploration.subject_means, &exploration.expected_phi); + assert!((exploration.covariance_target[[0, 0]] - 0.16).abs() <= 1e-12); + assert!((exploration.omega[[0, 0]] - 0.16).abs() <= 1e-12); + current_omega = exploration.omega.clone(); + + let first_smoothing = common_moment_cycle( + &mut statistics, + &first_smoothing_observed, + 1.0, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_nested_close( + &first_smoothing.expected_phi, + &[vec![-1.0], vec![1.0], vec![3.0]], + ); + assert!((first_smoothing.global_second_moment[[0, 0]] - 47.0 / 12.0).abs() <= 1e-12); + assert!((first_smoothing.beta[0] - 1.0).abs() <= 1e-12); + assert!((first_smoothing.beta[1] - 2.0).abs() <= 1e-12); + assert_nested_close( + &first_smoothing.subject_means, + &first_smoothing.expected_phi, + ); + assert!((first_smoothing.covariance_target[[0, 0]] - 0.25).abs() <= 1e-12); + assert!((first_smoothing.omega[[0, 0]] - 0.25).abs() <= 1e-12); + current_omega = first_smoothing.omega.clone(); + + let second_smoothing = common_moment_cycle( + &mut statistics, + &second_smoothing_observed, + 0.5, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_nested_close( + &second_smoothing.expected_phi, + &[vec![-1.5], vec![0.5], vec![2.5]], + ); + assert!((second_smoothing.global_second_moment[[0, 0]] - 91.0 / 24.0).abs() <= 1e-12); + assert!((second_smoothing.beta[0] - 0.5).abs() <= 1e-12); + assert!((second_smoothing.beta[1] - 2.0).abs() <= 1e-12); + assert_nested_close( + &second_smoothing.subject_means, + &second_smoothing.expected_phi, + ); + assert!((second_smoothing.covariance_target[[0, 0]] - 0.875).abs() <= 1e-12); + assert!((second_smoothing.omega[[0, 0]] - 0.875).abs() <= 1e-12); + + for cycle in [burn, exploration, first_smoothing, second_smoothing] { + let mean_square = cycle + .expected_phi + .iter() + .map(|row| row[0] * row[0]) + .sum::() + / cycle.expected_phi.len() as f64; + assert!(cycle.global_second_moment[[0, 0]] + 1e-12 >= mean_square); + assert!(cycle.covariance_target[[0, 0]] >= -1e-12); + } + } + + #[test] + fn coherent_covariance_target_precedes_structured_gem_constraints() { + let coherent_target = ndarray::array![[0.002, 0.0], [0.0, 0.04]]; + assert!(cholesky_lower(&coherent_target).is_ok()); + let parameters = [Parameter::log("ke"), Parameter::log("v")] + .into_iter() + .collect(); + let prior = ParametricPrior::new( + parameters, + Some( + Omega::new() + .variance("ke", 0.02) + .fixed_variance("v", 0.04) + .fixed_covariance("ke", "v", 0.012), + ), + None, + ) + .unwrap(); + + let constrained = prior + .resolved_omega() + .update_with_status(prior.omega(), &coherent_target, 0.0) + .unwrap(); + + assert_eq!(coherent_target[[0, 0]], 0.002); + assert!((constrained.matrix[[0, 0]] - 0.0092).abs() <= 1e-10); + assert_eq!(constrained.matrix[[0, 1]], 0.012); + assert_eq!(constrained.matrix[[1, 1]], 0.04); + assert_ne!(constrained.matrix, coherent_target); + } + + #[test] + fn covariate_update_uses_common_moments_and_no_second_smoothing_gain() { + let mut statistics = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![0.0], vec![2.0]]]) + .unwrap(); + let exploration_observed = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![2.0], vec![4.0]]]) + .unwrap(); + statistics + .stochastic_update(&exploration_observed, 1.0) + .unwrap(); + assert_eq!(statistics.expected_phi, vec![vec![3.0]]); + assert_eq!(statistics.global_second_moment, ndarray::array![[10.0]]); + let exploration_variance = statistics.global_second_moment[[0, 0]] + - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; + let exploration_candidate = ndarray::array![[exploration_variance]]; + assert_eq!(exploration_candidate, ndarray::array![[1.0]]); + + let parameters = [Parameter::log("x")].into_iter().collect(); + let prior = + ParametricPrior::new(parameters, Some(Omega::diagonal([("x", 0.25)])), None).unwrap(); + let exploration = prior + .resolved_omega() + .update_with_status_and_max_fraction( + prior.omega(), + &exploration_candidate, + 0.0, + covariate_omega_update_maximum_fraction(true, SaemPhase::Exploration, 0.1), + ) + .unwrap(); + assert!((exploration.matrix[[0, 0]] - 0.325).abs() <= 1e-12); + + let smoothing_observed = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![4.0], vec![6.0]]]) + .unwrap(); + statistics + .stochastic_update(&smoothing_observed, 0.5) + .unwrap(); + assert_eq!(statistics.expected_phi, vec![vec![4.0]]); + assert_eq!(statistics.global_second_moment, ndarray::array![[18.0]]); + let smoothing_variance = statistics.global_second_moment[[0, 0]] + - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; + let smoothing_candidate = ndarray::array![[smoothing_variance]]; + assert_eq!(smoothing_candidate, ndarray::array![[2.0]]); + + let smoothing = prior + .resolved_omega() + .update_with_status(&exploration.matrix, &smoothing_candidate, 0.0) + .unwrap(); + assert_eq!(smoothing.matrix, smoothing_candidate); + } + + #[test] + fn covariate_state_m_step_caps_exploration_and_does_not_resmooth_omega() { + let config = SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(2) + .omega_sa_max_step(0.1) + .compute_map(false); + let mut state = SaemState::from_problem(fixed_covariate_iiv_problem(), &config).unwrap(); + + for subject_chains in &mut state.etas { + subject_chains[0][0] = 2.0; + subject_chains[1][0] = -2.0; + } + state.cycle = 2; + assert_eq!( + state.initialization.schedule.phase(state.cycle), + SaemPhase::Exploration + ); + assert_eq!( + state + .initialization + .schedule + .stochastic_approximation_step(state.cycle), + 1.0 + ); + state.m_step().unwrap(); + + assert!((state.iiv_second_moment[[0, 0]] - 4.0).abs() <= 1e-12); + assert!((state.omega[[0, 0]] - 1.3).abs() <= 1e-12); + + for subject_chains in &mut state.etas { + subject_chains[0][0] = 4.0; + subject_chains[1][0] = -4.0; + } + state.cycle = 4; + assert_eq!( + state.initialization.schedule.phase(state.cycle), + SaemPhase::Smoothing + ); + assert_eq!( + state + .initialization + .schedule + .stochastic_approximation_step(state.cycle), + 0.5 + ); + state.m_step().unwrap(); + + // The common raw history moves from variance 4 toward 16 with gain 0.5, + // giving 10. Omega installs that coherent target directly. Applying the + // smoothing gain a second time would instead leave Omega below 10. + assert!((state.iiv_second_moment[[0, 0]] - 10.0).abs() <= 1e-12); + assert!((state.omega[[0, 0]] - 10.0).abs() <= 1e-12); + } + + #[test] + fn schedule_splits_burn_in_exploration_and_smoothing() { + let config = SaemConfig::new() + .k1_iterations(300) + .k2_iterations(100) + .burn_in(5); + let schedule = SaemSchedule::from_config(&config); + + assert_eq!(schedule.pure_burn_in, 5); + assert_eq!(schedule.exploration_iterations, 295); + assert_eq!(schedule.smoothing_iterations, 100); + assert_eq!(schedule.total_iterations, 400); + assert_eq!(schedule.variance_floor_iterations, 150); + assert_eq!(schedule.minimum_residual_sigma, 1e-6); + assert_eq!(schedule.stochastic_approximation_step(1), 0.0); + assert_eq!(schedule.stochastic_approximation_step(6), 1.0); + assert_eq!(schedule.stochastic_approximation_step(301), 1.0); + assert_eq!(schedule.stochastic_approximation_step(302), 0.5); + assert_eq!(schedule.covariance_step(1), 0.1); + assert_eq!(schedule.covariance_step(6), 0.1); + assert_eq!(schedule.covariance_step(300), 0.1); + assert_eq!(schedule.covariance_step(301), 1.0); + assert_eq!(schedule.covariance_step(302), 0.5); + assert!(!schedule.covariance_update_active(5)); + assert!(schedule.covariance_update_active(6)); + assert_eq!(schedule.guarded_residual_sigma(1, 1.0, 0.1), 0.97); + assert_eq!(schedule.guarded_residual_sigma(151, 1.0, 0.1), 0.1); + assert_eq!(schedule.guarded_residual_sigma(151, 1.0, 0.0), 1e-6); + } + + #[test] + fn averaged_schedule_uses_alpha_only_during_smoothing() { + let schedule = SaemSchedule::from_config( + &SaemConfig::new() + .k1_iterations(3) + .burn_in(1) + .k2_iterations(4) + .averaged_iterates(0.75), + ); + assert_eq!(schedule.stochastic_approximation_step(1), 0.0); + assert_eq!(schedule.stochastic_approximation_step(2), 1.0); + assert_eq!(schedule.stochastic_approximation_step(3), 1.0); + assert_eq!(schedule.stochastic_approximation_step(4), 1.0); + assert_eq!( + schedule.stochastic_approximation_step(5), + 2.0_f64.powf(-0.75) + ); + assert_eq!( + schedule.stochastic_approximation_step(7), + 4.0_f64.powf(-0.75) + ); + } + + #[test] + fn averaged_result_uses_only_completed_smoothing_iterates() { + let config = SaemConfig::new() + .k1_iterations(2) + .burn_in(1) + .k2_iterations(3) + .averaged_iterates(0.75) + .compute_map(false) + .seed(9981); + let result = problem().fit_with(config).unwrap(); + let metadata = result.estimator_metadata(); + assert!(metadata.average_applied); + assert_eq!(metadata.averaging_start_cycle, Some(3)); + assert_eq!(metadata.averaged_iterations, 3); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + + let smoothing = &result.cycle_diagnostics()[2..]; + for parameter_index in 0..result.population_parameters().len() { + if !result.estimated_parameters()[parameter_index] { + continue; + } + let expected = smoothing + .iter() + .map(|cycle| { + population_phi(&cycle.population_parameters, result.parameter_scales()).unwrap() + [parameter_index] + }) + .sum::() + / smoothing.len() as f64; + let installed = + population_phi(result.population_parameters(), result.parameter_scales()).unwrap() + [parameter_index]; + assert!((installed - expected).abs() < 1e-12); + } + for row in 0..result.omega().nrows() { + for col in 0..result.omega().ncols() { + let expected = smoothing + .iter() + .map(|cycle| cycle.omega[[row, col]]) + .sum::() + / smoothing.len() as f64; + assert!((result.omega()[[row, col]] - expected).abs() < 1e-12); + } + } + cholesky_lower(result.omega()).unwrap(); + } + + #[test] + fn averaged_iov_installation_is_canonical_and_preserves_latent_coordinates() { + let config = SaemConfig::new() + .n_chains(2) + .mcmc_iterations(2) + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .compute_map(false) + .seed(71_004); + let mut state = SaemState::from_problem(configured_iov_problem(), &config) + .expect("averaged IOV state should initialize"); + while matches!(state.status, Status::Continue) { + state.step().expect("averaged IOV cycle should complete"); + } + let cycle_records = state.cycle_diagnostics.clone(); + let smoothing = &cycle_records[1..]; + let terminal_phi = population_phi( + &state.population_parameters, + &state.initialization.parameter_scales, + ) + .expect("terminal population phi should be valid"); + let terminal_absolute_phi = state + .etas + .iter() + .map(|chains| { + chains + .iter() + .map(|eta| { + state + .initialization + .random_effect_indices + .iter() + .enumerate() + .map(|(eta_index, parameter_index)| { + terminal_phi[*parameter_index] + eta[eta_index] + }) + .collect::>() + }) + .collect::>() + }) + .collect::>(); + let terminal_kappas = state.kappas.clone(); + let average = state + .iterate_average + .clone() + .expect("completed smoothing average"); + + let metadata = state + .install_iterate_average() + .expect("averaged IOV state should install"); + assert!(metadata.average_applied); + assert_eq!(metadata.averaging_start_cycle, Some(2)); + assert_eq!(metadata.averaged_iterations, 3); + assert_eq!(state.cycle_diagnostics, cycle_records); + assert_eq!(state.kappas, terminal_kappas); + + let installed_phi = population_phi( + &state.population_parameters, + &state.initialization.parameter_scales, + ) + .expect("installed population phi should be valid"); + assert_eq!(installed_phi, average.population_phi); + for (subject_index, chains) in state.etas.iter().enumerate() { + for (chain_index, eta) in chains.iter().enumerate() { + for (eta_index, parameter_index) in state + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + assert!( + (installed_phi[parameter_index] + eta[eta_index] + - terminal_absolute_phi[subject_index][chain_index][eta_index]) + .abs() + < 1e-14 + ); + } + } + } + + let omega_iov = state.omega_iov.as_ref().expect("installed Omega_IOV"); + let iov_specification = state + .initialization + .omega_iov + .as_ref() + .expect("IOV specification"); + assert_eq!(omega_iov, &average.omega_iov.expect("averaged Omega_IOV")); + for row in 0..omega_iov.nrows() { + for col in 0..omega_iov.ncols() { + let expected = if iov_specification.estimated_mask()[[row, col]] { + smoothing + .iter() + .map(|cycle| { + cycle + .omega_iov + .as_ref() + .expect("smoothing cycle should retain Omega_IOV")[[row, col]] + }) + .sum::() + / smoothing.len() as f64 + } else { + iov_specification.initial()[[row, col]] + }; + assert!((omega_iov[[row, col]] - expected).abs() < 1e-12); + } + } + + let n_chains = state.initialization.n_chains as f64; + let mut direct_likelihoods = vec![0.0; state.initialization.subject_ids.len()]; + let mut direct_eta_priors = vec![0.0; state.initialization.subject_ids.len()]; + let mut direct_kappa_priors = vec![0.0; state.initialization.subject_ids.len()]; + for subject_index in 0..state.initialization.subject_ids.len() { + for chain_index in 0..state.initialization.n_chains { + let score = state + .score_subject_latents( + subject_index, + &state.etas[subject_index][chain_index], + &state.kappas[subject_index][chain_index], + ) + .expect("installed latent score should be directly calculable"); + direct_likelihoods[subject_index] += score.log_likelihood / n_chains; + direct_eta_priors[subject_index] += score.eta_log_prior / n_chains; + direct_kappa_priors[subject_index] += score.kappa_log_prior / n_chains; + } + } + assert_eq!(state.subject_log_likelihoods, direct_likelihoods); + assert_eq!(state.subject_log_priors, direct_eta_priors); + assert_eq!(state.subject_kappa_log_priors, direct_kappa_priors); + assert_eq!( + state.negative_log_likelihood, + negative_log_likelihood(&direct_likelihoods) + ); + } + + #[test] + fn frozen_markov_diagnostic_is_repeatable_and_canonical_result_is_unchanged() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + + let base = SaemConfig::new() + .k1_iterations(100) + .k2_iterations(50) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(true) + .seed(91) + .averaged_iterates(0.75); + let diagnostic_config = MarkovSimulationVarianceConfig::new( + 700, + 2, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 64 * 1024, + ); + let disabled = markov_iov_problem().fit_with(base.clone()).unwrap(); + let enabled = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diagnostic_config)) + .unwrap(); + let repeated = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diagnostic_config)) + .unwrap(); + let changed_seed = markov_iov_problem() + .fit_with( + base.clone() + .markov_simulation_variance(MarkovSimulationVarianceConfig::new( + 701, + 2, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 64 * 1024, + )), + ) + .unwrap(); + + assert_eq!( + enabled.markov_simulation_variance(), + repeated.markov_simulation_variance() + ); + assert_ne!( + enabled.markov_simulation_variance(), + changed_seed.markov_simulation_variance() + ); + assert_ne!( + enabled.markov_simulation_variance().status, + MarkovSimulationVarianceStatus::Disabled + ); + assert!(!enabled.markov_simulation_variance().chains.is_empty()); + // One subject, one eta block, one component eta, and two occasion-kappa + // blocks are attempted in that exact compound-kernel order per retained + // transition. Warmup attempts are absent from the exported count. + assert!(enabled + .markov_simulation_variance() + .chains + .iter() + .all(|chain| chain.proposals == 12 * (1 + 1 + 2))); + assert_eq!( + enabled.population_parameters(), + disabled.population_parameters() + ); + assert_eq!(enabled.omega(), disabled.omega()); + assert_eq!(enabled.omega_iov(), disabled.omega_iov()); + assert_eq!( + enabled.residual_error_estimates(), + disabled.residual_error_estimates() + ); + assert_eq!(enabled.eta_chain_means(), disabled.eta_chain_means()); + assert_eq!(enabled.kappa_chain_means(), disabled.kappa_chain_means()); + assert!(!enabled.conditional_modes().is_empty()); + assert_eq!(enabled.conditional_modes(), disabled.conditional_modes()); + assert_eq!( + enabled.information_diagnostics(), + disabled.information_diagnostics() + ); + assert_eq!(enabled.cycle_diagnostics(), disabled.cycle_diagnostics()); + assert_eq!(enabled.warnings(), disabled.warnings()); + assert_eq!(enabled.conditional_n2ll(), disabled.conditional_n2ll()); + assert_eq!(enabled.termination_reason(), disabled.termination_reason()); + assert_eq!( + enabled.population_parameters(), + changed_seed.population_parameters() + ); + assert_eq!(enabled.omega(), changed_seed.omega()); + assert_eq!( + enabled.residual_error_estimates(), + changed_seed.residual_error_estimates() + ); + assert_eq!(enabled.eta_chain_means(), changed_seed.eta_chain_means()); + assert_eq!( + enabled.cycle_diagnostics(), + changed_seed.cycle_diagnostics() + ); + assert_eq!(enabled.warnings(), changed_seed.warnings()); + assert_eq!(enabled.conditional_n2ll(), changed_seed.conditional_n2ll()); + let enabled_predictions = enabled.population_predictions(0.0, 0.0).unwrap(); + let disabled_predictions = disabled.population_predictions(0.0, 0.0).unwrap(); + assert_eq!(enabled_predictions.len(), disabled_predictions.len()); + for (actual, expected) in enabled_predictions.iter().zip(&disabled_predictions) { + assert_prediction_points_equal(actual, expected); + } + let enabled_conditional = enabled.conditional_predictions(0.0, 0.0).unwrap(); + let disabled_conditional = disabled.conditional_predictions(0.0, 0.0).unwrap(); + assert_eq!(enabled_conditional.len(), disabled_conditional.len()); + for (actual, expected) in enabled_conditional.iter().zip(&disabled_conditional) { + assert_prediction_points_equal(actual, expected); + } + } + + #[test] + fn rank_diagnostics_computed_for_multiple_chains_and_iov() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; + + let base = SaemConfig::new() + .k1_iterations(30) + .k2_iterations(20) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(91) + .averaged_iterates(0.75); + let diag = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let result = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diag)) + .unwrap(); + let rank = &result.markov_simulation_variance().rank_diagnostics; + assert_eq!(rank.diagnostic_chains, 2); + assert_eq!(rank.draws_per_chain, 12); + assert_eq!(rank.original_chains, 2); + assert_eq!(rank.status, RankDiagnosticStatus::Available); + assert!(!rank.traces.is_empty()); + // First trace is a score coordinate. + assert!(matches!( + rank.traces[0].trace, + DiagnosticTraceCoordinate::Score { .. } + )); + let score_count = result.information_diagnostics().coordinates.len(); + let eta_count = result + .eta_chain_means() + .iter() + .map(|estimate| estimate.values.len()) + .sum::(); + let kappa_count = result + .kappa_chain_means() + .iter() + .map(|estimate| estimate.values.len()) + .sum::(); + assert_eq!(rank.traces.len(), score_count + eta_count + kappa_count); + for (trace, coordinate) in rank + .traces + .iter() + .take(score_count) + .zip(&result.information_diagnostics().coordinates) + { + assert!(matches!( + &trace.trace, + DiagnosticTraceCoordinate::Score { index, .. } if *index == coordinate.index + )); + } + assert!(rank + .traces + .iter() + .skip(score_count) + .take(eta_count) + .all(|trace| matches!(trace.trace, DiagnosticTraceCoordinate::Eta { .. }))); + assert!(rank + .traces + .iter() + .skip(score_count + eta_count) + .all(|trace| matches!(trace.trace, DiagnosticTraceCoordinate::Kappa { .. }))); + assert!(rank.diagnostic_mean_lrv.is_some()); + assert!(rank.operational_lrv.is_some()); + + // Repeatability: same seed produces identical rank diagnostics. + let repeated = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diag)) + .unwrap(); + assert_eq!( + result.markov_simulation_variance().rank_diagnostics, + repeated.markov_simulation_variance().rank_diagnostics + ); + + // Canonical result is unchanged by rank diagnostic presence. + let disabled = markov_iov_problem().fit_with(base).unwrap(); + assert_eq!( + result.population_parameters(), + disabled.population_parameters() + ); + assert_eq!(result.omega(), disabled.omega()); + assert_eq!(result.conditional_n2ll(), disabled.conditional_n2ll()); + assert_eq!(result.termination_reason(), disabled.termination_reason()); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + } + + #[test] + fn score_failure_does_not_discard_valid_eta_rank_diagnostics() { + use crate::results::{ + DiagnosticTraceCoordinate, InformationCoordinateKind, RankDiagnosticStatus, + }; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![ + vec![vec![f64::NAN; 8], vec![f64::NAN; 8]], + vec![ + vec![1.0, 4.0, 2.0, 3.0, 2.0, 4.0, 1.0, 3.0], + vec![2.1, 3.1, 1.1, 4.1, 3.1, 1.1, 4.1, 2.1], + ], + ]; + let coordinates = vec![ + DiagnosticTraceCoordinate::Score { + index: 0, + name: "score".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }, + DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }, + ]; + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + assert_eq!( + diagnostics[0].rank_rhat_status, + RankDiagnosticStatus::ScoreUnavailable + ); + assert!(diagnostics[0].rank_rhat.is_none()); + assert_eq!( + diagnostics[1].rank_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostics[1].rank_rhat.is_some()); + } + + #[test] + fn multimodal_latent_trace_is_detected_while_mixed_score_trace_passes() { + use crate::results::{ + DiagnosticTraceCoordinate, InformationCoordinateKind, RankDiagnosticStatus, + }; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![ + vec![ + vec![1.0, 4.0, 2.0, 3.0, 2.0, 4.0, 1.0, 3.0], + vec![2.1, 3.1, 1.1, 4.1, 3.1, 1.1, 4.1, 2.1], + ], + vec![ + vec![-10.0, -9.0, -11.0, -8.0, -9.5, -8.5, -10.5, -7.5], + vec![8.0, 11.0, 9.0, 10.0, 8.5, 10.5, 7.5, 9.5], + ], + ]; + let coordinates = vec![ + DiagnosticTraceCoordinate::Score { + index: 0, + name: "score".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }, + DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }, + ]; + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + assert_eq!( + diagnostics[0].rank_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostics[0].rank_rhat.is_some_and(|rhat| rhat < 1.1)); + assert_eq!( + diagnostics[1].rank_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostics[1].rank_rhat.is_some_and(|rhat| rhat > 1.1)); + } + + #[test] + fn rank_coordinate_retains_valid_rhats_when_bulk_ess_is_unavailable() { + use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![vec![vec![1.0, 2.0, 4.0, 3.0], vec![1.5, 2.5, 4.5, 3.5]]]; + let coordinates = vec![DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }]; + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + let diagnostic = &diagnostics[0]; + assert!(diagnostic.rank_rhat.is_some()); + assert_eq!(diagnostic.rank_rhat_status, RankDiagnosticStatus::Available); + assert!(diagnostic.folded_rhat.is_some()); + assert_eq!( + diagnostic.folded_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostic.bulk_ess.is_none()); + assert!(diagnostic.tau.is_none()); + assert_eq!( + diagnostic.bulk_ess_status, + RankDiagnosticStatus::TooFewDraws + ); + assert_eq!(diagnostic.status, RankDiagnosticStatus::PartialAvailability); + } + + #[test] + fn derived_max_rhat_requires_both_rank_and_folded_components() { + use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![vec![ + vec![1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0], + vec![2.0, -2.0, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0], + ]]; + let coordinates = vec![DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }]; + + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + let diagnostic = &diagnostics[0]; + assert!(diagnostic.rank_rhat.is_some()); + assert_eq!(diagnostic.rank_rhat_status, RankDiagnosticStatus::Available); + assert!(diagnostic.folded_rhat.is_none()); + assert_eq!( + diagnostic.folded_rhat_status, + RankDiagnosticStatus::ConstantDraws + ); + assert!(diagnostic.max_rhat.is_none()); + assert_eq!( + diagnostic.max_rhat_status, + RankDiagnosticStatus::ConstantDraws + ); + assert_eq!(worst_valid_max_rhat(&diagnostics), None); + } + + #[test] + fn rank_diagnostics_available_when_markov_config_enabled() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::RankDiagnosticStatus; + + let base = SaemConfig::new() + .k1_iterations(100) + .k2_iterations(50) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(77) + .averaged_iterates(0.75); + let diag = MarkovSimulationVarianceConfig::new( + 42, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let result = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diag)) + .unwrap(); + let rank = &result.markov_simulation_variance().rank_diagnostics; + // Rank diagnostics object is always present when markov config enabled; + // status reflects whether data supported valid computation. + assert_eq!(rank.diagnostic_chains, 2); + assert_eq!(rank.original_chains, 2); + assert!(!matches!(rank.status, RankDiagnosticStatus::Disabled)); + } + + #[test] + fn one_diagnostic_chain_retains_markov_lrv_but_rank_is_unavailable() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::RankDiagnosticStatus; + + let config = SaemConfig::new() + .k1_iterations(30) + .k2_iterations(20) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(93) + .averaged_iterates(0.75) + .markov_simulation_variance(MarkovSimulationVarianceConfig::new( + 702, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 1, + 1024 * 1024, + )); + let result = markov_iov_problem().fit_with(config).unwrap(); + let markov = result.markov_simulation_variance(); + assert_eq!( + markov.rank_diagnostics.status, + RankDiagnosticStatus::TooFewChains + ); + assert_eq!(markov.chains.len(), 1); + assert!(!markov.lambda.is_empty()); + assert!(markov.rank_diagnostics.operational_lrv.is_some()); + assert!(markov.rank_diagnostics.traces.iter().all(|trace| { + trace.status == RankDiagnosticStatus::TooFewChains + && trace.rank_rhat.is_none() + && trace.bulk_ess.is_none() + })); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert!(!result.converged()); + } + + #[test] + fn rank_diagnostics_trace_byte_cap_exceeded_is_reported() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::RankDiagnosticStatus; + + let base = SaemConfig::new() + .k1_iterations(100) + .k2_iterations(50) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(91) + .averaged_iterates(0.75); + let tiny_cap = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1, // 1 byte cap — guaranteed to be exceeded + ); + let result = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(tiny_cap)) + .unwrap(); + let rank = &result.markov_simulation_variance().rank_diagnostics; + assert_eq!(rank.status, RankDiagnosticStatus::TraceByteCapExceeded); + assert!(rank.traces.is_empty()); + assert!(rank.diagnostic_mean_lrv.is_none()); + assert!(rank.operational_lrv.is_none()); + assert_eq!(rank.max_trace_bytes, 1); + assert!(rank.accounted_peak_trace_bytes_required > rank.max_trace_bytes); + assert_eq!(rank.accounted_peak_trace_bytes_used, 0); + let markov = result.markov_simulation_variance(); + assert!(matches!( + markov.status, + MarkovSimulationVarianceStatus::InvalidConfiguration(_) + )); + assert_eq!(markov.lambda_status, markov.status); + assert_eq!(markov.xi_status, markov.status); + assert_eq!(markov.simulation_covariance_status, markov.status); + assert!(markov.chains.is_empty()); + // Canonical result is unchanged. + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + + let generous = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let measured = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(generous)) + .unwrap(); + let measured_rank = &measured.markov_simulation_variance().rank_diagnostics; + let trace_count = measured_rank.traces.len(); + let score_width = measured.markov_simulation_variance().coordinates.len(); + let vec_header = std::mem::size_of::>(); + let persistent_bytes = 2 * 12 * trace_count * std::mem::size_of::() + + trace_count * 2 * vec_header + + trace_count * vec_header; + let score_transient_bytes = score_width * 12 * std::mem::size_of::() + 12 * vec_header; + let rank_transient_bytes = 2 * 12 * 8 * std::mem::size_of::() + 2 * 16 * vec_header; + let expected_bytes = persistent_bytes + score_transient_bytes.max(rank_transient_bytes); + assert_eq!( + measured_rank.accounted_peak_trace_bytes_required, + expected_bytes + ); + assert_eq!( + measured_rank.accounted_peak_trace_bytes_used, + expected_bytes + ); + + let exact_cap = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + expected_bytes, + ); + let exact = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(exact_cap)) + .unwrap(); + assert_eq!( + exact + .markov_simulation_variance() + .rank_diagnostics + .accounted_peak_trace_bytes_used, + expected_bytes + ); + assert!(!exact + .markov_simulation_variance() + .rank_diagnostics + .traces + .is_empty()); + + let under_cap = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + expected_bytes - 1, + ); + let rejected = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(under_cap)) + .unwrap(); + assert_eq!( + rejected + .markov_simulation_variance() + .rank_diagnostics + .status, + RankDiagnosticStatus::TraceByteCapExceeded + ); + assert_eq!( + rejected + .markov_simulation_variance() + .rank_diagnostics + .accounted_peak_trace_bytes_used, + 0 + ); + + let overflow = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + usize::MAX / 2 + 1, + usize::MAX, + ); + let overflowed = markov_iov_problem() + .fit_with(base.markov_simulation_variance(overflow)) + .unwrap(); + let overflowed = overflowed.markov_simulation_variance(); + assert_eq!( + overflowed.rank_diagnostics.status, + RankDiagnosticStatus::TraceMemoryAccountingOverflow + ); + assert_eq!( + overflowed.status, + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow + ); + assert_eq!( + overflowed + .rank_diagnostics + .accounted_peak_trace_bytes_required, + 0 + ); + assert_eq!( + overflowed.rank_diagnostics.accounted_peak_trace_bytes_used, + 0 + ); + assert!(overflowed.chains.is_empty()); + } + + #[test] + fn operational_and_frozen_iov_transitions_preserve_compound_kernel_order() { + let seed = 0x5eed; + let mut operational = SaemState::from_problem( + markov_iov_problem(), + &SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .eta_block_iterations(1) + .adapt_interval(50) + .seed(seed), + ) + .unwrap(); + let initial_eta_scales = operational.proposal_step_sizes.clone(); + let initial_eta_block_scales = operational.eta_block_step_sizes.clone(); + let initial_kappa_scales = operational.kappa_proposal_step_sizes.clone(); + let mut frozen = FrozenDiagnosticState { + etas: operational.etas.clone(), + kappas: operational.kappas.clone(), + }; + let mut frozen_rng = StdRng::seed_from_u64(seed); + let mut frozen_counts = vec![(0, 0, 0); operational.initialization.n_chains]; + + // This single compound transition is order-sensitive: eta blocks consume + // the stream first, followed by component etas and then occasion kappas. + operational + .frozen_diagnostic_transition(&mut frozen, &mut frozen_rng, &mut frozen_counts, None) + .unwrap(); + operational.e_step().unwrap(); + + assert_eq!(operational.etas, frozen.etas); + assert_eq!(operational.kappas, frozen.kappas); + assert_eq!(operational.proposal_step_sizes, initial_eta_scales); + assert_eq!(operational.eta_block_step_sizes, initial_eta_block_scales); + assert_eq!(operational.kappa_proposal_step_sizes, initial_kappa_scales); + + let diagnostics = operational.cycle_diagnostics.last().unwrap(); + let frozen_proposals = frozen_counts.iter().map(|count| count.0).sum::(); + let frozen_accepts = frozen_counts.iter().map(|count| count.1).sum::(); + let frozen_changes = frozen_counts.iter().map(|count| count.2).sum::(); + assert_eq!(diagnostics.eta_block_proposals, 2); + assert_eq!(diagnostics.eta_proposals, 4); + assert_eq!(diagnostics.kappa_proposals, 4); + assert_eq!(frozen_proposals, 8); + assert_eq!( + frozen_accepts, + diagnostics.eta_accepted + diagnostics.kappa_accepted + ); + assert_eq!(frozen_changes, frozen_accepts); + assert_eq!( + diagnostics.eta_rejected + diagnostics.kappa_rejected, + frozen_proposals - frozen_accepts + ); + assert_eq!(diagnostics.eta_non_finite, 0); + assert_eq!(diagnostics.kappa_non_finite, 0); + + let operational_continuation = operational.rng.random::(); + let frozen_continuation = frozen_rng.random::(); + assert_eq!(operational_continuation, frozen_continuation); + } + + #[test] + fn warmup_movement_cannot_satisfy_retained_movement_accounting() { + let mut counts = [(12, 7, 4), (8, 1, 1)]; + begin_retained_transition_accounting(&mut counts); + // Retained proposals that are accepted without an actual state change + // still leave the chain eligible for the exact stuck guard. + counts[0].0 += 3; + counts[0].1 += 3; + assert_eq!(counts, [(3, 3, 0), (0, 0, 0)]); + let stuck: Vec<_> = counts + .iter() + .enumerate() + .filter_map(|(chain, count)| (count.2 == 0).then_some(chain)) + .collect(); + assert_eq!(stuck, [0, 1]); + } + + #[test] + fn no_latent_state_reports_exact_zero_markov_variance() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + + let result = fixed_no_iiv_problem() + .fit_with( + SaemConfig::new() + .k1_iterations(30) + .k2_iterations(20) + .burn_in(1) + .compute_map(false) + .averaged_iterates(0.75) + .markov_simulation_variance(MarkovSimulationVarianceConfig::new( + 4, + 100, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024, + )), + ) + .unwrap(); + let diagnostic = result.markov_simulation_variance(); + assert_eq!( + diagnostic.status, + MarkovSimulationVarianceStatus::ExactZeroNoLatentState + ); + assert!(diagnostic.chains.is_empty()); + assert_eq!( + diagnostic.rank_diagnostics.status, + RankDiagnosticStatus::NoLatent + ); + assert!(diagnostic.rank_diagnostics.traces.is_empty()); + assert!(diagnostic + .lambda + .iter() + .flatten() + .all(|value| *value == 0.0)); + assert!(diagnostic.xi.iter().flatten().all(|value| *value == 0.0)); + assert!(diagnostic + .simulation_covariance + .iter() + .flatten() + .all(|value| *value == 0.0)); + } + + #[test] + fn explicit_terminal_policy_preserves_default_trajectory() { + let base = SaemConfig::new() + .k1_iterations(2) + .burn_in(1) + .k2_iterations(2) + .compute_map(false) + .seed(7788); + let default = problem().fit_with(base.clone()).unwrap(); + let explicit = problem() + .fit_with(base.estimator_policy(SaemEstimatorPolicy::TerminalIterate)) + .unwrap(); + assert_eq!(default.cycle_diagnostics(), explicit.cycle_diagnostics()); + assert_eq!( + default.population_parameters(), + explicit.population_parameters() + ); + assert_eq!(default.omega(), explicit.omega()); + assert_eq!(default.conditional_n2ll(), explicit.conditional_n2ll()); + assert_eq!(default.termination_reason(), Some(&StopReason::MaxCycles)); + assert_eq!(explicit.termination_reason(), Some(&StopReason::MaxCycles)); + } + + fn residual_phase_schedule() -> SaemSchedule { + let mut schedule = SaemSchedule::from_config( + &SaemConfig::new() + .burn_in(0) + .k1_iterations(4) + .k2_iterations(3), + ); + schedule.variance_floor_iterations = 1; + schedule + } + + #[test] + fn combined_residual_component_anneals_during_configured_period() { + let schedule = residual_phase_schedule(); + let applied = applied_combined_residual_component(&schedule, 1, 1.0, 0.1, true); + assert_eq!(applied, schedule.annealing_alpha); + } + + #[test] + fn combined_residual_component_replaces_directly_in_remaining_exploration() { + let schedule = residual_phase_schedule(); + assert_eq!( + applied_combined_residual_component(&schedule, 2, 1.0, 0.1, true), + 0.1 + ); + } + + #[test] + fn combined_residual_component_smooths_in_k2() { + let schedule = residual_phase_schedule(); + assert_eq!( + applied_combined_residual_component(&schedule, 6, 1.0, 0.2, true), + 0.6 + ); + } + + #[test] + fn combined_residual_component_preserves_fixed_value() { + let schedule = residual_phase_schedule(); + assert_eq!( + applied_combined_residual_component(&schedule, 1, 1.0, 0.1, false), + 1.0 + ); + assert_eq!( + applied_combined_residual_component(&schedule, 6, 1.0, 0.1, false), + 1.0 + ); + } + + #[test] + fn burn_in_warms_covariance_statistics_without_updating_parameters() { + let config = SaemConfig::new() + .n_chains(1) + .burn_in(2) + .k1_iterations(4) + .omega_sa_max_step(0.1); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + for subject_chains in &mut state.etas { + subject_chains[0].fill(2.0); + } + let initial_population = state.population_parameters.clone(); + let initial_omega = state.omega.clone(); + let initial_iiv_second_moment = state.iiv_second_moment.clone(); + let initial_phi_second_moment = state.sufficient_statistics.second_moment.clone(); + + state.step().unwrap(); + + assert_eq!(state.cycle, 1); + assert_eq!(state.cycle_diagnostics[0].phase, SaemPhase::BurnIn); + assert_eq!(state.population_parameters, initial_population); + assert_eq!(state.omega, initial_omega); + assert_ne!(state.iiv_second_moment, initial_iiv_second_moment); + assert_ne!( + state.sufficient_statistics.second_moment, + initial_phi_second_moment + ); + } + + #[test] + fn chain_count_auto_scales_for_small_datasets() { + assert_eq!(n_chains(&SaemConfig::default(), 2), 25); + assert_eq!(n_chains(&SaemConfig::new().n_chains(3), 2), 3); + assert_eq!(n_chains(&SaemConfig::default(), 100), 1); + } + + #[test] + fn result_retains_requested_config_and_separate_effective_chain_count() { + let config = SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false) + .seed(9876); + let serialized_config = serde_json::to_value(&config).unwrap(); + let state = SaemState::from_problem(problem(), &config).unwrap(); + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.config().n_chains, 1); + assert_eq!(result.effective_n_chains(), 25); + assert_eq!( + serde_json::to_value(result.config()).unwrap(), + serialized_config + ); + } + + #[test] + fn result_parameter_metadata_preserves_declaration_order() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false); + let state = SaemState::from_problem(ordered_metadata_problem(), &config).unwrap(); + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.parameter_names(), ["ke", "v"]); + assert_eq!( + result.parameter_scales(), + [ParameterScale::Identity, ParameterScale::Log] + ); + assert_eq!(result.estimated_parameters(), [true, false]); + assert_eq!(result.random_effect_indices(), [0]); + assert_eq!(result.random_effect_names(), ["ke"]); + assert_eq!(result.iov_effect_indices(), [1]); + assert_eq!(result.iov_effect_names(), ["v"]); + } + + #[test] + fn result_retains_exact_symmetric_iiv_covariance_masks() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false); + let configured = + Box::new(SaemState::from_problem(configured_omega_problem(), &config).unwrap()) + .into_result() + .unwrap(); + let correlated = + Box::new(SaemState::from_problem(correlated_omega_problem(), &config).unwrap()) + .into_result() + .unwrap(); + + assert_eq!(configured.random_effect_names(), ["ke", "v"]); + assert_eq!( + configured.omega_structural_mask(), + &ndarray::array![[true, false], [false, true]] + ); + assert_eq!( + configured.omega_estimated_mask(), + &ndarray::array![[true, false], [false, false]] + ); + assert_eq!( + correlated.omega_structural_mask(), + &ndarray::array![[true, true], [true, true]] + ); + assert_eq!( + correlated.omega_estimated_mask(), + &ndarray::array![[true, true], [true, true]] + ); + } + + #[test] + fn result_retains_ordered_iov_masks_and_none_without_iov() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false); + let iov = Box::new(SaemState::from_problem(configured_iov_problem(), &config).unwrap()) + .into_result() + .unwrap(); + let no_iov = Box::new(SaemState::from_problem(problem(), &config).unwrap()) + .into_result() + .unwrap(); + + assert_eq!(iov.iov_effect_indices(), [0, 1]); + assert_eq!(iov.iov_effect_names(), ["ke", "v"]); + assert_eq!( + iov.omega_iov_structural_mask(), + Some(&ndarray::array![[true, true], [true, true]]) + ); + assert_eq!( + iov.omega_iov_estimated_mask(), + Some(&ndarray::array![[true, false], [false, false]]) + ); + assert_eq!(no_iov.omega_iov_structural_mask(), None); + assert_eq!(no_iov.omega_iov_estimated_mask(), None); + } + + #[test] + fn state_initializes_zero_eta_chains() { + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + + assert_eq!(state.etas.len(), 2); + assert_eq!(state.etas[0].len(), 25); + assert_eq!(state.etas[0][0], vec![0.0, 0.0]); + assert_eq!(state.etas[1][24], vec![0.0, 0.0]); + assert_eq!(state.omega_diagonal(), Some(vec![1.0, 1.0])); + } + + #[test] + fn covariate_state_joint_gls_rebases_eta_and_builds_subject_omega() { + let mut state = SaemState::from_problem( + covariate_problem(), + &SaemConfig::new().n_chains(2).compute_map(false), + ) + .unwrap(); + let intercept = [0.2_f64.ln(), 10.0_f64.ln()]; + let beta = 0.35; + let expected_phi = [-1.0, 0.0, 1.0] + .into_iter() + .map(|design| vec![intercept[0] + beta * design, intercept[1]]) + .collect::>(); + let desired_omega = ndarray::array![[0.4, 0.1], [0.1, 0.3]]; + let mut second = desired_omega.clone(); + for mean in &expected_phi { + for row in 0..2 { + for column in 0..2 { + second[[row, column]] += mean[row] * mean[column] / 3.0; + } + } + } + let old_means = state.subject_mu_phi.clone().unwrap(); + for chains in &mut state.etas { + for eta in chains { + eta[0] = 0.1; + eta[1] = -0.2; + } + } + let absolute_before = old_means + .iter() + .map(|mean| vec![mean[0] + 0.1, mean[1] - 0.2]) + .collect::>(); + state.covariate_statistics = Some(CovariateSufficientStatistics { + expected_phi, + global_second_moment: second, + }); + + let candidate = state + .update_covariate_population_and_recenter_etas() + .unwrap(); + let model = state.covariate_model.as_ref().unwrap(); + assert!((model.estimates()[0].estimate() - beta).abs() < 1e-10); + assert!((candidate[[0, 0]] - desired_omega[[0, 0]]).abs() < 1e-10); + assert!((candidate[[0, 1]] - desired_omega[[0, 1]]).abs() < 1e-10); + for (subject, mean) in state.subject_mu_phi.as_ref().unwrap().iter().enumerate() { + for coordinate in 0..2 { + assert!( + (mean[coordinate] + state.etas[subject][0][coordinate] + - absolute_before[subject][coordinate]) + .abs() + < 1e-10 + ); + } + } + } + + #[test] + fn covariate_fit_executes_and_retains_subject_population_parameters() { + let result = covariate_problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(2) + .averaged_iterates(0.75) + .compute_map(false), + ) + .unwrap(); + assert!(result.estimator_metadata().average_applied); + assert_eq!(result.covariate_estimates().unwrap().len(), 2); + assert!(result.covariate_estimates().unwrap()[0].estimate() < 0.0); + assert_eq!( + result + .covariate_subject_population_parameters() + .unwrap() + .unwrap() + .len(), + 3 + ); + assert!(result.cycle_diagnostics().iter().all(|cycle| cycle + .covariate_betas + .as_ref() + .is_some_and(|values| values.len() == 2))); + let tables = result.tables(1.0, 0.0).unwrap(); + assert_eq!(tables.covariate_effects.len(), 2); + assert_eq!(tables.subject_covariates.len(), 6); + assert_eq!(tables.subject_population_parameters.len(), 6); + + let directory = + std::env::temp_dir().join(format!("pmcore-schema7-covariate-{}", std::process::id())); + result.write_outputs(&directory, 1.0, 0.0).unwrap(); + let record = + crate::results::ParametricResultRecord::read_json(directory.join("result.json")) + .unwrap(); + assert_eq!(record.schema_version, 9); + assert_eq!(record.source_metadata.covariate_effects.len(), 2); + let warm = record + .warm_start_problem(one_compartment(), result.data().clone()) + .unwrap(); + let warm_estimates = warm + .covariates() + .unwrap() + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + let result_estimates = result + .covariate_estimates() + .unwrap() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + assert!(warm_estimates + .iter() + .zip(result_estimates) + .all(|(warm, result)| (warm - result).abs() <= 2.0 * f64::EPSILON)); + std::fs::remove_dir_all(directory).unwrap(); + } + + #[test] + fn fixed_covariate_without_iiv_executes_subject_specific_predictions() { + let result = fixed_covariate_without_iiv_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .unwrap(); + assert!(result.random_effect_names().is_empty()); + let means = result + .covariate_subject_population_parameters() + .unwrap() + .unwrap(); + assert!((means[0].psi()[0] - 0.2).abs() < 1e-12); + assert!((means[1].psi()[0] - 0.2 * 0.2_f64.exp()).abs() < 1e-12); + let predictions = result.population_predictions(0.0, 0.0).unwrap(); + assert_ne!( + predictions[0].predictions()[0].prediction(), + predictions[1].predictions()[0].prediction() + ); + } + + #[test] + fn explicit_iiv_mask_controls_eta_and_omega_dimensions() { + let mut state = + SaemState::from_problem(partial_iiv_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + + assert_eq!(state.initialization.random_effect_indices, vec![0]); + assert_eq!(state.initialization.random_effect_names, vec!["ke"]); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| eta.len() == 1)); + assert_eq!(state.omega.dim(), (1, 1)); + assert_eq!(state.proposal_step_sizes.len(), 1); + + state.etas[0][0][0] = 2.0_f64.ln(); + let individual = state.individual_parameters(0, 0); + assert!((individual[0] - 0.4).abs() < 1e-12); + assert!((individual[1] - 10.0).abs() < 1e-12); + } + + #[test] + fn all_fixed_parameters_support_zero_dimensional_iiv() { + let config = SaemConfig::new() + .n_chains(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(1); + let state = SaemState::from_problem(fixed_no_iiv_problem(), &config).expect( + "fixed population plus estimated residual error should support zero-dimensional IIV", + ); + assert!(state.initialization.random_effect_names.is_empty()); + assert!(state.omega.is_empty()); + assert!(state.iiv_second_moment.is_empty()); + assert!(state + .etas + .iter() + .all(|chains| chains.iter().all(Vec::is_empty))); + + let result = fixed_no_iiv_problem().fit_with(config).unwrap(); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert_eq!(result.iterations(), 2); + assert!(result.objf().is_finite()); + assert!(result.conditional_modes().is_empty()); + assert_eq!(result.omega_structural_mask().dim(), (0, 0)); + assert_eq!(result.omega_estimated_mask().dim(), (0, 0)); + assert!(result.omega_structural_mask().is_empty()); + assert!(result.omega_estimated_mask().is_empty()); + assert_eq!(result.omega_iov_structural_mask(), None); + assert_eq!(result.omega_iov_estimated_mask(), None); + assert!(result + .eta_chain_means() + .iter() + .all(|estimate| estimate.values.is_empty())); + assert!(result.kappa_chain_means().is_empty()); + } + + #[test] + fn iov_state_tracks_one_kappa_per_subject_occasion_and_chain() { + let state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + + assert_eq!(state.initialization.iov_effect_names, vec!["ke"]); + assert_eq!(state.omega_iov, Some(ndarray::array![[0.1]])); + assert_eq!(state.kappas.len(), 1); + assert_eq!(state.kappas[0].len(), 2); + assert_eq!(state.kappas[0][0], vec![vec![0.0], vec![0.0]]); + } + + #[test] + fn uneven_occasion_counts_preserve_kappa_shapes_order_and_named_lookup() { + let result = uneven_iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(2) + .k2_iterations(0) + .compute_map(false), + ) + .unwrap(); + + assert_eq!(result.kappa_chain_means().len(), 6); + assert!(result.kappa_chain_mean("one", 0).is_some()); + assert!(result.kappa_chain_mean("one", 1).is_none()); + assert!(result.kappa_chain_mean("two", 0).is_some()); + assert!(result.kappa_chain_mean("two", 1).is_some()); + assert!(result.kappa_chain_mean("three", 0).is_some()); + assert!(result.kappa_chain_mean("three", 1).is_some()); + assert!(result.kappa_chain_mean("three", 2).is_some()); + assert!(result.eta_chain_mean("two").is_some()); + assert!(result.eta_chain_mean("missing").is_none()); + assert!(result.conditional_mode("two").is_none()); + assert!(result + .cycle_diagnostics() + .iter() + .all(|cycle| cycle.kappa_proposals == 12)); + } + + #[test] + fn iov_scores_per_occasion_kappa_prior_and_conditional_proposal() { + let state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + let score = state + .score_subject_latents(0, &state.etas[0][0], &state.kappas[0][0]) + .unwrap(); + + assert!((score.log_likelihood - state.subject_log_likelihoods[0]).abs() < 1e-12); + assert!((score.kappa_log_prior - state.subject_kappa_log_priors[0]).abs() < 1e-12); + assert!(score.kappa_log_prior.is_finite()); + assert_eq!( + state + .kappa_proposal_log_acceptance_ratio(0, 0, 0, &[0.0]) + .unwrap(), + 0.0 + ); + } + + #[test] + fn iov_controller_exposes_kappa_covariance_and_runs_conditional_mcmc() { + let mut controller = iov_problem() + .fit_controller( + SaemConfig::new() + .n_chains(2) + .k1_iterations(2) + .k2_iterations(0) + .burn_in(2), + ) + .unwrap(); + + assert_eq!( + controller.iov_effect_names(), + Some(["ke".to_string()].as_slice()) + ); + assert_eq!(controller.omega_iov(), Some(&ndarray::array![[0.1]])); + assert!(controller.kappa_log_prior().is_finite()); + assert_eq!( + controller.log_posterior(), + controller.likelihood() + controller.eta_log_prior() + controller.kappa_log_prior() + ); + + controller.step().unwrap(); + assert!(controller.likelihood().is_finite()); + assert!(controller.kappa_log_prior().is_finite()); + assert!(controller.acceptance_rate().is_some()); + assert!(controller + .kappa_acceptance_rate() + .is_some_and(|rate| (0.0..=1.0).contains(&rate))); + } + + #[test] + fn correlated_random_walk_reuses_one_standard_normal_vector() { + let proposed = + correlated_random_walk(&[1.0, 2.0], &[vec![2.0], vec![1.0, 3.0]], &[0.5, -1.0], 0.2) + .unwrap(); + + assert!((proposed[0] - 1.2).abs() < 1e-12); + assert!((proposed[1] - 1.5).abs() < 1e-12); + assert!(correlated_random_walk(&[0.0], &[vec![1.0]], &[0.0, 1.0], 1.0).is_err()); + } + + #[test] + fn eta_block_proposal_uses_covariance_scale_and_adaptation() { + let lower = vec![vec![1.0], vec![0.8, 0.6]]; + let normals = [[0.5, -1.0], [-0.25, 0.75], [1.2, 0.1], [-0.8, -0.4]]; + let uniforms = [0.2_f64, 0.9, 0.4, 0.7]; + let expected_trace = [ + [0.65, -0.3], + [0.525, -0.175], + [0.525, -0.175], + [0.525, -0.175], + ]; + let expected_ratios = [ + -0.9451955782312924, + 0.6944515306122447, + -2.211747363945578, + -0.4124850340136057, + ]; + let expected_accepts = [true, true, false, false]; + let expected_scales = [0.55, 0.495]; + let expected_checkpoint_counts = [(2, 2), (0, 2)]; + let log_likelihood = |eta: &[f64]| { + -0.5 * ((eta[0] - 0.3) / 0.5).powi(2) - 0.5 * ((eta[1] + 0.1) / 0.7).powi(2) + }; + let log_prior = |eta: &[f64]| { + -0.5 / (1.0 - 0.8_f64.powi(2)) + * (eta[0].powi(2) - 1.6 * eta[0] * eta[1] + eta[1].powi(2)) + }; + + let mut eta = vec![0.4, -0.2]; + let mut scale = 0.5; + let mut accepted = 0; + let mut proposed = 0; + let mut scale_index = 0; + for (step, (z, uniform)) in normals.iter().zip(uniforms).enumerate() { + let proposal = correlated_random_walk(&eta, &lower, z, scale).unwrap(); + let reference = [ + eta[0] + scale * lower[0][0] * z[0], + eta[1] + scale * (lower[1][0] * z[0] + lower[1][1] * z[1]), + ]; + assert!((proposal[0] - reference[0]).abs() < 1e-15); + assert!((proposal[1] - reference[1]).abs() < 1e-15); + + let current_score = SubjectPosteriorScore { + log_likelihood: log_likelihood(&eta), + eta_log_prior: log_prior(&eta), + kappa_log_prior: 0.0, + }; + let proposed_score = SubjectPosteriorScore { + log_likelihood: log_likelihood(&proposal), + eta_log_prior: log_prior(&proposal), + kappa_log_prior: 0.0, + }; + let ratio = current_score.log_acceptance_ratio(proposed_score); + let reference_ratio = proposed_score.log_posterior() - current_score.log_posterior(); + assert!((ratio - reference_ratio).abs() < 1e-15); + assert!((ratio - expected_ratios[step]).abs() < 1e-12); + + let accept = ratio >= 0.0 || uniform.ln() < ratio; + assert_eq!(accept, expected_accepts[step]); + proposed += 1; + if accept { + eta = proposal; + accepted += 1; + } + assert!((eta[0] - expected_trace[step][0]).abs() < 1e-12); + assert!((eta[1] - expected_trace[step][1]).abs() < 1e-12); + + if (step + 1) % 2 == 0 { + assert_eq!( + (accepted, proposed), + expected_checkpoint_counts[scale_index] + ); + scale = adapt_block_step_size( + scale, + accepted as f64 / proposed as f64, + ETA_BLOCK_TARGET_ACCEPTANCE, + ); + assert!((scale - expected_scales[scale_index]).abs() < 1e-12); + scale_index += 1; + accepted = 0; + proposed = 0; + } + } + + let eta_unchanged = [0.7, -0.3]; + let kappa_0_unchanged = [0.1, 0.2]; + let kappa_1 = correlated_random_walk( + &[-0.2, 0.4], + &[vec![0.5], vec![0.1, 0.4]], + &[-0.5, 0.25], + 0.3, + ) + .unwrap(); + assert_eq!(eta_unchanged, [0.7, -0.3]); + assert_eq!(kappa_0_unchanged, [0.1, 0.2]); + assert!((kappa_1[0] + 0.275).abs() < 1e-12); + assert!((kappa_1[1] - 0.415).abs() < 1e-12); + } + + #[test] + fn eta_block_kernel_runs_before_component_sweep_and_records_diagnostics() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .eta_block_iterations(2) + .adapt_interval(50) + .seed(2024), + ) + .unwrap(); + + state.e_step().unwrap(); + + let diagnostics = state.cycle_diagnostics.last().unwrap(); + assert_eq!(diagnostics.eta_block_proposals, 2 * 2 * 2); + assert_eq!( + diagnostics.eta_block_accepted + diagnostics.eta_block_rejected, + diagnostics.eta_block_proposals + ); + assert_eq!(diagnostics.eta_proposals, 2 * 2 * 2 + 2 * 2 * 2); + assert_eq!(diagnostics.eta_block_subject_acceptance_rates.len(), 2); + assert_eq!( + diagnostics.eta_block_step_sizes_before_adaptation, + vec![0.5, 0.5] + ); + assert_eq!( + diagnostics.eta_block_step_sizes_after_adaptation, + vec![0.5, 0.5] + ); + } + + #[test] + fn controller_exposes_opt_in_eta_block_acceptance_and_scales() { + let mut controller = problem() + .fit_controller( + SaemConfig::new() + .n_chains(2) + .eta_block_iterations(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + assert_eq!( + controller.eta_block_step_sizes(), + Some([0.5, 0.5].as_slice()) + ); + assert_eq!(controller.eta_block_acceptance_rate(), None); + controller.step().unwrap(); + assert!(controller + .eta_block_acceptance_rate() + .is_some_and(|rate| (0.0..=1.0).contains(&rate))); + } + + #[test] + fn eta_block_scale_adapts_per_subject_toward_acceptance_target() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .eta_block_iterations(1) + .adapt_interval(1), + ) + .unwrap(); + assert_eq!(state.eta_block_step_sizes, vec![0.5, 0.5]); + + state.eta_block_adaptation_accept_counts = vec![1, 0]; + state.eta_block_adaptation_proposal_counts = vec![1, 1]; + state.steps_since_adapt = 1; + state.adapt_proposal_step_sizes(); + assert_eq!(state.eta_block_step_sizes, vec![0.55, 0.45]); + assert_eq!(state.eta_block_adaptation_accept_counts, vec![0, 0]); + assert_eq!(state.eta_block_adaptation_proposal_counts, vec![0, 0]); + } + + #[test] + fn kappa_block_scale_adapts_per_subject_toward_acceptance_target() { + let mut state = SaemState::from_problem( + iov_problem(), + &SaemConfig::new().n_chains(2).adapt_interval(1), + ) + .unwrap(); + assert_eq!(state.kappa_proposal_step_sizes, vec![0.5]); + + state.kappa_adaptation_accept_counts[0] = 1; + state.kappa_adaptation_proposal_counts[0] = 1; + state.steps_since_adapt = 1; + state.adapt_proposal_step_sizes(); + assert!((state.kappa_proposal_step_sizes[0] - 0.55).abs() < 1e-12); + + state.kappa_adaptation_accept_counts[0] = 0; + state.kappa_adaptation_proposal_counts[0] = 1; + state.steps_since_adapt = 1; + state.adapt_proposal_step_sizes(); + assert!((state.kappa_proposal_step_sizes[0] - 0.495).abs() < 1e-12); + } + + #[test] + fn iov_second_moment_weights_each_occasion_chain_sample_equally() { + let kappas = vec![ + vec![vec![vec![1.0, 2.0]]], + vec![vec![vec![3.0, 4.0], vec![5.0, 6.0]]], + ]; + + let covariance = covariance_from_kappas(&kappas).unwrap(); + + assert!((covariance[[0, 0]] - 35.0 / 3.0).abs() < 1e-12); + assert!((covariance[[0, 1]] - 44.0 / 3.0).abs() < 1e-12); + assert!((covariance[[1, 0]] - 44.0 / 3.0).abs() < 1e-12); + assert!((covariance[[1, 1]] - 56.0 / 3.0).abs() < 1e-12); + } + + #[test] + fn iov_m_step_updates_omega_from_all_occasions() { + let mut state = SaemState::from_problem( + iov_problem(), + &SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0), + ) + .unwrap(); + state.cycle = 1; + state.e_step().unwrap(); + for kappas in &mut state.kappas[0] { + kappas[0][0] = 0.2; + kappas[1][0] = -0.1; + } + + state.m_step().unwrap(); + + assert!((state.omega_iov.as_ref().unwrap()[[0, 0]] - 0.025).abs() < 1e-12); + assert!( + !state + .cycle_diagnostics + .last() + .unwrap() + .omega_iov_update_rejected + ); + } + + #[test] + fn covariance_update_status_drives_iiv_and_iov_cycle_rejection_diagnostics() { + let config = SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + state.cycle = 1; + state.e_step().unwrap(); + state.iiv_second_moment.fill(f64::NAN); + state.iov_second_moment.as_mut().unwrap().fill(f64::NAN); + + state.m_step().unwrap(); + + let diagnostics = state.cycle_diagnostics.last().unwrap(); + assert!(diagnostics.omega_update_rejected); + assert!(diagnostics.omega_iov_update_rejected); + } + + #[test] + fn iov_second_moment_uses_saem_smoothing_step() { + let config = SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0) + .k1_iterations(1) + .k2_iterations(2); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + for kappas in &mut state.kappas[0] { + kappas[0][0] = 0.2; + kappas[1][0] = -0.1; + } + state.cycle = 1; + state.m_step().unwrap(); + + for kappas in &mut state.kappas[0] { + kappas[0][0] = 0.2; + kappas[1][0] = 0.2; + } + state.cycle = 3; // first smoothing iteration after K1: γ = 1/2 + state.m_step().unwrap(); + + assert!((state.omega_iov.as_ref().unwrap()[[0, 0]] - 0.0325).abs() < 1e-12); + } + + #[test] + fn iov_m_step_preserves_fixed_entries_and_positive_definiteness_jointly() { + let config = SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0); + let mut state = SaemState::from_problem(configured_iov_problem(), &config).unwrap(); + state.cycle = 1; + for chain in &mut state.kappas[0] { + for kappa in chain { + kappa[0] = 0.3; + kappa[1] = 1.0; + } + } + + state.m_step().unwrap(); + + let omega_iov = state.omega_iov.as_ref().unwrap(); + // With fixed b=.20 and c=.05, the exact constrained profile optimum is + // S11 - 2(c/b)S12 + c²/b + (c²/b²)S22 = .015. + assert!((omega_iov[[0, 0]] - 0.015).abs() < 1e-12); + assert_eq!(omega_iov[[0, 1]], 0.05); + assert_eq!(omega_iov[[1, 0]], 0.05); + assert_eq!(omega_iov[[1, 1]], 0.20); + assert!(omega_iov[[0, 0]] * omega_iov[[1, 1]] - omega_iov[[0, 1]].powi(2) > 0.0); + } + + #[test] + fn state_uses_declared_initial_omega() { + let state = + SaemState::from_problem(configured_omega_problem(), &SaemConfig::new().n_chains(2)) + .unwrap(); + + assert_eq!(state.omega, ndarray::array![[0.25, 0.0], [0.0, 0.5]]); + assert_eq!(state.proposal_step_sizes, vec![0.25, 0.25 * 2.0_f64.sqrt()]); + } + + #[test] + fn individual_parameters_add_eta_in_phi_space() { + let mut state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); + + let initial = state.individual_parameters(0, 0); + assert!((initial[0] - 0.2).abs() < 1e-12); + assert!((initial[1] - 10.0).abs() < 1e-12); + + state.etas[0][0][0] = 2.0_f64.ln(); + state.etas[0][0][1] = 0.5_f64.ln(); + let individual = state.individual_parameters(0, 0); + + assert!((individual[0] - 0.4).abs() < 1e-12); + assert!((individual[1] - 5.0).abs() < 1e-12); + } + + #[test] + fn bounded_transforms_round_trip() { + let logit = ParameterScale::Logit { + lower: 0.0, + upper: 1.0, + }; + let probit = ParameterScale::Probit { + lower: 0.0, + upper: 1.0, + }; + + assert!((phi_to_psi(psi_to_phi(0.25, logit), logit) - 0.25).abs() < 1e-12); + assert!((phi_to_psi(psi_to_phi(0.25, probit), probit) - 0.25).abs() < 1e-12); + } + + #[test] + fn parametric_fit_controller_steps_like_nonparametric_controller() { + let config = SaemConfig::new() + .k1_iterations(2) + .k2_iterations(1) + .burn_in(1); + let mut controller = problem().fit_controller(config).unwrap(); + + assert_eq!(controller.cycle(), 0); + assert!(controller.status().is_continue()); + assert!(controller.likelihood().is_finite()); + assert_eq!(controller.population_parameters(), &[0.2, 10.0]); + assert_eq!(controller.random_effect_names(), &["ke", "v"]); + assert_eq!(controller.iov_effect_names(), None); + assert_eq!(controller.omega_iov(), None); + assert_eq!(controller.residual_sigmas(), &[0.5]); + assert_eq!(controller.acceptance_rate(), None); + assert_eq!(controller.kappa_acceptance_rate(), None); + assert_eq!(controller.rejected_proposals(), None); + assert_eq!(controller.non_finite_proposals(), None); + assert_eq!(controller.parameter_acceptance_rates(), None); + assert_eq!( + controller.proposal_step_sizes(), + Some([0.5, 0.5].as_slice()) + ); + assert!(controller.eta_log_prior().is_finite()); + assert_eq!( + controller.log_posterior(), + controller.likelihood() + controller.eta_log_prior() + ); + assert!(controller.negative_log_likelihood().is_finite()); + assert_eq!( + controller.negative_log_likelihood(), + -controller.likelihood() + ); + assert!(controller.n2ll().is_finite()); + assert_eq!(controller.n_chains(), Some(25)); + assert_eq!( + controller.omega(), + Some(&ndarray::array![[1.0, 0.0], [0.0, 1.0]]) + ); + assert_eq!(controller.omega_diagonal(), Some(vec![1.0, 1.0])); + assert_eq!( + controller.log_acceptance_ratios(), + Some([0.0, 0.0].as_slice()) + ); + assert_eq!(controller.total_iterations(), 3); + assert_eq!(controller.step_size(), 0.0); + + assert!(controller.step().unwrap().is_continue()); + assert_eq!(controller.cycle(), 1); + assert_eq!(controller.step_size(), 0.0); + assert_eq!(controller.population_parameters(), &[0.2, 10.0]); + assert_eq!( + controller.omega(), + Some(&ndarray::array![[1.0, 0.0], [0.0, 1.0]]) + ); + assert!(controller.acceptance_rate().is_some()); + assert_eq!(controller.kappa_acceptance_rate(), None); + assert!(controller.rejected_proposals().is_some()); + assert_eq!(controller.non_finite_proposals(), Some(0)); + let parameter_acceptance_rates = controller.parameter_acceptance_rates().unwrap(); + assert_eq!(parameter_acceptance_rates.len(), 2); + assert!(parameter_acceptance_rates + .iter() + .all(|rate| (0.0..=1.0).contains(rate))); + assert!(controller.step().unwrap().is_continue()); + assert_eq!(controller.cycle(), 2); + assert_eq!(controller.step_size(), 1.0); + assert!(controller.step().unwrap().is_stop()); + assert_eq!(controller.cycle(), 3); + } + + #[test] + fn aborted_controller_preserves_typed_termination_reason() { + let mut controller = problem() + .fit_controller(SaemConfig::new().compute_map(false)) + .unwrap(); + controller.step().unwrap(); + controller.request_stop(); + + let result = controller.into_result().unwrap(); + + assert!(!result.converged()); + assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); + assert_ne!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert_ne!( + result.termination_reason(), + Some(&StopReason::NumericalFailure) + ); + assert_eq!(result.iterations(), 1); + } + + #[test] + fn expectation_numerical_failure_stops_and_blocks_result() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .compute_map(false), + ) + .unwrap(); + state.omega[[0, 0]] = f64::NAN; + + let error = state.step().unwrap_err(); + let failure = error + .downcast_ref::() + .expect("step error should retain its numerical failure type") + .clone(); + + assert_eq!(failure.attempted_cycle(), 1); + assert_eq!(failure.phase(), NumericalFailurePhase::Expectation); + assert!(!failure.source_message().is_empty()); + assert_eq!(state.status, Status::Stop(StopReason::NumericalFailure)); + assert_eq!( + state.step().unwrap(), + Status::Stop(StopReason::NumericalFailure) + ); + + let result_error = Box::new(state).into_result().unwrap_err(); + assert_eq!( + result_error.downcast_ref::(), + Some(&failure) + ); + } + + #[test] + fn maximization_numerical_failure_stops_fit() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .compute_map(false), + ) + .unwrap(); + state.sufficient_statistics.mean_phi.pop(); + + let error = state.step().unwrap_err(); + let failure = error + .downcast_ref::() + .expect("step error should retain its numerical failure type"); + + assert_eq!(failure.attempted_cycle(), 1); + assert_eq!(failure.phase(), NumericalFailurePhase::Maximization); + assert!(!failure.source_message().is_empty()); + assert_eq!(state.status, Status::Stop(StopReason::NumericalFailure)); + } + + #[test] + fn result_assembly_numerical_failure_returns_no_result() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1).compute_map(false)) + .unwrap(); + state.etas[0].clear(); + + let error = Box::new(state).into_result().unwrap_err(); + let failure = error + .downcast_ref::() + .expect("result error should retain its numerical failure type"); + + assert_eq!(failure.attempted_cycle(), 0); + assert_eq!(failure.phase(), NumericalFailurePhase::ResultAssembly); + assert!(!failure.source_message().is_empty()); + } + + #[test] + fn proposal_score_uses_pmcore_likelihood_and_eta_prior() { + let state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); + let current_eta = state.etas[0][0].clone(); + let score = state + .score_subject_latents(0, ¤t_eta, &state.kappas[0][0]) + .unwrap(); + + assert_eq!(score.log_likelihood, state.subject_log_likelihoods[0]); + assert_eq!(score.eta_log_prior, state.subject_log_priors[0]); + assert_eq!( + state + .proposal_log_acceptance_ratio(0, 0, ¤t_eta) + .unwrap(), + 0.0 + ); + } + + #[test] + fn component_random_walk_changes_only_selected_eta() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(2).seed(2024)).unwrap(); + let current = vec![1.0, 2.0]; + + let proposed = state.component_random_walk_eta(¤t, 1); + + assert_eq!(proposed[0], current[0]); + assert_ne!(proposed[1], current[1]); + } + + #[test] + fn component_scale_adaptation_uses_acceptance_bands_and_clamps() { + assert!((adapt_component_step_size(1.0, 0.45) - 1.1).abs() < 1e-12); + assert!((adapt_component_step_size(1.0, 0.44) - 0.9).abs() < 1e-12); + assert_eq!(adapt_component_step_size(5.0, 1.0), 5.0); + assert_eq!(adapt_component_step_size(1e-6, 0.0), 1e-6); + } + + #[test] + fn component_scale_adaptation_waits_for_interval_and_resets_counts() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(2).adapt_interval(2)) + .unwrap(); + state.adaptation_accept_counts = vec![9, 1]; + state.adaptation_proposal_counts = vec![10, 10]; + state.steps_since_adapt = 1; + + state.adapt_proposal_step_sizes(); + assert_eq!(state.proposal_step_sizes, vec![0.5, 0.5]); + + state.steps_since_adapt = 2; + state.adapt_proposal_step_sizes(); + assert_eq!(state.proposal_step_sizes, vec![0.55, 0.45]); + assert_eq!(state.adaptation_accept_counts, vec![0, 0]); + assert_eq!(state.adaptation_proposal_counts, vec![0, 0]); + assert_eq!(state.steps_since_adapt, 0); + } + + #[test] + fn e_step_runs_seeded_random_walk_for_all_chains_and_records_acceptance_rate() { + let config = SaemConfig::new().n_chains(3).mcmc_iterations(2).seed(2024); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + let initial_etas = state.etas.clone(); + + state.e_step().unwrap(); + + let acceptance_rate = state.acceptance_rate().unwrap(); + assert!((0.0..=1.0).contains(&acceptance_rate)); + assert_eq!(state.last_log_acceptance_ratios.len(), 2); + assert_eq!(state.last_parameter_acceptance_rates.len(), 2); + assert!(state + .last_parameter_acceptance_rates + .iter() + .all(|rate| (0.0..=1.0).contains(rate))); + assert!(state + .last_log_acceptance_ratios + .iter() + .all(|value| value.is_finite())); + assert_ne!(state.etas, initial_etas); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| eta.len() == 2)); + } + + #[test] + fn cycle_diagnostics_separate_eta_kappa_counts_and_schedule_phases() { + let config = SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(1); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + + state.step().unwrap(); + state.step().unwrap(); + state.step().unwrap(); + + assert_eq!(state.cycle_diagnostics.len(), 3); + assert_eq!(state.cycle_diagnostics[0].phase, SaemPhase::BurnIn); + assert_eq!(state.cycle_diagnostics[1].phase, SaemPhase::Exploration); + assert_eq!(state.cycle_diagnostics[2].phase, SaemPhase::Smoothing); + for diagnostics in &state.cycle_diagnostics { + assert_eq!(diagnostics.eta_proposals, 4); + assert_eq!( + diagnostics.eta_accepted + diagnostics.eta_rejected, + diagnostics.eta_proposals + ); + assert_eq!(diagnostics.kappa_proposals, 4); + assert_eq!( + diagnostics.kappa_accepted + diagnostics.kappa_rejected, + diagnostics.kappa_proposals + ); + assert_eq!(diagnostics.eta_parameter_acceptance_rates.len(), 2); + assert_eq!( + diagnostics.eta_proposal_step_sizes_before_adaptation.len(), + 2 + ); + assert_eq!( + diagnostics.eta_proposal_step_sizes_after_adaptation.len(), + 2 + ); + assert_eq!(diagnostics.kappa_subject_acceptance_rates.len(), 1); + assert_eq!( + diagnostics + .kappa_proposal_step_sizes_before_adaptation + .len(), + 1 + ); + assert_eq!( + diagnostics.kappa_proposal_step_sizes_after_adaptation.len(), + 1 + ); + } + assert_eq!( + state.cycle_diagnostics[0].stochastic_approximation_step, + 0.0 + ); + assert_eq!(state.cycle_diagnostics[0].covariance_step, 0.1); + } + + #[test] + fn warning_aggregation_preserves_kind_output_first_cycle_and_counts() { + let config = SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + let cycle = &mut state.cycle_diagnostics[0]; + cycle.omega_update_rejected = true; + cycle.eta_non_finite = 2; + cycle.eta_block_non_finite = 7; + let residual = &mut cycle.residual_diagnostics[0]; + residual.update_rejected = true; + residual.proportional_floor_count = 3; + residual.non_finite_prediction_count = 4; + residual.exponential_domain_violation_count = 5; + residual.combined_additive_collapse_warning = true; + residual.optimizer_converged = Some(false); + + let warnings = parametric_warnings(&state.cycle_diagnostics, None); + + assert!(warnings.contains(&ParametricWarning::OmegaUpdateRejected { + first_iteration: 1, + cycles: 1, + })); + assert!( + warnings.contains(&ParametricWarning::EtaNonFiniteProposals { + first_iteration: 1, + count: 2, + }) + ); + assert!( + warnings.contains(&ParametricWarning::EtaBlockNonFiniteProposals { + first_iteration: 1, + count: 7, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ResidualUpdateRejected { + output: "0".to_owned(), + first_iteration: 1, + cycles: 1, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ProportionalPredictionFloor { + output: "0".to_owned(), + first_iteration: 1, + count: 3, + }) + ); + assert!( + warnings.contains(&ParametricWarning::NonFiniteResidualPrediction { + output: "0".to_owned(), + first_iteration: 1, + count: 4, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ExponentialDomainViolation { + output: "0".to_owned(), + first_iteration: 1, + count: 5, + }) + ); + assert!( + warnings.contains(&ParametricWarning::CombinedAdditiveCollapse { + output: "0".to_owned(), + first_iteration: 1, + cycles: 1, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ResidualOptimizerNotConverged { + output: "0".to_owned(), + first_iteration: 1, + cycles: 1, + }) + ); + } + + #[test] + fn covariance_stability_records_fixed_iiv_and_iov_margins_and_output_rows() { + let result = markov_iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 1)), + ) + .unwrap(); + let cycle = &result.cycle_diagnostics()[0]; + assert!((cycle.omega_relative_spd_margin.unwrap() - 1.0).abs() < 1e-12); + assert!((cycle.omega_iov_relative_spd_margin.unwrap() - 1.0).abs() < 1e-12); + + let tables = result.tables(0.0, 0.0).unwrap(); + let stability_rows = tables + .statistics + .iter() + .filter(|row| row.kind == "covariance_stability") + .collect::>(); + assert_eq!(stability_rows.len(), 2); + assert!(stability_rows.iter().any(|row| { + row.name == "omega_relative_spd_margin" + && row.value.is_some_and(|value| (value - 1.0).abs() < 1e-12) + })); + assert!(stability_rows.iter().any(|row| { + row.name == "omega_iov_relative_spd_margin" + && row.value.is_some_and(|value| (value - 1.0).abs() < 1e-12) + })); + } + + #[test] + fn covariance_boundary_rejection_requires_a_complete_consecutive_window() { + let config = SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + let base = state.cycle_diagnostics[0].clone(); + let policy = CovarianceStabilityConfig::new(0.01, 3); + let pattern = [ + (1, 0.005, true), + (2, 0.004, true), + (3, 0.02, true), + (4, 0.003, true), + (5, 0.002, true), + (6, 0.001, true), + ]; + let cycles = pattern + .into_iter() + .map(|(iteration, margin, rejected)| { + let mut cycle = base.clone(); + cycle.iteration = iteration; + cycle.omega_relative_spd_margin = Some(margin); + cycle.omega_update_rejected = rejected; + cycle + }) + .collect::>(); + + assert_eq!( + covariance_boundary_rejection_summary(&cycles[..2], policy, false), + CovarianceBoundaryRejectionSummary { + first_iteration: None, + longest_run: 2, + } + ); + assert_eq!( + covariance_boundary_rejection_summary(&cycles, policy, false), + CovarianceBoundaryRejectionSummary { + first_iteration: Some(4), + longest_run: 3, + } + ); + let warnings = parametric_warnings(&cycles, Some(policy)); + assert!( + warnings.contains(&ParametricWarning::OmegaBoundaryRejection { + first_iteration: 4, + longest_run: 3, + }) + ); + + let mut mismatched_iov = base.clone(); + mismatched_iov.omega_iov_relative_spd_margin = Some(0.005); + mismatched_iov.omega_update_rejected = true; + mismatched_iov.omega_iov_update_rejected = false; + assert_eq!( + covariance_boundary_rejection_summary(&[mismatched_iov.clone()], policy, true), + CovarianceBoundaryRejectionSummary::default() + ); + mismatched_iov.omega_iov_update_rejected = true; + assert_eq!( + covariance_boundary_rejection_summary(&[mismatched_iov], policy, true).longest_run, + 1 + ); + + let iov_cycles = (1..=3) + .map(|iteration| { + let mut cycle = base.clone(); + cycle.iteration = iteration; + cycle.omega_iov_relative_spd_margin = Some(policy.minimum_relative_spd_margin); + cycle.omega_iov_update_rejected = true; + cycle + }) + .collect::>(); + assert_eq!( + covariance_boundary_rejection_summary(&iov_cycles, policy, true), + CovarianceBoundaryRejectionSummary { + first_iteration: Some(1), + longest_run: 3, + } + ); + assert!(parametric_warnings(&iov_cycles, Some(policy)).contains( + &ParametricWarning::OmegaIovBoundaryRejection { + first_iteration: 1, + longest_run: 3, + } + )); + + let criterion = evaluate_criterion( + "omega_boundary_rejection_run", + Some(3.0), + policy.rejection_window as f64, + |observed| observed < policy.rejection_window as f64, + ); + assert_eq!( + criterion.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + } + + #[test] + fn m_step_recenters_etas_before_updating_iiv_second_moment() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .burn_in(0) + .omega_sa_max_step(0.1), + ) + .unwrap(); + state.cycle = 1; + for subject_chains in &mut state.etas { + for eta in subject_chains { + eta[0] = 2.0_f64.ln(); + } + } + let individual_before = state.individual_parameters(0, 0); + + state.m_step().unwrap(); + + let individual_after = state.individual_parameters(0, 0); + assert!((individual_before[0] - individual_after[0]).abs() < 1e-12); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| eta[0].abs() < 1e-12)); + assert!((state.population_parameters[0] - 0.4).abs() < 1e-12); + assert!((state.population_parameters[1] - 10.0).abs() < 1e-12); + let information = state.information.diagnostics(); + let ke_coordinate = information + .coordinates + .iter() + .position(|coordinate| coordinate.name == "phi:ke") + .unwrap(); + // Two pre-M-step absolute phi values each differ from the old + // population by ln(2). Post-update or un-recentered evaluation would + // give a different score (zero or double-counted population shift). + assert!((information.delta[ke_coordinate] - 2.0 * 2.0_f64.ln()).abs() < 1e-12); + let expected_omega = ndarray::array![[0.9, 0.0], [0.0, 0.9]]; + assert!(state + .iiv_second_moment + .iter() + .zip(expected_omega.iter()) + .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); + assert!(state + .omega + .iter() + .zip(expected_omega.iter()) + .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); + } + + #[test] + fn exploration_covariance_cap_prevents_one_draw_rank_one_collapse() { + fn correlation(omega: &Array2) -> f64 { + omega[[0, 1]] / (omega[[0, 0]] * omega[[1, 1]]).sqrt() + } + + let make_state = |omega_sa_max_step| { + SaemState::from_problem( + correlated_omega_problem(), + &SaemConfig::new() + .n_chains(1) + .burn_in(0) + .omega_sa_max_step(omega_sa_max_step), + ) + .unwrap() + }; + let mut guarded = make_state(0.1); + let mut uncapped = make_state(1.0); + for state in [&mut guarded, &mut uncapped] { + state.cycle = 1; + state.etas[0][0] = vec![2.0, 2.0]; + state.etas[1][0] = vec![-2.0, -2.0]; + state.m_step().unwrap(); + assert!(state.omega[[0, 0]] >= state.initialization.schedule.minimum_variance); + assert!(state.omega[[1, 1]] >= state.initialization.schedule.minimum_variance); + assert!( + state.omega[[0, 0]] * state.omega[[1, 1]] - state.omega[[0, 1]].powi(2) > 0.0, + "omega: {:?}", + state.omega + ); + } + + let guarded_correlation = correlation(&guarded.omega); + let uncapped_correlation = correlation(&uncapped.omega); + assert!(guarded_correlation < 0.85); + assert!(uncapped_correlation > 0.85); + assert!(uncapped_correlation - guarded_correlation > 0.05); + } + + #[test] + fn m_step_preserves_fixed_omega_and_structural_zeros() { + let mut state = SaemState::from_problem( + configured_omega_problem(), + &SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0), + ) + .unwrap(); + state.cycle = 1; + for (subject_index, subject_chains) in state.etas.iter_mut().enumerate() { + let sign = if subject_index == 0 { 1.0 } else { -1.0 }; + for eta in subject_chains { + eta[0] = sign; + eta[1] = 2.0 * sign; + } + } + + state.m_step().unwrap(); + + assert!((state.omega[[0, 0]] - 1.0).abs() < 1e-12); + assert!((state.omega[[1, 1]] - 0.5).abs() < 1e-12); + assert_eq!(state.omega[[0, 1]], 0.0); + assert_eq!(state.omega[[1, 0]], 0.0); + } + + #[test] + fn fixed_population_effect_is_not_updated_and_omega_uses_fixed_center() { + let mut state = SaemState::from_problem( + fixed_population_iiv_problem(), + &SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0), + ) + .unwrap(); + state.cycle = 1; + for subject_chains in &mut state.etas { + for eta in subject_chains { + eta[0] = 2.0_f64.ln(); + } + } + + state.m_step().unwrap(); + + assert!((state.population_parameters[0] - 0.2).abs() < 1e-12); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| (eta[0] - 2.0_f64.ln()).abs() < 1e-12)); + assert!((state.omega[[0, 0]] - 2.0_f64.ln().powi(2)).abs() < 1e-12); + let individual = state.individual_parameters(0, 0); + assert!((individual[0] - 0.4).abs() < 1e-12); + } + + #[test] + fn m_step_updates_simple_residual_sigma_from_statrese() { + let mut state = SaemState::from_problem( + constant_error_problem(), + &SaemConfig::new().n_chains(1).burn_in(0), + ) + .unwrap(); + state.cycle = 1; + let candidate_sigma = state + .current_residual_statistics() + .unwrap() + .output(0) + .and_then(|statistic| statistic.sigma()) + .unwrap(); + let expected_sigma = state.initialization.schedule.guarded_residual_sigma( + state.cycle, + state.residual_sigmas[0], + candidate_sigma, + ); + + state.m_step().unwrap(); + + assert!((state.residual_sigmas[0] - expected_sigma).abs() < 1e-12); + assert_eq!( + state.error_models.get(0), + Some(&ResidualErrorModel::constant(expected_sigma)) + ); + } + + #[test] + fn sparse_second_output_reports_only_declared_residual_model() { + let result = sparse_second_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(0) + .compute_map(false), + ) + .unwrap(); + + assert_eq!(result.residual_sigmas().len(), 1); + assert_eq!(result.residual_error_estimates().len(), 1); + assert_eq!(result.residual_error_estimates()[0].output, "measured"); + assert_eq!(result.residual_error_estimates()[0].output_index, 1); + assert_eq!(result.cycle_diagnostics().len(), 1); + assert_eq!(result.cycle_diagnostics()[0].residual_diagnostics.len(), 1); + assert_eq!( + result.cycle_diagnostics()[0].residual_diagnostics[0].output, + "measured" + ); + assert_eq!( + result.cycle_diagnostics()[0].residual_diagnostics[0].output_index, + 1 + ); + } + + #[test] + fn averaged_sparse_second_output_preserves_index_name_and_arithmetic_mean() { + let result = sparse_second_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .compute_map(false) + .seed(71_002), + ) + .expect("averaged sparse-output fit should complete"); + + let metadata = result.estimator_metadata(); + assert!(metadata.average_applied); + assert_eq!(metadata.averaging_start_cycle, Some(2)); + assert_eq!(metadata.averaged_iterations, 3); + let estimate = result + .residual_error_estimates() + .first() + .expect("sparse residual estimate"); + assert_eq!( + (estimate.output_index, estimate.output.as_str()), + (1, "measured") + ); + let smoothing = &result.cycle_diagnostics()[1..]; + let expected = smoothing + .iter() + .map(|cycle| { + let residual = cycle + .residual_error_estimates + .first() + .expect("sparse cycle residual"); + assert_eq!( + (residual.output_index, residual.output.as_str()), + (1, "measured") + ); + primary_sigma_parameter(&residual.model) + }) + .sum::() + / smoothing.len() as f64; + assert!((primary_sigma_parameter(&estimate.model) - expected).abs() < 1e-12); + } + + #[test] + fn averaged_multi_output_residuals_preserve_fixed_and_fixed_zero_components() { + let result = mixed_residual_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .compute_map(false) + .seed(71_003), + ) + .expect("averaged mixed-output fit should complete"); + let estimates = result.residual_error_estimates(); + assert_eq!(estimates.len(), 2); + assert_eq!( + (estimates[0].output_index, estimates[0].output.as_str()), + (0, "fixed") + ); + assert_eq!(estimates[0].model, ResidualErrorModel::constant(0.5)); + assert!(!estimates[0].estimated); + assert_eq!( + (estimates[1].output_index, estimates[1].output.as_str()), + (1, "mixed") + ); + assert_eq!(estimates[1].combined_additive_estimated, Some(false)); + assert_eq!(estimates[1].combined_proportional_estimated, Some(true)); + let ResidualErrorModel::Combined { a, b } = estimates[1].model else { + panic!("expected combined residual model"); + }; + assert_eq!(a, 0.0); + let smoothing = &result.cycle_diagnostics()[1..]; + let expected_b = smoothing + .iter() + .map(|cycle| match cycle.residual_error_estimates[1].model { + ResidualErrorModel::Combined { a, b } => { + assert_eq!(a, 0.0); + b + } + _ => panic!("expected combined cycle residual model"), + }) + .sum::() + / smoothing.len() as f64; + assert!((b - expected_b).abs() < 1e-12); + assert!(result.cycle_diagnostics().iter().all(|cycle| { + cycle.residual_error_estimates[0].model == ResidualErrorModel::constant(0.5) + })); + } + + #[test] + fn correlated_residual_averaging_preserves_fixed_components_and_rejects_family_changes() { + let averaged = average_residual_model( + ResidualErrorModel::correlated_combined(0.3, 0.1, 0.2), + ResidualErrorModel::correlated_combined(0.5, 0.2, -0.4), + true, + [true, true], + [false, true, true], + 2, + ) + .unwrap(); + let ResidualErrorModel::CorrelatedCombined { a, b, rho } = averaged else { + panic!("expected correlated-combined average") + }; + assert_eq!(a, 0.3); + assert!((b - 0.15).abs() < 1e-15); + assert!((rho + 0.1).abs() < 1e-15); + assert!(average_residual_model( + averaged, + ResidualErrorModel::combined(0.3, 0.15), + true, + [true, true], + [true, true, true], + 3, + ) + .is_err()); + } + + fn assert_prediction_points_equal( + actual: &pharmsol::simulator::prediction::SubjectPredictions, + expected: &pharmsol::simulator::prediction::SubjectPredictions, + ) { + assert_eq!(actual.predictions().len(), expected.predictions().len()); + for (actual, expected) in actual.predictions().iter().zip(expected.predictions()) { + assert_eq!(actual.time(), expected.time()); + assert_eq!(actual.observation(), expected.observation()); + assert_eq!(actual.prediction(), expected.prediction()); + assert_eq!(actual.outeq(), expected.outeq()); + assert_eq!(actual.errorpoly(), expected.errorpoly()); + assert_eq!(actual.state(), expected.state()); + assert_eq!(actual.occasion(), expected.occasion()); + assert_eq!(actual.censoring(), expected.censoring()); + } + } + + #[test] + fn population_predictions_match_direct_execution_and_metadata() { + let result = problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + let predictions = result.population_predictions(0.25, 0.0).unwrap(); + let expanded = result.data().clone().expand(0.25, 0.0); + + assert_eq!(predictions.len(), expanded.subjects().len()); + assert_eq!(expanded.subjects()[0].id(), "s1"); + assert_eq!(expanded.subjects()[1].id(), "s2"); + for (subject, actual) in expanded.subjects().iter().zip(&predictions) { + let expected = result + .equation() + .estimate_predictions_dense(subject, result.population_parameters()) + .unwrap(); + assert_prediction_points_equal(actual, &expected); + } + } + + #[test] + fn fixed_zero_latent_conditional_predictions_equal_population_predictions() { + let result = fixed_no_iiv_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + assert!(result.conditional_modes().is_empty()); + let population = result.population_predictions(0.25, 0.0).unwrap(); + let conditional = result.conditional_predictions(0.25, 0.0).unwrap(); + assert_eq!(conditional.len(), population.len()); + for (conditional, population) in conditional.iter().zip(&population) { + assert_prediction_points_equal(conditional, population); + } + } + + #[test] + fn iov_conditional_predictions_use_each_occasion_kappa_in_order() { + let mut result = iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + result.conditional_modes[0].eta.fill(0.0); + result.conditional_modes[0].kappas[0].values[0] = -0.2; + result.conditional_modes[0].kappas[1].values[0] = 0.3; + + let actual = result.conditional_predictions(0.25, 0.0).unwrap(); + assert_eq!(actual.len(), 1); + let expanded = result.data().clone().expand(0.25, 0.0); + let subject = &expanded.subjects()[0]; + let mode = &result.conditional_modes()[0]; + let mut expected_points = Vec::new(); + for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { + let parameters = occasion_psi( + result.population_parameters(), + &result.parameter_scales, + &result.random_effect_indices, + &mode.eta, + &result.iov_effect_indices, + &kappa.values, + ) + .unwrap(); + let occasion_subject = + Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); + for mut prediction in result + .equation() + .estimate_predictions_dense(&occasion_subject, ¶meters) + .unwrap() + .predictions() + .iter() + .cloned() + { + *prediction.mut_occasion() = occasion.index(); + expected_points.push(prediction); + } + } + let expected = pharmsol::simulator::prediction::SubjectPredictions::from(expected_points); + assert_prediction_points_equal(&actual[0], &expected); + assert!(actual[0] + .predictions() + .windows(2) + .any(|pair| pair[0].occasion() != pair[1].occasion())); + let occasion_predictions = subject + .occasions() + .iter() + .map(|occasion| { + actual[0] + .predictions() + .iter() + .find(|prediction| { + prediction.occasion() == occasion.index() + && prediction.observation().is_some() + }) + .unwrap() + .prediction() + }) + .collect::>(); + assert_ne!(occasion_predictions[0], occasion_predictions[1]); + } + + #[test] + fn e_step_rescores_chain_zero_parameters() { + let mut state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); + let initial = state.log_likelihood(); + + state.etas[0][0][0] = 2.0_f64.ln(); + state.e_step().unwrap(); + + assert!(state.log_likelihood().is_finite()); + assert_ne!(state.log_likelihood(), initial); + assert_eq!(state.negative_log_likelihood(), -state.log_likelihood()); + } + + #[test] + fn iov_result_retains_named_omega_iov() { + let result = iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + assert_eq!(result.iov_effect_names(), &["ke"]); + assert_eq!(result.omega_iov(), Some(&ndarray::array![[0.1]])); + assert_eq!(result.conditional_modes().len(), 1); + assert_eq!(result.conditional_modes()[0].kappas.len(), 2); + assert!(result.conditional_modes()[0].objective.is_finite()); + } + + #[test] + fn result_reports_final_chain_means_for_eta_and_kappa() { + let mut state = + SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + state.etas[0][0][0] = 0.2; + state.etas[0][1][0] = 0.4; + state.kappas[0][0][0][0] = -0.2; + state.kappas[0][1][0][0] = 0.4; + state.kappas[0][0][1][0] = 0.1; + state.kappas[0][1][1][0] = 0.3; + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.eta_chain_means().len(), 1); + assert!((result.eta_chain_means()[0].values[0] - 0.3).abs() < 1e-12); + assert_eq!(result.kappa_chain_means().len(), 2); + assert_eq!(result.kappa_chain_means()[0].occasion_index, 0); + assert!((result.kappa_chain_means()[0].values[0] - 0.1).abs() < 1e-12); + assert_eq!(result.kappa_chain_means()[1].occasion_index, 1); + assert!((result.kappa_chain_means()[1].values[0] - 0.2).abs() < 1e-12); + } + + #[test] + fn result_retains_immutable_cycle_diagnostics() { + let config = SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(1) + .compute_map(false); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + state.step().unwrap(); + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.parameter_names(), ["ke", "v"]); + assert_eq!(result.data().subjects().len(), 2); + assert_eq!( + result + .equation() + .metadata() + .expect("retained equation metadata") + .outputs()[0] + .name(), + "0" + ); + assert_eq!(result.cycle_diagnostics().len(), 2); + assert_eq!(result.cycle_diagnostics()[0].iteration, 1); + assert_eq!(result.cycle_diagnostics()[0].phase, SaemPhase::BurnIn); + assert_eq!(result.cycle_diagnostics()[1].iteration, 2); + assert_eq!(result.cycle_diagnostics()[1].phase, SaemPhase::Smoothing); + assert_eq!( + result.cycle_diagnostics()[0].population_parameters, + vec![0.2, 10.0] + ); + let final_cycle = &result.cycle_diagnostics()[1]; + assert_eq!( + final_cycle.population_parameters, + result.population_parameters() + ); + assert_eq!(&final_cycle.omega, result.omega()); + assert_eq!(final_cycle.omega_iov.as_ref(), result.omega_iov()); + assert_eq!( + final_cycle.residual_error_estimates, + result.residual_error_estimates() + ); + assert!(final_cycle.conditional_negative_log_likelihood.is_finite()); + assert!(final_cycle.eta_log_prior.is_finite()); + assert!(final_cycle.kappa_log_prior.is_finite()); + } + + #[test] + fn conditional_modes_can_be_disabled_without_relabeling_chain_means() { + let result = problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false), + ) + .unwrap(); + + assert!(result.conditional_modes().is_empty()); + assert_eq!(result.eta_chain_means().len(), 2); + let error = result.conditional_predictions(0.25, 0.0).unwrap_err(); + assert_eq!( + error.to_string(), + "conditional predictions require conditional modes; rerun with compute_map(true)" + ); + } + + #[test] + fn population_uncertainty_wires_analytical_fit_summary_without_changing_estimates() { + let equation = analytical! { + name: "population_uncertainty_summary_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("uncertainty-1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.0, "cp") + .build(), + Subject::builder("uncertainty-2") + .infusion(0.0, 120.0, "iv", 0.5) + .observation(1.0, 5.4, "cp") + .observation(3.0, 3.2, "cp") + .build(), + ]); + let problem = EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.09)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.4)).fixed(), + ) + .build() + .expect("population uncertainty analytical fixture"); + let mut result = problem + .fit_with( + SaemConfig::new() + .seed(0x6a_2026) + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("population uncertainty analytical fit"); + let estimates_before = result.population_parameters().to_vec(); + let objective_before = result.objf(); + assert_eq!(estimates_before, vec![0.25, 20.0]); + assert_eq!(result.estimated_parameters(), &[true, false]); + assert_eq!( + result.population_uncertainty(), + &derive_population_uncertainty(result.information_diagnostics()) + ); + + let coordinates = result.information_diagnostics().coordinates.clone(); + assert_eq!(coordinates.len(), 1); + assert_eq!( + coordinates[0].kind, + InformationCoordinateKind::Population { parameter_index: 0 } + ); + result.population_uncertainty = PopulationUncertaintyDiagnostics { + coordinates, + free_covariance: Some(vec![vec![0.04]]), + free_standard_errors: Some(vec![0.2]), + spectral_condition_number: Some(1.0), + status: PopulationUncertaintyStatus::Available, + regularization: PopulationUncertaintyRegularization::None, + }; + + let summary = result.population_summary(); + assert_eq!(result.population_parameters(), estimates_before); + assert_eq!(result.objf().to_bits(), objective_before.to_bits()); + assert_eq!( + summary + .parameters + .iter() + .map(|parameter| parameter.estimate) + .collect::>(), + estimates_before + ); + assert!( + (summary.parameters[0] + .sd + .expect("free log-scale parameter SE") + - 0.2 * estimates_before[0]) + .abs() + < 1e-12 + ); + assert!( + (summary.parameters[0] + .cv_percent + .expect("free log-scale parameter CV") + - 20.0) + .abs() + < 1e-12 + ); + assert_eq!(summary.parameters[1].sd, None); + assert_eq!(summary.parameters[1].cv_percent, None); + } + + #[test] + fn initialization_result_is_non_converged_snapshot() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(1) + .burn_in(1); + let result = problem().fit_with(config).unwrap(); + let summary = result.summary(); + + assert!(!result.converged()); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert_ne!(result.termination_reason(), Some(&StopReason::Aborted)); + assert_ne!( + result.termination_reason(), + Some(&StopReason::NumericalFailure) + ); + assert_eq!(result.iterations(), 2); + assert_eq!(summary.subject_count, 2); + assert_eq!(summary.observation_count, 4); + assert_eq!(summary.parameter_count, 2); + assert!(result.objf().is_finite()); + assert_eq!(result.population_parameters().len(), 2); + assert_eq!(result.random_effect_names(), &["ke", "v"]); + assert_eq!(result.omega().dim(), (2, 2)); + assert_eq!(result.residual_sigmas().len(), 1); + assert_eq!(result.eta_chain_means().len(), 2); + assert!(result.kappa_chain_means().is_empty()); + assert_eq!(result.conditional_modes().len(), 2); + assert!(result + .conditional_modes() + .iter() + .all(|mode| mode.objective.is_finite())); + assert_eq!(result.population_summary().parameters.len(), 2); + assert_eq!(result.individual_summaries().len(), 2); + } + + // ─── Operational convergence tests ─────────────────────────────────── + + #[test] + fn operational_convergence_disabled_when_config_is_none() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let config = SaemConfig::new() + .k1_iterations(2) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .compute_map(false) + .seed(42); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + assert!(ops.checks.is_empty()); + assert!(!ops.used_for_termination); + assert!(!ops.final_check_reused); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + } + + #[test] + fn operational_convergence_records_checkpoints_when_configured() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(43); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + // Should have at least one checkpoint (smoothing phase produces checkpoints) + assert!(!ops.checks.is_empty(), "expected at least one checkpoint"); + // Each checkpoint should have all fields populated + for check in &ops.checks { + assert!(check.checkpoint_seed.is_some()); + assert!(check.z_quantile.is_some()); + assert!(check.implied_minimum_ess.is_some()); + assert!(!check.criteria.is_empty()); + assert!(check.markov.is_some()); + assert_eq!( + check.averaged_iterations, + check.markov.as_ref().unwrap().n_avg + ); + } + } + + #[test] + fn operational_convergence_has_exact_criterion_names() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(44); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + assert!(!ops.checks.is_empty()); + let first_check = &ops.checks[0]; + let names: Vec<&str> = first_check + .criteria + .iter() + .map(|c| c.name.as_str()) + .collect(); + assert!(names.contains(&"max_rhat")); + assert!(names.contains(&"min_bulk_ess")); + assert!(names.contains(&"min_average_bulk_ess_per_split_chain")); + assert!(names.contains(&"relative_fixed_width")); + assert!(names.contains(&"newton_displacement")); + assert!(names.contains(&"newton_displacement_mc_sd")); + assert!(names.contains(&"omega_boundary_rejection_run")); + assert!(names.contains(&"omega_iov_boundary_rejection_run")); + } + + #[test] + fn covariance_boundary_rejection_blocks_converged_stop_reason() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 100.0, 100.0); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.99, 1)) + .operational_convergence(oc) + .compute_map(false) + .seed(47); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + state.cycle_diagnostics[0].omega_relative_spd_margin = Some(0.5); + state.cycle_diagnostics[0].omega_update_rejected = true; + + state.step().unwrap(); + + let check = state + .operational_diagnostics + .checks + .last() + .expect("operational checkpoint"); + let boundary = check + .criteria + .iter() + .find(|criterion| criterion.name == "omega_boundary_rejection_run") + .expect("Omega boundary criterion"); + assert_eq!( + boundary.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + assert_ne!(state.status, Status::Stop(StopReason::Converged)); + } + + #[test] + fn iov_boundary_rejection_blocks_converged_stop_reason() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.99, 1)) + .operational_convergence(OperationalConvergenceConfig::literature_guided( + 1, 1, 1.0, 0.95, 100.0, 100.0, + )) + .compute_map(false) + .seed(48); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + state.step().unwrap(); + state.cycle_diagnostics[0].omega_iov_relative_spd_margin = Some(0.5); + state.cycle_diagnostics[0].omega_iov_update_rejected = true; + state.step().unwrap(); + + let check = state + .operational_diagnostics + .checks + .last() + .expect("operational checkpoint"); + let boundary = check + .criteria + .iter() + .find(|criterion| criterion.name == "omega_iov_boundary_rejection_run") + .expect("Omega_IOV boundary criterion"); + assert_eq!( + boundary.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + assert_ne!(state.status, Status::Stop(StopReason::Converged)); + } + + #[test] + fn operational_convergence_waits_for_complete_covariance_window() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(5) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 5)) + .operational_convergence(OperationalConvergenceConfig::literature_guided( + 1, 1, 1.0, 0.95, 100.0, 100.0, + )) + .compute_map(false) + .seed(49); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + state.step().unwrap(); + + let first = state + .operational_diagnostics + .checks + .last() + .expect("first operational checkpoint"); + let first_boundary = first + .criteria + .iter() + .find(|criterion| criterion.name == "omega_boundary_rejection_run") + .expect("Omega boundary criterion"); + assert!(matches!( + first_boundary.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + )); + assert!(matches!( + first.outcome, + OperationalConvergenceOutcome::Ineligible { .. } + )); + assert_ne!(state.status, Status::Stop(StopReason::Converged)); + + while state.cycle < 5 && !state.status.is_stop() { + state.step().unwrap(); + } + let eligible = state + .operational_diagnostics + .checks + .last() + .expect("fifth-cycle operational checkpoint"); + assert_eq!(eligible.iteration, 5); + let eligible_boundary = eligible + .criteria + .iter() + .find(|criterion| criterion.name == "omega_boundary_rejection_run") + .expect("Omega boundary criterion"); + assert_eq!( + eligible_boundary.status, + OperationalConvergenceCriterionStatus::Satisfied + ); + } + + #[test] + fn operational_convergence_final_checkpoint_runs_once_with_truthful_flags() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + // check_interval=1 means every smoothing iteration is a checkpoint, + // so the last scheduled checkpoint and the mandatory final will overlap. + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(45); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + assert!(!ops.final_check_reused); + let final_check = ops.checks.last().expect("final checkpoint"); + assert!(final_check.scheduled); + assert!(final_check.mandatory_final); + assert_eq!( + ops.checks + .iter() + .filter(|check| check.iteration == final_check.iteration) + .count(), + 1 + ); + } + + #[test] + fn operational_convergence_checkpoint_seed_is_deterministic_and_global_seed_is_unchanged() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(46); + let result1 = problem().fit_with(config.clone()).unwrap(); + let result2 = problem().fit_with(config).unwrap(); + + let ops1 = result1.operational_diagnostics(); + let ops2 = result2.operational_diagnostics(); + assert_eq!(ops1.checks.len(), ops2.checks.len()); + for (c1, c2) in ops1.checks.iter().zip(ops2.checks.iter()) { + assert_eq!(c1.checkpoint_seed, c2.checkpoint_seed); + assert_eq!(c1.z_quantile, c2.z_quantile); + assert_eq!(c1.outcome, c2.outcome); + } + // Canonical fit result must be unchanged by operational convergence + assert_eq!( + result1.population_parameters(), + result2.population_parameters() + ); + assert_eq!(result1.omega(), result2.omega()); + assert_eq!(result1.conditional_n2ll(), result2.conditional_n2ll()); + } + + #[test] + fn normal_two_sided_z_covers_common_confidence_levels() { + use statrs::distribution::{ContinuousCDF, Normal}; + let norm = Normal::new(0.0, 1.0).unwrap(); + for p in [0.90, 0.95, 0.99] { + let expected = norm.inverse_cdf(p + (1.0 - p) / 2.0); + let actual = normal_two_sided_z(p); + assert!((actual - expected).abs() < 1e-10); + } + } + + #[test] + fn gong_flegal_fixed_width_and_implied_ess_are_exact() { + let z = normal_two_sided_z(0.95); + let epsilon = 0.05; + let implied = 4.0 * z * z / (epsilon * epsilon); + assert!((implied - 6146.34).abs() < 0.1); + let boundary_fraction = epsilon / (2.0 * z); + assert!(2.0 * z * boundary_fraction <= epsilon); + assert!(2.0 * z * (boundary_fraction + 1e-12) > epsilon); + } + + #[test] + fn evaluate_criterion_detects_satisfied_not_satisfied_and_unavailable() { + let satisfied = evaluate_criterion("test", Some(0.5), 1.0, |v| v <= 1.0); + assert_eq!( + satisfied.status, + OperationalConvergenceCriterionStatus::Satisfied + ); + assert_eq!(satisfied.observed, Some(0.5)); + + let not_satisfied = evaluate_criterion("test", Some(2.0), 1.0, |v| v <= 1.0); + assert_eq!( + not_satisfied.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + assert_eq!(not_satisfied.observed, Some(2.0)); + + let unavailable_none = evaluate_criterion("test", None, 1.0, |v| v <= 1.0); + assert!(matches!( + unavailable_none.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + )); + assert_eq!(unavailable_none.observed, None); + + let unavailable_nan = evaluate_criterion("test", Some(f64::NAN), 1.0, |v| v <= 1.0); + assert!(matches!( + unavailable_nan.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + )); + } + + #[test] + fn newton_displacement_requires_matching_dimensions() { + let empty_info = InformationDiagnostics { + coordinates: vec![], + recursion_cycles: 0, + delta: vec![], + g: vec![], + expected_complete_hessian: vec![], + observed_hessian: vec![], + observed_information: vec![], + status: InformationStatus::Available, + }; + let empty_markov = MarkovSimulationVarianceDiagnostics::disabled(); + assert_eq!(newton_displacement(&empty_info, &empty_markov), None); + assert_eq!(newton_displacement_mc_sd(&empty_info, &empty_markov), None); + } +} diff --git a/src/algorithms/parametric/saem_config.rs b/src/algorithms/parametric/saem_config.rs index 289c41750..da0b519e2 100644 --- a/src/algorithms/parametric/saem_config.rs +++ b/src/algorithms/parametric/saem_config.rs @@ -1,32 +1,261 @@ +use anyhow::Result; use serde::{Deserialize, Serialize}; +use crate::estimation::parametric::{ + marginal_likelihood::MarginalLikelihoodConfig, residual::RESIDUAL_OPTIMIZER_MAX_SIGMA, +}; + +/// Lugsail batch-means parameters. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +pub struct LugsailConfig { + pub r: usize, + pub c: f64, +} + +impl LugsailConfig { + pub fn new(r: usize, c: f64) -> Self { + Self { r, c } + } + + /// High/extreme-MCMC over-lugsail convenience: Bartlett q=1, r=3, c=0.5. + pub fn over_lugsail_bartlett() -> Self { + Self::new(3, 0.5) + } +} + +/// Explicit post-fit frozen-kernel simulation-variance and rank-diagnostic budget. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +pub struct MarkovSimulationVarianceConfig { + pub seed: u64, + pub warmup_transitions: usize, + pub draws_per_chain: usize, + pub batch_size: usize, + pub lugsail: LugsailConfig, + pub diagnostic_chains: usize, + pub max_trace_bytes: usize, +} + +impl MarkovSimulationVarianceConfig { + pub fn new( + seed: u64, + warmup_transitions: usize, + draws_per_chain: usize, + batch_size: usize, + lugsail: LugsailConfig, + diagnostic_chains: usize, + max_trace_bytes: usize, + ) -> Self { + Self { + seed, + warmup_transitions, + draws_per_chain, + batch_size, + lugsail, + diagnostic_chains, + max_trace_bytes, + } + } +} + +/// Caller-declared policy for detecting sustained covariance-boundary stalls. +/// +/// The margin threshold is dimensionless and relative to the declared initial +/// covariance. No threshold is inferred from observed fit results. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct CovarianceStabilityConfig { + pub minimum_relative_spd_margin: f64, + pub rejection_window: usize, +} + +impl CovarianceStabilityConfig { + pub fn new(minimum_relative_spd_margin: f64, rejection_window: usize) -> Self { + Self { + minimum_relative_spd_margin, + rejection_window, + } + } +} + +/// Explicit opt-in PMcore operational convergence policy. +/// +/// This is an operational stopping rule, not proof of mathematical convergence, +/// stationarity, model correctness, or uncertainty. There is deliberately no +/// `Default`; schedule, precision, confidence, and stationarity thresholds are +/// caller choices. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct OperationalConvergenceConfig { + pub first_eligible_averaged_iteration: usize, + pub check_interval: usize, + pub max_rhat: f64, + pub min_bulk_ess: f64, + pub min_average_bulk_ess_per_split_chain: f64, + pub relative_fixed_width_epsilon: f64, + pub confidence_level: f64, + /// PMcore operational-policy threshold; the literature supplies none. + pub max_newton_displacement: f64, + /// PMcore operational-policy threshold; the literature supplies none. + pub max_newton_displacement_mc_sd: f64, +} + +impl OperationalConvergenceConfig { + #[allow(clippy::too_many_arguments)] + pub fn new( + first_eligible_averaged_iteration: usize, + check_interval: usize, + max_rhat: f64, + min_bulk_ess: f64, + min_average_bulk_ess_per_split_chain: f64, + relative_fixed_width_epsilon: f64, + confidence_level: f64, + max_newton_displacement: f64, + max_newton_displacement_mc_sd: f64, + ) -> Self { + Self { + first_eligible_averaged_iteration, + check_interval, + max_rhat, + min_bulk_ess, + min_average_bulk_ess_per_split_chain, + relative_fixed_width_epsilon, + confidence_level, + max_newton_displacement, + max_newton_displacement_mc_sd, + } + } + + /// Literature-guided Vehtari and Gong/Flegal policy with caller-supplied + /// schedule, fixed-width, confidence, and PMcore stationarity thresholds. + pub fn literature_guided( + first_eligible_averaged_iteration: usize, + check_interval: usize, + relative_fixed_width_epsilon: f64, + confidence_level: f64, + max_newton_displacement: f64, + max_newton_displacement_mc_sd: f64, + ) -> Self { + Self::new( + first_eligible_averaged_iteration, + check_interval, + 1.01, + 400.0, + 50.0, + relative_fixed_width_epsilon, + confidence_level, + max_newton_displacement, + max_newton_displacement_mc_sd, + ) + } + + pub fn first_eligible_averaged_iteration(mut self, iteration: usize) -> Self { + self.first_eligible_averaged_iteration = iteration; + self + } + + pub fn check_interval(mut self, interval: usize) -> Self { + self.check_interval = interval; + self + } + + pub fn max_rhat(mut self, value: f64) -> Self { + self.max_rhat = value; + self + } + + pub fn min_bulk_ess(mut self, value: f64) -> Self { + self.min_bulk_ess = value; + self + } + + pub fn min_average_bulk_ess_per_split_chain(mut self, value: f64) -> Self { + self.min_average_bulk_ess_per_split_chain = value; + self + } + + pub fn relative_fixed_width_epsilon(mut self, value: f64) -> Self { + self.relative_fixed_width_epsilon = value; + self + } + + pub fn confidence_level(mut self, value: f64) -> Self { + self.confidence_level = value; + self + } + + pub fn max_newton_displacement(mut self, value: f64) -> Self { + self.max_newton_displacement = value; + self + } + + pub fn max_newton_displacement_mc_sd(mut self, value: f64) -> Self { + self.max_newton_displacement_mc_sd = value; + self + } +} + +/// Final-estimate policy for SAEM. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize, Default)] +pub enum SaemEstimatorPolicy { + /// Use the final stochastic-approximation iterate. + #[default] + TerminalIterate, + /// Use an unweighted average of completed smoothing-phase iterates. + AveragedIterates { alpha: f64 }, +} + #[derive(Debug, Deserialize, Clone, Serialize)] #[serde(deny_unknown_fields, default)] pub struct SaemConfig { + /// Total pre-smoothing (K1) iterations, including `burn_in`. + /// + /// The exploration-phase count is `k1_iterations - burn_in`. pub k1_iterations: usize, + /// Number of smoothing-phase (K2) iterations. pub k2_iterations: usize, + /// Number of burn-in iterations within the total K1 period. pub burn_in: usize, + /// Residual variance-floor protection duration. Zero uses the runtime's + /// automatic K1/2 duration; a positive value sets the duration explicitly. pub sa_iterations: usize, pub sa_cooling_factor: f64, - pub mcmc_step_size: f64, pub rw_init: f64, pub n_chains: usize, pub mcmc_iterations: usize, + pub eta_block_iterations: usize, + pub adapt_interval: usize, + /// Maximum early covariance stabilization fraction. For covariate IIV, + /// this under-relaxes the accepted exploration Ω/GEM displacement. For + /// centered non-covariate IIV and IOV statistics, it limits their early + /// stochastic-approximation updates. + pub omega_sa_max_step: f64, + /// Minimum accepted estimated IIV variance. Every estimated initial Ω + /// diagonal must already be at least this floor; fixed diagonals are exempt. pub omega_min_variance: f64, - pub use_gibbs: bool, - pub n_kernels: usize, - pub transform_par: Vec, + /// Minimum accepted estimated IOV variance. Every estimated initial Ω_IOV + /// diagonal must already be at least this floor; fixed diagonals are exempt. + pub omega_iov_min_variance: f64, + pub residual_min_sigma: f64, + pub residual_optimizer_max_iterations: usize, pub compute_map: bool, - pub compute_fim: bool, - pub compute_ll_is: bool, - pub compute_ll_gq: bool, - pub n_mc_is: usize, - pub nu_is: usize, - pub n_nodes_gq: usize, - pub n_sd_gq: f64, - pub display_progress: usize, + pub map_max_iterations: usize, + pub map_sd_tolerance: f64, + pub map_initial_step: f64, pub seed: u64, - pub fix_seed: bool, + /// Final-estimate and smoothing-gain policy. + pub estimator_policy: SaemEstimatorPolicy, + /// Optional post-fit frozen-kernel Markov simulation-variance diagnostic. + pub markov_simulation_variance: Option, + /// Optional caller-declared covariance-boundary diagnostic policy. + #[serde(default, skip_serializing_if = "Option::is_none")] + pub covariance_stability: Option, + /// Optional explicit operational convergence policy. A joint passing + /// checkpoint may terminate with `Converged`; disabled, failed, or + /// ineligible finite schedules retain `MaxCycles`. Operational convergence + /// requires an explicit covariance-stability policy. + pub operational_convergence: Option, + /// Optional explicit post-fit population marginal-likelihood calculation. + pub marginal_likelihood: Option, } impl Default for SaemConfig { @@ -37,25 +266,29 @@ impl Default for SaemConfig { burn_in: 5, sa_iterations: 0, sa_cooling_factor: 0.97, - mcmc_step_size: 0.4, rw_init: 0.5, n_chains: 1, mcmc_iterations: 1, + // Disabled by default; opt in to the block-mixture kernel. + eta_block_iterations: 0, + adapt_interval: 50, + // Guard against one-draw correlated Ω collapse in exploration. + omega_sa_max_step: 0.1, omega_min_variance: 1e-6, - use_gibbs: false, - n_kernels: 4, - transform_par: vec![], + omega_iov_min_variance: 1e-8, + // Uses the established 1e-12 residual-variance guard on the SD scale. + residual_min_sigma: 1e-6, + residual_optimizer_max_iterations: 200, compute_map: true, - compute_fim: true, - compute_ll_is: true, - compute_ll_gq: false, - n_mc_is: 5000, - nu_is: 4, - n_nodes_gq: 12, - n_sd_gq: 4.0, - display_progress: 10, + map_max_iterations: 200, + map_sd_tolerance: 1e-8, + map_initial_step: 0.1, seed: 123456, - fix_seed: true, + estimator_policy: SaemEstimatorPolicy::TerminalIterate, + markov_simulation_variance: None, + covariance_stability: None, + operational_convergence: None, + marginal_likelihood: None, } } } @@ -66,7 +299,9 @@ impl SaemConfig { Self::default() } - /// Number of exploration-phase (K1) iterations. + /// Total pre-smoothing (K1) iterations, including burn-in. + /// + /// The exploration-phase count is `iterations - burn_in`. pub fn k1_iterations(mut self, iterations: usize) -> Self { self.k1_iterations = iterations; self @@ -78,7 +313,7 @@ impl SaemConfig { self } - /// Number of burn-in iterations. + /// Number of burn-in iterations within the total K1 period. pub fn burn_in(mut self, burn_in: usize) -> Self { self.burn_in = burn_in; self @@ -90,9 +325,87 @@ impl SaemConfig { self } - /// MCMC step size. + /// Initial random-walk scale multiplier. pub fn mcmc_step_size(mut self, step_size: f64) -> Self { - self.mcmc_step_size = step_size; + self.rw_init = step_size; + self + } + + /// Number of MCMC proposal sweeps per E-step. + pub fn mcmc_iterations(mut self, iterations: usize) -> Self { + self.mcmc_iterations = iterations; + self + } + + /// Number of Ω-scaled η block proposals per subject-chain and E-step. + /// + /// Each proposal precedes the componentwise decorrelation sweep and uses + /// `eta' = eta + scale_subject * chol(Omega) * z`, matching the reference + /// block kernel. The default is zero, which disables the block kernel. + pub fn eta_block_iterations(mut self, iterations: usize) -> Self { + self.eta_block_iterations = iterations; + self + } + + /// Number of E-steps between proposal-scale adaptations. + pub fn adapt_interval(mut self, iterations: usize) -> Self { + self.adapt_interval = iterations; + self + } + + /// Maximum early covariance stabilization fraction. + /// + /// Covariate IIV uses this as an exploration-only cap on the accepted + /// mask-aware Ω/GEM displacement, after constructing coherent raw moments. + /// Centered non-covariate IIV and IOV statistics retain their existing + /// burn-in/exploration stochastic-approximation interpretation. + pub fn omega_sa_max_step(mut self, step_size: f64) -> Self { + self.omega_sa_max_step = step_size; + self + } + + /// Minimum accepted estimated IOV variance. + /// + /// Every estimated initial Ω_IOV diagonal must already be at least this + /// value. Fixed diagonals are exempt. + pub fn omega_iov_min_variance(mut self, variance: f64) -> Self { + self.omega_iov_min_variance = variance; + self + } + + /// Minimum estimated residual standard deviation. + pub fn residual_min_sigma(mut self, sigma: f64) -> Self { + self.residual_min_sigma = sigma; + self + } + + /// Maximum iterations for each joint residual-parameter optimization. + pub fn residual_optimizer_max_iterations(mut self, iterations: usize) -> Self { + self.residual_optimizer_max_iterations = iterations; + self + } + + /// Enable or disable posthoc conditional-mode estimation. + pub fn compute_map(mut self, compute: bool) -> Self { + self.compute_map = compute; + self + } + + /// Maximum Nelder-Mead iterations for each subject's posthoc mode. + pub fn map_max_iterations(mut self, iterations: usize) -> Self { + self.map_max_iterations = iterations; + self + } + + /// Simplex objective-standard-deviation tolerance for posthoc modes. + pub fn map_sd_tolerance(mut self, tolerance: f64) -> Self { + self.map_sd_tolerance = tolerance; + self + } + + /// Initial simplex displacement as a fraction of each random-effect SD. + pub fn map_initial_step(mut self, step: f64) -> Self { + self.map_initial_step = step; self } @@ -102,64 +415,611 @@ impl SaemConfig { self } + /// Select the final-estimate policy directly. + pub fn estimator_policy(mut self, policy: SaemEstimatorPolicy) -> Self { + self.estimator_policy = policy; + self + } + + /// Use smoothing gain `s^-alpha` and the Cesaro average of smoothing iterates. + pub fn averaged_iterates(self, alpha: f64) -> Self { + self.estimator_policy(SaemEstimatorPolicy::AveragedIterates { alpha }) + } + + /// Enable the explicitly-budgeted post-fit frozen-kernel diagnostic. + pub fn markov_simulation_variance(mut self, config: MarkovSimulationVarianceConfig) -> Self { + self.markov_simulation_variance = Some(config); + self + } + + /// Enable caller-declared covariance-boundary and rejection diagnostics. + pub fn covariance_stability(mut self, policy: CovarianceStabilityConfig) -> Self { + self.covariance_stability = Some(policy); + self + } + + /// Enable explicit operational checkpoints and joint stopping criteria. + pub fn operational_convergence(mut self, criteria: OperationalConvergenceConfig) -> Self { + self.operational_convergence = Some(criteria); + self + } + + /// Request the explicit post-fit population marginal likelihood. + pub fn marginal_likelihood(mut self, config: MarginalLikelihoodConfig) -> Self { + self.marginal_likelihood = Some(config); + self + } + + pub(crate) fn validate(&self) -> Result<()> { + let total_iterations = self + .k1_iterations + .checked_add(self.k2_iterations) + .ok_or_else(|| { + anyhow::anyhow!("SAEM k1_iterations + k2_iterations must not overflow") + })?; + if total_iterations == 0 { + anyhow::bail!("SAEM k1_iterations + k2_iterations must be greater than zero"); + } + if self.burn_in > self.k1_iterations { + anyhow::bail!("SAEM burn_in must not exceed k1_iterations"); + } + if let Some(policy) = self.covariance_stability { + if !policy.minimum_relative_spd_margin.is_finite() + || policy.minimum_relative_spd_margin <= 0.0 + || policy.minimum_relative_spd_margin >= 1.0 + { + anyhow::bail!( + "SAEM covariance-stability minimum relative SPD margin must be finite and in (0, 1)" + ); + } + if policy.rejection_window == 0 { + anyhow::bail!("SAEM covariance-stability rejection window must be positive"); + } + let active_cycles = total_iterations - self.burn_in; + if policy.rejection_window > active_cycles { + anyhow::bail!( + "SAEM covariance-stability rejection window must be reachable during covariance-active cycles" + ); + } + } + if let SaemEstimatorPolicy::AveragedIterates { alpha } = self.estimator_policy { + if !alpha.is_finite() || alpha <= 0.5 || alpha >= 1.0 { + anyhow::bail!( + "SAEM averaged-iterate alpha must be finite and strictly between 0.5 and 1.0" + ); + } + if self.k2_iterations == 0 { + anyhow::bail!("SAEM averaged iterates require k2_iterations greater than zero"); + } + } + if let Some(diagnostic) = self.markov_simulation_variance { + if !matches!( + self.estimator_policy, + SaemEstimatorPolicy::AveragedIterates { .. } + ) { + anyhow::bail!("SAEM Markov simulation variance requires averaged iterates"); + } + if self.k2_iterations == 0 { + anyhow::bail!( + "SAEM Markov simulation variance requires a completed-capable K2 phase" + ); + } + let samples = diagnostic.draws_per_chain; + let batch = diagnostic.batch_size; + let r = diagnostic.lugsail.r; + let c = diagnostic.lugsail.c; + if samples == 0 || batch == 0 || samples % batch != 0 || samples / batch < 2 { + anyhow::bail!( + "SAEM Markov simulation variance draws must form at least two complete batches" + ); + } + if r == 0 || batch % r != 0 || batch / r == 0 { + anyhow::bail!("SAEM Markov simulation variance lugsail r must divide batch size with b/r >= 1"); + } + if !c.is_finite() || !(0.0..1.0).contains(&c) { + anyhow::bail!( + "SAEM Markov simulation variance lugsail c must be finite and in [0, 1)" + ); + } + if diagnostic.diagnostic_chains == 0 { + anyhow::bail!( + "SAEM Markov simulation variance diagnostic_chains must be at least 1" + ); + } + if diagnostic.max_trace_bytes == 0 { + anyhow::bail!( + "SAEM Markov simulation variance max_trace_bytes must be greater than zero" + ); + } + } + if let Some(policy) = self.operational_convergence { + let _covariance_stability = self.covariance_stability.ok_or_else(|| { + anyhow::anyhow!( + "SAEM operational convergence requires an explicit covariance-stability policy" + ) + })?; + if !matches!( + self.estimator_policy, + SaemEstimatorPolicy::AveragedIterates { .. } + ) { + anyhow::bail!("SAEM operational convergence requires averaged iterates"); + } + if policy.first_eligible_averaged_iteration == 0 + || policy.first_eligible_averaged_iteration > self.k2_iterations + { + anyhow::bail!("SAEM operational convergence first eligible averaged iteration must be reachable in K2"); + } + if policy.check_interval == 0 { + anyhow::bail!("SAEM operational convergence check interval must be positive"); + } + if !policy.max_rhat.is_finite() || policy.max_rhat <= 1.0 { + anyhow::bail!( + "SAEM operational convergence max Rhat must be finite and greater than 1" + ); + } + for (name, value) in [ + ("min bulk ESS", policy.min_bulk_ess), + ( + "min average bulk ESS per split chain", + policy.min_average_bulk_ess_per_split_chain, + ), + ( + "relative fixed-width epsilon", + policy.relative_fixed_width_epsilon, + ), + ("max Newton displacement", policy.max_newton_displacement), + ( + "max Newton-displacement MC SD", + policy.max_newton_displacement_mc_sd, + ), + ] { + if !value.is_finite() || value <= 0.0 { + anyhow::bail!( + "SAEM operational convergence {name} must be finite and positive" + ); + } + } + if !policy.confidence_level.is_finite() + || !(0.0..1.0).contains(&policy.confidence_level) + { + anyhow::bail!( + "SAEM operational convergence confidence level must be finite and in (0, 1)" + ); + } + let normal = statrs::distribution::Normal::new(0.0, 1.0) + .expect("standard normal parameters are valid"); + let one_sided = policy.confidence_level + (1.0 - policy.confidence_level) / 2.0; + let z = statrs::distribution::ContinuousCDF::inverse_cdf(&normal, one_sided); + if !z.is_finite() || z <= 0.0 { + anyhow::bail!( + "SAEM operational convergence confidence level produces an unusable normal quantile" + ); + } + let implied_minimum_ess = 4.0 * z * z / policy.relative_fixed_width_epsilon.powi(2); + if !implied_minimum_ess.is_finite() || implied_minimum_ess <= 0.0 { + anyhow::bail!( + "SAEM operational convergence confidence/epsilon produce an unusable implied minimum ESS" + ); + } + let diagnostic = self.markov_simulation_variance.ok_or_else(|| { + anyhow::anyhow!("SAEM operational convergence requires Markov diagnostics") + })?; + if diagnostic.diagnostic_chains < 4 { + anyhow::bail!("SAEM operational convergence requires at least 4 diagnostic chains"); + } + if diagnostic.draws_per_chain % 2 != 0 { + anyhow::bail!("SAEM operational convergence requires an even retained draw count"); + } + if diagnostic.lugsail != LugsailConfig::over_lugsail_bartlett() { + anyhow::bail!( + "SAEM operational termination requires over-lugsail Bartlett r=3,c=0.5" + ); + } + } + if let Some(config) = self.marginal_likelihood { + config.validate()?; + } + if !self.rw_init.is_finite() || self.rw_init <= 0.0 { + anyhow::bail!("SAEM rw_init must be finite and positive"); + } + if self.n_chains == 0 { + anyhow::bail!("SAEM n_chains must be greater than zero"); + } + if self.mcmc_iterations == 0 { + anyhow::bail!("SAEM mcmc_iterations must be greater than zero"); + } + if self.adapt_interval == 0 { + anyhow::bail!("SAEM adapt_interval must be greater than zero"); + } + if !self.sa_cooling_factor.is_finite() + || self.sa_cooling_factor <= 0.0 + || self.sa_cooling_factor > 1.0 + { + anyhow::bail!("SAEM sa_cooling_factor must be finite and in (0, 1]"); + } + if !self.omega_sa_max_step.is_finite() + || self.omega_sa_max_step <= 0.0 + || self.omega_sa_max_step > 1.0 + { + anyhow::bail!("SAEM omega_sa_max_step must be finite and in (0, 1]"); + } + for (name, variance) in [ + ("omega_min_variance", self.omega_min_variance), + ("omega_iov_min_variance", self.omega_iov_min_variance), + ] { + if !variance.is_finite() || variance <= 0.0 { + anyhow::bail!("SAEM {name} must be finite and positive"); + } + } + if !self.residual_min_sigma.is_finite() + || self.residual_min_sigma <= 0.0 + || self.residual_min_sigma >= RESIDUAL_OPTIMIZER_MAX_SIGMA + { + anyhow::bail!( + "SAEM residual_min_sigma must be finite, positive, and below {RESIDUAL_OPTIMIZER_MAX_SIGMA}" + ); + } + if self.residual_optimizer_max_iterations == 0 { + anyhow::bail!("SAEM residual_optimizer_max_iterations must be greater than zero"); + } + if self.compute_map { + if self.map_max_iterations == 0 { + anyhow::bail!("SAEM map_max_iterations must be greater than zero"); + } + if !self.map_sd_tolerance.is_finite() || self.map_sd_tolerance <= 0.0 { + anyhow::bail!("SAEM map_sd_tolerance must be finite and positive"); + } + if !self.map_initial_step.is_finite() || self.map_initial_step <= 0.0 { + anyhow::bail!("SAEM map_initial_step must be finite and positive"); + } + } + Ok(()) + } + pub fn total_iterations(&self) -> usize { self.k1_iterations + self.k2_iterations } +} + +#[cfg(test)] +mod tests { + use super::{ + CovarianceStabilityConfig, LugsailConfig, MarkovSimulationVarianceConfig, + OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, + RESIDUAL_OPTIMIZER_MAX_SIGMA, + }; + use crate::estimation::MarginalLikelihoodConfig; - pub fn is_exploration_phase(&self, iteration: usize) -> bool { - iteration <= self.k1_iterations + #[test] + fn mcmc_step_size_sets_rw_init() { + let config = SaemConfig::new().mcmc_step_size(0.75); + assert_eq!(config.rw_init, 0.75); } - pub fn is_smoothing_phase(&self, iteration: usize) -> bool { - iteration > self.k1_iterations + #[test] + fn eta_block_kernel_is_opt_in_and_serialized_configuration_is_operational() { + assert_eq!(SaemConfig::default().eta_block_iterations, 0); + let config = SaemConfig::new().eta_block_iterations(2); + assert_eq!(config.eta_block_iterations, 2); + let decoded: SaemConfig = serde_json::from_str(r#"{"eta_block_iterations":3}"#).unwrap(); + assert_eq!(decoded.eta_block_iterations, 3); } - pub fn is_sa_active(&self, iteration: usize) -> bool { - self.sa_iterations > 0 && iteration <= self.sa_iterations + #[test] + fn invalid_operational_values_fail_closed() { + let invalid = [ + ( + "empty schedule", + SaemConfig::new().k1_iterations(0).k2_iterations(0), + ), + ( + "schedule overflow", + SaemConfig { + k1_iterations: usize::MAX, + k2_iterations: 1, + ..SaemConfig::default() + }, + ), + ("burn-in", SaemConfig::new().k1_iterations(1).burn_in(2)), + ( + "random-walk scale", + SaemConfig::new().mcmc_step_size(f64::NAN), + ), + ("chains", SaemConfig::new().n_chains(0)), + ("MCMC iterations", SaemConfig::new().mcmc_iterations(0)), + ("adaptation interval", SaemConfig::new().adapt_interval(0)), + ( + "SA cooling", + SaemConfig { + sa_cooling_factor: 0.0, + ..SaemConfig::default() + }, + ), + ("omega step", SaemConfig::new().omega_sa_max_step(1.01)), + ( + "omega floor", + SaemConfig { + omega_min_variance: 0.0, + ..SaemConfig::default() + }, + ), + ( + "IOV omega floor", + SaemConfig::new().omega_iov_min_variance(f64::INFINITY), + ), + ("residual floor", SaemConfig::new().residual_min_sigma(-1.0)), + ( + "residual optimizer bound", + SaemConfig::new().residual_min_sigma(RESIDUAL_OPTIMIZER_MAX_SIGMA), + ), + ( + "residual optimizer", + SaemConfig::new().residual_optimizer_max_iterations(0), + ), + ("MAP iterations", SaemConfig::new().map_max_iterations(0)), + ( + "MAP tolerance", + SaemConfig::new().map_sd_tolerance(f64::NAN), + ), + ("MAP step", SaemConfig::new().map_initial_step(0.0)), + ]; + + for (case, config) in invalid { + assert!(config.validate().is_err(), "{case} must fail validation"); + } + + assert!(SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(0) + .eta_block_iterations(0) + .validate() + .is_ok()); + assert!(SaemConfig::new() + .compute_map(false) + .map_max_iterations(0) + .map_sd_tolerance(f64::NAN) + .map_initial_step(0.0) + .validate() + .is_ok()); } - pub fn sa_temperature(&self, iteration: usize) -> f64 { - if self.is_sa_active(iteration) { - self.sa_cooling_factor.powi(iteration as i32) - } else { - 1.0 + #[test] + fn covariance_stability_is_explicit_and_invalid_policies_fail_closed() { + assert!(SaemConfig::default().covariance_stability.is_none()); + let policy = CovarianceStabilityConfig::new(0.01, 2); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(1) + .burn_in(0) + .covariance_stability(policy); + assert!(config.validate().is_ok()); + let encoded = serde_json::to_string(&config).unwrap(); + assert!(encoded.contains("covariance_stability")); + assert_eq!( + serde_json::from_str::(&encoded) + .unwrap() + .covariance_stability, + Some(policy) + ); + for invalid in [ + CovarianceStabilityConfig::new(0.0, 1), + CovarianceStabilityConfig::new(1.0, 1), + CovarianceStabilityConfig::new(f64::NAN, 1), + CovarianceStabilityConfig::new(0.01, 0), + CovarianceStabilityConfig::new(0.01, 3), + ] { + assert!(SaemConfig::new() + .k1_iterations(1) + .k2_iterations(1) + .burn_in(0) + .covariance_stability(invalid) + .validate() + .is_err()); } } - pub fn step_size(&self, iteration: usize) -> f64 { - if iteration <= self.k1_iterations { - 1.0 - } else { - let k_smooth = iteration - self.k1_iterations; - 1.0 / (k_smooth as f64 + 1.0) + #[test] + fn marginal_likelihood_config_is_explicit_and_invalid_values_fail_closed() { + assert!(SaemConfig::default().marginal_likelihood.is_none()); + for config in [ + MarginalLikelihoodConfig::new(1, 1, 3, 1.0), + MarginalLikelihoodConfig::new(2, 1, 2, 1.0), + MarginalLikelihoodConfig::new(2, 1, 3, 0.0), + MarginalLikelihoodConfig::new(2, 1, 3, f64::NAN), + ] { + assert!(SaemConfig::new() + .marginal_likelihood(config) + .validate() + .is_err()); } + let valid = MarginalLikelihoodConfig::new(2, 9, 3, 0.5); + assert!(SaemConfig::new() + .marginal_likelihood(valid) + .validate() + .is_ok()); } - pub fn get_transform(&self, param_idx: usize) -> u8 { - self.transform_par.get(param_idx).copied().unwrap_or(1) + #[test] + fn estimator_policy_defaults_and_validates_alpha_schedule() { + assert_eq!( + SaemConfig::default().estimator_policy, + SaemEstimatorPolicy::TerminalIterate + ); + for alpha in [0.5, 1.0, f64::NAN, f64::INFINITY] { + assert!(SaemConfig::new() + .averaged_iterates(alpha) + .validate() + .is_err()); + } + assert!(SaemConfig::new() + .averaged_iterates(0.75) + .k2_iterations(0) + .validate() + .is_err()); + assert!(SaemConfig::new().averaged_iterates(0.75).validate().is_ok()); } - pub fn get_transforms(&self, n_params: usize) -> Vec { - let mut transforms = self.transform_par.clone(); - while transforms.len() < n_params { - transforms.push(1); + #[test] + fn markov_variance_config_is_explicit_and_all_invalid_shapes_fail() { + assert_eq!( + LugsailConfig::over_lugsail_bartlett(), + LugsailConfig::new(3, 0.5) + ); + let valid = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024, + ); + assert!(SaemConfig::new() + .averaged_iterates(0.75) + .markov_simulation_variance(valid) + .validate() + .is_ok()); + assert!(SaemConfig::new() + .markov_simulation_variance(valid) + .validate() + .is_err()); + for invalid in [ + MarkovSimulationVarianceConfig::new(1, 0, 0, 6, LugsailConfig::new(3, 0.5), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 10, 6, LugsailConfig::new(3, 0.5), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 6, 6, LugsailConfig::new(3, 0.5), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 12, 0, LugsailConfig::new(3, 0.5), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 12, 6, LugsailConfig::new(0, 0.5), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 12, 6, LugsailConfig::new(4, 0.5), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 12, 6, LugsailConfig::new(3, -0.1), 2, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 12, 6, LugsailConfig::new(3, 1.0), 2, 1024), + MarkovSimulationVarianceConfig::new( + 1, + 0, + 12, + 6, + LugsailConfig::new(3, f64::NAN), + 2, + 1024, + ), + MarkovSimulationVarianceConfig::new(1, 0, 12, 6, LugsailConfig::new(3, 0.5), 0, 1024), + MarkovSimulationVarianceConfig::new(1, 0, 12, 6, LugsailConfig::new(3, 0.5), 2, 0), + ] { + assert!(SaemConfig::new() + .averaged_iterates(0.75) + .markov_simulation_variance(invalid) + .validate() + .is_err()); } - transforms.truncate(n_params); - transforms } - pub fn infer_transforms_from_ranges(&mut self, ranges: &[(f64, f64)]) { - self.transform_par = ranges - .iter() - .map(|(lower, upper)| { - if *lower >= 0.0 && *upper > 0.0 && lower.is_finite() && upper.is_finite() { - 1 - } else if (*lower - 0.0).abs() < 1e-10 && (*upper - 1.0).abs() < 1e-10 { - 3 - } else { - 0 - } - }) - .collect(); + fn operational_policy() -> OperationalConvergenceConfig { + OperationalConvergenceConfig::literature_guided(2, 3, 0.05, 0.95, 0.1, 0.02) + } + + #[test] + fn operational_convergence_literature_guided_fixes_only_vehtari_values() { + let policy = operational_policy(); + assert_eq!(policy.max_rhat, 1.01); + assert_eq!(policy.min_bulk_ess, 400.0); + assert_eq!(policy.min_average_bulk_ess_per_split_chain, 50.0); + assert_eq!(policy.relative_fixed_width_epsilon, 0.05); + assert_eq!(policy.confidence_level, 0.95); + } + + #[test] + fn operational_convergence_serialization_has_no_policy_defaults() { + assert_eq!(SaemConfig::default().operational_convergence, None); + let policy = operational_policy().check_interval(4).max_rhat(1.02); + let config = SaemConfig::new() + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(policy); + let decoded: SaemConfig = + serde_json::from_str(&serde_json::to_string(&config).unwrap()).unwrap(); + assert_eq!(decoded.operational_convergence, Some(policy)); + assert!( + serde_json::from_str::(r#"{"max_rhat":1.01}"#).is_err() + ); + } + + #[test] + fn operational_convergence_validation_requires_complete_eligible_policy() { + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024, + ); + let base = || { + SaemConfig::new() + .k2_iterations(10) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + }; + assert!(base() + .operational_convergence(operational_policy()) + .validate() + .is_ok()); + // The five-cycle window is not available at the first K2 checkpoint, + // but it is reachable later in the declared eleven active cycles. + assert!(SaemConfig::new() + .k1_iterations(1) + .k2_iterations(10) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 5)) + .operational_convergence(operational_policy()) + .validate() + .is_ok()); + assert!(SaemConfig::new() + .averaged_iterates(0.75) + .operational_convergence(operational_policy()) + .validate() + .is_err()); + assert!(SaemConfig::new() + .k2_iterations(10) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .operational_convergence(operational_policy()) + .validate() + .is_err()); + for invalid in [ + operational_policy().first_eligible_averaged_iteration(0), + operational_policy().first_eligible_averaged_iteration(11), + operational_policy().check_interval(0), + operational_policy().max_rhat(1.0), + operational_policy().min_bulk_ess(0.0), + operational_policy().min_average_bulk_ess_per_split_chain(0.0), + operational_policy().relative_fixed_width_epsilon(0.0), + operational_policy().relative_fixed_width_epsilon(f64::MIN_POSITIVE), + operational_policy().confidence_level(1.0), + operational_policy().confidence_level(f64::from_bits(1.0_f64.to_bits() - 1)), + operational_policy().max_newton_displacement(f64::NAN), + operational_policy().max_newton_displacement_mc_sd(0.0), + ] { + assert!(base().operational_convergence(invalid).validate().is_err()); + } + } + + #[test] + fn removed_unwired_options_fail_closed_during_deserialization() { + for json in [ + r#"{"compute_fim":true}"#, + r#"{"compute_ll_is":true}"#, + r#"{"compute_ll_gq":true}"#, + r#"{"use_gibbs":true}"#, + r#"{"n_kernels":4}"#, + r#"{"transform_par":[1,1]}"#, + r#"{"fix_seed":false}"#, + ] { + let error = serde_json::from_str::(json) + .expect_err("unwired SAEM option must not deserialize silently"); + assert!(error.to_string().contains("unknown field")); + } } } diff --git a/src/bestdose/cost.rs b/src/bestdose/cost.rs index 6f59be9fb..a5da34b77 100644 --- a/src/bestdose/cost.rs +++ b/src/bestdose/cost.rs @@ -269,9 +269,8 @@ pub(crate) fn evaluate( // Simulate at observation times only let pred = problem .eq - .simulate_subject_dense(&target_subject, &spp, None)?; - pred.0 - .get_predictions() + .estimate_predictions_dense(&target_subject, &spp)?; + pred.get_predictions() .iter() .map(|p| p.prediction()) .collect() @@ -347,8 +346,8 @@ pub(crate) fn evaluate( // Simulate at dense times let pred = problem .eq - .simulate_subject_dense(&dense_subject, &spp, None)?; - let dense_predictions_with_outeq = pred.0.get_predictions(); + .estimate_predictions_dense(&dense_subject, &spp)?; + let dense_predictions_with_outeq = pred.get_predictions(); // Group predictions by outeq using the Prediction struct let mut outeq_predictions: std::collections::HashMap> = @@ -476,8 +475,8 @@ pub(crate) fn evaluate( // Simulate at dense times let pred = problem .eq - .simulate_subject_dense(&dense_subject, &spp, None)?; - let dense_predictions_with_outeq = pred.0.get_predictions(); + .estimate_predictions_dense(&dense_subject, &spp)?; + let dense_predictions_with_outeq = pred.get_predictions(); // Group predictions by outeq let mut outeq_predictions: std::collections::HashMap> = diff --git a/src/bestdose/mod.rs b/src/bestdose/mod.rs index b32d4104e..471c2e609 100644 --- a/src/bestdose/mod.rs +++ b/src/bestdose/mod.rs @@ -17,7 +17,7 @@ //! //! # fn example(eq: pharmsol::prelude::ODE, pop_data: pharmsol::prelude::Data, //! # prior_theta: pmcore::estimation::nonparametric::Theta, -//! # ems: pharmsol::prelude::AssayErrorModels, +//! # ems: pmcore::AssayErrorModels, //! # past_data: Option, //! # target: pharmsol::prelude::Subject) -> anyhow::Result<()> { //! // 1. Fit the population model with any algorithm. diff --git a/src/estimation/assay_error.rs b/src/estimation/assay_error.rs new file mode 100644 index 000000000..2f0f173f9 --- /dev/null +++ b/src/estimation/assay_error.rs @@ -0,0 +1,2044 @@ +use std::{ + collections::BTreeMap, + hash::{Hash, Hasher}, + ops::Deref, +}; + +pub use pharmsol::ErrorPoly; +use pharmsol::{prelude::Prediction, OutputLabel}; +use serde::{Deserialize, Serialize}; +use thiserror::Error; + +/// Parameter that can be either fixed or variable for estimation +/// +/// This enum allows specifying whether a factor parameter (like lambda or gamma) +/// should be fixed at a specific value or allowed to vary during estimation. +#[derive(Debug, Clone, Serialize, Deserialize, Copy, PartialEq)] +pub enum Factor { + /// Parameter can be estimated/varied during optimization + Variable(f64), + /// Parameter is fixed at this value and won't be estimated + Fixed(f64), +} + +impl Factor { + /// Get the current value of the parameter + pub fn value(&self) -> f64 { + match self { + Self::Variable(val) | Self::Fixed(val) => *val, + } + } + + /// Check if the parameter is fixed + pub fn is_fixed(&self) -> bool { + matches!(self, Self::Fixed(_)) + } + + /// Check if the parameter is variable (can be estimated) + pub fn is_variable(&self) -> bool { + matches!(self, Self::Variable(_)) + } + + /// Set the value while preserving the fixed/variable state + pub fn set_value(&mut self, new_value: f64) { + match self { + Self::Variable(val) => *val = new_value, + Self::Fixed(val) => *val = new_value, + } + } + + /// Convert the parameter to fixed at its current value + pub fn make_fixed(&mut self) { + if let Self::Variable(val) = self { + *self = Self::Fixed(*val); + } + } + + /// Convert the parameter to variable at its current value + pub fn make_variable(&mut self) { + if let Self::Fixed(val) = self { + *self = Self::Variable(*val); + } + } + + /// Replace the current factor with a new factor value + pub fn set_factor(&mut self, factor: &Factor) { + match factor { + Factor::Variable(val) => *self = Self::Variable(*val), + Factor::Fixed(val) => *self = Self::Fixed(*val), + } + } +} + +impl From> for AssayErrorModels { + fn from(models: Vec) -> Self { + Self { + models, + output_lookup: BTreeMap::new(), + named_models: BTreeMap::new(), + } + } +} + +/// Collection of assay/measurement error models for all outputs. +/// +/// This struct represents **measurement/assay noise** - the error associated with +/// quantification of drug concentration in biological samples. Sigma is computed +/// from the **observation** value. +/// +/// Used by non-parametric algorithms (NPAG, NPOD, etc.). +/// +/// For parametric algorithms (SAEM, FOCE), use [`crate::ResidualErrorModels`] instead, +/// which computes sigma from the **prediction**. +/// +/// This is a wrapper around a vector of [AssayErrorModel]s, its size is determined by +/// the number of outputs in the model/dataset. +#[derive(Serialize, Debug, Clone, Deserialize)] +pub struct AssayErrorModels { + models: Vec, + output_lookup: BTreeMap, + named_models: BTreeMap, +} + +/// Deprecated alias for [`AssayErrorModels`]. +/// +/// This type alias is provided for backward compatibility. +/// New code should use [`AssayErrorModels`] directly. +#[deprecated( + since = "0.23.0", + note = "Use AssayErrorModels instead. ErrorModels has been renamed to better reflect its purpose (assay/measurement error)." +)] +pub type ErrorModels = AssayErrorModels; + +/// Assay error models whose labels have been explicitly bound to ordered output slots. +/// +/// Create this view with [`AssayErrorModels::bind_outputs`]. Predictions carry +/// numeric output indices, so label-first declarations must be bound before +/// they can be scored. +#[derive(Debug)] +pub struct BoundAssayErrorModels<'a> { + storage: BoundAssayErrorModelsStorage<'a>, +} + +#[derive(Debug)] +enum BoundAssayErrorModelsStorage<'a> { + Borrowed(&'a AssayErrorModels), + Owned(AssayErrorModels), +} + +impl BoundAssayErrorModels<'_> { + /// Score predictions after explicit label-to-output binding. + pub fn log_likelihood

( + &self, + predictions: &P, + ) -> std::result::Result + where + P: pharmsol::Predictions, + { + crate::estimation::likelihood::observation::assay_error_model_log_likelihoods( + predictions, + self, + ) + } +} + +impl Deref for BoundAssayErrorModels<'_> { + type Target = AssayErrorModels; + + fn deref(&self) -> &Self::Target { + match &self.storage { + BoundAssayErrorModelsStorage::Borrowed(models) => models, + BoundAssayErrorModelsStorage::Owned(models) => models, + } + } +} + +impl Default for AssayErrorModels { + fn default() -> Self { + Self::new() + } +} + +impl AssayErrorModels { + /// Create a new reusable label-first [`AssayErrorModels`] definition. + /// + /// Before scoring predictions, bind labels to the model's canonical output + /// order with [`AssayErrorModels::bind_outputs`] or use + /// [`AssayErrorModels::log_likelihood_for_outputs`]. This lets the same + /// public declaration be reused safely without inferring label order. + /// + /// ```rust + /// # use pmcore::{AssayErrorModel, AssayErrorModels, ErrorPoly}; + /// let error_models = AssayErrorModels::new() + /// .add("cp", AssayErrorModel::additive(ErrorPoly::new(0.0, 0.05, 0.0, 0.0), 0.0))?; + /// # Ok::<(), pmcore::ErrorModelError>(()) + /// ``` + pub fn new() -> Self { + Self::empty() + } + + pub(crate) fn assert_compatible_output_names( + &self, + outputs: I, + ) -> Result<(), ErrorModelError> + where + I: IntoIterator, + S: AsRef, + { + if self.output_lookup.is_empty() { + return Ok(()); + } + + let expected = self.bound_output_names(); + let found = outputs + .into_iter() + .map(|output| output.as_ref().to_string()) + .collect::>(); + if expected == found { + return Ok(()); + } + + Err(ErrorModelError::IncompatibleOutputContext { expected, found }) + } + + /// Bind label-first declarations to an explicit canonical output order. + /// + /// The iterator order defines the numeric output indices carried by + /// predictions: its first name is output `0`, its second is output `1`, and + /// so on. No ordering is inferred from the declaration map. + /// + /// Numeric/dense declarations remain usable and are returned as a borrowed + /// bound view. A previously bound set must be rebound with the identical + /// output context. + pub fn bind_outputs( + &self, + outputs: I, + ) -> Result, ErrorModelError> + where + I: IntoIterator, + S: AsRef, + { + let outputs = outputs + .into_iter() + .map(|output| output.as_ref().to_string()) + .collect::>(); + + if !self.output_lookup.is_empty() { + self.assert_compatible_output_names(outputs.iter().map(String::as_str))?; + return Ok(BoundAssayErrorModels { + storage: BoundAssayErrorModelsStorage::Borrowed(self), + }); + } + + if self.named_models.is_empty() { + return Ok(BoundAssayErrorModels { + storage: BoundAssayErrorModelsStorage::Borrowed(self), + }); + } + + let mut bound = Self::with_output_names(outputs.iter().map(String::as_str)); + bound.models = self.models.clone(); + + for (label, model) in &self.named_models { + bound = bound.add(label.clone(), model.clone())?; + } + + Ok(BoundAssayErrorModels { + storage: BoundAssayErrorModelsStorage::Owned(bound), + }) + } + + /// Create an unbound error-model set for dense-slot callers. + /// + /// This keeps the pre-existing numeric-slot setup path available for low-level + /// tests or workflows that deliberately operate on dense output indices. + pub(crate) fn empty() -> Self { + Self { + models: vec![], + output_lookup: BTreeMap::new(), + named_models: BTreeMap::new(), + } + } + + /// Create an error-model set with output labels resolved up front. + /// + /// This is the label-aware constructor for public workflows. It binds names + /// to dense output slots once during setup so that likelihood evaluation can + /// keep using direct vector indexing with no additional runtime lookup cost. + pub(crate) fn with_output_names(outputs: I) -> Self + where + I: IntoIterator, + S: AsRef, + { + let output_lookup = outputs + .into_iter() + .enumerate() + .map(|(index, output)| (OutputLabel::new(output.as_ref()), index)) + .collect(); + + Self { + models: vec![], + output_lookup, + named_models: BTreeMap::new(), + } + } + + fn bound_output_names(&self) -> Vec { + let mut names = self + .output_lookup + .iter() + .map(|(label, index)| (*index, label.to_string())) + .collect::>(); + names.sort_by_key(|(index, _)| *index); + names.into_iter().map(|(_, label)| label).collect() + } + + fn resolve_output_binding(&self, outeq: impl ToString) -> Result { + let label = OutputLabel::new(outeq); + self.output_lookup + .get(&label) + .copied() + .or_else(|| label.index()) + .ok_or_else(|| ErrorModelError::UnknownOutputLabel(label.to_string())) + } + + fn insert_model_at( + &mut self, + outeq: usize, + model: AssayErrorModel, + ) -> Result<(), ErrorModelError> { + if outeq >= self.models.len() { + self.models.resize(outeq + 1, AssayErrorModel::None); + } + if self.models[outeq] != AssayErrorModel::None { + return Err(ErrorModelError::ExistingOutputEquation(outeq)); + } + self.models[outeq] = model; + Ok(()) + } + + /// Get the error model for a specific output equation + /// + /// # Arguments + /// * `outeq` - The index of the output equation for which to retrieve the error model. + /// # Returns + /// A reference to the [AssayErrorModel] for the specified output equation. + /// # Errors + /// If the output equation index is invalid, an [ErrorModelError::InvalidOutputEquation] is returned. + pub fn error_model(&self, outeq: usize) -> Result<&AssayErrorModel, ErrorModelError> { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + Ok(&self.models[outeq]) + } + + /// Add a new error model for a specific output equation or declared label. + /// # Arguments + /// * `outeq` - The output slot index or public output label. + /// * `model` - The [AssayErrorModel] to add for the specified output equation. + /// # Returns + /// A new instance of AssayErrorModels with the added model. + /// # Errors + /// If the output label is unknown or if a model already exists for that output equation, an error is returned. + pub fn add( + mut self, + outeq: impl ToString, + model: AssayErrorModel, + ) -> Result { + let label = OutputLabel::new(outeq); + + if !self.output_lookup.is_empty() { + let outeq = self.resolve_output_binding(label.clone())?; + self.insert_model_at(outeq, model)?; + return Ok(self); + } + + if let Some(outeq) = label.index() { + self.insert_model_at(outeq, model)?; + return Ok(self); + } + + if self.named_models.contains_key(&label) { + return Err(ErrorModelError::ExistingOutputLabel(label.to_string())); + } + self.named_models.insert(label, model); + Ok(self) + } + /// Returns an iterator over the error models in the collection. + /// + /// # Returns + /// An iterator that yields tuples containing the index and a reference to each [AssayErrorModel]. + pub fn iter(&self) -> impl Iterator { + self.models.iter().enumerate() + } + + /// Returns a mutable iterator that yields mutable references to the error models in the collection. + /// # Returns + /// An iterator that yields tuples containing the index and a mutable reference to each [AssayErrorModel]. + pub fn iter_mut(&mut self) -> impl Iterator { + self.models.iter_mut().enumerate() + } + + /// Computes a hash for the error models collection. + /// This hash is based on the output equations and their associated error models. + /// # Returns + /// A `u64` hash value representing the error models collection. + pub fn hash(&self) -> u64 { + fn hash_model(model: &AssayErrorModel, hasher: &mut impl Hasher) { + match model { + AssayErrorModel::Additive { lambda, poly } => { + 0u8.hash(hasher); + lambda.value().to_bits().hash(hasher); + lambda.is_fixed().hash(hasher); + let (c0, c1, c2, c3) = poly.coefficients(); + for coefficient in [c0, c1, c2, c3] { + coefficient.to_bits().hash(hasher); + } + } + AssayErrorModel::Proportional { gamma, poly } => { + 1u8.hash(hasher); + gamma.value().to_bits().hash(hasher); + gamma.is_fixed().hash(hasher); + let (c0, c1, c2, c3) = poly.coefficients(); + for coefficient in [c0, c1, c2, c3] { + coefficient.to_bits().hash(hasher); + } + } + AssayErrorModel::None => 2u8.hash(hasher), + } + } + + let mut hasher = ahash::AHasher::default(); + + // A dense slot has meaning only in the output context to which it was + // bound. Hash that context before the models so equal coefficients bound + // to different output names cannot share a cache key. + for (index, name) in self.bound_output_names().iter().enumerate() { + 0u8.hash(&mut hasher); + index.hash(&mut hasher); + name.hash(&mut hasher); + } + + for (label, model) in &self.named_models { + 1u8.hash(&mut hasher); + label.hash(&mut hasher); + hash_model(model, &mut hasher); + } + + for (outeq, model) in self.models.iter().enumerate() { + 2u8.hash(&mut hasher); + outeq.hash(&mut hasher); + hash_model(model, &mut hasher); + } + + hasher.finish() + } + /// Score generated predictions with assay likelihood semantics. + /// + /// Label-first declarations created with [`AssayErrorModels::add`] must be + /// explicitly bound first with [`AssayErrorModels::bind_outputs`], because + /// predictions contain only numeric output indices. This method never + /// infers label ordering. + pub fn log_likelihood

( + &self, + predictions: &P, + ) -> std::result::Result + where + P: pharmsol::Predictions, + { + if !self.named_models.is_empty() && self.output_lookup.is_empty() { + return Err(ErrorModelError::UnboundOutputModels { + outputs: self.named_models.keys().map(ToString::to_string).collect(), + } + .into()); + } + + crate::estimation::likelihood::observation::assay_error_model_log_likelihoods( + predictions, + self, + ) + } + + /// Bind an explicit ordered output-name context and score predictions. + /// + /// This is the convenience form of + /// `models.bind_outputs(outputs)?.log_likelihood(predictions)` and is the + /// recommended scoring path for label-first declarations. + pub fn log_likelihood_for_outputs( + &self, + predictions: &P, + outputs: I, + ) -> std::result::Result + where + P: pharmsol::Predictions, + I: IntoIterator, + S: AsRef, + { + self.bind_outputs(outputs)?.log_likelihood(predictions) + } + + /// Returns the number of error models in the collection. + pub fn len(&self) -> usize { + if self.models.is_empty() && !self.named_models.is_empty() && self.output_lookup.is_empty() + { + return self.named_models.len(); + } + self.models.len() + } + + /// Returns whether the collection contains no error models. + pub fn is_empty(&self) -> bool { + self.models.is_empty() && self.named_models.is_empty() + } + + /// Returns the error polynomial associated with the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// + /// # Returns + /// + /// The [`ErrorPoly`] for the given output equation. + pub fn errorpoly(&self, outeq: usize) -> Result { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].errorpoly() + } + + /// Returns the factor value associated with the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// + /// # Returns + /// + /// The factor value for the given output equation. + pub fn factor(&self, outeq: usize) -> Result { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].factor() + } + + /// Sets the error polynomial for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// * `poly` - The new [`ErrorPoly`] to set. + pub fn set_errorpoly(&mut self, outeq: usize, poly: ErrorPoly) -> Result<(), ErrorModelError> { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].set_errorpoly(poly); + Ok(()) + } + + /// Sets the factor value for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// * `factor` - The new factor value to set. + pub fn set_factor(&mut self, outeq: usize, factor: f64) -> Result<(), ErrorModelError> { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].set_factor(factor); + Ok(()) + } + + /// Gets the factor parameter (including fixed/variable state) for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// + /// # Returns + /// + /// The [`Factor`] for the given output equation. + pub fn factor_param(&self, outeq: usize) -> Result { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].factor_param() + } + + /// Sets the factor parameter (including fixed/variable state) for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// * `param` - The new [`Factor`] to set. + pub fn set_factor_param(&mut self, outeq: usize, param: Factor) -> Result<(), ErrorModelError> { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].set_factor_param(param); + Ok(()) + } + + /// Checks if the factor parameter is fixed for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// + /// # Returns + /// + /// `true` if the factor parameter is fixed, `false` if it's variable. + pub fn is_factor_fixed(&self, outeq: usize) -> Result { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].is_factor_fixed() + } + + /// Makes the factor parameter fixed at its current value for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + pub fn fix_factor(&mut self, outeq: usize) -> Result<(), ErrorModelError> { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].fix_factor(); + Ok(()) + } + + /// Makes the factor parameter variable at its current value for the specified output equation. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + pub fn unfix_factor(&mut self, outeq: usize) -> Result<(), ErrorModelError> { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].unfix_factor(); + Ok(()) + } + + /// Check if the error model for a specific output equation is proportional + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation + /// + /// # Returns + /// + /// `true` if the error model for `outeq` is proportional, `false` otherwise + pub fn is_proportional(&self, outeq: usize) -> bool { + if outeq >= self.models.len() { + return false; + } + self.models[outeq].is_proportional() + } + + /// Check if the error model for a specific output equation is additive + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation + /// + /// # Returns + /// + /// `true` if the error model for `outeq` is additive, `false` otherwise + pub fn is_additive(&self, outeq: usize) -> bool { + if outeq >= self.models.len() { + return false; + } + self.models[outeq].is_additive() + } + + /// Computes the standard deviation (sigma) for the specified output equation and prediction. + /// + /// This always uses the **observation** value to compute sigma, which is appropriate + /// for non-parametric algorithms (NPAG, NPOD). For parametric algorithms (SAEM, FOCE), + /// use [`crate::ResidualErrorModels`] instead, which computes sigma from the prediction. + /// + /// # Arguments + /// + /// * `prediction` - The [`Prediction`] to use for the calculation. + /// + /// # Returns + /// + /// A [`Result`] containing the computed sigma value or an [`ErrorModelError`] if the calculation fails. + pub fn sigma(&self, prediction: &Prediction) -> Result { + let outeq = prediction.outeq(); + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[prediction.outeq()].sigma(prediction) + } + + /// Computes the variance for the specified output equation and prediction. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// * `prediction` - The [`Prediction`] to use for the calculation. + /// + /// # Returns + /// + /// A [`Result`] containing the computed variance or an [`ErrorModelError`] if the calculation fails. + pub fn variance(&self, prediction: &Prediction) -> Result { + let outeq = prediction.outeq(); + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[prediction.outeq()].variance(prediction) + } + + /// Computes the standard deviation (sigma) for the specified output equation and value. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// * `value` - The value to use for the calculation. + /// + /// # Returns + /// + /// A [`Result`] containing the computed sigma value or an [`ErrorModelError`] if the calculation fails. + pub fn sigma_from_value(&self, outeq: usize, value: f64) -> Result { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].sigma_from_value(value) + } + + /// Computes the variance for the specified output equation and value. + /// + /// # Arguments + /// + /// * `outeq` - The index of the output equation. + /// * `value` - The value to use for the calculation. + /// + /// # Returns + /// + /// A [`Result`] containing the computed variance or an [`ErrorModelError`] if the calculation fails. + pub fn variance_from_value(&self, outeq: usize, value: f64) -> Result { + if outeq >= self.models.len() { + return Err(ErrorModelError::InvalidOutputEquation(outeq)); + } + if self.models[outeq] == AssayErrorModel::None { + return Err(ErrorModelError::NoneErrorModel(outeq)); + } + self.models[outeq].variance_from_value(value) + } +} + +impl IntoIterator for AssayErrorModels { + type Item = (usize, AssayErrorModel); + type IntoIter = std::vec::IntoIter; + + fn into_iter(self) -> Self::IntoIter { + self.models + .into_iter() + .enumerate() + .collect::>() + .into_iter() + } +} + +impl<'a> IntoIterator for &'a AssayErrorModels { + type Item = (usize, &'a AssayErrorModel); + type IntoIter = std::iter::Enumerate>; + + fn into_iter(self) -> Self::IntoIter { + self.models.iter().enumerate() + } +} + +impl<'a> IntoIterator for &'a mut AssayErrorModels { + type Item = (usize, &'a mut AssayErrorModel); + type IntoIter = std::iter::Enumerate>; + + fn into_iter(self) -> Self::IntoIter { + self.models.iter_mut().enumerate() + } +} + +/// Model for calculating observation errors in pharmacometric analyses +/// +/// An [AssayErrorModel] defines how the standard deviation of observations is calculated +/// based on the type of error model used and its parameters. +#[derive(Debug, Clone, Serialize, Deserialize, Default, PartialEq)] +pub enum AssayErrorModel { + /// Additive error model, where error is independent of concentration + /// + /// Contains: + /// * `lambda` - Lambda parameter for scaling errors (can be fixed or variable) + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + Additive { + /// Lambda parameter for scaling errors (can be fixed or variable) + lambda: Factor, + /// Error polynomial coefficients (c0, c1, c2, c3) + poly: ErrorPoly, + }, + + /// Proportional error model, where error scales with concentration + /// + /// Contains: + /// * `gamma` - Gamma parameter for scaling errors (can be fixed or variable) + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + Proportional { + /// Gamma parameter for scaling errors (can be fixed or variable) + gamma: Factor, + /// Error polynomial coefficients (c0, c1, c2, c3) + poly: ErrorPoly, + }, + #[default] + None, +} + +/// Deprecated alias for [`AssayErrorModel`]. +/// +/// This type alias is provided for backward compatibility. +/// New code should use [`AssayErrorModel`] directly. +#[deprecated( + since = "0.23.0", + note = "Use AssayErrorModel instead. ErrorModel has been renamed to better reflect its purpose (assay/measurement error)." +)] +pub type ErrorModel = AssayErrorModel; + +impl AssayErrorModel { + /// Create a new additive error model with a variable lambda parameter + /// + /// # Arguments + /// + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + /// * `lambda` - Lambda parameter for scaling errors (will be variable) + /// + /// # Returns + /// + /// A new additive error model + pub fn additive(poly: ErrorPoly, lambda: f64) -> Self { + Self::Additive { + lambda: Factor::Variable(lambda), + poly, + } + } + + /// Create a new additive error model with a fixed lambda parameter + /// + /// # Arguments + /// + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + /// * `lambda` - Lambda parameter for scaling errors (will be fixed) + /// + /// # Returns + /// + /// A new additive error model with fixed lambda + pub fn additive_fixed(poly: ErrorPoly, lambda: f64) -> Self { + Self::Additive { + lambda: Factor::Fixed(lambda), + poly, + } + } + + /// Create a new additive error model with a specified Factor for lambda + /// + /// # Arguments + /// + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + /// * `lambda` - Lambda parameter (can be Variable or Fixed) using [Factor] + /// + /// # Returns + /// + /// A new additive error model + pub fn additive_with_param(poly: ErrorPoly, lambda: Factor) -> Self { + Self::Additive { lambda, poly } + } + + /// Create a new proportional error model with a variable gamma parameter + /// + /// # Arguments + /// + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + /// * `gamma` - Gamma parameter for scaling errors (will be variable) + /// + /// # Returns + /// + /// A new proportional error model + pub fn proportional(poly: ErrorPoly, gamma: f64) -> Self { + Self::Proportional { + gamma: Factor::Variable(gamma), + poly, + } + } + + /// Create a new proportional error model with a fixed gamma parameter + /// + /// # Arguments + /// + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + /// * `gamma` - Gamma parameter for scaling errors (will be fixed) + /// + /// # Returns + /// + /// A new proportional error model with fixed gamma + pub fn proportional_fixed(poly: ErrorPoly, gamma: f64) -> Self { + Self::Proportional { + gamma: Factor::Fixed(gamma), + poly, + } + } + + /// Create a new proportional error model with a specified Factor for gamma + /// + /// # Arguments + /// + /// * `poly` - Error polynomial coefficients (c0, c1, c2, c3) + /// * `gamma` - Gamma parameter (can be Variable or Fixed) using [Factor] + /// + /// # Returns + /// + /// A new proportional error model + pub fn proportional_with_param(poly: ErrorPoly, gamma: Factor) -> Self { + Self::Proportional { gamma, poly } + } + + /// Get the error polynomial coefficients + /// + /// # Returns + /// + /// The error polynomial coefficients (c0, c1, c2, c3) + pub fn errorpoly(&self) -> Result { + match self { + Self::Additive { poly, .. } => Ok(*poly), + Self::Proportional { poly, .. } => Ok(*poly), + Self::None => Err(ErrorModelError::MissingErrorModel), + } + } + + /// Set the error polynomial coefficients + /// + /// # Arguments + /// + /// * `poly` - New error polynomial coefficients (c0, c1, c2, c3) + /// + /// # Returns + /// + /// The updated error model with the new polynomial coefficients + pub fn set_errorpoly(&mut self, poly: ErrorPoly) { + match self { + Self::Additive { poly: p, .. } => *p = poly, + Self::Proportional { poly: p, .. } => *p = poly, + Self::None => {} + } + } + + /// Get the scaling parameter value + pub fn factor(&self) -> Result { + match self { + Self::Additive { lambda, .. } => Ok(lambda.value()), + Self::Proportional { gamma, .. } => Ok(gamma.value()), + Self::None => Err(ErrorModelError::MissingErrorModel), + } + } + + /// Set the scaling parameter value (preserves fixed/variable state) + pub fn set_factor(&mut self, factor: f64) { + match self { + Self::Additive { lambda, .. } => lambda.set_value(factor), + Self::Proportional { gamma, .. } => gamma.set_value(factor), + Self::None => {} + } + } + + /// Get the scaling parameter (including its fixed/variable state) + pub fn factor_param(&self) -> Result { + match self { + Self::Additive { lambda, .. } => Ok(*lambda), + Self::Proportional { gamma, .. } => Ok(*gamma), + Self::None => Err(ErrorModelError::MissingErrorModel), + } + } + + /// Set the scaling parameter (including its fixed/variable state) + pub fn set_factor_param(&mut self, param: Factor) { + match self { + Self::Additive { lambda, .. } => *lambda = param, + Self::Proportional { gamma, .. } => *gamma = param, + Self::None => {} + } + } + + /// Check if the scaling parameter is fixed + pub fn is_factor_fixed(&self) -> Result { + match self { + Self::Additive { lambda, .. } => Ok(lambda.is_fixed()), + Self::Proportional { gamma, .. } => Ok(gamma.is_fixed()), + Self::None => Err(ErrorModelError::MissingErrorModel), + } + } + + /// Make the scaling parameter fixed at its current value + pub fn fix_factor(&mut self) { + match self { + Self::Additive { lambda, .. } => lambda.make_fixed(), + Self::Proportional { gamma, .. } => gamma.make_fixed(), + Self::None => {} + } + } + + /// Make the scaling parameter variable at its current value + pub fn unfix_factor(&mut self) { + match self { + Self::Additive { lambda, .. } => lambda.make_variable(), + Self::Proportional { gamma, .. } => gamma.make_variable(), + Self::None => {} + } + } + + /// Check if this is a proportional error model + /// + /// # Returns + /// + /// `true` if this is a `Proportional` variant, `false` otherwise + pub fn is_proportional(&self) -> bool { + matches!(self, Self::Proportional { .. }) + } + + /// Check if this is an additive error model + /// + /// # Returns + /// + /// `true` if this is an `Additive` variant, `false` otherwise + pub fn is_additive(&self) -> bool { + matches!(self, Self::Additive { .. }) + } + + /// Estimate the standard deviation for a prediction + /// + /// Calculates the standard deviation based on the error model type, + /// using either observation-specific error polynomial coefficients or + /// the model's default coefficients. + /// + /// # Arguments + /// + /// * `prediction` - The prediction for which to estimate the standard deviation + /// + /// # Returns + /// + /// The estimated standard deviation of the prediction + pub fn sigma(&self, prediction: &Prediction) -> Result { + if prediction.observation().is_none() { + return Err(ErrorModelError::MissingObservation); + } + + let errorpoly = prediction.errorpoly().unwrap_or(self.errorpoly()?); + + let (c0, c1, c2, c3) = errorpoly.coefficients(); + + // Calculate alpha term + let observation = prediction.observation().unwrap(); + let alpha = ((c3 * observation + c2) * observation + c1) * observation + c0; + + // Calculate standard deviation based on error model type + let sigma = match self { + Self::Additive { lambda, .. } => (alpha.powi(2) + lambda.value().powi(2)).sqrt(), + Self::Proportional { gamma, .. } => gamma.value() * alpha, + Self::None => { + return Err(ErrorModelError::MissingErrorModel); + } + }; + + if sigma < 0.0 { + Err(ErrorModelError::NegativeSigma) + } else if !sigma.is_finite() { + Err(ErrorModelError::NonFiniteSigma) + } else { + Ok(sigma) + } + } + + /// Estimate the variance of the observation + /// + /// This is a convenience function which calls [AssayErrorModel::sigma], and squares the result. + pub fn variance(&self, prediction: &Prediction) -> Result { + let sigma = self.sigma(prediction)?; + Ok(sigma.powi(2)) + } + + /// Estimate the standard deviation for a raw observation value + /// + /// Calculates the standard deviation based on the error model type, + /// using the model's default coefficients and a provided observation value. + /// + /// # Arguments + /// + /// * `value` - The observation value for which to estimate the standard deviation + /// + /// # Returns + /// + /// The estimated standard deviation for the given value + pub fn sigma_from_value(&self, value: f64) -> Result { + // Get polynomial coefficients from the model + let (c0, c1, c2, c3) = self.errorpoly()?.coefficients(); + + // Calculate alpha term + let alpha = ((c3 * value + c2) * value + c1) * value + c0; + + // Calculate standard deviation based on error model type + let sigma = match self { + Self::Additive { lambda, .. } => (alpha.powi(2) + lambda.value().powi(2)).sqrt(), + Self::Proportional { gamma, .. } => gamma.value() * alpha, + Self::None => { + return Err(ErrorModelError::MissingErrorModel); + } + }; + + if sigma < 0.0 { + Err(ErrorModelError::NegativeSigma) + } else if !sigma.is_finite() { + Err(ErrorModelError::NonFiniteSigma) + } else if sigma == 0.0 { + Err(ErrorModelError::ZeroSigma) + } else { + Ok(sigma) + } + } + + /// Estimate the variance for a raw observation value + /// + /// This is a convenience function which calls [AssayErrorModel::sigma_from_value], and squares the result. + pub fn variance_from_value(&self, value: f64) -> Result { + let sigma = self.sigma_from_value(value)?; + Ok(sigma.powi(2)) + } + + /// Get a boolean indicating if the error model should be optimized + /// + /// In other words, if the error model is not None, and the [Factor] is variable, it should be optimized. + pub fn optimize(&self) -> bool { + match self { + Self::Additive { lambda, .. } => lambda.is_variable(), + Self::Proportional { gamma, .. } => gamma.is_variable(), + Self::None => false, + } + } +} + +#[derive(Error, Debug, Clone)] +pub enum ErrorModelError { + #[error("The computed standard deviation is negative")] + NegativeSigma, + #[error("The computed standard deviation is zero")] + ZeroSigma, + #[error("The computed standard deviation is non-finite")] + NonFiniteSigma, + #[error("The output equation index {0} is invalid")] + InvalidOutputEquation(usize), + #[error("The output label `{0}` is not declared in this error model context")] + UnknownOutputLabel(String), + #[error( + "Named assay error models for outputs {outputs:?} are not bound to numeric output indices; call `bind_outputs` or `log_likelihood_for_outputs` with the model's canonical output order" + )] + UnboundOutputModels { outputs: Vec }, + #[error("The output label `{0}` already exists in this assay error model specification")] + ExistingOutputLabel(String), + #[error("The output equation number {0} already exists")] + ExistingOutputEquation(usize), + #[error( + "Assay error models were bound for outputs {expected:?} but used with outputs {found:?}" + )] + IncompatibleOutputContext { + expected: Vec, + found: Vec, + }, + #[error("An output equation does not have an error model defined")] + MissingErrorModel, + #[error("The output equation index {0} is of type ErrorModel::None")] + NoneErrorModel(usize), + #[error("The prediction does not have an observation associated with it")] + MissingObservation, +} + +#[cfg(test)] +mod tests { + use super::*; + use pharmsol::{Event, Observation, SubjectBuilderExt}; + + fn test_observation(value: f64, outeq: usize) -> Observation { + let subject = pharmsol::Subject::builder("test") + .observation(0.0, value, outeq) + .build(); + match &subject.occasions()[0].events()[0] { + Event::Observation(observation) => observation.clone(), + _ => unreachable!("builder created an observation"), + } + } + + #[test] + fn test_additive_error_model() { + let observation = test_observation(20.0, 0); + let prediction = observation.to_prediction(10.0, vec![]); + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + assert_eq!(model.sigma(&prediction).unwrap(), (26.0_f64).sqrt()); + } + + #[test] + fn test_proportional_error_model() { + let observation = test_observation(20.0, 0); + let prediction = observation.to_prediction(10.0, vec![]); + let model = AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + assert_eq!(model.sigma(&prediction).unwrap(), 2.0); + } + + #[test] + fn test_polynomial() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 2.0, 3.0, 4.0), 5.0); + assert_eq!( + model.errorpoly().unwrap().coefficients(), + (1.0, 2.0, 3.0, 4.0) + ); + } + + #[test] + fn test_set_errorpoly() { + let mut model = AssayErrorModel::additive(ErrorPoly::new(1.0, 2.0, 3.0, 4.0), 5.0); + assert_eq!( + model.errorpoly().unwrap().coefficients(), + (1.0, 2.0, 3.0, 4.0) + ); + model.set_errorpoly(ErrorPoly::new(5.0, 6.0, 7.0, 8.0)); + assert_eq!( + model.errorpoly().unwrap().coefficients(), + (5.0, 6.0, 7.0, 8.0) + ); + } + + #[test] + fn test_set_factor() { + let mut model = AssayErrorModel::additive(ErrorPoly::new(1.0, 2.0, 3.0, 4.0), 5.0); + assert_eq!(model.factor().unwrap(), 5.0); + model.set_factor(10.0); + assert_eq!(model.factor().unwrap(), 10.0); + } + + #[test] + fn test_sigma_from_value() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + assert_eq!(model.sigma_from_value(20.0).unwrap(), (26.0_f64).sqrt()); + + let model = AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + assert_eq!(model.sigma_from_value(20.0).unwrap(), 2.0); + } + + #[test] + fn test_error_models_new() { + let models = AssayErrorModels::new(); + assert_eq!(models.len(), 0); + } + + #[test] + fn test_error_models_default() { + let models = AssayErrorModels::default(); + assert_eq!(models.len(), 0); + } + + #[test] + fn test_error_models_add_single() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + assert_eq!(models.len(), 1); + } + + #[test] + fn test_error_models_add_multiple() { + let model1 = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let model2 = AssayErrorModel::proportional(ErrorPoly::new(2.0, 0.0, 0.0, 0.0), 3.0); + + let models = AssayErrorModels::empty() + .add(0, model1) + .unwrap() + .add(1, model2) + .unwrap(); + + assert_eq!(models.len(), 2); + } + + #[test] + fn test_error_models_add_label_with_output_names() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::with_output_names(["cp", "effect"]) + .add("effect", model) + .unwrap(); + + assert_eq!(models.len(), 2); + assert!(models.error_model(1).is_ok()); + } + + #[test] + fn test_error_models_bind_outputs() { + let error_models = AssayErrorModels::new() + .add( + "effect", + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0), + ) + .unwrap(); + + let models = error_models.bind_outputs(["cp", "effect"]).unwrap(); + assert_eq!(models.len(), 2); + assert!(models.error_model(1).is_ok()); + } + + #[test] + fn test_error_models_add_unknown_label_fails() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let result = AssayErrorModels::with_output_names(["cp"]).add("effect", model); + + assert!(result.is_err()); + match result { + Err(ErrorModelError::UnknownOutputLabel(label)) => assert_eq!(label, "effect"), + _ => panic!("Expected UnknownOutputLabel error"), + } + } + + #[test] + fn test_error_models_duplicate_label_fails() { + let result = AssayErrorModels::new() + .add( + "cp", + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0), + ) + .unwrap() + .add( + "cp", + AssayErrorModel::proportional(ErrorPoly::new(2.0, 0.0, 0.0, 0.0), 3.0), + ); + + match result { + Err(ErrorModelError::ExistingOutputLabel(label)) => assert_eq!(label, "cp"), + _ => panic!("Expected ExistingOutputLabel error"), + } + } + + #[test] + fn test_bound_error_models_reject_mismatched_output_context() { + let error_models = AssayErrorModels::new() + .add( + "cp", + AssayErrorModel::additive(ErrorPoly::new(0.0, 0.05, 0.0, 0.0), 0.0), + ) + .unwrap(); + let error_models = error_models.bind_outputs(["cp", "effect"]).unwrap(); + + match error_models.assert_compatible_output_names(["effect", "cp"]) { + Err(ErrorModelError::IncompatibleOutputContext { expected, found }) => { + assert_eq!(expected, vec!["cp".to_string(), "effect".to_string()]); + assert_eq!(found, vec!["effect".to_string(), "cp".to_string()]); + } + _ => panic!("Expected IncompatibleOutputContext error"), + } + } + + #[test] + fn test_error_models_sigma_from_label_bound_output() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::with_output_names(["cp"]) + .add("cp", model) + .unwrap(); + + let observation = test_observation(20.0, 0); + let prediction = observation.to_prediction(10.0, vec![]); + + assert_eq!(models.sigma(&prediction).unwrap(), (26.0_f64).sqrt()); + } + + #[test] + fn test_error_models_add_duplicate_outeq_fails() { + let model1 = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let model2 = AssayErrorModel::proportional(ErrorPoly::new(2.0, 0.0, 0.0, 0.0), 3.0); + + let result = AssayErrorModels::empty() + .add(0, model1) + .unwrap() + .add(0, model2); // Same outeq should fail + + assert!(result.is_err()); + match result { + Err(ErrorModelError::ExistingOutputEquation(outeq)) => assert_eq!(outeq, 0), + _ => panic!("Expected ExistingOutputEquation error"), + } + } + + #[test] + fn test_error_models_factor() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + assert_eq!(models.factor(0).unwrap(), 5.0); + } + + #[test] + fn test_error_models_factor_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let result = models.factor(1); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_set_factor() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let mut models = AssayErrorModels::empty().add(0, model).unwrap(); + + assert_eq!(models.factor(0).unwrap(), 5.0); + models.set_factor(0, 10.0).unwrap(); + assert_eq!(models.factor(0).unwrap(), 10.0); + } + + #[test] + fn test_error_models_set_factor_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let mut models = AssayErrorModels::empty().add(0, model).unwrap(); + + let result = models.set_factor(1, 10.0); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_errorpoly() { + let poly = ErrorPoly::new(1.0, 2.0, 3.0, 4.0); + let model = AssayErrorModel::additive(poly, 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let retrieved_poly = models.errorpoly(0).unwrap(); + assert_eq!(retrieved_poly.coefficients(), (1.0, 2.0, 3.0, 4.0)); + } + + #[test] + fn test_error_models_errorpoly_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let result = models.errorpoly(1); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_set_errorpoly() { + let poly1 = ErrorPoly::new(1.0, 2.0, 3.0, 4.0); + let poly2 = ErrorPoly::new(5.0, 6.0, 7.0, 8.0); + let model = AssayErrorModel::additive(poly1, 5.0); + let mut models = AssayErrorModels::empty().add(0, model).unwrap(); + + assert_eq!( + models.errorpoly(0).unwrap().coefficients(), + (1.0, 2.0, 3.0, 4.0) + ); + models.set_errorpoly(0, poly2).unwrap(); + assert_eq!( + models.errorpoly(0).unwrap().coefficients(), + (5.0, 6.0, 7.0, 8.0) + ); + } + + #[test] + fn test_error_models_set_errorpoly_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let mut models = AssayErrorModels::empty().add(0, model).unwrap(); + + let result = models.set_errorpoly(1, ErrorPoly::new(5.0, 6.0, 7.0, 8.0)); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_sigma() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let observation = test_observation(20.0, 0); + let prediction = observation.to_prediction(10.0, vec![]); + + // Non-parametric: sigma from observation + let sigma = models.sigma(&prediction).unwrap(); + assert_eq!(sigma, (26.0_f64).sqrt()); + } + + #[test] + fn test_error_models_sigma_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let observation = test_observation(20.0, 1); // outeq=1 not in models + let prediction = observation.to_prediction(10.0, vec![]); + + let result = models.sigma(&prediction); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_variance() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let observation = test_observation(20.0, 0); + let prediction = observation.to_prediction(10.0, vec![]); + + let variance = models.variance(&prediction).unwrap(); + let expected_sigma = (26.0_f64).sqrt(); + assert_eq!(variance, expected_sigma.powi(2)); + } + + #[test] + fn test_error_models_variance_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let observation = test_observation(20.0, 1); // outeq=1 not in models + let prediction = observation.to_prediction(10.0, vec![]); + + let result = models.variance(&prediction); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_sigma_from_value() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let sigma = models.sigma_from_value(0, 20.0).unwrap(); + assert_eq!(sigma, (26.0_f64).sqrt()); + } + + #[test] + fn test_error_models_sigma_from_value_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let result = models.sigma_from_value(1, 20.0); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_variance_from_value() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let variance = models.variance_from_value(0, 20.0).unwrap(); + let expected_sigma = (26.0_f64).sqrt(); + assert_eq!(variance, expected_sigma.powi(2)); + } + + #[test] + fn test_error_models_variance_from_value_invalid_outeq() { + let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let models = AssayErrorModels::empty().add(0, model).unwrap(); + + let result = models.variance_from_value(1, 20.0); + assert!(result.is_err()); + match result { + Err(ErrorModelError::InvalidOutputEquation(outeq)) => assert_eq!(outeq, 1), + _ => panic!("Expected InvalidOutputEquation error"), + } + } + + #[test] + fn test_error_models_hash_consistency() { + let model1 = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let model2 = AssayErrorModel::proportional(ErrorPoly::new(2.0, 0.0, 0.0, 0.0), 3.0); + + let models1 = AssayErrorModels::empty() + .add(0, model1.clone()) + .unwrap() + .add(1, model2.clone()) + .unwrap(); + + let models2 = AssayErrorModels::empty() + .add(0, model1) + .unwrap() + .add(1, model2) + .unwrap(); + + // Same models should produce same hash + assert_eq!(models1.hash(), models2.hash()); + } + + #[test] + fn test_error_models_hash_order_independence() { + let model1 = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let model2 = AssayErrorModel::proportional(ErrorPoly::new(2.0, 0.0, 0.0, 0.0), 3.0); + + // Add in different orders + let models1 = AssayErrorModels::empty() + .add(0, model1.clone()) + .unwrap() + .add(1, model2.clone()) + .unwrap(); + + let models2 = AssayErrorModels::empty() + .add(1, model2) + .unwrap() + .add(0, model1) + .unwrap(); + + // Hash should be the same regardless of insertion order + assert_eq!(models1.hash(), models2.hash()); + } + + #[test] + fn test_error_models_multiple_outeqs() { + let additive_model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.1, 0.0, 0.0), 0.5); + let proportional_model = + AssayErrorModel::proportional(ErrorPoly::new(0.0, 0.05, 0.0, 0.0), 0.1); + + let models = AssayErrorModels::empty() + .add(0, additive_model) + .unwrap() + .add(1, proportional_model) + .unwrap(); + + assert_eq!(models.len(), 2); + + // Test factor retrieval for different outeqs + assert_eq!(models.factor(0).unwrap(), 0.5); + assert_eq!(models.factor(1).unwrap(), 0.1); + + // Test polynomial retrieval for different outeqs + assert_eq!( + models.errorpoly(0).unwrap().coefficients(), + (1.0, 0.1, 0.0, 0.0) + ); + assert_eq!( + models.errorpoly(1).unwrap().coefficients(), + (0.0, 0.05, 0.0, 0.0) + ); + } + + #[test] + fn test_error_models_with_predictions_different_outeqs() { + let additive_model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let proportional_model = + AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + + let models = AssayErrorModels::empty() + .add(0, additive_model) + .unwrap() + .add(1, proportional_model) + .unwrap(); + + // Test with outeq=0 (additive model) + let obs1 = test_observation(20.0, 0); + let pred1 = obs1.to_prediction(10.0, vec![]); + let sigma1 = models.sigma(&pred1).unwrap(); + assert_eq!(sigma1, (26.0_f64).sqrt()); // additive: sqrt(alpha^2 + lambda^2) = sqrt(1^2 + 5^2) = sqrt(26) + + // Test with outeq=1 (proportional model) + let obs2 = test_observation(20.0, 1); + let pred2 = obs2.to_prediction(10.0, vec![]); + let sigma2 = models.sigma(&pred2).unwrap(); + assert_eq!(sigma2, 2.0); // proportional: gamma * alpha = 2 * 1 = 2 + } + + #[test] + fn test_factor_param_new_constructors() { + // Test variable constructors (default behavior) + let additive = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + assert_eq!(additive.factor().unwrap(), 5.0); + assert!(!additive.is_factor_fixed().unwrap()); + + let proportional = AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + assert_eq!(proportional.factor().unwrap(), 2.0); + assert!(!proportional.is_factor_fixed().unwrap()); + + // Test fixed constructors + let additive_fixed = + AssayErrorModel::additive_fixed(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + assert_eq!(additive_fixed.factor().unwrap(), 5.0); + assert!(additive_fixed.is_factor_fixed().unwrap()); + + let proportional_fixed = + AssayErrorModel::proportional_fixed(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + assert_eq!(proportional_fixed.factor().unwrap(), 2.0); + assert!(proportional_fixed.is_factor_fixed().unwrap()); + + // Test Factor constructors + let additive_with_param = AssayErrorModel::additive_with_param( + ErrorPoly::new(1.0, 0.0, 0.0, 0.0), + Factor::Fixed(5.0), + ); + assert_eq!(additive_with_param.factor().unwrap(), 5.0); + assert!(additive_with_param.is_factor_fixed().unwrap()); + + let proportional_with_param = AssayErrorModel::proportional_with_param( + ErrorPoly::new(1.0, 0.0, 0.0, 0.0), + Factor::Variable(2.0), + ); + assert_eq!(proportional_with_param.factor().unwrap(), 2.0); + assert!(!proportional_with_param.is_factor_fixed().unwrap()); + } + + #[test] + fn test_factor_param_methods() { + let mut model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + + // Test initial state + assert_eq!(model.factor().unwrap(), 5.0); + assert!(!model.is_factor_fixed().unwrap()); + + // Test fixing parameter + model.fix_factor(); + assert_eq!(model.factor().unwrap(), 5.0); + assert!(model.is_factor_fixed().unwrap()); + + // Test unfixing parameter + model.unfix_factor(); + assert_eq!(model.factor().unwrap(), 5.0); + assert!(!model.is_factor_fixed().unwrap()); + + // Test setting factor param directly + model.set_factor_param(Factor::Fixed(10.0)); + assert_eq!(model.factor().unwrap(), 10.0); + assert!(model.is_factor_fixed().unwrap()); + + // Test getting factor param + let param = model.factor_param().unwrap(); + assert_eq!(param.value(), 10.0); + assert!(param.is_fixed()); + } + + #[test] + fn test_factor_param_functionality() { + let mut param = Factor::Variable(5.0); + + // Test basic functionality + assert_eq!(param.value(), 5.0); + assert!(param.is_variable()); + assert!(!param.is_fixed()); + + // Test setting value + param.set_value(10.0); + assert_eq!(param.value(), 10.0); + assert!(param.is_variable()); + + // Test making fixed + param.make_fixed(); + assert_eq!(param.value(), 10.0); + assert!(param.is_fixed()); + assert!(!param.is_variable()); + + // Test making variable again + param.make_variable(); + assert_eq!(param.value(), 10.0); + assert!(param.is_variable()); + assert!(!param.is_fixed()); + } + + #[test] + fn test_error_models_factor_param_methods() { + let additive_model = + AssayErrorModel::additive_fixed(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let proportional_model = + AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + + let mut models = AssayErrorModels::empty() + .add(0, additive_model) + .unwrap() + .add(1, proportional_model) + .unwrap(); + + // Test factor param retrieval + let param0 = models.factor_param(0).unwrap(); + assert_eq!(param0.value(), 5.0); + assert!(param0.is_fixed()); + + let param1 = models.factor_param(1).unwrap(); + assert_eq!(param1.value(), 2.0); + assert!(param1.is_variable()); + + // Test is_factor_fixed + assert!(models.is_factor_fixed(0).unwrap()); + assert!(!models.is_factor_fixed(1).unwrap()); + + // Test fixing/unfixing + models.fix_factor(1).unwrap(); + assert!(models.is_factor_fixed(1).unwrap()); + + models.unfix_factor(0).unwrap(); + assert!(!models.is_factor_fixed(0).unwrap()); + + // Test setting factor param + models.set_factor_param(0, Factor::Fixed(10.0)).unwrap(); + assert_eq!(models.factor(0).unwrap(), 10.0); + assert!(models.is_factor_fixed(0).unwrap()); + } + + #[test] + fn test_fixed_parameters_in_calculations() { + // Test that fixed and variable parameters produce the same calculation results + let observation = test_observation(20.0, 0); + let prediction = observation.to_prediction(10.0, vec![]); + + let model_variable = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let model_fixed = AssayErrorModel::additive_fixed(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + + let sigma_variable = model_variable.sigma(&prediction).unwrap(); + let sigma_fixed = model_fixed.sigma(&prediction).unwrap(); + + assert_eq!(sigma_variable, sigma_fixed); + assert_eq!(sigma_variable, (26.0_f64).sqrt()); + + // Test with sigma_from_value + let sigma_variable_val = model_variable.sigma_from_value(20.0).unwrap(); + let sigma_fixed_val = model_fixed.sigma_from_value(20.0).unwrap(); + + assert_eq!(sigma_variable_val, sigma_fixed_val); + assert_eq!(sigma_variable_val, (26.0_f64).sqrt()); + } + + #[test] + fn test_hash_includes_fixed_state() { + let model1_variable = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let model1_fixed = AssayErrorModel::additive_fixed(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + + let models1 = AssayErrorModels::empty().add(0, model1_variable).unwrap(); + let models2 = AssayErrorModels::empty().add(0, model1_fixed).unwrap(); + + // Different fixed/variable states should produce different hashes + assert_ne!(models1.hash(), models2.hash()); + } + + #[test] + fn test_error_models_into_iter_functionality() { + let additive_model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); + let proportional_model = + AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); + + let mut models = AssayErrorModels::empty() + .add(0, additive_model) + .unwrap() + .add(1, proportional_model) + .unwrap(); + + // Verify initial state - both should be variable + assert!(!models.is_factor_fixed(0).unwrap()); + assert!(!models.is_factor_fixed(1).unwrap()); + assert_eq!(models.factor(0).unwrap(), 5.0); + assert_eq!(models.factor(1).unwrap(), 2.0); + + // First iteration: update values using iter_mut + for (outeq, model) in models.iter_mut() { + match outeq { + 0 => model.set_factor(10.0), // Update additive lambda from 5.0 to 10.0 + 1 => model.set_factor(4.0), // Update proportional gamma from 2.0 to 4.0 + _ => {} + } + } + + // Verify values were updated + assert_eq!(models.factor(0).unwrap(), 10.0); + assert_eq!(models.factor(1).unwrap(), 4.0); + assert!(!models.is_factor_fixed(0).unwrap()); // Still variable + assert!(!models.is_factor_fixed(1).unwrap()); // Still variable + + // Second iteration: fix all parameters using iter_mut + for (_outeq, model) in models.iter_mut() { + model.fix_factor(); + } + + // Verify all parameters are now fixed + assert!(models.is_factor_fixed(0).unwrap()); + assert!(models.is_factor_fixed(1).unwrap()); + assert_eq!(models.factor(0).unwrap(), 10.0); // Values should remain the same + assert_eq!(models.factor(1).unwrap(), 4.0); + + // Test read-only iteration with iter() + let mut count = 0; + for (outeq, model) in models.iter() { + count += 1; + match outeq { + 0 => { + assert!(model.is_factor_fixed().unwrap()); + assert_eq!(model.factor().unwrap(), 10.0); + } + 1 => { + assert!(model.is_factor_fixed().unwrap()); + assert_eq!(model.factor().unwrap(), 4.0); + } + _ => panic!("Unexpected outeq: {}", outeq), + } + } + assert_eq!(count, 2); + + // Test consuming iteration with into_iter() + let collected_models: Vec<(usize, AssayErrorModel)> = models.into_iter().collect(); + assert_eq!(collected_models.len(), 2); + + // Verify the collected models retain their state + let (outeq0, model0) = &collected_models[0]; + let (outeq1, model1) = &collected_models[1]; + + assert_eq!(*outeq0, 0); + assert_eq!(*outeq1, 1); + assert!(model0.is_factor_fixed().unwrap()); + assert!(model1.is_factor_fixed().unwrap()); + assert_eq!(model0.factor().unwrap(), 10.0); + assert_eq!(model1.factor().unwrap(), 4.0); + } + + #[test] + fn error_model_hash_deterministic() { + let models = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0), + ) + .unwrap(); + assert_eq!(models.hash(), models.hash()); + } + + #[test] + fn error_model_hash_differs_on_value() { + let a = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0), + ) + .unwrap(); + let b = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 10.0), + ) + .unwrap(); + assert_ne!(a.hash(), b.hash()); + } + + #[test] + fn error_model_hash_differs_on_type() { + let a = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0), + ) + .unwrap(); + let b = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0), + ) + .unwrap(); + assert_ne!(a.hash(), b.hash()); + } + + #[test] + fn error_model_hash_includes_every_polynomial_coefficient() { + let baseline = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::additive(ErrorPoly::new(1.0, 2.0, 3.0, 4.0), 5.0), + ) + .unwrap(); + for coefficients in [ + (1.5, 2.0, 3.0, 4.0), + (1.0, 2.5, 3.0, 4.0), + (1.0, 2.0, 3.5, 4.0), + (1.0, 2.0, 3.0, 4.5), + ] { + let changed = AssayErrorModels::empty() + .add( + 0, + AssayErrorModel::additive( + ErrorPoly::new( + coefficients.0, + coefficients.1, + coefficients.2, + coefficients.3, + ), + 5.0, + ), + ) + .unwrap(); + assert_ne!(baseline.hash(), changed.hash()); + } + } + + #[test] + fn error_model_hash_includes_named_and_bound_output_context() { + let model = || AssayErrorModel::additive(ErrorPoly::new(1.0, 2.0, 3.0, 4.0), 5.0); + let named_cp = AssayErrorModels::new().add("cp", model()).unwrap(); + let named_effect = AssayErrorModels::new().add("effect", model()).unwrap(); + assert_ne!(named_cp.hash(), named_effect.hash()); + + let bound_cp = AssayErrorModels::with_output_names(["cp"]) + .add("cp", model()) + .unwrap(); + let bound_effect = AssayErrorModels::with_output_names(["effect"]) + .add("effect", model()) + .unwrap(); + assert_ne!(bound_cp.hash(), bound_effect.hash()); + } +} diff --git a/src/estimation/error_models.rs b/src/estimation/error_models.rs index a48d06e57..21731312d 100644 --- a/src/estimation/error_models.rs +++ b/src/estimation/error_models.rs @@ -1,6 +1,7 @@ -use pharmsol::AssayErrorModels; use serde::{Deserialize, Serialize}; +use super::{AssayErrorModels, ResidualErrorModel, ResidualErrorModels}; + #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(tag = "family", content = "models", rename_all = "snake_case")] pub enum ErrorModels { @@ -24,3 +25,214 @@ impl From for AssayErrorModels { val.models().clone() } } + +/// One parametric residual-error declaration and its estimation status. +/// +/// Fixedness and residual-distribution semantics are both owned by PMcore. +#[derive(Debug, Clone, PartialEq)] +pub struct ParametricErrorModel { + model: ResidualErrorModel, + estimate: bool, + combined_component_estimated: [bool; 2], + correlated_combined_component_estimated: [bool; 3], +} + +impl ParametricErrorModel { + pub fn new(model: ResidualErrorModel) -> Self { + model.into() + } + + pub fn with_estimate(mut self, estimate: bool) -> Self { + self.estimate = estimate; + self.combined_component_estimated = [estimate; 2]; + self.correlated_combined_component_estimated = [estimate; 3]; + self + } + + /// Configure estimation of the additive component of a combined model. + pub fn with_combined_additive_estimate(mut self, estimate: bool) -> Self { + self.combined_component_estimated[0] = estimate; + self.estimate = self.combined_component_estimated.iter().any(|value| *value); + self.correlated_combined_component_estimated = [self.estimate; 3]; + self + } + + /// Configure estimation of the proportional component of a combined model. + pub fn with_combined_proportional_estimate(mut self, estimate: bool) -> Self { + self.combined_component_estimated[1] = estimate; + self.estimate = self.combined_component_estimated.iter().any(|value| *value); + self.correlated_combined_component_estimated = [self.estimate; 3]; + self + } + + pub fn fixed_combined_additive(self) -> Self { + self.with_combined_additive_estimate(false) + } + + pub fn fixed_combined_proportional(self) -> Self { + self.with_combined_proportional_estimate(false) + } + + /// Configure estimation of the additive SD of a correlated-combined model. + pub fn with_correlated_combined_additive_estimate(mut self, estimate: bool) -> Self { + self.correlated_combined_component_estimated[0] = estimate; + self.estimate = self + .correlated_combined_component_estimated + .iter() + .any(|value| *value); + self.combined_component_estimated = [self.estimate; 2]; + self + } + + /// Configure estimation of the proportional SD of a correlated-combined model. + pub fn with_correlated_combined_proportional_estimate(mut self, estimate: bool) -> Self { + self.correlated_combined_component_estimated[1] = estimate; + self.estimate = self + .correlated_combined_component_estimated + .iter() + .any(|value| *value); + self.combined_component_estimated = [self.estimate; 2]; + self + } + + /// Configure estimation of rho of a correlated-combined model. + pub fn with_correlated_combined_correlation_estimate(mut self, estimate: bool) -> Self { + self.correlated_combined_component_estimated[2] = estimate; + self.estimate = self + .correlated_combined_component_estimated + .iter() + .any(|value| *value); + self.combined_component_estimated = [self.estimate; 2]; + self + } + + pub fn fixed_correlated_combined_additive(self) -> Self { + self.with_correlated_combined_additive_estimate(false) + } + + pub fn fixed_correlated_combined_proportional(self) -> Self { + self.with_correlated_combined_proportional_estimate(false) + } + + pub fn fixed_correlated_combined_correlation(self) -> Self { + self.with_correlated_combined_correlation_estimate(false) + } + + pub fn fixed(self) -> Self { + self.with_estimate(false) + } + + pub fn model(&self) -> &ResidualErrorModel { + &self.model + } + + pub fn is_estimated(&self) -> bool { + self.estimate + } + + pub fn combined_component_estimated(&self) -> [bool; 2] { + self.combined_component_estimated + } + + pub fn correlated_combined_component_estimated(&self) -> [bool; 3] { + self.correlated_combined_component_estimated + } +} + +impl From for ParametricErrorModel { + fn from(model: ResidualErrorModel) -> Self { + Self { + model, + estimate: true, + combined_component_estimated: [true, true], + correlated_combined_component_estimated: [true, true, true], + } + } +} + +/// Resolved parametric residual models plus estimation masks. +#[derive(Debug, Clone)] +pub struct ParametricErrorModels { + models: ResidualErrorModels, + estimated: Vec, + combined_component_estimated: Vec<[bool; 2]>, + correlated_combined_component_estimated: Vec<[bool; 3]>, + output_names: Vec>, +} + +impl ParametricErrorModels { + pub(crate) fn new() -> Self { + Self { + models: ResidualErrorModels::new(), + estimated: Vec::new(), + combined_component_estimated: Vec::new(), + correlated_combined_component_estimated: Vec::new(), + output_names: Vec::new(), + } + } + + pub(crate) fn add( + mut self, + outeq: usize, + output_name: impl Into, + declaration: ParametricErrorModel, + ) -> Self { + if self.estimated.len() <= outeq { + self.estimated.resize(outeq + 1, false); + self.combined_component_estimated + .resize(outeq + 1, [false, false]); + self.correlated_combined_component_estimated + .resize(outeq + 1, [false, false, false]); + self.output_names.resize(outeq + 1, None); + } + self.estimated[outeq] = declaration.estimate; + self.combined_component_estimated[outeq] = declaration.combined_component_estimated; + self.correlated_combined_component_estimated[outeq] = + declaration.correlated_combined_component_estimated; + self.output_names[outeq] = Some(output_name.into()); + self.models = self.models.add(outeq, declaration.model); + self + } + + pub fn models(&self) -> &ResidualErrorModels { + &self.models + } + + pub fn get(&self, outeq: usize) -> Option<&ResidualErrorModel> { + self.models.get(outeq) + } + + pub fn len(&self) -> usize { + self.models.len() + } + + pub fn is_empty(&self) -> bool { + self.models.is_empty() + } + + pub(crate) fn models_mut(&mut self) -> &mut ResidualErrorModels { + &mut self.models + } + + pub fn is_estimated(&self, outeq: usize) -> bool { + self.estimated.get(outeq).copied().unwrap_or(false) + } + + pub fn combined_component_estimated(&self, outeq: usize) -> [bool; 2] { + self.combined_component_estimated + .get(outeq) + .copied() + .unwrap_or([false, false]) + } + + pub fn correlated_combined_component_estimated(&self, outeq: usize) -> [bool; 3] { + self.correlated_combined_component_estimated + .get(outeq) + .copied() + .unwrap_or([false, false, false]) + } + + pub fn output_name(&self, outeq: usize) -> Option<&str> { + self.output_names.get(outeq).and_then(Option::as_deref) + } +} diff --git a/src/estimation/likelihood/batch.rs b/src/estimation/likelihood/batch.rs new file mode 100644 index 000000000..1cf1c608b --- /dev/null +++ b/src/estimation/likelihood/batch.rs @@ -0,0 +1,171 @@ +#![allow(dead_code)] // wired when parametric algorithms cut over to PMcore scoring + +use anyhow::{bail, Result}; +use ndarray::{Array2, Axis}; +use pharmsol::{Data, Equation, Occasion, Subject}; + +use crate::ResidualErrorModels; +use rayon::prelude::*; + +use super::residual::residual_error_model_log_likelihoods; + +/// Compute one parametric subject log-likelihood in PMcore. +/// +/// pharmsol is used only to generate predictions; PMcore owns residual-error scoring. +pub(crate) fn parametric_subject_log_likelihood( + equation: &impl Equation, + subject: &Subject, + parameter_row: &[f64], + residual_error_models: &ResidualErrorModels, +) -> f64 { + let predictions = match equation.estimate_predictions_dense(subject, parameter_row) { + Ok(predictions) => predictions, + Err(_) => return f64::NEG_INFINITY, + }; + + residual_error_model_log_likelihoods(&predictions, residual_error_models) +} + +/// Score one occasion under its own κ-adjusted parameter vector. +/// +/// An occasion is simulated as an independent pharmsol subject so its reset +/// state and occasion-local covariates remain intact. PMcore supplies the +/// occasion-specific ψ parameters and owns residual scoring. +pub(crate) fn parametric_occasion_log_likelihood( + equation: &impl Equation, + subject_id: &str, + occasion: &Occasion, + parameter_row: &[f64], + residual_error_models: &ResidualErrorModels, +) -> f64 { + let occasion_subject = Subject::from_occasions(subject_id.to_owned(), vec![occasion.clone()]); + parametric_subject_log_likelihood( + equation, + &occasion_subject, + parameter_row, + residual_error_models, + ) +} + +/// Compute parametric subject log-likelihoods in PMcore. +/// +/// Each subject has one parameter row. pharmsol is used only to generate +/// predictions; PMcore owns residual-error scoring. +pub(crate) fn parametric_log_likelihood_batch( + equation: &impl Equation, + subjects: &Data, + parameters: &Array2, + residual_error_models: &ResidualErrorModels, +) -> Result> { + let subject_refs = subjects.subjects(); + if parameters.nrows() != subject_refs.len() { + bail!( + "parameters has {} rows but there are {} subjects", + parameters.nrows(), + subject_refs.len() + ); + } + + if let Some(flat_parameters) = parameters.as_slice() { + let width = parameters.ncols(); + Ok(subject_refs + .par_iter() + .enumerate() + .map(|(i, subject)| { + let start = i * width; + parametric_subject_log_likelihood( + equation, + subject, + &flat_parameters[start..start + width], + residual_error_models, + ) + }) + .collect()) + } else { + let parameter_rows = parameters + .axis_iter(Axis(0)) + .map(|row| row.to_vec()) + .collect::>(); + + Ok(subject_refs + .par_iter() + .enumerate() + .map(|(i, subject)| { + parametric_subject_log_likelihood( + equation, + subject, + ¶meter_rows[i], + residual_error_models, + ) + }) + .collect()) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::ResidualErrorModel; + use pharmsol::prelude::*; + use pharmsol::SubjectBuilderExt; + + fn one_compartment_metadata() -> pharmsol::equation::ModelMetadata { + equation::metadata::new("one_compartment_parametric_parity") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["0"]) + .route(equation::Route::bolus("0").to_state("central")) + } + + fn one_compartment() -> pharmsol::ODE { + equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata(one_compartment_metadata()) + .unwrap() + } + + fn data() -> Data { + Data::new(vec![ + Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .missing_observation(2.0, "0") + .observation(4.0, 4.0, "0") + .build(), + Subject::builder("s2") + .bolus(0.0, 80.0, "0") + .observation(0.5, 9.0, "0") + .observation(3.0, 2.5, "0") + .build(), + ]) + } + + #[test] + fn batch_scores_one_parameter_row_per_subject() { + let equation = one_compartment(); + let data = data(); + let parameters = ndarray::array![[0.15, 8.0], [0.30, 12.0]]; + let error_models = + ResidualErrorModels::new().add(0, ResidualErrorModel::combined(0.5, 0.1)); + + let scores = parametric_log_likelihood_batch(&equation, &data, ¶meters, &error_models) + .expect("pmcore batch"); + + assert_eq!(scores.len(), 2); + assert!(scores.iter().all(|score| score.is_finite())); + } +} diff --git a/src/estimation/likelihood/distributions.rs b/src/estimation/likelihood/distributions.rs new file mode 100644 index 000000000..f7e3899c4 --- /dev/null +++ b/src/estimation/likelihood/distributions.rs @@ -0,0 +1,140 @@ +use statrs::function::erf::erfc; +use thiserror::Error; + +const LOG_2PI: f64 = 1.8378770664093453_f64; +const SQRT_2: f64 = std::f64::consts::SQRT_2; + +/// Invalid inputs to PMcore's normal-distribution scoring primitives. +#[derive(Clone, Copy, Debug, Error, PartialEq)] +pub enum NormalDistributionError { + /// The standard deviation was non-finite, zero, or negative. + #[error("normal standard deviation must be finite and greater than zero, got {0}")] + InvalidSigma(f64), + /// The observation or prediction was non-finite. + #[error("normal observation and prediction must be finite")] + NonFiniteInput, +} + +fn standardized(obs: f64, pred: f64, sigma: f64) -> Result { + if !sigma.is_finite() || sigma <= 0.0 { + return Err(NormalDistributionError::InvalidSigma(sigma)); + } + if !obs.is_finite() || !pred.is_finite() { + return Err(NormalDistributionError::NonFiniteInput); + } + Ok((obs - pred) / sigma) +} + +/// Log of the standard-normal upper-tail probability for non-negative `z`. +/// +/// `erfc` evaluates the tail directly through the range where it is +/// representable. The Mills-ratio expansion avoids underflow beyond that range. +fn log_standard_normal_upper_tail(z: f64) -> f64 { + debug_assert!(z >= 0.0); + let direct = 0.5 * erfc(z / SQRT_2); + if direct > 0.0 { + return direct.ln(); + } + + let inverse_square = 1.0 / (z * z); + let mills_series = 1.0 + - inverse_square + * (1.0 + - inverse_square + * (3.0 + - inverse_square + * (15.0 - inverse_square * (105.0 - inverse_square * 945.0)))); + -0.5 * z * z - z.ln() - 0.5 * LOG_2PI + mills_series.ln() +} + +#[inline(always)] +pub(crate) fn log_normal_pdf( + obs: f64, + pred: f64, + sigma: f64, +) -> Result { + let z = standardized(obs, pred, sigma)?; + Ok(-0.5 * LOG_2PI - sigma.ln() - 0.5 * z * z) +} + +#[inline(always)] +pub(crate) fn log_normal_cdf( + obs: f64, + pred: f64, + sigma: f64, +) -> Result { + let z = standardized(obs, pred, sigma)?; + if z <= 0.0 { + Ok(log_standard_normal_upper_tail(-z)) + } else { + Ok((-log_standard_normal_upper_tail(z).exp()).ln_1p()) + } +} + +#[inline(always)] +pub(crate) fn log_normal_ccdf( + obs: f64, + pred: f64, + sigma: f64, +) -> Result { + let z = standardized(obs, pred, sigma)?; + if z >= 0.0 { + Ok(log_standard_normal_upper_tail(z)) + } else { + Ok((-log_standard_normal_upper_tail(-z).exp()).ln_1p()) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn pdf_matches_standard_normal_at_mean() { + let ll = log_normal_pdf(0.0, 0.0, 1.0).unwrap(); + assert!((ll + 0.5 * LOG_2PI).abs() < 1e-12); + } + + #[test] + fn cdf_and_survival_are_symmetric() { + for z in [-100.0, -37.0, -8.0, 0.0, 8.0, 37.0, 100.0] { + assert_eq!( + log_normal_cdf(z, 0.0, 1.0).unwrap(), + log_normal_ccdf(-z, 0.0, 1.0).unwrap() + ); + } + } + + #[test] + fn survival_is_finite_across_cancellation_and_extreme_tails() { + for z in [8.0, 12.0, 20.0, 37.0, 40.0, 100.0, 1_000.0] { + let value = log_normal_ccdf(z, 0.0, 1.0).unwrap(); + assert!(value.is_finite(), "z={z}, log survival={value}"); + assert!(value < 0.0); + } + } + + #[test] + fn invalid_sigma_is_typed() { + for sigma in [0.0, -1.0, f64::NAN, f64::INFINITY] { + assert!(matches!( + log_normal_pdf(0.0, 0.0, sigma), + Err(NormalDistributionError::InvalidSigma(value)) if value.to_bits() == sigma.to_bits() + )); + assert!(matches!( + log_normal_ccdf(0.0, 0.0, sigma), + Err(NormalDistributionError::InvalidSigma(value)) if value.to_bits() == sigma.to_bits() + )); + } + } + + #[test] + fn non_finite_pdf_inputs_are_typed() { + for (observation, prediction) in [(f64::NAN, 0.0), (0.0, f64::INFINITY)] { + assert_eq!( + log_normal_pdf(observation, prediction, 1.0), + Err(NormalDistributionError::NonFiniteInput) + ); + } + } +} diff --git a/src/estimation/likelihood/matrix.rs b/src/estimation/likelihood/matrix.rs new file mode 100644 index 000000000..559e046b9 --- /dev/null +++ b/src/estimation/likelihood/matrix.rs @@ -0,0 +1,193 @@ +use anyhow::{anyhow, Result}; +use ndarray::{Array2, Axis, ShapeBuilder}; +use pharmsol::{Data, Equation}; + +use crate::AssayErrorModels; +use rayon::prelude::*; + +use super::observation::{assay_error_model_log_likelihoods, AssayLikelihoodError}; + +/// Compute a nonparametric log-likelihood matrix with shape +/// `(n_subjects, n_support_points)`. +pub(crate) fn nonparametric_log_likelihood_matrix( + equation: &impl Equation, + subjects: &Data, + support_points: &Array2, + error_models: &AssayErrorModels, + progress: bool, +) -> Result> { + let n_support_points = support_points.nrows(); + let subject_refs = subjects.subjects(); + let support_point_rows = support_points + .axis_iter(Axis(0)) + .map(|row| row.to_vec()) + .collect::>(); + + if progress { + println!( + "Computing log-likelihood matrix: {} subjects × {} support points...", + subject_refs.len(), + n_support_points + ); + } + + let mut log_psi: Array2 = Array2::default((subjects.len(), n_support_points).f()); + + let result: Result<()> = log_psi + .axis_iter_mut(Axis(0)) + .into_par_iter() + .enumerate() + .try_for_each(|(i, mut row)| { + let subject = subject_refs[i]; + + for (element, support_point) in row.iter_mut().zip(support_point_rows.iter()) { + let predictions = equation + .estimate_predictions_dense(subject, support_point.as_slice()) + .map_err(|err| anyhow!(err))?; + *element = match assay_error_model_log_likelihoods(&predictions, error_models) { + Ok(score) => score, + Err(AssayLikelihoodError::Impossible) => f64::NEG_INFINITY, + Err(error) => return Err(error.into()), + }; + } + + Ok(()) + }); + + result?; + + if progress { + println!("Log-likelihood matrix complete."); + } + + Ok(log_psi) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::{AssayErrorModel, ErrorPoly}; + use pharmsol::prelude::*; + use pharmsol::{Censor, SubjectBuilderExt}; + + fn one_compartment_metadata() -> pharmsol::equation::ModelMetadata { + equation::metadata::new("one_compartment_likelihood_parity") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["0"]) + .route(equation::Route::bolus("0").to_state("central")) + } + + fn one_compartment() -> pharmsol::ODE { + equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata(one_compartment_metadata()) + .unwrap() + } + + fn direct_output() -> pharmsol::ODE { + equation::ODE::new( + |_x, _p, _t, dx, _b, _rateiv, _cov| dx[0] = 0.0, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |_x, p, _t, _cov, y| y[0] = p[0], + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata( + equation::metadata::new("direct_output_likelihood_matrix") + .parameters(["value"]) + .states(["state"]) + .outputs(["0"]) + .route(equation::Route::bolus("dose").to_state("state")), + ) + .unwrap() + } + + fn parity_data() -> Data { + let s1 = Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .missing_observation(2.0, "0") + .censored_observation(4.0, 1.0, "0", Censor::BLOQ) + .build(); + let s2 = Subject::builder("s2") + .bolus(0.0, 80.0, "0") + .observation(0.5, 9.0, "0") + .censored_observation(3.0, 15.0, "0", Censor::ALOQ) + .build(); + Data::new(vec![s1, s2]) + } + + fn error_models() -> AssayErrorModels { + AssayErrorModels::new() + .add( + "0", + AssayErrorModel::additive(ErrorPoly::new(0.0, 0.10, 0.0, 0.0), 0.0), + ) + .unwrap() + } + + #[test] + fn matrix_scores_all_subject_support_point_pairs() { + let equation = one_compartment(); + let data = parity_data(); + let support_points = ndarray::array![[0.15, 8.0], [0.30, 12.0], [0.55, 20.0]]; + let error_models = error_models(); + + let scores = nonparametric_log_likelihood_matrix( + &equation, + &data, + &support_points, + &error_models, + false, + ) + .expect("pmcore matrix"); + + assert_eq!(scores.dim(), (2, 3)); + assert!(scores.iter().all(|score| score.is_finite())); + } + + #[test] + fn matrix_preserves_impossible_cells_as_negative_infinity() { + let equation = direct_output(); + let data = Data::new(vec![Subject::builder("s1") + .observation(1.0, 1e155, "0") + .build()]); + let support_points = ndarray::array![[1e155], [0.0]]; + let error_models = AssayErrorModels::new() + .add( + "0", + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 0.0), + ) + .unwrap(); + + let scores = nonparametric_log_likelihood_matrix( + &equation, + &data, + &support_points, + &error_models, + false, + ) + .expect("an impossible support point must not abort matrix construction"); + + assert!(scores[(0, 0)].is_finite()); + assert_eq!(scores[(0, 1)], f64::NEG_INFINITY); + } +} diff --git a/src/estimation/likelihood/mod.rs b/src/estimation/likelihood/mod.rs new file mode 100644 index 000000000..bfce4b468 --- /dev/null +++ b/src/estimation/likelihood/mod.rs @@ -0,0 +1,14 @@ +//! Likelihood scoring. +//! +//! This module provides estimator-facing observation scoring and likelihood +//! aggregation over generated predictions. + +pub(crate) mod batch; +mod distributions; +pub use distributions::NormalDistributionError; +pub(crate) mod matrix; +pub(crate) mod objective; +pub(crate) mod observation; +pub use observation::AssayLikelihoodError; +pub(crate) mod particle; +pub(crate) mod residual; diff --git a/src/estimation/likelihood/objective.rs b/src/estimation/likelihood/objective.rs new file mode 100644 index 000000000..885cc0ef8 --- /dev/null +++ b/src/estimation/likelihood/objective.rs @@ -0,0 +1,139 @@ +use anyhow::{bail, Result}; +use ndarray::Array2; +use pharmsol::Equation; + +use crate::estimation::{EstimationProblem, Parametric}; + +use super::batch::parametric_log_likelihood_batch; + +/// Score one row of individual parameters per subject for a parametric problem. +/// +/// pharmsol is used only for prediction generation. PMcore owns residual-error +/// scoring through the likelihood module. +pub(crate) fn parametric_subject_log_likelihoods( + problem: &EstimationProblem, + individual_parameters: &Array2, +) -> Result> +where + E: Equation, +{ + validate_parameter_width(problem, individual_parameters)?; + + parametric_log_likelihood_batch( + &problem.model.equation, + &problem.data, + individual_parameters, + problem.error_models.models(), + ) +} + +fn validate_parameter_width( + problem: &EstimationProblem, + individual_parameters: &Array2, +) -> Result<()> +where + E: Equation, +{ + let expected = problem.parameters().len(); + if individual_parameters.ncols() != expected { + bail!( + "individual parameter matrix has {} columns but the parametric problem declares {} parameters", + individual_parameters.ncols(), + expected + ); + } + + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::likelihood::batch::parametric_log_likelihood_batch; + use crate::estimation::ParametricErrorModel; + use crate::model::Parameter; + use crate::ResidualErrorModel; + use pharmsol::prelude::*; + use pharmsol::SubjectBuilderExt; + + fn one_compartment_metadata() -> pharmsol::equation::ModelMetadata { + equation::metadata::new("one_compartment_parametric_objective") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["0"]) + .route(equation::Route::bolus("0").to_state("central")) + } + + fn one_compartment() -> pharmsol::ODE { + equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata(one_compartment_metadata()) + .unwrap() + } + + fn problem() -> EstimationProblem { + let data = Data::new(vec![ + Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .missing_observation(2.0, "0") + .observation(4.0, 4.0, "0") + .build(), + Subject::builder("s2") + .bolus(0.0, 80.0, "0") + .observation(0.5, 9.0, "0") + .observation(3.0, 2.5, "0") + .build(), + ]); + + EstimationProblem::parametric(one_compartment(), data) + .parameter(Parameter::log("ke")) + .parameter(Parameter::log("v")) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::combined(0.5, 0.1)).fixed(), + ) + .build() + .unwrap() + } + + #[test] + fn objective_uses_batch_subject_likelihoods() { + let problem = problem(); + let parameters = ndarray::array![[0.15, 8.0], [0.30, 12.0]]; + + let expected = parametric_log_likelihood_batch( + &problem.model.equation, + &problem.data, + ¶meters, + problem.error_models.models(), + ) + .unwrap(); + let actual = parametric_subject_log_likelihoods(&problem, ¶meters).unwrap(); + + assert_eq!(actual, expected); + } + + #[test] + fn objective_rejects_wrong_parameter_width() { + let problem = problem(); + let parameters = ndarray::array![[0.15], [0.30]]; + + let err = parametric_subject_log_likelihoods(&problem, ¶meters).unwrap_err(); + assert!(err.to_string().contains("declares 2 parameters")); + } +} diff --git a/src/estimation/likelihood/observation.rs b/src/estimation/likelihood/observation.rs new file mode 100644 index 000000000..084721b48 --- /dev/null +++ b/src/estimation/likelihood/observation.rs @@ -0,0 +1,102 @@ +use pharmsol::prelude::simulator::Prediction; +use pharmsol::Censor; +use pharmsol::Predictions; + +use crate::{AssayErrorModels, ErrorModelError}; + +use super::distributions::{ + log_normal_ccdf, log_normal_cdf, log_normal_pdf, NormalDistributionError, +}; + +/// Typed scoring error for assay observation likelihood. +/// +/// This is the public error type returned by [`crate::AssayErrorModels::log_likelihood`]. +/// It keeps a valid-but-impossible score ([`AssayLikelihoodError::Impossible`], +/// a log-likelihood of negative infinity) distinct from invalid inputs such as +/// an out-of-range standard deviation or non-finite observation/prediction +/// ([`AssayLikelihoodError::Distribution`]). +#[derive(Debug, thiserror::Error)] +pub enum AssayLikelihoodError { + /// The assay error model itself was invalid for the scored prediction. + #[error("invalid assay error model")] + ErrorModel(#[from] ErrorModelError), + /// The normal distribution rejected the standardized inputs (invalid sigma + /// or non-finite observation/prediction). + #[error(transparent)] + Distribution(#[from] NormalDistributionError), + /// The log-likelihood is negative infinity: a valid but impossible score. + #[error("assay log-likelihood is negative infinity")] + Impossible, + /// The log-likelihood is NaN or positive infinity, which is never valid. + #[error("assay log-likelihood is NaN or positive infinity: {0}")] + InvalidScore(f64), +} + +/// Apply assay error-model likelihood logic to one pharmsol prediction DTO. +/// +/// This preserves the current Pmetrics/non-parametric semantics: sigma is +/// observation/assay based and comes from [`AssayErrorModels`]. The likelihood +/// math and non-finite handling live here rather than in pharmsol. +#[inline] +pub(crate) fn assay_error_model_log_likelihood( + prediction: &Prediction, + error_models: &AssayErrorModels, +) -> std::result::Result { + let Some(obs) = prediction.observation() else { + return Ok(0.0); + }; + + let sigma = error_models.sigma(prediction)?; + + let log_lik = match prediction.censoring() { + Censor::None => log_normal_pdf(obs, prediction.prediction(), sigma)?, + Censor::BLOQ => log_normal_cdf(obs, prediction.prediction(), sigma)?, + Censor::ALOQ => log_normal_ccdf(obs, prediction.prediction(), sigma)?, + }; + + if log_lik.is_finite() { + Ok(log_lik) + } else if log_lik == f64::NEG_INFINITY { + Err(AssayLikelihoodError::Impossible) + } else { + Err(AssayLikelihoodError::InvalidScore(log_lik)) + } +} + +pub(crate) fn assay_error_model_log_likelihoods

( + predictions: &P, + error_models: &AssayErrorModels, +) -> std::result::Result +where + P: Predictions, +{ + let mut total = 0.0; + let mut error = None; + predictions.for_each_prediction(|prediction| { + if error.is_some() { + return; + } + match assay_error_model_log_likelihood(prediction, error_models) { + Ok(ll) => total += ll, + Err(err) => error = Some(err), + } + }); + + match error { + Some(err) => Err(err), + None => Ok(total), + } +} + +#[cfg(test)] +mod tests { + use super::*; + #[test] + fn missing_observations_score_zero() { + let prediction = Prediction::default(); + assert_eq!( + assay_error_model_log_likelihood(&prediction, &AssayErrorModels::new()).unwrap(), + 0.0 + ); + } +} diff --git a/src/estimation/likelihood/particle.rs b/src/estimation/likelihood/particle.rs new file mode 100644 index 000000000..701e9d3fc --- /dev/null +++ b/src/estimation/likelihood/particle.rs @@ -0,0 +1,228 @@ +use thiserror::Error; + +#[derive(Clone, Debug, Error, PartialEq)] +pub(crate) enum ParticleWeightError { + #[error("particle likelihood requires at least one particle")] + Empty, + #[error( + "particle likelihood increment count {found} does not match particle count {expected}" + )] + IncrementCount { expected: usize, found: usize }, + #[error("particle likelihood increment {index} is NaN or positive infinity")] + InvalidIncrement { index: usize }, + #[error("all particle likelihood weights are zero")] + AllImpossible, + #[error("particle likelihood normalization is non-finite")] + NormalizationFailure, +} + +#[derive(Clone, Debug, Error, PartialEq)] +pub(crate) enum ResamplingError { + #[error("systematic resampling requires at least one particle")] + Empty, + #[error("systematic resampling offset must be in [0, 1/N)")] + InvalidOffset, + #[error("systematic resampling weight {index} must be finite and non-negative")] + InvalidWeight { index: usize }, + #[error("systematic resampling weights must sum to one")] + InvalidWeightSum, +} + +/// Particle likelihood weights stored in normalized log space. +#[derive(Debug, Clone)] +pub(crate) struct ParticleWeights { + log_weights: Vec, +} + +impl ParticleWeights { + pub(crate) fn uniform(particle_count: usize) -> Result { + if particle_count == 0 { + return Err(ParticleWeightError::Empty); + } + let log_weight = -(particle_count as f64).ln(); + Ok(Self { + log_weights: vec![log_weight; particle_count], + }) + } + + pub(crate) fn update( + &mut self, + log_likelihood_increments: &[f64], + ) -> Result { + if log_likelihood_increments.len() != self.log_weights.len() { + return Err(ParticleWeightError::IncrementCount { + expected: self.log_weights.len(), + found: log_likelihood_increments.len(), + }); + } + if let Some(index) = log_likelihood_increments + .iter() + .position(|increment| increment.is_nan() || *increment == f64::INFINITY) + { + return Err(ParticleWeightError::InvalidIncrement { index }); + } + + for (log_weight, increment) in self.log_weights.iter_mut().zip(log_likelihood_increments) { + *log_weight += increment; + } + normalize_log_weights(&mut self.log_weights) + } + + pub(crate) fn effective_sample_size(&self) -> f64 { + let sum_squared_weights = self + .log_weights + .iter() + .map(|log_weight| (2.0 * log_weight).exp()) + .sum::(); + 1.0 / sum_squared_weights + } + + pub(crate) fn normalized_weights(&self) -> Vec { + self.log_weights + .iter() + .map(|log_weight| log_weight.exp()) + .collect() + } + + pub(crate) fn reset_uniform(&mut self) { + let log_weight = -(self.log_weights.len() as f64).ln(); + self.log_weights.fill(log_weight); + } + + pub(crate) fn systematic_ancestors( + weights: &[f64], + offset: f64, + ) -> Result, ResamplingError> { + systematic_ancestors(weights, offset) + } +} + +fn normalize_log_weights(log_weights: &mut [f64]) -> Result { + let maximum = log_weights + .iter() + .copied() + .fold(f64::NEG_INFINITY, f64::max); + if maximum == f64::NEG_INFINITY { + return Err(ParticleWeightError::AllImpossible); + } + if !maximum.is_finite() { + return Err(ParticleWeightError::NormalizationFailure); + } + + let scaled_sum = log_weights + .iter() + .map(|log_weight| (*log_weight - maximum).exp()) + .sum::(); + let log_normalizer = maximum + scaled_sum.ln(); + if !log_normalizer.is_finite() { + return Err(ParticleWeightError::NormalizationFailure); + } + for log_weight in log_weights { + *log_weight -= log_normalizer; + } + Ok(log_normalizer) +} + +/// Select particle ancestors by systematic resampling. +pub(crate) fn systematic_ancestors( + weights: &[f64], + offset: f64, +) -> Result, ResamplingError> { + if weights.is_empty() { + return Err(ResamplingError::Empty); + } + let particle_count = weights.len(); + let spacing = 1.0 / particle_count as f64; + if !offset.is_finite() || !(0.0..spacing).contains(&offset) { + return Err(ResamplingError::InvalidOffset); + } + if let Some(index) = weights + .iter() + .position(|weight| !weight.is_finite() || *weight < 0.0) + { + return Err(ResamplingError::InvalidWeight { index }); + } + let weight_sum = weights.iter().sum::(); + if (weight_sum - 1.0).abs() > 1e-10 { + return Err(ResamplingError::InvalidWeightSum); + } + + let mut ancestors = Vec::with_capacity(particle_count); + let mut cumulative = weights[0]; + let mut ancestor = 0usize; + for draw_index in 0..particle_count { + let threshold = offset + draw_index as f64 * spacing; + while threshold >= cumulative && ancestor + 1 < particle_count { + ancestor += 1; + cumulative += weights[ancestor]; + } + ancestors.push(ancestor); + } + Ok(ancestors) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn observation_update_returns_log_marginal_and_normalizes_weights() { + let mut weights = ParticleWeights::uniform(2).unwrap(); + let log_marginal = weights.update(&[0.25_f64.ln(), 0.75_f64.ln()]).unwrap(); + assert!((log_marginal - 0.5_f64.ln()).abs() < 1e-12); + let normalized = weights.normalized_weights(); + assert!((normalized[0] - 0.25).abs() < 1e-12); + assert!((normalized[1] - 0.75).abs() < 1e-12); + assert!((weights.effective_sample_size() - 1.6).abs() < 1e-12); + } + + #[test] + fn log_space_update_remains_stable_for_tiny_likelihoods() { + let mut weights = ParticleWeights::uniform(2).unwrap(); + let log_marginal = weights.update(&[-1_000.0, -1_002.0]).unwrap(); + assert!(log_marginal.is_finite()); + assert!((weights.normalized_weights().iter().sum::() - 1.0).abs() < 1e-12); + } + + #[test] + fn all_impossible_particles_are_typed() { + let mut weights = ParticleWeights::uniform(2).unwrap(); + assert_eq!( + weights.update(&[f64::NEG_INFINITY, f64::NEG_INFINITY]), + Err(ParticleWeightError::AllImpossible) + ); + } + + #[test] + fn invalid_increment_and_normalization_failures_are_distinct() { + let mut weights = ParticleWeights::uniform(2).unwrap(); + assert_eq!( + weights.update(&[f64::NAN, 0.0]), + Err(ParticleWeightError::InvalidIncrement { index: 0 }) + ); + } + + #[test] + fn systematic_resampling_is_deterministic_for_supplied_offset() { + let ancestors = systematic_ancestors(&[0.1, 0.2, 0.7], 0.05).unwrap(); + assert_eq!(ancestors, vec![0, 2, 2]); + } + + #[test] + fn resampling_failures_are_typed() { + assert_eq!( + systematic_ancestors(&[0.4, 0.4], 0.1), + Err(ResamplingError::InvalidWeightSum) + ); + } + + #[test] + fn resetting_after_resampling_restores_uniform_ess() { + let mut weights = ParticleWeights::uniform(3).unwrap(); + weights + .update(&[0.1_f64.ln(), 0.2_f64.ln(), 0.7_f64.ln()]) + .unwrap(); + weights.reset_uniform(); + assert!((weights.effective_sample_size() - 3.0).abs() < 1e-12); + } +} diff --git a/src/estimation/likelihood/residual.rs b/src/estimation/likelihood/residual.rs new file mode 100644 index 000000000..f8d0a6aef --- /dev/null +++ b/src/estimation/likelihood/residual.rs @@ -0,0 +1,208 @@ +use pharmsol::prelude::simulator::Prediction; +use pharmsol::Predictions; + +use crate::{ResidualErrorModel, ResidualErrorModels}; + +use super::distributions::log_normal_pdf; + +#[inline] +fn residual_log_likelihood_values( + model: &ResidualErrorModel, + observation: f64, + prediction: f64, +) -> f64 { + match model { + ResidualErrorModel::Exponential { .. } => { + if !observation.is_finite() + || observation <= 0.0 + || !prediction.is_finite() + || prediction <= 0.0 + { + return f64::NEG_INFINITY; + } + // Fit log(y) = log(f) + sigma*epsilon. The Jacobian + // converts the transformed Gaussian density back to the original + // observation scale; it is constant during latent-state MCMC but + // required for an honest likelihood value. + log_normal_pdf(observation.ln(), prediction.ln(), model.sigma(prediction)) + .unwrap_or(f64::NEG_INFINITY) + - observation.ln() + } + ResidualErrorModel::Constant { .. } + | ResidualErrorModel::Proportional { .. } + | ResidualErrorModel::Combined { .. } + | ResidualErrorModel::CorrelatedCombined { .. } => { + log_normal_pdf(observation, prediction, model.sigma(prediction)) + .unwrap_or(f64::NEG_INFINITY) + } + } +} + +/// Apply parametric residual-error-model likelihood semantics to one prediction. +/// +/// The Gaussian normalization term is retained through `log_normal_pdf`. +/// Proportional coefficients are SD-scale values and use `b * abs(prediction)`; +/// negative predictions remain signed in the residual, matching the direct +/// raw-prediction E-step. Exponential error uses a positive-only lognormal +/// observation model and includes the original-scale Jacobian. Censoring is not +/// handled on this path. +#[inline] +pub(crate) fn residual_error_model_log_likelihood( + prediction: &Prediction, + error_models: &ResidualErrorModels, +) -> f64 { + let Some(obs) = prediction.observation() else { + return 0.0; + }; + + let Some(model) = error_models.get(prediction.outeq()) else { + return f64::NEG_INFINITY; + }; + + residual_log_likelihood_values(model, obs, prediction.prediction()) +} + +pub(crate) fn residual_error_model_log_likelihoods

( + predictions: &P, + error_models: &ResidualErrorModels, +) -> f64 +where + P: Predictions, +{ + let mut total = 0.0; + let mut failed = false; + predictions.for_each_prediction(|prediction| { + if failed { + return; + } + let ll = residual_error_model_log_likelihood(prediction, error_models); + if ll.is_finite() { + total += ll; + } else { + failed = true; + } + }); + + if failed { + f64::NEG_INFINITY + } else { + total + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn proportional_sigma_is_sd_scaled_symmetric_and_floored_at_zero() { + let model = ResidualErrorModel::proportional(0.1); + + assert_eq!(model.sigma(10.0), 1.0); + assert_eq!(model.sigma(-10.0), 1.0); + assert_eq!(model.sigma(0.0), f64::EPSILON.sqrt()); + } + + #[test] + fn correlated_combined_likelihood_uses_exact_signed_variance_and_log_term() { + let model = ResidualErrorModel::correlated_combined(0.7, 0.2, -0.35); + for prediction in [-3.0_f64, 0.0, 2.5] { + let observation = prediction + 0.4; + let variance = 0.7_f64.powi(2) + + 2.0 * -0.35 * 0.7 * 0.2 * prediction + + 0.2_f64.powi(2) * prediction.powi(2); + let expected = -0.5 + * ((2.0 * std::f64::consts::PI).ln() + variance.ln() + 0.4_f64.powi(2) / variance); + assert!( + (residual_log_likelihood_values(&model, observation, prediction) - expected).abs() + < 1e-12 + ); + } + + let ordinary = ResidualErrorModel::combined(0.7, 0.2); + let independent = ResidualErrorModel::correlated_combined(0.7, 0.2, 0.0); + for prediction in [-3.0_f64, 0.0, 2.5] { + let observation = prediction + 0.4; + assert_eq!( + residual_log_likelihood_values(&ordinary, observation, prediction), + residual_log_likelihood_values(&independent, observation, prediction) + ); + } + } + + #[test] + fn exponential_likelihood_is_lognormal_with_original_scale_jacobian() { + let model = ResidualErrorModel::exponential(0.25); + let observation: f64 = 12.0; + let prediction: f64 = 10.0; + let expected = + log_normal_pdf(observation.ln(), prediction.ln(), 0.25).unwrap() - observation.ln(); + + assert!( + (residual_log_likelihood_values(&model, observation, prediction) - expected).abs() + < 1e-12 + ); + assert_eq!( + residual_log_likelihood_values(&model, 0.0, prediction), + f64::NEG_INFINITY + ); + assert_eq!( + residual_log_likelihood_values(&model, observation, -prediction), + f64::NEG_INFINITY + ); + } + + #[test] + fn exponential_likelihood_uses_the_canonical_scale_floor() { + let observation: f64 = 12.0; + let prediction: f64 = 10.0; + let floor = f64::EPSILON.sqrt(); + let expected = + log_normal_pdf(observation.ln(), prediction.ln(), floor).unwrap() - observation.ln(); + + for sigma in [floor / 2.0, floor] { + let model = ResidualErrorModel::exponential(sigma); + assert_eq!( + residual_log_likelihood_values(&model, observation, prediction), + expected + ); + } + + let above_floor = floor * 2.0; + let model = ResidualErrorModel::exponential(above_floor); + let expected_above = log_normal_pdf(observation.ln(), prediction.ln(), above_floor) + .unwrap() + - observation.ln(); + assert_eq!( + residual_log_likelihood_values(&model, observation, prediction), + expected_above + ); + } + + #[test] + fn exponential_fixed_trace_likelihood_matches_reference_checkpoint() { + let model = ResidualErrorModel::exponential(0.156_764_356_228_701_02); + let observations = [12.0, 8.0, 4.5, 1.2]; + let predictions = [10.0, 7.5, 5.0, 1.5]; + let log_likelihood = observations + .into_iter() + .zip(predictions) + .map(|(observation, prediction)| { + residual_log_likelihood_values(&model, observation, prediction) + }) + .sum::(); + + assert!((log_likelihood - -4.514_455_210_348_959).abs() < 1e-14); + } + + #[test] + fn missing_observations_score_zero() { + assert_eq!( + residual_error_model_log_likelihood( + &Prediction::default(), + &ResidualErrorModels::new(), + ), + 0.0 + ); + } +} diff --git a/src/estimation/mod.rs b/src/estimation/mod.rs index 5857dde14..5a50234fe 100644 --- a/src/estimation/mod.rs +++ b/src/estimation/mod.rs @@ -1,12 +1,45 @@ +pub mod assay_error; pub mod error_models; +pub(crate) mod likelihood; pub mod nonparametric; +pub mod parametric; pub mod problem; pub mod progress; +pub mod residual_error; +pub mod sde_particle; pub use crate::algorithms::nonparametric::{ NcnpagConfig, NonParametricAlgorithm, NpagConfig, NpmapConfig, NpodConfig, }; -pub use crate::algorithms::parametric::{ParametricAlgorithm, SaemConfig}; -pub use error_models::ErrorModels; +pub use crate::algorithms::parametric::{ + CovarianceStabilityConfig, LugsailConfig, MarkovSimulationVarianceConfig, + OperationalConvergenceConfig, ParametricAlgorithm, SaemConfig, SaemEstimatorPolicy, +}; +#[allow(deprecated)] +pub use assay_error::{ + AssayErrorModel, AssayErrorModels, BoundAssayErrorModels, ErrorModel, ErrorModelError, + ErrorPoly, Factor, +}; +pub use error_models::{ErrorModels, ParametricErrorModel, ParametricErrorModels}; +pub use likelihood::{AssayLikelihoodError, NormalDistributionError}; +pub use parametric::{ + rebase_eta, reject_constraints, solve_covariate_gls, subject_centered_omega, + ConditionalCurvatureAvailability, ConditionalCurvatureDiagnostics, + ConditionalCurvatureRegularization, ConditionalCurvatureStatus, + ConditionalCurvatureUnavailableReason, ConditionalModeMetadata, CovariateEffect, + CovariateEffectFamily, CovariateEstimate, CovariateGlsProblem, CovariateModel, + CovariateMstepError, CovariateValidationError, EtaMapShrinkage, EtaPosteriorMeanShrinkage, Iov, + JointLatentCoordinate, JointLatentCoordinateKind, KappaMapShrinkage, + KappaPosteriorMeanShrinkage, MarginalLikelihoodConfig, MarginalLikelihoodDiagnostics, + MarginalLikelihoodFailureReason, MarginalLikelihoodMethod, MarginalLikelihoodProposal, + MarginalLikelihoodStatus, MarginalLikelihoodSubjectFailure, Omega, ParametricConstraint, + ParametricPrior, ProposalScaleSource, ShrinkageDiagnostics, ShrinkageUnavailableReason, + ShrinkageValue, SubjectCovariateDesign, SubjectCovariateValue, + SubjectMarginalLikelihoodDiagnostics, SubjectPopulationParameters, +}; pub use problem::{EstimationProblem, Framework, NonParametric, Parametric, ParametricBuilder}; pub use progress::{FitProgress, NonparametricCycleProgress}; +pub use residual_error::{ResidualErrorModel, ResidualErrorModels}; +pub use sde_particle::{ + SdeParticleConfig, SdeParticleError, SdeParticleFilter, SdeParticleRecord, SdeParticleResult, +}; diff --git a/src/estimation/nonparametric/cycles.rs b/src/estimation/nonparametric/cycles.rs index 9c20d3539..00398ca33 100644 --- a/src/estimation/nonparametric/cycles.rs +++ b/src/estimation/nonparametric/cycles.rs @@ -2,9 +2,10 @@ use std::{fs::File, path::Path}; use anyhow::Result; use csv::WriterBuilder; -use pharmsol::{AssayErrorModel, AssayErrorModels}; use serde::Serialize; +use crate::{AssayErrorModel, AssayErrorModels}; + use crate::{ algorithms::Status, estimation::nonparametric::{median, theta::Theta, weights::Weights}, @@ -23,6 +24,7 @@ pub struct NPCycle { } impl NPCycle { + #[allow(clippy::too_many_arguments)] pub fn new( cycle: usize, objf: f64, diff --git a/src/estimation/nonparametric/mod.rs b/src/estimation/nonparametric/mod.rs index 57c980202..f430da957 100644 --- a/src/estimation/nonparametric/mod.rs +++ b/src/estimation/nonparametric/mod.rs @@ -2,6 +2,7 @@ mod cycles; mod expansion; pub(crate) mod ipm; +mod parameter_optimizer; mod posterior; mod predictions; @@ -17,6 +18,7 @@ mod weights; pub use cycles::{CycleLog, NPCycle}; pub(crate) use expansion::adaptative_grid; pub use ipm::burke; +pub(crate) use parameter_optimizer::ParameterOptimizer; pub use posterior::{posterior, Posterior}; pub use predictions::{NPPredictionRow, NPPredictions}; pub(crate) use psi::calculate_psi; diff --git a/src/estimation/nonparametric/parameter_optimizer.rs b/src/estimation/nonparametric/parameter_optimizer.rs new file mode 100644 index 000000000..0eeb1eacb --- /dev/null +++ b/src/estimation/nonparametric/parameter_optimizer.rs @@ -0,0 +1,119 @@ +//! Support-point refinement for NPOD. +//! +//! The optimizer sits beside the non-parametric objective because its cost +//! function assembles subject likelihoods over generated predictions. + +use argmin::{ + core::{CostFunction, Error, Executor}, + solver::neldermead::NelderMead, +}; +use ndarray::{Array1, Axis}; +use pharmsol::{Data, Equation}; + +use crate::estimation::likelihood::matrix::nonparametric_log_likelihood_matrix; +use crate::AssayErrorModels; + +pub(crate) struct ParameterOptimizer<'a, E: Equation> { + equation: &'a E, + data: &'a Data, + error_models: &'a AssayErrorModels, + reference_likelihoods: &'a Array1, +} + +impl CostFunction for ParameterOptimizer<'_, E> { + type Param = Vec; + type Output = f64; + + fn cost(&self, parameters: &Self::Param) -> Result { + let support_point = Array1::from(parameters.clone()).insert_axis(Axis(0)); + let log_likelihoods = nonparametric_log_likelihood_matrix( + self.equation, + self.data, + &support_point, + self.error_models, + false, + )?; + + if log_likelihoods.ncols() != 1 { + return Err(Error::msg(format!( + "support-point optimizer expected one likelihood column, found {}", + log_likelihoods.ncols() + ))); + } + if log_likelihoods.nrows() != self.reference_likelihoods.len() { + return Err(Error::msg(format!( + "support-point optimizer has {} subjects but {} reference likelihoods", + log_likelihoods.nrows(), + self.reference_likelihoods.len() + ))); + } + + let n_subjects = log_likelihoods.nrows() as f64; + let objective = log_likelihoods + .column(0) + .iter() + .zip(self.reference_likelihoods.iter()) + .fold(-n_subjects, |sum, (log_likelihood, reference)| { + sum + log_likelihood.exp() / reference + }); + Ok(-objective) + } +} + +impl<'a, E: Equation> ParameterOptimizer<'a, E> { + pub(crate) fn new( + equation: &'a E, + data: &'a Data, + error_models: &'a AssayErrorModels, + reference_likelihoods: &'a Array1, + ) -> Self { + Self { + equation, + data, + error_models, + reference_likelihoods, + } + } + + pub(crate) fn optimize_point(self, parameters: Array1) -> Result, Error> { + let solver = + NelderMead::new(initial_simplex(¶meters.to_vec())).with_sd_tolerance(1e-2)?; + let result = Executor::new(self, solver) + .configure(|state| state.max_iters(5)) + .run()?; + let best = result + .state + .best_param + .ok_or_else(|| Error::msg("support-point optimizer produced no best parameter"))?; + Ok(Array1::from(best)) + } +} + +fn initial_simplex(initial_point: &[f64]) -> Vec> { + let mut simplex = Vec::with_capacity(initial_point.len() + 1); + simplex.push(initial_point.to_vec()); + for dimension in 0..initial_point.len() { + let mut point = initial_point.to_vec(); + point[dimension] += if point[dimension] == 0.0 { + 0.00025 + } else { + 0.008 * point[dimension] + }; + simplex.push(point); + } + simplex +} + +#[cfg(test)] +mod tests { + use super::initial_simplex; + + #[test] + fn initial_simplex_perturbs_each_dimension() { + let simplex = initial_simplex(&[1.0, 0.0]); + assert_eq!( + simplex, + vec![vec![1.0, 0.0], vec![1.008, 0.0], vec![1.0, 0.00025]] + ); + } +} diff --git a/src/estimation/nonparametric/predictions.rs b/src/estimation/nonparametric/predictions.rs index d1c13e01a..5bb6ef0e4 100644 --- a/src/estimation/nonparametric/predictions.rs +++ b/src/estimation/nonparametric/predictions.rs @@ -138,8 +138,7 @@ impl NPPredictions { for spp in theta.matrix().row_iter() { let spp_values = spp.iter().cloned().collect::>(); let pred = equation - .simulate_subject_dense(subject, &spp_values, None)? - .0 + .estimate_predictions_dense(subject, &spp_values)? .get_predictions(); predictions.push(pred); } diff --git a/src/estimation/nonparametric/psi.rs b/src/estimation/nonparametric/psi.rs index c6a744854..fc903c672 100644 --- a/src/estimation/nonparametric/psi.rs +++ b/src/estimation/nonparametric/psi.rs @@ -2,13 +2,13 @@ use anyhow::bail; use anyhow::Result; use faer::Mat; use ndarray::{Array2, Axis}; -use pharmsol::prelude::simulator::log_likelihood_matrix; -use pharmsol::AssayErrorModels; use pharmsol::Data; use pharmsol::Equation; use serde::{Deserialize, Serialize}; use super::theta::Theta; +use crate::estimation::likelihood::matrix::nonparametric_log_likelihood_matrix; +use crate::AssayErrorModels; /// [Psi] is a structure that holds the likelihood for each subject (row), for each support point (column) #[derive(Debug, Clone, PartialEq)] @@ -264,8 +264,13 @@ pub(crate) fn calculate_psi( ) -> Result { let tm = theta.matrix(); let theta_ndarray = Array2::from_shape_fn((tm.nrows(), tm.ncols()), |(i, j)| tm[(i, j)]); - let log_psi = - log_likelihood_matrix(equation, subjects, &theta_ndarray, error_models, progress)?; + let log_psi = nonparametric_log_likelihood_matrix( + equation, + subjects, + &theta_ndarray, + error_models, + progress, + )?; Psi::from_log_likelihoods(log_psi) } diff --git a/src/estimation/nonparametric/result.rs b/src/estimation/nonparametric/result.rs index a0fc9f3f1..e5473f19d 100644 --- a/src/estimation/nonparametric/result.rs +++ b/src/estimation/nonparametric/result.rs @@ -5,8 +5,9 @@ use serde::Serialize; use crate::algorithms::Status; use crate::estimation::nonparametric::{CycleLog, NPPredictions, Posterior, Psi, Theta, Weights}; +use crate::AssayErrorModels; -use pharmsol::{AssayErrorModels, Data}; +use pharmsol::Data; /// Contains the results of a nonparametric estimation, including the final parameter #[derive(Debug)] @@ -429,9 +430,9 @@ mod tests { use crate::algorithms::nonparametric::{NpagConfig, NpodConfig}; use crate::estimation::EstimationProblem; use crate::model::ParameterSpace; + use crate::{AssayErrorModel, AssayErrorModels, ErrorPoly}; use pharmsol::equation::metadata; - use pharmsol::prelude::data::{AssayErrorModel, AssayErrorModels}; - use pharmsol::{ErrorPoly, SubjectBuilderExt}; + use pharmsol::SubjectBuilderExt; fn minimal_ode() -> pharmsol::ODE { pharmsol::equation::ODE::new( diff --git a/src/estimation/nonparametric/summaries.rs b/src/estimation/nonparametric/summaries.rs index 6d979aaef..fc3651121 100644 --- a/src/estimation/nonparametric/summaries.rs +++ b/src/estimation/nonparametric/summaries.rs @@ -13,6 +13,11 @@ pub fn fit_summary(result: &NonParametricResult) -> FitSummary { subject_count: result.data().subjects().len(), observation_count: count_observations(result.data()), parameter_count: result.get_theta().parameters().len(), + marginal_log_likelihood: None, + marginal_n2ll: None, + marginal_n2ll_mcse: None, + marginal_likelihood_status: None, + information_criteria: None, } } @@ -32,23 +37,26 @@ pub fn population_summary(result: &NonParametricResult) -> Popul let column = theta_matrix.column(index).to_vec(); let mean_value = mean[index]; let sd = weighted_sd(&column, &weights, mean_value); - let cv_percent = if mean_value.abs() > f64::EPSILON { - (sd / mean_value.abs()) * 100.0 - } else { - 0.0 - }; + let cv_percent = + (mean_value.abs() > f64::EPSILON).then_some((sd / mean_value.abs()) * 100.0); ParameterSummary { name, - mean: mean_value, - median: median[index], - sd, + estimate: mean_value, + mean: Some(mean_value), + median: Some(median[index]), + sd: Some(sd), cv_percent, } }) .collect(); - PopulationSummary { parameters } + PopulationSummary { + parameters, + information_criteria: None, + population_uncertainty: None, + shrinkage: None, + } } pub fn individual_summaries( @@ -71,6 +79,7 @@ pub fn individual_summaries( parameter_names: parameter_names.clone(), estimates: means.row(subject_index).to_vec(), standard_errors: None, + conditional_uncertainty: None, }) .collect() } diff --git a/src/estimation/parametric/conditional_uncertainty.rs b/src/estimation/parametric/conditional_uncertainty.rs new file mode 100644 index 000000000..e8af1d603 --- /dev/null +++ b/src/estimation/parametric/conditional_uncertainty.rs @@ -0,0 +1,956 @@ +//! Conditional-mode curvature via deterministic central finite-difference Hessian. +//! +//! Computes a strict SPD observed-Fisher-information matrix at a joint +//! latent-coordinate mode for one subject. No regularization, repair, ridge, +//! jitter, clipping, SVD, or pseudoinverse is applied. Any non-finite center, +//! perturbation, or non-SPD Hessian is classified as a typed unavailable +//! status. +//! +//! # Coordinate convention +//! +//! The flattened coordinate order is caller-provided `[eta, kappa...]`. Each +//! coordinate carries a prior standard deviation used for adaptive step sizing. + +use ndarray::Array2; +use serde::{Deserialize, Serialize}; + +use super::covariance::{cholesky_lower, eigenvalue_extrema_symmetric, inverse_spd_from_cholesky}; + +// ── public serializable types ────────────────────────────────────────────── + +/// Kind of one joint latent coordinate in the `[eta, kappa...]` ordering. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "kind", rename_all = "snake_case")] +pub enum JointLatentCoordinateKind { + /// Inter-individual random effect coordinate. + Eta { + /// Index of the associated population parameter. + parameter_index: usize, + }, + /// Inter-occasion random effect coordinate. + Kappa { + /// Subject-level occasion index. + occasion_index: usize, + /// Index within the declared IOV random-effect vector. + effect_index: usize, + /// Index of the associated population parameter. + parameter_index: usize, + }, +} + +/// One deterministic latent coordinate with its prior standard deviation. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct JointLatentCoordinate { + /// Zero-based index in the `[eta, kappa...]` flattened vector. + pub index: usize, + /// Human-readable name. + pub name: String, + /// Kind and source indices. + #[serde(flatten)] + pub kind: JointLatentCoordinateKind, + /// Prior standard deviation for adaptive step sizing. + pub prior_sd: f64, +} + +/// Explicit no-regularization marker for conditional-mode curvature. +/// +/// Always [`ConditionalCurvatureRegularization::None`]. No repair, ridge, +/// jitter, clipping, SVD, or pseudoinverse is ever applied. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub enum ConditionalCurvatureRegularization { + /// No regularization of any kind was applied. + None, +} + +/// Convergence metadata for the conditional-mode optimization. +/// +/// Informational only; the curvature computation does not depend on it. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ConditionalModeMetadata { + /// Whether the optimizer reported convergence. + pub converged: bool, + /// Number of iterations taken. + pub iterations: u64, + /// Final objective value at the mode. + pub objective_value: f64, + /// Human-readable termination reason. + pub termination_message: String, +} + +/// Typed reason why conditional-mode curvature is unavailable. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "reason", content = "detail", rename_all = "snake_case")] +pub enum ConditionalCurvatureUnavailableReason { + /// The mode or a perturbation produced a non-finite objective. + NonFiniteModeOrPerturbation, + /// The computed Hessian is not strictly positive definite. + NonSpdHessian, + /// The coordinate vector is empty. + ZeroSize, + /// Mode, prior-SD, and coordinate dimensions do not match. + DimensionMismatch { + mode: usize, + prior_sds: usize, + coordinates: usize, + }, + /// The mode or prior-SD metadata contains a non-finite value. + NonFiniteInput, + /// The finite-difference center objective is non-finite. + NonFiniteCenterObjective, + /// Cholesky inversion produced non-finite or non-positive diagonal entries. + InversionFailed, + /// The spectral condition number is non-finite. + NonFiniteConditionNumber, +} + +/// Availability status of conditional-mode curvature diagnostics. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "reason", rename_all = "snake_case")] +pub enum ConditionalCurvatureStatus { + /// Curvature is available with all fields populated. + Available, + /// Curvature could not be derived; see reason. + Unavailable(ConditionalCurvatureUnavailableReason), +} + +/// Compatibility name emphasizing that proposal selection consumes only availability. +pub type ConditionalCurvatureAvailability = ConditionalCurvatureStatus; + +/// Strict conditional-mode curvature diagnostics for one subject. +/// +/// All matrix fields are row-major `Vec>` in the same flattened +/// `[eta, kappa...]` order as the coordinates. The observed Fisher +/// information is the second derivative of the negative log-density at the +/// mode; the latent covariance is its strict Cholesky inverse. No +/// regularization, repair, or fallback is applied. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ConditionalCurvatureDiagnostics { + /// Flattened latent coordinate definitions. + pub coordinates: Vec, + /// Whether the curvature derivation succeeded. + pub status: ConditionalCurvatureStatus, + /// Regularization classification (always [`ConditionalCurvatureRegularization::None`]). + pub regularization: ConditionalCurvatureRegularization, + /// Observed-Fisher-information inverse (latent covariance). + #[serde(skip_serializing_if = "Option::is_none")] + pub latent_covariance: Option>>, + /// Diagonal square-roots of [`Self::latent_covariance`]. + #[serde(skip_serializing_if = "Option::is_none")] + pub latent_standard_errors: Option>, + /// Spectral condition number λ_max / λ_min of the observed-Fisher matrix. + #[serde(skip_serializing_if = "Option::is_none")] + pub spectral_condition_number: Option, + /// Retained finite-difference steps, one per coordinate. + pub finite_difference_steps: Vec, + /// Raw Hessian of the exact subject negative-log-posterior at the mode. + #[serde(skip_serializing_if = "Option::is_none")] + pub hessian: Option>>, + /// Convergence metadata passed through from the mode optimization. + pub mode_metadata: ConditionalModeMetadata, +} + +impl ConditionalCurvatureDiagnostics { + /// Construct an unavailable diagnostic with the given reason. + pub(crate) fn unavailable( + coordinates: Vec, + reason: ConditionalCurvatureUnavailableReason, + mode_metadata: ConditionalModeMetadata, + ) -> Self { + Self { + coordinates, + status: ConditionalCurvatureStatus::Unavailable(reason), + regularization: ConditionalCurvatureRegularization::None, + latent_covariance: None, + latent_standard_errors: None, + spectral_condition_number: None, + finite_difference_steps: Vec::new(), + hessian: None, + mode_metadata, + } + } +} + +// ── pub(crate) central finite-difference Hessian ─────────────────────────── + +/// Compute strict conditional-mode curvature via central finite-difference +/// Hessian of the exact objective. +/// +/// # Arguments +/// +/// * `mode` - The joint latent-coordinate mode `[eta, kappa...]`. +/// * `prior_sds` - Prior standard deviations, same length as `mode`. +/// * `coordinates` - Flattened coordinate metadata in mode order. +/// * `mode_metadata` - Convergence metadata (informational only). +/// * `objective` - Exact negative log-density `FnMut(&[f64]) -> f64`. +/// +/// # Step sizes +/// +/// For each coordinate `i`: +/// +/// ```text +/// h_i = ε^(1/4) × max(1, |mode_i|, prior_sd_i) +/// ``` +/// +/// where ε = [`f64::EPSILON`]. +/// +/// # Hessian formulas +/// +/// *Diagonal* (central second derivative): +/// +/// ```text +/// H_ii = [f(x + h_i e_i) - 2f(x) + f(x - h_i e_i)] / h_i² +/// ``` +/// +/// *Off-diagonal* (mixed central partial derivative): +/// +/// ```text +/// H_ij = [f_++ - f_+- - f_-+ + f_--] / (4 h_i h_j) +/// ``` +/// +/// where `f_ab = f(x + a·h_i e_i + b·h_j e_j)`. +/// +/// # Strictness +/// +/// No regularization, repair, ridge, jitter, clipping, SVD, or pseudoinverse +/// is applied. Non-finite center/perturbation and non-SPD Hessians produce a +/// typed [`ConditionalCurvatureStatus::Unavailable`] result. +pub(crate) fn conditional_mode_curvature f64>( + mode: &[f64], + prior_sds: &[f64], + coordinates: &[JointLatentCoordinate], + mode_metadata: &ConditionalModeMetadata, + mut objective: F, +) -> ConditionalCurvatureDiagnostics { + let n = mode.len(); + + // ── guard: zero-size ────────────────────────────────────────────── + if n == 0 { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::ZeroSize, + mode_metadata.clone(), + ); + } + if prior_sds.len() != n || coordinates.len() != n { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::DimensionMismatch { + mode: n, + prior_sds: prior_sds.len(), + coordinates: coordinates.len(), + }, + mode_metadata.clone(), + ); + } + if mode.iter().any(|value| !value.is_finite()) + || prior_sds + .iter() + .any(|value| !value.is_finite() || *value < 0.0) + { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteInput, + mode_metadata.clone(), + ); + } + + // ── step sizes ──────────────────────────────────────────────────── + let eps_quarter = f64::EPSILON.powf(0.25); + let steps: Vec = mode + .iter() + .zip(prior_sds) + .map(|(value, sd)| eps_quarter * f64::max(f64::max(1.0, value.abs()), *sd)) + .collect(); + + // ── center evaluation ───────────────────────────────────────────── + let f_center = objective(mode); + if !f_center.is_finite() { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteCenterObjective, + mode_metadata.clone(), + ); + } + + // ── forward / backward evaluations per coordinate ───────────────── + let mut point_plus = mode.to_vec(); + let mut point_minus = mode.to_vec(); + let mut f_plus = vec![0.0; n]; + let mut f_minus = vec![0.0; n]; + + for i in 0..n { + point_plus[i] = mode[i] + steps[i]; + f_plus[i] = objective(&point_plus); + point_plus[i] = mode[i]; // restore + + point_minus[i] = mode[i] - steps[i]; + f_minus[i] = objective(&point_minus); + point_minus[i] = mode[i]; // restore + } + + // ── check perturbation finiteness ───────────────────────────────── + if !f_center.is_finite() + || f_plus.iter().any(|v| !v.is_finite()) + || f_minus.iter().any(|v| !v.is_finite()) + { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteModeOrPerturbation, + mode_metadata.clone(), + ); + } + + // ── diagonal entries ────────────────────────────────────────────── + let mut hessian = Array2::::zeros((n, n)); + for i in 0..n { + let h_i = steps[i]; + hessian[[i, i]] = (f_plus[i] - 2.0 * f_center + f_minus[i]) / (h_i * h_i); + } + + // ── off-diagonal entries via mixed central differences ──────────── + let mut work = mode.to_vec(); + for i in 0..n { + for j in (i + 1)..n { + let h_i = steps[i]; + let h_j = steps[j]; + + // f_++: +h_i, +h_j + work[i] = mode[i] + h_i; + work[j] = mode[j] + h_j; + let f_pp = objective(&work); + + // f_+-: +h_i, -h_j + work[j] = mode[j] - h_j; + let f_pm = objective(&work); + + // f_-+: -h_i, +h_j + work[i] = mode[i] - h_i; + work[j] = mode[j] + h_j; + let f_mp = objective(&work); + + // f_--: -h_i, -h_j + work[j] = mode[j] - h_j; + let f_mm = objective(&work); + + // Restore + work[i] = mode[i]; + work[j] = mode[j]; + + if !f_pp.is_finite() || !f_pm.is_finite() || !f_mp.is_finite() || !f_mm.is_finite() { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteModeOrPerturbation, + mode_metadata.clone(), + ); + } + + let mixed = (f_pp - f_pm - f_mp + f_mm) / (4.0 * h_i * h_j); + hessian[[i, j]] = mixed; + hessian[[j, i]] = mixed; + } + } + + // ── check finite Hessian ────────────────────────────────────────── + if !hessian.iter().all(|v| v.is_finite()) { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteModeOrPerturbation, + mode_metadata.clone(), + ); + } + + // ── strict SPD Cholesky ─────────────────────────────────────────── + // The observed Fisher information is the Hessian itself at the mode. + let fisher = hessian.clone(); + + let lower = match cholesky_lower(&fisher) { + Ok(lower) => lower, + Err(_) => { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonSpdHessian, + mode_metadata.clone(), + ); + } + }; + + // ── strict Cholesky inverse ─────────────────────────────────────── + let covariance = match inverse_spd_from_cholesky(&lower) { + Ok(covariance) => covariance, + Err(_) => { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::InversionFailed, + mode_metadata.clone(), + ); + } + }; + + // ── latent standard errors ──────────────────────────────────────── + let latent_se: Vec = (0..n).map(|idx| covariance[[idx, idx]].sqrt()).collect(); + if latent_se.iter().any(|v| !v.is_finite()) { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::InversionFailed, + mode_metadata.clone(), + ); + } + + // ── spectral condition number ───────────────────────────────────── + let (lambda_min, lambda_max) = match eigenvalue_extrema_symmetric(&fisher) { + Ok(extrema) => extrema, + Err(_) => { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::InversionFailed, + mode_metadata.clone(), + ); + } + }; + if !lambda_min.is_finite() || !lambda_max.is_finite() || lambda_min <= 0.0 { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteConditionNumber, + mode_metadata.clone(), + ); + } + let cond = lambda_max / lambda_min; + if !cond.is_finite() { + return ConditionalCurvatureDiagnostics::unavailable( + coordinates.to_vec(), + ConditionalCurvatureUnavailableReason::NonFiniteConditionNumber, + mode_metadata.clone(), + ); + } + + // ── convert ndarray to Vec> for serialization ──────────── + let fisher_rows: Vec> = fisher.rows().into_iter().map(|row| row.to_vec()).collect(); + let cov_rows: Vec> = covariance + .rows() + .into_iter() + .map(|row| row.to_vec()) + .collect(); + + ConditionalCurvatureDiagnostics { + coordinates: coordinates.to_vec(), + status: ConditionalCurvatureStatus::Available, + regularization: ConditionalCurvatureRegularization::None, + latent_covariance: Some(cov_rows), + latent_standard_errors: Some(latent_se), + spectral_condition_number: Some(cond), + finite_difference_steps: steps, + hessian: Some(fisher_rows), + mode_metadata: mode_metadata.clone(), + } +} + +// ── tests ────────────────────────────────────────────────────────────────── + +#[cfg(test)] +mod tests { + use super::*; + + /// Build a minimal 2D coordinate list: eta on param 0, eta on param 1. + fn two_eta_coordinates(sd0: f64, sd1: f64) -> Vec { + vec![ + JointLatentCoordinate { + index: 0, + name: "eta:CL".into(), + kind: JointLatentCoordinateKind::Eta { parameter_index: 0 }, + prior_sd: sd0, + }, + JointLatentCoordinate { + index: 1, + name: "eta:V".into(), + kind: JointLatentCoordinateKind::Eta { parameter_index: 1 }, + prior_sd: sd1, + }, + ] + } + + fn single_eta_coordinate(sd: f64) -> Vec { + vec![JointLatentCoordinate { + index: 0, + name: "eta:CL".into(), + kind: JointLatentCoordinateKind::Eta { parameter_index: 0 }, + prior_sd: sd, + }] + } + + fn default_mode_metadata() -> ConditionalModeMetadata { + ConditionalModeMetadata { + converged: true, + iterations: 42, + objective_value: 0.0, + termination_message: "test".into(), + } + } + + // ── exact quadratic 2D ──────────────────────────────────────────────── + + /// Exact quadratic: f(eta) = 0.5 * eta^T H eta, where H = [[4, 1], [1, 2]]. + /// Mode at (0, 0) with f_center = 0. + fn quadratic_2d(eta: &[f64]) -> f64 { + let x = eta[0]; + let y = eta[1]; + 0.5 * (4.0 * x * x + 2.0 * x * y + 2.0 * y * y) + } + + #[test] + fn exact_quadratic_2d_hessian_and_covariance_recovery() { + let mode = vec![0.0, 0.0]; + let prior_sds = vec![1.0, 1.0]; + let coords = two_eta_coordinates(1.0, 1.0); + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, quadratic_2d); + + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + assert_eq!( + diagnostics.regularization, + ConditionalCurvatureRegularization::None + ); + assert_eq!(diagnostics.coordinates.len(), 2); + + // Hessian should be close to [[4, 1], [1, 2]]. + let fisher = diagnostics.hessian.unwrap(); + assert!( + (fisher[0][0] - 4.0).abs() < 1e-8, + "H[0,0] = {} expected 4.0", + fisher[0][0] + ); + assert!( + (fisher[0][1] - 1.0).abs() < 1e-8, + "H[0,1] = {} expected 1.0", + fisher[0][1] + ); + assert!( + (fisher[1][1] - 2.0).abs() < 1e-8, + "H[1,1] = {} expected 2.0", + fisher[1][1] + ); + + // Covariance (= H^{-1}): det = 8-1 = 7. + // H^{-1} = 1/7 * [[2, -1], [-1, 4]] ≈ [[0.285714, -0.142857], [-0.142857, 0.571429]] + let cov = diagnostics.latent_covariance.unwrap(); + assert!( + (cov[0][0] - 2.0 / 7.0).abs() < 1e-8, + "cov[0,0] = {}", + cov[0][0] + ); + assert!( + (cov[0][1] + 1.0 / 7.0).abs() < 1e-8, + "cov[0,1] = {}", + cov[0][1] + ); + assert!( + (cov[1][0] + 1.0 / 7.0).abs() < 1e-8, + "cov[1,0] = {}", + cov[1][0] + ); + assert!( + (cov[1][1] - 4.0 / 7.0).abs() < 1e-8, + "cov[1,1] = {}", + cov[1][1] + ); + + // Standard errors. + let se = diagnostics.latent_standard_errors.unwrap(); + assert!((se[0] - (2.0_f64 / 7.0).sqrt()).abs() < 1e-8); + assert!((se[1] - (4.0_f64 / 7.0).sqrt()).abs() < 1e-8); + + // Spectral condition number. + // Eigenvalues of H = [[4,1],[1,2]]: λ = (6 ± √(36-28))/2 = (6 ± √8)/2 = 3 ± √2. + // λ_max = 3 + √2, λ_min = 3 - √2. + let expected_condition = (3.0 + 2.0_f64.sqrt()) / (3.0 - 2.0_f64.sqrt()); + let cond = diagnostics.spectral_condition_number.unwrap(); + assert!( + (cond - expected_condition).abs() < 1e-8, + "condition = {} expected {}", + cond, + expected_condition + ); + } + + #[test] + fn three_dimensional_covariance_is_exactly_symmetric_for_proposal_reuse() { + let mode = vec![0.0; 3]; + let prior_sds = vec![1.0; 3]; + let coordinates = (0..3) + .map(|index| JointLatentCoordinate { + index, + name: format!("eta:{index}"), + kind: JointLatentCoordinateKind::Eta { + parameter_index: index, + }, + prior_sd: 1.0, + }) + .collect::>(); + let diagnostics = conditional_mode_curvature( + &mode, + &prior_sds, + &coordinates, + &default_mode_metadata(), + |x| { + 0.5 * (4.0 * x[0] * x[0] + + 3.0 * x[1] * x[1] + + 2.0 * x[2] * x[2] + + 2.0 * x[0] * x[1] + + x[0] * x[2] + + 0.5 * x[1] * x[2]) + }, + ); + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + let covariance = diagnostics.latent_covariance.unwrap(); + for (row, values) in covariance.iter().enumerate() { + for (column, value) in values.iter().enumerate().take(row) { + assert_eq!(value.to_bits(), covariance[column][row].to_bits()); + } + } + } + + // ── mode off-origin with high prior SD in step ──────────────────────── + + #[test] + fn quadratic_2d_mode_off_origin_with_high_prior_sd() { + // f(eta) = 0.5 * eta^T H eta with mode at (5, -3). + // Shift coordinates: let u = eta - mode, then f(u) = 0.5 * u^T H u. + // The Hessian is unchanged. + let mode = vec![5.0, -3.0]; + let prior_sds = vec![10.0, 20.0]; // high prior SDs → bigger steps + let coords = two_eta_coordinates(10.0, 20.0); + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |eta| { + let u0 = eta[0] - 5.0; + let u1 = eta[1] + 3.0; + 0.5 * (4.0 * u0 * u0 + 2.0 * u0 * u1 + 2.0 * u1 * u1) + }); + + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + let fisher = diagnostics.hessian.unwrap(); + assert!( + (fisher[0][0] - 4.0).abs() < 1e-6, + "off-origin H[0,0] = {}", + fisher[0][0] + ); + assert!( + (fisher[0][1] - 1.0).abs() < 1e-6, + "off-origin H[0,1] = {}", + fisher[0][1] + ); + } + + // ── 1D quadratic ────────────────────────────────────────────────────── + + #[test] + fn scalar_quadratic_hessian_and_covariance() { + // f(x) = 0.5 * 9 * x^2, Hessian = 9, covariance = 1/9. + let mode = vec![0.0]; + let prior_sds = vec![1.0]; + let coords = single_eta_coordinate(1.0); + let metadata = default_mode_metadata(); + + let diagnostics = conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |x| { + 4.5 * x[0] * x[0] + }); + + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + let fisher = diagnostics.hessian.unwrap(); + assert!((fisher[0][0] - 9.0).abs() < 1e-8); + let cov = diagnostics.latent_covariance.unwrap(); + assert!((cov[0][0] - 1.0 / 9.0).abs() < 1e-8); + let se = diagnostics.latent_standard_errors.unwrap(); + assert!((se[0] - 1.0 / 3.0).abs() < 1e-8); + assert_eq!(diagnostics.spectral_condition_number.unwrap(), 1.0); + } + + // ── typed unavailable: non-SPD ──────────────────────────────────────── + + #[test] + fn non_positive_definite_hessian_yields_typed_unavailable() { + // f(x,y) = 0.5 * (x^2 - y^2) has Hessian = [[1, 0], [0, -1]] (indefinite). + let mode = vec![0.0, 0.0]; + let prior_sds = vec![1.0, 1.0]; + let coords = two_eta_coordinates(1.0, 1.0); + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |eta| { + 0.5 * (eta[0] * eta[0] - eta[1] * eta[1]) + }); + + assert!( + matches!( + diagnostics.status, + ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::NonSpdHessian + ) + ), + "expected NonSpdHessian, got {:?}", + diagnostics.status + ); + assert!(diagnostics.latent_covariance.is_none()); + assert!(diagnostics.latent_standard_errors.is_none()); + assert!(diagnostics.spectral_condition_number.is_none()); + // The Hessian is omitted because strict Cholesky classification failed. + } + + // ── typed unavailable: NaN center ───────────────────────────────────── + + #[test] + fn nan_center_yields_typed_unavailable() { + let mode = vec![0.0]; + let prior_sds = vec![1.0]; + let coords = single_eta_coordinate(1.0); + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |_| f64::NAN); + + assert!(matches!( + diagnostics.status, + ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::NonFiniteCenterObjective + ) + )); + } + + // ── typed unavailable: NaN perturbation ─────────────────────────────── + + #[test] + fn nan_perturbation_yields_typed_unavailable() { + // Objective returns NaN when x[0] is perturbed away from 0. + let mode = vec![0.0]; + let prior_sds = vec![1.0]; + let coords = single_eta_coordinate(1.0); + let metadata = default_mode_metadata(); + + let diagnostics = conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |x| { + if (x[0] - mode[0]).abs() > f64::EPSILON { + f64::NAN + } else { + 0.0 + } + }); + + assert!(matches!( + diagnostics.status, + ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::NonFiniteModeOrPerturbation + ) + )); + } + + // ── typed unavailable: zero size ────────────────────────────────────── + + #[test] + fn zero_size_yields_typed_unavailable() { + let mode: Vec = vec![]; + let prior_sds: Vec = vec![]; + let coords: Vec = vec![]; + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |_| 0.0); + + assert!(matches!( + diagnostics.status, + ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::ZeroSize + ) + )); + } + + // ── typed unavailable: 2D non-SPD via zero eigenvalue ───────────────── + + #[test] + fn rank_deficient_hessian_yields_typed_unavailable() { + // f(x,y) = 0.5 * (x + y)^2 has Hessian = [[1,1],[1,1]] (rank 1). + let mode = vec![0.0, 0.0]; + let prior_sds = vec![1.0, 1.0]; + let coords = two_eta_coordinates(1.0, 1.0); + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |eta| { + let s = eta[0] + eta[1]; + 0.5 * s * s + }); + + assert!(matches!( + diagnostics.status, + ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::NonSpdHessian + ) + )); + } + + // ── serde roundtrip for all public types ────────────────────────────── + + #[test] + fn joint_latent_coordinate_kind_serde_roundtrip() { + for kind in [ + JointLatentCoordinateKind::Eta { parameter_index: 3 }, + JointLatentCoordinateKind::Kappa { + occasion_index: 7, + effect_index: 1, + parameter_index: 3, + }, + ] { + let json = serde_json::to_string(&kind).unwrap(); + let roundtripped: JointLatentCoordinateKind = serde_json::from_str(&json).unwrap(); + assert_eq!(roundtripped, kind); + } + } + + #[test] + fn conditional_curvature_status_serde_roundtrip() { + let available = ConditionalCurvatureStatus::Available; + let json = serde_json::to_string(&available).unwrap(); + assert!(json.contains("available")); + let rt: ConditionalCurvatureStatus = serde_json::from_str(&json).unwrap(); + assert_eq!(rt, ConditionalCurvatureStatus::Available); + + let unavailable = ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::NonSpdHessian, + ); + let json = serde_json::to_string(&unavailable).unwrap(); + assert!(json.contains("non_spd_hessian")); + let rt: ConditionalCurvatureStatus = serde_json::from_str(&json).unwrap(); + assert_eq!(rt, unavailable); + } + + #[test] + fn conditional_curvature_diagnostics_full_serde_roundtrip() { + let coords = two_eta_coordinates(1.0, 1.0); + let metadata = default_mode_metadata(); + let mode = vec![0.0, 0.0]; + let prior_sds = vec![1.0, 1.0]; + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, quadratic_2d); + + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + + let json = serde_json::to_string(&diagnostics).unwrap(); + let roundtripped: ConditionalCurvatureDiagnostics = serde_json::from_str(&json).unwrap(); + + assert_eq!(roundtripped.status, ConditionalCurvatureStatus::Available); + assert_eq!( + roundtripped.regularization, + ConditionalCurvatureRegularization::None + ); + assert!(roundtripped.latent_covariance.is_some()); + assert!(roundtripped.latent_standard_errors.is_some()); + assert!(roundtripped.spectral_condition_number.is_some()); + assert!(roundtripped.hessian.is_some()); + assert!(roundtripped.mode_metadata.converged); + assert_eq!(roundtripped.mode_metadata.iterations, 42); + } + + #[test] + fn unavailable_diagnostics_serde_roundtrip() { + let diagnostics = ConditionalCurvatureDiagnostics::unavailable( + two_eta_coordinates(1.0, 1.0), + ConditionalCurvatureUnavailableReason::NonSpdHessian, + default_mode_metadata(), + ); + + let json = serde_json::to_string(&diagnostics).unwrap(); + assert!(json.contains("non_spd_hessian")); + let roundtripped: ConditionalCurvatureDiagnostics = serde_json::from_str(&json).unwrap(); + + assert!(matches!( + roundtripped.status, + ConditionalCurvatureStatus::Unavailable( + ConditionalCurvatureUnavailableReason::NonSpdHessian + ) + )); + assert!(roundtripped.latent_covariance.is_none()); + assert!(roundtripped.latent_standard_errors.is_none()); + } + + #[test] + fn conditional_curvature_regularization_serde_roundtrip() { + let reg = ConditionalCurvatureRegularization::None; + let json = serde_json::to_string(®).unwrap(); + assert!(json.contains("none")); + let rt: ConditionalCurvatureRegularization = serde_json::from_str(&json).unwrap(); + assert_eq!(rt, ConditionalCurvatureRegularization::None); + } + + // ── metadata passthrough ────────────────────────────────────────────── + + #[test] + fn mode_metadata_is_preserved_in_output() { + let mode = vec![0.0, 0.0]; + let prior_sds = vec![1.0, 1.0]; + let coords = two_eta_coordinates(1.0, 1.0); + let metadata = ConditionalModeMetadata { + converged: false, + iterations: 100, + objective_value: -123.456, + termination_message: "max iterations".into(), + }; + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, quadratic_2d); + + assert!(!diagnostics.mode_metadata.converged); + assert_eq!(diagnostics.mode_metadata.iterations, 100); + assert_eq!(diagnostics.mode_metadata.objective_value, -123.456); + assert_eq!( + diagnostics.mode_metadata.termination_message, + "max iterations" + ); + // Curvature should still be available even for non-converged mode. + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + } + + // ── kappa coordinate kind ───────────────────────────────────────────── + + #[test] + fn kappa_coordinates_are_handled() { + // 1 eta + 1 kappa → 2D mode. + let coords = vec![ + JointLatentCoordinate { + index: 0, + name: "eta:CL".into(), + kind: JointLatentCoordinateKind::Eta { parameter_index: 0 }, + prior_sd: 1.0, + }, + JointLatentCoordinate { + index: 1, + name: "kappa:CL:0".into(), + kind: JointLatentCoordinateKind::Kappa { + occasion_index: 0, + effect_index: 0, + parameter_index: 0, + }, + prior_sd: 0.5, + }, + ]; + + let mode = vec![0.2, -0.1]; + let prior_sds = vec![1.0, 0.5]; + let metadata = default_mode_metadata(); + + let diagnostics = + conditional_mode_curvature(&mode, &prior_sds, &coords, &metadata, |eta| { + let x = eta[0]; + let y = eta[1]; + 0.5 * (4.0 * x * x + 2.0 * x * y + 3.0 * y * y) + }); + + assert_eq!(diagnostics.status, ConditionalCurvatureStatus::Available); + let fisher = diagnostics.hessian.unwrap(); + // Hessian = [[4, 1], [1, 3]] + assert!((fisher[0][0] - 4.0).abs() < 1e-6); + assert!((fisher[0][1] - 1.0).abs() < 1e-6); + assert!((fisher[1][1] - 3.0).abs() < 1e-6); + } +} diff --git a/src/estimation/parametric/covariance.rs b/src/estimation/parametric/covariance.rs new file mode 100644 index 000000000..829bc261a --- /dev/null +++ b/src/estimation/parametric/covariance.rs @@ -0,0 +1,618 @@ +use anyhow::Result; +use ndarray::Array2; + +/// Dense identity matrix used for initial Ω placeholders and tests. +pub(crate) fn identity_matrix(size: usize) -> Array2 { + Array2::from_shape_fn( + (size, size), + |(row, col)| if row == col { 1.0 } else { 0.0 }, + ) +} + +/// Lower Cholesky factor of a symmetric positive-definite covariance matrix. +/// +/// Kept small and ndarray-native for now. This is the shared PMcore path for +/// η/Ω prior scoring until a crate-wide linear algebra backend is selected. +pub(crate) fn cholesky_lower(matrix: &Array2) -> Result>> { + if matrix.nrows() != matrix.ncols() { + anyhow::bail!("omega must be square"); + } + + let n = matrix.nrows(); + let mut unit_lower = vec![vec![0.0; n]; n]; + let mut diagonal = vec![0.0; n]; + for row in 0..n { + for col in 0..row { + if matrix[[row, col]] != matrix[[col, row]] { + anyhow::bail!("omega must be symmetric"); + } + let sum = (0..col) + .map(|k| unit_lower[row][k] * unit_lower[col][k] * diagonal[k]) + .sum::(); + unit_lower[row][col] = (matrix[[row, col]] - sum) / diagonal[col]; + if !unit_lower[row][col].is_finite() { + anyhow::bail!("omega must be positive definite"); + } + } + unit_lower[row][row] = 1.0; + let sum = (0..row) + .map(|k| unit_lower[row][k].powi(2) * diagonal[k]) + .sum::(); + diagonal[row] = matrix[[row, row]] - sum; + if diagonal[row] <= 0.0 || !diagonal[row].is_finite() { + anyhow::bail!("omega must be positive definite"); + } + } + + Ok((0..n) + .map(|row| { + (0..n) + .map(|col| { + if col <= row { + unit_lower[row][col] * diagonal[col].sqrt() + } else { + 0.0 + } + }) + .collect() + }) + .collect()) +} + +/// Invert an SPD matrix from its unmodified lower Cholesky factor. +/// +/// The inverse is formed as `L^-T L^-1`; each lower-triangle value is computed +/// once and copied to its transpose, so the result is exactly symmetric without +/// regularization or numerical repair. +pub(crate) fn inverse_spd_from_cholesky(lower: &[Vec]) -> Result> { + let n = lower.len(); + if n == 0 || lower.iter().any(|row| row.len() != n) { + anyhow::bail!("Cholesky factor must be nonempty and square"); + } + let mut inverse_lower = Array2::::zeros((n, n)); + for column in 0..n { + for row in column..n { + let rhs = if row == column { 1.0 } else { 0.0 }; + let sum = (column..row) + .map(|k| lower[row][k] * inverse_lower[[k, column]]) + .sum::(); + let diagonal = lower[row][row]; + if !diagonal.is_finite() || diagonal <= 0.0 { + anyhow::bail!("Cholesky factor diagonal must be finite and positive"); + } + inverse_lower[[row, column]] = (rhs - sum) / diagonal; + } + } + if inverse_lower.iter().any(|value| !value.is_finite()) { + anyhow::bail!("Cholesky inversion produced a non-finite triangular inverse"); + } + + let mut inverse = Array2::::zeros((n, n)); + for row in 0..n { + for column in 0..=row { + let value = (row.max(column)..n) + .map(|k| inverse_lower[[k, row]] * inverse_lower[[k, column]]) + .sum::(); + if !value.is_finite() { + anyhow::bail!("Cholesky inversion produced a non-finite covariance"); + } + inverse[[row, column]] = value; + inverse[[column, row]] = value; + } + } + if (0..n).any(|index| inverse[[index, index]] <= 0.0) { + anyhow::bail!("Cholesky inversion produced a non-positive variance"); + } + Ok(inverse) +} + +/// Compute sqrt(λ_max(L^T V L)) where I = L L^T is the Cholesky decomposition +/// of the observed-information matrix. +/// +/// This is the worst-contrast metric: the largest standard-deviation scale of V +/// expressed in I-normalized coordinates. It is invariant under nonsingular +/// linear reparameterization. +/// +/// No clamp, jitter, projection, or repair is applied. Fails with a typed +/// error when inputs are non-finite, non-symmetric, dimensionally mismatched, +/// or when the information matrix is not strictly positive definite. +pub(crate) fn worst_contrast(information: &Array2, variance: &Array2) -> Result { + if information.nrows() != information.ncols() || variance.nrows() != variance.ncols() { + anyhow::bail!("worst-contrast inputs must be square"); + } + if information.nrows() != variance.nrows() { + anyhow::bail!("worst-contrast information and variance dimensions must match"); + } + + let n = information.nrows(); + + // Both matrices must be finite and symmetric. + for row in 0..n { + for col in 0..n { + if !information[[row, col]].is_finite() || !variance[[row, col]].is_finite() { + anyhow::bail!("worst-contrast inputs must be finite"); + } + } + for col in 0..row { + if information[[row, col]] != information[[col, row]] { + anyhow::bail!("worst-contrast information must be symmetric"); + } + if variance[[row, col]] != variance[[col, row]] { + anyhow::bail!("worst-contrast variance must be symmetric"); + } + } + } + + let (variance_min, _) = eigenvalue_extrema_symmetric(variance)?; + if variance_min < 0.0 { + anyhow::bail!("worst-contrast variance must be positive semidefinite"); + } + + // Cholesky requires strictly positive-definite information. + let lower = cholesky_lower(information)?; + + // Compute M = L^T V L as a full symmetric matrix. + let mut m = Array2::zeros((n, n)); + for i in 0..n { + for j in 0..=i { + let mut sum = 0.0; + for k in (i.max(j))..n { + // Accumulate L[k][i] * (V L)[k][j]. + let mut vl = 0.0; + for p in j..n { + vl += variance[[k, p]] * lower[p][j]; + } + sum += lower[k][i] * vl; + } + m[[i, j]] = sum; + m[[j, i]] = sum; + } + } + + let (_, max_eig) = eigenvalue_extrema_symmetric(&m)?; + if max_eig < 0.0 { + anyhow::bail!("worst-contrast eigenvalue must be nonnegative, got {max_eig}"); + } + Ok(max_eig.sqrt()) +} + +/// Largest eigenvalue of a symmetric matrix via Jacobi rotation. +/// +/// Operates on a mutable copy; the original matrix is never modified. +pub(crate) fn eigenvalue_extrema_symmetric(matrix: &Array2) -> Result<(f64, f64)> { + let n = matrix.nrows(); + if n == 0 { + anyhow::bail!("cannot compute eigenvalue of 0x0 matrix"); + } + + // Scalar: identity + if n == 1 { + return Ok((matrix[[0, 0]], matrix[[0, 0]])); + } + + // 2×2: closed form + if n == 2 { + let trace = matrix[[0, 0]] + matrix[[1, 1]]; + let det = matrix[[0, 0]] * matrix[[1, 1]] - matrix[[0, 1]] * matrix[[1, 0]]; + let discriminant = trace * trace - 4.0 * det; + if discriminant < 0.0 { + anyhow::bail!("2x2 worst-contrast discriminant must be nonnegative"); + } + let root = discriminant.sqrt(); + return Ok((0.5 * (trace - root), 0.5 * (trace + root))); + } + + // General n×n: symmetric Jacobi iteration. + let mut work = matrix.clone(); + let max_rotations = 50 * n * n; + let mut converged = false; + for _ in 0..max_rotations { + // Find the largest off-diagonal element. + let mut p = 0; + let mut q = 0; + let mut largest = 0.0; + for row in 0..n { + for col in 0..row { + let abs_val = work[[row, col]].abs(); + if abs_val > largest { + largest = abs_val; + p = row; + q = col; + } + } + } + let scale = work.iter().fold(1.0_f64, |s, x| s.max(x.abs())); + if largest <= 64.0 * f64::EPSILON * scale { + converged = true; + break; + } + let angle = 0.5 * (2.0 * work[[p, q]]).atan2(work[[q, q]] - work[[p, p]]); + let (sin, cos) = angle.sin_cos(); + for k in 0..n { + if k == p || k == q { + continue; + } + let kp = work[[k, p]]; + let kq = work[[k, q]]; + work[[k, p]] = cos * kp - sin * kq; + work[[p, k]] = work[[k, p]]; + work[[k, q]] = sin * kp + cos * kq; + work[[q, k]] = work[[k, q]]; + } + let pp = work[[p, p]]; + let qq = work[[q, q]]; + let pq = work[[p, q]]; + work[[p, p]] = cos * cos * pp - 2.0 * sin * cos * pq + sin * sin * qq; + work[[q, q]] = sin * sin * pp + 2.0 * sin * cos * pq + cos * cos * qq; + work[[p, q]] = 0.0; + work[[q, p]] = 0.0; + } + + if !converged { + anyhow::bail!("worst-contrast Jacobi eigensolver did not converge"); + } + let minimum = (0..n) + .map(|index| work[[index, index]]) + .fold(f64::INFINITY, f64::min); + let maximum = (0..n) + .map(|index| work[[index, index]]) + .fold(f64::NEG_INFINITY, f64::max); + Ok((minimum, maximum)) +} + +/// Smallest generalized eigenvalue of `current` relative to `reference`. +/// +/// This is the dimensionless SPD-boundary margin +/// `min(x' current x / x' reference x)`. It is positive exactly when both +/// covariance matrices are strictly positive definite and approaches zero as +/// `current` approaches the SPD boundary relative to the declared reference. +pub(crate) fn relative_spd_margin(current: &Array2, reference: &Array2) -> Result { + if current.dim() != reference.dim() || current.nrows() != current.ncols() { + anyhow::bail!("relative SPD margin covariance dimensions must match and be square"); + } + let n = current.nrows(); + if n == 0 { + anyhow::bail!("relative SPD margin requires a non-empty covariance"); + } + let reference_lower = cholesky_lower(reference)?; + cholesky_lower(current)?; + + let mut inverse_lower = Array2::zeros((n, n)); + for col in 0..n { + let mut unit = vec![0.0; n]; + unit[col] = 1.0; + let solution = solve_lower(&reference_lower, &unit)?; + for row in 0..n { + inverse_lower[[row, col]] = solution[row]; + } + } + + let mut whitened = Array2::zeros((n, n)); + for row in 0..n { + for col in 0..=row { + let mut value = 0.0; + for left in 0..n { + for right in 0..n { + value += inverse_lower[[row, left]] + * current[[left, right]] + * inverse_lower[[col, right]]; + } + } + whitened[[row, col]] = value; + whitened[[col, row]] = value; + } + } + let (minimum, _) = eigenvalue_extrema_symmetric(&whitened)?; + if !minimum.is_finite() || minimum <= 0.0 { + anyhow::bail!("relative SPD margin must be finite and positive"); + } + Ok(minimum) +} + +/// Solve `lower * x = rhs` for x. +pub(crate) fn solve_lower(lower: &[Vec], rhs: &[f64]) -> Result> { + if lower.len() != rhs.len() { + anyhow::bail!("eta length does not match omega dimension"); + } + + let mut solution = vec![0.0; rhs.len()]; + for row in 0..rhs.len() { + let sum = (0..row) + .map(|col| lower[row][col] * solution[col]) + .sum::(); + solution[row] = (rhs[row] - sum) / lower[row][row]; + } + Ok(solution) +} + +pub(crate) fn cholesky_log_determinant(lower: &[Vec]) -> f64 { + 2.0 * lower + .iter() + .enumerate() + .map(|(index, row)| row[index].ln()) + .sum::() +} + +/// Floor and regularize a covariance matrix until it is positive definite. +/// +/// Sparse early SAEM iterations can produce rank-deficient Ω estimates. +/// Numerical robustness work showed that allowing Ω to collapse starves the MCMC kernel, +/// so PMcore applies a diagonal floor and bounded jitter here. +#[cfg(test)] +pub(crate) fn ensure_positive_definite_covariance( + matrix: &Array2, + minimum_variance: f64, +) -> Array2 { + let n = matrix.nrows(); + let mut candidate = matrix.clone(); + for index in 0..n { + candidate[[index, index]] = candidate[[index, index]].max(minimum_variance); + } + + let mut jitter = minimum_variance.max(f64::EPSILON.sqrt()); + for _ in 0..8 { + if cholesky_lower(&candidate).is_ok() { + return candidate; + } + for index in 0..n { + candidate[[index, index]] += jitter; + } + jitter *= 10.0; + } + + Array2::from_shape_fn((n, n), |(row, col)| { + if row == col { + matrix[[row, row]].max(minimum_variance) + } else { + 0.0 + } + }) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn cholesky_log_determinant_handles_correlated_covariance() { + let omega = ndarray::array![[4.0, 1.0], [1.0, 2.0]]; + let lower = cholesky_lower(&omega).unwrap(); + assert!((cholesky_log_determinant(&lower) - 7.0_f64.ln()).abs() < 1e-12); + } + + #[test] + fn cholesky_scaling_rejects_rank_one_and_accepts_high_scale_covariance() { + assert!(cholesky_lower(&ndarray::array![[4.0, 4.0], [4.0, 4.0]]).is_err()); + assert!(cholesky_lower(&ndarray::array![[1e200, 5e199], [5e199, 1e200]]).is_ok()); + } + + #[test] + fn relative_spd_margin_matches_known_generalized_eigenvalue() { + let reference = ndarray::array![[4.0, 1.0], [1.0, 2.0]]; + let lower = cholesky_lower(&reference).unwrap(); + let whitened = ndarray::array![[0.95, -0.75], [-0.75, 0.95]]; + let mut current = Array2::zeros((2, 2)); + for row in 0..2 { + for col in 0..2 { + for left in 0..2 { + for right in 0..2 { + current[[row, col]] += + lower[row][left] * whitened[[left, right]] * lower[col][right]; + } + } + } + } + + assert!((relative_spd_margin(¤t, &reference).unwrap() - 0.2).abs() < 1e-12); + } + + #[test] + fn relative_spd_margin_is_invariant_under_dense_congruence() { + let reference = ndarray::array![[4.0, 1.0], [1.0, 2.0]]; + let current = ndarray::array![[2.0, 0.2], [0.2, 0.5]]; + let transform = ndarray::array![[1.2, -0.4], [0.7, 1.5]]; + let congruence = |matrix: &Array2| { + let mut transformed = Array2::zeros((2, 2)); + for row in 0..2 { + for col in 0..2 { + for left in 0..2 { + for right in 0..2 { + transformed[[row, col]] += transform[[row, left]] + * matrix[[left, right]] + * transform[[col, right]]; + } + } + } + } + transformed + }; + + let margin = relative_spd_margin(¤t, &reference).unwrap(); + let transformed_margin = + relative_spd_margin(&congruence(¤t), &congruence(&reference)).unwrap(); + assert!((margin - transformed_margin).abs() < 1e-12); + } + + #[test] + fn relative_spd_margin_handles_scalar_and_rejects_invalid_inputs() { + let reference = ndarray::array![[0.4]]; + let current = ndarray::array![[0.2]]; + assert!((relative_spd_margin(¤t, &reference).unwrap() - 0.5).abs() < 1e-12); + + let singular = ndarray::array![[1.0, 1.0], [1.0, 1.0]]; + let identity = identity_matrix(2); + assert!(relative_spd_margin(&singular, &identity).is_err()); + assert!(relative_spd_margin(&Array2::zeros((0, 0)), &Array2::zeros((0, 0))).is_err()); + } + + // ── worst-contrast scalar and 2D cases ── + + #[test] + fn scalar_identity_is_one() { + let info = ndarray::array![[4.0]]; + let var = ndarray::array![[4.0]]; + // L = [2], L^T V L = 2 * 4 * 2 = 16, sqrt(16) = 4 + assert!((worst_contrast(&info, &var).unwrap() - 4.0).abs() < 1e-12); + } + + #[test] + fn scalar_information_scale_invariance() { + let info = ndarray::array![[9.0]]; + let var = ndarray::array![[4.0]]; + // L = [3], M = 3 * 4 * 3 = 36, sqrt(36) = 6 + assert!((worst_contrast(&info, &var).unwrap() - 6.0).abs() < 1e-12); + } + + #[test] + fn two_dimensional_diagonal_has_exact_hand_calculated_value() { + let info = ndarray::array![[4.0, 0.0], [0.0, 1.0]]; + let var = ndarray::array![[8.0, 0.0], [0.0, 9.0]]; + // L = [[2, 0], [0, 1]], M = L^T V L = [[32, 0], [0, 9]] + // λ_max = 32, worst = sqrt(32) ≈ 5.656854249492381 + let contrast = worst_contrast(&info, &var).unwrap(); + let expected = 32_f64.sqrt(); + assert!((contrast - expected).abs() < 1e-12); + } + + #[test] + fn two_dimensional_correlated_information_variance_has_closed_form() { + // I = [[5, 2], [2, 2]] is SPD (det = 6 > 0). + // Cholesky: L = [[√5, 0], [2/√5, √(6/5)]] + // V = [[3, 1], [1, 2]] is PSD. + // M = L^T V L = [[20.6, 9√6/5], [9√6/5, 2.4]] + // trace = 23, det = 30, λ_max = (23+√409)/2 ≈ 21.61187 + // worst = √λ_max ≈ 4.648857 + let info = ndarray::array![[5.0, 2.0], [2.0, 2.0]]; + let var = ndarray::array![[3.0, 1.0], [1.0, 2.0]]; + let contrast = worst_contrast(&info, &var).unwrap(); + assert!((contrast - 4.648857301324525).abs() < 1e-10); + } + + // ── linear coordinate invariance ── + + #[test] + fn nonsingular_linear_coordinate_change_preserves_worst_contrast() { + let info = ndarray::array![[5.0, 2.0], [2.0, 2.0]]; + let var = ndarray::array![[3.0, 1.0], [1.0, 2.0]]; + let baseline = worst_contrast(&info, &var).unwrap(); + + // Nonsingular transformation A; new coordinates x' = A x. + // Then I' = A^{-T} I A^{-1}, V' = A V A^T. + let a = ndarray::array![[2.0, 1.0], [-1.0, 3.0]]; + let a_inv = ndarray::array![[3.0 / 7.0, -1.0 / 7.0], [1.0 / 7.0, 2.0 / 7.0]]; + let mut info_transformed = a_inv.t().dot(&info).dot(&a_inv); + let mut var_transformed = a.dot(&var).dot(&a.t()); + // Force exact symmetry after floating-point operations. + symmetrize(&mut info_transformed); + symmetrize(&mut var_transformed); + let contrast_transformed = worst_contrast(&info_transformed, &var_transformed).unwrap(); + + assert!((baseline - contrast_transformed).abs() < 1e-10); + } + + /// Replace M with (M + M^T)/2. + fn symmetrize(matrix: &mut Array2) { + let n = matrix.nrows(); + for i in 0..n { + for j in 0..i { + let avg = (matrix[[i, j]] + matrix[[j, i]]) / 2.0; + matrix[[i, j]] = avg; + matrix[[j, i]] = avg; + } + } + } + + #[test] + fn diagonal_scaling_coordinate_change_is_invariant() { + let info = ndarray::array![[4.0, 1.0], [1.0, 3.0]]; + let var = ndarray::array![[3.0, 0.0], [0.0, 2.0]]; + let baseline = worst_contrast(&info, &var).unwrap(); + + // Diagonal scaling: D = diag(3, 7). + // I' = D^{-1} I D^{-1}, V' = D V D. + let d_inv = ndarray::array![[1.0 / 3.0, 0.0], [0.0, 1.0 / 7.0]]; + let d = ndarray::array![[3.0, 0.0], [0.0, 7.0]]; + let mut info_scaled = d_inv.dot(&info).dot(&d_inv); + let mut var_scaled = d.dot(&var).dot(&d); + symmetrize(&mut info_scaled); + symmetrize(&mut var_scaled); + let contrast_scaled = worst_contrast(&info_scaled, &var_scaled).unwrap(); + + assert!((baseline - contrast_scaled).abs() < 1e-10); + } + + #[test] + fn three_dimensional_correlated_information_and_variance_invariance() { + // Use a 3×3 known SPD I and PSD V, then apply an orthogonal + // (rotation) coordinate change and confirm invariance. + let info = ndarray::array![[5.0, 1.0, 2.0], [1.0, 4.0, 0.0], [2.0, 0.0, 3.0]]; + let var = ndarray::array![[2.0, 0.5, 0.0], [0.5, 3.0, 1.0], [0.0, 1.0, 4.0]]; + let baseline = worst_contrast(&info, &var).unwrap(); + + // Rotation with det = 1: x' = A x. + // I' = A^{-T} I A^{-1} = A I A^T (orthogonal), V' = A V A^T. + let a = ndarray::array![[0.6, -0.8, 0.0], [0.8, 0.6, 0.0], [0.0, 0.0, 1.0]]; + let mut info_rotated = a.dot(&info).dot(&a.t()); + let mut var_rotated = a.dot(&var).dot(&a.t()); + symmetrize(&mut info_rotated); + symmetrize(&mut var_rotated); + let contrast_rotated = worst_contrast(&info_rotated, &var_rotated).unwrap(); + + assert!((baseline - contrast_rotated).abs() < 1e-10); + } + + // ── error branches ── + + #[test] + fn worst_contrast_rejects_non_square_inputs() { + let info = ndarray::Array2::::zeros((1, 2)); + let var = ndarray::Array2::::zeros((2, 1)); + assert!(worst_contrast(&info, &var).is_err()); + } + + #[test] + fn worst_contrast_rejects_dimension_mismatch() { + let info = ndarray::Array2::::zeros((2, 2)); + let var = ndarray::Array2::::zeros((3, 3)); + assert!(worst_contrast(&info, &var).is_err()); + } + + #[test] + fn worst_contrast_rejects_non_finite() { + let info = ndarray::array![[1.0]]; + let var = ndarray::array![[f64::NAN]]; + assert!(worst_contrast(&info, &var).is_err()); + } + + #[test] + fn worst_contrast_rejects_non_symmetric_information() { + let info = ndarray::array![[1.0, 2.0], [3.0, 4.0]]; + let var = ndarray::array![[1.0, 0.0], [0.0, 1.0]]; + assert!(worst_contrast(&info, &var).is_err()); + } + + #[test] + fn worst_contrast_rejects_non_symmetric_variance() { + let info = ndarray::array![[1.0, 0.0], [0.0, 1.0]]; + let var = ndarray::array![[1.0, 2.0], [3.0, 4.0]]; + assert!(worst_contrast(&info, &var).is_err()); + } + + #[test] + fn worst_contrast_rejects_rank_deficient_information() { + // I = [[1, 1], [1, 1]] has zero eigenvalue, not SPD. + let info = ndarray::array![[1.0, 1.0], [1.0, 1.0]]; + let var = ndarray::array![[1.0, 0.0], [0.0, 1.0]]; + assert!(worst_contrast(&info, &var).is_err()); + } + + #[test] + fn worst_contrast_accepts_psd_variance_that_is_not_spd() { + // V is rank-1 PSD but not SPD; should still compute. + let info = ndarray::array![[4.0, 0.0], [0.0, 1.0]]; + let var = ndarray::array![[1.0, 0.0], [0.0, 0.0]]; + // L = [[2, 0], [0, 1]], M = L^T V L = [[4, 0], [0, 0]] + // λ_max = 4, worst = 2 + assert!((worst_contrast(&info, &var).unwrap() - 2.0).abs() < 1e-12); + } +} diff --git a/src/estimation/parametric/covariates.rs b/src/estimation/parametric/covariates.rs new file mode 100644 index 000000000..636df7087 --- /dev/null +++ b/src/estimation/parametric/covariates.rs @@ -0,0 +1,1063 @@ +//! Named subject-static covariate effects in transformed population space. +//! +//! This module deliberately owns declaration, validation, design, and exact +//! GLS primitives without depending on pharmsol's interpolation semantics. + +use std::collections::{BTreeMap, HashMap, HashSet}; + +use ndarray::Array2; +use pharmsol::Data; +use serde::{Deserialize, Serialize}; +use thiserror::Error; + +use crate::model::{ParameterScale, ParameterSpace, UnboundedParameter}; + +use super::{covariance::cholesky_lower, transforms::phi_to_psi}; + +/// One named transformed-space population covariate effect. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct CovariateEffect { + parameter: String, + covariate: String, + kind: CovariateEffectKind, + initial: Option, + estimated: bool, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(tag = "family", rename_all = "snake_case", deny_unknown_fields)] +enum CovariateEffectKind { + Continuous { center: f64 }, + Categorical { reference: f64, level: f64 }, +} + +/// Public family metadata without exposing mutable declaration fields. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +#[serde(tag = "family", rename_all = "snake_case")] +pub enum CovariateEffectFamily { + Continuous { center: f64 }, + Categorical { reference: f64, level: f64 }, +} + +impl CovariateEffect { + pub fn continuous( + parameter: impl Into, + covariate: impl Into, + center: f64, + ) -> Self { + Self { + parameter: parameter.into(), + covariate: covariate.into(), + kind: CovariateEffectKind::Continuous { + center: canonical_zero(center), + }, + initial: None, + estimated: true, + } + } + + pub fn categorical( + parameter: impl Into, + covariate: impl Into, + reference: f64, + level: f64, + ) -> Self { + Self { + parameter: parameter.into(), + covariate: covariate.into(), + kind: CovariateEffectKind::Categorical { + reference: canonical_zero(reference), + level: canonical_zero(level), + }, + initial: None, + estimated: true, + } + } + + pub fn with_initial(mut self, beta: f64) -> Self { + self.initial = Some(beta); + self + } + + pub fn fixed(mut self) -> Self { + self.estimated = false; + self + } + + pub fn parameter(&self) -> &str { + &self.parameter + } + + pub fn covariate(&self) -> &str { + &self.covariate + } + + pub fn family(&self) -> CovariateEffectFamily { + match self.kind { + CovariateEffectKind::Continuous { center } => { + CovariateEffectFamily::Continuous { center } + } + CovariateEffectKind::Categorical { reference, level } => { + CovariateEffectFamily::Categorical { reference, level } + } + } + } + + pub fn initial(&self) -> Option { + self.initial + } + + pub fn estimated(&self) -> bool { + self.estimated + } + + pub fn name(&self) -> String { + match self.kind { + CovariateEffectKind::Continuous { .. } => { + format!("beta:{}:{}", self.parameter, self.covariate) + } + CovariateEffectKind::Categorical { level, .. } => format!( + "beta:{}:{}:{}", + self.parameter, + self.covariate, + stable_number(level) + ), + } + } + + fn design_value(&self, value: f64) -> f64 { + match self.kind { + CovariateEffectKind::Continuous { center } => value - center, + CovariateEffectKind::Categorical { level, .. } => { + if value == level { + 1.0 + } else { + 0.0 + } + } + } + } +} + +fn canonical_zero(value: f64) -> f64 { + if value == 0.0 { + 0.0 + } else { + value + } +} + +fn stable_number(value: f64) -> String { + canonical_zero(value).to_string() +} + +/// Rejection-only declaration for constraints outside the supported domains. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "kind", rename_all = "snake_case", deny_unknown_fields)] +pub enum ParametricConstraint { + Nonlinear { description: String }, +} + +impl ParametricConstraint { + pub fn nonlinear(description: impl Into) -> Self { + Self::Nonlinear { + description: description.into(), + } + } + + pub fn description(&self) -> &str { + match self { + Self::Nonlinear { description } => description, + } + } +} + +/// Immutable coefficient estimate in canonical declaration order. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct CovariateEstimate { + name: String, + declaration_index: usize, + estimate: f64, + estimated: bool, +} + +impl CovariateEstimate { + pub fn name(&self) -> &str { + &self.name + } + pub fn declaration_index(&self) -> usize { + self.declaration_index + } + pub fn estimate(&self) -> f64 { + self.estimate + } + pub fn estimated(&self) -> bool { + self.estimated + } +} + +/// One exact subject-static covariate value. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct SubjectCovariateValue { + subject: String, + covariate: String, + value: f64, +} + +impl SubjectCovariateValue { + pub fn subject(&self) -> &str { + &self.subject + } + pub fn covariate(&self) -> &str { + &self.covariate + } + pub fn value(&self) -> f64 { + self.value + } +} + +/// One subject's design values in canonical effect declaration order. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct SubjectCovariateDesign { + subject: String, + values: Vec, +} + +impl SubjectCovariateDesign { + pub fn subject(&self) -> &str { + &self.subject + } + pub fn values(&self) -> &[f64] { + &self.values + } +} + +/// Subject-specific transformed and execution-space population parameters. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct SubjectPopulationParameters { + subject: String, + phi: Vec, + psi: Vec, +} + +impl SubjectPopulationParameters { + pub fn subject(&self) -> &str { + &self.subject + } + pub fn phi(&self) -> &[f64] { + &self.phi + } + pub fn psi(&self) -> &[f64] { + &self.psi + } +} + +/// Fully validated subject-static covariate model. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct CovariateModel { + declarations: Vec, + estimates: Vec, + parameter_indices: Vec, + subject_values: Vec, + subject_design: Vec, +} + +impl CovariateModel { + pub fn resolve( + declarations: Vec, + parameters: &ParameterSpace, + data: &Data, + ) -> Result { + // Normalize signed zero even for declarations produced by serde rather + // than the public constructors. This makes equality, bit-set keys, + // names, design coding, and persisted source metadata agree. + let declarations = declarations + .into_iter() + .map(|mut effect| { + effect.kind = match effect.kind { + CovariateEffectKind::Continuous { center } => CovariateEffectKind::Continuous { + center: canonical_zero(center), + }, + CovariateEffectKind::Categorical { reference, level } => { + CovariateEffectKind::Categorical { + reference: canonical_zero(reference), + level: canonical_zero(level), + } + } + }; + effect + }) + .collect::>(); + validate_declarations(&declarations, parameters)?; + let covariate_names: Vec = declarations + .iter() + .map(|effect| effect.covariate.clone()) + .collect::>() + .into_iter() + .collect(); + let mut covariate_names = covariate_names; + covariate_names.sort(); + + let mut subject_values = Vec::new(); + let mut subject_maps = Vec::new(); + for subject in data.subjects() { + let mut values = BTreeMap::new(); + for name in &covariate_names { + let mut exact: Option = None; + for occasion in subject.occasions() { + let covariate = occasion.covariates().get_covariate(name).ok_or_else(|| { + CovariateValidationError::MissingSubjectCovariate { + subject: subject.id().clone(), + covariate: name.clone(), + occasion: occasion.index(), + } + })?; + let observations = covariate.observations(); + if observations.is_empty() { + return Err(CovariateValidationError::MissingSubjectCovariate { + subject: subject.id().clone(), + covariate: name.clone(), + occasion: occasion.index(), + }); + } + for (_, value) in observations { + if !value.is_finite() { + return Err(CovariateValidationError::NonFiniteSubjectCovariate { + subject: subject.id().clone(), + covariate: name.clone(), + }); + } + if exact.is_some_and(|prior| prior != value) { + return Err(CovariateValidationError::TimeVaryingSubjectCovariate { + subject: subject.id().clone(), + covariate: name.clone(), + }); + } + exact = Some(canonical_zero(value)); + } + } + let value = + exact.ok_or_else(|| CovariateValidationError::MissingSubjectCovariate { + subject: subject.id().clone(), + covariate: name.clone(), + occasion: 0, + })?; + values.insert(name.clone(), value); + subject_values.push(SubjectCovariateValue { + subject: subject.id().clone(), + covariate: name.clone(), + value, + }); + } + subject_maps.push((subject.id().clone(), values)); + } + validate_observed_categories(&declarations, &subject_maps)?; + + let mut subject_design = Vec::with_capacity(subject_maps.len()); + for (subject, values) in &subject_maps { + subject_design.push(SubjectCovariateDesign { + subject: subject.clone(), + values: declarations + .iter() + .map(|effect| effect.design_value(values[effect.covariate()])) + .collect(), + }); + } + let parameter_indices = declarations + .iter() + .map(|effect| { + parameters + .iter() + .position(|parameter| parameter.name == effect.parameter) + .expect("declaration parameter validated") + }) + .collect(); + let estimates = declarations + .iter() + .enumerate() + .map(|(index, effect)| CovariateEstimate { + name: effect.name(), + declaration_index: index, + estimate: effect.initial.expect("initial validated"), + estimated: effect.estimated, + }) + .collect(); + Ok(Self { + declarations, + estimates, + parameter_indices, + subject_values, + subject_design, + }) + } + + pub fn declarations(&self) -> &[CovariateEffect] { + &self.declarations + } + pub fn estimates(&self) -> &[CovariateEstimate] { + &self.estimates + } + pub fn subject_values(&self) -> &[SubjectCovariateValue] { + &self.subject_values + } + pub fn subject_design(&self) -> &[SubjectCovariateDesign] { + &self.subject_design + } + pub fn parameter_indices(&self) -> &[usize] { + &self.parameter_indices + } + + pub(crate) fn validate_initial_gls_rank( + &self, + parameters: &ParameterSpace, + random_effect_names: &[String], + omega: &Array2, + ) -> Result<(), CovariateValidationError> { + let random_rows: HashMap<&str, usize> = random_effect_names + .iter() + .enumerate() + .map(|(index, name)| (name.as_str(), index)) + .collect(); + let intercepts: Vec = parameters + .iter() + .enumerate() + .filter_map(|(parameter_index, parameter)| { + (parameter.estimate && parameter.random_effect).then_some(parameter_index) + }) + .collect(); + let estimated_effects: Vec = self + .declarations + .iter() + .enumerate() + .filter_map(|(index, effect)| { + (effect.estimated && parameters.items[self.parameter_indices[index]].random_effect) + .then_some(index) + }) + .collect(); + let width = intercepts.len() + estimated_effects.len(); + if width == 0 { + return Ok(()); + } + let mut designs = Vec::with_capacity(self.subject_design.len()); + for subject in &self.subject_design { + let mut design = Array2::::zeros((random_effect_names.len(), width)); + for (column, parameter_index) in intercepts.iter().copied().enumerate() { + let parameter = ¶meters.items[parameter_index]; + let row = random_rows[parameter.name.as_str()]; + design[[row, column]] = 1.0; + } + for (effect_column, effect_index) in estimated_effects.iter().copied().enumerate() { + let row = random_rows[self.declarations[effect_index].parameter()]; + design[[row, intercepts.len() + effect_column]] = subject.values[effect_index]; + } + designs.push(design); + } + let expected = vec![vec![0.0; random_effect_names.len()]; designs.len()]; + let offsets = expected.clone(); + solve_covariate_gls(CovariateGlsProblem { + design: &designs, + expected_phi: &expected, + offset: &offsets, + omega, + }) + .map(|_| ()) + .map_err(|error| CovariateValidationError::SingularDesign { + detail: error.to_string(), + }) + } + + pub fn with_estimates(&self, values: &[f64]) -> Result { + if values.len() != self.estimates.len() { + return Err(CovariateMstepError::DimensionMismatch { + detail: format!( + "expected {} coefficients, got {}", + self.estimates.len(), + values.len() + ), + }); + } + if values.iter().any(|value| !value.is_finite()) { + return Err(CovariateMstepError::NonFiniteSolution); + } + let mut updated = self.clone(); + for (estimate, value) in updated.estimates.iter_mut().zip(values) { + if estimate.estimated { + estimate.estimate = *value; + } else if estimate.estimate != *value { + return Err(CovariateMstepError::FixedCoefficientChanged { + name: estimate.name.clone(), + }); + } + } + Ok(updated) + } + + pub fn subject_population_parameters( + &self, + population_phi: &[f64], + scales: &[ParameterScale], + ) -> Result, CovariateMstepError> { + if population_phi.len() != scales.len() { + return Err(CovariateMstepError::DimensionMismatch { + detail: "population phi and scale widths differ".to_string(), + }); + } + let mut rows = Vec::with_capacity(self.subject_design.len()); + for design in &self.subject_design { + let mut phi = population_phi.to_vec(); + for (effect_index, design_value) in design.values.iter().enumerate() { + phi[self.parameter_indices[effect_index]] += + design_value * self.estimates[effect_index].estimate; + } + let psi: Vec = phi + .iter() + .zip(scales) + .map(|(value, scale)| phi_to_psi(*value, *scale)) + .collect(); + if phi.iter().chain(&psi).any(|value| !value.is_finite()) { + return Err(CovariateMstepError::NonFiniteSubjectMean { + subject: design.subject.clone(), + }); + } + rows.push(SubjectPopulationParameters { + subject: design.subject.clone(), + phi, + psi, + }); + } + Ok(rows) + } +} + +fn validate_declarations( + declarations: &[CovariateEffect], + parameters: &ParameterSpace, +) -> Result<(), CovariateValidationError> { + let by_parameter: HashMap<&str, &UnboundedParameter> = parameters + .iter() + .map(|parameter| (parameter.name.as_str(), parameter)) + .collect(); + let mut keys = HashSet::new(); + let mut families: HashMap<&str, CovariateEffectFamily> = HashMap::new(); + for effect in declarations { + if effect.parameter.is_empty() { + return Err(CovariateValidationError::UnknownParameter( + effect.parameter.clone(), + )); + } + if effect.covariate.is_empty() { + return Err(CovariateValidationError::UnknownCovariate( + effect.covariate.clone(), + )); + } + if !by_parameter.contains_key(effect.parameter.as_str()) { + return Err(CovariateValidationError::UnknownParameter( + effect.parameter.clone(), + )); + } + let initial = effect + .initial + .ok_or_else(|| CovariateValidationError::MissingInitial(effect.name()))?; + if !initial.is_finite() { + return Err(CovariateValidationError::NonFiniteInitial(effect.name())); + } + let family = effect.family(); + match family { + CovariateEffectFamily::Continuous { center } if !center.is_finite() => { + return Err(CovariateValidationError::NonFiniteCenter { + covariate: effect.covariate.clone(), + }); + } + CovariateEffectFamily::Categorical { reference, level } + if !reference.is_finite() || !level.is_finite() => + { + return Err(CovariateValidationError::NonFiniteCategory { + covariate: effect.covariate.clone(), + }); + } + CovariateEffectFamily::Categorical { reference, level } if reference == level => { + return Err(CovariateValidationError::ReferenceLevelCollision { + covariate: effect.covariate.clone(), + value: reference, + }); + } + _ => {} + } + if let Some(existing) = families.get(effect.covariate.as_str()) { + let compatible = match (*existing, family) { + ( + CovariateEffectFamily::Continuous { center: left }, + CovariateEffectFamily::Continuous { center: right }, + ) => left == right, + ( + CovariateEffectFamily::Categorical { + reference: left, .. + }, + CovariateEffectFamily::Categorical { + reference: right, .. + }, + ) => left == right, + _ => false, + }; + if !compatible { + return Err(CovariateValidationError::InconsistentFamily { + covariate: effect.covariate.clone(), + }); + } + } else { + families.insert(effect.covariate.as_str(), family); + } + let level = match effect.kind { + CovariateEffectKind::Continuous { .. } => None, + CovariateEffectKind::Categorical { level, .. } => Some(level.to_bits()), + }; + if !keys.insert((effect.parameter.as_str(), effect.covariate.as_str(), level)) { + return Err(CovariateValidationError::DuplicateEffect(effect.name())); + } + } + Ok(()) +} + +fn validate_observed_categories( + declarations: &[CovariateEffect], + subjects: &[(String, BTreeMap)], +) -> Result<(), CovariateValidationError> { + let categorical_covariates: HashSet<&str> = declarations + .iter() + .filter_map(|effect| match effect.kind { + CovariateEffectKind::Categorical { .. } => Some(effect.covariate.as_str()), + _ => None, + }) + .collect(); + for covariate in categorical_covariates { + let Some(reference) = declarations.iter().find_map(|effect| { + (effect.covariate == covariate) + .then_some(effect) + .and_then(|effect| match effect.kind { + CovariateEffectKind::Categorical { reference, .. } => Some(reference), + _ => None, + }) + }) else { + return Err(CovariateValidationError::InconsistentFamily { + covariate: covariate.to_string(), + }); + }; + let observed: HashSet = subjects + .iter() + .map(|(_, values)| values[covariate].to_bits()) + .collect(); + let declared_all: HashSet = declarations + .iter() + .filter_map(|effect| match effect.kind { + CovariateEffectKind::Categorical { level, .. } if effect.covariate == covariate => { + Some(level.to_bits()) + } + _ => None, + }) + .collect(); + for value in &observed { + if *value != reference.to_bits() && !declared_all.contains(value) { + return Err(CovariateValidationError::UnknownCategory { + covariate: covariate.to_string(), + value: f64::from_bits(*value), + }); + } + } + let targets: HashSet<&str> = declarations + .iter() + .filter(|effect| effect.covariate == covariate) + .map(|effect| effect.parameter.as_str()) + .collect(); + for parameter in targets { + let levels: HashSet = declarations + .iter() + .filter_map(|effect| match effect.kind { + CovariateEffectKind::Categorical { level, .. } + if effect.covariate == covariate && effect.parameter == parameter => + { + Some(level.to_bits()) + } + _ => None, + }) + .collect(); + for value in &observed { + if *value != reference.to_bits() && !levels.contains(value) { + return Err(CovariateValidationError::IncompleteCategoricalLevels { + parameter: parameter.to_string(), + covariate: covariate.to_string(), + level: f64::from_bits(*value), + }); + } + } + } + } + Ok(()) +} + +/// Exact joint GLS input. `design[i]` is `A_i`, `expected_phi[i]` is `Ephi_i`, +/// and `offset[i]` contains all fixed-coordinate contributions. +#[derive(Debug, Clone)] +pub struct CovariateGlsProblem<'a> { + pub design: &'a [Array2], + pub expected_phi: &'a [Vec], + pub offset: &'a [Vec], + pub omega: &'a Array2, +} + +/// Strict finite Cholesky GLS with no tolerance, repair, or generalized inverse. +pub fn solve_covariate_gls( + problem: CovariateGlsProblem<'_>, +) -> Result, CovariateMstepError> { + let n = problem.design.len(); + if n == 0 || problem.expected_phi.len() != n || problem.offset.len() != n { + return Err(CovariateMstepError::DimensionMismatch { + detail: "subject GLS inputs differ".to_string(), + }); + } + let q = problem.omega.nrows(); + if q == 0 || problem.omega.ncols() != q { + return Err(CovariateMstepError::DimensionMismatch { + detail: "Omega must be nonempty and square".to_string(), + }); + } + let p = problem.design[0].ncols(); + if p == 0 { + return Ok(Vec::new()); + } + let omega_lower = cholesky_lower(problem.omega) + .map_err(|error| CovariateMstepError::InvalidOmega(error.to_string()))?; + let mut h = Array2::::zeros((p, p)); + let mut g = vec![0.0; p]; + for subject in 0..n { + let a = &problem.design[subject]; + if a.nrows() != q + || a.ncols() != p + || problem.expected_phi[subject].len() != q + || problem.offset[subject].len() != q + { + return Err(CovariateMstepError::DimensionMismatch { + detail: format!("subject {subject} GLS width differs"), + }); + } + let d: Vec = problem.expected_phi[subject] + .iter() + .zip(&problem.offset[subject]) + .map(|(mean, offset)| mean - offset) + .collect(); + let wd = solve_spd_from_lower(&omega_lower, &d)?; + let mut wa = Array2::::zeros((q, p)); + for column in 0..p { + let rhs: Vec = (0..q).map(|row| a[[row, column]]).collect(); + let solved = solve_spd_from_lower(&omega_lower, &rhs)?; + for row in 0..q { + wa[[row, column]] = solved[row]; + } + } + for left in 0..p { + g[left] += (0..q).map(|row| a[[row, left]] * wd[row]).sum::(); + for right in 0..=left { + h[[left, right]] += (0..q) + .map(|row| a[[row, left]] * wa[[row, right]]) + .sum::(); + } + } + } + for left in 0..p { + for right in 0..left { + h[[right, left]] = h[[left, right]]; + } + } + let lower = cholesky_lower(&h).map_err(|_| CovariateMstepError::SingularDesign)?; + let solution = solve_spd_from_lower(&lower, &g)?; + if solution.iter().any(|value| !value.is_finite()) { + return Err(CovariateMstepError::NonFiniteSolution); + } + Ok(solution) +} + +fn solve_spd_from_lower(lower: &[Vec], rhs: &[f64]) -> Result, CovariateMstepError> { + if lower.len() != rhs.len() || lower.iter().any(|row| row.len() != rhs.len()) { + return Err(CovariateMstepError::DimensionMismatch { + detail: "Cholesky solve width differs".to_string(), + }); + } + let n = rhs.len(); + let mut y = vec![0.0; n]; + for row in 0..n { + let residual = rhs[row] - (0..row).map(|col| lower[row][col] * y[col]).sum::(); + y[row] = residual / lower[row][row]; + if !y[row].is_finite() { + return Err(CovariateMstepError::NonFiniteSolution); + } + } + let mut x = vec![0.0; n]; + for row in (0..n).rev() { + let residual = y[row] + - ((row + 1)..n) + .map(|col| lower[col][row] * x[col]) + .sum::(); + x[row] = residual / lower[row][row]; + if !x[row].is_finite() { + return Err(CovariateMstepError::NonFiniteSolution); + } + } + Ok(x) +} + +/// Rebase one eta vector while preserving absolute transformed parameters. +pub fn rebase_eta( + eta: &mut [f64], + old_mu: &[f64], + new_mu: &[f64], +) -> Result<(), CovariateMstepError> { + if eta.len() != old_mu.len() || eta.len() != new_mu.len() { + return Err(CovariateMstepError::DimensionMismatch { + detail: "eta and subject means differ".to_string(), + }); + } + for index in 0..eta.len() { + eta[index] -= new_mu[index] - old_mu[index]; + if !eta[index].is_finite() { + return Err(CovariateMstepError::NonFiniteSolution); + } + } + Ok(()) +} + +/// Subject-specific raw covariance candidate from SA-updated moments. +pub fn subject_centered_omega( + global_second_moment: &Array2, + expected_phi: &[Vec], + subject_mu: &[Vec], +) -> Result, CovariateMstepError> { + let q = global_second_moment.nrows(); + if q == 0 + || global_second_moment.ncols() != q + || expected_phi.is_empty() + || expected_phi.len() != subject_mu.len() + { + return Err(CovariateMstepError::DimensionMismatch { + detail: "Omega moment inputs differ".to_string(), + }); + } + let mut candidate = global_second_moment.clone(); + let n = expected_phi.len() as f64; + for (mean, mu) in expected_phi.iter().zip(subject_mu) { + if mean.len() != q || mu.len() != q { + return Err(CovariateMstepError::DimensionMismatch { + detail: "subject mean width differs".to_string(), + }); + } + for row in 0..q { + for column in 0..q { + candidate[[row, column]] += + (-mean[row] * mu[column] - mu[row] * mean[column] + mu[row] * mu[column]) / n; + } + } + } + if candidate.iter().any(|value| !value.is_finite()) { + return Err(CovariateMstepError::NonFiniteOmegaCandidate); + } + Ok(candidate) +} + +#[derive(Debug, Clone, PartialEq, Error, Serialize, Deserialize)] +#[serde(tag = "failure", rename_all = "snake_case")] +pub enum CovariateValidationError { + #[error("unknown parameter '{0}' in covariate effect")] + UnknownParameter(String), + #[error("unknown or empty covariate '{0}'")] + UnknownCovariate(String), + #[error("covariate effect '{0}' has no initial coefficient")] + MissingInitial(String), + #[error("covariate effect '{0}' has a nonfinite initial coefficient")] + NonFiniteInitial(String), + #[error("continuous covariate '{covariate}' has a nonfinite center")] + NonFiniteCenter { covariate: String }, + #[error("categorical covariate '{covariate}' has a nonfinite reference or level")] + NonFiniteCategory { covariate: String }, + #[error("categorical covariate '{covariate}' reference collides with level {value}")] + ReferenceLevelCollision { covariate: String, value: f64 }, + #[error("covariate '{covariate}' has inconsistent family, center, or reference declarations")] + InconsistentFamily { covariate: String }, + #[error("duplicate covariate effect '{0}'")] + DuplicateEffect(String), + #[error("subject '{subject}' is missing covariate '{covariate}' in occasion {occasion}")] + MissingSubjectCovariate { + subject: String, + covariate: String, + occasion: usize, + }, + #[error("subject '{subject}' covariate '{covariate}' is nonfinite")] + NonFiniteSubjectCovariate { subject: String, covariate: String }, + #[error("subject '{subject}' covariate '{covariate}' is time-varying")] + TimeVaryingSubjectCovariate { subject: String, covariate: String }, + #[error("covariate '{covariate}' has undeclared observed category {value}")] + UnknownCategory { covariate: String, value: f64 }, + #[error("parameter '{parameter}' does not declare observed level {level} for covariate '{covariate}'")] + IncompleteCategoricalLevels { + parameter: String, + covariate: String, + level: f64, + }, + #[error("covariate GLS design is not strict full rank: {detail}")] + SingularDesign { detail: String }, + #[error("unsupported nonlinear parametric constraint: {description}")] + UnsupportedNonlinearConstraint { description: String }, +} + +#[derive(Debug, Clone, PartialEq, Error, Serialize, Deserialize)] +#[serde(tag = "failure", rename_all = "snake_case")] +pub enum CovariateMstepError { + #[error("covariate M-step dimension mismatch: {detail}")] + DimensionMismatch { detail: String }, + #[error("invalid accepted Omega for covariate GLS: {0}")] + InvalidOmega(String), + #[error("covariate GLS design is singular or collinear")] + SingularDesign, + #[error("covariate GLS produced a nonfinite solution")] + NonFiniteSolution, + #[error("fixed coefficient '{name}' changed")] + FixedCoefficientChanged { name: String }, + #[error("subject '{subject}' has a nonfinite population mean")] + NonFiniteSubjectMean { subject: String }, + #[error("subject-centered Omega candidate is nonfinite")] + NonFiniteOmegaCandidate, +} + +pub fn reject_constraints( + constraints: &[ParametricConstraint], +) -> Result<(), CovariateValidationError> { + if let Some(constraint) = constraints.first() { + return Err(CovariateValidationError::UnsupportedNonlinearConstraint { + description: constraint.description().to_string(), + }); + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn canonical_names_and_design_are_stable() { + let continuous = CovariateEffect::continuous("cl", "wt", 70.0).with_initial(0.0); + let categorical = CovariateEffect::categorical("v", "sex", 0.0, 2.0).with_initial(0.1); + assert_eq!(continuous.name(), "beta:cl:wt"); + assert_eq!(categorical.name(), "beta:v:sex:2"); + assert_eq!(continuous.design_value(82.0), 12.0); + assert_eq!(categorical.design_value(2.0), 1.0); + assert_eq!(categorical.design_value(0.0), 0.0); + } + + #[test] + fn signed_zero_is_canonical_before_names_bits_and_design() { + let positive = CovariateEffect::categorical("v", "group", 1.0, 0.0); + let negative = CovariateEffect::categorical("v", "group", 1.0, -0.0); + assert_eq!(positive, negative); + assert_eq!(negative.name(), "beta:v:group:0"); + assert_eq!(negative.design_value(0.0), 1.0); + assert_eq!(negative.design_value(-0.0), 1.0); + match negative.family() { + CovariateEffectFamily::Categorical { reference, level } => { + assert_eq!(reference.to_bits(), 1.0f64.to_bits()); + assert_eq!(level.to_bits(), 0.0f64.to_bits()); + } + _ => panic!("expected categorical effect"), + } + + let collision = CovariateEffect::categorical("v", "group", -0.0, 0.0); + match collision.family() { + CovariateEffectFamily::Categorical { reference, level } => { + assert_eq!(reference.to_bits(), level.to_bits()); + } + _ => panic!("expected categorical effect"), + } + let centered = CovariateEffect::continuous("v", "wt", -0.0); + match centered.family() { + CovariateEffectFamily::Continuous { center } => { + assert_eq!(center.to_bits(), 0.0f64.to_bits()); + } + _ => panic!("expected continuous effect"), + } + } + + #[test] + fn correlated_gls_with_fixed_offsets_is_exact() { + let omega = Array2::from_shape_vec((2, 2), vec![2.0, 0.6, 0.6, 1.0]).unwrap(); + let design = vec![ + Array2::from_shape_vec((2, 2), vec![1.0, -1.0, 0.0, 1.0]).unwrap(), + Array2::from_shape_vec((2, 2), vec![1.0, 2.0, 0.0, -0.5]).unwrap(), + Array2::from_shape_vec((2, 2), vec![1.0, 0.5, 0.0, 2.0]).unwrap(), + ]; + let truth = [0.7, -0.2]; + let offsets = vec![vec![0.1, 1.5]; 3]; + let expected_phi: Vec> = design + .iter() + .map(|a| { + (0..2) + .map(|row| { + offsets[0][row] + + (0..2) + .map(|column| a[[row, column]] * truth[column]) + .sum::() + }) + .collect() + }) + .collect(); + let estimate = solve_covariate_gls(CovariateGlsProblem { + design: &design, + expected_phi: &expected_phi, + offset: &offsets, + omega: &omega, + }) + .unwrap(); + assert!((estimate[0] - truth[0]).abs() <= 1e-10); + assert!((estimate[1] - truth[1]).abs() <= 1e-10); + } + + #[test] + fn singular_design_fails_without_repair() { + let omega = Array2::from_shape_vec((1, 1), vec![1.0]).unwrap(); + let design = vec![Array2::from_shape_vec((1, 2), vec![1.0, 1.0]).unwrap()]; + let means = vec![vec![2.0]]; + let offsets = vec![vec![0.0]]; + assert_eq!( + solve_covariate_gls(CovariateGlsProblem { + design: &design, + expected_phi: &means, + offset: &offsets, + omega: &omega + }), + Err(CovariateMstepError::SingularDesign) + ); + } + + #[test] + fn eta_rebase_preserves_absolute_phi() { + let old_mu = [1.0, -2.0]; + let new_mu = [1.25, -2.5]; + let mut eta = [0.4, 0.8]; + let absolute = [old_mu[0] + eta[0], old_mu[1] + eta[1]]; + rebase_eta(&mut eta, &old_mu, &new_mu).unwrap(); + assert!((new_mu[0] + eta[0] - absolute[0]).abs() <= 1e-10); + assert!((new_mu[1] + eta[1] - absolute[1]).abs() <= 1e-10); + } + + #[test] + fn subject_specific_omega_formula_is_exact() { + let m2 = Array2::from_shape_vec((1, 1), vec![10.0]).unwrap(); + let means = vec![vec![2.0], vec![4.0]]; + let mu = vec![vec![1.0], vec![3.0]]; + let omega = subject_centered_omega(&m2, &means, &mu).unwrap(); + let expected = 10.0 + (-2.0 - 2.0 + 1.0 - 12.0 - 12.0 + 9.0) / 2.0; + assert!((omega[[0, 0]] - expected).abs() <= 1e-10); + } +} diff --git a/src/estimation/parametric/individual.rs b/src/estimation/parametric/individual.rs new file mode 100644 index 000000000..a8de04b1b --- /dev/null +++ b/src/estimation/parametric/individual.rs @@ -0,0 +1,279 @@ +use anyhow::{bail, Result}; + +use crate::model::ParameterScale; + +use super::transforms::{phi_to_psi, psi_to_phi}; + +pub(crate) fn population_phi( + population_psi: &[f64], + scales: &[ParameterScale], +) -> Result> { + validate_widths( + population_psi.len(), + scales.len(), + "population parameters", + "scales", + )?; + Ok(population_psi + .iter() + .zip(scales.iter()) + .map(|(psi, scale)| psi_to_phi(*psi, *scale)) + .collect()) +} + +pub(crate) fn population_psi( + population_phi: &[f64], + scales: &[ParameterScale], +) -> Result> { + validate_widths( + population_phi.len(), + scales.len(), + "population phi", + "scales", + )?; + Ok(population_phi + .iter() + .zip(scales.iter()) + .map(|(phi, scale)| phi_to_psi(*phi, *scale)) + .collect()) +} + +pub(crate) fn individual_phi( + population_psi: &[f64], + scales: &[ParameterScale], + random_effect_indices: &[usize], + eta: &[f64], +) -> Result> { + validate_widths( + population_psi.len(), + scales.len(), + "population parameters", + "scales", + )?; + validate_widths( + random_effect_indices.len(), + eta.len(), + "random-effect indices", + "eta", + )?; + + let mut phi = population_phi(population_psi, scales)?; + let mut seen = vec![false; population_psi.len()]; + for (eta_index, parameter_index) in random_effect_indices.iter().copied().enumerate() { + if parameter_index >= population_psi.len() { + bail!( + "random-effect parameter index {parameter_index} exceeds parameter width {}", + population_psi.len() + ); + } + if seen[parameter_index] { + bail!("random-effect parameter index {parameter_index} is duplicated"); + } + seen[parameter_index] = true; + phi[parameter_index] += eta[eta_index]; + } + Ok(phi) +} + +pub(crate) fn individual_psi( + population_parameters: &[f64], + scales: &[ParameterScale], + random_effect_indices: &[usize], + eta: &[f64], +) -> Result> { + let phi = individual_phi(population_parameters, scales, random_effect_indices, eta)?; + population_psi(&phi, scales) +} + +/// Construct one occasion's ψ-space parameters by adding subject η and +/// occasion κ in transformed φ-space. +pub(crate) fn occasion_psi( + population_parameters: &[f64], + scales: &[ParameterScale], + random_effect_indices: &[usize], + eta: &[f64], + iov_effect_indices: &[usize], + kappa: &[f64], +) -> Result> { + validate_widths( + iov_effect_indices.len(), + kappa.len(), + "IOV-effect indices", + "kappa", + )?; + let mut phi = individual_phi(population_parameters, scales, random_effect_indices, eta)?; + let mut seen = vec![false; population_parameters.len()]; + for (kappa_index, parameter_index) in iov_effect_indices.iter().copied().enumerate() { + if parameter_index >= population_parameters.len() { + bail!( + "IOV-effect parameter index {parameter_index} exceeds parameter width {}", + population_parameters.len() + ); + } + if seen[parameter_index] { + bail!("IOV-effect parameter index {parameter_index} is duplicated"); + } + seen[parameter_index] = true; + phi[parameter_index] += kappa[kappa_index]; + } + population_psi(&phi, scales) +} + +/// Add η to an already resolved subject-specific population mean in φ-space. +/// +/// The no-covariate helpers above intentionally remain unchanged; covariate +/// execution calls this helper only after resolving the subject mean. +#[allow(dead_code, reason = "N5 subject-mean execution helper")] +pub(crate) fn individual_phi_from_subject_mean( + subject_mu_phi: &[f64], + random_effect_indices: &[usize], + eta: &[f64], +) -> Result> { + validate_widths( + random_effect_indices.len(), + eta.len(), + "random-effect indices", + "eta", + )?; + let mut phi = subject_mu_phi.to_vec(); + let mut seen = vec![false; phi.len()]; + for (eta_index, parameter_index) in random_effect_indices.iter().copied().enumerate() { + if parameter_index >= phi.len() { + bail!( + "random-effect parameter index {parameter_index} exceeds parameter width {}", + phi.len() + ); + } + if seen[parameter_index] { + bail!("random-effect parameter index {parameter_index} is duplicated"); + } + seen[parameter_index] = true; + phi[parameter_index] += eta[eta_index]; + } + Ok(phi) +} + +#[allow(dead_code, reason = "N5 subject-mean execution helper")] +pub(crate) fn individual_psi_from_subject_mean( + subject_mu_phi: &[f64], + scales: &[ParameterScale], + random_effect_indices: &[usize], + eta: &[f64], +) -> Result> { + validate_widths(subject_mu_phi.len(), scales.len(), "subject mean", "scales")?; + population_psi( + &individual_phi_from_subject_mean(subject_mu_phi, random_effect_indices, eta)?, + scales, + ) +} + +#[allow(dead_code, reason = "N5 subject-mean execution helper")] +pub(crate) fn occasion_psi_from_subject_mean( + subject_mu_phi: &[f64], + scales: &[ParameterScale], + random_effect_indices: &[usize], + eta: &[f64], + iov_effect_indices: &[usize], + kappa: &[f64], +) -> Result> { + validate_widths( + iov_effect_indices.len(), + kappa.len(), + "IOV-effect indices", + "kappa", + )?; + let mut phi = individual_phi_from_subject_mean(subject_mu_phi, random_effect_indices, eta)?; + let mut seen = vec![false; phi.len()]; + for (kappa_index, parameter_index) in iov_effect_indices.iter().copied().enumerate() { + if parameter_index >= phi.len() { + bail!( + "IOV-effect parameter index {parameter_index} exceeds parameter width {}", + phi.len() + ); + } + if seen[parameter_index] { + bail!("IOV-effect parameter index {parameter_index} is duplicated"); + } + seen[parameter_index] = true; + phi[parameter_index] += kappa[kappa_index]; + } + population_psi(&phi, scales) +} + +fn validate_widths(left: usize, right: usize, left_name: &str, right_name: &str) -> Result<()> { + if left != right { + bail!("{left_name} has width {left} but {right_name} has width {right}"); + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn eta_is_additive_in_phi_space() { + let population = vec![0.2, 10.0]; + let scales = vec![ParameterScale::Log, ParameterScale::Log]; + let eta = vec![2.0_f64.ln(), 0.5_f64.ln()]; + let individual = individual_psi(&population, &scales, &[0, 1], &eta).unwrap(); + + assert!((individual[0] - 0.4).abs() < 1e-12); + assert!((individual[1] - 5.0).abs() < 1e-12); + } + + #[test] + fn occasion_parameters_add_eta_and_kappa_in_phi_space() { + let population = vec![0.2, 10.0]; + let scales = vec![ParameterScale::Log, ParameterScale::Log]; + let parameters = occasion_psi( + &population, + &scales, + &[0], + &[2.0_f64.ln()], + &[0, 1], + &[0.5_f64.ln(), 2.0_f64.ln()], + ) + .unwrap(); + + assert!((parameters[0] - 0.2).abs() < 1e-12); + assert!((parameters[1] - 20.0).abs() < 1e-12); + } + + #[test] + fn subject_means_convert_exactly_across_all_supported_transforms() { + let mean = vec![2.0, 3.0_f64.ln(), 0.0, 0.0]; + let scales = vec![ + ParameterScale::Identity, + ParameterScale::Log, + ParameterScale::Logit { + lower: -2.0, + upper: 6.0, + }, + ParameterScale::Probit { + lower: 10.0, + upper: 14.0, + }, + ]; + let psi = individual_psi_from_subject_mean(&mean, &scales, &[], &[]).unwrap(); + assert!((psi[0] - 2.0).abs() < 1e-12); + assert!((psi[1] - 3.0).abs() < 1e-12); + assert!((psi[2] - 2.0).abs() < 1e-12); + assert!((psi[3] - 12.0).abs() < 1e-12); + let occasion = + occasion_psi_from_subject_mean(&mean, &scales, &[], &[], &[0, 1], &[1.5, 2.0_f64.ln()]) + .unwrap(); + assert!((occasion[0] - 3.5).abs() < 1e-12); + assert!((occasion[1] - 6.0).abs() < 1e-12); + } + + #[test] + fn parameters_without_random_effects_ignore_eta() { + let population = vec![0.2, 10.0]; + let scales = vec![ParameterScale::Log, ParameterScale::Log]; + let individual = individual_psi(&population, &scales, &[0], &[2.0_f64.ln()]).unwrap(); + + assert!((individual[0] - 0.4).abs() < 1e-12); + assert!((individual[1] - 10.0).abs() < 1e-12); + } +} diff --git a/src/estimation/parametric/information.rs b/src/estimation/parametric/information.rs new file mode 100644 index 000000000..245458ce9 --- /dev/null +++ b/src/estimation/parametric/information.rs @@ -0,0 +1,2267 @@ +//! Analytic complete-data derivatives and observed-information recursion. +//! +//! Coordinates are population φ values, raw free covariance entries, and raw +//! positive residual standard-deviation components. No model sensitivities, +//! finite differences, regularization, or matrix repair are used here. + +use anyhow::{bail, Result}; +use ndarray::Array2; +use pharmsol::prelude::simulator::Prediction; +use pharmsol::{Censor, Predictions}; + +use crate::estimation::ParametricErrorModels; +use crate::results::{ + InformationCoordinate, InformationCoordinateKind, InformationDiagnostics, InformationStatus, + PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, + PopulationUncertaintyStatus, PopulationUncertaintyUnavailableReason, +}; +use crate::ResidualErrorModel; + +use super::covariance::{cholesky_lower, eigenvalue_extrema_symmetric, inverse_spd_from_cholesky}; + +#[derive(Debug, Clone)] +pub(crate) struct InformationLayout { + pub(crate) coordinates: Vec, + population: Vec>, + covariate_effects: Vec>, + omega: Vec, + omega_iov: Vec, + residual: Vec, +} + +#[derive(Debug, Clone, Copy)] +pub(crate) struct CovarianceCoordinate { + coordinate: usize, + row: usize, + column: usize, +} + +#[derive(Debug, Clone, Copy, Default)] +pub(crate) struct ResidualCoordinates { + pub(crate) additive: Option, + pub(crate) proportional: Option, + pub(crate) correlation: Option, +} + +impl InformationLayout { + #[allow(clippy::too_many_arguments)] + pub(crate) fn new( + parameter_names: &[String], + estimated_parameters: &[bool], + covariate_effect_names: &[String], + covariate_estimated: &[bool], + random_effect_names: &[String], + omega_structural: &Array2, + omega_estimated: &Array2, + iov_effect_names: &[String], + omega_iov_structural: Option<&Array2>, + omega_iov_estimated: Option<&Array2>, + error_models: &ParametricErrorModels, + ) -> Result { + if parameter_names.len() != estimated_parameters.len() { + bail!("population parameter metadata dimension mismatch"); + } + if covariate_effect_names.len() != covariate_estimated.len() { + bail!("covariate effect metadata dimension mismatch"); + } + validate_masks( + random_effect_names.len(), + omega_structural, + omega_estimated, + "omega", + )?; + match (omega_iov_structural, omega_iov_estimated) { + (Some(structural), Some(estimated)) => { + validate_masks(iov_effect_names.len(), structural, estimated, "omega_iov")?; + } + (None, None) if iov_effect_names.is_empty() => {} + _ => bail!("omega_iov metadata dimension mismatch"), + } + + let mut coordinates = Vec::new(); + let mut population = vec![None; parameter_names.len()]; + for (parameter_index, (name, estimated)) in + parameter_names.iter().zip(estimated_parameters).enumerate() + { + if *estimated { + population[parameter_index] = Some(push_coordinate( + &mut coordinates, + format!("phi:{name}"), + InformationCoordinateKind::Population { parameter_index }, + )); + } + } + let covariate_effects = covariate_effect_names + .iter() + .zip(covariate_estimated) + .enumerate() + .map(|(effect_index, (name, estimated))| { + estimated.then(|| { + push_coordinate( + &mut coordinates, + name.clone(), + InformationCoordinateKind::CovariateEffect { effect_index }, + ) + }) + }) + .collect(); + let omega = covariance_coordinates( + &mut coordinates, + random_effect_names, + omega_structural, + omega_estimated, + false, + ); + let omega_iov = match (omega_iov_structural, omega_iov_estimated) { + (Some(structural), Some(estimated)) => covariance_coordinates( + &mut coordinates, + iov_effect_names, + structural, + estimated, + true, + ), + _ => Vec::new(), + }; + + let mut residual = vec![ResidualCoordinates::default(); error_models.len()]; + for (output_index, residual_coordinates) in residual.iter_mut().enumerate() { + let Some(model) = error_models.get(output_index) else { + continue; + }; + let output = error_models + .output_name(output_index) + .map(str::to_owned) + .unwrap_or_else(|| format!("output_{output_index}")); + match *model { + ResidualErrorModel::Constant { .. } | ResidualErrorModel::Exponential { .. } => { + if error_models.is_estimated(output_index) { + residual_coordinates.additive = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:sigma"), + InformationCoordinateKind::Residual { + output_index, + component: "sigma".to_string(), + }, + )); + } + } + ResidualErrorModel::Proportional { .. } => { + if error_models.is_estimated(output_index) { + residual_coordinates.proportional = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:proportional"), + InformationCoordinateKind::Residual { + output_index, + component: "proportional".to_string(), + }, + )); + } + } + ResidualErrorModel::Combined { .. } => { + let estimated = error_models.combined_component_estimated(output_index); + if estimated[0] { + residual_coordinates.additive = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:additive"), + InformationCoordinateKind::Residual { + output_index, + component: "additive".to_string(), + }, + )); + } + if estimated[1] { + residual_coordinates.proportional = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:proportional"), + InformationCoordinateKind::Residual { + output_index, + component: "proportional".to_string(), + }, + )); + } + } + ResidualErrorModel::CorrelatedCombined { .. } => { + let estimated = + error_models.correlated_combined_component_estimated(output_index); + if estimated[0] { + residual_coordinates.additive = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:additive"), + InformationCoordinateKind::Residual { + output_index, + component: "additive".to_string(), + }, + )); + } + if estimated[1] { + residual_coordinates.proportional = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:proportional"), + InformationCoordinateKind::Residual { + output_index, + component: "proportional".to_string(), + }, + )); + } + if estimated[2] { + residual_coordinates.correlation = Some(push_coordinate( + &mut coordinates, + format!("residual:{output}:correlation"), + InformationCoordinateKind::Residual { + output_index, + component: "correlation".to_string(), + }, + )); + } + } + } + } + Ok(Self { + coordinates, + population, + covariate_effects, + omega, + omega_iov, + residual, + }) + } + + pub(crate) fn len(&self) -> usize { + self.coordinates.len() + } + + pub(crate) fn residual(&self, output: usize) -> ResidualCoordinates { + self.residual.get(output).copied().unwrap_or_default() + } +} + +fn validate_masks( + width: usize, + structural: &Array2, + estimated: &Array2, + label: &str, +) -> Result<()> { + if structural.dim() != (width, width) || estimated.dim() != (width, width) { + bail!("{label} mask dimension mismatch"); + } + for row in 0..width { + for column in 0..width { + if structural[[row, column]] != structural[[column, row]] + || estimated[[row, column]] != estimated[[column, row]] + || (estimated[[row, column]] && !structural[[row, column]]) + { + bail!("{label} masks must be symmetric and estimated entries structural"); + } + } + } + Ok(()) +} + +fn push_coordinate( + coordinates: &mut Vec, + name: String, + kind: InformationCoordinateKind, +) -> usize { + let index = coordinates.len(); + coordinates.push(InformationCoordinate { index, name, kind }); + index +} + +fn covariance_coordinates( + coordinates: &mut Vec, + names: &[String], + structural: &Array2, + estimated: &Array2, + iov: bool, +) -> Vec { + let mut result = Vec::new(); + for row in 0..names.len() { + for column in 0..=row { + if !structural[[row, column]] || !estimated[[row, column]] { + continue; + } + let kind = if iov { + InformationCoordinateKind::OmegaIov { row, column } + } else { + InformationCoordinateKind::Omega { row, column } + }; + let prefix = if iov { "omega_iov" } else { "omega" }; + result.push(CovarianceCoordinate { + coordinate: push_coordinate( + coordinates, + format!("{prefix}:{}:{}", names[row], names[column]), + kind, + ), + row, + column, + }); + } + } + result +} + +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct CompleteDerivative { + pub(crate) score: Vec, + pub(crate) hessian: Array2, +} + +impl CompleteDerivative { + pub(crate) fn zero(width: usize) -> Self { + Self { + score: vec![0.0; width], + hessian: Array2::zeros((width, width)), + } + } + + /// Add one Gaussian log-density contribution. `deviation` is x-mu. + pub(crate) fn add_gaussian( + &mut self, + deviation: &[f64], + covariance: &Array2, + mean_coordinates: &[Option], + covariance_coordinates: &[CovarianceCoordinate], + ) -> Result<()> { + let n = deviation.len(); + if covariance.dim() != (n, n) || mean_coordinates.len() != n { + bail!("Gaussian derivative dimension mismatch"); + } + if n == 0 { + if covariance_coordinates.is_empty() { + return self.ensure_finite(); + } + bail!("zero-dimensional Gaussian prior has covariance coordinates"); + } + let inverse = inverse_spd(covariance)?; + let z = mat_vec(&inverse, deviation); + if !z.iter().all(|value| value.is_finite()) { + bail!("non-finite Gaussian derivative"); + } + for (row, coordinate) in mean_coordinates.iter().enumerate() { + let Some(coordinate) = coordinate else { + continue; + }; + self.score[*coordinate] += z[row]; + for (column, other) in mean_coordinates.iter().enumerate() { + if let Some(other) = other { + self.hessian[[*coordinate, *other]] -= inverse[[row, column]]; + } + } + } + for covariance_coordinate in covariance_coordinates { + let basis = symmetric_basis(n, covariance_coordinate.row, covariance_coordinate.column); + let ab = inverse.dot(&basis); + let abz = ab.dot(&Array2::from_shape_vec((n, 1), z.clone())?); + let quadratic = deviation + .iter() + .enumerate() + .map(|(index, value)| value * abz[[index, 0]]) + .sum::(); + let coordinate = covariance_coordinate.coordinate; + self.score[coordinate] += -0.5 * trace(&ab) + 0.5 * quadratic; + + for (mean_row, mean_coordinate) in mean_coordinates.iter().enumerate() { + if let Some(mean_coordinate) = mean_coordinate { + let cross = -ab + .row(mean_row) + .iter() + .zip(&z) + .map(|(left, right)| left * right) + .sum::(); + self.hessian[[*mean_coordinate, coordinate]] += cross; + self.hessian[[coordinate, *mean_coordinate]] += cross; + } + } + for other in covariance_coordinates { + let other_basis = symmetric_basis(n, other.row, other.column); + let abs = inverse.dot(&other_basis); + let trace_term = 0.5 * trace(&ab.dot(&abs)); + let first = ab.dot(&abs).dot(&inverse); + let second = abs.dot(&ab).dot(&inverse); + let quadratic_term = -0.5 * quadratic_form(deviation, &(first + second)); + self.hessian[[coordinate, other.coordinate]] += trace_term + quadratic_term; + } + } + self.ensure_finite() + } + + pub(crate) fn add_population_prior( + &mut self, + eta: &[f64], + covariance: &Array2, + random_effect_parameter_indices: &[usize], + layout: &InformationLayout, + ) -> Result<()> { + if eta.len() != random_effect_parameter_indices.len() { + bail!("eta dimension mismatch"); + } + let means = random_effect_parameter_indices + .iter() + .map(|index| layout.population.get(*index).copied().flatten()) + .collect::>(); + self.add_gaussian(eta, covariance, &means, &layout.omega) + } + + pub(crate) fn add_covariate_population_prior( + &mut self, + eta: &[f64], + covariance: &Array2, + random_effect_parameter_indices: &[usize], + effect_parameter_indices: &[usize], + subject_design_values: &[f64], + layout: &InformationLayout, + ) -> Result<()> { + if eta.len() != random_effect_parameter_indices.len() + || effect_parameter_indices.len() != subject_design_values.len() + || effect_parameter_indices.len() != layout.covariate_effects.len() + { + bail!("covariate Gaussian derivative dimension mismatch"); + } + if eta.is_empty() { + return Ok(()); + } + let mut columns = Vec::>::new(); + let mut coordinates = Vec::new(); + for (row, parameter_index) in random_effect_parameter_indices.iter().copied().enumerate() { + if let Some(coordinate) = layout.population.get(parameter_index).copied().flatten() { + let mut column = vec![0.0; eta.len()]; + column[row] = 1.0; + columns.push(column); + coordinates.push(coordinate); + } + } + for (effect_index, coordinate) in layout.covariate_effects.iter().enumerate() { + let Some(coordinate) = coordinate else { + continue; + }; + let Some(row) = random_effect_parameter_indices + .iter() + .position(|parameter| *parameter == effect_parameter_indices[effect_index]) + else { + bail!("estimated covariate effect does not target an IIV coordinate"); + }; + let mut column = vec![0.0; eta.len()]; + column[row] = subject_design_values[effect_index]; + columns.push(column); + coordinates.push(*coordinate); + } + let design = Array2::from_shape_fn((eta.len(), columns.len()), |(row, column)| { + columns[column][row] + }); + self.add_design_mean_prior(eta, covariance, &design, &coordinates, &layout.omega) + } + + pub(crate) fn add_iov_prior( + &mut self, + kappa: &[f64], + covariance: &Array2, + layout: &InformationLayout, + ) -> Result<()> { + self.add_gaussian( + kappa, + covariance, + &vec![None; kappa.len()], + &layout.omega_iov, + ) + } + + /// Add one generalized subject-design Gaussian log-density contribution. + /// + /// `design` is an `n_random_effects × n_coefficients` matrix where each + /// column is the design vector for the corresponding coefficient coordinate. + /// Score: `score_c = A_col_c' * W * eta` + /// Hessian: `H_cc = -A' * W * A` and `H_{c,Omega_h} = -A_col_c' * W * S_h * W * eta` + pub(crate) fn add_design_mean_prior( + &mut self, + eta: &[f64], + covariance: &Array2, + design: &Array2, + coefficient_coordinates: &[usize], + covariance_coordinates: &[CovarianceCoordinate], + ) -> Result<()> { + let n_random = eta.len(); + let n_coefficients = coefficient_coordinates.len(); + if covariance.dim() != (n_random, n_random) || design.dim() != (n_random, n_coefficients) { + bail!("design mean-prior derivative dimension mismatch"); + } + let inverse = inverse_spd(covariance)?; + let z = mat_vec(&inverse, eta); + if !z.iter().all(|value| value.is_finite()) { + bail!("non-finite design mean-prior derivative"); + } + // Score: score_c = A[:,c]' * z + for (col, coordinate) in coefficient_coordinates.iter().enumerate() { + let score_c: f64 = design + .column(col) + .iter() + .zip(&z) + .map(|(a, zi)| a * zi) + .sum(); + self.score[*coordinate] += score_c; + } + // Hessian beta-beta: H_c1,c2 = -A[:,c1]' * W * A[:,c2] + for (c1, coord1) in coefficient_coordinates.iter().enumerate() { + for (c2, coord2) in coefficient_coordinates.iter().enumerate() { + let mut value = 0.0; + for row in 0..n_random { + let wa_c2 = (0..n_random) + .map(|k| inverse[[row, k]] * design[[k, c2]]) + .sum::(); + value += design[[row, c1]] * wa_c2; + } + self.hessian[[*coord1, *coord2]] -= value; + } + } + // Hessian beta-Omega and Omega score: same as add_gaussian for Omega-on-Omega + for covariance_coordinate in covariance_coordinates { + let basis = symmetric_basis( + n_random, + covariance_coordinate.row, + covariance_coordinate.column, + ); + let ab = inverse.dot(&basis); + let abz = ab.dot(&Array2::from_shape_vec((n_random, 1), z.clone())?); + let quadratic = eta + .iter() + .enumerate() + .map(|(index, value)| value * abz[[index, 0]]) + .sum::(); + let omega_coord = covariance_coordinate.coordinate; + self.score[omega_coord] += -0.5 * trace(&ab) + 0.5 * quadratic; + // Beta-Omega cross: H_{c,Omega_h} = -A[:,c]' * W * S_h * z + for (col, coordinate) in coefficient_coordinates.iter().enumerate() { + let cross = -(0..n_random) + .map(|row| { + design[[row, col]] * (0..n_random).map(|k| ab[[row, k]] * z[k]).sum::() + }) + .sum::(); + self.hessian[[*coordinate, omega_coord]] += cross; + self.hessian[[omega_coord, *coordinate]] += cross; + } + // Omega-Omega (same as original add_gaussian) + for other in covariance_coordinates { + let other_basis = symmetric_basis(n_random, other.row, other.column); + let abs = inverse.dot(&other_basis); + let trace_term = 0.5 * trace(&ab.dot(&abs)); + let first = ab.dot(&abs).dot(&inverse); + let second = abs.dot(&ab).dot(&inverse); + let quadratic_term = -0.5 * quadratic_form(eta, &(first + second)); + self.hessian[[omega_coord, other.coordinate]] += trace_term + quadratic_term; + } + } + self.ensure_finite() + } + + pub(crate) fn add_predictions( + &mut self, + predictions: &P, + error_models: &ParametricErrorModels, + layout: &InformationLayout, + ) -> Result<()> { + let mut failure = None; + predictions.for_each_prediction(|prediction: &Prediction| { + if failure.is_some() { + return; + } + let Some(model) = error_models.get(prediction.outeq()).copied() else { + return; + }; + if let Err(error) = self.add_residual( + prediction.outeq(), + prediction.observation(), + prediction.prediction(), + prediction.censoring(), + model, + layout, + ) { + failure = Some(error); + } + }); + match failure { + Some(error) => Err(error), + None => Ok(()), + } + } + + /// Strict retained-Markov score semantics: censoring and every active or + /// equal residual likelihood-floor branch are unsupported. + pub(crate) fn add_predictions_strict( + &mut self, + predictions: &P, + error_models: &ParametricErrorModels, + layout: &InformationLayout, + ) -> Result<()> { + let mut failure = None; + predictions.for_each_prediction(|prediction: &Prediction| { + if failure.is_some() || prediction.observation().is_none() { + return; + } + let Some(model) = error_models.get(prediction.outeq()).copied() else { + return; + }; + let raw_scale = match model { + ResidualErrorModel::Constant { a } => a, + ResidualErrorModel::Proportional { b } => b * prediction.prediction().abs(), + ResidualErrorModel::Combined { a, b } => { + (a * a + b * b * prediction.prediction().powi(2)).sqrt() + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } => (a * a + + 2.0 * rho * a * b * prediction.prediction() + + b * b * prediction.prediction().powi(2)) + .sqrt(), + ResidualErrorModel::Exponential { sigma } => sigma, + }; + if prediction.censoring() != Censor::None { + failure = Some(anyhow::anyhow!( + "retained Markov scores are unsupported for censored observations" + )); + } else if !raw_scale.is_finite() || raw_scale <= f64::EPSILON.sqrt() { + failure = Some(anyhow::anyhow!( + "retained Markov scores are unsupported on an active or equal likelihood-floor branch" + )); + } else if let Err(error) = self.add_residual( + prediction.outeq(), + prediction.observation(), + prediction.prediction(), + prediction.censoring(), + model, + layout, + ) { + failure = Some(error); + } + }); + match failure { + Some(error) => Err(error), + None => Ok(()), + } + } + + pub(crate) fn add_residual( + &mut self, + output: usize, + observation: Option, + prediction: f64, + censor: Censor, + model: ResidualErrorModel, + layout: &InformationLayout, + ) -> Result<()> { + let Some(observation) = observation else { + return Ok(()); + }; + if censor != Censor::None { + bail!("analytic information is unsupported for censored observations"); + } + if !observation.is_finite() || !prediction.is_finite() { + bail!("non-finite residual derivative input"); + } + let coordinates = layout.residual(output); + match model { + ResidualErrorModel::Constant { a } => { + if scale_floor_branch(a, "constant")? == ScaleFloorBranch::Above { + add_simple_residual(self, coordinates.additive, observation - prediction, a)?; + } + } + ResidualErrorModel::Proportional { b } => { + let raw_sigma = b * prediction.abs(); + if scale_floor_branch(raw_sigma, "proportional")? == ScaleFloorBranch::Above { + let residual = observation - prediction; + if let Some(coordinate) = coordinates.proportional { + let q = prediction * prediction; + add_scale_derivative(self, coordinate, residual, b, q)?; + } + } + } + ResidualErrorModel::Combined { a, b } => { + let raw_sigma = (a * a + b * b * prediction * prediction).sqrt(); + if scale_floor_branch(raw_sigma, "combined")? == ScaleFloorBranch::Above { + add_combined_residual( + self, + coordinates, + observation - prediction, + prediction, + a, + b, + )?; + } + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + let raw_sigma = + (a * a + 2.0 * rho * a * b * prediction + b * b * prediction * prediction) + .sqrt(); + if scale_floor_branch(raw_sigma, "correlated-combined")? == ScaleFloorBranch::Above + { + add_correlated_combined_residual( + self, + coordinates, + observation - prediction, + prediction, + a, + b, + rho, + )?; + } + } + ResidualErrorModel::Exponential { sigma } => { + if observation <= 0.0 || prediction <= 0.0 { + bail!("exponential residual information requires positive observation and prediction"); + } + if scale_floor_branch(sigma, "exponential")? == ScaleFloorBranch::Above { + add_simple_residual( + self, + coordinates.additive, + observation.ln() - prediction.ln(), + sigma, + )?; + } + } + } + self.ensure_finite() + } + + fn ensure_finite(&self) -> Result<()> { + if !self.score.iter().all(|value| value.is_finite()) + || !self.hessian.iter().all(|value| value.is_finite()) + { + bail!("non-finite complete-data derivative"); + } + Ok(()) + } +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum ScaleFloorBranch { + Below, + Above, +} + +fn scale_floor_branch(raw_sigma: f64, family: &str) -> Result { + if !raw_sigma.is_finite() || raw_sigma < 0.0 { + bail!("{family} residual scale must be finite and nonnegative"); + } + let floor = f64::EPSILON.sqrt(); + if raw_sigma == floor { + bail!( + "{family} residual scale is exactly at the nondifferentiable likelihood floor boundary" + ); + } + Ok(if raw_sigma < floor { + ScaleFloorBranch::Below + } else { + ScaleFloorBranch::Above + }) +} + +fn add_simple_residual( + derivative: &mut CompleteDerivative, + coordinate: Option, + residual: f64, + sigma: f64, +) -> Result<()> { + let Some(coordinate) = coordinate else { + return Ok(()); + }; + if !sigma.is_finite() || sigma <= 0.0 { + bail!("residual standard deviation must be finite and positive"); + } + add_scale_derivative(derivative, coordinate, residual, sigma, 1.0) +} + +fn add_scale_derivative( + derivative: &mut CompleteDerivative, + coordinate: usize, + residual: f64, + sigma_parameter: f64, + prediction_squared: f64, +) -> Result<()> { + if sigma_parameter <= 0.0 || prediction_squared <= 0.0 { + bail!("invalid residual derivative scale"); + } + let residual_squared = residual * residual; + derivative.score[coordinate] += + -1.0 / sigma_parameter + residual_squared / sigma_parameter.powi(3) / prediction_squared; + derivative.hessian[[coordinate, coordinate]] += 1.0 / sigma_parameter.powi(2) + - 3.0 * residual_squared / sigma_parameter.powi(4) / prediction_squared; + Ok(()) +} + +fn add_combined_residual( + derivative: &mut CompleteDerivative, + coordinates: ResidualCoordinates, + residual: f64, + prediction: f64, + a: f64, + b: f64, +) -> Result<()> { + if a < 0.0 || b < 0.0 || !a.is_finite() || !b.is_finite() { + bail!("combined residual components must be finite and nonnegative"); + } + let f2 = prediction * prediction; + let variance = a * a + b * b * f2; + let r2 = residual * residual; + let common = 1.0 / variance - r2 / variance.powi(2); + let curvature = -1.0 / variance.powi(2) + 2.0 * r2 / variance.powi(3); + if let Some(ai) = coordinates.additive { + derivative.score[ai] -= a * common; + derivative.hessian[[ai, ai]] -= common + 2.0 * a * a * curvature; + } + if let Some(bi) = coordinates.proportional { + derivative.score[bi] -= b * f2 * common; + derivative.hessian[[bi, bi]] -= f2 * common + 2.0 * b * b * f2 * f2 * curvature; + } + if let (Some(ai), Some(bi)) = (coordinates.additive, coordinates.proportional) { + let cross = -2.0 * a * b * f2 * curvature; + derivative.hessian[[ai, bi]] += cross; + derivative.hessian[[bi, ai]] += cross; + } + Ok(()) +} + +fn add_correlated_combined_residual( + derivative: &mut CompleteDerivative, + coordinates: ResidualCoordinates, + residual: f64, + prediction: f64, + a: f64, + b: f64, + rho: f64, +) -> Result<()> { + if !a.is_finite() + || a <= 0.0 + || !b.is_finite() + || b <= 0.0 + || !rho.is_finite() + || rho <= -1.0 + || rho >= 1.0 + { + bail!("correlated-combined residual components are outside their declared domains"); + } + let f = prediction; + let f2 = f * f; + let variance = a * a + 2.0 * rho * a * b * f + b * b * f2; + if !variance.is_finite() || variance <= 0.0 { + bail!("correlated-combined residual variance must be finite and positive"); + } + let residual_squared = residual * residual; + let common = 1.0 / variance - residual_squared / variance.powi(2); + let curvature = -1.0 / variance.powi(2) + 2.0 * residual_squared / variance.powi(3); + let indices = [ + coordinates.additive, + coordinates.proportional, + coordinates.correlation, + ]; + let first = [ + 2.0 * (a + rho * b * f), + 2.0 * (b * f2 + rho * a * f), + 2.0 * a * b * f, + ]; + let second = [ + [2.0, 2.0 * rho * f, 2.0 * b * f], + [2.0 * rho * f, 2.0 * f2, 2.0 * a * f], + [2.0 * b * f, 2.0 * a * f, 0.0], + ]; + for left in 0..3 { + let Some(left_index) = indices[left] else { + continue; + }; + derivative.score[left_index] -= 0.5 * common * first[left]; + for right in 0..3 { + let Some(right_index) = indices[right] else { + continue; + }; + derivative.hessian[[left_index, right_index]] -= + 0.5 * (common * second[left][right] + curvature * first[left] * first[right]); + } + } + Ok(()) +} + +#[derive(Debug, Clone)] +pub(crate) struct InformationRecursion { + layout: InformationLayout, + cycles: usize, + delta: Vec, + g: Array2, + complete_hessian: Array2, + failure: Option, +} + +impl InformationRecursion { + pub(crate) fn new(layout: InformationLayout) -> Self { + let width = layout.len(); + Self { + layout, + cycles: 0, + delta: vec![0.0; width], + g: Array2::zeros((width, width)), + complete_hessian: Array2::zeros((width, width)), + failure: None, + } + } + + pub(crate) fn layout(&self) -> &InformationLayout { + &self.layout + } + + /// Apply one SA update from full-dataset chain replicates. + pub(crate) fn update(&mut self, replicates: &[CompleteDerivative], gamma: f64) { + if gamma == 0.0 || self.failure.is_some() { + return; + } + let width = self.layout.len(); + if replicates.is_empty() + || !gamma.is_finite() + || gamma <= 0.0 + || gamma > 1.0 + || replicates.iter().any(|replicate| { + replicate.score.len() != width + || replicate.hessian.dim() != (width, width) + || !replicate.score.iter().all(|value| value.is_finite()) + || !replicate.hessian.iter().all(|value| value.is_finite()) + }) + { + self.failure = Some(InformationStatus::NonFinite); + return; + } + let count = replicates.len() as f64; + let mut mean_score = vec![0.0; width]; + let mut mean_hessian = Array2::::zeros((width, width)); + let mut mean_augmented = Array2::::zeros((width, width)); + for replicate in replicates { + for row in 0..width { + mean_score[row] += replicate.score[row] / count; + for column in 0..width { + mean_hessian[[row, column]] += replicate.hessian[[row, column]] / count; + mean_augmented[[row, column]] += (replicate.hessian[[row, column]] + + replicate.score[row] * replicate.score[column]) + / count; + } + } + } + for row in 0..width { + self.delta[row] += gamma * (mean_score[row] - self.delta[row]); + for column in 0..width { + self.complete_hessian[[row, column]] += + gamma * (mean_hessian[[row, column]] - self.complete_hessian[[row, column]]); + self.g[[row, column]] += + gamma * (mean_augmented[[row, column]] - self.g[[row, column]]); + } + } + self.cycles += 1; + } + + pub(crate) fn mark_unavailable(&mut self, status: InformationStatus) { + if self.failure.is_none() { + self.failure = Some(status); + } + } + + pub(crate) fn diagnostics(&self) -> InformationDiagnostics { + let width = self.layout.len(); + let mut observed_hessian = self.g.clone(); + for row in 0..width { + for column in 0..width { + observed_hessian[[row, column]] -= self.delta[row] * self.delta[column]; + } + } + let symmetric = is_finite_symmetric(&observed_hessian) + && is_finite_symmetric(&self.g) + && is_finite_symmetric(&self.complete_hessian); + if symmetric { + symmetrize_roundoff(&mut observed_hessian); + } + let observed_information = observed_hessian.mapv(|value| -value); + let status = if let Some(status) = &self.failure { + status.clone() + } else if width == 0 { + InformationStatus::NoFreeCoordinates + } else if !symmetric { + InformationStatus::NonFinite + } else if cholesky_lower(&observed_information).is_err() { + InformationStatus::ObservedInformationNotPositiveDefinite + } else { + InformationStatus::Available + }; + InformationDiagnostics { + coordinates: self.layout.coordinates.clone(), + recursion_cycles: self.cycles, + delta: self.delta.clone(), + g: rows(&self.g), + expected_complete_hessian: rows(&self.complete_hessian), + observed_hessian: rows(&observed_hessian), + observed_information: rows(&observed_information), + status, + } + } +} + +/// Derive free-coordinate population uncertainty from observed-information diagnostics. +/// +/// Inverts only when [`InformationStatus::Available`]; never applies regularization, +/// repair, or fallback. The returned covariance and standard errors are in the +/// estimation (φ) space coordinated with the diagnostic free-coordinate order. +pub(crate) fn derive_population_uncertainty( + diagnostics: &InformationDiagnostics, +) -> PopulationUncertaintyDiagnostics { + if diagnostics.status != InformationStatus::Available { + let reason = match &diagnostics.status { + InformationStatus::NonFinite => PopulationUncertaintyUnavailableReason::NonFinite, + InformationStatus::ObservedInformationNotPositiveDefinite => { + PopulationUncertaintyUnavailableReason::ObservedInformationNotPositiveDefinite + } + other => PopulationUncertaintyUnavailableReason::SourceUnavailable(other.clone()), + }; + return unavailable_population_uncertainty(diagnostics, reason); + } + + let n = diagnostics.coordinates.len(); + if n == 0 { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::SourceUnavailable( + InformationStatus::NoFreeCoordinates, + ), + ); + } + + let observed_information = match rows_to_array2(&diagnostics.observed_information, n) { + Ok(matrix) => matrix, + Err(_) => { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::InversionFailed, + ); + } + }; + if !observed_information.iter().all(|value| value.is_finite()) { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::NonFinite, + ); + } + + // Strict SPD classification and inversion through one unmodified Cholesky factor. + let lower = match cholesky_lower(&observed_information) { + Ok(lower) => lower, + Err(_) => { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::ObservedInformationNotPositiveDefinite, + ); + } + }; + let free_covariance = match inverse_spd_from_cholesky(&lower) { + Ok(inverse) => inverse, + Err(_) => { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::InversionFailed, + ); + } + }; + + let (minimum_eigenvalue, maximum_eigenvalue) = + match eigenvalue_extrema_symmetric(&observed_information) { + Ok(extrema) => extrema, + Err(_) => { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::InversionFailed, + ); + } + }; + if !minimum_eigenvalue.is_finite() || !maximum_eigenvalue.is_finite() { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::NonFinite, + ); + } + if minimum_eigenvalue <= 0.0 { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::ObservedInformationNotPositiveDefinite, + ); + } + let spectral_condition_number = maximum_eigenvalue / minimum_eigenvalue; + if !spectral_condition_number.is_finite() { + return unavailable_population_uncertainty( + diagnostics, + PopulationUncertaintyUnavailableReason::NonFinite, + ); + } + + PopulationUncertaintyDiagnostics { + coordinates: diagnostics.coordinates.clone(), + free_standard_errors: Some( + (0..n) + .map(|index| free_covariance[[index, index]].sqrt()) + .collect(), + ), + free_covariance: Some(rows(&free_covariance)), + spectral_condition_number: Some(spectral_condition_number), + status: PopulationUncertaintyStatus::Available, + regularization: PopulationUncertaintyRegularization::None, + } +} + +fn unavailable_population_uncertainty( + diagnostics: &InformationDiagnostics, + reason: PopulationUncertaintyUnavailableReason, +) -> PopulationUncertaintyDiagnostics { + let mut result = PopulationUncertaintyDiagnostics::unavailable(reason); + result.coordinates = diagnostics.coordinates.clone(); + result +} + +fn rows_to_array2(rows: &[Vec], n: usize) -> Result> { + if rows.len() != n || rows.iter().any(|row| row.len() != n) { + bail!("observed-information matrix dimensions must match coordinate count"); + } + let flat: Vec = rows.iter().flat_map(|row| row.iter().copied()).collect(); + Ok(Array2::from_shape_vec((n, n), flat)?) +} + +fn inverse_spd(matrix: &Array2) -> Result> { + let lower = cholesky_lower(matrix)?; + inverse_spd_from_cholesky(&lower) +} + +fn symmetric_basis(width: usize, row: usize, column: usize) -> Array2 { + let mut basis = Array2::zeros((width, width)); + basis[[row, column]] = 1.0; + basis[[column, row]] = 1.0; + basis +} + +fn mat_vec(matrix: &Array2, vector: &[f64]) -> Vec { + (0..matrix.nrows()) + .map(|row| { + (0..matrix.ncols()) + .map(|column| matrix[[row, column]] * vector[column]) + .sum() + }) + .collect() +} + +fn quadratic_form(vector: &[f64], matrix: &Array2) -> f64 { + let product = mat_vec(matrix, vector); + vector + .iter() + .zip(product) + .map(|(left, right)| left * right) + .sum() +} + +fn trace(matrix: &Array2) -> f64 { + (0..matrix.nrows()) + .map(|index| matrix[[index, index]]) + .sum() +} + +fn rows(matrix: &Array2) -> Vec> { + matrix.rows().into_iter().map(|row| row.to_vec()).collect() +} + +fn is_finite_symmetric(matrix: &Array2) -> bool { + if matrix.nrows() != matrix.ncols() || !matrix.iter().all(|value| value.is_finite()) { + return false; + } + for row in 0..matrix.nrows() { + for column in 0..row { + let scale = matrix[[row, column]] + .abs() + .max(matrix[[column, row]].abs()) + .max(1.0); + if (matrix[[row, column]] - matrix[[column, row]]).abs() > 64.0 * f64::EPSILON * scale { + return false; + } + } + } + true +} + +fn symmetrize_roundoff(matrix: &mut Array2) { + for row in 0..matrix.nrows() { + for column in 0..row { + let value = 0.5 * matrix[[row, column]] + 0.5 * matrix[[column, row]]; + matrix[[row, column]] = value; + matrix[[column, row]] = value; + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + fn one_coordinate_layout() -> InformationLayout { + InformationLayout { + coordinates: vec![InformationCoordinate { + index: 0, + name: "phi:x".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }], + population: vec![Some(0)], + covariate_effects: Vec::new(), + omega: Vec::new(), + omega_iov: Vec::new(), + residual: Vec::new(), + } + } + + fn two_coordinate_layout() -> InformationLayout { + let mut layout = one_coordinate_layout(); + layout.coordinates.push(InformationCoordinate { + index: 1, + name: "phi:y".into(), + kind: InformationCoordinateKind::Population { parameter_index: 1 }, + }); + layout.population.push(Some(1)); + layout + } + + fn diagnostics_with_information( + coordinates: Vec, + observed_information: Vec>, + status: InformationStatus, + ) -> InformationDiagnostics { + let width = coordinates.len(); + InformationDiagnostics { + coordinates, + recursion_cycles: 1, + delta: vec![0.0; width], + g: vec![vec![0.0; width]; width], + expected_complete_hessian: vec![vec![0.0; width]; width], + observed_hessian: observed_information + .iter() + .map(|row| row.iter().map(|value| -*value).collect()) + .collect(), + observed_information, + status, + } + } + + #[test] + fn population_uncertainty_inverts_one_and_two_coordinate_information() { + let one = diagnostics_with_information( + one_coordinate_layout().coordinates, + vec![vec![4.0]], + InformationStatus::Available, + ); + let one_uncertainty = derive_population_uncertainty(&one); + assert_eq!( + one_uncertainty.status, + PopulationUncertaintyStatus::Available + ); + assert_eq!(one_uncertainty.free_covariance, Some(vec![vec![0.25]])); + assert_eq!(one_uncertainty.free_standard_errors, Some(vec![0.5])); + assert_eq!(one_uncertainty.spectral_condition_number, Some(1.0)); + assert_eq!( + one_uncertainty.regularization, + PopulationUncertaintyRegularization::None + ); + + let two = diagnostics_with_information( + two_coordinate_layout().coordinates, + vec![vec![4.0, 2.0], vec![2.0, 3.0]], + InformationStatus::Available, + ); + let two_uncertainty = derive_population_uncertainty(&two); + let covariance = two_uncertainty.free_covariance.unwrap(); + assert!((covariance[0][0] - 0.375).abs() < 1e-12); + assert!((covariance[0][1] + 0.25).abs() < 1e-12); + assert!((covariance[1][0] + 0.25).abs() < 1e-12); + assert!((covariance[1][1] - 0.5).abs() < 1e-12); + let standard_errors = two_uncertainty.free_standard_errors.unwrap(); + assert!((standard_errors[0] - 0.375_f64.sqrt()).abs() < 1e-12); + assert!((standard_errors[1] - 0.5_f64.sqrt()).abs() < 1e-12); + let expected_condition = (7.0 + 17.0_f64.sqrt()) / (7.0 - 17.0_f64.sqrt()); + assert!( + (two_uncertainty.spectral_condition_number.unwrap() - expected_condition).abs() < 1e-12 + ); + } + + #[test] + fn population_uncertainty_has_typed_unavailable_reasons() { + let coordinate = one_coordinate_layout().coordinates; + for (status, expected) in [ + ( + InformationStatus::Ineligible("not accumulated".into()), + PopulationUncertaintyUnavailableReason::SourceUnavailable( + InformationStatus::Ineligible("not accumulated".into()), + ), + ), + ( + InformationStatus::NonFinite, + PopulationUncertaintyUnavailableReason::NonFinite, + ), + ( + InformationStatus::ObservedInformationNotPositiveDefinite, + PopulationUncertaintyUnavailableReason::ObservedInformationNotPositiveDefinite, + ), + ] { + let diagnostics = + diagnostics_with_information(coordinate.clone(), vec![vec![1.0]], status); + assert_eq!( + derive_population_uncertainty(&diagnostics).status, + PopulationUncertaintyStatus::Unavailable(expected) + ); + } + + let nonfinite = diagnostics_with_information( + coordinate.clone(), + vec![vec![f64::NAN]], + InformationStatus::Available, + ); + assert_eq!( + derive_population_uncertainty(&nonfinite).status, + PopulationUncertaintyStatus::Unavailable( + PopulationUncertaintyUnavailableReason::NonFinite + ) + ); + + let non_positive_definite = diagnostics_with_information( + coordinate.clone(), + vec![vec![-1.0]], + InformationStatus::Available, + ); + assert_eq!( + derive_population_uncertainty(&non_positive_definite).status, + PopulationUncertaintyStatus::Unavailable( + PopulationUncertaintyUnavailableReason::ObservedInformationNotPositiveDefinite + ) + ); + + let inversion_overflow = diagnostics_with_information( + coordinate, + vec![vec![f64::from_bits(1)]], + InformationStatus::Available, + ); + assert_eq!( + derive_population_uncertainty(&inversion_overflow).status, + PopulationUncertaintyStatus::Unavailable( + PopulationUncertaintyUnavailableReason::InversionFailed + ) + ); + } + + #[test] + fn one_dimensional_gaussian_mean_score_and_hessian_are_exact() { + let layout = one_coordinate_layout(); + let mut derivative = CompleteDerivative::zero(1); + derivative + .add_gaussian(&[2.0], &ndarray::array![[4.0]], &[Some(0)], &[]) + .unwrap(); + assert_eq!(derivative.score, vec![0.5]); + assert_eq!(derivative.hessian[[0, 0]], -0.25); + assert_eq!(layout.len(), 1); + } + + #[test] + fn population_uncertainty_layout_preserves_mixed_fixed_free_masks_and_order() { + use crate::estimation::ParametricErrorModel; + + let structural = ndarray::array![[true, true], [true, true]]; + let estimated = ndarray::array![[true, true], [true, false]]; + let iov_mask = ndarray::array![[true]]; + let models = ParametricErrorModels::new() + .add( + 0, + "fixed", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .add( + 1, + "combined", + ParametricErrorModel::new(ResidualErrorModel::combined(0.2, 0.3)) + .fixed_combined_additive(), + ) + .add(2, "prop", ResidualErrorModel::proportional(0.1).into()); + let layout = InformationLayout::new( + &["a".into(), "b".into(), "c".into()], + &[true, false, true], + &[], + &[], + &["a".into(), "c".into()], + &structural, + &estimated, + &["c".into()], + Some(&iov_mask), + Some(&iov_mask), + &models, + ) + .unwrap(); + assert_eq!( + layout + .coordinates + .iter() + .map(|c| c.name.as_str()) + .collect::>(), + vec![ + "phi:a", + "phi:c", + "omega:a:a", + "omega:c:a", + "omega_iov:c:c", + "residual:combined:proportional", + "residual:prop:proportional", + ] + ); + let coordinates = layout.coordinates.clone(); + let width = coordinates.len(); + let observed_information = (0..width) + .map(|row| { + (0..width) + .map(|column| if row == column { 1.0 } else { 0.0 }) + .collect() + }) + .collect(); + let diagnostics = diagnostics_with_information( + coordinates.clone(), + observed_information, + InformationStatus::Available, + ); + let uncertainty = derive_population_uncertainty(&diagnostics); + assert_eq!(uncertainty.coordinates, coordinates); + assert_eq!(uncertainty.status, PopulationUncertaintyStatus::Available); + assert_eq!(uncertainty.free_standard_errors, Some(vec![1.0; width])); + } + + #[test] + fn two_dimensional_gaussian_has_exact_mean_covariance_cross_block() { + let coordinates = vec![ + CovarianceCoordinate { + coordinate: 2, + row: 0, + column: 0, + }, + CovarianceCoordinate { + coordinate: 3, + row: 1, + column: 0, + }, + CovarianceCoordinate { + coordinate: 4, + row: 1, + column: 1, + }, + ]; + let mut derivative = CompleteDerivative::zero(5); + derivative + .add_gaussian( + &[1.0, -2.0], + &ndarray::array![[2.0, 1.0], [1.0, 2.0]], + &[Some(0), Some(1)], + &coordinates, + ) + .unwrap(); + let expected_score = [4.0 / 3.0, -5.0 / 3.0, 5.0 / 9.0, -17.0 / 9.0, 19.0 / 18.0]; + let expected_hessian = ndarray::array![ + [-2.0 / 3.0, 1.0 / 3.0, -8.0 / 9.0, 14.0 / 9.0, -5.0 / 9.0], + [1.0 / 3.0, -2.0 / 3.0, 4.0 / 9.0, -13.0 / 9.0, 10.0 / 9.0], + [ + -8.0 / 9.0, + 4.0 / 9.0, + -26.0 / 27.0, + 50.0 / 27.0, + -37.0 / 54.0 + ], + [ + 14.0 / 9.0, + -13.0 / 9.0, + 50.0 / 27.0, + -107.0 / 27.0, + 59.0 / 27.0 + ], + [ + -5.0 / 9.0, + 10.0 / 9.0, + -37.0 / 54.0, + 59.0 / 27.0, + -44.0 / 27.0 + ], + ]; + for (actual, expected) in derivative.score.iter().zip(expected_score) { + assert!((actual - expected).abs() < 1e-12); + } + for (actual, expected) in derivative.hessian.iter().zip(expected_hessian.iter()) { + assert!((actual - expected).abs() < 1e-12); + } + // The raw symmetric off-diagonal covariance coordinate has no hidden + // half-vectorization factor: its score is exactly -17/9. + assert!((derivative.score[3] + 17.0 / 9.0).abs() < 1e-12); + } + + fn residual_layout() -> InformationLayout { + InformationLayout { + coordinates: vec![ + InformationCoordinate { + index: 0, + name: "a".into(), + kind: InformationCoordinateKind::Residual { + output_index: 0, + component: "additive".into(), + }, + }, + InformationCoordinate { + index: 1, + name: "b".into(), + kind: InformationCoordinateKind::Residual { + output_index: 0, + component: "proportional".into(), + }, + }, + ], + population: Vec::new(), + covariate_effects: Vec::new(), + omega: Vec::new(), + omega_iov: Vec::new(), + residual: vec![ResidualCoordinates { + additive: Some(0), + proportional: Some(1), + correlation: None, + }], + } + } + + #[test] + fn multiple_iov_occasions_aggregate_into_one_complete_derivative() { + let layout = InformationLayout { + coordinates: vec![InformationCoordinate { + index: 0, + name: "omega_iov:k:k".into(), + kind: InformationCoordinateKind::OmegaIov { row: 0, column: 0 }, + }], + population: Vec::new(), + covariate_effects: Vec::new(), + omega: Vec::new(), + omega_iov: vec![CovarianceCoordinate { + coordinate: 0, + row: 0, + column: 0, + }], + residual: Vec::new(), + }; + let mut derivative = CompleteDerivative::zero(1); + derivative + .add_iov_prior(&[1.0], &ndarray::array![[4.0]], &layout) + .unwrap(); + derivative + .add_iov_prior(&[2.0], &ndarray::array![[4.0]], &layout) + .unwrap(); + assert!((derivative.score[0] + 3.0 / 32.0).abs() < 1e-12); + assert!((derivative.hessian[[0, 0]] + 1.0 / 64.0).abs() < 1e-12); + } + + #[test] + fn multi_output_residual_coordinates_preserve_masks_and_order() { + use crate::estimation::ParametricErrorModel; + + let models = ParametricErrorModels::new() + .add(0, "first", ResidualErrorModel::constant(0.5).into()) + .add( + 1, + "second", + ParametricErrorModel::new(ResidualErrorModel::proportional(0.25)), + ); + let empty = Array2::from_shape_vec((0, 0), Vec::::new()).unwrap(); + let layout = InformationLayout::new( + &[], + &[], + &[], + &[], + &[], + &empty, + &empty, + &[], + None, + None, + &models, + ) + .unwrap(); + assert_eq!( + layout + .coordinates + .iter() + .map(|coordinate| coordinate.name.as_str()) + .collect::>(), + ["residual:first:sigma", "residual:second:proportional"] + ); + let mut derivative = CompleteDerivative::zero(2); + derivative + .add_residual( + 0, + Some(2.0), + 1.0, + Censor::None, + ResidualErrorModel::constant(0.5), + &layout, + ) + .unwrap(); + derivative + .add_residual( + 1, + Some(3.0), + 2.0, + Censor::None, + ResidualErrorModel::proportional(0.25), + &layout, + ) + .unwrap(); + assert_eq!(derivative.score, vec![6.0, 12.0]); + assert_eq!(derivative.hessian[[0, 0]], -44.0); + assert_eq!(derivative.hessian[[1, 1]], -176.0); + assert_eq!(derivative.hessian[[0, 1]], 0.0); + } + + #[test] + fn residual_family_derivatives_match_hard_coded_analytic_values() { + let layout = residual_layout(); + for model in [ + ResidualErrorModel::constant(0.5), + ResidualErrorModel::proportional(0.5), + ] { + let mut derivative = CompleteDerivative::zero(2); + derivative + .add_residual(0, Some(2.0), 1.0, Censor::None, model, &layout) + .unwrap(); + let coordinate = if matches!(model, ResidualErrorModel::Proportional { .. }) { + 1 + } else { + 0 + }; + assert_eq!(derivative.score[coordinate], 6.0); + assert_eq!(derivative.hessian[[coordinate, coordinate]], -44.0); + } + + let mut combined = CompleteDerivative::zero(2); + combined + .add_residual( + 0, + Some(2.0), + 1.0, + Censor::None, + ResidualErrorModel::combined(0.3, 0.4), + &layout, + ) + .unwrap(); + assert!((combined.score[0] - 3.6).abs() < 1e-12); + assert!((combined.score[1] - 4.8).abs() < 1e-12); + assert!((combined.hessian[[0, 0]] + 8.16).abs() < 1e-12); + assert!((combined.hessian[[1, 1]] + 23.84).abs() < 1e-12); + assert!((combined.hessian[[0, 1]] + 26.88).abs() < 1e-12); + assert_eq!(combined.hessian[[0, 1]], combined.hessian[[1, 0]]); + + let mut exponential = CompleteDerivative::zero(2); + exponential + .add_residual( + 0, + Some(std::f64::consts::E), + 1.0, + Censor::None, + ResidualErrorModel::exponential(0.5), + &layout, + ) + .unwrap(); + assert!((exponential.score[0] - 6.0).abs() < 1e-12); + assert!((exponential.hessian[[0, 0]] + 44.0).abs() < 1e-12); + } + + #[test] + fn correlated_combined_layout_and_analytic_derivatives_match_finite_differences() { + let models = ParametricErrorModels::new().add( + 0, + "cp", + crate::estimation::ParametricErrorModel::new(ResidualErrorModel::correlated_combined( + 0.6, 0.3, -0.25, + )), + ); + let empty = Array2::from_shape_vec((0, 0), Vec::::new()).unwrap(); + let layout = InformationLayout::new( + &[], + &[], + &[], + &[], + &[], + &empty, + &empty, + &[], + None, + None, + &models, + ) + .unwrap(); + assert_eq!( + layout + .coordinates + .iter() + .map(|coordinate| coordinate.name.as_str()) + .collect::>(), + [ + "residual:cp:additive", + "residual:cp:proportional", + "residual:cp:correlation" + ] + ); + + let values = [0.6_f64, 0.3, -0.25]; + let prediction = 1.2_f64; + let observation = 1.8_f64; + let log_likelihood = |parameters: [f64; 3]| { + let variance = parameters[0].powi(2) + + 2.0 * parameters[2] * parameters[0] * parameters[1] * prediction + + parameters[1].powi(2) * prediction.powi(2); + -0.5 * (variance.ln() + (observation - prediction).powi(2) / variance) + }; + let mut derivative = CompleteDerivative::zero(3); + derivative + .add_residual( + 0, + Some(observation), + prediction, + Censor::None, + ResidualErrorModel::correlated_combined(values[0], values[1], values[2]), + &layout, + ) + .unwrap(); + let h = 1e-4; + for left in 0..3 { + let mut plus = values; + plus[left] += h; + let mut minus = values; + minus[left] -= h; + let numeric_score = (log_likelihood(plus) - log_likelihood(minus)) / (2.0 * h); + assert!((derivative.score[left] - numeric_score).abs() < 2e-7); + for right in 0..3 { + let numeric_hessian = if left == right { + (log_likelihood(plus) - 2.0 * log_likelihood(values) + log_likelihood(minus)) + / h.powi(2) + } else { + let mut pp = values; + pp[left] += h; + pp[right] += h; + let mut pm = values; + pm[left] += h; + pm[right] -= h; + let mut mp = values; + mp[left] -= h; + mp[right] += h; + let mut mm = values; + mm[left] -= h; + mm[right] -= h; + (log_likelihood(pp) - log_likelihood(pm) - log_likelihood(mp) + + log_likelihood(mm)) + / (4.0 * h.powi(2)) + }; + assert!( + (derivative.hessian[[left, right]] - numeric_hessian).abs() < 2e-5, + "hessian ({left}, {right})" + ); + } + } + } + + #[test] + fn missing_and_censor_semantics_are_explicit() { + let layout = residual_layout(); + let mut missing = CompleteDerivative::zero(2); + missing + .add_residual( + 0, + None, + 1.0, + Censor::None, + ResidualErrorModel::constant(0.5), + &layout, + ) + .unwrap(); + assert_eq!(missing.score, vec![0.0, 0.0]); + + let mut censored = CompleteDerivative::zero(2); + assert!(censored + .add_residual( + 0, + Some(1.0), + 1.0, + Censor::BLOQ, + ResidualErrorModel::constant(0.5), + &layout + ) + .unwrap_err() + .to_string() + .contains("censored")); + } + + #[test] + fn every_residual_family_matches_below_equal_and_above_likelihood_floor() { + let layout = residual_layout(); + let floor = f64::EPSILON.sqrt(); + let branches = [floor / 2.0, floor, floor * 2.0]; + for family in ["constant", "proportional", "combined", "exponential"] { + for (branch, scale) in branches.into_iter().enumerate() { + let model = match family { + "constant" => ResidualErrorModel::constant(scale), + "proportional" => ResidualErrorModel::proportional(scale), + "combined" => ResidualErrorModel::combined(scale, 0.0), + "exponential" => ResidualErrorModel::exponential(scale), + _ => unreachable!(), + }; + let mut derivative = CompleteDerivative::zero(2); + let result = + derivative.add_residual(0, Some(1.0), 1.0, Censor::None, model, &layout); + match branch { + 0 => { + result.unwrap(); + assert_eq!(derivative.score, vec![0.0, 0.0]); + assert_eq!(derivative.hessian, Array2::::zeros((2, 2))); + } + 1 => assert_eq!( + result.unwrap_err().to_string(), + format!( + "{family} residual scale is exactly at the nondifferentiable likelihood floor boundary" + ) + ), + 2 => { + result.unwrap(); + let coordinate = usize::from(family == "proportional"); + assert_eq!(derivative.score[coordinate], -1.0 / scale); + assert_eq!( + derivative.hessian[[coordinate, coordinate]], + 1.0 / scale.powi(2) + ); + } + _ => unreachable!(), + } + } + } + + let mut prediction_floored = CompleteDerivative::zero(2); + prediction_floored + .add_residual( + 0, + Some(1.0), + 0.0, + Censor::None, + ResidualErrorModel::proportional(0.5), + &layout, + ) + .unwrap(); + assert_eq!(prediction_floored.score, vec![0.0, 0.0]); + } + + #[test] + fn recursion_uses_mean_outer_scores_and_two_cycle_sa_updates() { + let layout = one_coordinate_layout(); + let mut recursion = InformationRecursion::new(layout); + let a = CompleteDerivative { + score: vec![1.0], + hessian: ndarray::array![[-2.0]], + }; + let b = CompleteDerivative { + score: vec![-1.0], + hessian: ndarray::array![[-2.0]], + }; + recursion.update(&[a, b], 1.0); + let c = CompleteDerivative { + score: vec![2.0], + hessian: ndarray::array![[-4.0]], + }; + recursion.update(&[c], 0.5); + let diagnostics = recursion.diagnostics(); + assert_eq!(diagnostics.recursion_cycles, 2); + assert_eq!(diagnostics.delta, vec![1.0]); + assert_eq!(diagnostics.expected_complete_hessian, vec![vec![-3.0]]); + assert_eq!(diagnostics.g, vec![vec![-0.5]]); + assert_eq!(diagnostics.observed_hessian, vec![vec![-1.5]]); + assert_eq!(diagnostics.observed_information, vec![vec![1.5]]); + assert_eq!(diagnostics.status, InformationStatus::Available); + } + + #[test] + fn diagnostics_canonicalize_only_accepted_symmetric_roundoff() { + let mut recursion = InformationRecursion::new(two_coordinate_layout()); + recursion.g = ndarray::array![[-4.0, -1.0], [-1.0 - f64::EPSILON, -3.0]]; + recursion.complete_hessian = ndarray::array![[-4.0, -1.0], [-1.0, -3.0]]; + + let diagnostics = recursion.diagnostics(); + assert_eq!(diagnostics.status, InformationStatus::Available); + assert_eq!( + diagnostics.observed_information[0][1], + diagnostics.observed_information[1][0] + ); + assert_eq!( + diagnostics.observed_hessian[0][1], + diagnostics.observed_hessian[1][0] + ); + } + + #[test] + fn accepted_high_scale_roundoff_canonicalization_remains_finite() { + let high = 0.25 * f64::MAX; + let near = high * (1.0 + 8.0 * f64::EPSILON); + let mut recursion = InformationRecursion::new(two_coordinate_layout()); + recursion.g = ndarray::array![[-3.0 * high, -high], [-near, -3.0 * high]]; + recursion.complete_hessian = ndarray::array![[-3.0 * high, -high], [-high, -3.0 * high]]; + + let diagnostics = recursion.diagnostics(); + assert_eq!(diagnostics.status, InformationStatus::Available); + assert!(diagnostics + .observed_information + .iter() + .flatten() + .all(|value| value.is_finite())); + assert_eq!( + diagnostics.observed_information[0][1], + diagnostics.observed_information[1][0] + ); + } + + #[test] + fn above_tolerance_asymmetry_is_retained_and_rejected() { + let asymmetry = 128.0 * f64::EPSILON; + let mut raw_g = InformationRecursion::new(two_coordinate_layout()); + raw_g.g = ndarray::array![[-4.0, -1.0], [-1.0 - asymmetry, -3.0]]; + raw_g.complete_hessian = ndarray::array![[-4.0, -1.0], [-1.0, -3.0]]; + let g_diagnostics = raw_g.diagnostics(); + assert_eq!(g_diagnostics.status, InformationStatus::NonFinite); + assert_eq!(g_diagnostics.g[1][0], -1.0 - asymmetry); + assert_eq!(g_diagnostics.observed_hessian[1][0], -1.0 - asymmetry); + assert_eq!(g_diagnostics.observed_hessian[0][1], -1.0); + + let mut raw_complete = InformationRecursion::new(two_coordinate_layout()); + raw_complete.g = ndarray::array![[-4.0, -1.0], [-1.0, -3.0]]; + raw_complete.complete_hessian = ndarray::array![[-4.0, -1.0], [-1.0 - asymmetry, -3.0]]; + let complete_diagnostics = raw_complete.diagnostics(); + assert_eq!(complete_diagnostics.status, InformationStatus::NonFinite); + assert_eq!( + complete_diagnostics.expected_complete_hessian[1][0], + -1.0 - asymmetry + ); + assert_eq!(complete_diagnostics.observed_hessian[0][1], -1.0); + assert_eq!(complete_diagnostics.observed_hessian[1][0], -1.0); + } + + #[test] + fn nonfinite_and_indefinite_statuses_retain_unmodified_values() { + let mut nonfinite = InformationRecursion::new(one_coordinate_layout()); + nonfinite.mark_unavailable(InformationStatus::NonFinite); + assert_eq!(nonfinite.diagnostics().status, InformationStatus::NonFinite); + + let mut indefinite = InformationRecursion::new(one_coordinate_layout()); + indefinite.update( + &[CompleteDerivative { + score: vec![0.0], + hessian: ndarray::array![[1.0]], + }], + 1.0, + ); + let diagnostics = indefinite.diagnostics(); + assert_eq!( + diagnostics.status, + InformationStatus::ObservedInformationNotPositiveDefinite + ); + assert_eq!(diagnostics.observed_information, vec![vec![-1.0]]); + } + + #[test] + fn zero_coordinates_are_labeled_without_fabricated_information() { + let layout = InformationLayout { + coordinates: Vec::new(), + population: Vec::new(), + covariate_effects: Vec::new(), + omega: Vec::new(), + omega_iov: Vec::new(), + residual: Vec::new(), + }; + assert_eq!( + InformationRecursion::new(layout).diagnostics().status, + InformationStatus::NoFreeCoordinates + ); + } + + // ─── Covariate information coordinate tests ───────────────────────── + + fn covariate_effect_layout( + covariate_names: &[String], + covariate_estimated: &[bool], + ) -> InformationLayout { + let _empty = Array2::from_shape_vec((0, 0), Vec::::new()).unwrap(); + InformationLayout::new( + &["CL".into()], + &[true], + covariate_names, + covariate_estimated, + &["CL".into()], + &ndarray::array![[true]], + &ndarray::array![[true]], + &[], + None, + None, + &crate::estimation::ParametricErrorModels::new(), + ) + .unwrap() + } + + #[test] + fn covariate_coordinates_follow_intercepts_in_canonical_order() { + let layout = covariate_effect_layout(&["beta:CL:WT".into()], &[true]); + assert_eq!( + layout + .coordinates + .iter() + .map(|c| (c.name.as_str(), &c.kind)) + .collect::>(), + vec![ + ( + "phi:CL", + &InformationCoordinateKind::Population { parameter_index: 0 } + ), + ( + "beta:CL:WT", + &InformationCoordinateKind::CovariateEffect { effect_index: 0 } + ), + ( + "omega:CL:CL", + &InformationCoordinateKind::Omega { row: 0, column: 0 } + ), + ] + ); + assert_eq!(layout.len(), 3); + } + + #[test] + fn fixed_covariate_effects_are_excluded_from_coordinates() { + let layout = + covariate_effect_layout(&["beta:CL:WT".into(), "beta:CL:AGE".into()], &[false, true]); + assert_eq!( + layout + .coordinates + .iter() + .map(|c| c.name.as_str()) + .collect::>(), + vec!["phi:CL", "beta:CL:AGE", "omega:CL:CL"] + ); + assert_eq!(layout.len(), 3); + } + + #[test] + fn design_mean_prior_matches_separate_add_gaussian_for_single_intercept() { + // When the design matrix is an identity (one intercept per eta row), + // add_design_mean_prior should match add_gaussian with the same mean + // coordinate mapping. + let layout = InformationLayout { + coordinates: vec![ + InformationCoordinate { + index: 0, + name: "phi:CL".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }, + InformationCoordinate { + index: 1, + name: "omega:CL:CL".into(), + kind: InformationCoordinateKind::Omega { row: 0, column: 0 }, + }, + ], + population: vec![Some(0)], + covariate_effects: Vec::new(), + omega: vec![CovarianceCoordinate { + coordinate: 1, + row: 0, + column: 0, + }], + omega_iov: Vec::new(), + residual: Vec::new(), + }; + let covariance = ndarray::array![[4.0]]; + let eta = vec![2.0]; + + // Via add_gaussian (established path) + let mut ref_deriv = CompleteDerivative::zero(2); + ref_deriv + .add_gaussian(&eta, &covariance, &[Some(0)], &layout.omega) + .unwrap(); + + // Via add_design_mean_prior (identity design) + let mut test_deriv = CompleteDerivative::zero(2); + test_deriv + .add_design_mean_prior( + &eta, + &covariance, + &ndarray::array![[1.0]], // identity design for one intercept + &[0], + &layout.omega, + ) + .unwrap(); + + assert_eq!(ref_deriv.score, test_deriv.score); + assert_eq!(ref_deriv.hessian, test_deriv.hessian); + } + + #[test] + fn design_mean_prior_beta_beta_block_is_exact() { + // Two covariate effects on one eta row, no Omega coordinates. + // design = [[x1, x2]] where x1=1.5, x2=-0.5 + // eta = 3.0, covariance = [[2.0]] + // W = 0.5, z = 1.5 + // Score: score_1 = x1 * z = 2.25, score_2 = x2 * z = -0.75 + // Hessian: H_cc = -A' * W * A = -[[x1^2*W, x1*x2*W], [x2*x1*W, x2^2*W]] + // = -[[1.125, -0.375], [-0.375, 0.125]] + let _layout = InformationLayout { + coordinates: vec![ + InformationCoordinate { + index: 0, + name: "beta:CL:WT".into(), + kind: InformationCoordinateKind::CovariateEffect { effect_index: 0 }, + }, + InformationCoordinate { + index: 1, + name: "beta:CL:AGE".into(), + kind: InformationCoordinateKind::CovariateEffect { effect_index: 1 }, + }, + ], + population: Vec::new(), + covariate_effects: vec![Some(0), Some(1)], + omega: Vec::new(), + omega_iov: Vec::new(), + residual: Vec::new(), + }; + let mut derivative = CompleteDerivative::zero(2); + derivative + .add_design_mean_prior( + &[3.0], + &ndarray::array![[2.0]], + &ndarray::array![[1.5, -0.5]], + &[0, 1], + &[], + ) + .unwrap(); + assert!((derivative.score[0] - 2.25).abs() < 1e-12); + assert!((derivative.score[1] + 0.75).abs() < 1e-12); + assert!((derivative.hessian[[0, 0]] + 1.125).abs() < 1e-12); + assert!((derivative.hessian[[0, 1]] - 0.375).abs() < 1e-12); + assert_eq!(derivative.hessian[[0, 1]], derivative.hessian[[1, 0]]); + assert!((derivative.hessian[[1, 1]] + 0.125).abs() < 1e-12); + } + + #[test] + fn design_mean_prior_beta_omega_cross_block_is_exact() { + // One covariate effect and one Omega coordinate. + // design = [[x]], eta = e, Omega = [[sigma^2]] + // W = 1/sigma^2, z = e/sigma^2 + // S = [[1]] (symmetric basis for diagonal) + // W*S = W, W*S*z = z/sigma^2 = e/sigma^4 + // H_{beta,omega} = -x * (W*S*z) = -x * e / sigma^4 + let layout = InformationLayout { + coordinates: vec![ + InformationCoordinate { + index: 0, + name: "beta:CL:WT".into(), + kind: InformationCoordinateKind::CovariateEffect { effect_index: 0 }, + }, + InformationCoordinate { + index: 1, + name: "omega:CL:CL".into(), + kind: InformationCoordinateKind::Omega { row: 0, column: 0 }, + }, + ], + population: Vec::new(), + covariate_effects: vec![Some(0)], + omega: vec![CovarianceCoordinate { + coordinate: 1, + row: 0, + column: 0, + }], + omega_iov: Vec::new(), + residual: Vec::new(), + }; + let covariance = ndarray::array![[4.0]]; // sigma^2 = 4, sigma = 2 + let eta = vec![3.0]; + let x = 1.5; + let mut derivative = CompleteDerivative::zero(2); + derivative + .add_design_mean_prior( + &eta, + &covariance, + &ndarray::array![[x]], + &[0], + &layout.omega, + ) + .unwrap(); + // Score beta: x * z = x * eta / sigma^2 = 1.5 * 3/4 = 1.125 + assert!((derivative.score[0] - 1.125).abs() < 1e-12); + // Score omega: -0.5*trace(W) + 0.5*eta'*W*S*W*eta + // = -0.5*(1/4) + 0.5 * 9/16 = -0.125 + 0.28125 = 0.15625... + // Wait: quadratic = eta' * (W * S * z) = 3 * (1/4 * 1 * 3/4) = 3 * 3/16 = 9/16 + // score_omega = -0.5*trace(W*S) + 0.5*quadratic = -0.5*(1/4) + 0.5*9/16 = -0.125 + 0.28125 + let expected_omega_score = -0.125 + 0.28125; + assert!((derivative.score[1] - expected_omega_score).abs() < 1e-12); + // H_beta_beta = -x^2 * W = -2.25 * 0.25 = -0.5625 + assert!((derivative.hessian[[0, 0]] + 0.5625).abs() < 1e-12); + // H_beta_omega = -x * W * S * z = -1.5 * 0.25 * 1 * 3/4 = -0.28125 + assert!((derivative.hessian[[0, 1]] + 0.28125).abs() < 1e-12); + assert_eq!(derivative.hessian[[0, 1]], derivative.hessian[[1, 0]]); + } + + #[test] + fn design_mean_prior_matches_add_gaussian_for_iov_identity_design() { + // IOV with identity design: kappa deviation and Omega_IOV. + // add_design_mean_prior with the beta coordinate as a mean coordinate + // should match add_gaussian with the same mapping. + let omega_iov = vec![CovarianceCoordinate { + coordinate: 1, + row: 0, + column: 0, + }]; + let _layout = InformationLayout { + coordinates: vec![ + InformationCoordinate { + index: 0, + name: "beta:CL:WT".into(), + kind: InformationCoordinateKind::CovariateEffect { effect_index: 0 }, + }, + InformationCoordinate { + index: 1, + name: "omega_iov:CL:CL".into(), + kind: InformationCoordinateKind::OmegaIov { row: 0, column: 0 }, + }, + ], + population: Vec::new(), + covariate_effects: vec![Some(0)], + omega: Vec::new(), + omega_iov: omega_iov.clone(), + residual: Vec::new(), + }; + let covariance = ndarray::array![[4.0]]; + let kappa = vec![2.0]; + + // add_gaussian with Some(0) mean coordinate and omega_iov + let mut ref_deriv = CompleteDerivative::zero(2); + ref_deriv + .add_gaussian(&kappa, &covariance, &[Some(0)], &omega_iov) + .unwrap(); + + // add_design_mean_prior with identity design matching the intercept mapping + let mut test_deriv = CompleteDerivative::zero(2); + test_deriv + .add_design_mean_prior( + &kappa, + &covariance, + &ndarray::array![[1.0]], + &[0], + &omega_iov, + ) + .unwrap(); + + assert_eq!(ref_deriv.score, test_deriv.score); + assert_eq!(ref_deriv.hessian, test_deriv.hessian); + } + + #[test] + fn covariate_coordinate_kind_roundtrips_in_serde() { + let kind = InformationCoordinateKind::CovariateEffect { effect_index: 7 }; + let json = serde_json::to_string(&kind).unwrap(); + assert!(json.contains("covariate_effect")); + assert!(json.contains("7")); + let roundtripped: InformationCoordinateKind = serde_json::from_str(&json).unwrap(); + assert_eq!(roundtripped, kind); + } +} diff --git a/src/estimation/parametric/marginal_likelihood.rs b/src/estimation/parametric/marginal_likelihood.rs new file mode 100644 index 000000000..8c8a73087 --- /dev/null +++ b/src/estimation/parametric/marginal_likelihood.rs @@ -0,0 +1,1383 @@ +//! Explicit post-fit population marginal likelihood by importance sampling. +//! +//! This module owns normalized latent densities and streaming importance-weight +//! moments. It does not participate in the SAEM fit random stream. + +use anyhow::Result; +use ndarray::Array2; +use rand::{distr::Distribution, rngs::StdRng, SeedableRng}; +use rand_distr::{ChiSquared, StandardNormal}; +use serde::{Deserialize, Serialize}; +use statrs::function::gamma::ln_gamma; + +use super::covariance::{cholesky_log_determinant, cholesky_lower, solve_lower}; +use crate::estimation::parametric::conditional_uncertainty::ConditionalCurvatureAvailability; + +/// Fixed domain separator for deterministic subject-specific integration streams. +pub const N2_SEED_DOMAIN: u64 = 0x4e32_5f50_4d43_4f52; + +/// Explicit marginal-likelihood sampling budget and proposal configuration. +/// +/// There is deliberately no `Default`: every stochastic choice is explicit. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields)] +pub struct MarginalLikelihoodConfig { + pub samples_per_subject: usize, + pub seed: u64, + pub degrees_of_freedom: u32, + pub covariance_scale_multiplier: f64, + pub proposal: MarginalLikelihoodProposal, +} + +impl MarginalLikelihoodConfig { + pub fn new( + samples_per_subject: usize, + seed: u64, + degrees_of_freedom: u32, + covariance_scale_multiplier: f64, + ) -> Self { + Self { + samples_per_subject, + seed, + degrees_of_freedom, + covariance_scale_multiplier, + proposal: MarginalLikelihoodProposal::FinalRawOmegaBlocks, + } + } + + /// Use the conditional-mode curvature covariance as the proposal scale matrix. + /// + /// When selected, each subject must supply a valid conditional curvature + /// covariance in its retained subject input; if unavailable, mismatched, non-finite, + /// or not strictly positive definite, the calculation returns a typed failure + /// with no fallback to the raw Omega blocks. + pub fn conditional_mode_curvature_proposal(mut self) -> Self { + self.proposal = MarginalLikelihoodProposal::ConditionalModeCurvature; + self + } + + pub(crate) fn validate(&self) -> Result<()> { + if self.samples_per_subject < 2 { + anyhow::bail!("N2 samples_per_subject must be at least 2"); + } + if self.degrees_of_freedom < 3 { + anyhow::bail!("N2 degrees_of_freedom must be at least 3"); + } + if !self.covariance_scale_multiplier.is_finite() || self.covariance_scale_multiplier <= 0.0 + { + anyhow::bail!("N2 covariance_scale_multiplier must be finite and positive"); + } + Ok(()) + } +} + +/// Subject integration method. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub enum MarginalLikelihoodMethod { + ExactNoLatent, + StudentTImportanceSampling, +} + +/// Which proposal covariance-scale matrix to use for importance sampling. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub enum MarginalLikelihoodProposal { + FinalRawOmegaBlocks, + ConditionalModeCurvature, +} + +/// Source of the proposal covariance-scale matrix. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub enum ProposalScaleSource { + FinalRawOmegaBlocks, + ConditionalModeCurvature, + NotApplicableNoLatent, +} + +/// Typed reason that a subject marginal-likelihood calculation is unavailable. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "reason", content = "detail", rename_all = "snake_case")] +pub enum MarginalLikelihoodFailureReason { + MissingConditionalMode, + ConditionalModeCalculationFailed(String), + SubjectIdMismatch { + expected: String, + actual: String, + }, + EtaWidthMismatch { + expected: usize, + actual: usize, + }, + KappaCountMismatch { + expected: usize, + actual: usize, + }, + KappaOccasionMismatch { + position: usize, + expected: usize, + actual: usize, + }, + KappaWidthMismatch { + position: usize, + expected: usize, + actual: usize, + }, + NonFiniteModeCoordinate, + InvalidRawCovariance(String), + NonFiniteDraw, + NonFiniteDensity, + NonFiniteWeight, + NonFiniteMoments, + PopulationAggregationOverflow, + AllZeroEffectiveWeights, + ConditionalCurvatureUnavailable, + ConditionalCurvatureDimensionMismatch { + expected: usize, + actual: usize, + }, + ConditionalCurvatureNonFinite, + ConditionalCurvatureNotSPD(String), + ScoringFailure(String), +} + +/// Availability of the complete population marginal-likelihood calculation. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "detail", rename_all = "snake_case")] +pub enum MarginalLikelihoodStatus { + Available, + AvailableWithNonconvergedModes { + subjects: Vec, + }, + Unavailable { + failures: Vec, + }, +} + +/// Subject and typed reason retained when marginal likelihood cannot be calculated. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +pub struct MarginalLikelihoodSubjectFailure { + pub subject_id: String, + pub reason: MarginalLikelihoodFailureReason, +} + +/// Immutable per-subject marginal-likelihood diagnostics. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct SubjectMarginalLikelihoodDiagnostics { + pub subject_id: String, + pub method: MarginalLikelihoodMethod, + pub proposal_scale_source: ProposalScaleSource, + pub seed: Option, + pub dimension: usize, + pub occasion_indices: Vec, + pub mode: Vec, + pub mode_converged: Option, + pub samples: usize, + pub log_marginal_likelihood: Option, + pub n2ll: Option, + pub effective_sample_size: Option, + pub effective_sample_fraction: Option, + pub zero_weight_count: usize, + pub var_log: Option, + pub n2ll_mcse: Option, + pub failure: Option, +} + +/// Immutable post-fit population marginal-likelihood diagnostics. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct MarginalLikelihoodDiagnostics { + pub config: MarginalLikelihoodConfig, + pub status: MarginalLikelihoodStatus, + pub log_marginal_likelihood: Option, + pub n2ll: Option, + pub n2ll_mcse: Option, + pub subjects: Vec, +} + +#[derive(Debug, Clone)] +pub(crate) struct MarginalSubject<'a> { + pub subject_id: &'a str, + pub occasion_indices: &'a [usize], + pub mode: &'a [f64], + pub mode_converged: Option, + pub eta_dimension: usize, + pub kappa_dimension: usize, + pub validation_failure: Option, + pub curvature_availability: Option<&'a ConditionalCurvatureAvailability>, + pub curvature_covariance: Option<&'a Array2>, +} + +/// Derive a stable subject stream without consuming any fit RNG state. +pub fn marginal_likelihood_subject_seed(base_seed: u64, subject_index: usize) -> u64 { + let mut value = + base_seed ^ N2_SEED_DOMAIN ^ (subject_index as u64).wrapping_mul(0x9e37_79b9_7f4a_7c15); + value = (value ^ (value >> 30)).wrapping_mul(0xbf58_476d_1ce4_e5b9); + value = (value ^ (value >> 27)).wrapping_mul(0x94d0_49bb_1331_11eb); + value ^ (value >> 31) +} + +pub(crate) fn calculate_population_marginal_likelihood( + config: MarginalLikelihoodConfig, + subjects: &[MarginalSubject<'_>], + omega: &Array2, + omega_iov: Option<&Array2>, + mut score: F, +) -> MarginalLikelihoodDiagnostics +where + F: FnMut(usize, &[f64], &[Vec]) -> Result, +{ + let mut diagnostics = Vec::with_capacity(subjects.len()); + let mut failures = Vec::new(); + let mut nonconverged = Vec::new(); + let mut total_log_marginal = 0.0; + let mut total_var_log = 0.0; + + for (subject_index, subject) in subjects.iter().enumerate() { + let dimension = + subject.eta_dimension + subject.occasion_indices.len() * subject.kappa_dimension; + let mut result = if let Some(reason) = subject.validation_failure.clone() { + let method = if dimension == 0 { + MarginalLikelihoodMethod::ExactNoLatent + } else { + MarginalLikelihoodMethod::StudentTImportanceSampling + }; + let seed = (dimension > 0) + .then(|| marginal_likelihood_subject_seed(config.seed, subject_index)); + let proposal_scale_source = if dimension == 0 { + ProposalScaleSource::NotApplicableNoLatent + } else { + match config.proposal { + MarginalLikelihoodProposal::FinalRawOmegaBlocks => { + ProposalScaleSource::FinalRawOmegaBlocks + } + MarginalLikelihoodProposal::ConditionalModeCurvature => { + ProposalScaleSource::ConditionalModeCurvature + } + } + }; + let samples = if dimension == 0 { + 0 + } else { + config.samples_per_subject + }; + let mut failed = empty_subject(subject, method, proposal_scale_source, seed, samples); + failed.failure = Some(reason); + failed + } else if dimension == 0 { + exact_subject(subject_index, subject, &mut score) + } else { + importance_subject(config, subject_index, subject, omega, omega_iov, &mut score) + }; + if subject.mode_converged == Some(false) && dimension > 0 && result.failure.is_none() { + nonconverged.push(subject.subject_id.to_owned()); + } + if let Some(reason) = result.failure.clone() { + failures.push(MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.to_owned(), + reason, + }); + } else if let (Some(subject_log), Some(subject_var)) = + (result.log_marginal_likelihood, result.var_log) + { + let next_log = total_log_marginal + subject_log; + let next_var = total_var_log + subject_var; + if !next_log.is_finite() || !next_var.is_finite() { + clear_subject_numerics(&mut result); + result.failure = + Some(MarginalLikelihoodFailureReason::PopulationAggregationOverflow); + failures.push(MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.to_owned(), + reason: MarginalLikelihoodFailureReason::PopulationAggregationOverflow, + }); + } else { + total_log_marginal = next_log; + total_var_log = next_var; + } + } else { + clear_subject_numerics(&mut result); + result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteMoments); + failures.push(MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.to_owned(), + reason: MarginalLikelihoodFailureReason::NonFiniteMoments, + }); + } + diagnostics.push(result); + } + + let final_n2ll = -2.0 * total_log_marginal; + let final_mcse = 2.0 * total_var_log.sqrt(); + if failures.is_empty() && (!final_n2ll.is_finite() || !final_mcse.is_finite()) { + if let Some(last) = diagnostics.last_mut() { + clear_subject_numerics(last); + last.failure = Some(MarginalLikelihoodFailureReason::PopulationAggregationOverflow); + failures.push(MarginalLikelihoodSubjectFailure { + subject_id: last.subject_id.clone(), + reason: MarginalLikelihoodFailureReason::PopulationAggregationOverflow, + }); + } + } + + let (status, log_marginal_likelihood, n2ll, n2ll_mcse) = if failures.is_empty() { + let status = if nonconverged.is_empty() { + MarginalLikelihoodStatus::Available + } else { + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { + subjects: nonconverged, + } + }; + ( + status, + Some(total_log_marginal), + Some(final_n2ll), + Some(final_mcse), + ) + } else { + ( + MarginalLikelihoodStatus::Unavailable { failures }, + None, + None, + None, + ) + }; + + MarginalLikelihoodDiagnostics { + config, + status, + log_marginal_likelihood, + n2ll, + n2ll_mcse, + subjects: diagnostics, + } +} + +pub(crate) fn unavailable_population_marginal_likelihood( + config: MarginalLikelihoodConfig, + subjects: &[MarginalSubject<'_>], + reason: MarginalLikelihoodFailureReason, +) -> MarginalLikelihoodDiagnostics { + let failures = subjects + .iter() + .map(|subject| MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.to_owned(), + reason: reason.clone(), + }) + .collect(); + let subject_diagnostics = subjects + .iter() + .enumerate() + .map(|(subject_index, subject)| { + let dimension = + subject.eta_dimension + subject.occasion_indices.len() * subject.kappa_dimension; + let method = if dimension == 0 { + MarginalLikelihoodMethod::ExactNoLatent + } else { + MarginalLikelihoodMethod::StudentTImportanceSampling + }; + let seed = (dimension > 0) + .then(|| marginal_likelihood_subject_seed(config.seed, subject_index)); + let proposal_scale_source = if dimension == 0 { + ProposalScaleSource::NotApplicableNoLatent + } else { + match config.proposal { + MarginalLikelihoodProposal::FinalRawOmegaBlocks => { + ProposalScaleSource::FinalRawOmegaBlocks + } + MarginalLikelihoodProposal::ConditionalModeCurvature => { + ProposalScaleSource::ConditionalModeCurvature + } + } + }; + let samples = if dimension == 0 { + 0 + } else { + config.samples_per_subject + }; + let mut diagnostics = + empty_subject(subject, method, proposal_scale_source, seed, samples); + if matches!( + reason, + MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed(_) + ) { + diagnostics.mode.clear(); + diagnostics.mode_converged = None; + } + diagnostics.failure = Some(reason.clone()); + diagnostics + }) + .collect(); + MarginalLikelihoodDiagnostics { + config, + status: MarginalLikelihoodStatus::Unavailable { failures }, + log_marginal_likelihood: None, + n2ll: None, + n2ll_mcse: None, + subjects: subject_diagnostics, + } +} + +fn exact_subject( + subject_index: usize, + subject: &MarginalSubject<'_>, + score: &mut F, +) -> SubjectMarginalLikelihoodDiagnostics +where + F: FnMut(usize, &[f64], &[Vec]) -> Result, +{ + let mut result = empty_subject( + subject, + MarginalLikelihoodMethod::ExactNoLatent, + ProposalScaleSource::NotApplicableNoLatent, + None, + 0, + ); + match score(subject_index, &[], &[]) { + Ok(value) if value.is_finite() && (-2.0 * value).is_finite() => { + result.log_marginal_likelihood = Some(value); + result.n2ll = Some(-2.0 * value); + result.var_log = Some(0.0); + result.n2ll_mcse = Some(0.0); + } + Ok(_) => result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteMoments), + Err(error) => { + result.failure = Some(MarginalLikelihoodFailureReason::ScoringFailure(format!( + "{error:#}" + ))) + } + } + result +} + +fn importance_subject( + config: MarginalLikelihoodConfig, + subject_index: usize, + subject: &MarginalSubject<'_>, + omega: &Array2, + omega_iov: Option<&Array2>, + score: &mut F, +) -> SubjectMarginalLikelihoodDiagnostics +where + F: FnMut(usize, &[f64], &[Vec]) -> Result, +{ + let seed = marginal_likelihood_subject_seed(config.seed, subject_index); + let dimension = + subject.eta_dimension + subject.occasion_indices.len() * subject.kappa_dimension; + let proposal_scale_source = match config.proposal { + MarginalLikelihoodProposal::FinalRawOmegaBlocks => ProposalScaleSource::FinalRawOmegaBlocks, + MarginalLikelihoodProposal::ConditionalModeCurvature => { + ProposalScaleSource::ConditionalModeCurvature + } + }; + let mut result = empty_subject( + subject, + MarginalLikelihoodMethod::StudentTImportanceSampling, + proposal_scale_source, + Some(seed), + config.samples_per_subject, + ); + if subject.mode.len() != dimension || subject.mode.iter().any(|value| !value.is_finite()) { + result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteModeCoordinate); + return result; + } + + let lower = match config.proposal { + MarginalLikelihoodProposal::ConditionalModeCurvature => { + let curvature_cov = match subject.curvature_availability { + Some(ConditionalCurvatureAvailability::Available) => subject + .curvature_covariance + .ok_or(MarginalLikelihoodFailureReason::ConditionalCurvatureUnavailable), + _ => Err(MarginalLikelihoodFailureReason::ConditionalCurvatureUnavailable), + }; + let curvature_cov = match curvature_cov { + Ok(cov) => cov, + Err(reason) => { + result.failure = Some(reason); + return result; + } + }; + if curvature_cov.nrows() != dimension || curvature_cov.ncols() != dimension { + result.failure = Some( + MarginalLikelihoodFailureReason::ConditionalCurvatureDimensionMismatch { + expected: dimension, + actual: curvature_cov.nrows(), + }, + ); + return result; + } + if curvature_cov.iter().any(|v| !v.is_finite()) { + result.failure = + Some(MarginalLikelihoodFailureReason::ConditionalCurvatureNonFinite); + return result; + } + for i in 0..dimension { + for j in 0..i { + if curvature_cov[(i, j)] != curvature_cov[(j, i)] { + result.failure = + Some(MarginalLikelihoodFailureReason::ConditionalCurvatureNotSPD( + "curvature covariance is not symmetric".to_string(), + )); + return result; + } + } + } + let cov_lower = match cholesky_lower(curvature_cov) { + Ok(lower) => lower, + Err(error) => { + result.failure = + Some(MarginalLikelihoodFailureReason::ConditionalCurvatureNotSPD( + format!("{error:#}"), + )); + return result; + } + }; + let root_scale = config.covariance_scale_multiplier.sqrt(); + let mut lower = vec![vec![0.0; dimension]; dimension]; + for row in 0..dimension { + for column in 0..=row { + lower[row][column] = root_scale * cov_lower[row][column]; + } + } + lower + } + MarginalLikelihoodProposal::FinalRawOmegaBlocks => { + match block_scaled_cholesky( + omega, + omega_iov, + subject.eta_dimension, + subject.kappa_dimension, + subject.occasion_indices.len(), + config.covariance_scale_multiplier, + ) { + Ok(value) => value, + Err(error) => { + result.failure = Some(MarginalLikelihoodFailureReason::InvalidRawCovariance( + format!("{error:#}"), + )); + return result; + } + } + } + }; + let log_det = cholesky_log_determinant(&lower); + if !log_det.is_finite() { + result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteDensity); + return result; + } + + let chi = match ChiSquared::new(config.degrees_of_freedom as f64) { + Ok(value) => value, + Err(error) => { + result.failure = Some(MarginalLikelihoodFailureReason::ScoringFailure( + error.to_string(), + )); + return result; + } + }; + let mut rng = StdRng::seed_from_u64(seed); + let mut moments = OnlineLogWeightMoments::default(); + for _ in 0..config.samples_per_subject { + let z = (0..dimension) + .map(|_| StandardNormal.sample(&mut rng)) + .collect::>(); + let u = chi.sample(&mut rng); + let factor = (config.degrees_of_freedom as f64 / u).sqrt(); + let mut draw = subject.mode.to_vec(); + for row in 0..dimension { + draw[row] += factor + * (0..=row) + .map(|column| lower[row][column] * z[column]) + .sum::(); + } + if draw.iter().any(|value| !value.is_finite()) { + result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteDraw); + return result; + } + let eta = &draw[..subject.eta_dimension]; + let kappas = draw[subject.eta_dimension..] + .chunks(subject.kappa_dimension.max(1)) + .take(subject.occasion_indices.len()) + .map(|values| values.to_vec()) + .collect::>(); + let target = match score(subject_index, eta, &kappas) { + Ok(value) => value, + Err(error) => { + result.failure = Some(MarginalLikelihoodFailureReason::ScoringFailure(format!( + "{error:#}" + ))); + return result; + } + }; + let log_q = match multivariate_t_log_density( + &draw, + subject.mode, + &lower, + config.degrees_of_freedom, + log_det, + ) { + Ok(value) => value, + Err(reason) => { + result.failure = Some(reason); + return result; + } + }; + let log_weight = target - log_q; + if log_weight.is_nan() || log_weight == f64::INFINITY { + result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteWeight); + return result; + } + if let Err(reason) = moments.push(log_weight) { + result.failure = Some(reason); + return result; + } + } + + result.zero_weight_count = moments.zero_weight_count; + match moments.finish(config.samples_per_subject) { + Ok(summary) => { + let n2ll = -2.0 * summary.log_mean_weight; + let fraction = summary.ess / config.samples_per_subject as f64; + let mcse = 2.0 * summary.var_log.sqrt(); + if n2ll.is_finite() + && fraction.is_finite() + && fraction > 0.0 + && fraction <= 1.0 + && mcse.is_finite() + { + result.log_marginal_likelihood = Some(summary.log_mean_weight); + result.n2ll = Some(n2ll); + result.effective_sample_size = Some(summary.ess); + result.effective_sample_fraction = Some(fraction); + result.var_log = Some(summary.var_log); + result.n2ll_mcse = Some(mcse); + } else { + result.failure = Some(MarginalLikelihoodFailureReason::NonFiniteMoments); + } + } + Err(reason) => result.failure = Some(reason), + } + result +} + +fn clear_subject_numerics(result: &mut SubjectMarginalLikelihoodDiagnostics) { + result.log_marginal_likelihood = None; + result.n2ll = None; + result.effective_sample_size = None; + result.effective_sample_fraction = None; + result.var_log = None; + result.n2ll_mcse = None; +} + +fn empty_subject( + subject: &MarginalSubject<'_>, + method: MarginalLikelihoodMethod, + proposal_scale_source: ProposalScaleSource, + seed: Option, + samples: usize, +) -> SubjectMarginalLikelihoodDiagnostics { + SubjectMarginalLikelihoodDiagnostics { + subject_id: subject.subject_id.to_owned(), + method, + proposal_scale_source, + seed, + dimension: subject.eta_dimension + subject.occasion_indices.len() * subject.kappa_dimension, + occasion_indices: subject.occasion_indices.to_vec(), + mode: subject.mode.to_vec(), + mode_converged: if matches!(method, MarginalLikelihoodMethod::ExactNoLatent) { + None + } else { + subject.mode_converged + }, + samples, + log_marginal_likelihood: None, + n2ll: None, + effective_sample_size: None, + effective_sample_fraction: None, + zero_weight_count: 0, + var_log: None, + n2ll_mcse: None, + failure: None, + } +} + +fn block_scaled_cholesky( + omega: &Array2, + omega_iov: Option<&Array2>, + eta_dimension: usize, + kappa_dimension: usize, + occasions: usize, + scale: f64, +) -> Result>> { + if omega.nrows() != eta_dimension || omega.ncols() != eta_dimension { + anyhow::bail!("raw Omega dimension does not match eta width"); + } + let eta_lower = cholesky_lower(omega)?; + let iov_lower = if kappa_dimension > 0 { + let matrix = omega_iov.ok_or_else(|| anyhow::anyhow!("raw Omega_IOV is missing"))?; + if matrix.nrows() != kappa_dimension || matrix.ncols() != kappa_dimension { + anyhow::bail!("raw Omega_IOV dimension does not match kappa width"); + } + Some(cholesky_lower(matrix)?) + } else { + None + }; + let dimension = eta_dimension + occasions * kappa_dimension; + let mut lower = vec![vec![0.0; dimension]; dimension]; + let root_scale = scale.sqrt(); + for row in 0..eta_dimension { + for column in 0..=row { + lower[row][column] = root_scale * eta_lower[row][column]; + } + } + if let Some(block) = iov_lower { + for occasion in 0..occasions { + let offset = eta_dimension + occasion * kappa_dimension; + for row in 0..kappa_dimension { + for column in 0..=row { + lower[offset + row][offset + column] = root_scale * block[row][column]; + } + } + } + } + Ok(lower) +} + +fn multivariate_t_log_density( + value: &[f64], + center: &[f64], + lower: &[Vec], + degrees_of_freedom: u32, + log_det: f64, +) -> std::result::Result { + if value.len() != center.len() { + return Err(MarginalLikelihoodFailureReason::NonFiniteDensity); + } + let difference = value + .iter() + .zip(center) + .map(|(value, center)| value - center) + .collect::>(); + let standardized = solve_lower(lower, &difference) + .map_err(|_| MarginalLikelihoodFailureReason::NonFiniteDensity)?; + let delta = standardized.iter().map(|value| value * value).sum::(); + let d = value.len() as f64; + let nu = degrees_of_freedom as f64; + let result = ln_gamma((nu + d) / 2.0) + - ln_gamma(nu / 2.0) + - d / 2.0 * (nu * std::f64::consts::PI).ln() + - 0.5 * log_det + - (nu + d) / 2.0 * (delta / nu).ln_1p(); + if result.is_finite() { + Ok(result) + } else { + Err(MarginalLikelihoodFailureReason::NonFiniteDensity) + } +} + +#[derive(Debug, Default)] +struct OnlineLogWeightMoments { + max_log_weight: Option, + sum_scaled: f64, + sum_scaled_squares: f64, + finite_count: usize, + zero_weight_count: usize, +} + +impl OnlineLogWeightMoments { + fn push( + &mut self, + log_weight: f64, + ) -> std::result::Result<(), MarginalLikelihoodFailureReason> { + if log_weight == f64::NEG_INFINITY { + self.zero_weight_count += 1; + return Ok(()); + } + if !log_weight.is_finite() { + return Err(MarginalLikelihoodFailureReason::NonFiniteWeight); + } + match self.max_log_weight { + None => { + self.max_log_weight = Some(log_weight); + self.sum_scaled = 1.0; + self.sum_scaled_squares = 1.0; + } + Some(maximum) if log_weight > maximum => { + let scale = (maximum - log_weight).exp(); + self.sum_scaled = self.sum_scaled * scale + 1.0; + self.sum_scaled_squares = self.sum_scaled_squares * scale * scale + 1.0; + self.max_log_weight = Some(log_weight); + } + Some(maximum) => { + let scaled = (log_weight - maximum).exp(); + self.sum_scaled += scaled; + self.sum_scaled_squares += scaled * scaled; + } + } + self.finite_count += 1; + if !self.sum_scaled.is_finite() + || !self.sum_scaled_squares.is_finite() + || self.sum_scaled <= 0.0 + || self.sum_scaled_squares <= 0.0 + { + return Err(MarginalLikelihoodFailureReason::NonFiniteMoments); + } + Ok(()) + } + + fn finish( + &self, + samples: usize, + ) -> std::result::Result { + if self.finite_count == 0 { + return Err(MarginalLikelihoodFailureReason::AllZeroEffectiveWeights); + } + let maximum = self + .max_log_weight + .ok_or(MarginalLikelihoodFailureReason::AllZeroEffectiveWeights)?; + let k = samples as f64; + if samples < 2 || !k.is_finite() { + return Err(MarginalLikelihoodFailureReason::NonFiniteMoments); + } + let log_mean_weight = maximum + (self.sum_scaled.ln() - k.ln()); + let ess = self.sum_scaled * self.sum_scaled / self.sum_scaled_squares; + let cv2 = k / ess - 1.0; + let var_log = cv2 / (k - 1.0); + if !log_mean_weight.is_finite() + || !ess.is_finite() + || ess <= 0.0 + || ess > k + || !cv2.is_finite() + || cv2 < 0.0 + || !var_log.is_finite() + || var_log < 0.0 + { + return Err(MarginalLikelihoodFailureReason::NonFiniteMoments); + } + Ok(LogWeightSummary { + log_mean_weight, + ess, + var_log, + }) + } +} + +#[derive(Debug, PartialEq)] +struct LogWeightSummary { + log_mean_weight: f64, + ess: f64, + var_log: f64, +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::parametric::posterior::eta_log_prior_from_omega; + + #[test] + fn normalized_student_t_density_matches_scalar_formula() { + let lower = vec![vec![2.0]]; + let actual = multivariate_t_log_density(&[1.0], &[0.0], &lower, 5, 4.0_f64.ln()).unwrap(); + let expected = ln_gamma(3.0) + - ln_gamma(2.5) + - 0.5 * (5.0 * std::f64::consts::PI).ln() + - 0.5 * 4.0_f64.ln() + - 3.0 * (1.0_f64 / 20.0).ln_1p(); + assert!((actual - expected).abs() < 1e-12); + } + + #[test] + fn block_determinant_and_mahalanobis_are_exact() { + let omega = ndarray::array![[4.0, 1.0], [1.0, 2.0]]; + let iov = ndarray::array![[3.0]]; + let lower = block_scaled_cholesky(&omega, Some(&iov), 2, 1, 2, 2.0).unwrap(); + assert!( + (cholesky_log_determinant(&lower) - (7.0_f64 * 3.0 * 3.0 * 16.0).ln()).abs() < 1e-12 + ); + let z = solve_lower(&lower, &[1.0, -1.0, 2.0, -2.0]).unwrap(); + let delta = z.iter().map(|value| value * value).sum::(); + let expected = 0.5 * (8.0 / 7.0 + 4.0 / 3.0 + 4.0 / 3.0); + assert!((delta - expected).abs() < 1e-12); + } + + #[test] + fn online_moments_match_offline_weights() { + let logs = [-1000.0, -999.0, -1002.0, f64::NEG_INFINITY]; + let mut moments = OnlineLogWeightMoments::default(); + for value in logs { + moments.push(value).unwrap(); + } + let summary = moments.finish(logs.len()).unwrap(); + let shifted = [0.0_f64, 1.0, -2.0].map(f64::exp); + let sum = shifted.iter().sum::(); + let sum2 = shifted.iter().map(|value| value * value).sum::(); + let expected_ess = sum * sum / sum2; + assert!((summary.ess - expected_ess).abs() < 1e-12); + assert_eq!(moments.zero_weight_count, 1); + } + + #[test] + fn extreme_and_all_zero_weights_are_typed() { + let mut moments = OnlineLogWeightMoments::default(); + moments.push(-10_000.0).unwrap(); + moments.push(-10_001.0).unwrap(); + assert!(moments.finish(2).unwrap().log_mean_weight.is_finite()); + let mut zeros = OnlineLogWeightMoments::default(); + zeros.push(f64::NEG_INFINITY).unwrap(); + zeros.push(f64::NEG_INFINITY).unwrap(); + assert_eq!( + zeros.finish(2), + Err(MarginalLikelihoodFailureReason::AllZeroEffectiveWeights) + ); + } + + #[test] + fn max_shifted_moments_accept_large_positive_and_mixed_extreme_logs() { + let mut large = OnlineLogWeightMoments::default(); + large.push(f64::MAX).unwrap(); + large.push(f64::MAX).unwrap(); + let summary = large.finish(2).unwrap(); + assert_eq!(summary.log_mean_weight, f64::MAX); + assert_eq!(summary.ess, 2.0); + assert_eq!(summary.var_log, 0.0); + + let mut mixed = OnlineLogWeightMoments::default(); + mixed.push(f64::MAX).unwrap(); + mixed.push(-f64::MAX).unwrap(); + let summary = mixed.finish(2).unwrap(); + assert_eq!(summary.log_mean_weight, f64::MAX); + assert_eq!(summary.ess, 1.0); + assert_eq!(summary.var_log, 1.0); + } + + #[test] + fn population_n2_overflow_is_typed_without_population_totals() { + let config = MarginalLikelihoodConfig::new(2, 17, 3, 1.0); + let first = MarginalSubject { + subject_id: "first", + occasion_indices: &[], + mode: &[], + mode_converged: Some(true), + eta_dimension: 0, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let second = MarginalSubject { + subject_id: "second", + ..first.clone() + }; + let diagnostics = calculate_population_marginal_likelihood( + config, + &[first, second], + &Array2::zeros((0, 0)), + None, + |_, _, _| Ok(-f64::MAX / 2.0), + ); + assert!(matches!( + diagnostics.status, + MarginalLikelihoodStatus::Unavailable { ref failures } + if failures.len() == 1 + && failures[0].subject_id == "second" + && failures[0].reason + == MarginalLikelihoodFailureReason::PopulationAggregationOverflow + )); + assert!(diagnostics.log_marginal_likelihood.is_none()); + assert!(diagnostics.n2ll.is_none()); + assert!(diagnostics.n2ll_mcse.is_none()); + assert!(diagnostics.subjects[0].failure.is_none()); + assert!(diagnostics.subjects[1].log_marginal_likelihood.is_none()); + assert!(diagnostics.subjects[1].failure.is_some()); + } + + #[test] + fn mixed_valid_and_failed_subjects_retain_ordered_local_failures() { + let config = MarginalLikelihoodConfig::new(2, 19, 3, 1.0); + let valid = MarginalSubject { + subject_id: "valid", + occasion_indices: &[], + mode: &[], + mode_converged: Some(true), + eta_dimension: 0, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let failed = MarginalSubject { + subject_id: "failed", + validation_failure: Some(MarginalLikelihoodFailureReason::EtaWidthMismatch { + expected: 1, + actual: 0, + }), + eta_dimension: 1, + mode: &[], + ..valid.clone() + }; + let diagnostics = calculate_population_marginal_likelihood( + config, + &[valid, failed], + &ndarray::array![[1.0]], + None, + |_, _, _| Ok(-1.0), + ); + assert_eq!(diagnostics.subjects[0].log_marginal_likelihood, Some(-1.0)); + assert_eq!(diagnostics.subjects[1].dimension, 1); + assert_eq!(diagnostics.subjects[1].samples, 2); + assert_eq!( + diagnostics.subjects[1].seed, + Some(marginal_likelihood_subject_seed(19, 1)) + ); + assert!(matches!( + diagnostics.status, + MarginalLikelihoodStatus::Unavailable { ref failures } + if failures.len() == 1 && failures[0].subject_id == "failed" + )); + } + + #[test] + fn seed_derivation_is_stable_and_subject_specific() { + assert_eq!( + marginal_likelihood_subject_seed(17, 0), + 10_096_700_463_465_019_373 + ); + assert_ne!( + marginal_likelihood_subject_seed(17, 0), + marginal_likelihood_subject_seed(17, 1) + ); + } + + #[test] + fn exact_conjugate_correlated_iiv_fixture_is_within_reported_mcse() { + let omega = ndarray::array![[1.0, 0.3], [0.3, 0.7]]; + let residual = ndarray::array![[0.4, 0.0], [0.0, 0.2]]; + let marginal = &omega + &residual; + let observation = [0.8, -0.4]; + let determinant = marginal[[0, 0]] * marginal[[1, 1]] - marginal[[0, 1]] * marginal[[1, 0]]; + let solved = [ + (marginal[[1, 1]] * observation[0] - marginal[[0, 1]] * observation[1]) / determinant, + (-marginal[[1, 0]] * observation[0] + marginal[[0, 0]] * observation[1]) / determinant, + ]; + let mode = [ + omega[[0, 0]] * solved[0] + omega[[0, 1]] * solved[1], + omega[[1, 0]] * solved[0] + omega[[1, 1]] * solved[1], + ]; + let subject = MarginalSubject { + subject_id: "correlated", + occasion_indices: &[], + mode: &mode, + mode_converged: Some(true), + eta_dimension: 2, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let diagnostics = calculate_population_marginal_likelihood( + MarginalLikelihoodConfig::new(65_536, 201, 5, 1.5), + &[subject], + &omega, + None, + |_, eta, _| { + let error = [observation[0] - eta[0], observation[1] - eta[1]]; + Ok(eta_log_prior_from_omega(eta, &omega)? + + eta_log_prior_from_omega(&error, &residual)?) + }, + ); + let exact_n2ll = -2.0 * eta_log_prior_from_omega(&observation, &marginal).unwrap(); + let estimated = diagnostics.n2ll.unwrap(); + let mcse = diagnostics.n2ll_mcse.unwrap(); + eprintln!( + "correlated IIV N2: exact={exact_n2ll:.17}, estimated={estimated:.17}, mcse={mcse:.17}, abs_error={:.17}", + (estimated - exact_n2ll).abs() + ); + assert!((estimated - exact_n2ll).abs() <= 5.0 * mcse + 1e-10); + } + + #[test] + fn exact_conjugate_joint_iiv_iov_fixture_is_within_reported_mcse() { + let omega = ndarray::array![[0.8]]; + let omega_iov = ndarray::array![[0.5]]; + let residual = ndarray::array![[0.3]]; + let marginal = ndarray::array![[1.6]]; + let observation = 1.2; + let mode = [0.8 / 1.6 * observation, 0.5 / 1.6 * observation]; + let subject = MarginalSubject { + subject_id: "joint", + occasion_indices: &[7], + mode: &mode, + mode_converged: Some(true), + eta_dimension: 1, + kappa_dimension: 1, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let diagnostics = calculate_population_marginal_likelihood( + MarginalLikelihoodConfig::new(65_536, 301, 5, 1.5), + &[subject], + &omega, + Some(&omega_iov), + |_, eta, kappas| { + let error = [observation - eta[0] - kappas[0][0]]; + Ok(eta_log_prior_from_omega(eta, &omega)? + + eta_log_prior_from_omega(&kappas[0], &omega_iov)? + + eta_log_prior_from_omega(&error, &residual)?) + }, + ); + let exact_n2ll = -2.0 * eta_log_prior_from_omega(&[observation], &marginal).unwrap(); + let estimated = diagnostics.n2ll.unwrap(); + let mcse = diagnostics.n2ll_mcse.unwrap(); + eprintln!( + "joint IIV+IOV N2: exact={exact_n2ll:.17}, estimated={estimated:.17}, mcse={mcse:.17}, abs_error={:.17}", + (estimated - exact_n2ll).abs() + ); + assert!((estimated - exact_n2ll).abs() <= 5.0 * mcse + 1e-10); + assert_eq!(diagnostics.subjects[0].occasion_indices, vec![7]); + } + + #[test] + fn t_draw_stream_is_bit_exact_and_has_heavier_tail_than_normal_scale() { + let config = MarginalLikelihoodConfig::new(4096, 91, 3, 1.0); + let subject = MarginalSubject { + subject_id: "1", + occasion_indices: &[], + mode: &[0.0], + mode_converged: Some(true), + eta_dimension: 1, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let omega = ndarray::array![[1.0]]; + let calculate = || { + calculate_population_marginal_likelihood( + config, + std::slice::from_ref(&subject), + &omega, + None, + |_, eta, _| eta_log_prior_from_omega(eta, &omega), + ) + }; + assert_eq!(calculate(), calculate()); + let diagnostics = calculate(); + assert!(diagnostics.subjects[0].effective_sample_size.unwrap() > 0.0); + } + + #[test] + fn nonconverged_mode_remains_available_and_invalid_covariance_is_typed() { + let config = MarginalLikelihoodConfig::new(128, 401, 3, 1.0); + let subject = MarginalSubject { + subject_id: "finite_mode", + occasion_indices: &[], + mode: &[0.0], + mode_converged: Some(false), + eta_dimension: 1, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let omega = ndarray::array![[1.0]]; + let available = calculate_population_marginal_likelihood( + config, + std::slice::from_ref(&subject), + &omega, + None, + |_, eta, _| eta_log_prior_from_omega(eta, &omega), + ); + assert!(matches!( + available.status, + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { ref subjects } + if subjects == &["finite_mode"] + )); + + let invalid = ndarray::array![[0.0]]; + let unavailable = calculate_population_marginal_likelihood( + config, + &[subject], + &invalid, + None, + |_, _, _| Ok(0.0), + ); + assert!(matches!( + unavailable.status, + MarginalLikelihoodStatus::Unavailable { ref failures } + if matches!(failures[0].reason, MarginalLikelihoodFailureReason::InvalidRawCovariance(_)) + )); + assert!(unavailable.n2ll.is_none()); + } + + #[test] + fn global_posthoc_failure_retains_requested_subject_metadata() { + let config = MarginalLikelihoodConfig::new(32, 411, 5, 1.5); + let modes = [vec![0.1, 0.2], vec![0.3, 0.4]]; + let first = MarginalSubject { + subject_id: "first", + occasion_indices: &[3], + mode: &modes[0], + mode_converged: Some(true), + eta_dimension: 1, + kappa_dimension: 1, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let second = MarginalSubject { + subject_id: "second", + occasion_indices: &[7], + mode: &modes[1], + mode_converged: Some(false), + eta_dimension: 1, + kappa_dimension: 1, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let diagnostics = unavailable_population_marginal_likelihood( + config, + &[first, second], + MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed( + "global conditional mode calculation failed".to_string(), + ), + ); + assert!(diagnostics.log_marginal_likelihood.is_none()); + assert!(diagnostics.n2ll.is_none()); + assert!(diagnostics.n2ll_mcse.is_none()); + for (index, subject) in diagnostics.subjects.iter().enumerate() { + assert_eq!(subject.dimension, 2); + assert_eq!(subject.samples, 32); + assert_eq!( + subject.seed, + Some(marginal_likelihood_subject_seed(411, index)) + ); + assert_eq!( + subject.occasion_indices, + vec![if index == 0 { 3 } else { 7 }] + ); + assert!(matches!( + subject.failure, + Some(MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed(_)) + )); + assert!(subject.mode.is_empty()); + assert_eq!(subject.mode_converged, None); + assert!(subject.log_marginal_likelihood.is_none()); + assert!(subject.n2ll.is_none()); + assert!(subject.effective_sample_size.is_none()); + assert!(subject.effective_sample_fraction.is_none()); + assert!(subject.var_log.is_none()); + assert!(subject.n2ll_mcse.is_none()); + } + } + + #[test] + fn all_zero_effective_weights_are_unavailable_without_thresholding_low_ess() { + let config = MarginalLikelihoodConfig::new(16, 501, 3, 1.0); + let subject = MarginalSubject { + subject_id: "zero", + occasion_indices: &[], + mode: &[0.0], + mode_converged: Some(true), + eta_dimension: 1, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let omega = ndarray::array![[1.0]]; + let diagnostics = calculate_population_marginal_likelihood( + config, + &[subject], + &omega, + None, + |_, _, _| Ok(f64::NEG_INFINITY), + ); + assert!(matches!( + diagnostics.subjects[0].failure, + Some(MarginalLikelihoodFailureReason::AllZeroEffectiveWeights) + )); + assert_eq!(diagnostics.subjects[0].zero_weight_count, 16); + assert!(diagnostics.n2ll.is_none()); + } + + #[test] + fn config_default_proposal_is_final_raw_omega_blocks() { + let config = MarginalLikelihoodConfig::new(100, 42, 3, 1.0); + assert_eq!( + config.proposal, + MarginalLikelihoodProposal::FinalRawOmegaBlocks + ); + } + + #[test] + fn config_builder_sets_conditional_mode_curvature_proposal() { + let config = + MarginalLikelihoodConfig::new(100, 42, 3, 1.0).conditional_mode_curvature_proposal(); + assert_eq!( + config.proposal, + MarginalLikelihoodProposal::ConditionalModeCurvature + ); + } + + #[test] + fn curvature_proposal_without_curvature_data_is_typed_failure() { + let config = + MarginalLikelihoodConfig::new(32, 701, 3, 1.0).conditional_mode_curvature_proposal(); + let subject = MarginalSubject { + subject_id: "no_curv", + occasion_indices: &[], + mode: &[0.1], + mode_converged: Some(true), + eta_dimension: 1, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let omega = ndarray::array![[1.0]]; + let diagnostics = calculate_population_marginal_likelihood( + config, + &[subject], + &omega, + None, + |_, _, _| Ok(0.0), + ); + assert!(matches!( + diagnostics.subjects[0].failure, + Some(MarginalLikelihoodFailureReason::ConditionalCurvatureUnavailable) + )); + assert_eq!( + diagnostics.subjects[0].proposal_scale_source, + ProposalScaleSource::ConditionalModeCurvature + ); + } + + #[test] + fn raw_omega_proposal_default_unchanged_with_new_config_field() { + let config = MarginalLikelihoodConfig::new(128, 801, 3, 1.0); + let subject = MarginalSubject { + subject_id: "default_proposal", + occasion_indices: &[], + mode: &[0.0], + mode_converged: Some(true), + eta_dimension: 1, + kappa_dimension: 0, + validation_failure: None, + curvature_availability: None, + curvature_covariance: None, + }; + let omega = ndarray::array![[1.0]]; + let diagnostics = calculate_population_marginal_likelihood( + config, + &[subject], + &omega, + None, + |_, eta, _| eta_log_prior_from_omega(eta, &omega), + ); + assert_eq!( + diagnostics.subjects[0].proposal_scale_source, + ProposalScaleSource::FinalRawOmegaBlocks + ); + assert!(diagnostics.subjects[0].log_marginal_likelihood.is_some()); + } +} diff --git a/src/estimation/parametric/markov_variance.rs b/src/estimation/parametric/markov_variance.rs new file mode 100644 index 000000000..3c084a9a8 --- /dev/null +++ b/src/estimation/parametric/markov_variance.rs @@ -0,0 +1,338 @@ +//! Frozen-kernel Markov simulation-variance algebra for averaged SAEM. +//! +//! This deliberately small module implements non-overlapping multivariate +//! batch means and the Vats/Flegal lugsail combination. It never repairs, +//! projects, regularizes, or replaces a reported matrix. + +use anyhow::{bail, Result}; +use ndarray::Array2; + +use crate::algorithms::parametric::LugsailConfig; +use crate::estimation::parametric::covariance::cholesky_lower; + +pub(crate) fn batch_means(samples: &[Vec], batch_size: usize) -> Result> { + if samples.is_empty() || batch_size == 0 || !samples.len().is_multiple_of(batch_size) { + bail!("batch-means samples must be non-empty and divisible by batch size"); + } + let batches = samples.len() / batch_size; + if batches < 2 { + bail!("batch means require at least two batches"); + } + let width = samples[0].len(); + if samples + .iter() + .any(|sample| sample.len() != width || sample.iter().any(|x| !x.is_finite())) + { + bail!("batch-means samples must have one finite coordinate width"); + } + let mut overall = vec![0.0; width]; + for sample in samples { + for j in 0..width { + overall[j] += sample[j] / samples.len() as f64; + } + } + let mut result = Array2::zeros((width, width)); + for batch in samples.chunks(batch_size) { + let mut mean = vec![0.0; width]; + for sample in batch { + for j in 0..width { + mean[j] += sample[j] / batch_size as f64; + } + } + for row in 0..width { + for column in 0..width { + result[[row, column]] += + (mean[row] - overall[row]) * (mean[column] - overall[column]); + } + } + } + result.mapv_inplace(|value| value * batch_size as f64 / (batches - 1) as f64); + Ok(result) +} + +pub(crate) fn lugsail_batch_means( + samples: &[Vec], + batch_size: usize, + lugsail: LugsailConfig, +) -> Result<(Array2, Array2, Array2)> { + let coarse = batch_means(samples, batch_size)?; + let fine = batch_means(samples, batch_size / lugsail.r)?; + let lrv = (&coarse - &fine * lugsail.c) / (1.0 - lugsail.c); + Ok((coarse, fine, lrv)) +} + +/// Independent chain averages have covariance C^-2 sum_c Lambda_c. +#[allow(dead_code)] +pub(crate) fn combine_independent_chain_lrvs(chains: &[Array2]) -> Result> { + let Some(first) = chains.first() else { + bail!("at least one chain LRV is required"); + }; + let shape = first.dim(); + if shape.0 != shape.1 || chains.iter().any(|matrix| matrix.dim() != shape) { + bail!("chain LRV matrices must have one square dimension"); + } + let mut combined = Array2::zeros(shape); + for chain in chains { + combined += chain; + } + combined /= (chains.len() * chains.len()) as f64; + Ok(combined) +} + +/// Scale a sum of `C_d` independent-chain LRVs both for the diagnostic-chain +/// mean (`sum / C_d^2`) and for the operational fit-chain mean +/// (`sum / (C_d C_f)`). +pub(crate) fn scale_lrv_sum( + sum: &Array2, + diagnostic_chains: usize, + fit_chains: usize, +) -> (Array2, Array2) { + debug_assert!(diagnostic_chains > 0 && fit_chains > 0); + let diagnostic = sum / (diagnostic_chains as f64 * diagnostic_chains as f64); + let operational = sum / (diagnostic_chains as f64 * fit_chains as f64); + (diagnostic, operational) +} + +/// Compute Iobs^-1 Lambda Iobs^-T and its Cesaro-average covariance Xi/n_avg. +pub(crate) fn transform_simulation_variance( + information: &Array2, + lambda: &Array2, + n_avg: usize, +) -> Result<(Array2, Array2)> { + if n_avg == 0 || information.dim() != lambda.dim() || information.nrows() != information.ncols() + { + bail!("simulation-variance transform dimensions are invalid"); + } + let inverse = inverse_spd_no_jitter(information)?; + let n = inverse.nrows(); + let mut xi = Array2::zeros((n, n)); + for row in 0..n { + for column in 0..=row { + let mut value = 0.0; + for left in 0..n { + for right in 0..n { + value += + inverse[[row, left]] * lambda[[left, right]] * inverse[[column, right]]; + } + } + xi[[row, column]] = value; + xi[[column, row]] = value; + } + } + let covariance = &xi / n_avg as f64; + Ok((xi, covariance)) +} + +fn inverse_spd_no_jitter(matrix: &Array2) -> Result> { + let lower = cholesky_lower(matrix)?; + let n = lower.len(); + let mut inverse = Array2::zeros((n, n)); + for column in 0..n { + let mut y = vec![0.0; n]; + for row in 0..n { + let rhs = usize::from(row == column) as f64; + y[row] = (rhs - (0..row).map(|k| lower[row][k] * y[k]).sum::()) / lower[row][row]; + } + let mut x = vec![0.0; n]; + for row in (0..n).rev() { + x[row] = (y[row] - ((row + 1)..n).map(|k| lower[k][row] * x[k]).sum::()) + / lower[row][row]; + } + for row in 0..n { + inverse[[row, column]] = x[row]; + } + } + Ok(inverse) +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub(crate) enum MatrixClassification { + EligiblePsd, + NonFinite, + NonSymmetric, + Indefinite, +} + +pub(crate) fn classify_psd(matrix: &Array2) -> MatrixClassification { + if matrix.nrows() != matrix.ncols() || matrix.iter().any(|value| !value.is_finite()) { + return MatrixClassification::NonFinite; + } + let n = matrix.nrows(); + for row in 0..n { + for column in 0..row { + let scale = matrix[[row, column]] + .abs() + .max(matrix[[column, row]].abs()) + .max(1.0); + if (matrix[[row, column]] - matrix[[column, row]]).abs() > 64.0 * f64::EPSILON * scale { + return MatrixClassification::NonSymmetric; + } + } + } + // Symmetric Jacobi eigenvalues are used only for classification. The input + // and every reported matrix remain byte-for-byte unmodified. + let mut work = matrix.clone(); + for _ in 0..(50 * n.max(1) * n.max(1)) { + let mut p = 0; + let mut q = 0; + let mut largest = 0.0; + for row in 0..n { + for column in 0..row { + if work[[row, column]].abs() > largest { + largest = work[[row, column]].abs(); + p = row; + q = column; + } + } + } + if largest <= 64.0 * f64::EPSILON * work.iter().fold(1.0_f64, |s, x| s.max(x.abs())) { + break; + } + let angle = 0.5 * (2.0 * work[[p, q]]).atan2(work[[q, q]] - work[[p, p]]); + let (sin, cos) = angle.sin_cos(); + for k in 0..n { + if k == p || k == q { + continue; + } + let kp = work[[k, p]]; + let kq = work[[k, q]]; + work[[k, p]] = cos * kp - sin * kq; + work[[p, k]] = work[[k, p]]; + work[[k, q]] = sin * kp + cos * kq; + work[[q, k]] = work[[k, q]]; + } + let pp = work[[p, p]]; + let qq = work[[q, q]]; + let pq = work[[p, q]]; + work[[p, p]] = cos * cos * pp - 2.0 * sin * cos * pq + sin * sin * qq; + work[[q, q]] = sin * sin * pp + 2.0 * sin * cos * pq + cos * cos * qq; + work[[p, q]] = 0.0; + work[[q, p]] = 0.0; + } + let scale = matrix.iter().fold(1.0_f64, |s, x| s.max(x.abs())); + if (0..n).any(|index| work[[index, index]] < -256.0 * f64::EPSILON * scale) { + MatrixClassification::Indefinite + } else { + MatrixClassification::EligiblePsd + } +} + +pub(crate) fn rows(matrix: &Array2) -> Vec> { + matrix.rows().into_iter().map(|row| row.to_vec()).collect() +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn scalar_and_two_dimensional_batch_means_are_hand_calculated() { + let scalar = batch_means(&[vec![1.0], vec![3.0], vec![5.0], vec![7.0]], 2).unwrap(); + assert_eq!(scalar[[0, 0]], 16.0); + let two = batch_means( + &[ + vec![1.0, 2.0], + vec![3.0, 4.0], + vec![5.0, 8.0], + vec![7.0, 10.0], + ], + 2, + ) + .unwrap(); + assert_eq!(two, ndarray::array![[16.0, 24.0], [24.0, 36.0]]); + } + + #[test] + fn over_lugsail_and_chain_scaling_are_exact() { + let samples = [ + vec![1.0], + vec![3.0], + vec![5.0], + vec![7.0], + vec![9.0], + vec![11.0], + ]; + let (b, fine, lrv) = + lugsail_batch_means(&samples, 3, LugsailConfig::over_lugsail_bartlett()).unwrap(); + assert_eq!(b[[0, 0]], 54.0); + assert_eq!(fine[[0, 0]], 14.0); + assert_eq!(lrv[[0, 0]], 94.0); + assert_eq!( + combine_independent_chain_lrvs(&[lrv.clone(), lrv]).unwrap()[[0, 0]], + 47.0 + ); + } + + #[test] + fn unequal_diagnostic_and_fit_chain_scaling_is_exact() { + // Hard-coded sum of three 2x2 chain LRVs with C_d=3 and C_f=2. + let sum = ndarray::array![[18.0, 9.0], [9.0, 36.0]]; + let (diagnostic, operational) = scale_lrv_sum(&sum, 3, 2); + assert_eq!(diagnostic, ndarray::array![[2.0, 1.0], [1.0, 4.0]]); + assert_eq!(operational, ndarray::array![[3.0, 1.5], [1.5, 6.0]]); + } + + #[test] + fn transform_and_indefinite_classification_retain_raw_values() { + let information = ndarray::array![[2.0, 0.0], [0.0, 4.0]]; + let lambda = ndarray::array![[8.0, 4.0], [4.0, 8.0]]; + let (xi, covariance) = transform_simulation_variance(&information, &lambda, 5).unwrap(); + for (actual, expected) in xi.iter().zip([2.0, 0.5, 0.5, 0.5]) { + assert!((actual - expected).abs() < 1e-14); + } + for (actual, expected) in covariance.iter().zip([0.4, 0.1, 0.1, 0.1]) { + assert!((actual - expected).abs() < 1e-14); + } + let indefinite = ndarray::array![[1.0, 2.0], [2.0, 1.0]]; + assert_eq!(classify_psd(&indefinite), MatrixClassification::Indefinite); + assert_eq!(indefinite[[0, 1]], 2.0); + } + + #[test] + fn per_chain_indefiniteness_survives_psd_aggregate_cancellation() { + let first = ndarray::array![[-1.0, 0.0], [0.0, 3.0]]; + let second = ndarray::array![[3.0, 0.0], [0.0, -1.0]]; + assert_eq!(classify_psd(&first), MatrixClassification::Indefinite); + assert_eq!(classify_psd(&second), MatrixClassification::Indefinite); + let combined = combine_independent_chain_lrvs(&[first.clone(), second.clone()]).unwrap(); + assert_eq!(combined, ndarray::array![[0.5, 0.0], [0.0, 0.5]]); + assert_eq!(classify_psd(&combined), MatrixClassification::EligiblePsd); + assert_eq!(first[[0, 0]], -1.0); + assert_eq!(second[[1, 1]], -1.0); + } + + #[test] + fn transformed_indefinite_matrices_are_finite_and_remain_indefinite() { + let information = ndarray::array![[2.0, 0.0], [0.0, 4.0]]; + let lambda = ndarray::array![[-2.0, 0.0], [0.0, 4.0]]; + let (xi, covariance) = transform_simulation_variance(&information, &lambda, 2).unwrap(); + for (actual, expected) in xi.iter().zip([-0.5, 0.0, 0.0, 0.25]) { + assert!((actual - expected).abs() < 1e-14); + } + for (actual, expected) in covariance.iter().zip([-0.25, 0.0, 0.0, 0.125]) { + assert!((actual - expected).abs() < 1e-14); + } + assert_eq!(classify_psd(&xi), MatrixClassification::Indefinite); + assert_eq!(classify_psd(&covariance), MatrixClassification::Indefinite); + assert!(xi + .iter() + .chain(covariance.iter()) + .all(|value| value.is_finite())); + } + + #[test] + fn finite_symmetry_and_psd_are_classified_without_repair() { + assert_eq!( + classify_psd(&ndarray::array![[1.0, 1.0], [1.0, 1.0]]), + MatrixClassification::EligiblePsd + ); + assert_eq!( + classify_psd(&ndarray::array![[1.0, 0.5], [0.25, 1.0]]), + MatrixClassification::NonSymmetric + ); + assert_eq!( + classify_psd(&ndarray::array![[f64::NAN]]), + MatrixClassification::NonFinite + ); + } +} diff --git a/src/estimation/parametric/mod.rs b/src/estimation/parametric/mod.rs new file mode 100644 index 000000000..a770dde2a --- /dev/null +++ b/src/estimation/parametric/mod.rs @@ -0,0 +1,46 @@ +//! Shared parametric-estimation utilities. +//! +//! Algorithm implementations such as SAEM, FO, FOCE, and FOCEI should use these +//! modules for parameter transforms, covariance algebra, and η/Ω posterior +//! scoring instead of keeping algorithm-local copies. + +pub(crate) mod conditional_uncertainty; +pub(crate) mod covariance; +pub mod covariates; +pub(crate) mod individual; +pub(crate) mod information; +pub mod marginal_likelihood; +pub(crate) mod markov_variance; +pub(crate) mod posterior; +pub(crate) mod posthoc; +pub(crate) mod prior; +pub(crate) mod rank_diagnostics; +pub(crate) mod residual; +pub(crate) mod shrinkage; +pub(crate) mod sufficient; +pub(crate) mod transforms; + +pub use conditional_uncertainty::{ + ConditionalCurvatureAvailability, ConditionalCurvatureDiagnostics, + ConditionalCurvatureRegularization, ConditionalCurvatureStatus, + ConditionalCurvatureUnavailableReason, ConditionalModeMetadata, JointLatentCoordinate, + JointLatentCoordinateKind, +}; +pub use covariates::{ + rebase_eta, reject_constraints, solve_covariate_gls, subject_centered_omega, CovariateEffect, + CovariateEffectFamily, CovariateEstimate, CovariateGlsProblem, CovariateModel, + CovariateMstepError, CovariateValidationError, ParametricConstraint, SubjectCovariateDesign, + SubjectCovariateValue, SubjectPopulationParameters, +}; +pub use marginal_likelihood::{ + marginal_likelihood_subject_seed, MarginalLikelihoodConfig, MarginalLikelihoodDiagnostics, + MarginalLikelihoodFailureReason, MarginalLikelihoodMethod, MarginalLikelihoodProposal, + MarginalLikelihoodStatus, MarginalLikelihoodSubjectFailure, ProposalScaleSource, + SubjectMarginalLikelihoodDiagnostics, N2_SEED_DOMAIN, +}; +pub(crate) use prior::{CovarianceUpdateStatus, ResolvedOmega}; +pub use prior::{Iov, Omega, ParametricPrior}; +pub use shrinkage::{ + EtaMapShrinkage, EtaPosteriorMeanShrinkage, KappaMapShrinkage, KappaPosteriorMeanShrinkage, + ShrinkageDiagnostics, ShrinkageUnavailableReason, ShrinkageValue, +}; diff --git a/src/estimation/parametric/posterior.rs b/src/estimation/parametric/posterior.rs new file mode 100644 index 000000000..c35102904 --- /dev/null +++ b/src/estimation/parametric/posterior.rs @@ -0,0 +1,95 @@ +use anyhow::Result; +use ndarray::Array2; + +use super::covariance::{cholesky_log_determinant, cholesky_lower, solve_lower}; + +const LOG_2PI: f64 = 1.8378770664093453_f64; + +/// Subject-level proposal score used by SAEM MCMC kernels and future FOCE diagnostics. +#[derive(Debug, Clone, Copy, PartialEq)] +pub(crate) struct SubjectPosteriorScore { + pub(crate) log_likelihood: f64, + pub(crate) eta_log_prior: f64, + pub(crate) kappa_log_prior: f64, +} + +impl SubjectPosteriorScore { + pub(crate) fn log_posterior(self) -> f64 { + self.log_likelihood + self.eta_log_prior + self.kappa_log_prior + } + + pub(crate) fn log_acceptance_ratio(self, proposed: Self) -> f64 { + proposed.log_posterior() - self.log_posterior() + } +} + +/// Log-density of η under a multivariate normal N(0, Ω). +/// +/// The normalizing constant is retained for diagnostics and objective assembly. +/// MCMC acceptance ratios cancel it when Ω is unchanged, matching established +/// kernel logic while keeping PMcore's score explicit. +pub(crate) fn eta_log_prior(eta: &[f64], cholesky: &[Vec], log_det: f64) -> Result { + let z = solve_lower(cholesky, eta)?; + let quadratic = z.iter().map(|value| value * value).sum::(); + Ok(-0.5 * (eta.len() as f64 * LOG_2PI + log_det + quadratic)) +} + +pub(crate) fn eta_log_prior_from_omega(eta: &[f64], omega: &Array2) -> Result { + let cholesky = cholesky_lower(omega)?; + let log_det = cholesky_log_determinant(&cholesky); + eta_log_prior(eta, &cholesky, log_det) +} + +pub(crate) fn eta_log_priors( + etas: &[Vec>], + omega: &Array2, + chain_index: usize, +) -> Result> { + let cholesky = cholesky_lower(omega)?; + let log_det = cholesky_log_determinant(&cholesky); + + etas.iter() + .map(|subject_chains| eta_log_prior(&subject_chains[chain_index], &cholesky, log_det)) + .collect() +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::parametric::covariance::{cholesky_lower, identity_matrix}; + + #[test] + fn eta_log_prior_uses_full_normal_density() { + let omega = identity_matrix(2); + let eta = vec![1.0, 2.0]; + let cholesky = cholesky_lower(&omega).unwrap(); + let actual = eta_log_prior(&eta, &cholesky, 0.0).unwrap(); + let expected = -0.5 * (2.0 * LOG_2PI + 5.0); + assert!((actual - expected).abs() < 1e-12); + } + + #[test] + fn eta_log_prior_handles_correlated_omega() { + let omega = ndarray::array![[4.0, 1.0], [1.0, 2.0]]; + let eta = vec![1.0, -1.0]; + let cholesky = cholesky_lower(&omega).unwrap(); + let actual = eta_log_prior(&eta, &cholesky, 7.0_f64.ln()).unwrap(); + let expected = -0.5 * (2.0 * LOG_2PI + 7.0_f64.ln() + 8.0 / 7.0); + assert!((actual - expected).abs() < 1e-12); + } + + #[test] + fn posterior_score_produces_acceptance_ratio() { + let current = SubjectPosteriorScore { + log_likelihood: -10.0, + eta_log_prior: -1.0, + kappa_log_prior: 0.0, + }; + let proposed = SubjectPosteriorScore { + log_likelihood: -9.0, + eta_log_prior: -1.5, + kappa_log_prior: 0.0, + }; + assert_eq!(current.log_acceptance_ratio(proposed), 0.5); + } +} diff --git a/src/estimation/parametric/posthoc.rs b/src/estimation/parametric/posthoc.rs new file mode 100644 index 000000000..61e7e376c --- /dev/null +++ b/src/estimation/parametric/posthoc.rs @@ -0,0 +1,158 @@ +use argmin::{ + core::{CostFunction, Error, Executor, State, TerminationReason}, + solver::neldermead::NelderMead, +}; + +const NON_FINITE_PENALTY: f64 = 1e100; + +#[derive(Debug, Clone)] +pub(crate) struct ConditionalModeSolution { + pub(crate) coordinates: Vec, + pub(crate) objective: f64, + pub(crate) converged: bool, + pub(crate) iterations: u64, + pub(crate) termination: String, +} + +struct ConditionalModeCost { + cost: F, +} + +impl CostFunction for ConditionalModeCost +where + F: Fn(&[f64]) -> f64, +{ + type Param = Vec; + type Output = f64; + + fn cost(&self, coordinates: &Self::Param) -> Result { + let objective = (self.cost)(coordinates); + Ok(if objective.is_finite() { + objective + } else { + NON_FINITE_PENALTY + }) + } +} + +pub(crate) fn optimize_conditional_mode( + initial: Vec, + coordinate_scales: &[f64], + max_iterations: u64, + sd_tolerance: f64, + cost: F, +) -> anyhow::Result +where + F: Fn(&[f64]) -> f64, +{ + anyhow::ensure!( + !initial.is_empty(), + "conditional mode requires latent coordinates" + ); + anyhow::ensure!( + initial.len() == coordinate_scales.len(), + "conditional-mode coordinate and scale dimensions differ" + ); + anyhow::ensure!( + initial.iter().all(|value| value.is_finite()), + "conditional-mode initial coordinates must be finite" + ); + anyhow::ensure!( + coordinate_scales + .iter() + .all(|scale| scale.is_finite() && *scale > 0.0), + "conditional-mode coordinate scales must be finite and positive" + ); + anyhow::ensure!( + sd_tolerance.is_finite() && sd_tolerance > 0.0, + "conditional-mode tolerance must be finite and positive" + ); + + let initial_objective = cost(&initial); + anyhow::ensure!( + initial_objective.is_finite(), + "conditional-mode warm-start objective is non-finite" + ); + + let solver = NelderMead::new(initial_simplex(&initial, coordinate_scales)) + .with_sd_tolerance(sd_tolerance)?; + let result = Executor::new(ConditionalModeCost { cost }, solver) + .configure(|state| state.max_iters(max_iterations)) + .run()?; + let state = result.state; + let coordinates = state + .best_param + .clone() + .filter(|_coordinates| state.best_cost.is_finite() && state.best_cost < NON_FINITE_PENALTY) + .unwrap_or_else(|| initial.clone()); + let objective = if state.best_cost.is_finite() && state.best_cost < NON_FINITE_PENALTY { + state.best_cost + } else { + initial_objective + }; + let termination_reason = state.get_termination_reason(); + let converged = matches!(termination_reason, Some(TerminationReason::SolverConverged)); + let termination = termination_reason + .map(ToString::to_string) + .unwrap_or_else(|| "unknown termination".to_owned()); + + Ok(ConditionalModeSolution { + coordinates, + objective, + converged, + iterations: state.iter, + termination, + }) +} + +fn initial_simplex(initial: &[f64], coordinate_scales: &[f64]) -> Vec> { + let mut simplex = Vec::with_capacity(initial.len() + 1); + simplex.push(initial.to_vec()); + for dimension in 0..initial.len() { + let mut point = initial.to_vec(); + point[dimension] += coordinate_scales[dimension]; + simplex.push(point); + } + simplex +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn optimizer_recovers_known_joint_mode() { + let solution = + optimize_conditional_mode(vec![0.0, 0.0], &[0.2, 0.2], 200, 1e-10, |coordinates| { + (coordinates[0] - 1.0).powi(2) + 2.0 * (coordinates[1] + 0.5).powi(2) + }) + .unwrap(); + + assert!((solution.coordinates[0] - 1.0).abs() < 1e-4); + assert!((solution.coordinates[1] + 0.5).abs() < 1e-4); + assert!(solution.objective < 1e-8); + assert!(solution.converged); + } + + #[test] + fn non_finite_regions_receive_finite_penalty() { + let solution = optimize_conditional_mode(vec![0.0], &[0.1], 100, 1e-8, |coordinates| { + if coordinates[0] > 0.5 { + f64::NAN + } else { + (coordinates[0] - 0.25).powi(2) + } + }) + .unwrap(); + + assert!((solution.coordinates[0] - 0.25).abs() < 1e-3); + assert!(solution.objective.is_finite()); + } + + #[test] + fn non_finite_warm_start_is_rejected() { + let error = + optimize_conditional_mode(vec![0.0], &[0.1], 10, 1e-6, |_| f64::INFINITY).unwrap_err(); + assert!(error.to_string().contains("warm-start objective")); + } +} diff --git a/src/estimation/parametric/prior.rs b/src/estimation/parametric/prior.rs new file mode 100644 index 000000000..025f1a6e8 --- /dev/null +++ b/src/estimation/parametric/prior.rs @@ -0,0 +1,2736 @@ +use std::collections::{HashMap, HashSet}; + +use anyhow::{bail, Result}; +use ndarray::Array2; + +use crate::model::{ParameterSpace, UnboundedParameter}; +use crate::results::{CovarianceTrialRejectionReason, CovarianceUpdateRejectionReason}; + +use super::{ + covariance::{cholesky_log_determinant, cholesky_lower, identity_matrix}, + covariates::CovariateModel, +}; + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum OmegaEntryUnit { + VarianceOrCovariance, + StandardDeviation, +} + +#[derive(Debug, Clone, PartialEq)] +struct OmegaEntry { + left: String, + right: String, + value: f64, + unit: OmegaEntryUnit, + estimated: bool, +} + +/// Named declaration of the initial IIV covariance matrix. +/// +/// Variances and covariances omitted from an explicit declaration are +/// structural zeros. Fixed entries remain part of Ω but are not updated by an +/// estimation algorithm. This preserves the distinction between covariance +/// structure and structural/free and fixed masks. +#[derive(Debug, Clone, Default, PartialEq)] +pub struct Omega { + entries: Vec, +} + +impl Omega { + pub fn new() -> Self { + Self::default() + } + + /// Builds an estimated diagonal Ω declaration from variances. + /// + /// This compatibility constructor retains its original variance semantics. + pub fn diagonal(variances: I) -> Self + where + N: Into, + I: IntoIterator, + { + Self::diagonal_variances(variances) + } + + /// Builds an estimated diagonal Ω declaration from variances. + pub fn diagonal_variances(variances: I) -> Self + where + N: Into, + I: IntoIterator, + { + variances + .into_iter() + .fold(Self::new(), |omega, (name, variance)| { + omega.variance(name, variance) + }) + } + + /// Builds an estimated diagonal Ω declaration from standard deviations. + /// + /// SD domain validation and conversion to variances occur during final + /// problem construction, preserving fail-closed builder semantics. + pub fn diagonal_standard_deviations(standard_deviations: I) -> Self + where + N: Into, + I: IntoIterator, + { + standard_deviations.into_iter().fold( + Self::new(), + |mut omega, (name, standard_deviation)| { + let name = name.into(); + omega.entries.push(OmegaEntry { + left: name.clone(), + right: name, + value: standard_deviation, + unit: OmegaEntryUnit::StandardDeviation, + estimated: true, + }); + omega + }, + ) + } + + /// Declares an estimated variance for one random effect. + pub fn variance(mut self, name: impl Into, value: f64) -> Self { + let name = name.into(); + self.entries.push(OmegaEntry { + left: name.clone(), + right: name, + value, + unit: OmegaEntryUnit::VarianceOrCovariance, + estimated: true, + }); + self + } + + /// Declares a fixed variance for one random effect. + pub fn fixed_variance(mut self, name: impl Into, value: f64) -> Self { + let name = name.into(); + self.entries.push(OmegaEntry { + left: name.clone(), + right: name, + value, + unit: OmegaEntryUnit::VarianceOrCovariance, + estimated: false, + }); + self + } + + /// Declares an estimated covariance. Undeclared covariances remain + /// structural zeros. + pub fn covariance( + mut self, + left: impl Into, + right: impl Into, + value: f64, + ) -> Self { + self.entries.push(OmegaEntry { + left: left.into(), + right: right.into(), + value, + unit: OmegaEntryUnit::VarianceOrCovariance, + estimated: true, + }); + self + } + + /// Declares a fixed covariance. + pub fn fixed_covariance( + mut self, + left: impl Into, + right: impl Into, + value: f64, + ) -> Self { + self.entries.push(OmegaEntry { + left: left.into(), + right: right.into(), + value, + unit: OmegaEntryUnit::VarianceOrCovariance, + estimated: false, + }); + self + } +} + +/// Named declaration of the initial inter-occasion covariance matrix. +/// +/// κ is additive in transformed φ-space, independently for every occasion of +/// a subject. Its names refer to model parameters, not to IIV membership: a +/// parameter may have IOV with or without IIV. +#[derive(Debug, Clone, Default, PartialEq)] +pub struct Iov { + omega: Omega, +} + +impl Iov { + pub fn new() -> Self { + Self::default() + } + + /// Builds an estimated diagonal IOV declaration from variances. + /// + /// This compatibility constructor retains its original variance semantics. + pub fn diagonal(variances: I) -> Self + where + N: Into, + I: IntoIterator, + { + Self::diagonal_variances(variances) + } + + /// Builds an estimated diagonal IOV declaration from variances. + pub fn diagonal_variances(variances: I) -> Self + where + N: Into, + I: IntoIterator, + { + Self { + omega: Omega::diagonal_variances(variances), + } + } + + /// Builds an estimated diagonal IOV declaration from standard deviations. + pub fn diagonal_standard_deviations(standard_deviations: I) -> Self + where + N: Into, + I: IntoIterator, + { + Self { + omega: Omega::diagonal_standard_deviations(standard_deviations), + } + } + + pub fn variance(mut self, name: impl Into, value: f64) -> Self { + self.omega = self.omega.variance(name, value); + self + } + + pub fn fixed_variance(mut self, name: impl Into, value: f64) -> Self { + self.omega = self.omega.fixed_variance(name, value); + self + } + + pub fn covariance( + mut self, + left: impl Into, + right: impl Into, + value: f64, + ) -> Self { + self.omega = self.omega.covariance(left, right, value); + self + } + + pub fn fixed_covariance( + mut self, + left: impl Into, + right: impl Into, + value: f64, + ) -> Self { + self.omega = self.omega.fixed_covariance(left, right, value); + self + } +} + +/// Initial population, IIV, and optional IOV distribution shared by all +/// parametric algorithms. +#[derive(Debug, Clone, PartialEq)] +pub struct ParametricPrior { + parameters: ParameterSpace, + omega: ResolvedOmega, + iov: Option, + covariates: Option, +} + +impl ParametricPrior { + #[cfg(test)] + pub(crate) fn new( + parameters: ParameterSpace, + omega: Option, + iov: Option, + ) -> Result { + Self::new_with_covariates(parameters, omega, iov, None) + } + + pub(crate) fn new_with_covariates( + parameters: ParameterSpace, + omega: Option, + iov: Option, + covariates: Option, + ) -> Result { + let resolved = ResolvedOmega::resolve(¶meters, omega.as_ref())?; + let iov = iov + .as_ref() + .map(|declaration| ResolvedIov::resolve(¶meters, declaration)) + .transpose()?; + Ok(Self { + parameters, + omega: resolved, + iov, + covariates, + }) + } + + pub fn parameters(&self) -> &ParameterSpace { + &self.parameters + } + + /// Random-effect names in η/Ω order. + pub fn random_effect_names(&self) -> &[String] { + &self.omega.names + } + + /// Initial IIV covariance matrix. + pub fn omega(&self) -> &Array2 { + &self.omega.initial + } + + pub(crate) fn resolved_omega(&self) -> &ResolvedOmega { + &self.omega + } + + pub fn iov_effect_names(&self) -> Option<&[String]> { + self.iov.as_ref().map(|iov| iov.omega.names.as_slice()) + } + + pub fn omega_iov(&self) -> Option<&Array2> { + self.iov.as_ref().map(|iov| iov.omega.initial()) + } + + pub(crate) fn resolved_iov(&self) -> Option<&ResolvedIov> { + self.iov.as_ref() + } + + /// Fully validated subject-static covariate population model, when declared. + pub fn covariates(&self) -> Option<&CovariateModel> { + self.covariates.as_ref() + } +} + +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct ResolvedIov { + parameter_indices: Vec, + omega: ResolvedOmega, +} + +impl ResolvedIov { + fn resolve(parameters: &ParameterSpace, declaration: &Iov) -> Result { + let mut names = Vec::new(); + for entry in &declaration.omega.entries { + for name in [&entry.left, &entry.right] { + if !names.iter().any(|existing| existing == name) { + names.push(name.clone()); + } + } + } + if names.is_empty() { + bail!("IOV declaration must contain at least one variance"); + } + let parameter_indices = names + .iter() + .map(|name| { + parameters + .iter() + .position(|parameter| parameter.name == *name) + .ok_or_else(|| { + anyhow::anyhow!("IOV entry references unknown model parameter '{name}'") + }) + }) + .collect::>>()?; + let omega = ResolvedOmega::resolve_names(&names, &declaration.omega, "IOV")?; + Ok(Self { + parameter_indices, + omega, + }) + } + + pub(crate) fn parameter_indices(&self) -> &[usize] { + &self.parameter_indices + } + + pub(crate) fn omega(&self) -> &ResolvedOmega { + &self.omega + } +} + +// ── Connected-component classification ──────────────────────────── + +/// Connected component of the declared potentially-nonzero graph. +#[derive(Debug, Clone)] +struct OmegaComponent { + kind: OmegaComponentKind, + indices: Vec, + free_coords: Vec<(usize, usize)>, + fixed_coords: Vec<(usize, usize)>, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum OmegaComponentKind { + /// Every declared entry is fixed. + AllFixed, + /// Every declared entry is free and all cross-covariances are declared. + DenseAllFree, + /// Every declared entry is free but not all cross-covariances are + /// declared (sparse structural mask). + SparseAllFree, + /// Both free and fixed entries coexist. + Mixed, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub(crate) enum CovarianceUpdateStatus { + Accepted, + NoOp, + Rejected, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum VarianceFloorMode { + LegacyAfterInterpolation, + CappedSolvedTarget, +} + +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct CovarianceUpdateResult { + pub(crate) matrix: Array2, + pub(crate) status: CovarianceUpdateStatus, + pub(crate) solved_target: Option>, + pub(crate) accepted_fraction: Option, + pub(crate) attempted_fractions: Vec, + pub(crate) trial_rejections: Vec, + pub(crate) rejection_reason: Option, +} + +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct ResolvedOmega { + names: Vec, + initial: Array2, + structural_mask: Array2, + estimated_mask: Array2, +} + +fn resolve_entry_value(entry: &OmegaEntry, domain: &str) -> Result { + match entry.unit { + OmegaEntryUnit::VarianceOrCovariance => { + if !entry.value.is_finite() { + bail!( + "{domain} entry for '{}' and '{}' must be finite", + entry.left, + entry.right + ); + } + Ok(entry.value) + } + OmegaEntryUnit::StandardDeviation => { + if entry.left != entry.right { + bail!("{domain} standard-deviation declarations must be diagonal"); + } + if !entry.value.is_finite() || entry.value <= 0.0 { + bail!( + "{domain} standard deviation for '{}' must be finite and strictly positive", + entry.left + ); + } + if entry.value > f64::MAX / entry.value { + bail!( + "{domain} standard deviation for '{}' would overflow its variance representation", + entry.left + ); + } + let variance = entry.value * entry.value; + if !variance.is_finite() || variance <= 0.0 { + bail!( + "{domain} standard deviation for '{}' cannot be represented as a finite positive variance", + entry.left + ); + } + Ok(variance) + } + } +} + +impl ResolvedOmega { + fn resolve( + parameters: &ParameterSpace, + declaration: Option<&Omega>, + ) -> Result { + let names = parameters + .iter() + .filter(|parameter| parameter.random_effect) + .map(|parameter| parameter.name.clone()) + .collect::>(); + let indices = names + .iter() + .enumerate() + .map(|(index, name)| (name.as_str(), index)) + .collect::>(); + let size = names.len(); + + let Some(declaration) = declaration else { + let initial = identity_matrix(size); + let structural_mask = Array2::from_shape_fn((size, size), |(row, col)| row == col); + return Ok(Self { + names, + initial, + estimated_mask: structural_mask.clone(), + structural_mask, + }); + }; + + let mut initial = Array2::zeros((size, size)); + let mut structural_mask = Array2::from_elem((size, size), false); + let mut estimated_mask = Array2::from_elem((size, size), false); + let mut declared = HashSet::new(); + + for entry in &declaration.entries { + let Some(&left) = indices.get(entry.left.as_str()) else { + bail!( + "omega entry references '{}' which is not a declared IIV random effect", + entry.left + ); + }; + let Some(&right) = indices.get(entry.right.as_str()) else { + bail!( + "omega entry references '{}' which is not a declared IIV random effect", + entry.right + ); + }; + let value = resolve_entry_value(entry, "omega")?; + if left == right && value <= 0.0 { + bail!("omega variance for '{}' must be positive", entry.left); + } + + let key = if left <= right { + (left, right) + } else { + (right, left) + }; + if !declared.insert(key) { + bail!( + "omega entry for '{}' and '{}' is declared more than once", + entry.left, + entry.right + ); + } + + initial[[left, right]] = value; + initial[[right, left]] = value; + structural_mask[[left, right]] = true; + structural_mask[[right, left]] = true; + estimated_mask[[left, right]] = entry.estimated; + estimated_mask[[right, left]] = entry.estimated; + } + + for (index, name) in names.iter().enumerate() { + if !structural_mask[[index, index]] { + bail!("omega variance for random effect '{name}' is not declared"); + } + } + if size > 0 { + cholesky_lower(&initial)?; + } + + Ok(Self { + names, + initial, + structural_mask, + estimated_mask, + }) + } + + fn resolve_names(names: &[String], declaration: &Omega, domain: &str) -> Result { + let indices = names + .iter() + .enumerate() + .map(|(index, name)| (name.as_str(), index)) + .collect::>(); + let size = names.len(); + let mut initial = Array2::zeros((size, size)); + let mut structural_mask = Array2::from_elem((size, size), false); + let mut estimated_mask = Array2::from_elem((size, size), false); + let mut declared = HashSet::new(); + + for entry in &declaration.entries { + let Some(&left) = indices.get(entry.left.as_str()) else { + bail!("{domain} entry references unknown effect '{}'", entry.left); + }; + let Some(&right) = indices.get(entry.right.as_str()) else { + bail!("{domain} entry references unknown effect '{}'", entry.right); + }; + let value = resolve_entry_value(entry, domain)?; + if left == right && value <= 0.0 { + bail!("{domain} variance for '{}' must be positive", entry.left); + } + let key = if left <= right { + (left, right) + } else { + (right, left) + }; + if !declared.insert(key) { + bail!( + "{domain} entry for '{}' and '{}' is declared more than once", + entry.left, + entry.right + ); + } + initial[[left, right]] = value; + initial[[right, left]] = value; + structural_mask[[left, right]] = true; + structural_mask[[right, left]] = true; + estimated_mask[[left, right]] = entry.estimated; + estimated_mask[[right, left]] = entry.estimated; + } + + for (index, name) in names.iter().enumerate() { + if !structural_mask[[index, index]] { + bail!("{domain} variance for effect '{name}' is not declared"); + } + } + cholesky_lower(&initial)?; + Ok(Self { + names: names.to_vec(), + initial, + structural_mask, + estimated_mask, + }) + } + + pub(crate) fn names(&self) -> &[String] { + &self.names + } + + pub(crate) fn initial(&self) -> &Array2 { + &self.initial + } + + pub(crate) fn update_with_status( + &self, + current: &Array2, + candidate: &Array2, + minimum_variance: f64, + ) -> Result { + self.update_with_status_and_floor_mode( + current, + candidate, + minimum_variance, + 1.0, + VarianceFloorMode::LegacyAfterInterpolation, + ) + } + + /// Perform a mask-aware covariance GEM update whose accepted displacement + /// is at most `maximum_fraction` of the solved target displacement. + /// + /// This under-relaxes the accepted covariance iterate; it does not alter + /// the stochastic-approximation history used to construct `candidate`. + pub(crate) fn update_with_status_and_max_fraction( + &self, + current: &Array2, + candidate: &Array2, + minimum_variance: f64, + maximum_fraction: f64, + ) -> Result { + self.update_with_status_and_floor_mode( + current, + candidate, + minimum_variance, + maximum_fraction, + VarianceFloorMode::CappedSolvedTarget, + ) + } + + fn update_with_status_and_floor_mode( + &self, + current: &Array2, + candidate: &Array2, + minimum_variance: f64, + maximum_fraction: f64, + floor_mode: VarianceFloorMode, + ) -> Result { + let dimensions = (self.names.len(), self.names.len()); + if current.dim() != dimensions || candidate.dim() != dimensions { + bail!("omega update dimensions do not match the declared random effects"); + } + if !minimum_variance.is_finite() || minimum_variance < 0.0 { + bail!("omega minimum variance must be finite and non-negative"); + } + if !maximum_fraction.is_finite() || maximum_fraction <= 0.0 || maximum_fraction > 1.0 { + bail!("omega update maximum fraction must be finite and in (0, 1]"); + } + if cholesky_lower(current).is_err() { + bail!("omega update cannot retain a positive-definite current matrix"); + } + if !self.has_estimated_entries() { + return Ok(CovarianceUpdateResult { + matrix: current.clone(), + status: CovarianceUpdateStatus::NoOp, + solved_target: None, + accepted_fraction: None, + attempted_fractions: Vec::new(), + trial_rejections: Vec::new(), + rejection_reason: None, + }); + } + + let components = self.classify_components(); + let n = self.names.len(); + // Cross-component second moments cannot affect a block-diagonal + // covariance objective. Preserve the original finite path exactly, but + // sanitize malformed values that occur only in those irrelevant slots. + let mut relevant_candidate = Array2::zeros((n, n)); + for component in &components { + if component.free_coords.is_empty() { + continue; + } + for &row in &component.indices { + for &col in &component.indices { + relevant_candidate[[row, col]] = candidate[[row, col]]; + } + } + } + let candidate = if validate_finite_symmetric(candidate, "omega second moment").is_ok() { + candidate + } else if validate_finite_symmetric(&relevant_candidate, "omega relevant second moment") + .is_ok() + { + &relevant_candidate + } else { + return Ok(CovarianceUpdateResult { + matrix: current.clone(), + status: CovarianceUpdateStatus::Rejected, + solved_target: None, + accepted_fraction: None, + attempted_fractions: Vec::new(), + trial_rejections: Vec::new(), + rejection_reason: Some( + CovarianceUpdateRejectionReason::CandidateNotFiniteSymmetric, + ), + }); + }; + let Ok(current_objective) = covariance_objective(current, candidate) else { + return Ok(CovarianceUpdateResult { + matrix: current.clone(), + status: CovarianceUpdateStatus::Rejected, + solved_target: None, + accepted_fraction: None, + attempted_fractions: Vec::new(), + trial_rejections: Vec::new(), + rejection_reason: Some( + CovarianceUpdateRejectionReason::CurrentObjectiveUnavailable, + ), + }); + }; + + // Solve each connected declared component independently. Dense all-free + // blocks use S as the exact unconstrained target before the outer + // floor, interpolation, SPD, and objective checks. Sparse all-free and + // mixed blocks use the deterministic constrained local GEM solver. + // Structural zeros between components never enter either solve. + let mut target = Array2::zeros((n, n)); + // Carry accepted component solutions only to remove irrelevant + // cross-component floating-point work from later component solves. + let mut gem_working = current.clone(); + for component in &components { + match component.kind { + OmegaComponentKind::AllFixed => { + copy_component_coordinates(&mut target, &self.initial, &component.fixed_coords); + } + OmegaComponentKind::DenseAllFree => { + copy_component_coordinates(&mut target, candidate, &component.free_coords); + } + OmegaComponentKind::SparseAllFree | OmegaComponentKind::Mixed => { + let Ok((gem_result, _)) = + self.local_gem_minimizer(&gem_working, candidate, &component.free_coords) + else { + return Ok(CovarianceUpdateResult { + matrix: current.clone(), + status: CovarianceUpdateStatus::Rejected, + solved_target: None, + accepted_fraction: None, + attempted_fractions: Vec::new(), + trial_rejections: Vec::new(), + rejection_reason: Some( + CovarianceUpdateRejectionReason::ConstrainedSolveFailed, + ), + }); + }; + copy_component_coordinates(&mut target, &gem_result, &component.free_coords); + copy_component_coordinates(&mut target, &self.initial, &component.fixed_coords); + copy_component_coordinates( + &mut gem_working, + &gem_result, + &component.free_coords, + ); + } + } + } + + // Capped covariate exploration applies the variance floor to the solved + // target before interpolation, so the floor cannot bypass the requested + // displacement fraction. The uncapped path retains the established + // floor-after-interpolation order used by non-covariate IIV and IOV. + // Every accepted trial must be strictly SPD and must not increase the + // covariance objective beyond the explicit matrix-arithmetic roundoff + // allowance in `objective_nonincrease`. + if floor_mode == VarianceFloorMode::CappedSolvedTarget { + for index in 0..n { + if self.estimated_mask[[index, index]] { + target[[index, index]] = target[[index, index]].max(minimum_variance); + } + } + } + let mut attempted_fractions = Vec::with_capacity(16); + let mut trial_rejections = Vec::with_capacity(16); + for attempt in 0..16 { + let fraction = maximum_fraction * 0.5_f64.powi(attempt); + attempted_fractions.push(fraction); + let mut updated = current.clone(); + for row in 0..n { + for col in 0..n { + if !self.structural_mask[[row, col]] { + updated[[row, col]] = 0.0; + } else if self.estimated_mask[[row, col]] { + updated[[row, col]] = if floor_mode == VarianceFloorMode::CappedSolvedTarget + && fraction == 1.0 + { + target[[row, col]] + } else { + current[[row, col]] + + fraction * (target[[row, col]] - current[[row, col]]) + }; + } else { + updated[[row, col]] = self.initial[[row, col]]; + } + } + } + match floor_mode { + VarianceFloorMode::LegacyAfterInterpolation => { + for index in 0..n { + if self.estimated_mask[[index, index]] { + updated[[index, index]] = updated[[index, index]].max(minimum_variance); + } + } + } + VarianceFloorMode::CappedSolvedTarget + if (0..n).any(|index| { + self.estimated_mask[[index, index]] + && updated[[index, index]] < minimum_variance + }) => + { + trial_rejections.push(CovarianceTrialRejectionReason::VarianceFloorInfeasible); + continue; + } + VarianceFloorMode::CappedSolvedTarget => {} + } + if cholesky_lower(&updated).is_err() { + trial_rejections.push(CovarianceTrialRejectionReason::NotPositiveDefinite); + continue; + } + let Ok(updated_objective) = covariance_objective(&updated, candidate) else { + trial_rejections.push(CovarianceTrialRejectionReason::ObjectiveUnavailable); + continue; + }; + if objective_nonincrease(updated_objective, current_objective, n) { + let changed = (0..n).any(|row| { + (row..n).any(|col| { + self.estimated_mask[[row, col]] + && updated[[row, col]] != current[[row, col]] + }) + }); + return Ok(CovarianceUpdateResult { + matrix: updated, + status: if changed { + CovarianceUpdateStatus::Accepted + } else { + CovarianceUpdateStatus::NoOp + }, + solved_target: Some(target), + accepted_fraction: Some(fraction), + attempted_fractions, + trial_rejections, + rejection_reason: None, + }); + } + trial_rejections.push(CovarianceTrialRejectionReason::ObjectiveIncrease); + } + + Ok(CovarianceUpdateResult { + matrix: current.clone(), + status: CovarianceUpdateStatus::Rejected, + solved_target: Some(target), + accepted_fraction: None, + attempted_fractions, + trial_rejections, + rejection_reason: Some(CovarianceUpdateRejectionReason::BacktrackingExhausted), + }) + } + + #[cfg(test)] + pub(crate) fn update( + &self, + current: &Array2, + candidate: &Array2, + minimum_variance: f64, + ) -> Result> { + Ok(self + .update_with_status(current, candidate, minimum_variance)? + .matrix) + } + + fn classify_components(&self) -> Vec { + let n = self.names.len(); + let mut component_id = vec![usize::MAX; n]; + let mut next_id = 0; + // BFS on structural_mask to find connected components. + for start in 0..n { + if component_id[start] != usize::MAX { + continue; + } + component_id[start] = next_id; + let mut pending = vec![start]; + while let Some(row) = pending.pop() { + for (col, id) in component_id.iter_mut().enumerate() { + if row != col && self.structural_mask[[row, col]] && *id == usize::MAX { + *id = next_id; + pending.push(col); + } + } + } + next_id += 1; + } + + (0..next_id) + .map(|id| { + let indices: Vec = (0..n).filter(|&idx| component_id[idx] == id).collect(); + let mut free_coords = Vec::new(); + let mut fixed_coords = Vec::new(); + let mut has_free = false; + let mut has_fixed = false; + let mut declared_count = 0usize; + for &ri in &indices { + for &ci in &indices { + if ri <= ci && self.structural_mask[[ri, ci]] { + declared_count += 1; + if self.estimated_mask[[ri, ci]] { + free_coords.push((ri, ci)); + has_free = true; + } else { + fixed_coords.push((ri, ci)); + has_fixed = true; + } + } + } + } + let kind = match (has_free, has_fixed) { + (false, true) => OmegaComponentKind::AllFixed, + (true, false) => { + // Dense iff every intra-component pair is declared. + let sz = indices.len(); + let dense = declared_count == sz * (sz + 1) / 2; + if dense { + OmegaComponentKind::DenseAllFree + } else { + OmegaComponentKind::SparseAllFree + } + } + (true, true) => OmegaComponentKind::Mixed, + (false, false) => unreachable!("empty component"), + }; + OmegaComponent { + kind, + indices, + free_coords, + fixed_coords, + } + }) + .collect() + } + + // ── Deterministic local GEM minimizer ───────────────────────────── + // + /// Minimise `f(Ω)=logdet(Ω)+tr(Ω⁻¹S)` locally over one component's + /// declared free symmetric coordinates. The negative gradient is + /// `Ω⁻¹(S-Ω)Ω⁻¹`; off-diagonal coordinates use the full symmetric basis. + /// Newton uses the analytic Hessian, with Fisher scoring only when that + /// Hessian is not strictly PD or its strict solve fails. Armijo therefore + /// uses `f(Ω+αp) <= f(Ω)-c α (-∇f)'p` with a positive directional decrease. + fn local_gem_minimizer( + &self, + current: &Array2, + second_moment: &Array2, + free_coords: &[(usize, usize)], + ) -> Result<(Array2, GemTrace)> { + validate_finite_symmetric(second_moment, "local GEM second moment")?; + if free_coords.is_empty() { + return Ok((current.clone(), GemTrace::default())); + } + + let mut omega = current.clone(); + self.restore_constraints(&mut omega); + let start_objective = covariance_objective(&omega, second_moment)?; + let mut objective = start_objective; + let mut trace = GemTrace::new(start_objective); + + // Dimensionless terminal tolerance: + // 4*ε^(2/3)*sqrt(max(1,m)). The factor four is a fixed dot/inverse + // operation roundoff budget. The rule depends only on binary64 machine + // precision and free-coordinate count and is tighter than frozen D0 + // relative accuracy at covariance-scale coordinates. + let terminal_tolerance = + 4.0 * f64::EPSILON.powf(2.0 / 3.0) * (free_coords.len().max(1) as f64).sqrt(); + + for _ in 0..512 { + let inverse = inverse_spd(&omega)?; + let score_matrix = inverse.dot(&(second_moment - &omega)).dot(&inverse); + let raw_score = free_coords + .iter() + .map(|&(row, col)| { + if row == col { + score_matrix[[row, col]] + } else { + score_matrix[[row, col]] + score_matrix[[col, row]] + } + }) + .collect::>(); + if raw_score.iter().any(|value| !value.is_finite()) { + bail!("local GEM produced a non-finite score"); + } + + // Overflow-safe coordinate scale d_k=sqrt(Ω_ii)*sqrt(Ω_jj). + let scales = free_coords + .iter() + .map(|&(row, col)| omega[[row, row]].sqrt() * omega[[col, col]].sqrt()) + .collect::>(); + if scales + .iter() + .any(|scale| !scale.is_finite() || *scale <= 0.0) + { + bail!("local GEM produced a non-finite coordinate scale"); + } + let scaled_score = raw_score + .iter() + .zip(&scales) + .map(|(score, scale)| score * scale) + .collect::>(); + if scaled_score.iter().any(|value| !value.is_finite()) { + bail!("local GEM produced a non-finite scaled score"); + } + let scaled_score_norm = scaled_score + .iter() + .map(|value| value * value) + .sum::() + .sqrt(); + + let hessian_scaled = scale_symmetric_matrix( + covariance_hessian(&inverse, second_moment, free_coords), + &scales, + "local GEM Hessian", + )?; + let hessian_pd = dense_is_positive_definite(&hessian_scaled); + + if scaled_score_norm <= terminal_tolerance { + certify_gem_terminal(hessian_pd, objective, start_objective, omega.nrows())?; + return Ok((omega, trace)); + } + + let information_scaled = scale_symmetric_matrix( + covariance_information(&inverse, free_coords), + &scales, + "local GEM Fisher information", + )?; + if !dense_is_positive_definite(&information_scaled) { + bail!("local GEM Fisher information is not strictly positive definite"); + } + + let (scaled_direction, used_newton) = if hessian_pd { + match solve_dense_strict(hessian_scaled.clone(), scaled_score.clone()) { + Ok(direction) => (direction, true), + Err(_) => ( + solve_dense_strict(information_scaled, scaled_score.clone())?, + false, + ), + } + } else { + ( + solve_dense_strict(information_scaled, scaled_score.clone())?, + false, + ) + }; + let directional_decrease = scaled_score + .iter() + .zip(&scaled_direction) + .map(|(score, direction)| score * direction) + .sum::(); + if !directional_decrease.is_finite() || directional_decrease <= 0.0 { + bail!("local GEM produced a non-descent direction"); + } + + if directional_decrease.sqrt() <= terminal_tolerance { + certify_gem_terminal(hessian_pd, objective, start_objective, omega.nrows())?; + return Ok((omega, trace)); + } + + // x=Dz, so a scaled direction maps back as p=D p_s. + let raw_direction = scaled_direction + .iter() + .zip(&scales) + .map(|(direction, scale)| direction * scale) + .collect::>(); + if raw_direction.iter().any(|value| !value.is_finite()) { + bail!("local GEM produced a non-finite raw direction"); + } + + let mut accepted = None; + for attempt in 0..64 { + let fraction = 0.5_f64.powi(attempt); + let mut trial = omega.clone(); + for (&(row, col), delta) in free_coords.iter().zip(&raw_direction) { + let value = omega[[row, col]] + fraction * delta; + trial[[row, col]] = value; + trial[[col, row]] = value; + } + self.restore_constraints(&mut trial); + let Ok(trial_objective) = covariance_objective(&trial, second_moment) else { + continue; + }; + if trial_objective < objective + && trial_objective <= objective - 1e-4 * fraction * directional_decrease + { + accepted = Some((trial, trial_objective)); + break; + } + } + let Some((trial, trial_objective)) = accepted else { + if certify_roundoff_stalled_newton_iterate( + hessian_pd, + objective, + start_objective, + omega.nrows(), + used_newton, + directional_decrease, + ) { + trace.newton_used = true; + return Ok((omega, trace)); + } + bail!( + "local GEM line search did not find a strict SPD descent step (scaled score norm {scaled_score_norm}, directional decrease {directional_decrease}, terminal tolerance {terminal_tolerance})" + ); + }; + trace.accept(trial_objective, used_newton); + omega = trial; + objective = trial_objective; + } + + bail!("local GEM did not converge in 512 iterations") + } + + fn restore_constraints(&self, matrix: &mut Array2) { + for row in 0..self.names.len() { + for col in 0..self.names.len() { + if !self.structural_mask[[row, col]] { + matrix[[row, col]] = 0.0; + } else if !self.estimated_mask[[row, col]] { + matrix[[row, col]] = self.initial[[row, col]]; + } + } + } + } + + pub(crate) fn has_estimated_entries(&self) -> bool { + self.estimated_mask.iter().any(|value| *value) + } + + pub(crate) fn structural_mask(&self) -> &Array2 { + &self.structural_mask + } + + pub(crate) fn estimated_mask(&self) -> &Array2 { + &self.estimated_mask + } +} + +#[derive(Debug, Clone, Default)] +struct GemTrace { + newton_used: bool, + fisher_used: bool, + #[cfg(test)] + objective_sequence: Vec, +} + +impl GemTrace { + #[allow(unused_variables)] + fn new(start_objective: f64) -> Self { + Self { + #[cfg(test)] + objective_sequence: vec![start_objective], + ..Self::default() + } + } + + #[allow(unused_variables)] + fn accept(&mut self, objective: f64, used_newton: bool) { + self.newton_used |= used_newton; + self.fisher_used |= !used_newton; + #[cfg(test)] + self.objective_sequence.push(objective); + } +} + +/// Post-hoc convergence diagnostics for the local GEM solver. +#[cfg(test)] +#[derive(Debug, Clone)] +pub(crate) struct CovarianceConvergenceMetrics { + pub gradient_norm: f64, + pub hessian_positive_definite: bool, + pub objective: f64, + pub convergence_threshold: f64, + pub newton_used: bool, + pub fisher_used: bool, + /// Initial objective followed by every accepted inner objective. + pub objective_sequence: Vec, +} + +#[cfg(test)] +impl ResolvedOmega { + pub(crate) fn local_gem_with_metrics( + &self, + current: &Array2, + second_moment: &Array2, + ) -> Result<(Array2, CovarianceConvergenceMetrics)> { + let all_free_coords = (0..self.names.len()) + .flat_map(|row| (row..self.names.len()).map(move |col| (row, col))) + .filter(|(row, col)| self.estimated_mask[[*row, *col]]) + .collect::>(); + let gem_components = self + .classify_components() + .into_iter() + .filter(|component| { + matches!( + component.kind, + OmegaComponentKind::SparseAllFree | OmegaComponentKind::Mixed + ) + }) + .collect::>(); + + let mut result = current.clone(); + let mut aggregate = GemTrace::new(covariance_objective(current, second_moment)?); + if gem_components.is_empty() { + result = self.update(current, second_moment, 0.0)?; + } else { + for component in gem_components { + let (component_result, component_trace) = + self.local_gem_minimizer(&result, second_moment, &component.free_coords)?; + result = component_result; + aggregate.newton_used |= component_trace.newton_used; + aggregate.fisher_used |= component_trace.fisher_used; + aggregate + .objective_sequence + .extend(component_trace.objective_sequence.into_iter().skip(1)); + } + } + + let inverse = inverse_spd(&result) + .map_err(|error| anyhow::anyhow!("converged omega is not SPD: {error}"))?; + let score_matrix = inverse.dot(&(second_moment - &result)).dot(&inverse); + let scales = all_free_coords + .iter() + .map(|&(row, col)| result[[row, row]].sqrt() * result[[col, col]].sqrt()) + .collect::>(); + let gradient_norm = all_free_coords + .iter() + .zip(&scales) + .map(|(&(row, col), scale)| { + let score = if row == col { + score_matrix[[row, col]] + } else { + score_matrix[[row, col]] + score_matrix[[col, row]] + }; + (score * scale).powi(2) + }) + .sum::() + .sqrt(); + let reduced_hessian = scale_symmetric_matrix( + covariance_hessian(&inverse, second_moment, &all_free_coords), + &scales, + "test local GEM Hessian", + )?; + let objective = covariance_objective(&result, second_moment)?; + let convergence_threshold = + 4.0 * f64::EPSILON.powf(2.0 / 3.0) * (all_free_coords.len().max(1) as f64).sqrt(); + Ok(( + result, + CovarianceConvergenceMetrics { + gradient_norm, + hessian_positive_definite: dense_is_positive_definite(&reduced_hessian), + objective, + convergence_threshold, + newton_used: aggregate.newton_used, + fisher_used: aggregate.fisher_used, + objective_sequence: aggregate.objective_sequence, + }, + )) + } +} + +fn copy_component_coordinates( + destination: &mut Array2, + source: &Array2, + coordinates: &[(usize, usize)], +) { + for &(row, col) in coordinates { + destination[[row, col]] = source[[row, col]]; + destination[[col, row]] = source[[row, col]]; + } +} + +fn objective_evaluation_roundoff_allowance( + candidate: f64, + current: f64, + dimension: usize, +) -> Option { + // A Cholesky/inverse/trace evaluation is O(n^3). The only equality slack + // admitted is 64*n^3 binary64 roundoffs at the objective's finite scale. + let relative_roundoffs = 64.0 * (dimension.max(1) as f64).powi(3) * f64::EPSILON; + let allowance = candidate.abs().max(current.abs()).max(1.0) * relative_roundoffs; + allowance.is_finite().then_some(allowance) +} + +fn objective_nonincrease(candidate: f64, current: f64, dimension: usize) -> bool { + if candidate <= current { + return true; + } + objective_evaluation_roundoff_allowance(candidate, current, dimension).is_some_and( + |allowance| (candidate - current).is_finite() && candidate - current <= allowance, + ) +} + +fn certify_roundoff_stalled_newton_iterate( + hessian_positive_definite: bool, + objective: f64, + start_objective: f64, + dimension: usize, + used_newton: bool, + objective_scale_newton_decrement: f64, +) -> bool { + hessian_positive_definite + && used_newton + && objective_nonincrease(objective, start_objective, dimension) + && objective_scale_newton_decrement.is_finite() + && objective_scale_newton_decrement >= 0.0 + && objective_evaluation_roundoff_allowance(objective, start_objective, dimension) + .is_some_and(|allowance| objective_scale_newton_decrement <= allowance) +} + +fn certify_gem_terminal( + hessian_positive_definite: bool, + objective: f64, + start_objective: f64, + dimension: usize, +) -> Result<()> { + if !hessian_positive_definite { + bail!( + "local GEM converged to a non-minimum stationary point: analytic Hessian is not strictly positive definite" + ); + } + if !objective_nonincrease(objective, start_objective, dimension) { + bail!( + "local GEM terminal objective increased relative to its initial objective ({objective} > {start_objective})" + ); + } + Ok(()) +} + +fn validate_finite_symmetric(matrix: &Array2, label: &str) -> Result<()> { + if matrix.nrows() != matrix.ncols() { + bail!("{label} must be square"); + } + for row in 0..matrix.nrows() { + for col in 0..matrix.ncols() { + if !matrix[[row, col]].is_finite() { + bail!("{label} must be finite"); + } + if row > col && matrix[[row, col]] != matrix[[col, row]] { + bail!("{label} must be symmetric"); + } + } + } + Ok(()) +} + +fn inverse_spd(matrix: &Array2) -> Result> { + let lower = cholesky_lower(matrix)?; + let n = matrix.nrows(); + let mut inverse = Array2::zeros((n, n)); + for column in 0..n { + let mut forward = vec![0.0; n]; + for row in 0..n { + let prior = (0..row) + .map(|index| lower[row][index] * forward[index]) + .sum::(); + forward[row] = (f64::from(row == column) - prior) / lower[row][row]; + } + let mut solution = vec![0.0; n]; + for row in (0..n).rev() { + let prior = ((row + 1)..n) + .map(|index| lower[index][row] * solution[index]) + .sum::(); + solution[row] = (forward[row] - prior) / lower[row][row]; + } + for row in 0..n { + inverse[[row, column]] = solution[row]; + } + } + if inverse.iter().any(|value| !value.is_finite()) { + bail!("local GEM inverse is non-finite"); + } + Ok(inverse) +} + +fn covariance_objective(omega: &Array2, second_moment: &Array2) -> Result { + let lower = cholesky_lower(omega)?; + let inverse = inverse_spd(omega)?; + let trace = (0..omega.nrows()) + .map(|row| { + (0..omega.ncols()) + .map(|col| inverse[[row, col]] * second_moment[[col, row]]) + .sum::() + }) + .sum::(); + let objective = cholesky_log_determinant(&lower) + trace; + if !objective.is_finite() { + bail!("local GEM objective is non-finite"); + } + Ok(objective) +} + +fn covariance_information(inverse: &Array2, coordinates: &[(usize, usize)]) -> Vec> { + let n = inverse.nrows(); + let basis = |row: usize, col: usize| { + Array2::from_shape_fn((n, n), |(left, right)| { + if (left == row && right == col) || (row != col && left == col && right == row) { + 1.0 + } else { + 0.0 + } + }) + }; + coordinates + .iter() + .map(|&(left_row, left_col)| { + let left = inverse.dot(&basis(left_row, left_col)); + coordinates + .iter() + .map(|&(right_row, right_col)| { + let product = left.dot(inverse).dot(&basis(right_row, right_col)); + (0..n).map(|index| product[[index, index]]).sum() + }) + .collect() + }) + .collect() +} + +fn covariance_hessian( + inverse: &Array2, + second_moment: &Array2, + coordinates: &[(usize, usize)], +) -> Vec> { + let n = inverse.nrows(); + let bases = coordinates + .iter() + .map(|&(row, col)| { + Array2::from_shape_fn((n, n), |(left, right)| { + if (left == row && right == col) || (row != col && left == col && right == row) { + 1.0 + } else { + 0.0 + } + }) + }) + .collect::>(); + bases + .iter() + .map(|left| { + bases + .iter() + .map(|right| { + let first = inverse.dot(right).dot(inverse).dot(left); + let second = first.dot(inverse).dot(second_moment); + let third = inverse + .dot(left) + .dot(inverse) + .dot(right) + .dot(inverse) + .dot(second_moment); + (0..n) + .map(|index| { + -first[[index, index]] + second[[index, index]] + third[[index, index]] + }) + .sum() + }) + .collect() + }) + .collect() +} + +fn scale_symmetric_matrix( + matrix: Vec>, + scales: &[f64], + label: &str, +) -> Result>> { + let n = scales.len(); + if matrix.len() != n || matrix.iter().any(|row| row.len() != n) { + bail!("{label} dimensions differ from the free-coordinate count"); + } + let mut scaled = vec![vec![0.0; n]; n]; + for row in 0..n { + for col in 0..n { + scaled[row][col] = matrix[row][col] * scales[row] * scales[col]; + if !scaled[row][col].is_finite() { + bail!("{label} is non-finite after coordinate scaling"); + } + } + } + symmetrize_roundoff_equivalent(scaled, label) +} + +fn symmetrize_roundoff_equivalent(mut matrix: Vec>, label: &str) -> Result>> { + let n = matrix.len(); + if matrix.iter().any(|row| row.len() != n) { + bail!("{label} must be square"); + } + let scale = matrix.iter().flatten().try_fold(0.0_f64, |scale, value| { + if value.is_finite() { + Ok(scale.max(value.abs())) + } else { + Err(anyhow::anyhow!("{label} must be finite")) + } + })?; + let asymmetry_allowance = scale * 64.0 * (n.max(1) as f64) * f64::EPSILON; + let mut row = 0; + while row < n { + let mut col = 0; + while col < row { + let left = matrix[row][col]; + let right = matrix[col][row]; + if (left - right).abs() > asymmetry_allowance { + bail!("{label} has material floating-point asymmetry"); + } + // Overflow-safe average; this changes only entries already proven + // equal within the explicit matrix-roundoff allowance. + let symmetric = 0.5 * left + 0.5 * right; + if !symmetric.is_finite() { + bail!("{label} symmetrization is non-finite"); + } + matrix[row][col] = symmetric; + matrix[col][row] = symmetric; + col += 1; + } + row += 1; + } + Ok(matrix) +} + +fn dense_is_positive_definite(matrix: &[Vec]) -> bool { + let n = matrix.len(); + if n == 0 { + return true; + } + if matrix.iter().any(|row| row.len() != n) { + return false; + } + let array = Array2::from_shape_fn((n, n), |(row, col)| matrix[row][col]); + cholesky_lower(&array).is_ok() +} + +#[cfg(test)] +fn hessian_is_positive_definite(hessian: &[Vec]) -> bool { + symmetrize_roundoff_equivalent(hessian.to_vec(), "local GEM Hessian") + .is_ok_and(|matrix| dense_is_positive_definite(&matrix)) +} + +fn solve_dense_strict(mut matrix: Vec>, mut rhs: Vec) -> Result> { + let n = rhs.len(); + if matrix.len() != n || matrix.iter().any(|row| row.len() != n) { + bail!("local GEM linear system dimensions differ"); + } + for pivot in 0..n { + let pivot_row = (pivot..n) + .max_by(|left, right| { + matrix[*left][pivot] + .abs() + .total_cmp(&matrix[*right][pivot].abs()) + }) + .ok_or_else(|| anyhow::anyhow!("local GEM pivot range is empty"))?; + matrix.swap(pivot, pivot_row); + rhs.swap(pivot, pivot_row); + let diagonal = matrix[pivot][pivot]; + if diagonal == 0.0 || !diagonal.is_finite() { + bail!("local GEM linear system is singular or non-finite"); + } + let pivot_values = matrix[pivot][pivot..].to_vec(); + for row in (pivot + 1)..n { + let factor = matrix[row][pivot] / diagonal; + if !factor.is_finite() { + bail!("local GEM linear solve is non-finite"); + } + for (entry, pivot_entry) in matrix[row][pivot..].iter_mut().zip(&pivot_values) { + *entry -= factor * pivot_entry; + } + rhs[row] -= factor * rhs[pivot]; + } + } + let mut solution = vec![0.0; n]; + for row in (0..n).rev() { + let prior = ((row + 1)..n) + .map(|col| matrix[row][col] * solution[col]) + .sum::(); + solution[row] = (rhs[row] - prior) / matrix[row][row]; + if !solution[row].is_finite() { + bail!("local GEM linear solve is non-finite"); + } + } + Ok(solution) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::model::Parameter; + + fn parameters() -> ParameterSpace { + [Parameter::log("ke"), Parameter::log("v")] + .into_iter() + .collect() + } + + #[test] + fn default_omega_is_estimated_diagonal_identity() { + let prior = ParametricPrior::new(parameters(), None, None).unwrap(); + + assert_eq!(prior.random_effect_names(), &["ke", "v"]); + assert_eq!(prior.omega(), &ndarray::array![[1.0, 0.0], [0.0, 1.0]]); + assert!(!prior.resolved_omega().structural_mask()[[0, 1]]); + assert!(!prior.resolved_omega().estimated_mask()[[0, 1]]); + } + + #[test] + fn diagonal_sd_overflow_is_rejected_before_multiplication() { + let error = ParametricPrior::new( + parameters(), + Some(Omega::diagonal_standard_deviations([ + ("ke", f64::MAX), + ("v", 1.0), + ])), + None, + ) + .unwrap_err() + .to_string(); + assert!(error.contains("would overflow its variance representation")); + } + + #[test] + fn explicit_omega_tracks_structural_and_fixed_entries() { + let omega = Omega::diagonal([("ke", 0.2)]) + .fixed_variance("v", 0.4) + .covariance("ke", "v", 0.1); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + + assert_eq!(prior.omega(), &ndarray::array![[0.2, 0.1], [0.1, 0.4]]); + assert!(prior.resolved_omega().estimated_mask()[[0, 0]]); + assert!(!prior.resolved_omega().estimated_mask()[[1, 1]]); + assert!(prior.resolved_omega().structural_mask()[[0, 1]]); + } + + #[test] + fn omega_update_preserves_fixed_values_and_structural_zeros() { + let omega = Omega::diagonal([("ke", 0.2)]).fixed_variance("v", 0.4); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let candidate = ndarray::array![[0.8, 0.3], [0.3, 2.0]]; + + let updated = prior + .resolved_omega() + .update(prior.omega(), &candidate, 1e-6) + .unwrap(); + + assert!((updated[[0, 0]] - 0.8).abs() < 1e-12); + assert!((updated[[1, 1]] - 0.4).abs() < 1e-12); + assert_eq!(updated[[0, 1]], 0.0); + assert_eq!(updated[[1, 0]], 0.0); + } + + #[test] + fn omega_update_preserves_fixed_covariance_and_positive_definiteness_jointly() { + let omega = Omega::diagonal([("ke", 0.2)]) + .fixed_variance("v", 0.4) + .fixed_covariance("ke", "v", 0.1); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let candidate = ndarray::array![[0.3, 0.1], [0.1, 0.4]]; + + let updated = prior + .resolved_omega() + .update(prior.omega(), &candidate, 1e-6) + .unwrap(); + + assert_eq!(updated[[0, 1]], 0.1); + assert_eq!(updated[[1, 0]], 0.1); + assert_eq!(updated[[1, 1]], 0.4); + assert!(cholesky_lower(&updated).is_ok()); + } + + #[test] + fn mixed_mask_under_relaxation_caps_only_the_free_profile_coordinate() { + let omega = Omega::new() + .variance("ke", 0.02) + .fixed_variance("v", 0.04) + .fixed_covariance("ke", "v", 0.012); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let current = prior.omega().clone(); + let second_moment = ndarray::array![[0.002, 0.0], [0.0, 0.04]]; + + let update = prior + .resolved_omega() + .update_with_status_and_max_fraction(¤t, &second_moment, 0.0, 0.1) + .unwrap(); + + // The unconstrained profile target is 0.0092, so a 0.1 accepted + // displacement from 0.02 is 0.01892. Fixed coordinates remain exact. + assert_eq!(update.status, CovarianceUpdateStatus::Accepted); + assert!((update.matrix[[0, 0]] - 0.01892).abs() <= 1e-10); + assert_eq!(update.matrix[[0, 1]], 0.012); + assert_eq!(update.matrix[[1, 0]], 0.012); + assert_eq!(update.matrix[[1, 1]], 0.04); + assert!(cholesky_lower(&update.matrix).is_ok()); + assert!( + covariance_objective(&update.matrix, &second_moment).unwrap() + <= covariance_objective(¤t, &second_moment).unwrap() + ); + } + + #[test] + fn mixed_mask_update_reports_bit_exact_capped_result() { + let omega = Omega::new() + .variance("ke", 0.02) + .fixed_variance("v", 0.04) + .fixed_covariance("ke", "v", 0.012); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let current = prior.omega().clone(); + let proposal = ndarray::array![[0.002, 0.0], [0.0, 0.04]]; + let update = prior + .resolved_omega() + .update_with_status_and_max_fraction(¤t, &proposal, 0.0, 0.1) + .unwrap(); + + let expected_matrix = ndarray::array![[0.01892_f64, 0.012], [0.012, 0.04]]; + for (actual, expected) in update.matrix.iter().zip(expected_matrix.iter()) { + assert_eq!(actual.to_bits(), expected.to_bits()); + } + assert_eq!(update.status, CovarianceUpdateStatus::Accepted); + assert_eq!(update.accepted_fraction, Some(0.1)); + assert_eq!(update.attempted_fractions, vec![0.1]); + assert!(update.trial_rejections.is_empty()); + assert!(update.rejection_reason.is_none()); + assert_eq!(current, prior.omega().clone()); + } + + #[test] + fn mixed_mask_two_by_two_matches_exact_profile_score_and_objective() { + let omega = Omega::new() + .variance("ke", 0.02) + .fixed_variance("v", 0.04) + .fixed_covariance("ke", "v", 0.012); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let second_moment = ndarray::array![[0.002, 0.0], [0.0, 0.04]]; + + let updated = prior + .resolved_omega() + .update(prior.omega(), &second_moment, 0.0) + .unwrap(); + let expected = 0.0092; + assert!((updated[[0, 0]] - expected).abs() <= 1e-10); + assert!((updated[[0, 0]] / expected - 1.0).abs() <= 1e-9); + assert_eq!(updated[[0, 1]], 0.012); + assert_eq!(updated[[1, 1]], 0.04); + + let inverse = inverse_spd(&updated).unwrap(); + let score = inverse.dot(&(&second_moment - &updated)).dot(&inverse); + assert!(score[[0, 0]].abs() <= 1e-10); + let objective = covariance_objective(&updated, &second_moment).unwrap(); + for delta in [-1e-5, 1e-5] { + let mut profile = updated.clone(); + profile[[0, 0]] += delta; + assert!(covariance_objective(&profile, &second_moment).unwrap() > objective); + } + } + + #[test] + fn mixed_mask_three_by_three_matches_exact_score_and_finite_difference_oracle() { + let parameters = [ + Parameter::log("a"), + Parameter::log("b"), + Parameter::log("c"), + ] + .into_iter() + .collect(); + let omega = Omega::new() + .variance("a", 0.2) + .variance("b", 0.4) + .fixed_variance("c", 0.4) + .fixed_covariance("a", "b", 0.04) + .fixed_covariance("a", "c", -0.02) + .covariance("b", "c", 0.03); + let prior = ParametricPrior::new(parameters, Some(omega), None).unwrap(); + let expected = ndarray::array![[0.3, 0.04, -0.02], [0.04, 0.5, 0.06], [-0.02, 0.06, 0.4]]; + // Construct S=Ω+ΩGΩ with G exactly zero on every free symmetric + // coordinate. Thus `expected` is an independent analytic stationary + // oracle while fixed-coordinate scores remain nonzero. + let fixed_score = ndarray::array![[0.0, 0.04, -0.03], [0.04, 0.0, 0.0], [-0.03, 0.0, 0.05]]; + let second_moment = &expected + expected.dot(&fixed_score).dot(&expected); + let updated = prior + .resolved_omega() + .update(prior.omega(), &second_moment, 0.0) + .unwrap(); + for row in 0..3 { + for col in 0..3 { + assert!( + (updated[[row, col]] - expected[[row, col]]).abs() <= 1e-10, + "updated={updated:?}, expected={expected:?}" + ); + } + } + + let inverse = inverse_spd(&updated).unwrap(); + let score = inverse.dot(&(&second_moment - &updated)).dot(&inverse); + for &(row, col) in &[(0, 0), (1, 1), (1, 2)] { + assert!(score[[row, col]].abs() <= 1e-10); + let step = 1e-6; + let mut plus = updated.clone(); + let mut minus = updated.clone(); + plus[[row, col]] += step; + minus[[row, col]] -= step; + if row != col { + plus[[col, row]] += step; + minus[[col, row]] -= step; + } + let finite_difference = (covariance_objective(&plus, &second_moment).unwrap() + - covariance_objective(&minus, &second_moment).unwrap()) + / (2.0 * step); + assert!(finite_difference.abs() <= 1e-9); + } + } + + #[test] + fn omega_update_accepts_finite_high_scale_positive_definite_candidate() { + let prior = ParametricPrior::new( + parameters(), + Some(Omega::diagonal([("ke", 0.2), ("v", 0.4)]).covariance("ke", "v", 0.1)), + None, + ) + .unwrap(); + let candidate = ndarray::array![[1e200, 5e199], [5e199, 1e200]]; + + let updated = prior + .resolved_omega() + .update(prior.omega(), &candidate, 1e-6) + .unwrap(); + + assert_eq!(updated, candidate); + assert!(cholesky_lower(&updated).is_ok()); + } + + #[test] + fn omega_update_rejects_non_finite_candidate_by_retaining_current_matrix() { + let prior = ParametricPrior::new(parameters(), None, None).unwrap(); + let current = prior.omega().clone(); + let candidate = ndarray::array![[f64::NAN, 0.0], [0.0, f64::INFINITY]]; + + let updated = prior + .resolved_omega() + .update(¤t, &candidate, 1e-6) + .unwrap(); + + assert_eq!(updated, current); + } + + #[test] + fn covariance_update_status_reports_accepted_change() { + let prior = ParametricPrior::new(parameters(), None, None).unwrap(); + let update = prior + .resolved_omega() + .update_with_status(prior.omega(), &ndarray::array![[0.8, 0.0], [0.0, 1.5]], 0.0) + .unwrap(); + + assert_eq!(update.status, CovarianceUpdateStatus::Accepted); + assert_eq!(update.matrix, ndarray::array![[0.8, 0.0], [0.0, 1.5]]); + assert_eq!( + update.solved_target, + Some(ndarray::array![[0.8, 0.0], [0.0, 1.5]]) + ); + assert_eq!(update.accepted_fraction, Some(1.0)); + assert_eq!(update.attempted_fractions, vec![1.0]); + assert!(update.trial_rejections.is_empty()); + assert!(update.rejection_reason.is_none()); + } + + #[test] + fn covariance_update_status_reports_all_fixed_no_op() { + let prior = ParametricPrior::new( + parameters(), + Some( + Omega::new() + .fixed_variance("ke", 0.2) + .fixed_variance("v", 0.4), + ), + None, + ) + .unwrap(); + let update = prior + .resolved_omega() + .update_with_status(prior.omega(), &ndarray::array![[0.8, 0.3], [0.3, 2.0]], 0.0) + .unwrap(); + + assert_eq!(update.status, CovarianceUpdateStatus::NoOp); + assert_eq!(&update.matrix, prior.omega()); + assert!(update.solved_target.is_none()); + assert_eq!(update.accepted_fraction, None); + assert!(update.attempted_fractions.is_empty()); + assert!(update.trial_rejections.is_empty()); + assert!(update.rejection_reason.is_none()); + } + + #[test] + fn covariance_update_status_ignores_irrelevant_cross_component_candidate() { + let prior = ParametricPrior::new(parameters(), None, None).unwrap(); + let update = prior + .resolved_omega() + .update_with_status( + prior.omega(), + &ndarray::array![[1.0, 0.75], [0.75, 1.0]], + 0.0, + ) + .unwrap(); + + assert_eq!(update.status, CovarianceUpdateStatus::NoOp); + assert_eq!(&update.matrix, prior.omega()); + } + + #[test] + fn explicit_omega_rejects_missing_or_unknown_variances() { + let missing = + ParametricPrior::new(parameters(), Some(Omega::diagonal([("ke", 0.2)])), None) + .unwrap_err(); + assert!(missing + .to_string() + .contains("omega variance for random effect 'v' is not declared")); + + let unknown = ParametricPrior::new( + parameters(), + Some(Omega::diagonal([("ke", 0.2), ("v", 0.4)]).covariance("ke", "ka", 0.1)), + None, + ) + .unwrap_err(); + assert!(unknown + .to_string() + .contains("not a declared IIV random effect")); + } + + #[test] + fn iov_resolves_against_model_parameters_independently_of_iiv() { + let parameters = [ + Parameter::log("ke").without_random_effect(), + Parameter::log("v"), + ] + .into_iter() + .collect(); + let prior = + ParametricPrior::new(parameters, None, Some(Iov::diagonal([("ke", 0.3)]))).unwrap(); + + assert_eq!( + prior.iov_effect_names(), + Some(["ke".to_string()].as_slice()) + ); + assert_eq!(prior.omega_iov(), Some(&ndarray::array![[0.3]])); + assert_eq!(prior.resolved_iov().unwrap().parameter_indices(), &[0]); + } + + #[test] + fn explicit_omega_must_be_positive_definite() { + let error = ParametricPrior::new( + parameters(), + Some(Omega::diagonal([("ke", 0.2), ("v", 0.4)]).covariance("ke", "v", 1.0)), + None, + ) + .unwrap_err(); + + assert_eq!(error.to_string(), "omega must be positive definite"); + } + + // ── PD Hessian and stationary-point certification ────────────────── + + #[test] + fn local_gem_certifies_pd_hessian_at_convergence() { + // Two free coordinates (ke, v) + fixed covariance. The exact solution + // has a PD 2×2 Hessian by construction; verify the certification passes. + let omega = Omega::new() + .variance("ke", 0.2) + .variance("v", 0.4) + .fixed_covariance("ke", "v", 0.05); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + // S = Ω at the solution: score vanishes, Ω = S simplifies the Hessian. + let second_moment = ndarray::array![[0.2, 0.05], [0.05, 0.4]]; + let current = prior.omega().clone(); + let (result, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &second_moment) + .unwrap(); + assert!(metrics.hessian_positive_definite); + assert!(metrics.gradient_norm <= 1e-8); + assert!(metrics.gradient_norm <= metrics.convergence_threshold); + // The result must match the exact solution. + assert!((result[[0, 0]] - 0.2).abs() <= 1e-10); + assert!((result[[1, 1]] - 0.4).abs() <= 1e-10); + assert!((result[[0, 1]] - 0.05).abs() <= 1e-10); + } + + #[test] + fn roundoff_stalled_mixed_mask_newton_iterate_is_certified() { + let parameters = [ + Parameter::log("ke"), + Parameter::log("v"), + Parameter::log("bio"), + ] + .into_iter() + .collect(); + let current_a = f64::from_bits(0x3fb3_ae45_0de5_60fe); + let prior = ParametricPrior::new( + parameters, + Some( + Omega::new() + .variance("ke", current_a) + .fixed_variance("v", 0.04) + .fixed_variance("bio", 0.03) + .fixed_covariance("ke", "v", 0.012), + ), + None, + ) + .unwrap(); + let proposal = ndarray::array![ + [ + f64::from_bits(0x3fb3_cdaf_fb74_e0a1), + f64::from_bits(0x3f89_6735_4993_5f80), + f64::from_bits(0xbf80_be7b_bd4e_b876), + ], + [ + f64::from_bits(0x3f89_6735_4993_5f80), + f64::from_bits(0x3fa1_40d8_5314_f738), + f64::from_bits(0xbf62_706b_63ca_bde0), + ], + [ + f64::from_bits(0xbf80_be7b_bd4e_b876), + f64::from_bits(0xbf62_706b_63ca_bde0), + f64::from_bits(0x3f9d_9f0e_1372_9e0b), + ], + ]; + let fixed_ratio = 0.012 / 0.04; + let analytic_target = 0.0036 + proposal[[0, 0]] - 2.0 * fixed_ratio * proposal[[0, 1]] + + fixed_ratio * fixed_ratio * proposal[[1, 1]]; + assert_eq!(analytic_target.to_bits(), 0x3fb3_98a2_6afb_8fc1); + + let current = prior.omega().clone(); + let (local_target, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &proposal) + .unwrap(); + assert_eq!(local_target[[0, 0]].to_bits(), 0x3fb3_98a2_6a73_c4bb); + assert!(metrics.gradient_norm > metrics.convergence_threshold); + assert!(metrics.hessian_positive_definite); + assert!(metrics.newton_used); + + let update = prior + .resolved_omega() + .update_with_status_and_max_fraction(¤t, &proposal, 1e-6, 0.1) + .unwrap(); + assert_eq!(update.status, CovarianceUpdateStatus::Accepted); + assert_eq!(update.accepted_fraction, Some(0.1)); + assert_eq!( + update.solved_target.as_ref().unwrap()[[0, 0]].to_bits(), + 0x3fb3_98a2_6a73_c4bb + ); + assert_eq!(update.matrix[[0, 0]].to_bits(), 0x3fb3_ac1b_30c0_6af7); + } + + #[test] + fn roundoff_stall_certification_rejects_a_nonstationary_or_unsafe_iterate() { + let objective = -6.5; + let start_objective = -6.4; + let allowance = + objective_evaluation_roundoff_allowance(objective, start_objective, 3).unwrap(); + assert!(certify_roundoff_stalled_newton_iterate( + true, + objective, + start_objective, + 3, + true, + allowance, + )); + assert!(!certify_roundoff_stalled_newton_iterate( + true, + objective, + start_objective, + 3, + true, + 2.0 * allowance, + )); + assert!(!certify_roundoff_stalled_newton_iterate( + false, + objective, + start_objective, + 3, + true, + allowance, + )); + assert!(!certify_roundoff_stalled_newton_iterate( + true, + objective, + start_objective, + 3, + false, + allowance, + )); + assert!(!certify_roundoff_stalled_newton_iterate( + true, + start_objective + 2.0 * allowance, + start_objective, + 3, + true, + allowance, + )); + } + + #[test] + fn three_by_three_mixed_mask_has_pd_hessian_at_exact_solution() { + let parameters = [ + Parameter::log("a"), + Parameter::log("b"), + Parameter::log("c"), + ] + .into_iter() + .collect(); + let omega = Omega::new() + .variance("a", 0.3) + .variance("b", 0.5) + .fixed_variance("c", 0.4) + .fixed_covariance("a", "b", 0.04) + .fixed_covariance("a", "c", -0.02) + .covariance("b", "c", 0.06); + let prior = ParametricPrior::new(parameters, Some(omega), None).unwrap(); + // S = Ω at the solution. + let second_moment = + ndarray::array![[0.3, 0.04, -0.02], [0.04, 0.5, 0.06], [-0.02, 0.06, 0.4]]; + let current = prior.omega().clone(); + let (_result, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &second_moment) + .unwrap(); + // Gradient must vanish and Hessian must be PD. + assert!(metrics.gradient_norm <= 1e-8); + assert!(metrics.hessian_positive_definite); + } + + #[test] + fn stationary_nonminimum_zero_score_rejected() { + // With fixed unit variances and only the covariance free, c=0 is + // stationary for S=0.2I, but f''(0)=-2+2tr(S)=-1.2 < 0. + let omega = Omega::new() + .fixed_variance("ke", 1.0) + .fixed_variance("v", 1.0) + .covariance("ke", "v", 0.0); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let current = prior.omega().clone(); + let second_moment = ndarray::array![[0.2, 0.0], [0.0, 0.2]]; + let error = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &second_moment) + .unwrap_err(); + assert!(error.to_string().contains("non-minimum stationary point")); + + // Production update follows the explicit reject-and-retain-current path. + let retained = prior + .resolved_omega() + .update(¤t, &second_moment, 0.0) + .unwrap(); + assert_eq!(retained, current); + } + + // ── Scale-invariant convergence ──────────────────────────────────── + + #[test] + fn scaled_problem_converges_with_same_iteration_budget() { + // The covariance M-step target is scale-equivariant: scaling S and + // current by the same factor preserves the GEM iteration behaviour. + // For the DenseAllFree component, the target = S directly; for mixed + // components, the GEM converges to the same relative optimum. + // Verify that the convergence threshold (epsilon-only) is independent + // of the objective magnitude. + let omega = Omega::new() + .variance("ke", 1.0) + .variance("v", 1.0) + .covariance("ke", "v", 0.3); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let base = ndarray::array![[0.8, 0.24], [0.24, 1.2]]; + for scale in [1.0, 100.0, 0.01_f64] { + let second_moment = &base * scale; + let current = &prior.omega().clone() * scale; + let (_result, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &second_moment) + .unwrap(); + // Convergence must be reached. The threshold is epsilon-only. + assert!( + metrics.gradient_norm <= metrics.convergence_threshold, + "scale={scale}: gradient_norm={} > threshold={}", + metrics.gradient_norm, + metrics.convergence_threshold + ); + assert!(metrics.hessian_positive_definite); + } + } + + // ── Component dispatch preserving legacy all-free behavior ───────── + + #[test] + fn all_free_diagonal_bypasses_gem_and_preserves_candidate_exactly() { + // All-free Omega with no fixed entries must return the candidate + // unchanged — no GEM optimisation runs. + let omega = Omega::diagonal([("ke", 0.2), ("v", 0.4)]); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let candidate = ndarray::array![[0.8, 0.0], [0.0, 1.5]]; + let result = prior + .resolved_omega() + .update(prior.omega(), &candidate, 1e-6) + .unwrap(); + // All-free dispatch returns candidate unchanged (no GEM, no mixed mask). + assert_eq!(result, candidate); + } + + #[test] + fn all_free_correlated_bypasses_gem_and_returns_candidate() { + let omega = Omega::diagonal([("ke", 0.2), ("v", 0.4)]).covariance("ke", "v", 0.1); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let candidate = ndarray::array![[0.5, 0.15], [0.15, 0.7]]; + let result = prior + .resolved_omega() + .update(prior.omega(), &candidate, 1e-6) + .unwrap(); + assert_eq!(result, candidate); + } + + #[test] + fn mixed_mask_dispatch_uses_constrained_gem_not_legacy_path() { + let omega = Omega::new().variance("ke", 0.2).fixed_variance("v", 0.4); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let candidate = ndarray::array![[0.8, 0.0], [0.0, 0.4]]; + let result = prior + .resolved_omega() + .update(prior.omega(), &candidate, 1e-6) + .unwrap(); + // Free variance (ke) is updated; fixed variance (v) stays at 0.4. + assert!((result[[0, 0]] - 0.8).abs() <= 1e-10); + assert!((result[[1, 1]] - 0.4).abs() <= 1e-10); + assert_eq!(result[[0, 1]], 0.0); + } + + // ── Objective nonincrease ────────────────────────────────────────── + + #[test] + fn gem_objective_does_not_increase_at_any_step() { + // This distant mixed-mask start has an indefinite analytic Hessian + // initially (Fisher fallback) and a PD Hessian later (Newton). + let omega = Omega::new() + .fixed_variance("ke", 1.0) + .fixed_variance("v", 1.0) + .covariance("ke", "v", 0.0); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let second_moment = ndarray::array![[0.2, 0.1], [0.1, 0.2]]; + let current = prior.omega().clone(); + let (result, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &second_moment) + .unwrap(); + assert!( + metrics.fisher_used, + "fixture did not exercise Fisher fallback" + ); + assert!( + metrics.newton_used, + "fixture did not exercise Newton scoring" + ); + assert!(metrics + .objective_sequence + .windows(2) + .all(|pair| pair[1] <= pair[0])); + assert!(metrics.objective < metrics.objective_sequence[0]); + assert!(cholesky_lower(&result).is_ok()); + } + + // ── Derivative analytic correctness ──────────────────────────────── + + #[test] + fn analytic_score_matches_finite_difference_to_machine_precision() { + // For the one-free-coordinate case, the analytic score formula + // must match central finite differences within D0 tolerance. + let omega = Omega::new() + .variance("ke", 0.3) + .fixed_variance("v", 0.4) + .fixed_covariance("ke", "v", 0.06); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let second_moment = ndarray::array![[0.25, 0.06], [0.06, 0.4]]; + let current = prior.omega().clone(); + let inverse = inverse_spd(¤t).unwrap(); + let score_matrix = inverse.dot(&(&second_moment - ¤t)).dot(&inverse); + let analytic = score_matrix[[0, 0]]; + let step = 1e-6; + let mut plus = current.clone(); + let mut minus = current.clone(); + plus[[0, 0]] += step; + minus[[0, 0]] -= step; + let finite = (covariance_objective(&plus, &second_moment).unwrap() + - covariance_objective(&minus, &second_moment).unwrap()) + / (2.0 * step); + assert!((analytic + finite).abs() <= 1e-9); + } + + #[test] + fn analytic_gradient_and_hessian_match_independent_central_differences() { + let current = ndarray::array![[0.7, 0.08, -0.04], [0.08, 0.9, 0.11], [-0.04, 0.11, 0.6]]; + let second_moment = + ndarray::array![[0.5, -0.03, 0.07], [-0.03, 1.1, 0.02], [0.07, 0.02, 0.8]]; + let coordinates = (0..3) + .flat_map(|row| (row..3).map(move |col| (row, col))) + .collect::>(); + let inverse = inverse_spd(¤t).unwrap(); + let score_matrix = inverse.dot(&(&second_moment - ¤t)).dot(&inverse); + let negative_gradient = coordinates + .iter() + .map(|&(row, col)| { + if row == col { + score_matrix[[row, col]] + } else { + score_matrix[[row, col]] + score_matrix[[col, row]] + } + }) + .collect::>(); + let hessian = covariance_hessian(&inverse, &second_moment, &coordinates); + let objective_step = 2e-6; + let score_step = 2e-6; + + for (column, &(row, col)) in coordinates.iter().enumerate() { + let mut plus = current.clone(); + let mut minus = current.clone(); + plus[[row, col]] += objective_step; + minus[[row, col]] -= objective_step; + if row != col { + plus[[col, row]] += objective_step; + minus[[col, row]] -= objective_step; + } + let objective_derivative = (covariance_objective(&plus, &second_moment).unwrap() + - covariance_objective(&minus, &second_moment).unwrap()) + / (2.0 * objective_step); + assert!( + (objective_derivative + negative_gradient[column]).abs() <= 2e-8, + "gradient coordinate {column}: analytic={}, finite={objective_derivative}", + -negative_gradient[column] + ); + + let score = |delta: f64| { + let mut perturbed = current.clone(); + perturbed[[row, col]] += delta; + if row != col { + perturbed[[col, row]] += delta; + } + let inverse = inverse_spd(&perturbed).unwrap(); + let matrix = inverse.dot(&(&second_moment - &perturbed)).dot(&inverse); + coordinates + .iter() + .map(|&(score_row, score_col)| { + if score_row == score_col { + matrix[[score_row, score_col]] + } else { + matrix[[score_row, score_col]] + matrix[[score_col, score_row]] + } + }) + .collect::>() + }; + let plus_score = score(score_step); + let minus_score = score(-score_step); + for row_index in 0..coordinates.len() { + let finite = -(plus_score[row_index] - minus_score[row_index]) / (2.0 * score_step); + assert!( + (hessian[row_index][column] - finite).abs() <= 2e-7, + "H[{row_index}][{column}] analytic={} finite={finite}", + hessian[row_index][column] + ); + } + } + } + + #[test] + fn convergence_metrics_instrumentation_is_consistent() { + // The test-only instrumentation wrapper must report metrics consistent + // with independently computed values at the returned point. + let omega = Omega::new() + .variance("ke", 0.5) + .fixed_variance("v", 0.5) + .covariance("ke", "v", 0.2); + let prior = ParametricPrior::new(parameters(), Some(omega), None).unwrap(); + let second_moment = ndarray::array![[0.3, 0.2], [0.2, 0.5]]; + let current = prior.omega().clone(); + let (result, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(¤t, &second_moment) + .unwrap(); + // Independently compute the objective. + let independent_objective = covariance_objective(&result, &second_moment).unwrap(); + assert!((metrics.objective - independent_objective).abs() <= 1e-12); + // Hessian PD flag must match direct check. + let inverse = inverse_spd(&result).unwrap(); + let coords: Vec<_> = (0..2) + .flat_map(|r| (r..2).map(move |c| (r, c))) + .filter(|(r, c)| prior.resolved_omega().estimated_mask()[[*r, *c]]) + .collect(); + let direct_hessian = covariance_hessian(&inverse, &second_moment, &coords); + assert_eq!( + metrics.hessian_positive_definite, + hessian_is_positive_definite(&direct_hessian) + ); + } + + #[test] + fn sparse_and_mixed_gem_are_unit_rescaling_equivariant() { + let sparse_parameters = || { + [ + Parameter::log("a"), + Parameter::log("b"), + Parameter::log("c"), + ] + .into_iter() + .collect() + }; + let sparse_second = ndarray::array![[0.7, 0.05, 0.3], [0.05, 1.1, 0.08], [0.3, 0.08, 0.9]]; + let mixed_second = ndarray::array![[0.18, 0.025], [0.025, 0.5]]; + let mut sparse_reference: Option> = None; + let mut mixed_reference: Option> = None; + for scale in [0.01, 1.0, 100.0] { + let sparse = ParametricPrior::new( + sparse_parameters(), + Some( + Omega::diagonal([("a", 1.0 * scale), ("b", 0.8 * scale), ("c", 1.2 * scale)]) + .covariance("a", "b", 0.15 * scale) + .covariance("b", "c", 0.10 * scale), + ), + None, + ) + .unwrap(); + assert_eq!( + sparse.resolved_omega().classify_components()[0].kind, + OmegaComponentKind::SparseAllFree + ); + let (sparse_result, sparse_metrics) = sparse + .resolved_omega() + .local_gem_with_metrics(sparse.omega(), &(&sparse_second * scale)) + .unwrap(); + assert!(sparse_metrics.newton_used || sparse_metrics.fisher_used); + let normalized_sparse = sparse_result / scale; + if let Some(reference) = &sparse_reference { + assert!((&normalized_sparse - reference) + .iter() + .all(|difference| difference.abs() <= 2e-9)); + } else { + sparse_reference = Some(normalized_sparse); + } + + let mixed = ParametricPrior::new( + parameters(), + Some( + Omega::new() + .variance("ke", 0.3 * scale) + .fixed_variance("v", 0.5 * scale) + .fixed_covariance("ke", "v", 0.04 * scale), + ), + None, + ) + .unwrap(); + assert_eq!( + mixed.resolved_omega().classify_components()[0].kind, + OmegaComponentKind::Mixed + ); + let (mixed_result, mixed_metrics) = mixed + .resolved_omega() + .local_gem_with_metrics(mixed.omega(), &(&mixed_second * scale)) + .unwrap(); + assert!(mixed_metrics.newton_used || mixed_metrics.fisher_used); + let normalized_mixed = mixed_result / scale; + if let Some(reference) = &mixed_reference { + assert!((&normalized_mixed - reference) + .iter() + .all(|difference| difference.abs() <= 2e-9)); + } else { + mixed_reference = Some(normalized_mixed); + } + } + } + + #[test] + fn connected_sparse_gem_beats_legacy_masked_second_moment_and_satisfies_score() { + let parameters = [ + Parameter::log("a"), + Parameter::log("b"), + Parameter::log("c"), + ] + .into_iter() + .collect(); + let prior = ParametricPrior::new( + parameters, + Some( + Omega::diagonal([("a", 1.0), ("b", 0.8), ("c", 1.2)]) + .covariance("a", "b", 0.15) + .covariance("b", "c", 0.10), + ), + None, + ) + .unwrap(); + let second_moment = ndarray::array![[0.7, 0.05, 0.3], [0.05, 1.1, 0.08], [0.3, 0.08, 0.9]]; + let (result, metrics) = prior + .resolved_omega() + .local_gem_with_metrics(prior.omega(), &second_moment) + .unwrap(); + let mut legacy_masked = second_moment.clone(); + legacy_masked[[0, 2]] = 0.0; + legacy_masked[[2, 0]] = 0.0; + assert!(covariance_objective(&legacy_masked, &second_moment).unwrap() > metrics.objective); + assert!(metrics.gradient_norm <= metrics.convergence_threshold); + assert!(result.diag().iter().all(|variance| *variance > 1e-8)); + assert!(metrics + .objective_sequence + .windows(2) + .all(|pair| pair[1] <= pair[0])); + } + + #[test] + fn disconnected_mixed_components_ignore_cross_component_second_moments() { + let four_parameters = [ + Parameter::log("a"), + Parameter::log("b"), + Parameter::log("c"), + Parameter::log("d"), + ] + .into_iter() + .collect(); + let declaration = Omega::new() + .variance("a", 0.3) + .fixed_variance("b", 0.5) + .fixed_covariance("a", "b", 0.04) + .variance("c", 0.6) + .fixed_variance("d", 0.9) + .fixed_covariance("c", "d", -0.05); + let prior = ParametricPrior::new(four_parameters, Some(declaration), None).unwrap(); + let second_moment = ndarray::array![ + [0.18, 0.025, 0.30, -0.20], + [0.025, 0.5, 0.10, 0.15], + [0.30, 0.10, 0.45, -0.03], + [-0.20, 0.15, -0.03, 0.9] + ]; + let (direct_result, direct_metrics) = prior + .resolved_omega() + .local_gem_with_metrics(prior.omega(), &second_moment) + .unwrap(); + let initial_objective = covariance_objective(prior.omega(), &second_moment).unwrap(); + assert!( + direct_metrics.objective <= initial_objective, + "direct={} initial={initial_objective}, result={direct_result:?}", + direct_metrics.objective + ); + let result = prior + .resolved_omega() + .update(prior.omega(), &second_moment, 0.0) + .unwrap(); + for row in 0..2 { + for col in 2..4 { + assert_eq!(result[[row, col]], 0.0); + assert_eq!(result[[col, row]], 0.0); + } + } + let first = ParametricPrior::new( + parameters(), + Some( + Omega::new() + .variance("ke", 0.3) + .fixed_variance("v", 0.5) + .fixed_covariance("ke", "v", 0.04), + ), + None, + ) + .unwrap(); + let expected_first = first + .resolved_omega() + .update( + first.omega(), + &second_moment.slice(ndarray::s![0..2, 0..2]).to_owned(), + 0.0, + ) + .unwrap(); + let difference = (result[[0, 0]] - expected_first[[0, 0]]).abs(); + let relative = difference / expected_first[[0, 0]].abs().max(f64::MIN_POSITIVE); + assert!( + difference <= 1e-10 || relative <= 1e-9, + "disconnected={}, independent={}, abs={difference}, rel={relative}", + result[[0, 0]], + expected_first[[0, 0]] + ); + assert_eq!(result[[0, 1]], expected_first[[0, 1]]); + } + + #[test] + fn covariance_update_status_reports_mixed_nonfinite_rejection() { + let prior = ParametricPrior::new( + parameters(), + Some( + Omega::new() + .variance("ke", 0.3) + .fixed_variance("v", 0.5) + .fixed_covariance("ke", "v", 0.04), + ), + None, + ) + .unwrap(); + let current = prior.omega().clone(); + let candidate = ndarray::array![[f64::NAN, 0.04], [0.04, 0.5]]; + let update = prior + .resolved_omega() + .update_with_status(¤t, &candidate, 0.0) + .unwrap(); + assert_eq!(update.status, CovarianceUpdateStatus::Rejected); + assert_eq!(update.matrix, current); + assert_eq!( + update.rejection_reason, + Some(CovarianceUpdateRejectionReason::CandidateNotFiniteSymmetric) + ); + assert!(update.solved_target.is_none()); + assert!(update.attempted_fractions.is_empty()); + assert!(update.trial_rejections.is_empty()); + } + + #[test] + fn covariance_update_fraction_under_relaxes_the_accepted_iterate() { + let one_parameter: ParameterSpace = + [Parameter::log("x")].into_iter().collect(); + let prior = + ParametricPrior::new(one_parameter, Some(Omega::diagonal([("x", 1.0)])), None).unwrap(); + let current = ndarray::array![[1.0]]; + let candidate = ndarray::array![[4.0]]; + + let full = prior + .resolved_omega() + .update_with_status(¤t, &candidate, 0.0) + .unwrap(); + let limited = prior + .resolved_omega() + .update_with_status_and_max_fraction(¤t, &candidate, 0.0, 0.1) + .unwrap(); + + assert_eq!(full.status, CovarianceUpdateStatus::Accepted); + assert_eq!(full.matrix, candidate); + assert_eq!(limited.status, CovarianceUpdateStatus::Accepted); + assert!((limited.matrix[[0, 0]] - 1.3).abs() <= 1e-12); + assert!( + covariance_objective(&limited.matrix, &candidate).unwrap() + <= covariance_objective(¤t, &candidate).unwrap() + ); + } + + #[test] + fn uncapped_update_preserves_legacy_floor_after_backtracking() { + let three_parameters: ParameterSpace = [ + Parameter::log("x"), + Parameter::log("y"), + Parameter::log("z"), + ] + .into_iter() + .collect(); + let declaration = Omega::new() + .variance("x", 1.0) + .variance("y", 1.0) + .variance("z", 1.0) + .covariance("x", "y", 0.0); + let prior = ParametricPrior::new(three_parameters, Some(declaration), None).unwrap(); + let current = ndarray::Array2::eye(3); + let candidate = ndarray::array![[1.0, 1.0, 0.0], [1.0, 1.0, 0.0], [0.0, 0.0, 0.01]]; + + let legacy = prior + .resolved_omega() + .update_with_status(¤t, &candidate, 0.1) + .unwrap(); + let capped = prior + .resolved_omega() + .update_with_status_and_max_fraction(¤t, &candidate, 0.1, 1.0) + .unwrap(); + + assert_eq!(legacy.status, CovarianceUpdateStatus::Accepted); + assert_eq!(capped.status, CovarianceUpdateStatus::Accepted); + assert!((legacy.matrix[[0, 1]] - 0.5).abs() <= 1e-12); + assert!((legacy.matrix[[2, 2]] - 0.505).abs() <= 1e-12); + assert!((capped.matrix[[2, 2]] - 0.55).abs() <= 1e-12); + } + + #[test] + fn covariance_floor_cannot_bypass_the_displacement_fraction() { + let one_parameter: ParameterSpace = + [Parameter::log("x")].into_iter().collect(); + let prior = ParametricPrior::new(one_parameter, Some(Omega::diagonal([("x", 0.01)])), None) + .unwrap(); + let current = ndarray::array![[0.01]]; + let candidate = ndarray::array![[0.2]]; + + let update = prior + .resolved_omega() + .update_with_status_and_max_fraction(¤t, &candidate, 0.1, 0.1) + .unwrap(); + + // Reaching the 0.1 floor would require a displacement larger than 0.1 + // of the solved-target displacement. Reject rather than bypass the cap. + assert_eq!(update.status, CovarianceUpdateStatus::Rejected); + assert_eq!(update.matrix, current); + } + + #[test] + fn covariance_update_status_reports_floor_rejection_when_candidate_equals_current() { + let one_parameter: ParameterSpace = + [Parameter::log("x")].into_iter().collect(); + let prior = + ParametricPrior::new(one_parameter, Some(Omega::diagonal([("x", 1.0)])), None).unwrap(); + let improving = prior + .resolved_omega() + .update_with_status(&ndarray::array![[1.0]], &ndarray::array![[0.01]], 0.1) + .unwrap(); + assert_eq!(improving.status, CovarianceUpdateStatus::Accepted); + assert_eq!(improving.matrix, ndarray::array![[0.1]]); + + let current = ndarray::array![[0.01]]; + let rejected = prior + .resolved_omega() + .update_with_status(¤t, ¤t, 0.1) + .unwrap(); + + assert_eq!(rejected.status, CovarianceUpdateStatus::Rejected); + assert_eq!(rejected.matrix, current); + assert_eq!( + rejected.rejection_reason, + Some(CovarianceUpdateRejectionReason::BacktrackingExhausted) + ); + assert_eq!(rejected.attempted_fractions.len(), 16); + assert_eq!(rejected.trial_rejections.len(), 16); + assert!(rejected + .trial_rejections + .iter() + .all(|reason| *reason == CovarianceTrialRejectionReason::ObjectiveIncrease)); + } +} diff --git a/src/estimation/parametric/rank_diagnostics.rs b/src/estimation/parametric/rank_diagnostics.rs new file mode 100644 index 000000000..3c8ff37bd --- /dev/null +++ b/src/estimation/parametric/rank_diagnostics.rs @@ -0,0 +1,976 @@ +//! Rank-normalized convergence diagnostics (Vehtari et al. 2021). +//! +//! Foundation module providing split-R̂ (rank-normalized and folded) and bulk +//! effective sample size (ESS). The implementation follows the posterior R +//! package semantics: +//! +//! * pooled average-rank ties, +//! * Blom normal-score transform via statrs, +//! * even split of each chain into two halves, +//! * classical split-R̂ on the transformed chains, +//! * folded split-R̂ via absolute deviations from the pooled median, +//! * biased-N autocovariance for bulk ESS, +//! * Geyer initial-positive-sequence + monotone sequence estimator for τ, +//! * no clipping of R̂ below 1 or ESS above the total draw count. +//! +//! All internal errors are typed and no value is silently patched. + +#![allow(dead_code)] // foundation module pending integration + +use statrs::distribution::{ContinuousCDF, Normal}; +use thiserror::Error; + +// --------------------------------------------------------------------------- +// Errors +// --------------------------------------------------------------------------- + +/// Errors returned by rank-based convergence diagnostics. +#[derive(Debug, Clone, PartialEq, Error)] +pub(crate) enum RankDiagnosticError { + /// No chains were provided. + #[error("at least one chain is required for rank diagnostics")] + NoChains, + + /// Fewer than two chains: split requires ≥ 2 original chains. + #[error("at least two chains are required for split diagnostics; found {found}")] + TooFewChains { found: usize }, + + /// Chains have different lengths. + #[error( + "all chains must have the same length; found lengths ranging from {shortest} to {longest}" + )] + UnequalChainLengths { shortest: usize, longest: usize }, + + /// Chain length is odd (cannot be split evenly). + #[error("chain length must be even for split diagnostics; found length {len}")] + OddChainLength { len: usize }, + + /// A draw is non-finite. + #[error("rank diagnostics require finite draws; found non-finite value")] + NonFiniteDraw, + + /// Chain has fewer draws than the minimum required. + #[error("chain length {len} must be at least {min_len} for {diagnostic}")] + TooFewDraws { + len: usize, + min_len: usize, + diagnostic: &'static str, + }, + + /// All pooled draws are identical after rank normalization (W = 0), so + /// R̂ is undefined. + #[error("all pooled draws are constant; split-R̂ is undefined (W = 0)")] + ConstantDraws, + + /// A required within-chain or pooled variance is non-positive/non-finite. + #[error("rank diagnostic variance is invalid")] + InvalidVariance, + + /// Integrated autocorrelation time τ ≤ 0, making ESS undefined. + #[error("integrated autocorrelation time τ = {tau} is non-positive; ESS is undefined")] + NonPositiveTau { tau: f64 }, +} + +// --------------------------------------------------------------------------- +// Input validation helpers +// --------------------------------------------------------------------------- + +/// Validate chains are non-empty, equal-length, even-length, finite, and +/// at least two chains for split diagnostics. +fn validate_chains( + chains: &[Vec], + min_len: usize, + diagnostic: &'static str, +) -> Result<(), RankDiagnosticError> { + if chains.is_empty() { + return Err(RankDiagnosticError::NoChains); + } + if chains.len() < 2 { + return Err(RankDiagnosticError::TooFewChains { + found: chains.len(), + }); + } + let first_len = chains[0].len(); + if chains.iter().any(|c| c.len() != first_len) { + let shortest = chains.iter().map(|c| c.len()).min().unwrap_or(0); + let longest = chains.iter().map(|c| c.len()).max().unwrap_or(0); + return Err(RankDiagnosticError::UnequalChainLengths { shortest, longest }); + } + if first_len < min_len { + return Err(RankDiagnosticError::TooFewDraws { + len: first_len, + min_len, + diagnostic, + }); + } + if !first_len.is_multiple_of(2) { + return Err(RankDiagnosticError::OddChainLength { len: first_len }); + } + if chains.iter().any(|c| c.iter().any(|x| !x.is_finite())) { + return Err(RankDiagnosticError::NonFiniteDraw); + } + Ok(()) +} + +/// Check that the pooled set of draws across chains is not all identical. +/// +/// This is applied *after* rank normalization (or folding) so that a constant +/// multiset of z-scores is detected before computing R̂. One internally +/// constant chain does not make the pooled within-chain variance zero when +/// other chains vary; the variance calculations below decide eligibility. +fn assert_non_constant_draws(chains: &[Vec]) -> Result<(), RankDiagnosticError> { + let first = chains[0][0]; + if chains.iter().all(|c| c.iter().all(|&x| x == first)) { + return Err(RankDiagnosticError::ConstantDraws); + } + Ok(()) +} + +// --------------------------------------------------------------------------- +// Split +// --------------------------------------------------------------------------- + +/// Split each chain evenly into two halves (first n/2, last n/2). +fn split_even(chains: &[Vec]) -> Vec> { + let half = chains[0].len() / 2; + chains + .iter() + .flat_map(|c| { + let first = c[..half].to_vec(); + let second = c[half..].to_vec(); + vec![first, second] + }) + .collect() +} + +// --------------------------------------------------------------------------- +// Ranking +// --------------------------------------------------------------------------- + +/// Compute pooled average ranks across all chains. +/// +/// Tied values receive the average of the 1-based ranks they span. +fn pooled_average_ranks(chains: &[Vec]) -> Vec> { + let total = chains.iter().map(|c| c.len()).sum::(); + // Collect (value, chain_index, position_index). + let mut indexed: Vec<(f64, usize, usize)> = chains + .iter() + .enumerate() + .flat_map(|(ci, c)| c.iter().enumerate().map(move |(pi, &v)| (v, ci, pi))) + .collect(); + + // Stable sort by value (f64::total_cmp gives total ordering, but we want + // exact-equality tie handling). For post-rank-normalization z-scores ties + // are rare; we use simple partial_cmp with well-defined float ordering. + indexed.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal)); + + let mut ranks: Vec> = chains.iter().map(|c| vec![0.0; c.len()]).collect(); + let mut i = 0; + while i < total { + let mut j = i; + while j + 1 < total && indexed[j + 1].0 == indexed[i].0 { + j += 1; + } + // Average of 1-based ranks (i+1)..(j+1). + let avg = (i + j) as f64 / 2.0 + 1.0; + for &(_, ci, pi) in &indexed[i..=j] { + ranks[ci][pi] = avg; + } + i = j + 1; + } + ranks +} + +// --------------------------------------------------------------------------- +// Blom transform +// --------------------------------------------------------------------------- + +/// Convert pooled ranks to normal scores using Blom's formula: +/// `z = Φ⁻¹((rank − 3/8) / (S + 1/4))`. +fn blom_scores(chains: &[Vec]) -> Vec> { + let total = chains.iter().map(|c| c.len()).sum::() as f64; + let norm = standard_normal(); + pooled_average_ranks(chains) + .into_iter() + .map(|c| { + c.into_iter() + .map(|r| norm.inverse_cdf((r - 0.375) / (total + 0.25))) + .collect() + }) + .collect() +} + +fn standard_normal() -> Normal { + Normal::new(0.0, 1.0).expect("standard normal parameters are valid") +} + +// --------------------------------------------------------------------------- +// Classical split-R̂ +// --------------------------------------------------------------------------- + +/// Compute the classical split-R̂ (sqrt variant) on *post-split* chains. +/// +/// The chains must already be rank-normalized and split. Returns `ConstantDraws` +/// if within-chain variance W is zero. +fn split_rhat_of(chains: &[Vec]) -> Result { + let m = chains.len() as f64; + let n = chains[0].len() as f64; + + let means: Vec = chains.iter().map(|c| c.iter().sum::() / n).collect(); + let grand = means.iter().sum::() / m; + + let b = if m > 1.0 { + n * means.iter().map(|mu| (mu - grand).powi(2)).sum::() / (m - 1.0) + } else { + 0.0 + }; + + let w = chains + .iter() + .zip(means.iter()) + .map(|(c, mu)| c.iter().map(|x| (x - mu).powi(2)).sum::() / (n - 1.0)) + .sum::() + / m; + + if w == 0.0 { + return Err(RankDiagnosticError::ConstantDraws); + } + if !w.is_finite() || w < 0.0 || !b.is_finite() || b < 0.0 { + return Err(RankDiagnosticError::InvalidVariance); + } + + let var_plus = (n - 1.0) / n * w + b / n; + if !var_plus.is_finite() || var_plus <= 0.0 { + return Err(RankDiagnosticError::InvalidVariance); + } + Ok((var_plus / w).sqrt()) +} + +// --------------------------------------------------------------------------- +// Rank-normalized split-R̂ +// --------------------------------------------------------------------------- + +/// Rank-normalized split-R̂. +/// +/// Splits each chain evenly, ranks across all split chains, applies Blom +/// normal scores, then computes the classical split-R̂ on the transformed data. +pub(crate) fn rank_normalized_split_rhat(chains: &[Vec]) -> Result { + validate_chains(chains, 4, "rank-normalized split-R̂")?; + assert_non_constant_draws(chains)?; + let split = split_even(chains); + assert_non_constant_draws(&split)?; + let z = blom_scores(&split); + assert_non_constant_draws(&z)?; + split_rhat_of(&z) +} + +// --------------------------------------------------------------------------- +// Folded split-R̂ +// --------------------------------------------------------------------------- + +/// Folded split-R̂. +/// +/// Computes absolute deviations from the pooled median across all chains, then +/// applies rank-normalized split-R̂ to the folded values. +pub(crate) fn folded_split_rhat(chains: &[Vec]) -> Result { + validate_chains(chains, 4, "folded split-R̂")?; + + // Pooled median. + let total = chains.iter().map(|c| c.len()).sum::(); + let mut pooled: Vec = chains.iter().flat_map(|c| c.iter()).copied().collect(); + pooled.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + // total is even because all chain lengths are even, validated above. + // Avoid overflowing the sum for large same-sign finite values and the + // subtraction for opposite-sign values. + let lower = pooled[total / 2 - 1]; + let upper = pooled[total / 2]; + let median = if lower.is_sign_negative() == upper.is_sign_negative() { + lower + (upper - lower) / 2.0 + } else { + lower / 2.0 + upper / 2.0 + }; + if !median.is_finite() { + return Err(RankDiagnosticError::NonFiniteDraw); + } + + let mut folded = Vec::with_capacity(chains.len()); + for chain in chains { + let mut folded_chain = Vec::with_capacity(chain.len()); + for &draw in chain { + let difference = draw - median; + if !difference.is_finite() { + return Err(RankDiagnosticError::NonFiniteDraw); + } + let value = difference.abs(); + if !value.is_finite() { + return Err(RankDiagnosticError::NonFiniteDraw); + } + folded_chain.push(value); + } + folded.push(folded_chain); + } + + assert_non_constant_draws(chains)?; + let split = split_even(&folded); + assert_non_constant_draws(&split)?; + let z = blom_scores(&split); + assert_non_constant_draws(&z)?; + split_rhat_of(&z) +} + +/// Maximum of rank-normalized and folded split-R̂. +pub(crate) fn max_split_rhat(chains: &[Vec]) -> Result { + let r_rank = rank_normalized_split_rhat(chains)?; + let r_fold = folded_split_rhat(chains)?; + Ok(r_rank.max(r_fold)) +} + +// --------------------------------------------------------------------------- +// Bulk ESS +// --------------------------------------------------------------------------- + +/// Biased (divisor N) autocovariance of a single series. +fn acov_biased(x: &[f64]) -> Vec { + let n = x.len(); + let mu = x.iter().sum::() / n as f64; + let centered: Vec = x.iter().map(|xi| xi - mu).collect(); + + (0..n) + .map(|t| { + let mut s = 0.0; + for i in 0..(n - t) { + s += centered[i] * centered[i + t]; + } + s / n as f64 + }) + .collect() +} + +/// Compute τ̂ from rank-normalized split chains using Geyer's initial positive +/// sequence + monotone sequence estimator, then return ESS = m·n / τ̂. +/// +/// Returns `NonPositiveTau` if τ̂ ≤ 0 or `ConstantDraws` if W = 0. +fn ess_from_split_z(z: &[Vec]) -> Result<(f64, f64), RankDiagnosticError> { + let m = z.len() as f64; + let n_float = z[0].len() as f64; + let n = z[0].len(); + + // Per-chain biased autocovariance. + let acovs: Vec> = z.iter().map(|c| acov_biased(c)).collect(); + + // Average autocovariance across chains at each lag. + let acov_means: Vec = (0..n) + .map(|t| acovs.iter().map(|a| a[t]).sum::() / m) + .collect(); + + // Var⁺ = mean_var · (n-1)/n + var(chain means) [Vehtari et al. 2021 eq 13.3] + let mean_var = acov_means[0] * n_float / (n_float - 1.0); + let mut var_plus = mean_var * (n_float - 1.0) / n_float; + if m > 1.0 { + let means: Vec = z.iter().map(|c| c.iter().sum::() / n_float).collect(); + let grand = means.iter().sum::() / m; + var_plus += means.iter().map(|mu| (mu - grand).powi(2)).sum::() / (m - 1.0); + } + if !mean_var.is_finite() || mean_var <= 0.0 || !var_plus.is_finite() || var_plus <= 0.0 { + return Err(RankDiagnosticError::InvalidVariance); + } + + // Vehtari/Geyer indexing: rho_0 is exactly one and P_t contains the + // consecutive lag pair (rho_(2t), rho_(2t+1)). Keep positive pairs from + // t=0 through the pair immediately before the first non-positive or + // unavailable pair, then enforce the initial monotone sequence on P itself. + let mut rho = Vec::with_capacity(n); + rho.push(1.0); + rho.extend( + acov_means + .iter() + .skip(1) + .map(|acov| 1.0 - (mean_var - acov) / var_plus), + ); + let monotone_pairs = geyer_initial_monotone_pairs(&rho); + let tau = -1.0 + 2.0 * monotone_pairs.iter().sum::(); + + if tau <= 0.0 || !tau.is_finite() { + return Err(RankDiagnosticError::NonPositiveTau { tau }); + } + + let total = m * n_float; + Ok((total / tau, tau)) +} + +/// Return Geyer's initial-positive, initial-monotone sequence of paired +/// autocorrelations. An incomplete final pair is unavailable and is excluded. +fn geyer_initial_monotone_pairs(rho: &[f64]) -> Vec { + let mut pairs = Vec::with_capacity(rho.len() / 2); + for pair in rho.chunks_exact(2) { + let paired_sum = pair[0] + pair[1]; + if !paired_sum.is_finite() || paired_sum <= 0.0 { + break; + } + pairs.push(match pairs.last() { + Some(previous) => paired_sum.min(*previous), + None => paired_sum, + }); + } + pairs +} + +/// Bulk effective sample size via rank-normalized split chains. +/// +/// Splits each chain evenly, rank-normalizes all split chains with Blom scores, +/// then computes ESS via Geyer's IPS+monotone estimator. Requires chain length +/// ≥ 6 (3 per split half). +pub(crate) fn bulk_ess(chains: &[Vec]) -> Result<(f64, f64), RankDiagnosticError> { + validate_chains(chains, 6, "bulk ESS")?; + assert_non_constant_draws(chains)?; + let split = split_even(chains); + assert_non_constant_draws(&split)?; + let z = blom_scores(&split); + ess_from_split_z(&z) +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- +#[cfg(test)] +mod tests { + use super::*; + use approx::assert_relative_eq; + + // Tolerance for values that go through inverse_cdf (Blom). + const Z_TOL: f64 = 1e-8; + // Tolerance for pure arithmetic on ranks, variances, etc. + const ARITH_TOL: f64 = 1e-12; + + // ──────────────────────────────────────────────────── + // A. Pooled average ranks with ties + // ──────────────────────────────────────────────────── + #[test] + fn pooled_average_ranks_handles_ties_and_absence() { + // Two chains of length 4; expected ranks from Python reference. + let chains = vec![vec![1.0, 2.0, 2.0, 4.0], vec![2.0, 5.0, 6.0, 7.0]]; + let ranks = pooled_average_ranks(&chains); + let expected = [vec![1.0, 3.0, 3.0, 5.0], vec![3.0, 6.0, 7.0, 8.0]]; + for (actual_row, expected_row) in ranks.iter().zip(expected.iter()) { + for (&a, &e) in actual_row.iter().zip(expected_row.iter()) { + assert_relative_eq!(a, e, max_relative = ARITH_TOL); + } + } + } + + #[test] + fn pooled_average_ranks_no_ties_all_distinct() { + let chains = vec![vec![2.0, 1.0, 4.0, 3.0], vec![5.0, 6.0, 7.0, 8.0]]; + let ranks = pooled_average_ranks(&chains); + // Values: [2,1,4,3,5,6,7,8] → sorted [1,2,3,4,5,6,7,8] → ranks 1..8. + let expected = [vec![2.0, 1.0, 4.0, 3.0], vec![5.0, 6.0, 7.0, 8.0]]; + for (actual_row, expected_row) in ranks.iter().zip(expected.iter()) { + for (&a, &e) in actual_row.iter().zip(expected_row.iter()) { + assert_relative_eq!(a, e, max_relative = ARITH_TOL); + } + } + } + + // ──────────────────────────────────────────────────── + // B. Blom normal scores + // ──────────────────────────────────────────────────── + #[test] + fn blom_scores_for_four_distinct_draws() { + // Single chain of 4; S=4. + // Python: r=1 → z ≈ -1.0491313979639711, r=3.5 → z ≈ 0.62890421763219 + // Values: [1, 3, 3, 2] mapped to chains for pooled rank context. + let chains = vec![vec![1.0, 3.0], vec![3.0, 2.0]]; + let z = blom_scores(&chains); + // Ranks: [[1.0, 3.5], [3.5, 2.0]] + // z[0][0] for rank 1: -1.0491313979639711 + // z[0][1] for rank 3.5: 0.62890421763219 + assert_relative_eq!(z[0][0], -1.0491313979639711, max_relative = Z_TOL); + assert_relative_eq!(z[0][1], 0.62890421763219, max_relative = Z_TOL); + } + + // ──────────────────────────────────────────────────── + // C. Rank-normalized split-R̂ — simple hand-checked + // ──────────────────────────────────────────────────── + #[test] + fn rank_normalized_split_rhat_single_ascending_chain() { + // Two identical chains [1,2,4,3]: split halves not fully constant. + let chains = vec![vec![1.0, 2.0, 4.0, 3.0], vec![1.0, 2.0, 4.0, 3.0]]; + let r = rank_normalized_split_rhat(&chains).unwrap(); + assert!(r > 1.0, "R̂ should exceed 1 for non-mixed identical chains"); + } + + // ──────────────────────────────────────────────────── + // D. Folded split-R̂ — single chain, value-agnostic + // ──────────────────────────────────────────────────── + #[test] + fn folded_split_rhat_is_sqrt_half_for_single_ascending_chain() { + // Two identical chains [1,2,4,3]: folded split-R̂ still √0.5. + let chains = vec![vec![1.0, 2.0, 4.0, 3.0], vec![1.0, 2.0, 4.0, 3.0]]; + let r = folded_split_rhat(&chains).unwrap(); + assert_relative_eq!(r, std::f64::consts::FRAC_1_SQRT_2, max_relative = Z_TOL); + } + + // ──────────────────────────────────────────────────── + // E. Ties in multiple chains → rank rhat + // ──────────────────────────────────────────────────── + #[test] + fn rank_normalized_split_rhat_with_ties() { + // Python: tie rank rhat = 1.687130053098945 + let chains = vec![vec![1.0, 2.0, 2.0, 4.0], vec![2.0, 5.0, 6.0, 7.0]]; + let r = rank_normalized_split_rhat(&chains).unwrap(); + assert_relative_eq!(r, 1.687130053098945, max_relative = Z_TOL); + } + + // ──────────────────────────────────────────────────── + // F. Two monotone chains x 8 (poor mixing) + // ──────────────────────────────────────────────────── + #[test] + fn diagnostics_for_two_monotone_chains() { + let chains = vec![ + vec![0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8], + vec![7.1, 6.2, 5.3, 4.4, 3.5, 2.6, 1.7, 0.8], + ]; + // Python: rank rhat F = 1.7299566224270406 + let r = rank_normalized_split_rhat(&chains).unwrap(); + assert_relative_eq!(r, 1.7299566224270406, max_relative = Z_TOL); + // Python: folded rhat F = 0.9129284180922413 + let f = folded_split_rhat(&chains).unwrap(); + assert_relative_eq!(f, 0.9129284180922413, max_relative = Z_TOL); + } + + // ──────────────────────────────────────────────────── + // F-long. Monotone adjustment exercised (long well-mixed chains). + // ──────────────────────────────────────────────────── + #[test] + fn rank_rhat_and_ess_for_long_well_mixed_chains() { + let chains = vec![ + vec![ + 11.0, 5.0, 13.0, 2.0, 3.0, 14.0, 9.0, 17.0, 6.0, 10.0, 1.0, 15.0, 4.0, 20.0, 7.0, + 8.0, 19.0, 12.0, 16.0, 18.0, + ], + vec![ + 38.0, 34.0, 22.0, 24.0, 28.0, 31.0, 35.0, 30.0, 21.0, 33.0, 39.0, 27.0, 32.0, 40.0, + 36.0, 25.0, 29.0, 37.0, 23.0, 26.0, + ], + ]; + // Python: long rank rhat = 1.81651170432963 + let r = rank_normalized_split_rhat(&chains).unwrap(); + assert_relative_eq!(r, 1.81651170432963, max_relative = Z_TOL); + } + + // ──────────────────────────────────────────────────── + // G. Antithetic case: ESS > total·log10(total) — no upper cap. + // ──────────────────────────────────────────────────── + #[test] + fn bulk_ess_exceeds_total_times_log10_total_for_mildly_antithetic_chain() { + // Two identical chains preserve autocorrelation structure; tau > 0 still. + let chain = vec![ + 5.0, 3.0, 2.0, 12.0, 9.0, 10.0, 4.0, 11.0, 7.0, 8.0, 6.0, 1.0, + ]; + let chains = vec![chain.clone(), chain]; + let (ess, tau) = bulk_ess(&chains).unwrap(); + assert_relative_eq!(ess, 32.491_591_364_927_49, max_relative = Z_TOL); + assert_relative_eq!(tau, 0.7386526480173083, max_relative = Z_TOL); + } + + // ──────────────────────────────────────────────────── + // H. NonPositiveTau error via perfectly antithetic chain. + // ──────────────────────────────────────────────────── + #[test] + fn bulk_ess_errors_on_non_positive_tau() { + // Two identical perfectly antithetic chains. + let chain = vec![ + 1.0, 12.0, 2.0, 11.0, 3.0, 10.0, 4.0, 9.0, 5.0, 8.0, 6.0, 7.0, + ]; + let chains = vec![chain.clone(), chain]; + let err = bulk_ess(&chains).unwrap_err(); + assert!(matches!(err, RankDiagnosticError::NonPositiveTau { .. })); + if let RankDiagnosticError::NonPositiveTau { tau } = err { + assert!(tau < 0.0, "tau should be negative"); + } + } + + // ──────────────────────────────────────────────────── + // I. Max split-R̂ returns the larger of the two. + // ──────────────────────────────────────────────────── + #[test] + fn max_split_rhat_is_max_of_rank_and_folded() { + let chains = vec![ + vec![0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8], + vec![7.1, 6.2, 5.3, 4.4, 3.5, 2.6, 1.7, 0.8], + ]; + let r_rank = rank_normalized_split_rhat(&chains).unwrap(); + let r_fold = folded_split_rhat(&chains).unwrap(); + let r_max = max_split_rhat(&chains).unwrap(); + assert_eq!(r_max, r_rank.max(r_fold)); + // sanity: floor should be at r_fold < r_rank for this input. + assert!(r_fold < r_rank); + } + + #[test] + fn monotone_transform_preserves_rank_diagnostics() { + let chains = vec![ + vec![0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8], + vec![7.1, 6.2, 5.3, 4.4, 3.5, 2.6, 1.7, 0.8], + ]; + let transformed = chains + .iter() + .map(|chain| chain.iter().map(|value| f64::exp(*value)).collect()) + .collect::>>(); + assert_eq!( + rank_normalized_split_rhat(&chains).unwrap(), + rank_normalized_split_rhat(&transformed).unwrap() + ); + assert_eq!(bulk_ess(&chains).unwrap(), bulk_ess(&transformed).unwrap()); + } + + #[test] + fn shifted_location_and_drift_are_detected_by_rank_rhat() { + let shifted = vec![ + (0..20).map(|i| i as f64 * 0.1).collect::>(), + (0..20).map(|i| 8.0 + i as f64 * 0.1).collect::>(), + ]; + assert!(rank_normalized_split_rhat(&shifted).unwrap() > 1.5); + + let drifting = vec![ + (0..20).map(|i| i as f64).collect::>(), + (0..20).map(|i| i as f64 + 0.25).collect::>(), + ]; + assert!(rank_normalized_split_rhat(&drifting).unwrap() > 1.5); + } + + #[test] + fn scale_only_mismatch_is_stronger_after_folding() { + let narrow = [-1.0, -0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8, 1.0]; + let wide = [-8.0, -6.4, -4.8, -3.2, -1.6, 1.6, 3.2, 4.8, 6.4, 8.0]; + let chains = vec![narrow.repeat(2), wide.repeat(2)]; + let rank = rank_normalized_split_rhat(&chains).unwrap(); + let folded = folded_split_rhat(&chains).unwrap(); + assert!(folded > rank); + assert!(folded > 1.1); + } + + #[test] + fn sticky_trace_has_low_bulk_ess() { + let chains = (0..4) + .map(|chain| { + (0..80) + .map(|draw| (draw / 10) as f64 + chain as f64 * 0.01) + .collect::>() + }) + .collect::>(); + let (ess, _) = bulk_ess(&chains).unwrap(); + assert!( + ess < 80.0, + "sticky ESS {ess} should be well below 320 draws" + ); + } + + // ──────────────────────────────────────────────────── + // J. Edge cases — input validation errors + // ──────────────────────────────────────────────────── + #[test] + fn no_chains_rejected() { + let chains: Vec> = vec![]; + assert_eq!( + rank_normalized_split_rhat(&chains).unwrap_err(), + RankDiagnosticError::NoChains + ); + assert_eq!( + folded_split_rhat(&chains).unwrap_err(), + RankDiagnosticError::NoChains + ); + assert_eq!( + bulk_ess(&chains).unwrap_err(), + RankDiagnosticError::NoChains + ); + } + + #[test] + fn unequal_chain_lengths_rejected() { + let chains = vec![ + vec![1.0, 2.0, 3.0, 4.0], + vec![5.0, 6.0, 7.0, 8.0, 9.0, 10.0], + ]; + assert_eq!( + rank_normalized_split_rhat(&chains).unwrap_err(), + RankDiagnosticError::UnequalChainLengths { + shortest: 4, + longest: 6 + } + ); + } + + #[test] + fn odd_chain_length_rejected() { + let chains = vec![vec![1.0, 2.0, 3.0, 4.0, 5.0], vec![1.0, 2.0, 3.0, 4.0, 5.0]]; + let err = rank_normalized_split_rhat(&chains).unwrap_err(); + assert_eq!(err, RankDiagnosticError::OddChainLength { len: 5 }); + } + + #[test] + fn too_few_draws_for_rhat() { + // 2 chains, length 2: minimum is 4. + let chains = vec![vec![1.0, 2.0], vec![1.0, 2.0]]; + assert_eq!( + rank_normalized_split_rhat(&chains).unwrap_err(), + RankDiagnosticError::TooFewDraws { + len: 2, + min_len: 4, + diagnostic: "rank-normalized split-R̂" + } + ); + } + + #[test] + fn too_few_draws_for_ess() { + // 2 chains, length 4: ESS minimum is 6. + let chains = vec![vec![1.0, 2.0, 3.0, 4.0], vec![1.0, 2.0, 3.0, 4.0]]; + assert_eq!( + bulk_ess(&chains).unwrap_err(), + RankDiagnosticError::TooFewDraws { + len: 4, + min_len: 6, + diagnostic: "bulk ESS" + } + ); + } + + #[test] + fn non_finite_draws_rejected() { + // 2 chains: NAN in first, valid in second. + let chains = vec![vec![1.0, f64::NAN, 3.0, 4.0], vec![1.0, 2.0, 3.0, 4.0]]; + assert_eq!( + rank_normalized_split_rhat(&chains).unwrap_err(), + RankDiagnosticError::NonFiniteDraw + ); + // ESS needs len ≥ 6; 2 chains with INF. + let chains_inf = vec![ + vec![1.0, f64::INFINITY, 3.0, 4.0, 5.0, 6.0], + vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], + ]; + assert_eq!( + bulk_ess(&chains_inf).unwrap_err(), + RankDiagnosticError::NonFiniteDraw + ); + } + + #[test] + fn constant_draws_produce_error_in_rhat() { + let chains = vec![vec![5.0, 5.0, 5.0, 5.0], vec![6.0, 6.0, 6.0, 6.0]]; + let err = rank_normalized_split_rhat(&chains).unwrap_err(); + assert_eq!(err, RankDiagnosticError::ConstantDraws); + } + + #[test] + fn folded_median_is_overflow_safe_and_rejects_overflowing_differences() { + let same_sign = vec![ + vec![ + f64::MAX * 0.75, + f64::MAX * 0.8, + f64::MAX * 0.85, + f64::MAX * 0.9, + ], + vec![ + f64::MAX * 0.7, + f64::MAX * 0.78, + f64::MAX * 0.88, + f64::MAX * 0.95, + ], + ]; + assert!(folded_split_rhat(&same_sign).is_ok()); + + let overflowing_difference = vec![ + vec![-f64::MAX, f64::MAX * 0.70, f64::MAX * 0.80, f64::MAX * 0.90], + vec![ + -f64::MAX * 0.90, + f64::MAX * 0.75, + f64::MAX * 0.85, + f64::MAX * 0.95, + ], + ]; + assert_eq!( + folded_split_rhat(&overflowing_difference).unwrap_err(), + RankDiagnosticError::NonFiniteDraw + ); + } + + #[test] + fn folded_constant_draws_produce_error() { + // Both chains produce folded absolute deviations from median 0: + // first → [1,1,1,1], second → [2,2,2,2] → split halves constant. + let chains = vec![vec![1.0, -1.0, 1.0, -1.0], vec![2.0, -2.0, 2.0, -2.0]]; + let err = folded_split_rhat(&chains).unwrap_err(); + assert_eq!(err, RankDiagnosticError::ConstantDraws); + } + + // ──────────────────────────────────────────────────── + // K. Constant-chain eligibility follows pooled variance mathematics. + // ──────────────────────────────────────────────────── + #[test] + fn all_chains_internally_constant_are_ineligible_for_rhat_and_ess() { + // Each chain is internally constant but chains have different values. + let chains = vec![ + vec![1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + vec![2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0], + ]; + // R̂: post-split produces constant z per half-chain → pooled W=0. + assert_eq!( + rank_normalized_split_rhat(&chains).unwrap_err(), + RankDiagnosticError::ConstantDraws + ); + assert_eq!( + bulk_ess(&chains).unwrap_err(), + RankDiagnosticError::InvalidVariance + ); + } + + #[test] + fn one_stuck_chain_with_an_independently_varying_chain_remains_diagnostic() { + let chains = vec![vec![1.0; 8], vec![0.0, 2.0, 4.0, 6.0, 1.0, 3.0, 5.0, 7.0]]; + + let rank = rank_normalized_split_rhat(&chains).unwrap(); + let folded = folded_split_rhat(&chains).unwrap(); + let (ess, tau) = bulk_ess(&chains).unwrap(); + + assert!(rank.is_finite() && rank > 1.0); + assert!(folded.is_finite()); + assert!(ess.is_finite() && ess > 0.0); + assert!(tau.is_finite() && tau > 0.0); + } + + fn assert_values(actual: &[f64], expected: &[f64]) { + assert_eq!(actual.len(), expected.len()); + for (&actual, &expected) in actual.iter().zip(expected) { + assert_relative_eq!(actual, expected, epsilon = ARITH_TOL); + } + } + + #[test] + fn geyer_fixture_keeps_first_pair_only() { + // Fixture and all expected arithmetic were calculated independently. + let z = vec![ + vec![0.0, 3.0, 4.0, -2.0, -4.0, -4.0, 3.0, -2.0], + vec![-1.0, -3.0, 3.0, -2.0, 3.0, 3.0, 3.0, 2.0], + ]; + assert_values( + &acov_biased(&z[0]), + &[ + 9.1875, 1.2421875, -2.453125, -3.4609375, 0.46875, 0.2734375, -0.609375, -0.0546875, + ], + ); + assert_values( + &acov_biased(&z[1]), + &[5.75, -0.25, 1.5, -0.25, -1.375, -1.25, -1.0, -0.25], + ); + let rho = [ + 1.0, + 0.02550054112554101, + -0.09239718614718617, + -0.2595373376623378, + ]; + assert_values(&geyer_initial_monotone_pairs(&rho), &[1.025500541125541]); + let (ess, tau) = ess_from_split_z(&z).unwrap(); + assert_relative_eq!(tau, 1.0510010822510818, epsilon = ARITH_TOL); + assert_relative_eq!(ess, 15.223580898442535, epsilon = ARITH_TOL); + } + + #[test] + fn geyer_fixture_truncates_at_later_nonpositive_pair() { + let z = vec![ + vec![-1.0, -4.0, -2.0, 0.0, -1.0, 3.0, -2.0, 1.0], + vec![0.0, 2.0, 4.0, 2.0, 4.0, -1.0, -1.0, -3.0], + ]; + assert_values( + &acov_biased(&z[0]), + &[ + 3.9375, -0.5078125, 0.984375, -0.6796875, -1.15625, 0.1171875, -0.671875, + -0.0546875, + ], + ); + assert_values( + &acov_biased(&z[1]), + &[ + 5.609375, + 1.810546875, + 0.94921875, + -2.193359375, + -1.8828125, + -1.572265625, + -0.33984375, + 0.423828125, + ], + ); + let rho = [ + 1.0, + 0.21165293040293032, + 0.26341575091575087, + -0.13097527472527482, + -0.14459706959706975, + -0.014629120879120938, + ]; + assert_values( + &geyer_initial_monotone_pairs(&rho), + &[1.2116529304029302, 0.13244047619047605], + ); + let (ess, tau) = ess_from_split_z(&z).unwrap(); + assert_relative_eq!(tau, 1.6881868131868125, epsilon = ARITH_TOL); + assert_relative_eq!(ess, 9.477624084621647, epsilon = ARITH_TOL); + } + + #[test] + fn geyer_fixture_applies_monotone_pair_adjustment() { + let z = vec![ + vec![2.0, -1.0, -1.0, 2.0, 3.0, 2.0, 4.0, 4.0], + vec![-2.0, 4.0, 0.0, 3.0, -2.0, -4.0, -2.0, 2.0], + ]; + assert_values( + &acov_biased(&z[0]), + &[ + 3.359375, + 1.576171875, + -0.16015625, + -0.115234375, + -0.7578125, + -1.525390625, + -0.73046875, + 0.033203125, + ], + ); + assert_values( + &acov_biased(&z[1]), + &[ + 7.109375, + -0.267578125, + -0.55078125, + -2.990234375, + -0.7578125, + -0.025390625, + 1.53515625, + -0.498046875, + ], + ); + let rho = [ + 1.0, + 0.2635374884294971, + 0.12395865473619261, + -0.041538105522986646, + 0.068343103980253, + 0.06591329836470228, + 0.22871027460660298, + 0.14096729404504793, + ]; + assert_values( + &geyer_initial_monotone_pairs(&rho), + &[ + 1.2635374884294972, + 0.08242054921320596, + 0.08242054921320596, + 0.08242054921320596, + ], + ); + let (ess, tau) = ess_from_split_z(&z).unwrap(); + assert_relative_eq!(tau, 2.02159827213823, epsilon = ARITH_TOL); + assert_relative_eq!(ess, 7.9145299145299095, epsilon = ARITH_TOL); + } +} diff --git a/src/estimation/parametric/residual.rs b/src/estimation/parametric/residual.rs new file mode 100644 index 000000000..d069f79ad --- /dev/null +++ b/src/estimation/parametric/residual.rs @@ -0,0 +1,1079 @@ +use anyhow::Result; +use argmin::{ + core::{CostFunction, Error, Executor, State, TerminationReason}, + solver::neldermead::NelderMead, +}; +use pharmsol::prelude::simulator::Prediction; +use pharmsol::{Equation, Predictions, Subject}; + +use crate::estimation::{ParametricErrorModels, ResidualErrorModel, ResidualErrorModels}; + +/// One output's SAEM residual-error sufficient statistic (`statrese`). +#[derive(Debug, Clone, Copy, Default, PartialEq)] +pub(crate) struct ResidualOutputStatistic { + pub(crate) weighted_sum: f64, + pub(crate) observation_count: usize, + pub(crate) proportional_floor_count: usize, + pub(crate) non_finite_prediction_count: usize, + pub(crate) exponential_domain_violation_count: usize, +} + +impl ResidualOutputStatistic { + pub(crate) fn sigma(self) -> Option { + if self.observation_count == 0 || !self.weighted_sum.is_finite() { + return None; + } + Some((self.weighted_sum / self.observation_count as f64).sqrt()) + } +} + +/// Output-indexed residual sufficient statistics. +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct ResidualSufficientStatistics { + outputs: Vec, + observations: Vec>, +} + +impl ResidualSufficientStatistics { + pub(crate) fn zero(output_count: usize) -> Self { + Self { + outputs: vec![ResidualOutputStatistic::default(); output_count], + observations: vec![Vec::new(); output_count], + } + } + + pub(crate) fn from_predictions

(predictions: &P, error_models: &ResidualErrorModels) -> Self + where + P: Predictions, + { + let mut statistics = Self::zero(error_models.len()); + predictions.for_each_prediction(|prediction| { + statistics.accumulate_prediction(prediction, error_models); + }); + statistics + } + + fn accumulate_prediction( + &mut self, + prediction: &Prediction, + error_models: &ResidualErrorModels, + ) { + let Some(observation) = prediction.observation() else { + return; + }; + let outeq = prediction.outeq(); + let Some(model) = error_models.get(outeq) else { + return; + }; + self.accumulate_values(outeq, model, observation, prediction.prediction()); + } + + fn accumulate_values( + &mut self, + outeq: usize, + model: &ResidualErrorModel, + observation: f64, + prediction: f64, + ) { + let Some(statistic) = self.outputs.get_mut(outeq) else { + return; + }; + statistic.weighted_sum += weighted_squared_residual(model, observation, prediction); + statistic.observation_count += 1; + if let Some(observations) = self.observations.get_mut(outeq) { + observations.push(ResidualObservation { + observation, + prediction, + }); + } + if !prediction.is_finite() { + statistic.non_finite_prediction_count += 1; + } else if matches!(model, ResidualErrorModel::Proportional { .. }) + && prediction.powi(2) <= PROPORTIONAL_PREDICTION_SQUARED_FLOOR + { + statistic.proportional_floor_count += 1; + } + if matches!(model, ResidualErrorModel::Exponential { .. }) + && (!observation.is_finite() + || observation <= 0.0 + || !prediction.is_finite() + || prediction <= 0.0) + { + statistic.exponential_domain_violation_count += 1; + } + } + + pub(crate) fn add_assign(&mut self, observed: &Self) { + if self.outputs.len() < observed.outputs.len() { + self.outputs + .resize(observed.outputs.len(), ResidualOutputStatistic::default()); + self.observations + .resize_with(observed.outputs.len(), Vec::new); + } + for (output_index, (total, value)) in + self.outputs.iter_mut().zip(&observed.outputs).enumerate() + { + total.weighted_sum += value.weighted_sum; + total.observation_count += value.observation_count; + total.proportional_floor_count += value.proportional_floor_count; + total.non_finite_prediction_count += value.non_finite_prediction_count; + total.exponential_domain_violation_count += value.exponential_domain_violation_count; + self.observations[output_index].extend_from_slice(&observed.observations[output_index]); + } + } + + pub(crate) fn stochastic_update(&self, observed: Self, step_size: f64) -> Self { + let mut updated = self.clone(); + if updated.outputs.len() < observed.outputs.len() { + updated + .outputs + .resize(observed.outputs.len(), ResidualOutputStatistic::default()); + updated + .observations + .resize_with(observed.outputs.len(), Vec::new); + } + updated.observations = observed.observations; + for (current, value) in updated.outputs.iter_mut().zip(observed.outputs) { + current.weighted_sum += step_size * (value.weighted_sum - current.weighted_sum); + current.observation_count = value.observation_count.max(current.observation_count); + current.proportional_floor_count = value.proportional_floor_count; + current.non_finite_prediction_count = value.non_finite_prediction_count; + current.exponential_domain_violation_count = value.exponential_domain_violation_count; + } + updated + } + + pub(crate) fn output(&self, outeq: usize) -> Option { + self.outputs.get(outeq).copied() + } + + pub(crate) fn observations(&self, outeq: usize) -> Option<&[ResidualObservation]> { + self.observations.get(outeq).map(Vec::as_slice) + } +} + +pub(crate) fn residual_statistics_for_subject( + equation: &E, + subject: &Subject, + parameters: &[f64], + error_models: &ResidualErrorModels, +) -> Result { + let predictions = equation.estimate_predictions_dense(subject, parameters)?; + Ok(ResidualSufficientStatistics::from_predictions( + &predictions, + error_models, + )) +} + +/// Denominator floor used by the proportional residual-statistic update. +/// +/// This applies to squared predictions, not to the proportional coefficient. +/// It keeps an exactly zero prediction in the statistic with a very large +/// penalty instead of silently dropping the observation. +const PROPORTIONAL_PREDICTION_SQUARED_FLOOR: f64 = f64::EPSILON; + +pub(crate) fn weighted_squared_residual( + model: &ResidualErrorModel, + observation: f64, + prediction: f64, +) -> f64 { + match model { + ResidualErrorModel::Exponential { .. } => { + if observation.is_finite() + && observation > 0.0 + && prediction.is_finite() + && prediction > 0.0 + { + (observation.ln() - prediction.ln()).powi(2) + } else { + f64::NAN + } + } + ResidualErrorModel::Constant { .. } => (observation - prediction).powi(2), + ResidualErrorModel::Proportional { .. } => { + let residual_sq = (observation - prediction).powi(2); + residual_sq + / prediction + .powi(2) + .max(PROPORTIONAL_PREDICTION_SQUARED_FLOOR) + } + ResidualErrorModel::Combined { a, b } => { + let residual_sq = (observation - prediction).powi(2); + let variance = (a.powi(2) + b.powi(2) * prediction.powi(2)).max(f64::EPSILON); + residual_sq / variance + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + let residual_sq = (observation - prediction).powi(2); + let variance = + (a.powi(2) + 2.0 * rho * a * b * prediction + b.powi(2) * prediction.powi(2)) + .max(f64::EPSILON); + residual_sq / variance + } + } +} + +pub(crate) fn primary_sigma_parameter(model: &ResidualErrorModel) -> f64 { + match model { + ResidualErrorModel::Constant { a } => *a, + ResidualErrorModel::Proportional { b } => *b, + ResidualErrorModel::Combined { a, .. } + | ResidualErrorModel::CorrelatedCombined { a, .. } => *a, + ResidualErrorModel::Exponential { sigma } => *sigma, + } +} + +pub(crate) fn primary_sigma_parameters(error_models: &ResidualErrorModels) -> Vec { + error_models + .iter() + .map(|(_, model)| primary_sigma_parameter(model)) + .collect() +} + +pub(crate) fn update_estimated_combined_residual_model( + error_models: &mut ParametricErrorModels, + outeq: usize, + additive_sd: f64, + proportional_sd: f64, +) { + if !error_models.is_estimated(outeq) { + return; + } + if let Some(slot) = error_models.models_mut().get_mut(outeq) { + *slot = ResidualErrorModel::combined(additive_sd, proportional_sd); + } +} + +pub(crate) fn update_estimated_correlated_combined_residual_model( + error_models: &mut ParametricErrorModels, + outeq: usize, + additive_sd: f64, + proportional_sd: f64, + correlation: f64, +) { + if !error_models.is_estimated(outeq) { + return; + } + if let Some(slot) = error_models.models_mut().get_mut(outeq) { + *slot = ResidualErrorModel::correlated_combined(additive_sd, proportional_sd, correlation); + } +} + +pub(crate) fn update_estimated_simple_residual_model_with_sigma( + error_models: &mut ParametricErrorModels, + outeq: usize, + sigma: f64, +) { + if !error_models.is_estimated(outeq) { + return; + } + let Some(model) = error_models.models().get(outeq).cloned() else { + return; + }; + let Some(updated) = simple_model_with_sigma(&model, sigma) else { + return; + }; + if let Some(slot) = error_models.models_mut().get_mut(outeq) { + *slot = updated; + } +} + +fn simple_model_with_sigma(model: &ResidualErrorModel, sigma: f64) -> Option { + match model { + ResidualErrorModel::Constant { .. } => Some(ResidualErrorModel::constant(sigma)), + ResidualErrorModel::Proportional { .. } => Some(ResidualErrorModel::proportional(sigma)), + ResidualErrorModel::Exponential { .. } => Some(ResidualErrorModel::exponential(sigma)), + ResidualErrorModel::Combined { .. } | ResidualErrorModel::CorrelatedCombined { .. } => None, + } +} + +/// Observation/prediction pair retained for residual-NLL optimization. +#[derive(Debug, Clone, Copy, PartialEq)] +pub(crate) struct ResidualObservation { + pub(crate) observation: f64, + pub(crate) prediction: f64, +} + +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct CombinedResidualSolution { + pub(crate) additive_sd: f64, + pub(crate) proportional_sd: f64, + pub(crate) objective: f64, + pub(crate) converged: bool, + pub(crate) iterations: u64, + pub(crate) termination: String, +} + +const RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY: f64 = 1e100; +const COMBINED_ADDITIVE_COLLAPSE_THRESHOLD: f64 = 1e-3; +// Upper log-sigma bound of 5. +pub(crate) const RESIDUAL_OPTIMIZER_MAX_SIGMA: f64 = 148.413_159_102_576_6; + +/// Conditional Gaussian residual NLL for the quadrature combined model. +/// +/// This is the active `sqrt(a^2 + b^2 f^2)` model, equivalently the +/// `sigma_add^2 + f^2 sigma_prop^2` variance, including the log-variance term. +pub(crate) fn combined_additive_sigma_collapsed(additive_sd: f64, estimated: bool) -> bool { + estimated && additive_sd.is_finite() && additive_sd <= COMBINED_ADDITIVE_COLLAPSE_THRESHOLD +} + +pub(crate) fn combined_residual_nll( + observations: &[ResidualObservation], + additive_sd: f64, + proportional_sd: f64, +) -> f64 { + if observations.is_empty() + || !additive_sd.is_finite() + || !proportional_sd.is_finite() + || additive_sd < 0.0 + || proportional_sd < 0.0 + || (additive_sd == 0.0 && proportional_sd == 0.0) + { + return f64::INFINITY; + } + + observations.iter().fold(0.0, |total, value| { + if !value.observation.is_finite() || !value.prediction.is_finite() { + return f64::INFINITY; + } + let variance = (additive_sd.powi(2) + proportional_sd.powi(2) * value.prediction.powi(2)) + .max(f64::EPSILON); + total + 0.5 * (variance.ln() + (value.observation - value.prediction).powi(2) / variance) + }) +} + +struct CombinedResidualCost<'a> { + observations: &'a [ResidualObservation], + minimum_sigma: f64, + initial_sigmas: [f64; 2], + estimated: [bool; 2], +} + +impl CombinedResidualCost<'_> { + fn unpack(&self, log_sigmas: &[f64]) -> Option<[f64; 2]> { + if log_sigmas.len() != self.estimated.iter().filter(|value| **value).count() { + return None; + } + let mut sigmas = self.initial_sigmas; + let mut coordinate = 0; + for (sigma, estimated) in sigmas.iter_mut().zip(self.estimated) { + if estimated { + *sigma = log_sigmas[coordinate].exp(); + coordinate += 1; + } + } + Some(sigmas) + } +} + +impl CostFunction for CombinedResidualCost<'_> { + type Param = Vec; + type Output = f64; + + fn cost(&self, log_sigmas: &Self::Param) -> std::result::Result { + let Some([additive_sd, proportional_sd]) = self.unpack(log_sigmas) else { + return Ok(RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY); + }; + for (component, sigma) in [additive_sd, proportional_sd].into_iter().enumerate() { + if self.estimated[component] + && (sigma < self.minimum_sigma || sigma > RESIDUAL_OPTIMIZER_MAX_SIGMA) + { + return Ok(RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY); + } + } + let objective = combined_residual_nll(self.observations, additive_sd, proportional_sd); + Ok(if objective.is_finite() { + objective + } else { + RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY + }) + } +} + +pub(crate) fn optimize_combined_residual( + observations: &[ResidualObservation], + initial_additive_sd: f64, + initial_proportional_sd: f64, + estimated: [bool; 2], + minimum_sigma: f64, + max_iterations: u64, +) -> Result { + anyhow::ensure!( + !observations.is_empty(), + "combined residual optimization requires observations" + ); + anyhow::ensure!( + estimated.iter().any(|value| *value), + "combined residual optimization requires an estimated component" + ); + anyhow::ensure!( + minimum_sigma.is_finite() + && minimum_sigma > 0.0 + && minimum_sigma < RESIDUAL_OPTIMIZER_MAX_SIGMA, + "combined residual minimum sigma must be finite, positive, and below the maximum" + ); + let initial_sigmas = [initial_additive_sd, initial_proportional_sd]; + for (component, sigma) in initial_sigmas.into_iter().enumerate() { + anyhow::ensure!( + sigma.is_finite() + && if estimated[component] { + sigma > 0.0 + } else { + sigma >= 0.0 + }, + "combined residual SD components must be finite and estimated components must be positive" + ); + } + + let initial = initial_sigmas + .into_iter() + .zip(estimated) + .filter(|&(_sigma, estimate)| estimate) + .map(|(sigma, _estimate)| { + sigma + .clamp(minimum_sigma, RESIDUAL_OPTIMIZER_MAX_SIGMA) + .ln() + }) + .collect::>(); + let mut simplex = Vec::with_capacity(initial.len() + 1); + simplex.push(initial.clone()); + for component in 0..initial.len() { + let mut point = initial.clone(); + point[component] += 0.2; + simplex.push(point); + } + let cost = CombinedResidualCost { + observations, + minimum_sigma, + initial_sigmas, + estimated, + }; + let solver = NelderMead::new(simplex).with_sd_tolerance(1e-8)?; + let result = Executor::new(cost, solver) + .configure(|state| state.max_iters(max_iterations)) + .run()?; + let state = result.state; + let best_is_valid = + state.best_cost.is_finite() && state.best_cost < RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY; + let best = state + .best_param + .clone() + .filter(|_| best_is_valid) + .unwrap_or(initial); + let cost = CombinedResidualCost { + observations, + minimum_sigma, + initial_sigmas, + estimated, + }; + let [additive_sd, proportional_sd] = cost.unpack(&best).ok_or_else(|| { + anyhow::anyhow!("combined residual optimizer returned invalid coordinates") + })?; + let objective = combined_residual_nll(observations, additive_sd, proportional_sd); + let termination_reason = state.get_termination_reason(); + + Ok(CombinedResidualSolution { + additive_sd, + proportional_sd, + objective, + converged: best_is_valid + && matches!(termination_reason, Some(TerminationReason::SolverConverged)), + iterations: state.iter, + termination: termination_reason + .map(ToString::to_string) + .unwrap_or_else(|| "unknown termination".to_owned()), + }) +} + +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct CorrelatedCombinedResidualSolution { + pub(crate) additive_sd: f64, + pub(crate) proportional_sd: f64, + pub(crate) correlation: f64, + pub(crate) objective: f64, + pub(crate) converged: bool, + pub(crate) iterations: u64, + pub(crate) termination: String, +} + +/// Conditional Gaussian residual NLL for the within-observation correlated +/// additive/proportional family, including the log-variance term. +pub(crate) fn correlated_combined_residual_nll( + observations: &[ResidualObservation], + additive_sd: f64, + proportional_sd: f64, + correlation: f64, +) -> f64 { + if observations.is_empty() + || !additive_sd.is_finite() + || additive_sd <= 0.0 + || !proportional_sd.is_finite() + || proportional_sd <= 0.0 + || !correlation.is_finite() + || correlation <= -1.0 + || correlation >= 1.0 + { + return f64::INFINITY; + } + + observations.iter().fold(0.0, |total, value| { + if !total.is_finite() || !value.observation.is_finite() || !value.prediction.is_finite() { + return f64::INFINITY; + } + let variance = additive_sd.powi(2) + + 2.0 * correlation * additive_sd * proportional_sd * value.prediction + + proportional_sd.powi(2) * value.prediction.powi(2); + if !variance.is_finite() || variance <= 0.0 { + return f64::INFINITY; + } + total + 0.5 * (variance.ln() + (value.observation - value.prediction).powi(2) / variance) + }) +} + +struct CorrelatedCombinedResidualCost<'a> { + observations: &'a [ResidualObservation], + minimum_sigma: f64, + initial: [f64; 3], + estimated: [bool; 3], +} + +impl CorrelatedCombinedResidualCost<'_> { + fn unpack(&self, coordinates: &[f64]) -> Option<[f64; 3]> { + if coordinates.len() != self.estimated.iter().filter(|value| **value).count() { + return None; + } + let mut values = self.initial; + let mut coordinate = 0; + for (component, estimated) in self.estimated.into_iter().enumerate() { + if estimated { + values[component] = if component < 2 { + coordinates[coordinate].exp() + } else { + coordinates[coordinate].tanh() + }; + coordinate += 1; + } + } + Some(values) + } + + fn valid(&self, values: [f64; 3]) -> bool { + values.into_iter().all(f64::is_finite) + && values[0] > 0.0 + && values[1] > 0.0 + && values[2] > -1.0 + && values[2] < 1.0 + && (!self.estimated[0] + || (values[0] >= self.minimum_sigma && values[0] <= RESIDUAL_OPTIMIZER_MAX_SIGMA)) + && (!self.estimated[1] + || (values[1] >= self.minimum_sigma && values[1] <= RESIDUAL_OPTIMIZER_MAX_SIGMA)) + } +} + +impl CostFunction for CorrelatedCombinedResidualCost<'_> { + type Param = Vec; + type Output = f64; + + fn cost(&self, coordinates: &Self::Param) -> std::result::Result { + let Some(values) = self.unpack(coordinates) else { + return Ok(RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY); + }; + if !self.valid(values) { + return Ok(RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY); + } + let objective = + correlated_combined_residual_nll(self.observations, values[0], values[1], values[2]); + Ok(if objective.is_finite() { + objective + } else { + RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY + }) + } +} + +pub(crate) fn optimize_correlated_combined_residual( + observations: &[ResidualObservation], + initial_additive_sd: f64, + initial_proportional_sd: f64, + initial_correlation: f64, + estimated: [bool; 3], + minimum_sigma: f64, + max_iterations: u64, +) -> Result { + anyhow::ensure!( + !observations.is_empty(), + "correlated-combined residual optimization requires observations" + ); + anyhow::ensure!( + estimated.iter().any(|value| *value), + "correlated-combined residual optimization requires an estimated component" + ); + anyhow::ensure!( + minimum_sigma.is_finite() + && minimum_sigma > 0.0 + && minimum_sigma < RESIDUAL_OPTIMIZER_MAX_SIGMA, + "correlated-combined residual minimum sigma must be finite, positive, and below the maximum" + ); + let initial_values = [ + initial_additive_sd, + initial_proportional_sd, + initial_correlation, + ]; + anyhow::ensure!( + initial_values.into_iter().all(f64::is_finite) + && initial_additive_sd > 0.0 + && initial_proportional_sd > 0.0 + && initial_correlation > -1.0 + && initial_correlation < 1.0, + "correlated-combined residual components require positive SDs and correlation strictly inside (-1, 1)" + ); + for component in 0..2 { + if estimated[component] { + anyhow::ensure!( + initial_values[component] >= minimum_sigma + && initial_values[component] <= RESIDUAL_OPTIMIZER_MAX_SIGMA, + "estimated correlated-combined residual SD is outside optimizer bounds" + ); + } + } + + let initial = initial_values + .into_iter() + .enumerate() + .filter(|(component, _)| estimated[*component]) + .map(|(component, value)| { + if component < 2 { + value.ln() + } else { + value.atanh() + } + }) + .collect::>(); + let mut simplex = Vec::with_capacity(initial.len() + 1); + simplex.push(initial.clone()); + for component in 0..initial.len() { + let mut point = initial.clone(); + point[component] += 0.2; + simplex.push(point); + } + let cost = CorrelatedCombinedResidualCost { + observations, + minimum_sigma, + initial: initial_values, + estimated, + }; + let solver = NelderMead::new(simplex).with_sd_tolerance(1e-8)?; + let result = Executor::new(cost, solver) + .configure(|state| state.max_iters(max_iterations)) + .run()?; + let state = result.state; + let best_is_valid = + state.best_cost.is_finite() && state.best_cost < RESIDUAL_OPTIMIZER_NON_FINITE_PENALTY; + anyhow::ensure!( + best_is_valid, + "correlated-combined residual optimizer did not return a finite valid objective" + ); + let best = state.best_param.clone().ok_or_else(|| { + anyhow::anyhow!("correlated-combined residual optimizer returned no candidate") + })?; + let cost = CorrelatedCombinedResidualCost { + observations, + minimum_sigma, + initial: initial_values, + estimated, + }; + let [additive_sd, proportional_sd, correlation] = cost.unpack(&best).ok_or_else(|| { + anyhow::anyhow!("correlated-combined residual optimizer returned invalid coordinates") + })?; + anyhow::ensure!( + cost.valid([additive_sd, proportional_sd, correlation]), + "correlated-combined residual optimizer returned an invalid candidate" + ); + let objective = + correlated_combined_residual_nll(observations, additive_sd, proportional_sd, correlation); + anyhow::ensure!( + objective.is_finite(), + "correlated-combined residual optimizer returned a non-finite objective" + ); + let termination_reason = state.get_termination_reason(); + + Ok(CorrelatedCombinedResidualSolution { + additive_sd, + proportional_sd, + correlation, + objective, + converged: best_is_valid + && matches!(termination_reason, Some(TerminationReason::SolverConverged)), + iterations: state.iter, + termination: termination_reason + .map(ToString::to_string) + .unwrap_or_else(|| "unknown termination".to_owned()), + }) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn weighted_residual_matches_constant_and_proportional_forms() { + assert_eq!( + weighted_squared_residual(&ResidualErrorModel::constant(99.0), 5.0, 3.0), + 4.0 + ); + assert!( + (weighted_squared_residual(&ResidualErrorModel::proportional(99.0), 12.0, 10.0) - 0.04) + .abs() + < 1e-12 + ); + assert!( + (weighted_squared_residual(&ResidualErrorModel::proportional(99.0), -8.0, -10.0) + - 0.04) + .abs() + < 1e-12 + ); + assert_eq!( + weighted_squared_residual(&ResidualErrorModel::proportional(99.0), 1.0, 0.0), + 1.0 / PROPORTIONAL_PREDICTION_SQUARED_FLOOR + ); + } + + #[test] + fn exponential_statistic_is_squared_log_residual_and_rejects_nonpositive_domain() { + let model = ResidualErrorModel::exponential(0.25); + let expected = (12.0_f64.ln() - 10.0_f64.ln()).powi(2); + + assert!((weighted_squared_residual(&model, 12.0, 10.0) - expected).abs() < 1e-12); + assert!(weighted_squared_residual(&model, 0.0, 10.0).is_nan()); + assert!(weighted_squared_residual(&model, 12.0, 0.0).is_nan()); + + let mut statistics = ResidualSufficientStatistics::zero(1); + statistics.accumulate_values(0, &model, 12.0, 10.0); + statistics.accumulate_values(0, &model, 0.0, 10.0); + statistics.accumulate_values(0, &model, 12.0, -1.0); + statistics.accumulate_values(0, &model, 12.0, f64::NAN); + let statistic = statistics.output(0).unwrap(); + assert_eq!(statistic.exponential_domain_violation_count, 3); + assert_eq!(statistic.non_finite_prediction_count, 1); + } + + #[test] + fn exponential_fixed_trace_statistic_matches_reference_checkpoint() { + let model = ResidualErrorModel::exponential(0.25); + let observations = [12.0, 8.0, 4.5, 1.2]; + let predictions = [10.0, 7.5, 5.0, 1.5]; + let statrese = observations + .into_iter() + .zip(predictions) + .map(|(observation, prediction)| { + weighted_squared_residual(&model, observation, prediction) + }) + .sum::(); + let sigma = (statrese / observations.len() as f64).sqrt(); + + assert!((statrese - 0.098_300_253_535_196_28).abs() < 1e-15); + assert!((sigma - 0.156_764_356_228_701_02).abs() < 1e-15); + } + + #[test] + fn residual_statistics_remain_separate_by_output() { + let model = ResidualErrorModel::constant(1.0); + let mut statistics = ResidualSufficientStatistics::zero(2); + statistics.accumulate_values(0, &model, 10.0, 8.0); + statistics.accumulate_values(1, &model, 10.0, 5.0); + + assert_eq!(statistics.output(0).unwrap().weighted_sum, 4.0); + assert_eq!(statistics.output(0).unwrap().observation_count, 1); + assert_eq!(statistics.output(1).unwrap().weighted_sum, 25.0); + assert_eq!(statistics.output(1).unwrap().observation_count, 1); + } + + #[test] + fn proportional_statistics_count_floor_and_non_finite_predictions() { + let model = ResidualErrorModel::proportional(0.1); + let mut statistics = ResidualSufficientStatistics::zero(1); + statistics.accumulate_values(0, &model, 1.0, 0.0); + statistics.accumulate_values(0, &model, 1.0, f64::NAN); + + let output = statistics.output(0).unwrap(); + assert_eq!(output.observation_count, 2); + assert_eq!(output.proportional_floor_count, 1); + assert_eq!(output.non_finite_prediction_count, 1); + assert!(!output.weighted_sum.is_finite()); + assert_eq!(output.sigma(), None); + } + + #[test] + fn residual_statistic_stochastic_update_matches_reference_shape() { + let current = ResidualSufficientStatistics { + outputs: vec![ + ResidualOutputStatistic { + weighted_sum: 10.0, + observation_count: 5, + ..Default::default() + }, + ResidualOutputStatistic { + weighted_sum: 8.0, + observation_count: 4, + ..Default::default() + }, + ], + observations: vec![Vec::new(), Vec::new()], + }; + let observed = ResidualSufficientStatistics { + outputs: vec![ + ResidualOutputStatistic { + weighted_sum: 20.0, + observation_count: 5, + ..Default::default() + }, + ResidualOutputStatistic { + weighted_sum: 4.0, + observation_count: 4, + ..Default::default() + }, + ], + observations: vec![Vec::new(), Vec::new()], + }; + let updated = current.stochastic_update(observed, 0.25); + assert_eq!(updated.output(0).unwrap().weighted_sum, 12.5); + assert_eq!( + updated.output(0).and_then(ResidualOutputStatistic::sigma), + Some((12.5_f64 / 5.0).sqrt()) + ); + assert_eq!(updated.output(1).unwrap().weighted_sum, 7.0); + assert_eq!( + updated.output(1).and_then(ResidualOutputStatistic::sigma), + Some((7.0_f64 / 4.0).sqrt()) + ); + } + + #[test] + fn combined_additive_collapse_warning_ignores_fixed_components() { + assert!(combined_additive_sigma_collapsed(5e-4, true)); + assert!(!combined_additive_sigma_collapsed(5e-4, false)); + assert!(!combined_additive_sigma_collapsed(0.5, true)); + } + + #[test] + fn combined_residual_nll_matches_quadrature_gaussian_formula() { + let observations = [ResidualObservation { + observation: 3.0, + prediction: 2.0, + }]; + let variance = 0.5_f64.powi(2) + 0.1_f64.powi(2) * 2.0_f64.powi(2); + let expected = 0.5 * (variance.ln() + 1.0 / variance); + + assert!((combined_residual_nll(&observations, 0.5, 0.1) - expected).abs() < 1e-12); + } + + #[test] + fn combined_residual_optimizer_recovers_known_additive_and_proportional_scales() { + let true_additive = 0.3; + let true_proportional = 0.1; + let mut observations = Vec::new(); + for prediction in [1.0_f64, 2.0, 4.0, 8.0] { + let sd = (true_additive * true_additive + + true_proportional * true_proportional * prediction * prediction) + .sqrt(); + observations.push(ResidualObservation { + observation: prediction - sd, + prediction, + }); + observations.push(ResidualObservation { + observation: prediction + sd, + prediction, + }); + } + + let initial_objective = combined_residual_nll(&observations, 0.6, 0.2); + let solution = + optimize_combined_residual(&observations, 0.6, 0.2, [true, true], 1e-6, 500).unwrap(); + + assert!(solution.objective < initial_objective); + assert!(solution.converged); + assert!((solution.additive_sd - true_additive).abs() / true_additive < 0.05); + assert!((solution.proportional_sd - true_proportional).abs() / true_proportional < 0.05); + assert!(solution.iterations > 0); + assert!(!solution.termination.is_empty()); + + let fixed_additive = + optimize_combined_residual(&observations, true_additive, 0.2, [false, true], 1e-6, 500) + .unwrap(); + assert_eq!(fixed_additive.additive_sd, true_additive); + assert!( + (fixed_additive.proportional_sd - true_proportional).abs() / true_proportional < 0.05 + ); + + let fixed_proportional = optimize_combined_residual( + &observations, + 0.6, + true_proportional, + [true, false], + 1e-6, + 500, + ) + .unwrap(); + assert_eq!(fixed_proportional.proportional_sd, true_proportional); + assert!((fixed_proportional.additive_sd - true_additive).abs() / true_additive < 0.05); + } + + fn correlated_truth_observations() -> Vec { + let (a, b, rho) = (0.7_f64, 0.25_f64, -0.3_f64); + let mut observations = Vec::new(); + for prediction in [-2.0_f64, 0.5, 3.0] { + let variance = a * a + 2.0 * rho * a * b * prediction + b * b * prediction * prediction; + let sd = variance.sqrt(); + observations.push(ResidualObservation { + observation: prediction - sd, + prediction, + }); + observations.push(ResidualObservation { + observation: prediction + sd, + prediction, + }); + } + observations + } + + #[test] + fn correlated_combined_nll_matches_signed_formula_and_rho_zero_combined() { + for prediction in [-2.0_f64, 0.0, 3.0] { + let observation = prediction + 0.4; + let observations = [ResidualObservation { + observation, + prediction, + }]; + let variance = 0.7_f64.powi(2) + + 2.0 * -0.3 * 0.7 * 0.25 * prediction + + 0.25_f64.powi(2) * prediction.powi(2); + let expected = 0.5 * (variance.ln() + 0.4_f64.powi(2) / variance); + assert!( + (correlated_combined_residual_nll(&observations, 0.7, 0.25, -0.3) - expected).abs() + < 1e-12 + ); + assert_eq!( + correlated_combined_residual_nll(&observations, 0.7, 0.25, 0.0), + combined_residual_nll(&observations, 0.7, 0.25) + ); + } + } + + #[test] + fn correlated_combined_optimizer_recovers_all_components_and_preserves_fixed_values() { + let observations = correlated_truth_observations(); + let truth = [0.7, 0.25, -0.3]; + let all_free = optimize_correlated_combined_residual( + &observations, + 0.45, + 0.4, + 0.25, + [true, true, true], + 1e-6, + 1_000, + ) + .unwrap(); + assert!(all_free.converged); + assert!((all_free.additive_sd - truth[0]).abs() < 2e-3); + assert!((all_free.proportional_sd - truth[1]).abs() < 2e-3); + assert!((all_free.correlation - truth[2]).abs() < 2e-3); + assert!(all_free.objective.is_finite()); + assert!(all_free.iterations > 0); + + for estimated in [ + [false, true, true], + [true, false, true], + [true, true, false], + [false, false, true], + [false, true, false], + [true, false, false], + ] { + let solution = optimize_correlated_combined_residual( + &observations, + truth[0], + truth[1], + truth[2], + estimated, + 1e-6, + 1_000, + ) + .unwrap(); + let values = [ + solution.additive_sd, + solution.proportional_sd, + solution.correlation, + ]; + for component in 0..3 { + assert!(values[component].is_finite()); + if !estimated[component] { + assert_eq!(values[component], truth[component]); + } else { + assert!((values[component] - truth[component]).abs() < 2e-3); + } + } + assert!(solution.correlation > -1.0 && solution.correlation < 1.0); + } + } + + #[test] + fn correlated_combined_optimizer_rejects_when_no_valid_candidate_exists() { + let invalid = [ResidualObservation { + observation: f64::NAN, + prediction: 1.0, + }]; + let result = optimize_correlated_combined_residual( + &invalid, + 0.7, + 0.25, + -0.3, + [true, true, true], + 1e-6, + 50, + ); + assert!(result.is_err()); + } + + #[test] + fn sigma_update_mutates_only_estimated_simple_models() { + use crate::estimation::ParametricErrorModel; + + let mut models = ParametricErrorModels::new() + .add(0, "first", ResidualErrorModel::constant(0.5).into()) + .add( + 1, + "second", + ParametricErrorModel::from(ResidualErrorModel::proportional(0.1)).fixed(), + ) + .add(2, "third", ResidualErrorModel::combined(0.5, 0.1).into()) + .add(3, "fourth", ResidualErrorModel::exponential(0.2).into()); + + update_estimated_simple_residual_model_with_sigma(&mut models, 0, 2.0); + update_estimated_simple_residual_model_with_sigma(&mut models, 1, 3.0); + update_estimated_simple_residual_model_with_sigma(&mut models, 2, 4.0); + update_estimated_simple_residual_model_with_sigma(&mut models, 3, 0.3); + + assert_eq!( + primary_sigma_parameters(models.models()), + vec![2.0, 0.1, 0.5, 0.3] + ); + assert_eq!(models.output_name(0), Some("first")); + assert_eq!(models.output_name(1), Some("second")); + assert_eq!(models.output_name(2), Some("third")); + assert_eq!(models.output_name(3), Some("fourth")); + assert_eq!( + models.models().get(0), + Some(&ResidualErrorModel::constant(2.0)) + ); + assert_eq!( + models.models().get(1), + Some(&ResidualErrorModel::proportional(0.1)) + ); + assert_eq!( + models.models().get(2), + Some(&ResidualErrorModel::combined(0.5, 0.1)) + ); + assert_eq!( + models.models().get(3), + Some(&ResidualErrorModel::exponential(0.3)) + ); + } +} diff --git a/src/estimation/parametric/shrinkage.rs b/src/estimation/parametric/shrinkage.rs new file mode 100644 index 000000000..adef010e0 --- /dev/null +++ b/src/estimation/parametric/shrinkage.rs @@ -0,0 +1,946 @@ +//! Eta and kappa shrinkage diagnostics. +//! +//! Shrinkage quantifies how much individual empirical Bayes estimates are +//! pulled toward the population mean. A value near 100% indicates that +//! individual estimates are tightly clustered around the population mean +//! and provide little independent information; a value near 0% indicates +//! that individual estimates spread out about as much as the estimated +//! population variance. +//! +//! ## Units and denominators +//! +//! - **Eta (η) shrinkage**: one estimate per subject — the denominator +//! `unit_count` equals the number of subjects. +//! - **Kappa (κ) shrinkage**: one estimate per subject-occasion pair, +//! pooled across all subjects — the denominator `unit_count` equals +//! the total number of subject-occasion pairs. +//! +//! ## Formula +//! +//! ```text +//! shrinkage = 100 × (1 − sample_variance_{N−1} / final_variance) +//! ``` +//! +//! where `sample_variance_{N−1}` is the unbiased (N − 1) sample variance of +//! the individual estimates across units, and `final_variance` is the +//! corresponding diagonal element of the final population covariance matrix +//! (Ω for eta, inter-occasion covariance for kappa). +//! +//! No clamping is applied. Values below 0% (sample variance exceeds final +//! variance) and above 100% (floating-point edge cases) are preserved as-is. + +use serde::{Deserialize, Serialize}; + +// --------------------------------------------------------------------------- +// Shrinkage value and unavailable reasons +// --------------------------------------------------------------------------- + +/// Computed shrinkage for one effect, or a typed reason the value is +/// unavailable. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(tag = "status", rename_all = "snake_case")] +pub enum ShrinkageValue { + /// Shrinkage was successfully computed. + Available { + /// Shrinkage percentage (no clamping applied). + value: f64, + /// Number of independent units in the denominator. + /// + /// For eta: number of subjects. + /// For kappa: number of pooled subject-occasion pairs. + unit_count: usize, + /// Human-readable documentation of what `unit_count` counts. + denominator_documentation: String, + }, + /// Shrinkage could not be computed. + Unavailable { reason: ShrinkageUnavailableReason }, +} + +/// Typed reason that a shrinkage value is unavailable. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(tag = "reason", content = "detail", rename_all = "snake_case")] +pub enum ShrinkageUnavailableReason { + /// Fewer than 2 independent units. + /// + /// At least 2 units are required to compute a meaningful sample variance + /// with the N−1 denominator. + TooFewUnits { count: usize }, + /// MAP estimates were requested but are not available. + MissingMap, + /// One or more input values are non-finite (NaN or infinity). + NonFiniteValue, + /// The reference population variance is non-positive (zero, negative, or + /// non-finite). + NonPositiveReferenceVariance { variance: f64 }, + /// Width mismatch between effect names, posterior rows, and covariance + /// diagonal. + WidthMismatch { + effect_names: usize, + row_width: usize, + variance_len: usize, + }, +} + +// --------------------------------------------------------------------------- +// Per-effect shrinkage result types +// --------------------------------------------------------------------------- + +/// Eta (η) shrinkage computed from subject posterior means. +/// +/// One value per random effect. The denominator `unit_count` equals the +/// number of subjects. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct EtaPosteriorMeanShrinkage { + /// Random-effect name. + pub effect: String, + /// Computed shrinkage or unavailable reason. + pub shrinkage: ShrinkageValue, +} + +/// Eta (η) shrinkage computed from subject MAP (maximum a posteriori) +/// estimates. +/// +/// One value per random effect. The denominator `unit_count` equals the +/// number of subjects. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct EtaMapShrinkage { + /// Random-effect name. + pub effect: String, + /// Computed shrinkage or unavailable reason. + pub shrinkage: ShrinkageValue, +} + +/// Kappa (κ) shrinkage computed from occasion posterior means. +/// +/// One value per inter-occasion random effect. The denominator `unit_count` +/// equals the number of pooled subject-occasion pairs. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct KappaPosteriorMeanShrinkage { + /// Random-effect name. + pub effect: String, + /// Computed shrinkage or unavailable reason. + pub shrinkage: ShrinkageValue, +} + +/// Kappa (κ) shrinkage computed from occasion MAP (maximum a posteriori) +/// estimates. +/// +/// One value per inter-occasion random effect. The denominator `unit_count` +/// equals the number of pooled subject-occasion pairs. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct KappaMapShrinkage { + /// Random-effect name. + pub effect: String, + /// Computed shrinkage or unavailable reason. + pub shrinkage: ShrinkageValue, +} + +/// Source-explicit shrinkage diagnostics retained by a parametric result. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ShrinkageDiagnostics { + pub eta_posterior_mean: Vec, + pub eta_map: Vec, + pub kappa_posterior_mean: Vec, + pub kappa_map: Vec, +} + +// --------------------------------------------------------------------------- +// Internal constants +// --------------------------------------------------------------------------- + +const ETA_DENOMINATOR: &str = "number of subjects"; +const KAPPA_DENOMINATOR: &str = "number of pooled subject-occasion pairs"; + +// --------------------------------------------------------------------------- +// Core computation +// --------------------------------------------------------------------------- + +/// Compute 100 × (1 − sample_variance_{N−1} / final_variance). +/// +/// Returns `Ok(value)` on success or `Err(reason)` on any precondition +/// failure. No clamping is applied — the caller receives the raw result. +fn compute_shrinkage( + unit_values: &[f64], + final_variance: f64, + unit_count: usize, +) -> Result { + // Validate final variance must be strictly positive and finite. + if !final_variance.is_finite() || final_variance <= 0.0 { + return Err(ShrinkageUnavailableReason::NonPositiveReferenceVariance { + variance: final_variance, + }); + } + + // Validate all unit values are finite. + if unit_values.iter().any(|v| !v.is_finite()) { + return Err(ShrinkageUnavailableReason::NonFiniteValue); + } + + // At least two units required for N−1 sample variance. + if unit_count < 2 { + return Err(ShrinkageUnavailableReason::TooFewUnits { count: unit_count }); + } + + // Sample mean. + let mean = unit_values.iter().sum::() / unit_count as f64; + + // Unbiased sample variance (N−1 denominator). + let sample_var = unit_values + .iter() + .map(|v| { + let diff = v - mean; + diff * diff + }) + .sum::() + / (unit_count - 1) as f64; + + // No clamping: negative and >100 preserved if achievable. + let shrinkage = 100.0 * (1.0 - sample_var / final_variance); + Ok(shrinkage) +} + +// --------------------------------------------------------------------------- +// Input validation +// --------------------------------------------------------------------------- + +/// Validate that effect names, row widths, and final variance diagonal have +/// matching dimensions and that all rows are uniformly wide. +fn validate_widths( + effect_names: &[String], + rows: &[Vec], + final_var_diag: &[f64], +) -> Result<(), ShrinkageUnavailableReason> { + let effect_count = effect_names.len(); + let row_width = rows.first().map_or(effect_count, |r| r.len()); + let variance_len = final_var_diag.len(); + + if effect_count != row_width || row_width != variance_len { + return Err(ShrinkageUnavailableReason::WidthMismatch { + effect_names: effect_count, + row_width, + variance_len, + }); + } + + // Every row must have the same width. + if rows.iter().any(|r| r.len() != row_width) { + return Err(ShrinkageUnavailableReason::WidthMismatch { + effect_names: effect_count, + row_width, + variance_len, + }); + } + + Ok(()) +} + +/// Extract the column (by effect index) from all rows. +fn extract_column(rows: &[Vec], col: usize) -> Vec { + rows.iter().map(|r| r[col]).collect() +} + +// --------------------------------------------------------------------------- +// Public derivation functions +// --------------------------------------------------------------------------- + +/// Derive eta (η) shrinkage for each random effect from subject posterior +/// means. +/// +/// # Parameters +/// +/// - `effect_names` — one name per random effect, in order. +/// - `final_var_diag` — diagonal of the final Ω covariance matrix, one entry +/// per effect. +/// - `posterior_mean_rows` — one row per subject, one column per effect. +/// Each row is the posterior mean η vector for that subject. +/// +/// # Denominator +/// +/// `unit_count` = number of rows = number of subjects. +pub(crate) fn derive_eta_posterior_mean_shrinkage( + effect_names: &[String], + final_var_diag: &[f64], + posterior_mean_rows: &[Vec], +) -> Vec { + if let Err(reason) = validate_widths(effect_names, posterior_mean_rows, final_var_diag) { + return effect_names + .iter() + .map(|effect| EtaPosteriorMeanShrinkage { + effect: effect.clone(), + shrinkage: ShrinkageValue::Unavailable { + reason: reason.clone(), + }, + }) + .collect(); + } + + let unit_count = posterior_mean_rows.len(); + effect_names + .iter() + .enumerate() + .map(|(i, effect)| { + let col = extract_column(posterior_mean_rows, i); + let shrinkage = match compute_shrinkage(&col, final_var_diag[i], unit_count) { + Ok(value) => ShrinkageValue::Available { + value, + unit_count, + denominator_documentation: ETA_DENOMINATOR.to_owned(), + }, + Err(reason) => ShrinkageValue::Unavailable { reason }, + }; + EtaPosteriorMeanShrinkage { + effect: effect.clone(), + shrinkage, + } + }) + .collect() +} + +/// Derive eta (η) shrinkage for each random effect from subject MAP estimates. +/// +/// # Parameters +/// +/// - `effect_names` — one name per random effect, in order. +/// - `final_var_diag` — diagonal of the final Ω covariance matrix, one entry +/// per effect. +/// - `map_rows` — one row per subject, one column per effect. +/// Each row is the MAP η vector for that subject. +/// +/// # Denominator +/// +/// `unit_count` = number of rows = number of subjects. +pub(crate) fn derive_eta_map_shrinkage( + effect_names: &[String], + final_var_diag: &[f64], + map_rows: Option<&[Vec]>, +) -> Vec { + let map_rows = match map_rows { + Some(rows) => rows, + None => { + return effect_names + .iter() + .map(|effect| EtaMapShrinkage { + effect: effect.clone(), + shrinkage: ShrinkageValue::Unavailable { + reason: ShrinkageUnavailableReason::MissingMap, + }, + }) + .collect(); + } + }; + + if let Err(reason) = validate_widths(effect_names, map_rows, final_var_diag) { + return effect_names + .iter() + .map(|effect| EtaMapShrinkage { + effect: effect.clone(), + shrinkage: ShrinkageValue::Unavailable { + reason: reason.clone(), + }, + }) + .collect(); + } + + let unit_count = map_rows.len(); + effect_names + .iter() + .enumerate() + .map(|(i, effect)| { + let col = extract_column(map_rows, i); + let shrinkage = match compute_shrinkage(&col, final_var_diag[i], unit_count) { + Ok(value) => ShrinkageValue::Available { + value, + unit_count, + denominator_documentation: ETA_DENOMINATOR.to_owned(), + }, + Err(reason) => ShrinkageValue::Unavailable { reason }, + }; + EtaMapShrinkage { + effect: effect.clone(), + shrinkage, + } + }) + .collect() +} + +/// Derive kappa (κ) shrinkage for each inter-occasion random effect from +/// occasion posterior means. +/// +/// # Parameters +/// +/// - `effect_names` — one name per inter-occasion random effect, in order. +/// - `final_var_diag` — diagonal of the final inter-occasion covariance +/// matrix, one entry per effect. +/// - `posterior_mean_rows` — one row per subject-occasion pair, one column +/// per effect. Rows from all subjects are pooled together. +/// +/// # Denominator +/// +/// `unit_count` = number of rows = number of pooled subject-occasion pairs. +pub(crate) fn derive_kappa_posterior_mean_shrinkage( + effect_names: &[String], + final_var_diag: &[f64], + posterior_mean_rows: &[Vec], +) -> Vec { + if let Err(reason) = validate_widths(effect_names, posterior_mean_rows, final_var_diag) { + return effect_names + .iter() + .map(|effect| KappaPosteriorMeanShrinkage { + effect: effect.clone(), + shrinkage: ShrinkageValue::Unavailable { + reason: reason.clone(), + }, + }) + .collect(); + } + + let unit_count = posterior_mean_rows.len(); + effect_names + .iter() + .enumerate() + .map(|(i, effect)| { + let col = extract_column(posterior_mean_rows, i); + let shrinkage = match compute_shrinkage(&col, final_var_diag[i], unit_count) { + Ok(value) => ShrinkageValue::Available { + value, + unit_count, + denominator_documentation: KAPPA_DENOMINATOR.to_owned(), + }, + Err(reason) => ShrinkageValue::Unavailable { reason }, + }; + KappaPosteriorMeanShrinkage { + effect: effect.clone(), + shrinkage, + } + }) + .collect() +} + +/// Derive kappa (κ) shrinkage for each inter-occasion random effect from +/// occasion MAP estimates. +/// +/// # Parameters +/// +/// - `effect_names` — one name per inter-occasion random effect, in order. +/// - `final_var_diag` — diagonal of the final inter-occasion covariance +/// matrix, one entry per effect. +/// - `map_rows` — one row per subject-occasion pair, one column per effect. +/// Rows from all subjects are pooled together. +/// +/// # Denominator +/// +/// `unit_count` = number of rows = number of pooled subject-occasion pairs. +pub(crate) fn derive_kappa_map_shrinkage( + effect_names: &[String], + final_var_diag: &[f64], + map_rows: Option<&[Vec]>, +) -> Vec { + let map_rows = match map_rows { + Some(rows) => rows, + None => { + return effect_names + .iter() + .map(|effect| KappaMapShrinkage { + effect: effect.clone(), + shrinkage: ShrinkageValue::Unavailable { + reason: ShrinkageUnavailableReason::MissingMap, + }, + }) + .collect(); + } + }; + + if let Err(reason) = validate_widths(effect_names, map_rows, final_var_diag) { + return effect_names + .iter() + .map(|effect| KappaMapShrinkage { + effect: effect.clone(), + shrinkage: ShrinkageValue::Unavailable { + reason: reason.clone(), + }, + }) + .collect(); + } + + let unit_count = map_rows.len(); + effect_names + .iter() + .enumerate() + .map(|(i, effect)| { + let col = extract_column(map_rows, i); + let shrinkage = match compute_shrinkage(&col, final_var_diag[i], unit_count) { + Ok(value) => ShrinkageValue::Available { + value, + unit_count, + denominator_documentation: KAPPA_DENOMINATOR.to_owned(), + }, + Err(reason) => ShrinkageValue::Unavailable { reason }, + }; + KappaMapShrinkage { + effect: effect.clone(), + shrinkage, + } + }) + .collect() +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::parametric::shrinkage::ShrinkageUnavailableReason::{ + MissingMap, NonFiniteValue, NonPositiveReferenceVariance, TooFewUnits, WidthMismatch, + }; + + // ── Helpers ── + + fn names(names: &[&str]) -> Vec { + names.iter().map(|s| s.to_string()).collect() + } + + fn unpack_available(value: &ShrinkageValue) -> (f64, usize, &str) { + match value { + ShrinkageValue::Available { + value, + unit_count, + denominator_documentation, + } => (*value, *unit_count, denominator_documentation.as_str()), + ShrinkageValue::Unavailable { reason } => { + panic!("expected Available, got Unavailable: {reason:?}") + } + } + } + + fn unpack_unavailable(value: &ShrinkageValue) -> &ShrinkageUnavailableReason { + match value { + ShrinkageValue::Unavailable { reason } => reason, + ShrinkageValue::Available { .. } => panic!("expected Unavailable, got Available"), + } + } + + // ── Formula correctness ── + + #[test] + fn shrinkage_zero_when_sample_variance_equals_final_variance() { + // Two subjects with η values [-1, 1]. Sample variance: + // mean = 0, var = ((-1)^2 + 1^2) / (2-1) = 2/1 = 2. + // final_variance = 2 → shrinkage = 100*(1 - 2/2) = 0. + let rows = vec![vec![-1.0], vec![1.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[2.0], &rows); + let (val, count, doc) = unpack_available(&results[0].shrinkage); + assert_eq!(results[0].effect, "CL"); + assert!((val - 0.0).abs() < 1e-12); + assert_eq!(count, 2); + assert_eq!(doc, "number of subjects"); + } + + #[test] + fn shrinkage_fifty_when_sample_variance_is_half_of_final() { + // Three subjects with values [0, 2, 4]. mean = 2. + // sample var: ((0-2)^2 + (2-2)^2 + (4-2)^2) / 2 = (4 + 0 + 4)/2 = 4. + // final_variance = 8 → shrinkage = 100*(1 - 4/8) = 50. + let rows = vec![vec![0.0], vec![2.0], vec![4.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["V"]), &[8.0], &rows); + let (val, _, _) = unpack_available(&results[0].shrinkage); + assert!((val - 50.0).abs() < 1e-12); + } + + #[test] + fn shrinkage_one_hundred_when_all_posterior_means_identical() { + // All identical → sample variance = 0 → shrinkage = 100%. + let rows = vec![vec![3.0], vec![3.0], vec![3.0], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["KA"]), &[2.0], &rows); + let (val, count, _) = unpack_available(&results[0].shrinkage); + assert!((val - 100.0).abs() < 1e-12); + assert_eq!(count, 4); + } + + #[test] + fn negative_shrinkage_preserved_when_sample_variance_exceeds_final_variance() { + // Two subjects: values [-3, 3]. mean = 0. + // sample var: ((-3)^2 + 3^2) / 1 = 18. + // final_variance = 2 → shrinkage = 100*(1 - 18/2) = 100*(1 - 9) = -800. + let rows = vec![vec![-3.0], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[2.0], &rows); + let (val, _, _) = unpack_available(&results[0].shrinkage); + assert!((val - (-800.0)).abs() < 1e-12); + assert!(val < 0.0, "negative shrinkage must be preserved, got {val}"); + } + + #[test] + fn posterior_mean_differs_from_map() { + // Two effects, three subjects. + // Posterior means and MAP differ → shrinkage values must differ. + let effect_names = names(&["CL", "V"]); + let final_var_diag = vec![4.0, 9.0]; + + // Posterior mean rows + let post_mean_rows = vec![ + vec![1.0, 3.0], // subject 1 + vec![2.0, 6.0], // subject 2 + vec![3.0, 9.0], // subject 3 + ]; + // MAP rows (different values) + let map_rows = vec![ + vec![0.5, 1.0], // subject 1 + vec![2.5, 5.0], // subject 2 + vec![4.0, 14.0], // subject 3 + ]; + + let post_results = + derive_eta_posterior_mean_shrinkage(&effect_names, &final_var_diag, &post_mean_rows); + let map_results = derive_eta_map_shrinkage(&effect_names, &final_var_diag, Some(&map_rows)); + + let (post_cl, _, _) = unpack_available(&post_results[0].shrinkage); + let (map_cl, _, _) = unpack_available(&map_results[0].shrinkage); + let (post_v, _, _) = unpack_available(&post_results[1].shrinkage); + let (map_v, _, _) = unpack_available(&map_results[1].shrinkage); + + // Both effects should differ between posterior mean and MAP. + assert!( + (post_cl - map_cl).abs() > 1e-12, + "CL shrinkage should differ: posterior_mean={post_cl}, map={map_cl}" + ); + assert!( + (post_v - map_v).abs() > 1e-12, + "V shrinkage should differ: posterior_mean={post_v}, map={map_v}" + ); + } + + #[test] + fn kappa_pooled_units_use_correct_denominator() { + // 2 subjects × 3 occasions = 6 pooled rows. + let rows = vec![ + vec![0.0], + vec![1.0], + vec![2.0], // subject 1 occasions + vec![3.0], + vec![4.0], + vec![5.0], // subject 2 occasions + ]; + let results = derive_kappa_posterior_mean_shrinkage(&names(&["IOV_CL"]), &[10.0], &rows); + let (val, count, doc) = unpack_available(&results[0].shrinkage); + assert_eq!(count, 6); + assert_eq!(doc, "number of pooled subject-occasion pairs"); + // mean = 2.5, sample var = ((0-2.5)^2 + ... + (5-2.5)^2) / 5 + // = (6.25 + 2.25 + 0.25 + 0.25 + 2.25 + 6.25) / 5 = 17.5/5 = 3.5 + // shrinkage = 100*(1 - 3.5/10) = 65 + assert!((val - 65.0).abs() < 1e-12); + } + + // ── Multiple effects ── + + #[test] + fn multiple_effects_each_have_independent_shrinkage() { + let effect_names = names(&["CL", "V", "KA"]); + let final_var_diag = vec![4.0, 16.0, 1.0]; + // 4 subjects + let rows = vec![ + vec![1.0, 2.0, 0.5], + vec![3.0, 6.0, 1.5], + vec![1.0, 2.0, 0.5], + vec![3.0, 6.0, 1.5], + ]; + let results = derive_eta_posterior_mean_shrinkage(&effect_names, &final_var_diag, &rows); + assert_eq!(results.len(), 3); + assert_eq!(results[0].effect, "CL"); + assert_eq!(results[1].effect, "V"); + assert_eq!(results[2].effect, "KA"); + + // CL: post means [1,3,1,3], mean=2, sample var = (1+1+1+1)/3 = 4/3 ≈ 1.333 + // shrinkage = 100*(1 - 1.333/4) = 100*(1 - 0.3333) ≈ 66.667 + let (cl_val, _, _) = unpack_available(&results[0].shrinkage); + assert!((cl_val - 100.0 * (1.0 - 4.0 / 3.0 / 4.0)).abs() < 1e-12); + + // V: squared deviations sum to 16, so sample variance = 16/3. + let (v_val, _, _) = unpack_available(&results[1].shrinkage); + assert!((v_val - 100.0 * (1.0 - 16.0 / 3.0 / 16.0)).abs() < 1e-12); + + // KA: squared deviations sum to 1, so sample variance = 1/3. + let (ka_val, _, _) = unpack_available(&results[2].shrinkage); + assert!((ka_val - 100.0 * (1.0 - 1.0 / 3.0 / 1.0)).abs() < 1e-12); + } + + // ── Unavailable: TooFewUnits ── + + #[test] + fn single_subject_yields_too_few_units() { + let rows = vec![vec![5.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[2.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &TooFewUnits { count: 1 }); + } + + #[test] + fn zero_subjects_yields_too_few_units() { + let rows: Vec> = vec![]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[2.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &TooFewUnits { count: 0 }); + } + + // ── Unavailable: MissingMap ── + + #[test] + fn missing_map_yields_missing_map_reason() { + let results = derive_eta_map_shrinkage(&names(&["CL"]), &[2.0], None); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &MissingMap); + } + + #[test] + fn missing_kappa_map_yields_missing_map_reason() { + let results = derive_kappa_map_shrinkage(&names(&["IOV_V"]), &[3.0], None); + assert_eq!(results.len(), 1); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &MissingMap); + } + + // ── Unavailable: NonFiniteValue ── + + #[test] + fn nan_in_posterior_mean_yields_non_finite() { + let rows = vec![vec![f64::NAN], vec![1.0], vec![2.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[4.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &NonFiniteValue); + } + + #[test] + fn infinity_in_posterior_mean_yields_non_finite() { + let rows = vec![vec![1.0], vec![f64::INFINITY], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[4.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &NonFiniteValue); + } + + #[test] + fn neg_infinity_in_posterior_mean_yields_non_finite() { + let rows = vec![vec![1.0], vec![f64::NEG_INFINITY], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[4.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &NonFiniteValue); + } + + // ── Unavailable: NonPositiveReferenceVariance ── + + #[test] + fn zero_final_variance_yields_non_positive_reference() { + let rows = vec![vec![1.0], vec![2.0], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[0.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &NonPositiveReferenceVariance { variance: 0.0 }); + } + + #[test] + fn negative_final_variance_yields_non_positive_reference() { + let rows = vec![vec![1.0], vec![2.0], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[-1.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &NonPositiveReferenceVariance { variance: -1.0 }); + } + + #[test] + fn nan_final_variance_yields_non_positive_reference() { + let rows = vec![vec![1.0], vec![2.0], vec![3.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[f64::NAN], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert!(matches!(reason, NonPositiveReferenceVariance { .. })); + } + + // ── Unavailable: WidthMismatch ── + + #[test] + fn effect_names_fewer_than_row_columns_yields_width_mismatch() { + let rows = vec![vec![1.0, 2.0], vec![3.0, 4.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[4.0, 9.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!( + reason, + &WidthMismatch { + effect_names: 1, + row_width: 2, + variance_len: 2, + } + ); + } + + #[test] + fn variance_diag_shorter_than_effects_yields_width_mismatch() { + let rows = vec![vec![1.0, 2.0], vec![3.0, 4.0]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL", "V"]), &[4.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!( + reason, + &WidthMismatch { + effect_names: 2, + row_width: 2, + variance_len: 1, + } + ); + } + + #[test] + fn jagged_rows_yield_width_mismatch() { + let rows = vec![vec![1.0], vec![2.0, 3.0], vec![4.0]]; + let results = derive_kappa_posterior_mean_shrinkage(&names(&["IOV_CL"]), &[5.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert!(matches!(reason, WidthMismatch { .. })); + } + + #[test] + fn empty_rows_with_effects_yields_too_few_units() { + let rows: Vec> = vec![]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL", "V"]), &[4.0, 9.0], &rows); + let reason = unpack_unavailable(&results[0].shrinkage); + assert_eq!(reason, &TooFewUnits { count: 0 }); + } + + // ── Kappa-specific: pooled subject-occasion pairs ── + + #[test] + fn kappa_map_differs_from_posterior_mean() { + let effect_names = names(&["IOV_CL", "IOV_V"]); + let final_var_diag = vec![4.0, 9.0]; + + // 2 subjects × 3 occasions = 6 pooled rows + let post_mean_rows = vec![ + vec![1.0, 2.0], + vec![2.0, 4.0], + vec![3.0, 6.0], + vec![4.0, 8.0], + vec![5.0, 10.0], + vec![6.0, 12.0], + ]; + let map_rows = vec![ + vec![0.0, 1.0], + vec![1.5, 3.0], + vec![3.5, 7.0], + vec![4.5, 9.0], + vec![4.0, 8.0], + vec![7.0, 14.0], + ]; + + let post_results = + derive_kappa_posterior_mean_shrinkage(&effect_names, &final_var_diag, &post_mean_rows); + let map_results = + derive_kappa_map_shrinkage(&effect_names, &final_var_diag, Some(&map_rows)); + + let (post_cl, post_count, post_doc) = unpack_available(&post_results[0].shrinkage); + let (map_cl, map_count, map_doc) = unpack_available(&map_results[0].shrinkage); + + assert_eq!(post_count, 6); + assert_eq!(map_count, 6); + assert_eq!(post_doc, "number of pooled subject-occasion pairs"); + assert_eq!(map_doc, "number of pooled subject-occasion pairs"); + assert!( + (post_cl - map_cl).abs() > 1e-12, + "kappa CL shrinkage should differ: posterior_mean={post_cl}, map={map_cl}" + ); + } + + // ── MAP paths with valid data work identically to posterior-mean paths ── + + #[test] + fn eta_map_with_valid_rows_computes_correctly() { + let rows = vec![vec![0.0], vec![2.0], vec![4.0]]; + let results = derive_eta_map_shrinkage(&names(&["V"]), &[8.0], Some(&rows)); + let (val, count, doc) = unpack_available(&results[0].shrinkage); + assert!((val - 50.0).abs() < 1e-12); + assert_eq!(count, 3); + assert_eq!(doc, "number of subjects"); + } + + #[test] + fn kappa_map_with_valid_rows_computes_correctly() { + let rows = vec![vec![1.0], vec![2.0], vec![3.0], vec![4.0]]; + let results = derive_kappa_map_shrinkage(&names(&["IOV_CL"]), &[5.0], Some(&rows)); + let (val, count, doc) = unpack_available(&results[0].shrinkage); + // mean = 2.5, sample var = (2.25+0.25+0.25+2.25)/3 = 5/3 ≈ 1.667 + // shrinkage = 100*(1 - 1.667/5) ≈ 66.667 + assert!((val - 100.0 * (1.0 - 5.0 / 3.0 / 5.0)).abs() < 1e-12); + assert_eq!(count, 4); + assert_eq!(doc, "number of pooled subject-occasion pairs"); + } + + // ── Serde round-trip ── + + #[test] + fn serde_round_trip_available() { + let value = ShrinkageValue::Available { + value: 42.5, + unit_count: 10, + denominator_documentation: "number of subjects".to_owned(), + }; + let json = serde_json::to_string(&value).unwrap(); + let round_tripped: ShrinkageValue = serde_json::from_str(&json).unwrap(); + assert_eq!(value, round_tripped); + } + + #[test] + fn serde_round_trip_unavailable() { + let value = ShrinkageValue::Unavailable { + reason: ShrinkageUnavailableReason::TooFewUnits { count: 1 }, + }; + let json = serde_json::to_string(&value).unwrap(); + let round_tripped: ShrinkageValue = serde_json::from_str(&json).unwrap(); + assert_eq!(value, round_tripped); + } + + #[test] + fn serde_round_trip_missing_map() { + let value = ShrinkageValue::Unavailable { reason: MissingMap }; + let json = serde_json::to_string(&value).unwrap(); + let round_tripped: ShrinkageValue = serde_json::from_str(&json).unwrap(); + assert_eq!(value, round_tripped); + } + + #[test] + fn serde_json_structure_is_stable() { + let effect = EtaPosteriorMeanShrinkage { + effect: "CL".to_owned(), + shrinkage: ShrinkageValue::Available { + value: 25.0, + unit_count: 3, + denominator_documentation: "number of subjects".to_owned(), + }, + }; + let json = serde_json::to_string_pretty(&effect).unwrap(); + // Verify key fields are present with expected values. + assert!(json.contains("\"effect\": \"CL\"")); + assert!(json.contains("\"status\": \"available\"")); + assert!(json.contains("\"value\": 25.0")); + assert!(json.contains("\"unit_count\": 3")); + assert!(json.contains("\"denominator_documentation\": \"number of subjects\"")); + } + + // ── No clamping: edge cases ── + + #[test] + fn near_zero_sample_variance_produces_near_100_shrinkage_no_clamp() { + // Very tiny but non-zero sample variance → shrinkage < 100 but very close. + // Two values almost identical. + let rows = vec![vec![5.0], vec![5.0 + 1e-6]]; + let results = derive_eta_posterior_mean_shrinkage(&names(&["CL"]), &[1.0], &rows); + let (val, _, _) = unpack_available(&results[0].shrinkage); + // The nonzero variance is small enough to be near 100%, but large + // enough that the subtraction remains representable in binary64. + assert!(val < 100.0, "should be slightly below 100, got {val}"); + assert!(val > 99.999, "should be very close to 100, got {val}"); + } + + #[test] + fn negative_shrinkage_round_trips_through_serde() { + let value = ShrinkageValue::Available { + value: -150.0, + unit_count: 5, + denominator_documentation: "number of subjects".to_owned(), + }; + let json = serde_json::to_string(&value).unwrap(); + assert!(json.contains("-150.0")); + let round_tripped: ShrinkageValue = serde_json::from_str(&json).unwrap(); + assert_eq!(value, round_tripped); + } +} diff --git a/src/estimation/parametric/sufficient.rs b/src/estimation/parametric/sufficient.rs new file mode 100644 index 000000000..f40065efb --- /dev/null +++ b/src/estimation/parametric/sufficient.rs @@ -0,0 +1,320 @@ +use anyhow::{bail, Result}; +use ndarray::Array2; + +#[cfg(test)] +use super::covariance::ensure_positive_definite_covariance; + +/// SAEM φ sufficient statistics for models without covariate effects. +/// +/// This uses first and second φ moments in the no-covariate case: +/// `mean_phi = E[φ]` and `second_moment = E[φφᵀ]`. Ω is then the centered +/// covariance `E[(φ-μ)(φ-μ)ᵀ]`, with a diagonal floor to prevent the early +/// collapse identified by numerical robustness analysis. +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct PhiSufficientStatistics { + pub(crate) mean_phi: Vec, + pub(crate) second_moment: Array2, +} + +/// Subject-resolved moments used only when a covariate model is active. +/// +/// `expected_phi` preserves deterministic subject order while +/// `global_second_moment` averages all subject/chain outer products in the IIV +/// coordinate system. +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct CovariateSufficientStatistics { + pub(crate) expected_phi: Vec>, + pub(crate) global_second_moment: Array2, +} + +impl CovariateSufficientStatistics { + pub(crate) fn from_subject_chains(subject_phi: &[Vec>]) -> Result { + let Some(first_chain) = subject_phi.first().and_then(|subject| subject.first()) else { + bail!("cannot build covariate statistics without subjects and chains"); + }; + let width = first_chain.len(); + if subject_phi + .iter() + .any(|subject| subject.is_empty() || subject.iter().any(|phi| phi.len() != width)) + { + bail!("covariate phi statistic dimensions do not match"); + } + let mut expected_phi = Vec::with_capacity(subject_phi.len()); + let mut global_second_moment = Array2::zeros((width, width)); + let mut samples = 0usize; + for subject in subject_phi { + let mut mean = vec![0.0; width]; + for phi in subject { + samples += 1; + for row in 0..width { + mean[row] += phi[row]; + for column in 0..width { + global_second_moment[[row, column]] += phi[row] * phi[column]; + } + } + } + let chains = subject.len() as f64; + mean.iter_mut().for_each(|value| *value /= chains); + expected_phi.push(mean); + } + global_second_moment.mapv_inplace(|value| value / samples as f64); + Ok(Self { + expected_phi, + global_second_moment, + }) + } + + /// Update both raw moments with one coherent stochastic-approximation gain. + /// + /// These moments are combined later to form a centered covariance. Using + /// different gain histories can make that algebraic combination indefinite + /// even when every observed moment pair is valid. + pub(crate) fn stochastic_update(&mut self, observed: &Self, step_size: f64) -> Result<()> { + if self.expected_phi.len() != observed.expected_phi.len() + || self.global_second_moment.raw_dim() != observed.global_second_moment.raw_dim() + || self + .expected_phi + .iter() + .zip(&observed.expected_phi) + .any(|(left, right)| left.len() != right.len()) + { + bail!("covariate sufficient statistics dimensions do not match"); + } + for (current, target) in self.expected_phi.iter_mut().zip(&observed.expected_phi) { + for (value, observed) in current.iter_mut().zip(target) { + *value += step_size * (*observed - *value); + } + } + self.global_second_moment = &self.global_second_moment + + &((&observed.global_second_moment - &self.global_second_moment) * step_size); + Ok(()) + } +} + +impl PhiSufficientStatistics { + pub(crate) fn from_subject_phi(subject_phi: &[Vec]) -> Result { + let Some(first) = subject_phi.first() else { + bail!("cannot build phi statistics without subjects"); + }; + let n_subjects = subject_phi.len(); + let n_parameters = first.len(); + if n_parameters == 0 { + bail!("cannot build phi statistics without parameters"); + } + if subject_phi.iter().any(|row| row.len() != n_parameters) { + bail!("all subject phi rows must have the same width"); + } + + let mut mean_phi = vec![0.0; n_parameters]; + let mut second_moment = Array2::zeros((n_parameters, n_parameters)); + for phi in subject_phi { + for parameter_index in 0..n_parameters { + mean_phi[parameter_index] += phi[parameter_index]; + for other_index in 0..n_parameters { + second_moment[[parameter_index, other_index]] += + phi[parameter_index] * phi[other_index]; + } + } + } + + let scale = n_subjects as f64; + for value in &mut mean_phi { + *value /= scale; + } + second_moment.mapv_inplace(|value| value / scale); + + Ok(Self { + mean_phi, + second_moment, + }) + } + + pub(crate) fn stochastic_update_with_steps( + &mut self, + observed: &Self, + mean_step_size: f64, + second_moment_step_size: f64, + ) -> Result<()> { + if self.mean_phi.len() != observed.mean_phi.len() + || self.second_moment.raw_dim() != observed.second_moment.raw_dim() + { + bail!("phi sufficient statistics dimensions do not match"); + } + + for (current, target) in self.mean_phi.iter_mut().zip(observed.mean_phi.iter()) { + *current += mean_step_size * (*target - *current); + } + self.second_moment = &self.second_moment + + &((&observed.second_moment - &self.second_moment) * second_moment_step_size); + Ok(()) + } + + #[cfg(test)] + pub(crate) fn omega(&self, minimum_variance: f64) -> Array2 { + let all_indices = (0..self.mean_phi.len()).collect::>(); + self.omega_for_indices(&all_indices, minimum_variance) + .expect("all parameter indices are valid") + } + + #[cfg(test)] + pub(crate) fn omega_for_indices( + &self, + random_effect_indices: &[usize], + minimum_variance: f64, + ) -> Result> { + self.omega_around_mean(random_effect_indices, &self.mean_phi, minimum_variance) + } + + #[cfg(test)] + pub(crate) fn omega_around_mean( + &self, + random_effect_indices: &[usize], + population_phi: &[f64], + minimum_variance: f64, + ) -> Result> { + let covariance = self.covariance_around_mean(random_effect_indices, population_phi)?; + Ok(ensure_positive_definite_covariance( + &covariance, + minimum_variance, + )) + } + + /// Raw centered covariance before structural/fixed Ω constraints and + /// positive-definite guardrails are applied. + #[cfg(test)] + pub(crate) fn covariance_around_mean( + &self, + random_effect_indices: &[usize], + population_phi: &[f64], + ) -> Result> { + if population_phi.len() != self.mean_phi.len() { + bail!( + "population phi has width {} but statistics have width {}", + population_phi.len(), + self.mean_phi.len() + ); + } + + let mut seen = vec![false; self.mean_phi.len()]; + for parameter_index in random_effect_indices.iter().copied() { + if parameter_index >= self.mean_phi.len() { + bail!( + "random-effect parameter index {parameter_index} exceeds parameter width {}", + self.mean_phi.len() + ); + } + if seen[parameter_index] { + bail!("random-effect parameter index {parameter_index} is duplicated"); + } + seen[parameter_index] = true; + } + + Ok(Array2::from_shape_fn( + (random_effect_indices.len(), random_effect_indices.len()), + |(row, col)| { + let parameter_row = random_effect_indices[row]; + let parameter_col = random_effect_indices[col]; + self.second_moment[[parameter_row, parameter_col]] + - self.mean_phi[parameter_row] * population_phi[parameter_col] + - population_phi[parameter_row] * self.mean_phi[parameter_col] + + population_phi[parameter_row] * population_phi[parameter_col] + }, + )) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::parametric::covariance::cholesky_lower; + + #[test] + fn covariate_raw_moments_share_one_gain_and_remain_coherent() { + let mut statistics = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![0.0]]]).unwrap(); + let observed = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![10.0]]]).unwrap(); + + statistics.stochastic_update(&observed, 0.1).unwrap(); + + assert_eq!(statistics.expected_phi, vec![vec![1.0]]); + assert!((statistics.global_second_moment[[0, 0]] - 10.0).abs() < 1e-12); + let centered = statistics.global_second_moment[[0, 0]] + - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; + assert!((centered - 9.0).abs() < 1e-12); + } + + #[test] + fn legacy_split_raw_moment_gains_can_leave_the_gaussian_moment_cone() { + let initial = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![0.0]]]).unwrap(); + let observed = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![10.0]]]).unwrap(); + + // Reproduce the legacy exploration update: full gain for E[phi] but + // 0.1 gain for E[phi phi']. Each input moment pair is realizable, but + // their differently weighted combination is not. + let legacy_mean = observed.expected_phi[0][0]; + let legacy_second = initial.global_second_moment[[0, 0]] + + 0.1 * (observed.global_second_moment[[0, 0]] - initial.global_second_moment[[0, 0]]); + let legacy_centered = legacy_second - legacy_mean * legacy_mean; + + assert_eq!(legacy_centered, -90.0); + assert!(legacy_centered < 0.0); + } + + #[test] + fn phi_statistics_compute_mean_and_floored_covariance() { + let stats = + PhiSufficientStatistics::from_subject_phi(&[vec![1.0, 2.0], vec![3.0, 4.0]]).unwrap(); + + assert_eq!(stats.mean_phi, vec![2.0, 3.0]); + let omega = stats.omega(1e-6); + assert!(omega[[0, 0]] >= 1.0); + assert!(omega[[1, 1]] >= 1.0); + assert!((omega[[0, 1]] - 1.0).abs() < 1e-12); + assert!((omega[[1, 0]] - 1.0).abs() < 1e-12); + assert!(cholesky_lower(&omega).is_ok()); + } + + #[test] + fn omega_diagonal_is_floored_when_eta_has_not_moved() { + let stats = + PhiSufficientStatistics::from_subject_phi(&[vec![1.0, 2.0], vec![1.0, 2.0]]).unwrap(); + + let omega = stats.omega(1e-6); + assert_eq!(omega[[0, 0]], 1e-6); + assert_eq!(omega[[1, 1]], 1e-6); + } + + #[test] + fn omega_is_regularized_to_positive_definite() { + let stats = + PhiSufficientStatistics::from_subject_phi(&[vec![1.0, 2.0], vec![3.0, 4.0]]).unwrap(); + + let omega = stats.omega(1e-6); + assert!(cholesky_lower(&omega).is_ok()); + } + + #[test] + fn omega_uses_only_declared_random_effect_dimensions() { + let stats = PhiSufficientStatistics::from_subject_phi(&[ + vec![1.0, 10.0, 2.0], + vec![3.0, 10.0, 6.0], + ]) + .unwrap(); + + let omega = stats.omega_for_indices(&[0, 2], 1e-6).unwrap(); + assert_eq!(omega.dim(), (2, 2)); + assert!((omega[[0, 1]] - 2.0).abs() < 1e-12); + assert!((omega[[1, 0]] - 2.0).abs() < 1e-12); + } + + #[test] + fn omega_centers_around_fixed_population_mean() { + let stats = PhiSufficientStatistics::from_subject_phi(&[vec![2.0], vec![4.0]]).unwrap(); + + let omega = stats.omega_around_mean(&[0], &[1.0], 1e-6).unwrap(); + assert!((omega[[0, 0]] - 5.0).abs() < 1e-12); + } +} diff --git a/src/estimation/parametric/transforms.rs b/src/estimation/parametric/transforms.rs new file mode 100644 index 000000000..3552d7889 --- /dev/null +++ b/src/estimation/parametric/transforms.rs @@ -0,0 +1,130 @@ +use statrs::distribution::{Continuous, ContinuousCDF, Normal}; + +use crate::model::ParameterScale; + +/// Transform model-space ψ to estimation-space φ. +/// +/// SAEM/FOCE-style η values are additive in φ-space. Model execution remains in +/// ψ-space, so every algorithm should use these shared helpers for consistency. +pub(crate) fn psi_to_phi(psi: f64, scale: ParameterScale) -> f64 { + match scale { + ParameterScale::Identity => psi, + ParameterScale::Log => psi.ln(), + ParameterScale::Logit { lower, upper } => ((psi - lower) / (upper - psi)).ln(), + ParameterScale::Probit { lower, upper } => { + standard_normal().inverse_cdf((psi - lower) / (upper - lower)) + } + } +} + +/// Transform estimation-space φ back to model-space ψ. +pub(crate) fn phi_to_psi(phi: f64, scale: ParameterScale) -> f64 { + match scale { + ParameterScale::Identity => phi, + ParameterScale::Log => phi.exp(), + ParameterScale::Logit { lower, upper } => { + let exp_phi = phi.exp(); + lower + (upper - lower) * exp_phi / (1.0 + exp_phi) + } + ParameterScale::Probit { lower, upper } => { + lower + (upper - lower) * standard_normal().cdf(phi) + } + } +} + +fn standard_normal() -> Normal { + Normal::new(0.0, 1.0).expect("standard normal parameters are valid") +} + +/// Exact derivative dψ/dφ of the inverse transform φ → ψ. +/// +/// Used for the delta-method transformation of free-coordinate standard errors +/// from estimation (φ) space to natural (ψ) space. The absolute value is the +/// Jacobian scaling factor: sd_ψ = |dψ/dφ| × sd_φ. +pub(crate) fn phi_to_psi_derivative(phi: f64, scale: ParameterScale) -> f64 { + match scale { + ParameterScale::Identity => 1.0, + ParameterScale::Log => phi.exp(), + ParameterScale::Logit { lower, upper } => { + let exp_negative_abs_phi = (-phi.abs()).exp(); + (upper - lower) * exp_negative_abs_phi / (1.0 + exp_negative_abs_phi).powi(2) + } + ParameterScale::Probit { lower, upper } => { + let norm = standard_normal(); + (upper - lower) * norm.pdf(phi) + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn bounded_transforms_round_trip() { + let scales = [ + ParameterScale::Identity, + ParameterScale::Log, + ParameterScale::Logit { + lower: 0.0, + upper: 1.0, + }, + ParameterScale::Probit { + lower: -2.0, + upper: 3.0, + }, + ]; + let values = [0.5, 2.0, 0.75, 1.25]; + + for (value, scale) in values.into_iter().zip(scales.into_iter()) { + let phi = psi_to_phi(value, scale); + let psi = phi_to_psi(phi, scale); + assert!((psi - value).abs() < 1e-10, "scale={scale:?}"); + } + } + + #[test] + fn population_uncertainty_identity_derivative_is_one() { + assert_eq!(phi_to_psi_derivative(0.0, ParameterScale::Identity), 1.0); + assert_eq!(phi_to_psi_derivative(5.0, ParameterScale::Identity), 1.0); + } + + #[test] + fn population_uncertainty_log_derivative_is_exp_phi() { + for phi in [-2.0_f64, 0.0, 1.5] { + let expected = phi.exp(); + let actual = phi_to_psi_derivative(phi, ParameterScale::Log); + assert!((actual - expected).abs() < 1e-12); + } + } + + #[test] + fn population_uncertainty_logit_derivative_is_stable_logistic_probability() { + let scale = ParameterScale::Logit { + lower: 0.0, + upper: 1.0, + }; + // At phi = 0, p = 0.5, derivative = 1 * 0.5 * 0.5 = 0.25 + assert!((phi_to_psi_derivative(0.0, scale) - 0.25).abs() < 1e-12); + // At large positive phi, p ≈ 1, derivative ≈ 0 + let large = 50.0; + let d_large = phi_to_psi_derivative(large, scale); + assert!(d_large > 0.0); + assert!(d_large < 1e-10); + assert!(d_large.is_finite()); + } + + #[test] + fn population_uncertainty_probit_derivative_is_standard_normal_pdf_scaled() { + let scale = ParameterScale::Probit { + lower: 10.0, + upper: 20.0, + }; + let norm = Normal::new(0.0, 1.0).expect("valid standard normal"); + for phi in [-2.0, 0.0, 1.5] { + let expected = (20.0 - 10.0) * norm.pdf(phi); + let actual = phi_to_psi_derivative(phi, scale); + assert!((actual - expected).abs() < 1e-12); + } + } +} diff --git a/src/estimation/problem.rs b/src/estimation/problem.rs index 2ff908be8..c46994ad9 100644 --- a/src/estimation/problem.rs +++ b/src/estimation/problem.rs @@ -1,12 +1,19 @@ use anyhow::{anyhow, Result}; -use pharmsol::{ - AssayErrorModel, AssayErrorModels, Data, Equation, Event, ResidualErrorModel, - ResidualErrorModels, -}; +use pharmsol::{Data, Equation, Event}; use std::collections::{BTreeSet, HashSet}; +use crate::estimation::error_models::{ParametricErrorModel, ParametricErrorModels}; use crate::estimation::nonparametric::Theta; -use crate::model::parameter_space::{BoundedParameter, ParameterSpace, UnboundedParameter}; +use crate::estimation::parametric::residual::RESIDUAL_OPTIMIZER_MAX_SIGMA; +use crate::estimation::parametric::transforms::psi_to_phi; +use crate::estimation::parametric::{ + reject_constraints, CovariateEffect, CovariateModel, Iov, Omega, ParametricConstraint, + ParametricPrior, +}; +use crate::estimation::{AssayErrorModel, AssayErrorModels, ResidualErrorModel}; +use crate::model::parameter_space::{ + BoundedParameter, ParameterScale, ParameterSpace, UnboundedParameter, +}; use crate::model::{EquationMetadataSource, Model, ModelBuilder}; pub trait Framework { @@ -15,7 +22,8 @@ pub trait Framework { /// /// For the non-parametric framework this is a [`Theta`] (a discrete prior /// distribution that also carries the parameter space). For the parametric - /// framework it is the [`ParameterSpace`] of unbounded parameters. + /// framework it is a [`ParametricPrior`] carrying the population parameters + /// and initial IIV covariance model. type Prior; } @@ -23,8 +31,8 @@ pub trait Framework { pub struct Parametric; impl Framework for Parametric { - type ErrorModels = ResidualErrorModels; - type Prior = ParameterSpace; + type ErrorModels = ParametricErrorModels; + type Prior = ParametricPrior; } #[derive(Debug, Clone, Copy)] @@ -61,13 +69,14 @@ impl EstimationProblem { prior: Theta, error_models: AssayErrorModels, ) -> Result { + reject_sde_estimation::()?; let model_builder = Model::builder(equation); validate_nonparametric_parameters(&model_builder, prior.parameters())?; let model = model_builder.build()?; - validate_nonparametric_error_models(&model, &data, &error_models)?; + let error_models = validate_nonparametric_error_models(&model, &data, &error_models)?; Ok(EstimationProblem { model, @@ -85,13 +94,47 @@ impl EstimationProblem { model: Model::builder(equation), data, parameters: ParameterSpace::::new(), + omega: None, + iov: None, + covariate_effects: Vec::new(), + constraints: Vec::new(), error_models: Vec::new(), } } /// Returns the parameter space defined for this problem. pub fn parameters(&self) -> &ParameterSpace { - &self.prior + self.prior.parameters() + } + + /// Returns the initial IIV covariance matrix in random-effect order. + pub fn omega(&self) -> &ndarray::Array2 { + self.prior.omega() + } + + /// Returns random-effect names in η/Ω order. + pub fn random_effect_names(&self) -> &[String] { + self.prior.random_effect_names() + } + + /// Returns IOV effect names in κ/Ω_IOV order, when configured. + pub fn iov_effect_names(&self) -> Option<&[String]> { + self.prior.iov_effect_names() + } + + /// Returns the initial IOV covariance matrix, when configured. + pub fn omega_iov(&self) -> Option<&ndarray::Array2> { + self.prior.omega_iov() + } + + /// Returns the declared residual-error models and estimation masks. + pub fn residual_error_models(&self) -> &ParametricErrorModels { + &self.error_models + } + + /// Returns the fully validated subject-static covariate model, when declared. + pub fn covariates(&self) -> Option<&CovariateModel> { + self.prior.covariates() } } @@ -106,7 +149,11 @@ pub struct ParametricBuilder { model: ModelBuilder, data: Data, parameters: ParameterSpace, - error_models: Vec<(String, ResidualErrorModel)>, + omega: Option, + iov: Option, + covariate_effects: Vec, + constraints: Vec, + error_models: Vec<(String, ParametricErrorModel)>, } impl ParametricBuilder { @@ -126,36 +173,129 @@ impl ParametricBuilder { self } - pub fn error_model(mut self, name: impl Into, model: ResidualErrorModel) -> Self { - self.error_models.push((name.into(), model)); + /// Defines the initial IIV covariance structure. If omitted, PMcore uses an + /// estimated diagonal identity Ω over declared random effects. + pub fn omega(mut self, omega: Omega) -> Self { + self.omega = Some(omega); + self + } + + /// Defines inter-occasion variability. κ effects are additive in φ-space + /// for every occasion and have their own Ω_IOV. + pub fn iov(mut self, iov: Iov) -> Self { + self.iov = Some(iov); + self + } + + /// Adds one named subject-static transformed-space covariate effect. + pub fn covariate_effect(mut self, effect: CovariateEffect) -> Self { + self.covariate_effects.push(effect); + self + } + + /// Adds named subject-static covariate effects in stable iterator order. + pub fn covariate_effects(mut self, effects: I) -> Self + where + I: IntoIterator, + { + self.covariate_effects.extend(effects); + self + } + + /// Declares a parametric constraint. Unsupported nonlinear constraints are + /// retained only long enough to fail explicitly during `build`. + pub fn constraint(mut self, constraint: ParametricConstraint) -> Self { + self.constraints.push(constraint); + self + } + + pub fn error_model( + mut self, + name: impl Into, + model: impl Into, + ) -> Self { + self.error_models.push((name.into(), model.into())); self } } impl ParametricBuilder { pub fn build(self) -> Result> { + reject_sde_estimation::()?; validate_parametric_parameters(&self.model, &self.parameters)?; validate_parametric_error_models(&self.model, &self.error_models)?; - - let mut all_errors = ResidualErrorModels::new(); + reject_constraints(&self.constraints)?; + let covariates = if self.covariate_effects.is_empty() { + None + } else { + Some(CovariateModel::resolve( + self.covariate_effects, + &self.parameters, + &self.data, + )?) + }; + + let model = self.model.build()?; + let mut all_errors = ParametricErrorModels::new(); for (name, error_model) in self.error_models { - let outeq = self - .model + let outeq = model .output_index(&name) .ok_or_else(|| anyhow!("unknown equation output label: {name}"))?; - all_errors = all_errors.add(outeq, error_model); + all_errors = all_errors.add(outeq, name, error_model); + } + validate_parametric_data(&model, &self.data, &all_errors)?; + + let prior = ParametricPrior::new_with_covariates( + self.parameters, + self.omega, + self.iov, + covariates, + )?; + if let Some(covariates) = prior.covariates() { + covariates.validate_initial_gls_rank( + prior.parameters(), + prior.random_effect_names(), + prior.omega(), + )?; + let scales: Vec<_> = prior + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect(); + let population_phi: Vec<_> = prior + .parameters() + .iter() + .map(|parameter| { + let psi = parameter.initial.unwrap_or(match parameter.scale { + ParameterScale::Identity | ParameterScale::Log => 1.0, + ParameterScale::Logit { lower, upper } + | ParameterScale::Probit { lower, upper } => 0.5 * (lower + upper), + }); + psi_to_phi(psi, parameter.scale) + }) + .collect(); + covariates.subject_population_parameters(&population_phi, &scales)?; } - Ok(EstimationProblem { - model: self.model.build()?, + model, data: self.data, error_models: all_errors, - prior: self.parameters, + prior, }) } } +fn reject_sde_estimation() -> Result<()> { + if matches!(E::kind(), pharmsol::equation::EqnKind::SDE) { + anyhow::bail!( + "EstimationProblem does not support SDE models; use SdeParticleFilter for \ + observation-conditioned filtering." + ); + } + Ok(()) +} + fn validate_nonparametric_parameters( model: &ModelBuilder, parameters: &ParameterSpace, @@ -197,6 +337,46 @@ fn validate_parametric_parameters( anyhow::bail!("at least one parameter is required for parametric models"); } + for parameter in parameters.iter() { + if let Some(initial) = parameter.initial { + if !initial.is_finite() { + anyhow::bail!( + "invalid initial value for parameter '{}': initial values must be finite", + parameter.name + ); + } + } + + match parameter.scale { + ParameterScale::Identity => {} + ParameterScale::Log => { + if parameter.initial.is_some_and(|initial| initial <= 0.0) { + anyhow::bail!( + "invalid initial value for log-scale parameter '{}': the initial value must be greater than zero", + parameter.name + ); + } + } + ParameterScale::Logit { lower, upper } | ParameterScale::Probit { lower, upper } => { + if !lower.is_finite() || !upper.is_finite() || lower >= upper { + anyhow::bail!( + "invalid bounds for parameter '{}': bounds must be finite and lower ({lower}) must be strictly less than upper ({upper})", + parameter.name + ); + } + if parameter + .initial + .is_some_and(|initial| initial <= lower || initial >= upper) + { + anyhow::bail!( + "invalid initial value for bounded parameter '{}': the initial value must lie strictly inside ({lower}, {upper})", + parameter.name + ); + } + } + } + } + let names: Vec = parameters .iter() .map(|parameter| parameter.name.clone()) @@ -258,12 +438,13 @@ fn validate_nonparametric_error_models( model: &Model, data: &Data, error_models: &AssayErrorModels, -) -> Result<()> { - // Bind the (label-first) error models to the equation. This resolves and - // validates that every declared output label maps to a valid model output. - let bound = model - .equation - .bind_error_models(error_models) +) -> Result { + // Bind the label-first PMcore error models against neutral equation metadata. + let output_names = (0..model.output_count()) + .filter_map(|index| model.output_name(index)) + .collect::>(); + let bound = error_models + .bind_outputs(output_names) .map_err(|e| anyhow!("invalid assay error model output(s): {e}"))?; // Collect the set of model output indices that are actually observed in the @@ -321,33 +502,198 @@ fn validate_nonparametric_error_models( } } - Ok(()) + Ok((*bound).clone()) } /// Resolves an observation output `label` to a model output index, mirroring the -/// simulator: first by exact output name, then via the `outeq_` numeric alias. +/// simulator: exact metadata name, then numeric `N` only for a declared `outeq_N`. fn resolve_output_index( model: &Model, label: &str, ) -> Option { model.output_index(label).or_else(|| { - if !label.is_empty() && label.bytes().all(|b| b.is_ascii_digit()) { - model.output_index(&format!("outeq_{label}")) - } else { - None - } + (!label.is_empty() && label.chars().all(|ch| ch.is_ascii_digit())) + .then(|| format!("outeq_{label}")) + .and_then(|alias| model.output_index(&alias)) }) } +fn validate_parametric_data( + model: &Model, + data: &Data, + error_models: &ParametricErrorModels, +) -> Result<()> { + if data.subjects().is_empty() { + anyhow::bail!("parametric estimation requires at least one subject"); + } + + let mut measured_observations = 0; + for subject in data.subjects() { + for occasion in subject.occasions() { + for event in occasion.events() { + let Event::Observation(observation) = event else { + continue; + }; + let label = observation.outeq().to_string(); + let output_index = resolve_output_index(model, &label).ok_or_else(|| { + anyhow!( + "parametric observation for subject '{}' at time {} references unknown model output '{}'; valid outputs are: {}", + subject.id(), + observation.time(), + label, + (0..model.output_count()) + .filter_map(|index| model.output_name(index)) + .collect::>() + .join(", ") + ) + })?; + if observation.censored() { + anyhow::bail!( + "parametric estimation does not support {:?} censoring for subject '{}' at time {} on output '{}'; only missing and uncensored observations are supported", + observation.censoring(), + subject.id(), + observation.time(), + label + ); + } + if observation.value().is_none() { + continue; + } + measured_observations += 1; + if error_models.output_name(output_index).is_none() { + anyhow::bail!( + "no parametric residual model is defined for measured output '{}' (index {}) referenced by subject '{}' at time {}", + model.output_name(output_index).unwrap_or(&label), + output_index, + subject.id(), + observation.time() + ); + } + } + } + } + if measured_observations == 0 { + anyhow::bail!("parametric estimation requires at least one measured observation"); + } + Ok(()) +} + fn validate_parametric_error_models( model: &ModelBuilder, - error_models: &[(String, ResidualErrorModel)], + error_models: &[(String, ParametricErrorModel)], ) -> Result<()> { if error_models.is_empty() { anyhow::bail!("at least one residual error model is required"); } - validate_error_model_labels(model, error_models.iter().map(|(name, _)| name.as_str())) + validate_error_model_labels(model, error_models.iter().map(|(name, _)| name.as_str()))?; + + for (output, declaration) in error_models { + let combined_component_estimated = declaration.combined_component_estimated(); + let correlated_component_estimated = declaration.correlated_combined_component_estimated(); + if !matches!(declaration.model(), ResidualErrorModel::Combined { .. }) + && combined_component_estimated != [declaration.is_estimated(); 2] + { + anyhow::bail!( + "combined-component estimation controls for output '{output}' require a combined residual model" + ); + } + if !matches!( + declaration.model(), + ResidualErrorModel::CorrelatedCombined { .. } + ) && correlated_component_estimated != [declaration.is_estimated(); 3] + { + anyhow::bail!( + "correlated-combined component estimation controls for output '{output}' require a correlated-combined residual model" + ); + } + match declaration.model() { + ResidualErrorModel::Constant { a } if !a.is_finite() || *a <= 0.0 => { + anyhow::bail!( + "constant residual SD for output '{output}' must be finite and greater than zero" + ) + } + ResidualErrorModel::Proportional { b } if !b.is_finite() || *b <= 0.0 => { + anyhow::bail!( + "proportional residual SD coefficient for output '{output}' must be finite and greater than zero" + ) + } + ResidualErrorModel::Exponential { sigma } if !sigma.is_finite() || *sigma <= 0.0 => { + anyhow::bail!( + "exponential residual log-scale SD for output '{output}' must be finite and greater than zero" + ) + } + ResidualErrorModel::Combined { a, b } + if !a.is_finite() + || !b.is_finite() + || *a < 0.0 + || *b < 0.0 + || (*a == 0.0 && *b == 0.0) => + { + anyhow::bail!( + "combined residual SD coefficients for output '{output}' must be finite, non-negative, and not both zero" + ) + } + ResidualErrorModel::Combined { a, .. } + if combined_component_estimated[0] && *a == 0.0 => + { + anyhow::bail!( + "estimated combined additive SD for output '{output}' must be greater than zero" + ) + } + ResidualErrorModel::Combined { b, .. } + if combined_component_estimated[1] && *b == 0.0 => + { + anyhow::bail!( + "estimated combined proportional SD for output '{output}' must be greater than zero" + ) + } + ResidualErrorModel::Combined { a, .. } + if combined_component_estimated[0] && *a > RESIDUAL_OPTIMIZER_MAX_SIGMA => + { + anyhow::bail!( + "estimated combined additive SD for output '{output}' must not exceed the optimizer maximum {RESIDUAL_OPTIMIZER_MAX_SIGMA}" + ) + } + ResidualErrorModel::Combined { b, .. } + if combined_component_estimated[1] && *b > RESIDUAL_OPTIMIZER_MAX_SIGMA => + { + anyhow::bail!( + "estimated combined proportional SD for output '{output}' must not exceed the optimizer maximum {RESIDUAL_OPTIMIZER_MAX_SIGMA}" + ) + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } + if !a.is_finite() + || !b.is_finite() + || *a <= 0.0 + || *b <= 0.0 + || !rho.is_finite() + || *rho <= -1.0 + || *rho >= 1.0 => + { + anyhow::bail!( + "correlated-combined residual declaration for output '{output}' requires finite positive a and b and finite rho strictly inside (-1, 1)" + ) + } + ResidualErrorModel::CorrelatedCombined { a, .. } + if correlated_component_estimated[0] && *a > RESIDUAL_OPTIMIZER_MAX_SIGMA => + { + anyhow::bail!( + "estimated correlated-combined additive SD for output '{output}' must not exceed the optimizer maximum {RESIDUAL_OPTIMIZER_MAX_SIGMA}" + ) + } + ResidualErrorModel::CorrelatedCombined { b, .. } + if correlated_component_estimated[1] && *b > RESIDUAL_OPTIMIZER_MAX_SIGMA => + { + anyhow::bail!( + "estimated correlated-combined proportional SD for output '{output}' must not exceed the optimizer maximum {RESIDUAL_OPTIMIZER_MAX_SIGMA}" + ) + } + _ => {} + } + } + + Ok(()) } fn validate_error_model_labels<'a, E, I>(model: &ModelBuilder, labels: I) -> Result<()> @@ -378,3 +724,284 @@ where Ok(()) } + +#[cfg(test)] +mod tests { + use super::{reject_sde_estimation, EstimationProblem, RESIDUAL_OPTIMIZER_MAX_SIGMA}; + use crate::estimation::ParametricErrorModel; + use crate::model::parameter_space::Parameter; + use crate::ResidualErrorModel; + use pharmsol::prelude::*; + use pharmsol::{Censor, Data, Subject, SubjectBuilderExt}; + + fn equation_with_outputs(outputs: [&str; 2]) -> pharmsol::ODE { + pharmsol::equation::ODE::new( + |_x, _p, _t, dx, _b, _rateiv, _cov| dx[0] = 0.0, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |_x, p, _t, _cov, y| { + y[0] = p[0]; + y[1] = p[0]; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + equation::metadata::new("parametric_validation") + .parameters(["value"]) + .states(["state"]) + .outputs(outputs) + .route(equation::Route::bolus("dose").to_state("state")), + ) + .unwrap() + } + + fn equation() -> pharmsol::ODE { + equation_with_outputs(["cp", "effect"]) + } + + fn measured_data(output: &str) -> Data { + Data::new(vec![Subject::builder("subject-1") + .observation(3.5, 1.25, output) + .build()]) + } + + #[test] + fn deterministic_model_kind_support_is_fail_closed() { + assert!(reject_sde_estimation::().is_ok()); + assert!(reject_sde_estimation::().is_ok()); + + let error = reject_sde_estimation::() + .expect_err("EstimationProblem must reject SDE models") + .to_string(); + assert!(error.contains("SDE")); + assert!(error.contains("SdeParticleFilter")); + } + + #[test] + fn parametric_parameter_domains_fail_closed() { + let invalid = [ + Parameter::real("value").with_initial(f64::NAN), + Parameter::log("value").with_initial(0.0), + Parameter::logit("value", f64::NEG_INFINITY, 1.0).with_initial(0.5), + Parameter::probit("value", 1.0, 1.0).with_initial(1.0), + Parameter::logit("value", 0.0, 1.0).with_initial(0.0), + Parameter::probit("value", 0.0, 1.0).with_initial(1.0), + ]; + + for parameter in invalid { + let error = EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter(parameter) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("invalid parameter domain must fail") + .to_string(); + assert!(error.contains("value")); + } + } + + #[test] + fn valid_parameter_scales_and_default_initials_are_preserved() { + for parameter in [ + Parameter::real("value"), + Parameter::log("value"), + Parameter::logit("value", 0.0, 2.0), + Parameter::probit("value", 0.0, 2.0), + ] { + assert!( + EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter(parameter) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .is_ok(), + "supported parameter scale must build" + ); + } + } + + #[test] + fn combined_estimated_components_respect_optimizer_bound() { + let too_large = RESIDUAL_OPTIMIZER_MAX_SIGMA * 2.0; + let error = EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::combined(too_large, 0.1)) + .build() + .expect_err("estimated combined components above the optimizer bound must fail") + .to_string(); + assert!(error.contains("optimizer maximum")); + + assert!( + EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter(Parameter::log("value")) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::combined(too_large, 0.1)) + .fixed_combined_additive(), + ) + .build() + .is_ok() + ); + } + + #[test] + fn parametric_construction_rejects_empty_or_unmeasured_data() { + let empty_error = EstimationProblem::parametric(equation(), Data::new(vec![])) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("parametric data must contain a subject") + .to_string(); + assert!(empty_error.contains("at least one subject")); + + let missing_only = Data::new(vec![Subject::builder("missing-subject") + .missing_observation(2.0, "cp") + .build()]); + let missing_error = EstimationProblem::parametric(equation(), missing_only) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("parametric data must contain a measured observation") + .to_string(); + assert!(missing_error.contains("measured observation")); + } + + #[test] + fn parametric_observation_outputs_fail_closed() { + let unknown = EstimationProblem::parametric(equation(), measured_data("unknown")) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("unknown observation output must fail") + .to_string(); + assert!(unknown.contains("subject-1")); + assert!(unknown.contains("3.5")); + assert!(unknown.contains("unknown")); + + let unknown_missing_data = Data::new(vec![Subject::builder("missing-subject") + .missing_observation(4.5, "unknown") + .build()]); + let unknown_missing = EstimationProblem::parametric(equation(), unknown_missing_data) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("unknown missing-observation output must fail") + .to_string(); + assert!(unknown_missing.contains("missing-subject")); + assert!(unknown_missing.contains("4.5")); + assert!(unknown_missing.contains("unknown")); + + let missing_residual = EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter(Parameter::log("value")) + .error_model("effect", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("measured output without residual model must fail") + .to_string(); + assert!(missing_residual.contains("cp")); + assert!(missing_residual.contains("subject-1")); + } + + #[test] + fn missing_observations_with_declared_output_names_remain_supported() { + let data = Data::new(vec![Subject::builder("subject-1") + .observation(1.0, 1.0, "cp") + .missing_observation(2.0, "effect") + .build()]); + assert!( + EstimationProblem::parametric(equation(), data) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .is_ok(), + "missing values need no residual model" + ); + + let numeric = EstimationProblem::parametric(equation(), measured_data("0")) + .parameter(Parameter::log("value")) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("numeric labels must not alias arbitrarily named metadata outputs") + .to_string(); + assert!(numeric.contains("unknown model output '0'")); + } + + #[test] + fn numeric_output_aliases_preserve_leading_zeroes() { + assert!(EstimationProblem::parametric( + equation_with_outputs(["outeq_00", "effect"]), + measured_data("00"), + ) + .parameter(Parameter::log("value")) + .error_model("outeq_00", ResidualErrorModel::constant(1.0)) + .build() + .is_ok()); + + let error = EstimationProblem::parametric( + equation_with_outputs(["outeq_0", "effect"]), + measured_data("00"), + ) + .parameter(Parameter::log("value")) + .error_model("outeq_0", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("00 must not resolve to outeq_0") + .to_string(); + assert!(error.contains("unknown model output '00'")); + } + + #[test] + fn estimated_and_fixed_parameters_without_iiv_are_declared_independently() { + let estimated = EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter( + Parameter::log("value") + .with_initial(1.0) + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect("estimated non-IIV theta should use the numerical M-step"); + assert!(estimated.parameters().items[0].estimate); + assert!(!estimated.parameters().items[0].random_effect); + + assert!( + EstimationProblem::parametric(equation(), measured_data("cp")) + .parameter( + Parameter::log("value") + .with_initial(1.0) + .fixed() + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .is_ok() + ); + } + + #[test] + fn parametric_construction_rejects_bloq_observations() { + assert_parametric_censoring_rejected(Censor::BLOQ, "BLOQ"); + } + + #[test] + fn parametric_construction_rejects_aloq_observations() { + assert_parametric_censoring_rejected(Censor::ALOQ, "ALOQ"); + } + + fn assert_parametric_censoring_rejected(censoring: Censor, label: &str) { + let data = Data::new(vec![Subject::builder("censored-subject") + .censored_observation(3.5, 1.25, "cp", censoring) + .build()]); + + let error = EstimationProblem::parametric(equation(), data) + .parameter(Parameter::log("value").with_initial(1.0)) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect_err("ParametricBuilder::build must reject censored observations") + .to_string(); + + assert!(error.contains(label)); + assert!(error.contains("censored-subject")); + assert!(error.contains("3.5")); + assert!(error.contains("cp")); + } +} diff --git a/src/estimation/residual_error.rs b/src/estimation/residual_error.rs new file mode 100644 index 000000000..fe744db8d --- /dev/null +++ b/src/estimation/residual_error.rs @@ -0,0 +1,399 @@ +//! Prediction-dependent residual error models for parametric estimation. +//! +//! Constant, proportional, combined, correlated-combined, and exponential +//! models provide residual scale calculations and simulation. Estimation uses +//! separate canonical +//! likelihood scoring routines. + +use serde::{Deserialize, Serialize}; + +/// Residual standard deviation as a function of the model prediction. +/// +/// # Examples +/// +/// ```rust +/// use pmcore::ResidualErrorModel; +/// +/// // Constant (additive) error: σ = 0.5 +/// let constant = ResidualErrorModel::Constant { a: 0.5 }; +/// assert!((constant.sigma(100.0) - 0.5).abs() < 1e-10); +/// +/// // Proportional error: σ = 0.1 * |f| +/// let proportional = ResidualErrorModel::Proportional { b: 0.1 }; +/// assert!((proportional.sigma(100.0) - 10.0).abs() < 1e-10); +/// +/// // Combined error: σ = sqrt(0.5² + 0.1² * f²) +/// let combined = ResidualErrorModel::Combined { a: 0.5, b: 0.1 }; +/// // For f=100: σ = sqrt(0.25 + 100) = sqrt(100.25) ≈ 10.01 +/// ``` +#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq)] +pub enum ResidualErrorModel { + /// Constant (additive) error model + /// + /// σ = a + /// + /// Error is independent of the predicted value. + /// Appropriate when measurement error is constant regardless of concentration. + Constant { + /// Additive error standard deviation + a: f64, + }, + + /// Proportional error model + /// + /// σ = b * |f| + /// + /// Error scales linearly with the prediction. + /// Appropriate when measurement error is a constant percentage of the value. + /// + /// Note: Uses |f| to handle negative predictions gracefully. + Proportional { + /// Proportional coefficient (e.g., 0.1 = 10% CV) + b: f64, + }, + + /// Combined (additive + proportional) error model + /// + /// σ = sqrt(a² + b² * f²) + /// + /// This is the standard quadrature combined-error model: + /// ```R + /// g <- cutoff(sqrt(ab[1]^2 + ab[2]^2 * f^2)) + /// ``` + /// + /// The combined model: + /// - Dominates at low concentrations (a term) + /// - Scales proportionally at high concentrations (b term) + Combined { + /// Additive component (a) + a: f64, + /// Proportional component (b) + b: f64, + }, + + /// Within-observation correlated additive/proportional error model. + /// + /// `Y = f + epsilon_a + f * epsilon_p`, where the component standard + /// deviations are `a` and `b` and their correlation is `rho`. Therefore + /// `Var(Y | f) = a² + 2 rho a b f + b² f²`. Observations remain + /// conditionally independent: this does not model serial or cross-output + /// residual correlation. + CorrelatedCombined { + /// Additive component standard deviation. + a: f64, + /// Proportional component standard deviation. + b: f64, + /// Within-observation additive/proportional correlation. + rho: f64, + }, + + /// Exponential error model (for log-transformed data) + /// + /// σ = σ_exp (constant on log scale) + /// + /// When data is analyzed on the log scale: + /// ```text + /// log(Y) = log(f) + ε, where ε ~ N(0, σ²) + /// ``` + /// + /// This corresponds to multiplicative error on the original scale. + Exponential { + /// Error standard deviation on log scale + sigma: f64, + }, +} + +impl Default for ResidualErrorModel { + fn default() -> Self { + // Default to constant error with σ = 1.0 + ResidualErrorModel::Constant { a: 1.0 } + } +} + +impl ResidualErrorModel { + /// Create a constant (additive) error model + /// + /// # Arguments + /// * `a` - Standard deviation (must be positive) + pub fn constant(a: f64) -> Self { + ResidualErrorModel::Constant { a } + } + + /// Create a proportional error model + /// + /// # Arguments + /// * `b` - Proportional coefficient (e.g., 0.1 for 10% CV) + pub fn proportional(b: f64) -> Self { + ResidualErrorModel::Proportional { b } + } + + /// Create a combined (additive + proportional) error model + /// + /// # Arguments + /// * `a` - Additive component + /// * `b` - Proportional component + pub fn combined(a: f64, b: f64) -> Self { + ResidualErrorModel::Combined { a, b } + } + + /// Create a within-observation correlated additive/proportional model. + /// + /// Valid parametric declarations require finite `a, b > 0` and finite + /// `rho` strictly inside `(-1, 1)`. + pub fn correlated_combined(a: f64, b: f64, rho: f64) -> Self { + ResidualErrorModel::CorrelatedCombined { a, b, rho } + } + + /// Create an exponential error model + /// + /// # Arguments + /// * `sigma` - Standard deviation on log scale + pub fn exponential(sigma: f64) -> Self { + ResidualErrorModel::Exponential { sigma } + } + + /// Compute sigma (standard deviation) for a given prediction + /// + /// # Arguments + /// * `prediction` - The model prediction (f) + /// + /// # Returns + /// The standard deviation σ at this prediction value. + /// Returns a cutoff minimum to avoid numerical issues with very small σ. + pub fn sigma(&self, prediction: f64) -> f64 { + let raw_sigma = match self { + ResidualErrorModel::Constant { a } => *a, + ResidualErrorModel::Proportional { b } => b * prediction.abs(), + ResidualErrorModel::Combined { a, b } => { + (a.powi(2) + b.powi(2) * prediction.powi(2)).sqrt() + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + let proportional = b * prediction; + (a + rho * proportional).hypot((1.0 - rho * rho).sqrt() * proportional) + } + ResidualErrorModel::Exponential { sigma } => *sigma, + }; + + // Apply a machine-precision cutoff to prevent division by zero. + raw_sigma.max(f64::EPSILON.sqrt()) + } + + /// Simulate one observation from a supplied standard-normal draw. + /// + /// Exponential residual error is lognormal on the observation scale and + /// therefore requires a finite, strictly positive prediction. The other + /// models are additive normal errors with their prediction-dependent sigma. + pub fn simulate_with_standard_normal( + &self, + prediction: f64, + standard_normal: f64, + ) -> Option { + if !prediction.is_finite() || !standard_normal.is_finite() { + return None; + } + + let observation = match self { + Self::Constant { .. } + | Self::Proportional { .. } + | Self::Combined { .. } + | Self::CorrelatedCombined { .. } => { + prediction + self.sigma(prediction) * standard_normal + } + Self::Exponential { sigma } => { + if prediction <= 0.0 { + return None; + } + prediction * (sigma * standard_normal).exp() + } + }; + + observation.is_finite().then_some(observation) + } + + /// Compute the residual variance for a prediction. + pub fn variance(&self, prediction: f64) -> f64 { + self.sigma(prediction).powi(2) + } + + /// Return the model's primary scale parameter. + pub fn primary_parameter(&self) -> f64 { + match self { + Self::Constant { a } => *a, + Self::Proportional { b } => *b, + Self::Combined { a, .. } | Self::CorrelatedCombined { a, .. } => *a, + Self::Exponential { sigma } => *sigma, + } + } + + /// Return whether this is a proportional model. + pub fn is_proportional(&self) -> bool { + matches!(self, Self::Proportional { .. }) + } + + /// Return whether this is a constant model. + pub fn is_constant(&self) -> bool { + matches!(self, Self::Constant { .. }) + } + + /// Return whether this is a combined model. + pub fn is_combined(&self) -> bool { + matches!(self, Self::Combined { .. }) + } + + /// Return whether this is a correlated additive/proportional model. + pub fn is_correlated_combined(&self) -> bool { + matches!(self, Self::CorrelatedCombined { .. }) + } + + /// Return whether this is an exponential model. + pub fn is_exponential(&self) -> bool { + matches!(self, Self::Exponential { .. }) + } +} + +/// Residual error models indexed by output equation. +#[derive(Debug, Clone, Serialize, Deserialize, Default)] +pub struct ResidualErrorModels { + models: Vec>, +} + +impl ResidualErrorModels { + /// Create an empty collection + pub fn new() -> Self { + Self { models: vec![] } + } + + /// Add an error model for a specific output equation + pub fn add(mut self, outeq: usize, model: ResidualErrorModel) -> Self { + if outeq >= self.models.len() { + self.models.resize(outeq + 1, None); + } + self.models[outeq] = Some(model); + self + } + + /// Get the error model for a specific output equation + pub fn get(&self, outeq: usize) -> Option<&ResidualErrorModel> { + self.models.get(outeq).and_then(Option::as_ref) + } + + /// Get a mutable reference to the error model for a specific output equation + pub fn get_mut(&mut self, outeq: usize) -> Option<&mut ResidualErrorModel> { + self.models.get_mut(outeq).and_then(Option::as_mut) + } + + /// Compute sigma for an output equation and prediction. + pub fn sigma(&self, outeq: usize, prediction: f64) -> Option { + self.get(outeq).map(|model| model.sigma(prediction)) + } + + /// Number of error models + pub fn len(&self) -> usize { + self.models.len() + } + + /// Check if collection has no declared models. + pub fn is_empty(&self) -> bool { + self.models.iter().all(Option::is_none) + } + + /// Iterate over declared output indices and models. + pub fn iter(&self) -> impl Iterator { + self.models + .iter() + .enumerate() + .filter_map(|(index, model)| model.as_ref().map(|model| (index, model))) + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_constant_error() { + let model = ResidualErrorModel::constant(0.5); + assert!((model.sigma(0.0) - 0.5).abs() < 1e-10); + assert!((model.sigma(100.0) - 0.5).abs() < 1e-10); + assert!((model.sigma(-50.0) - 0.5).abs() < 1e-10); + } + + #[test] + fn test_proportional_error() { + let model = ResidualErrorModel::proportional(0.1); + assert!((model.sigma(100.0) - 10.0).abs() < 1e-10); + assert!((model.sigma(50.0) - 5.0).abs() < 1e-10); + // Uses absolute value, so negative predictions work + assert!((model.sigma(-100.0) - 10.0).abs() < 1e-10); + } + + #[test] + fn test_combined_error() { + let model = ResidualErrorModel::combined(0.5, 0.1); + // At f=0: sigma = sqrt(0.25 + 0) = 0.5 + assert!((model.sigma(0.0) - 0.5).abs() < 1e-10); + // At f=100: sigma = sqrt(0.25 + 100) = sqrt(100.25) + assert!((model.sigma(100.0) - 100.25_f64.sqrt()).abs() < 1e-10); + } + + #[test] + fn correlated_combined_matches_loading_formula_for_signed_predictions() { + let model = ResidualErrorModel::correlated_combined(0.7, 0.2, -0.35); + for prediction in [-3.0, 0.0, 2.5] { + let direct = 0.7_f64.powi(2) + + 2.0 * -0.35 * 0.7 * 0.2 * prediction + + 0.2_f64.powi(2) * prediction.powi(2); + let loading = (0.7 + -0.35 * 0.2 * prediction).powi(2) + + (1.0 - (-0.35_f64).powi(2)) * (0.2 * prediction).powi(2); + assert!((direct - loading).abs() < 1e-14); + assert!((model.variance(prediction) - direct).abs() < 1e-14); + + let draw = model + .simulate_with_standard_normal(prediction, -1.25) + .unwrap(); + assert!(((draw - prediction).powi(2) / 1.25_f64.powi(2) - direct).abs() < 1e-13); + } + + let ordinary = ResidualErrorModel::combined(0.7, 0.2); + let independent = ResidualErrorModel::correlated_combined(0.7, 0.2, 0.0); + for prediction in [-3.0, 0.0, 2.5] { + assert_eq!( + ordinary.variance(prediction), + independent.variance(prediction) + ); + } + } + + #[test] + fn test_sigma_cutoff() { + let model = ResidualErrorModel::proportional(0.1); + // At prediction = 0, raw sigma would be 0, but cutoff prevents this + let sigma = model.sigma(0.0); + assert!(sigma > 0.0); + assert!(sigma >= f64::EPSILON.sqrt()); + } + + #[test] + fn test_residual_error_models_collection() { + let models = ResidualErrorModels::new() + .add(0, ResidualErrorModel::constant(0.5)) + .add(1, ResidualErrorModel::proportional(0.1)); + + assert_eq!(models.len(), 2); + assert_eq!(models.get(0), Some(&ResidualErrorModel::constant(0.5))); + assert_eq!(models.get(1), Some(&ResidualErrorModel::proportional(0.1))); + assert!(models.get(2).is_none()); + assert!((models.get(0).unwrap().sigma(100.0) - 0.5).abs() < 1e-10); + assert!((models.get(1).unwrap().sigma(100.0) - 10.0).abs() < 1e-10); + + let sparse = ResidualErrorModels::new().add(1, ResidualErrorModel::constant(0.25)); + assert_eq!(sparse.len(), 2); + assert!(!sparse.is_empty()); + assert_eq!(sparse.get(0), None); + assert_eq!(sparse.sigma(0, 1.0), None); + assert_eq!( + sparse.iter().collect::>(), + vec![(1, sparse.get(1).unwrap())] + ); + } +} diff --git a/src/estimation/sde_particle.rs b/src/estimation/sde_particle.rs new file mode 100644 index 000000000..dffe85c7b --- /dev/null +++ b/src/estimation/sde_particle.rs @@ -0,0 +1,277 @@ +use pharmsol::equation::SdeSessionError; +use pharmsol::{Parameters, Subject, SDE}; +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; +use thiserror::Error; + +use crate::estimation::likelihood::observation::{ + assay_error_model_log_likelihood, AssayLikelihoodError, +}; +use crate::estimation::likelihood::particle::{ParticleWeightError, ParticleWeights}; +use crate::estimation::likelihood::NormalDistributionError; +use crate::{AssayErrorModels, ErrorModelError}; + +/// Reproducible controls for explicit SDE particle filtering. +#[derive(Clone, Debug, PartialEq)] +pub struct SdeParticleConfig { + pub particle_count: usize, + /// Resample when ESS is less than or equal to this fraction of the particle count. + pub ess_threshold: f64, + pub process_seed: u64, + pub resampling_seed: u64, +} + +impl SdeParticleConfig { + pub fn new(particle_count: usize) -> Self { + Self { + particle_count, + ess_threshold: 0.5, + process_seed: 0, + resampling_seed: 1, + } + } + + pub fn with_ess_threshold(mut self, threshold: f64) -> Self { + self.ess_threshold = threshold; + self + } + + pub fn with_process_seed(mut self, seed: u64) -> Self { + self.process_seed = seed; + self + } + + pub fn with_resampling_seed(mut self, seed: u64) -> Self { + self.resampling_seed = seed; + self + } +} + +/// One sequential observation update. +/// +/// `normalized_weights` and `effective_sample_size` always describe the same +/// pre-resampling phase. When `resampled` is true, they are the weights and ESS +/// that triggered ancestor selection, not the reset-uniform continuation state. +#[derive(Clone, Debug, PartialEq)] +pub struct SdeParticleRecord { + pub time: f64, + pub output: usize, + pub log_increment: f64, + pub effective_sample_size: f64, + pub resampled: bool, + pub ancestors: Option>, + pub normalized_weights: Vec, +} + +/// Complete sequential particle-filter result for one subject. +#[derive(Clone, Debug, PartialEq)] +pub struct SdeParticleResult { + pub log_value: f64, + pub records: Vec, + pub final_normalized_weights: Vec, +} + +/// Contextual failures from explicit SDE particle filtering. +#[derive(Debug, Error)] +pub enum SdeParticleError { + #[error("particle_count must be greater than zero")] + InvalidParticleCount, + #[error("ESS threshold must be finite and in (0, 1]")] + InvalidEssThreshold, + #[error(transparent)] + Session(#[from] SdeSessionError), + #[error("assay models are invalid for this SDE output context")] + InvalidAssayModels(#[source] ErrorModelError), + #[error( + "particle {particle} has an invalid assay model at time {time}, output {output}: {source}" + )] + InvalidParticleModel { + time: f64, + output: usize, + particle: usize, + #[source] + source: ErrorModelError, + }, + #[error("particle {particle} has invalid sigma {sigma} at time {time}, output {output}")] + InvalidSigma { + time: f64, + output: usize, + particle: usize, + sigma: f64, + }, + #[error("particle {particle} produced NaN or positive-infinity score at time {time}, output {output}")] + InvalidParticleScore { + time: f64, + output: usize, + particle: usize, + }, + #[error("all particles are impossible at time {time}, output {output}")] + ImpossibleObservation { time: f64, output: usize }, + #[error("particle weight normalization failed at time {time}, output {output}")] + NormalizationFailure { time: f64, output: usize }, + #[error("systematic resampling failed at time {time}, output {output}")] + ResamplingFailure { time: f64, output: usize }, +} + +/// Observation-conditioned particle filtering for pharmsol SDEs. +pub trait SdeParticleFilter { + fn particle_filter( + &self, + subject: &Subject, + parameters: &Parameters, + assay_models: &AssayErrorModels, + config: &SdeParticleConfig, + ) -> Result; +} + +impl SdeParticleFilter for SDE { + fn particle_filter( + &self, + subject: &Subject, + parameters: &Parameters, + assay_models: &AssayErrorModels, + config: &SdeParticleConfig, + ) -> Result { + if config.particle_count == 0 { + return Err(SdeParticleError::InvalidParticleCount); + } + if !config.ess_threshold.is_finite() + || !(0.0..=1.0).contains(&config.ess_threshold) + || config.ess_threshold == 0.0 + { + return Err(SdeParticleError::InvalidEssThreshold); + } + + let bound_models = if let Some(metadata) = self.metadata() { + assay_models.bind_outputs(metadata.outputs().iter().map(|output| output.name())) + } else { + assay_models.bind_outputs(std::iter::empty::<&str>()) + } + .map_err(SdeParticleError::InvalidAssayModels)?; + + let mut process_rng = StdRng::seed_from_u64(config.process_seed); + let mut resampling_rng = StdRng::seed_from_u64(config.resampling_seed); + let mut session = + self.particle_session(subject, parameters, config.particle_count, &mut process_rng)?; + let mut weights = ParticleWeights::uniform(config.particle_count).map_err(|_| { + SdeParticleError::NormalizationFailure { + time: 0.0, + output: 0, + } + })?; + let mut total = 0.0; + let mut records = Vec::new(); + + while let Some(boundary) = session.next_observation()? { + let time = boundary.time(); + let output = boundary.output_index(); + if boundary.observation().value().is_none() { + let ess = weights.effective_sample_size(); + let normalized = weights.normalized_weights(); + session.retain_particles()?; + records.push(SdeParticleRecord { + time, + output, + log_increment: 0.0, + effective_sample_size: ess, + resampled: false, + ancestors: None, + normalized_weights: normalized, + }); + continue; + } + + let mut increments = Vec::with_capacity(config.particle_count); + for (particle, prediction) in boundary.predictions().iter().enumerate() { + match assay_error_model_log_likelihood(prediction, &bound_models) { + Ok(increment) => increments.push(increment), + Err(AssayLikelihoodError::Impossible) => { + // A zero likelihood is valid for one particle; only an + // observation where every particle is impossible fails. + increments.push(f64::NEG_INFINITY); + } + Err(AssayLikelihoodError::InvalidScore(_)) + | Err(AssayLikelihoodError::Distribution( + NormalDistributionError::NonFiniteInput, + )) => { + return Err(SdeParticleError::InvalidParticleScore { + time, + output, + particle, + }); + } + Err(AssayLikelihoodError::Distribution( + NormalDistributionError::InvalidSigma(sigma), + )) => { + return Err(SdeParticleError::InvalidSigma { + time, + output, + particle, + sigma, + }); + } + Err(AssayLikelihoodError::ErrorModel(source)) => { + return Err(SdeParticleError::InvalidParticleModel { + time, + output, + particle, + source, + }); + } + } + } + if increments.iter().all(|value| *value == f64::NEG_INFINITY) { + return Err(SdeParticleError::ImpossibleObservation { time, output }); + } + let log_increment = match weights.update(&increments) { + Ok(value) => value, + Err(ParticleWeightError::AllImpossible) => { + return Err(SdeParticleError::ImpossibleObservation { time, output }); + } + Err(_) => { + return Err(SdeParticleError::NormalizationFailure { time, output }); + } + }; + total += log_increment; + let ess = weights.effective_sample_size(); + let should_resample = ess <= config.ess_threshold * config.particle_count as f64; + + if should_resample { + let normalized = weights.normalized_weights(); + let spacing = 1.0 / config.particle_count as f64; + let offset = resampling_rng.random_range(0.0..spacing); + let ancestors = ParticleWeights::systematic_ancestors(&normalized, offset) + .map_err(|_| SdeParticleError::ResamplingFailure { time, output })?; + session.select_ancestors(&ancestors)?; + records.push(SdeParticleRecord { + time, + output, + log_increment, + effective_sample_size: ess, + resampled: true, + ancestors: Some(ancestors), + normalized_weights: normalized, + }); + weights.reset_uniform(); + } else { + let normalized = weights.normalized_weights(); + session.retain_particles()?; + records.push(SdeParticleRecord { + time, + output, + log_increment, + effective_sample_size: ess, + resampled: false, + ancestors: None, + normalized_weights: normalized, + }); + } + } + + Ok(SdeParticleResult { + log_value: total, + records, + final_normalized_weights: weights.normalized_weights(), + }) + } +} diff --git a/src/iov/mod.rs b/src/iov/mod.rs index 2ba67803c..8a9f00b62 100644 --- a/src/iov/mod.rs +++ b/src/iov/mod.rs @@ -1,8 +1,9 @@ -//! SDE-based Inter-Occasion Variability (IOV) analysis. +//! SDE diffusion optimization for nonparametric support points. //! //! This module provides [`optimize_diffusion`](crate::iov::DiffusionOptimize::optimize_diffusion), //! which optimizes SDE diffusion (sigma) parameters for each support point independently, using the -//! NelderMead algorithm. The optimization runs in parallel over support points via rayon. +//! NelderMead algorithm. The optimization runs in parallel over support points via rayon. Each +//! objective evaluation uses sequential observation-conditioned particle filtering. //! //! # Workflow //! @@ -26,50 +27,25 @@ //! //! let diff = sde.optimize_diffusion( //! &r_ode.data(), &mut joint, -//! &["ske"], &r_ode.error_models(), -//! DiffusionConfig::default(), +//! &["ske".to_string()], &r_ode.error_models(), +//! None, DiffusionConfig::default(), //! )?; //! ``` mod optimizer; -use anyhow::bail; -use rayon::prelude::*; - -use pharmsol::prelude::data::AssayErrorModels; +use anyhow::{bail, Context}; use pharmsol::{Data, SDE}; +use rayon::prelude::*; use crate::estimation::nonparametric::Theta; +use crate::AssayErrorModels; -/// Configuration for SDE diffusion parameter optimization. #[derive(Debug, Clone)] pub struct DiffusionConfig { - /// Maximum NelderMead iterations per support point. - /// - /// Default: 50. Set lower for speed, higher for difficult surfaces. pub max_iter: usize, - - /// Convergence tolerance on simplex standard deviation. - /// - /// NelderMead stops when the standard deviation of function values - /// across the simplex vertices falls below this threshold. - /// Default: 1e-3. pub sd_tolerance: f64, - - /// Fraction of the distance to the upper bound for simplex construction. - /// - /// The second simplex vertex is placed at `init + perturbation × (upper − init)`. - /// This spans the simplex toward the upper bound without overshooting. - /// Default: 0.15 (for init=0.01, bounds [0,0.5] → vertex at 0.084). pub initial_perturbation: f64, - - /// Number of resampled evaluations per cost function call. - /// - /// The SDE particle filter produces noisy likelihood estimates. Averaging - /// over multiple evaluations reduces variance and makes NelderMead decisions - /// reliable by giving every vertex the same precision. - /// - /// Default: 3. Set to 1 for speed (raw NelderMead), higher for precision. pub resampling_samples: usize, } @@ -84,37 +60,36 @@ impl Default for DiffusionConfig { } } -/// Results of SDE diffusion parameter optimization. +impl DiffusionConfig { + fn validate(&self) -> anyhow::Result<()> { + if self.max_iter == 0 { + bail!("max_iter must be greater than zero"); + } + if !self.sd_tolerance.is_finite() || self.sd_tolerance <= 0.0 { + bail!("sd_tolerance must be finite and greater than zero"); + } + if !self.initial_perturbation.is_finite() + || self.initial_perturbation <= 0.0 + || self.initial_perturbation > 1.0 + { + bail!("initial_perturbation must be finite and in the interval (0, 1]"); + } + if self.resampling_samples == 0 { + bail!("resampling_samples must be greater than zero"); + } + Ok(()) + } +} + #[derive(Debug, Clone)] pub struct DiffusionResult { - /// Final log-likelihood for each support point after optimization. - /// Length equals `theta.nspp()`. pub per_point_likelihood: Vec, - - /// Number of NelderMead iterations used for each point. pub per_point_iterations: Vec, - - /// Whether NelderMead converged within `max_iter` for each point. pub per_point_converged: Vec, } -/// Trait for SDEs that support diffusion parameter optimization. -/// -/// This enables method-style calls: `sde.optimize_diffusion(...)`. pub trait DiffusionOptimize { - /// Optimize SDE diffusion parameters for each support point independently. - /// - /// Modifies `theta` **in-place**: for each support point, the sigma parameter - /// columns are replaced with values that maximize the log-likelihood of all - /// subjects under this SDE. Primary (non-sigma) parameter values are held fixed. - /// - /// If `posterior` is provided, subject contributions are weighted by their - /// posterior responsibility for each support point: `p(z_i=j)` from Stage 1. - /// If `None`, falls back to uniform weighting. - /// - /// # Panics - /// - /// Panics if any name in `sigma_params` is not found in `theta.parameters()`. + /// Optimize diffusion columns in place while holding all other columns fixed. fn optimize_diffusion( &self, data: &Data, @@ -136,155 +111,158 @@ impl DiffusionOptimize for SDE { posterior: Option<&crate::estimation::nonparametric::Posterior>, config: DiffusionConfig, ) -> anyhow::Result { - optimize_diffusion( - self, - data, - theta, - sigma_params, - error_models, - posterior, - config, - ) - } -} + config.validate()?; -/// Optimize SDE diffusion parameters for each support point independently. -/// -/// Free-function form of [`DiffusionOptimize::optimize_diffusion`]. -/// Prefer `sde.optimize_diffusion(...)` for readability. -/// -/// # Important: disable SDE caching -/// -/// SDEs cache likelihood results by default. This optimization is a Monte Carlo -/// method that requires fresh random evaluations every iteration. Ensure the SDE -/// is constructed with `.disable_cache()` before passing it here. This function -/// warns (but does not error) if caching may be enabled. -pub(crate) fn optimize_diffusion( - sde: &SDE, - data: &Data, - theta: &mut Theta, - sigma_params: &[String], - error_models: &AssayErrorModels, - posterior: Option<&crate::estimation::nonparametric::Posterior>, - config: DiffusionConfig, -) -> anyhow::Result { - let n_spp = theta.nspp(); - if n_spp == 0 { - bail!("theta has no support points"); - } + let sde = self; + let support_points = theta.nspp(); + if support_points == 0 { + bail!("theta has no support points"); + } + if sigma_params.is_empty() { + bail!("at least one diffusion parameter must be selected"); + } - // Resolve sigma parameter indices in theta - let sigma_indices: Vec = sigma_params - .iter() - .map(|name| { - theta - .parameters() - .iter() - .position(|p| p.name.as_str() == name.as_str()) - .unwrap_or_else(|| { - panic!( - "sigma parameter '{}' not found in theta parameters: {:?}", - name, - theta.parameters().names() - ) - }) - }) - .collect(); + let parameter_names = theta.parameters().names(); + let mut sigma_set = std::collections::HashSet::with_capacity(sigma_params.len()); + let sigma_indices = sigma_params + .iter() + .map(|name| { + let index = parameter_names + .iter() + .position(|candidate| candidate == name) + .ok_or_else(|| { + anyhow::anyhow!("diffusion parameter `{name}` is not in theta") + })?; + if !sigma_set.insert(index) { + bail!("duplicate diffusion parameter `{name}`"); + } + Ok(index) + }) + .collect::>>()?; + let primary_indices = (0..theta.matrix().ncols()) + .filter(|index| !sigma_set.contains(index)) + .collect::>(); + let sigma_bounds = sigma_indices + .iter() + .map(|&index| { + let parameter = &theta.parameters().items[index]; + if !parameter.lower.is_finite() + || !parameter.upper.is_finite() + || parameter.lower < 0.0 + || parameter.lower > parameter.upper + { + bail!( + "diffusion parameter `{}` must have finite nonnegative inclusive bounds, got [{}, {}]", + parameter.name, + parameter.lower, + parameter.upper + ); + } + Ok((parameter.lower, parameter.upper)) + }) + .collect::>>()?; - // Identify primary parameter indices (all others) - let n_total = theta.matrix().ncols(); - let sigma_set: std::collections::HashSet = sigma_indices.iter().copied().collect(); - let primary_indices: Vec = (0..n_total).filter(|i| !sigma_set.contains(i)).collect(); + for support_index in 0..support_points { + for (sigma_position, ¶meter_index) in sigma_indices.iter().enumerate() { + let value = theta.matrix()[(support_index, parameter_index)]; + let (lower, upper) = sigma_bounds[sigma_position]; + if !value.is_finite() || value < lower || value > upper { + bail!( + "initial diffusion parameter `{}` for support point {} must be finite and within inclusive bounds [{}, {}], got {}", + parameter_names[parameter_index], + support_index, + lower, + upper, + value + ); + } + } + } - // Check for sigma initialized to zero - for &si in &sigma_indices { - for r in 0..n_spp { - if theta.matrix()[(r, si)] == 0.0 { - tracing::warn!( - "sigma parameter at column {} (support point {}) initialized to 0.0; \ - the SDE degenerates to an ODE at sigma=0. Consider using a small \ - non-zero initial value (e.g., 0.01)", - si, - r + if let Some(posterior) = posterior { + let matrix = posterior.matrix(); + let subject_count = data.subjects().len(); + if matrix.nrows() != subject_count { + bail!( + "posterior row count ({}) must match data subject count ({}) for diffusion optimization", + matrix.nrows(), + subject_count ); } + if matrix.ncols() != support_points { + bail!( + "posterior column count ({}) must match theta support point count ({}) for diffusion optimization", + matrix.ncols(), + support_points + ); + } + for row in 0..matrix.nrows() { + for column in 0..matrix.ncols() { + let value = matrix[(row, column)]; + if !value.is_finite() { + bail!( + "posterior value at row {}, column {} must be finite, got {}", + row, + column, + value + ); + } + } + } } - } - - // Extract sigma parameter bounds for simplex construction - let sigma_bounds: Vec<(f64, f64)> = sigma_indices - .iter() - .map(|&si| { - let bp = &theta.parameters().items[si]; - (bp.lower, bp.upper) - }) - .collect(); - // Parallel optimization over support points — each SP optimized independently. - // If a Stage 1 posterior is provided, subject contributions are weighted by - // p(z_i=j), correctly modeling population structure without inner-loop Burke. - let results: Vec = (0..n_spp) - .into_par_iter() - .map(|i| { - let primary: Vec = primary_indices - .iter() - .map(|&pi| theta.matrix()[(i, pi)]) - .collect(); + let particle_count = sde + .metadata() + .and_then(|metadata| metadata.particles()) + .ok_or_else(|| anyhow::anyhow!("SDE metadata must declare its particle count"))?; - let sigma_init: Vec = sigma_indices - .iter() - .map(|&si| theta.matrix()[(i, si)]) - .collect(); - - // Extract posterior responsibilities for this SP (if available) - let responsibilities: Option> = posterior.map(|p| { - (0..data.subjects().len()) - .map(|s| p.matrix()[(s, i)]) - .collect() - }); - let resp_slice: Option<&[f64]> = responsibilities.as_deref(); - - let cost = optimizer::SigmaCost::new( - sde, - data, - &primary, - &primary_indices, - &sigma_indices, - error_models, - resp_slice, - ); - - optimizer::optimize_sigma(cost, &sigma_init, &sigma_bounds, &config) - }) - .collect(); - - // Update theta with optimized sigma values - let mut per_point_likelihood = Vec::with_capacity(n_spp); - let mut per_point_iterations = Vec::with_capacity(n_spp); - let mut per_point_converged = Vec::with_capacity(n_spp); + let outcomes = (0..support_points) + .into_par_iter() + .map(|support_index| { + let primary = primary_indices + .iter() + .map(|&index| theta.matrix()[(support_index, index)]) + .collect::>(); + let sigma = sigma_indices + .iter() + .map(|&index| theta.matrix()[(support_index, index)]) + .collect::>(); + let responsibilities = posterior.map(|posterior| { + (0..data.subjects().len()) + .map(|subject| posterior.matrix()[(subject, support_index)]) + .collect::>() + }); + let cost = optimizer::SigmaCost::new( + sde, + data, + &primary, + &primary_indices, + &sigma_indices, + parameter_names.clone(), + error_models, + responsibilities.as_deref(), + particle_count, + ); + optimizer::optimize_sigma(cost, &sigma, &sigma_bounds, &config).with_context(|| { + format!("diffusion optimization failed for support point {support_index}") + }) + }) + .collect::>>()?; - for (i, outcome) in results.iter().enumerate() { - for (j, &si) in sigma_indices.iter().enumerate() { - theta.matrix_mut()[(i, si)] = outcome.optimized_params[j]; + let mut result = DiffusionResult { + per_point_likelihood: Vec::with_capacity(support_points), + per_point_iterations: Vec::with_capacity(support_points), + per_point_converged: Vec::with_capacity(support_points), + }; + for (support_index, outcome) in outcomes.into_iter().enumerate() { + for (sigma_position, ¶meter_index) in sigma_indices.iter().enumerate() { + theta.matrix_mut()[(support_index, parameter_index)] = + outcome.optimized_params[sigma_position]; + } + result.per_point_likelihood.push(-outcome.final_cost); + result.per_point_iterations.push(outcome.iterations); + result.per_point_converged.push(outcome.converged); } - per_point_likelihood.push(-outcome.final_cost); - per_point_iterations.push(outcome.iterations); - per_point_converged.push(outcome.converged); + Ok(result) } - - let n_converged = per_point_converged.iter().filter(|&&c| c).count(); - tracing::info!( - "SDE IOV optimization: {}/{} support points converged, \ - mean iterations: {:.1}, mean log-likelihood: {:.2}", - n_converged, - n_spp, - per_point_iterations.iter().sum::() as f64 / n_spp.max(1) as f64, - per_point_likelihood.iter().sum::() / n_spp.max(1) as f64, - ); - - Ok(DiffusionResult { - per_point_likelihood, - per_point_iterations, - per_point_converged, - }) } diff --git a/src/iov/optimizer.rs b/src/iov/optimizer.rs index c07918400..921002b41 100644 --- a/src/iov/optimizer.rs +++ b/src/iov/optimizer.rs @@ -1,75 +1,88 @@ -//! NelderMead-based sigma optimization for SDE IOV. -//! -//! Internal module — not exposed publicly. +use std::sync::{ + atomic::{AtomicU64, Ordering}, + Mutex, +}; use argmin::core::{CostFunction, Error, Executor}; use argmin::solver::neldermead::NelderMead; -use pharmsol::prelude::data::AssayErrorModels; -use pharmsol::prelude::simulator::Equation; -use pharmsol::{Data, SDE}; +use pharmsol::{Data, Parameters, SDE}; use super::DiffusionConfig; +use crate::{AssayErrorModels, SdeParticleConfig, SdeParticleFilter}; + +const INVALID_CANDIDATE_COST: f64 = 1e10; -/// Cost function: posterior-weighted negative log-likelihood. -/// -/// ```text -/// cost(sigma_j) = -sum_i r_i * log P(data_i | theta_j, sigma_j) -/// ``` -/// -/// Where `rᵢ = p(zᵢ=j)` is subject i's posterior responsibility for support -/// point j, computed during Stage 1 (NPAG). If responsibilities are `None`, -/// falls back to uniform weighting (all subjects contribute equally). -/// -/// Subjects with near-zero responsibility contribute near-zero to the gradient, -/// correctly modeling the population structure without running Burke in the -/// inner optimization loop. pub(crate) struct SigmaCost<'a> { sde: &'a SDE, data: &'a Data, primary: Vec, primary_indices: Vec, sigma_indices: Vec, + parameter_names: Vec, error_models: &'a AssayErrorModels, - /// Per-subject responsibilities for this support point. - /// None → uniform weighting (all subjects equal). - #[allow(dead_code)] responsibilities: Option<&'a [f64]>, n_total: usize, + particle_count: usize, + seed: AtomicU64, + execution_error: Mutex>, } impl<'a> SigmaCost<'a> { + #[allow(clippy::too_many_arguments)] pub(crate) fn new( sde: &'a SDE, data: &'a Data, primary: &[f64], primary_indices: &[usize], sigma_indices: &[usize], + parameter_names: Vec, error_models: &'a AssayErrorModels, responsibilities: Option<&'a [f64]>, + particle_count: usize, ) -> Self { - let n_total = primary.len() + sigma_indices.len(); Self { sde, data, primary: primary.to_vec(), primary_indices: primary_indices.to_vec(), sigma_indices: sigma_indices.to_vec(), + parameter_names, error_models, responsibilities, - n_total, + n_total: primary.len() + sigma_indices.len(), + particle_count, + seed: AtomicU64::new(0), + execution_error: Mutex::new(None), } } fn build_params(&self, sigma: &[f64]) -> Vec { let mut full = vec![0.0; self.n_total]; - for (&pi, &val) in self.primary_indices.iter().zip(self.primary.iter()) { - full[pi] = val; + for (&index, &value) in self.primary_indices.iter().zip(&self.primary) { + full[index] = value; } - for (&si, &val) in self.sigma_indices.iter().zip(sigma) { - full[si] = val; + for (&index, &value) in self.sigma_indices.iter().zip(sigma) { + full[index] = value; } full } + + fn record_execution_error(&self, error: String) { + let mut slot = self + .execution_error + .lock() + .unwrap_or_else(std::sync::PoisonError::into_inner); + if slot.is_none() { + *slot = Some(error); + } + } + + fn take_execution_error(&self) -> Option { + self.execution_error + .lock() + .unwrap_or_else(std::sync::PoisonError::into_inner) + .take() + } } impl CostFunction for SigmaCost<'_> { @@ -77,42 +90,58 @@ impl CostFunction for SigmaCost<'_> { type Output = f64; fn cost(&self, sigma: &Self::Param) -> Result { - if sigma.iter().any(|&s| s < 0.0) { + if sigma.iter().any(|value| !value.is_finite() || *value < 0.0) { return Ok(1e10); } - - let full_params = self.build_params(sigma); - let mut total_ll = 0.0f64; - - for subject in self.data.subjects().iter() { - let (_, likelihood) = self - .sde - .simulate_subject_dense(subject, &full_params, Some(self.error_models)) - .map_err(|e| Error::msg(e.to_string()))?; - - match likelihood { - Some(ll) if ll > 0.0 => total_ll += ll.ln(), - _ => return Ok(1e10), + let full = self.build_params(sigma); + let parameters = match Parameters::with_model( + self.sde, + self.parameter_names + .iter() + .map(String::as_str) + .zip(full.iter().copied()), + ) { + Ok(parameters) => parameters, + Err(error) => { + self.record_execution_error(error.to_string()); + return Ok(INVALID_CANDIDATE_COST); } - } + }; + let call = self.seed.fetch_add(1, Ordering::Relaxed); + let config = SdeParticleConfig::new(self.particle_count) + .with_process_seed(call.wrapping_mul(2)) + .with_resampling_seed(call.wrapping_mul(2).wrapping_add(1)); - if !total_ll.is_finite() { - return Ok(1e10); + let mut total = 0.0; + for (subject_index, subject) in self.data.subjects().iter().enumerate() { + let result = + match self + .sde + .particle_filter(subject, ¶meters, self.error_models, &config) + { + Ok(result) => result, + Err(error) => { + self.record_execution_error(error.to_string()); + return Ok(INVALID_CANDIDATE_COST); + } + }; + let responsibility = self + .responsibilities + .map_or(1.0, |values| values[subject_index]); + total += responsibility * result.log_value; } - - Ok(-total_ll) + if !total.is_finite() { + self.record_execution_error("particle-filter objective is non-finite".to_string()); + return Ok(INVALID_CANDIDATE_COST); + } + Ok(-total) } } -/// Wraps a [`SigmaCost`] and evaluates it `n` times per cost call, returning -/// the mean. NelderMead makes decisions based on these averaged values, -/// preventing noise-triggered premature simplex collapse. -/// -/// Every vertex gets the same number of samples — this ensures unbiased -/// comparisons within the simplex. struct ResampledCost<'a> { inner: &'a SigmaCost<'a>, samples: usize, + bounds: &'a [(f64, f64)], } impl CostFunction for ResampledCost<'_> { @@ -120,14 +149,72 @@ impl CostFunction for ResampledCost<'_> { type Output = f64; fn cost(&self, sigma: &Self::Param) -> Result { - let sum: f64 = (0..self.samples) - .map(|_| self.inner.cost(sigma).unwrap_or(1e10)) - .sum(); - Ok(sum / self.samples as f64) + if !candidate_within_bounds(sigma, self.bounds) { + return Ok(INVALID_CANDIDATE_COST); + } + + let mut sum = 0.0; + for _ in 0..self.samples.max(1) { + sum += self.inner.cost(sigma)?; + } + Ok(sum / self.samples.max(1) as f64) } } -/// Outcome of a single support point's sigma optimization. +fn candidate_within_bounds(candidate: &[f64], bounds: &[(f64, f64)]) -> bool { + candidate.len() == bounds.len() + && candidate + .iter() + .zip(bounds) + .all(|(&value, &(lower, upper))| value.is_finite() && value >= lower && value <= upper) +} + +#[derive(Debug, thiserror::Error, PartialEq)] +pub(crate) enum DiffusionOptimizationError { + #[error("failed to configure diffusion optimizer: {0}")] + SolverConfiguration(String), + #[error("diffusion optimizer execution failed: {0}")] + Execution(String), + #[error("diffusion optimizer did not return a best parameter")] + MissingBestParameter, + #[error("diffusion optimizer returned non-finite final cost {0}")] + NonFiniteFinalCost(f64), + #[error("optimizer returned {actual} diffusion parameters, expected {expected}")] + ReturnedDimension { expected: usize, actual: usize }, + #[error( + "optimizer returned invalid diffusion parameter at position {position}: {value} is not finite or outside inclusive bounds [{lower}, {upper}]" + )] + ReturnedParameter { + position: usize, + value: f64, + lower: f64, + upper: f64, + }, +} + +fn validate_returned_params( + candidate: &[f64], + bounds: &[(f64, f64)], +) -> Result<(), DiffusionOptimizationError> { + if candidate.len() != bounds.len() { + return Err(DiffusionOptimizationError::ReturnedDimension { + expected: bounds.len(), + actual: candidate.len(), + }); + } + for (position, (&value, &(lower, upper))) in candidate.iter().zip(bounds).enumerate() { + if !value.is_finite() || value < lower || value > upper { + return Err(DiffusionOptimizationError::ReturnedParameter { + position, + value, + lower, + upper, + }); + } + } + Ok(()) +} + pub(crate) struct OptimizationOutcome { pub optimized_params: Vec, pub final_cost: f64, @@ -135,78 +222,61 @@ pub(crate) struct OptimizationOutcome { pub converged: bool, } -/// Run NelderMead with resampled cost evaluations to handle particle-filter noise. pub(crate) fn optimize_sigma( cost: SigmaCost<'_>, sigma_init: &[f64], sigma_bounds: &[(f64, f64)], config: &DiffusionConfig, -) -> OptimizationOutcome { +) -> Result { let simplex = build_simplex(sigma_init, sigma_bounds, config.initial_perturbation); - let resampled = ResampledCost { inner: &cost, samples: config.resampling_samples, + bounds: sigma_bounds, }; - let solver: NelderMead, f64> = NelderMead::new(simplex) .with_sd_tolerance(config.sd_tolerance) - .expect("NelderMead construction should succeed with valid parameters"); - - let result = Executor::new(resampled, solver) + .map_err(|error| DiffusionOptimizationError::SolverConfiguration(error.to_string()))?; + let execution = Executor::new(resampled, solver) .configure(|state| state.max_iters(config.max_iter as u64)) .run(); - - match result { - Ok(res) => { - let best = res.state.best_param.unwrap_or_else(|| sigma_init.to_vec()); - let cost_val = res.state.best_cost; - let iterations = res.state.iter as usize; - - OptimizationOutcome { - optimized_params: best, - final_cost: cost_val, - iterations, - converged: iterations < config.max_iter, - } - } - Err(e) => { - tracing::warn!( - "NelderMead optimization failed for a support point: {}. Returning initial values.", - e - ); - OptimizationOutcome { - optimized_params: sigma_init.to_vec(), - final_cost: f64::INFINITY, - iterations: 0, - converged: false, - } - } + if let Some(error) = cost.take_execution_error() { + return Err(DiffusionOptimizationError::Execution(error)); + } + let result = + execution.map_err(|error| DiffusionOptimizationError::Execution(error.to_string()))?; + let iterations = result.state.iter as usize; + let optimized_params = result + .state + .best_param + .ok_or(DiffusionOptimizationError::MissingBestParameter)?; + let final_cost = result.state.best_cost; + if !final_cost.is_finite() { + return Err(DiffusionOptimizationError::NonFiniteFinalCost(final_cost)); } + validate_returned_params(&optimized_params, sigma_bounds)?; + + Ok(OptimizationOutcome { + optimized_params, + final_cost, + iterations, + converged: iterations < config.max_iter, + }) } -/// Build the initial simplex for NelderMead. -/// -/// For N-dimensional optimization, the simplex has N+1 vertices. -/// Vertex 0 is the initial point. Vertex i+1 is placed at -/// `init + perturbation × (upper − init)`, clamped to bounds. -/// This keeps the simplex within the search space while providing enough -/// spread to reach the optimum even from a small initial value. fn build_simplex(initial: &[f64], bounds: &[(f64, f64)], perturbation: f64) -> Vec> { - let n = initial.len(); - let mut vertices = Vec::with_capacity(n + 1); + let mut vertices = Vec::with_capacity(initial.len() + 1); vertices.push(initial.to_vec()); - - for i in 0..n { + for index in 0..initial.len() { let mut vertex = initial.to_vec(); - let (lower, upper) = bounds[i]; - vertex[i] = (initial[i] + perturbation * (upper - initial[i])).clamp(lower, upper); - if (vertex[i] - initial[i]).abs() < 1e-10 { - vertex[i] = (lower + upper) * 0.5; + let (lower, upper) = bounds[index]; + vertex[index] = + (initial[index] + perturbation * (upper - initial[index])).clamp(lower, upper); + if (vertex[index] - initial[index]).abs() < 1e-10 { + vertex[index] = (lower + upper) * 0.5; } vertices.push(vertex); } - vertices } @@ -216,14 +286,39 @@ mod tests { #[test] fn simplex_reaches_optimum() { - // init=0.01, bounds [0, 0.5], perturbation=0.15 - // vertex = 0.01 + 0.15 * (0.5 - 0.01) = 0.0835 - let s = build_simplex(&[0.01], &[(0.0, 0.5)], 0.15); - assert!((s[1][0] - 0.0835).abs() < 1e-10); - - // init=0.3, bounds [0, 1.0], perturbation=0.15 - // vertex = 0.3 + 0.15 * (1.0 - 0.3) = 0.405 - let s = build_simplex(&[0.3], &[(0.0, 1.0)], 0.15); - assert!((s[1][0] - 0.405).abs() < 1e-10); + let simplex = build_simplex(&[0.01], &[(0.0, 0.5)], 0.15); + assert!((simplex[1][0] - 0.0835).abs() < 1e-10); + let simplex = build_simplex(&[0.3], &[(0.0, 1.0)], 0.15); + assert!((simplex[1][0] - 0.405).abs() < 1e-10); + } + + #[test] + fn candidate_bounds_enforce_positive_lower_and_upper_limits() { + let bounds = [(0.25, 0.75), (1.0, 2.0)]; + + assert!(candidate_within_bounds(&[0.25, 2.0], &bounds)); + assert!(!candidate_within_bounds(&[0.249, 1.5], &bounds)); + assert!(!candidate_within_bounds(&[0.5, 2.001], &bounds)); + assert!(!candidate_within_bounds(&[f64::NAN, 1.5], &bounds)); + assert!(!candidate_within_bounds(&[0.5], &bounds)); + } + + #[test] + fn returned_point_validation_rejects_each_bound_violation() { + let bounds = [(0.25, 0.75), (1.0, 2.0)]; + + assert!(validate_returned_params(&[0.25, 2.0], &bounds).is_ok()); + assert!(matches!( + validate_returned_params(&[0.249, 1.5], &bounds), + Err(DiffusionOptimizationError::ReturnedParameter { position: 0, .. }) + )); + assert!(matches!( + validate_returned_params(&[0.5, 2.001], &bounds), + Err(DiffusionOptimizationError::ReturnedParameter { position: 1, .. }) + )); + assert!(matches!( + validate_returned_params(&[0.5, f64::INFINITY], &bounds), + Err(DiffusionOptimizationError::ReturnedParameter { position: 1, .. }) + )); } } diff --git a/src/lib.rs b/src/lib.rs index 824aae3f9..1998eb936 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,31 +1,10 @@ -//! PMcore is a framework for developing and running population pharmacokinetic algorithms. -//! -//! # Algorithm Types -//! -//! ## Non-Parametric Algorithms -//! Represent the population distribution as a discrete set of support points with associated weights. -//! - NPAG (Non-Parametric Adaptive Grid) -//! - NPOD (Non-Parametric Optimal Design) -//! - NPMAP (Maximum a posteriori reweighting) -//! -//! ## Parametric Algorithms (planned) -//! Represent the population distribution with a parametric form (e.g. a normal distribution) and -//! estimate the parameters of that distribution. This family is not yet implemented; the API is -//! present but calling it will panic until a solver (SAEM) is available. -//! -//! # Public Interface -//! -//! PMcore centers on the estimation interface in [estimation]. Models are defined in -//! [model], configured with [estimation::EstimationProblem], and then executed with the -//! selected algorithm. -//! -//! # Data format -//! -//! PMcore is heavily linked to [pharmsol], which provides the data structures and routines for handling -//! pharmacokinetic data. The data is stored in a [pharmsol::Data] structure, and can either be read -//! from a CSV file, using `pharmsol::data::parse_pmetrics::read_pmetrics`, or created dynamically -//! using the [pharmsol::data::builder::SubjectBuilder]. +//! Population pharmacokinetic estimation algorithms and result types. //! +//! PMcore provides nonparametric algorithms, SAEM for deterministic analytical +//! and ODE models, and explicit SDE particle filtering. Models and data are +//! supplied through [pharmsol], configured with [`estimation::EstimationProblem`], +//! and run with an algorithm from [`algorithms`]. Generic SDE fitting through +//! [`estimation::EstimationProblem`] is unsupported. /// Provides the various algorithms used within the framework pub mod algorithms; @@ -46,6 +25,24 @@ pub mod logs; pub use anyhow::Result; pub use std::collections::HashMap; +#[allow(deprecated)] +pub use estimation::{ + AssayErrorModel, AssayErrorModels, AssayLikelihoodError, BoundAssayErrorModels, + ConditionalCurvatureDiagnostics, ConditionalCurvatureRegularization, + ConditionalCurvatureStatus, ConditionalCurvatureUnavailableReason, ConditionalModeMetadata, + CovariateEffect, CovariateEffectFamily, CovariateEstimate, CovariateGlsProblem, CovariateModel, + CovariateMstepError, CovariateValidationError, ErrorModel, ErrorModelError, ErrorPoly, + EtaMapShrinkage, EtaPosteriorMeanShrinkage, Factor, JointLatentCoordinate, + JointLatentCoordinateKind, KappaMapShrinkage, KappaPosteriorMeanShrinkage, + MarginalLikelihoodConfig, MarginalLikelihoodDiagnostics, MarginalLikelihoodFailureReason, + MarginalLikelihoodMethod, MarginalLikelihoodProposal, MarginalLikelihoodStatus, + MarginalLikelihoodSubjectFailure, NormalDistributionError, ParametricConstraint, + ProposalScaleSource, ResidualErrorModel, ResidualErrorModels, SdeParticleConfig, + SdeParticleError, SdeParticleFilter, SdeParticleRecord, SdeParticleResult, + ShrinkageDiagnostics, ShrinkageUnavailableReason, ShrinkageValue, SubjectCovariateDesign, + SubjectCovariateValue, SubjectPopulationParameters, +}; + /// Dose optimization and forecasting (BestDose). pub mod bestdose; @@ -62,10 +59,27 @@ pub mod prelude { pub use crate::estimation::NonParametric; pub use crate::estimation::Parametric; + #[allow(deprecated)] pub use crate::estimation::{ - ErrorModels, EstimationProblem, FitProgress, NcnpagConfig, NonParametricAlgorithm, - NonparametricCycleProgress, NpagConfig, NpmapConfig, NpodConfig, ParametricAlgorithm, - SaemConfig, + AssayErrorModel, AssayErrorModels, AssayLikelihoodError, BoundAssayErrorModels, + ConditionalCurvatureDiagnostics, ConditionalCurvatureRegularization, + ConditionalCurvatureStatus, ConditionalCurvatureUnavailableReason, ConditionalModeMetadata, + CovarianceStabilityConfig, CovariateEffect, CovariateEffectFamily, CovariateEstimate, + CovariateGlsProblem, CovariateModel, CovariateMstepError, CovariateValidationError, + ErrorModel, ErrorModelError, ErrorModels, ErrorPoly, EstimationProblem, EtaMapShrinkage, + EtaPosteriorMeanShrinkage, Factor, FitProgress, Iov, JointLatentCoordinate, + JointLatentCoordinateKind, KappaMapShrinkage, KappaPosteriorMeanShrinkage, LugsailConfig, + MarginalLikelihoodConfig, MarginalLikelihoodDiagnostics, MarginalLikelihoodFailureReason, + MarginalLikelihoodMethod, MarginalLikelihoodProposal, MarginalLikelihoodStatus, + MarginalLikelihoodSubjectFailure, MarkovSimulationVarianceConfig, NcnpagConfig, + NonParametricAlgorithm, NonparametricCycleProgress, NormalDistributionError, NpagConfig, + NpmapConfig, NpodConfig, Omega, OperationalConvergenceConfig, ParametricAlgorithm, + ParametricConstraint, ParametricErrorModel, ParametricErrorModels, ParametricPrior, + ProposalScaleSource, ResidualErrorModel, ResidualErrorModels, SaemConfig, + SaemEstimatorPolicy, SdeParticleConfig, SdeParticleError, SdeParticleFilter, + SdeParticleRecord, SdeParticleResult, ShrinkageDiagnostics, ShrinkageUnavailableReason, + ShrinkageValue, SubjectCovariateDesign, SubjectCovariateValue, + SubjectMarginalLikelihoodDiagnostics, SubjectPopulationParameters, }; pub use crate::model::parameter_space::{ @@ -73,13 +87,37 @@ pub mod prelude { }; pub use crate::algorithms::nonparametric::{CycleFlow, FitController, FitObserver}; + pub use crate::algorithms::parametric::{ + CycleFlow as ParametricCycleFlow, FitController as ParametricFitController, + FitObserver as ParametricFitObserver, NumericalFailure, NumericalFailurePhase, + ParametricFitSnapshot, + }; pub use crate::estimation::nonparametric::{ CycleLog, NPCycle, NPPredictions, NonParametricResult, Posterior, Psi, Theta, Weights, }; pub use crate::iov::{DiffusionConfig, DiffusionOptimize, DiffusionResult}; pub use crate::model::{EquationMetadataSource, ModelMetadata}; pub use crate::results::{ - FitResult, FitSummary, IndividualSummary, ParameterSummary, PopulationSummary, + CovarianceCycleUpdateDiagnostics, CovarianceCycleUpdateOutcome, + CovarianceTrialRejectionReason, CovarianceUpdateNotAttemptedReason, + CovarianceUpdateRejectionReason, DiagnosticTraceCoordinate, FitResult, FitSummary, + IndividualEffectRow, IndividualParameterRow, IndividualSummary, + InformationCriteriaDiagnostics, InformationCriteriaParameterCount, InformationCriteriaRow, + InformationCriteriaSampleSizeConvention, InformationCriteriaStatus, + InformationCriteriaUnavailableReason, IterationRow, MarginalLikelihoodRow, + MarkovSimulationVarianceChainDiagnostics, MarkovSimulationVarianceDiagnostics, + MarkovSimulationVarianceStatus, OccasionKappaEstimate, OmegaRow, + OperationalConvergenceCheck, OperationalConvergenceCriterion, + OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, + OperationalConvergenceOutcome, ParameterSummary, ParametricResult, ParametricResultRecord, + ParametricResultTables, ParametricSourceCovariance, ParametricSourceEffect, + ParametricSourceMetadata, ParametricSourceParameter, ParametricSourceResidual, + ParametricWarning, ParametricWarningRecord, PopulationParameterRow, PopulationSummary, + PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, + PopulationUncertaintyStatus, PopulationUncertaintyUnavailableReason, PredictionRow, + RankDiagnosticStatus, RankMixingDiagnostic, RankMixingDiagnostics, + ResidualCycleDiagnostics, ResidualErrorEstimate, ResidualErrorRow, SaemCycleDiagnostics, + SaemEstimatorMetadata, SaemPhase, StatisticRow, SubjectConditionalMode, SubjectEtaEstimate, }; // pharmsol: re-export the crate itself and its curated prelude. diff --git a/src/model/parameter_space.rs b/src/model/parameter_space.rs index b6cf5658e..3e0120d9d 100644 --- a/src/model/parameter_space.rs +++ b/src/model/parameter_space.rs @@ -149,6 +149,7 @@ impl From for UnboundedParameter { }, initial: None, estimate: true, + random_effect: true, } } } @@ -160,6 +161,11 @@ pub struct UnboundedParameter { pub scale: ParameterScale, pub initial: Option, pub estimate: bool, + /// Whether this population parameter has an IIV random effect η. + /// + /// Defaults to `true`. + #[serde(default = "default_true")] + pub random_effect: bool, } impl UnboundedParameter { @@ -170,6 +176,7 @@ impl UnboundedParameter { scale, initial: None, estimate: true, + random_effect: true, } } @@ -178,11 +185,40 @@ impl UnboundedParameter { Self::new(name, ParameterScale::Identity) } - /// Sets an initial value. + /// Sets the natural-scale typical value at zero eta, kappa, and covariate offsets. pub fn with_initial(mut self, value: f64) -> Self { self.initial = Some(value); self } + + /// Enables or disables estimation of the population parameter value. + pub fn with_estimate(mut self, enabled: bool) -> Self { + self.estimate = enabled; + self + } + + /// Fixes the population parameter at its initial value. + /// + /// This is independent of IIV: a fixed population parameter may still have + /// a random effect unless [`without_random_effect`](Self::without_random_effect) is used. + pub fn fixed(self) -> Self { + self.with_estimate(false) + } + + /// Enables or disables the parameter's IIV random effect η. + pub fn with_random_effect(mut self, enabled: bool) -> Self { + self.random_effect = enabled; + self + } + + /// Declares that this population parameter has no IIV random effect. + pub fn without_random_effect(self) -> Self { + self.with_random_effect(false) + } +} + +fn default_true() -> bool { + true } impl ParameterMeta for UnboundedParameter { @@ -252,3 +288,21 @@ impl Parameter { UnboundedParameter::new(name, ParameterScale::Probit { lower, upper }) } } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn parametric_parameters_default_to_iiv() { + assert!(Parameter::log("cl").random_effect); + assert!(!Parameter::log("v").without_random_effect().random_effect); + } + + #[test] + fn fixing_population_estimation_is_independent_from_iiv() { + let parameter = Parameter::log("cl").fixed(); + assert!(!parameter.estimate); + assert!(parameter.random_effect); + } +} diff --git a/src/results/fit_result.rs b/src/results/fit_result.rs index eadcfeaaf..b8b600617 100644 --- a/src/results/fit_result.rs +++ b/src/results/fit_result.rs @@ -1,7 +1,31 @@ -use pharmsol::Equation; +use anyhow::{bail, Context}; +use ndarray::Array2; +use pharmsol::simulator::prediction::SubjectPredictions; +use pharmsol::{Data, Equation, Event, Subject}; +use serde::{Deserialize, Serialize}; +use crate::estimation::parametric::{ + covariates::{ + CovariateEffect, CovariateEstimate, CovariateModel, CovariateMstepError, + SubjectCovariateDesign, SubjectCovariateValue, SubjectPopulationParameters, + }, + individual::{occasion_psi, occasion_psi_from_subject_mean, population_phi}, + marginal_likelihood::{MarginalLikelihoodDiagnostics, MarginalLikelihoodStatus}, + transforms::{phi_to_psi_derivative, psi_to_phi}, + ConditionalCurvatureDiagnostics, ShrinkageDiagnostics, +}; +use crate::model::ParameterScale; +use crate::ResidualErrorModel; + +use crate::algorithms::parametric::{ + MarkovSimulationVarianceConfig, OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, +}; +use crate::algorithms::StopReason; use crate::estimation::nonparametric::NonParametricResult; -use crate::results::{FitSummary, IndividualSummary, PopulationSummary}; +use crate::results::{ + FitSummary, IndividualSummary, InformationCriteriaDiagnostics, ParameterSummary, + PopulationSummary, +}; /// A shared trait for the output of any estimation algorithm. pub trait FitResult { @@ -12,28 +36,1447 @@ pub trait FitResult { fn individual_summaries(&self) -> Vec; } -// TODO: Implement ParametricResult once parametric fitting is available. -#[derive(Debug)] -#[allow(unused)] +/// SAEM schedule phase associated with an immutable cycle diagnostic. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize)] +pub enum SaemPhase { + BurnIn, + Exploration, + Smoothing, +} + +/// Per-output residual M-step diagnostics for one SAEM cycle. +#[derive(Debug, Clone, PartialEq, Serialize)] +pub struct ResidualCycleDiagnostics { + pub output: String, + pub output_index: usize, + pub prediction_evaluation_count: usize, + pub proportional_floor_count: usize, + pub non_finite_prediction_count: usize, + pub exponential_domain_violation_count: usize, + pub update_rejected: bool, + pub optimizer_objective: Option, + pub optimizer_converged: Option, + pub optimizer_iterations: Option, + pub optimizer_termination: Option, + pub combined_additive_collapse_warning: bool, +} + +/// Why a covariance update was not attempted during a cycle. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize)] +#[serde(rename_all = "snake_case")] +pub enum CovarianceUpdateNotAttemptedReason { + BurnIn, + UpdateInactive, + NoEstimatedEntries, + NotConfigured, +} + +/// Terminal reason for rejecting a covariance proposal before or after trial steps. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize)] +#[serde(rename_all = "snake_case")] +pub enum CovarianceUpdateRejectionReason { + CandidateNotFiniteSymmetric, + CurrentObjectiveUnavailable, + ConstrainedSolveFailed, + BacktrackingExhausted, +} + +/// Reason one deterministic covariance trial was rejected. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize)] +#[serde(rename_all = "snake_case")] +pub enum CovarianceTrialRejectionReason { + VarianceFloorInfeasible, + NotPositiveDefinite, + ObjectiveUnavailable, + ObjectiveIncrease, +} + +/// Terminal classification of one cycle's covariance update. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize)] +#[serde(tag = "status", rename_all = "snake_case")] +pub enum CovarianceCycleUpdateOutcome { + NotAttempted { + reason: CovarianceUpdateNotAttemptedReason, + }, + Accepted, + NoOp, + Rejected { + reason: CovarianceUpdateRejectionReason, + }, +} + +/// Complete proposal and acceptance diagnostics for one covariance M-step. +#[derive(Debug, Clone, PartialEq, Serialize)] +pub struct CovarianceCycleUpdateDiagnostics { + /// Coherent covariance second-moment proposal supplied to the updater. + pub proposal: Option>, + /// Mask-aware solved target after any capped-path variance floor. + pub solved_target: Option>, + pub outcome: CovarianceCycleUpdateOutcome, + /// Accepted interpolation fraction; absent when no trial was accepted. + pub accepted_fraction: Option, + /// Fractions evaluated in deterministic order. + pub attempted_fractions: Vec, + /// One entry for every rejected trial, aligned with the corresponding + /// prefix of `attempted_fractions` before an accepted trial, if any. + pub trial_rejections: Vec, +} + +impl CovarianceCycleUpdateDiagnostics { + pub(crate) fn not_attempted(reason: CovarianceUpdateNotAttemptedReason) -> Self { + Self { + proposal: None, + solved_target: None, + outcome: CovarianceCycleUpdateOutcome::NotAttempted { reason }, + accepted_fraction: None, + attempted_fractions: Vec::new(), + trial_rejections: Vec::new(), + } + } +} + +/// MCMC, covariance, and residual diagnostics captured after one complete SAEM cycle. +#[derive(Debug, Clone, PartialEq, serde::Serialize)] +pub struct SaemCycleDiagnostics { + pub iteration: usize, + pub phase: SaemPhase, + pub stochastic_approximation_step: f64, + pub covariance_step: f64, + pub eta_proposals: usize, + pub eta_accepted: usize, + pub eta_rejected: usize, + pub eta_non_finite: usize, + pub eta_parameter_acceptance_rates: Vec, + pub eta_proposal_step_sizes_before_adaptation: Vec, + pub eta_proposal_step_sizes_after_adaptation: Vec, + pub eta_block_proposals: usize, + pub eta_block_accepted: usize, + pub eta_block_rejected: usize, + pub eta_block_non_finite: usize, + pub eta_block_subject_acceptance_rates: Vec, + pub eta_block_step_sizes_before_adaptation: Vec, + pub eta_block_step_sizes_after_adaptation: Vec, + pub kappa_proposals: usize, + pub kappa_accepted: usize, + pub kappa_rejected: usize, + pub kappa_non_finite: usize, + pub kappa_subject_acceptance_rates: Vec, + pub kappa_proposal_step_sizes_before_adaptation: Vec, + pub kappa_proposal_step_sizes_after_adaptation: Vec, + pub simulated_annealing_active: bool, + pub population_parameters: Vec, + pub omega: Array2, + pub omega_iov: Option>, + pub residual_error_estimates: Vec, + pub residual_diagnostics: Vec, + pub conditional_negative_log_likelihood: f64, + pub eta_log_prior: f64, + pub kappa_log_prior: f64, + pub omega_update_rejected: bool, + pub omega_iov_update_rejected: bool, + /// Detailed Ω proposal and acceptance record for this cycle. + pub omega_update: CovarianceCycleUpdateDiagnostics, + /// Detailed Ω_IOV proposal and acceptance record for this cycle. + pub omega_iov_update: CovarianceCycleUpdateDiagnostics, + /// Dimensionless generalized SPD margin relative to the declared initial Ω. + #[serde(skip_serializing_if = "Option::is_none")] + pub omega_relative_spd_margin: Option, + /// Dimensionless generalized SPD margin relative to the declared initial Ω_IOV. + #[serde(skip_serializing_if = "Option::is_none")] + pub omega_iov_relative_spd_margin: Option, + /// Current covariate coefficient values (beta) after this cycle's M-step. + /// + /// Present only when a covariate model was declared. The vector is in + /// canonical declaration order and includes both estimated and fixed + /// coefficients. + pub covariate_betas: Option>, + /// Fixed/free status aligned with `covariate_betas`. + pub covariate_beta_estimated: Option>, +} + +impl SaemCycleDiagnostics { + /// Residual M-step diagnostics for a named model output in this cycle. + pub fn residual_diagnostic(&self, output: &str) -> Option<&ResidualCycleDiagnostics> { + self.residual_diagnostics + .iter() + .find(|diagnostic| diagnostic.output == output) + } +} + +/// Named final residual-error estimate for one model output. +#[derive(Debug, Clone, PartialEq, Serialize)] +pub struct ResidualErrorEstimate { + pub output: String, + pub output_index: usize, + pub model: ResidualErrorModel, + pub estimated: bool, + /// Additive-component status for ordinary or correlated combined models. + pub combined_additive_estimated: Option, + /// Proportional-component status for ordinary or correlated combined models. + pub combined_proportional_estimated: Option, + /// Correlation-component status for the correlated-combined model. + pub correlation_estimated: Option, +} + +/// Structured warning aggregated from immutable parametric cycle diagnostics. +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum ParametricWarning { + OmegaUpdateRejected { + first_iteration: usize, + cycles: usize, + }, + OmegaIovUpdateRejected { + first_iteration: usize, + cycles: usize, + }, + OmegaBoundaryRejection { + first_iteration: usize, + longest_run: usize, + }, + OmegaIovBoundaryRejection { + first_iteration: usize, + longest_run: usize, + }, + EtaNonFiniteProposals { + first_iteration: usize, + count: usize, + }, + EtaBlockNonFiniteProposals { + first_iteration: usize, + count: usize, + }, + KappaNonFiniteProposals { + first_iteration: usize, + count: usize, + }, + ResidualUpdateRejected { + output: String, + first_iteration: usize, + cycles: usize, + }, + ProportionalPredictionFloor { + output: String, + first_iteration: usize, + count: usize, + }, + NonFiniteResidualPrediction { + output: String, + first_iteration: usize, + count: usize, + }, + ExponentialDomainViolation { + output: String, + first_iteration: usize, + count: usize, + }, + CombinedAdditiveCollapse { + output: String, + first_iteration: usize, + cycles: usize, + }, + ResidualOptimizerNotConverged { + output: String, + first_iteration: usize, + cycles: usize, + }, + MarginalLikelihoodUnavailable { + subjects: Vec, + }, + MarginalLikelihoodNonconvergedModes { + subjects: Vec, + }, +} + +/// Kind and exact source indices of one free information coordinate. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "kind", rename_all = "snake_case")] +pub enum InformationCoordinateKind { + Population { + parameter_index: usize, + }, + CovariateEffect { + effect_index: usize, + }, + Omega { + row: usize, + column: usize, + }, + OmegaIov { + row: usize, + column: usize, + }, + Residual { + output_index: usize, + component: String, + }, +} + +/// One deterministic free coordinate used by complete-data derivatives. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +pub struct InformationCoordinate { + pub index: usize, + pub name: String, + pub kind: InformationCoordinateKind, +} + +/// Scientific and numerical availability of the observed-information diagnostic. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "reason", rename_all = "snake_case")] +pub enum InformationStatus { + Available, + NoFreeCoordinates, + NonFinite, + ObservedInformationNotPositiveDefinite, + Unsupported(String), + Ineligible(String), +} + +/// Immutable complete-data score/Hessian stochastic-approximation diagnostics. +/// +/// This is diagnostic only. The persistent-MCMC assumptions required for an +/// inferential interpretation have not been verified. It is the source for the +/// separately classified population uncertainty result, not a marginal-likelihood +/// result or evidence of convergence. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct InformationDiagnostics { + pub coordinates: Vec, + pub recursion_cycles: usize, + pub delta: Vec, + pub g: Vec>, + pub expected_complete_hessian: Vec>, + pub observed_hessian: Vec>, + pub observed_information: Vec>, + pub status: InformationStatus, +} + +/// Explicit classification of why population uncertainty is unavailable. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "reason", content = "detail", rename_all = "snake_case")] +pub enum PopulationUncertaintyUnavailableReason { + SourceUnavailable(InformationStatus), + NonFinite, + ObservedInformationNotPositiveDefinite, + InversionFailed, +} + +/// Top-level status of a population uncertainty derivation. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "reason", rename_all = "snake_case")] +pub enum PopulationUncertaintyStatus { + Available, + Unavailable(PopulationUncertaintyUnavailableReason), +} + +/// Regularization applied while deriving population uncertainty. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub enum PopulationUncertaintyRegularization { + None, +} + +/// Free-coordinate population uncertainty derived from the observed information. +/// +/// The covariance and standard errors are in estimation (φ) space. Natural-scale +/// (ψ) parameter standard errors are computed separately via the first-order delta +/// method. No confidence intervals or stronger inferential claims are provided; the +/// required persistent-MCMC assumptions have not been verified. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct PopulationUncertaintyDiagnostics { + /// Canonical free-coordinate indices, names, and kinds. + pub coordinates: Vec, + /// Unregularized free-coordinate observed-information inverse (φ-space covariance). + /// Rows are coordinates in the same order as [`Self::coordinates`]. + #[serde(skip_serializing_if = "Option::is_none")] + pub free_covariance: Option>>, + /// Diagonal square-root of [`Self::free_covariance`] (φ-space SEs). + #[serde(skip_serializing_if = "Option::is_none")] + pub free_standard_errors: Option>, + /// Spectral condition number (λ_max / λ_min) of the observed-information matrix. + #[serde(skip_serializing_if = "Option::is_none")] + pub spectral_condition_number: Option, + /// Derivation outcome. + pub status: PopulationUncertaintyStatus, + /// Explicit regularization classification (always [`PopulationUncertaintyRegularization::None`]). + pub regularization: PopulationUncertaintyRegularization, +} + +impl PopulationUncertaintyDiagnostics { + pub(crate) fn unavailable(reason: PopulationUncertaintyUnavailableReason) -> Self { + Self { + coordinates: Vec::new(), + free_covariance: None, + free_standard_errors: None, + spectral_condition_number: None, + status: PopulationUncertaintyStatus::Unavailable(reason), + regularization: PopulationUncertaintyRegularization::None, + } + } +} + +/// Scientific/numerical status of the optional frozen-kernel diagnostic. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "detail", rename_all = "snake_case")] +pub enum MarkovSimulationVarianceStatus { + Disabled, + AverageNotApplied, + NoFreeCoordinates, + ExactZeroNoLatentState, + InformationUnavailable(String), + InvalidConfiguration(String), + /// Checked trace-memory accounting overflowed `usize` before allocation. + TraceMemoryAccountingOverflow, + CoordinateMismatch, + UnsupportedScore(String), + NonFinite, + NonSymmetric, + Indefinite, + StuckChain { + chain: usize, + }, + /// Finite algebra was produced, but stationarity, mixing, and the Markov + /// Poisson-equation/CLT assumptions were not verified. + AssumptionsUnverified, +} + +/// Batch-means output and movement accounting for one independent chain. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct MarkovSimulationVarianceChainDiagnostics { + pub chain: usize, + pub bm_batch: Vec>, + pub bm_batch_over_r: Vec>, + pub lugsail_lrv: Vec>, + /// Classification of this chain's raw lugsail LRV. + pub status: MarkovSimulationVarianceStatus, + /// Retained-transition proposals only; diagnostic warmup is excluded. + pub proposals: usize, + /// Retained-transition accepts only; diagnostic warmup is excluded. + pub accepts: usize, + /// Retained-transition actual state changes only; diagnostic warmup is excluded. + pub state_changes: usize, +} + +/// Diagnostic-only frozen-kernel simulation variance for the Cesaro estimate. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct MarkovSimulationVarianceDiagnostics { + pub config: Option, + pub coordinates: Vec, + pub chain_count: usize, + pub n_avg: usize, + pub chains: Vec, + /// Grand complete-score mean across all retained independent diagnostic draws. + pub grand_score_mean: Vec, + pub lambda: Vec>, + pub lambda_status: MarkovSimulationVarianceStatus, + pub xi: Vec>, + pub xi_status: MarkovSimulationVarianceStatus, + pub simulation_covariance: Vec>, + pub simulation_covariance_status: MarkovSimulationVarianceStatus, + pub status: MarkovSimulationVarianceStatus, + pub assumptions: String, + /// Rank/mixing convergence diagnostics from independent prior-drawn chains. + /// Always present; status indicates availability. + pub rank_diagnostics: RankMixingDiagnostics, +} + +impl MarkovSimulationVarianceDiagnostics { + pub(crate) fn disabled() -> Self { + Self { + config: None, + coordinates: Vec::new(), + chain_count: 0, + n_avg: 0, + chains: Vec::new(), + grand_score_mean: Vec::new(), + lambda: Vec::new(), + lambda_status: MarkovSimulationVarianceStatus::Disabled, + xi: Vec::new(), + xi_status: MarkovSimulationVarianceStatus::Disabled, + simulation_covariance: Vec::new(), + simulation_covariance_status: MarkovSimulationVarianceStatus::Disabled, + status: MarkovSimulationVarianceStatus::Disabled, + assumptions: "diagnostic disabled; no stationarity, mixing, Poisson-equation, or Markov-CLT claim".into(), + rank_diagnostics: RankMixingDiagnostics { + diagnostic_chains: 0, + draws_per_chain: 0, + original_chains: 0, + traces: Vec::new(), + lrv_per_chain: Vec::new(), + lrv_chain_statuses: Vec::new(), + diagnostic_mean_lrv: None, + operational_lrv: None, + max_trace_bytes: 0, + accounted_peak_trace_bytes_required: 0, + accounted_peak_trace_bytes_used: 0, + worst_rhat: None, + min_bulk_ess: None, + min_avg_ess_per_split_chain: None, + assumptions: String::new(), + status: RankDiagnosticStatus::Disabled, + }, + } + } +} + +/// Immutable status of a rank/mixing diagnostic coordinate or aggregate. +/// +/// Variants carry explicit reasons; no silent fallback hides the cause. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "detail", rename_all = "snake_case")] +pub enum RankDiagnosticStatus { + Disabled, + /// The requested diagnostic has no latent coordinates to trace. + NoLatent, + /// A complete-score coordinate could not be evaluated; latent traces remain independent. + ScoreUnavailable, + Unavailable, + /// Some coordinate diagnostics are valid while others are unavailable. + PartialAvailability, + NoChains, + /// Fewer than two diagnostic chains: split-R̂ requires ≥ 2 chains. + TooFewChains, + /// Chains did not retain the same number of draws. + UnequalChainLengths, + /// Too few retained draws for the requested statistic. + TooFewDraws, + /// The retained draw count cannot be split evenly. + OddDraws, + /// max_trace_bytes would be exceeded by the accounted trace-memory peak. + TraceByteCapExceeded, + /// Checked trace-memory accounting overflowed `usize` before allocation. + TraceMemoryAccountingOverflow, + /// At least one draw or derived folded value is non-finite. + NonFiniteDraws, + /// The coordinate is constant (W = 0): R̂/ESS is undefined. + ConstantDraws, + /// A required variance was invalid. + InvalidVariance, + /// Integrated autocorrelation time τ ≤ 0: ESS undefined. + NonPositiveTau, + /// Valid rank diagnostics were computed; no convergence claim is made. + Available, +} + +/// Per-coordinate trace metadata: identifies the quantity being traced. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +#[serde(tag = "kind", content = "detail", rename_all = "snake_case")] +pub enum DiagnosticTraceCoordinate { + /// One coordinate of the score vector (information coordinate). + Score { + index: usize, + name: String, + kind: InformationCoordinateKind, + }, + /// One η element for a specific subject and random effect. + Eta { + subject: String, + effect_index: usize, + effect_name: String, + }, + /// One κ element for a specific subject, occasion, and random effect. + Kappa { + subject: String, + occasion_index: usize, + effect_index: usize, + effect_name: String, + }, +} + +/// Per-coordinate rank/mixing diagnostic with split-R̂ and bulk ESS. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct RankMixingDiagnostic { + /// Metadata identifying the traced coordinate. + pub trace: DiagnosticTraceCoordinate, + /// Rank-normalized split-R̂, or None if inapplicable. + pub rank_rhat: Option, + /// Exact status of the rank-normalized split-R̂ computation. + pub rank_rhat_status: RankDiagnosticStatus, + /// Folded split-R̂, or None if inapplicable. + pub folded_rhat: Option, + /// Exact status of the folded split-R̂ computation. + pub folded_rhat_status: RankDiagnosticStatus, + /// Maximum of rank and folded split-R̂, present only when both components succeed. + pub max_rhat: Option, + /// Derived status of the maximum; available only when both component R̂ values succeed. + pub max_rhat_status: RankDiagnosticStatus, + /// Bulk effective sample size (total across all split chains). + pub bulk_ess: Option, + /// Exact status of the bulk ESS computation. + pub bulk_ess_status: RankDiagnosticStatus, + /// Average ESS per split chain = bulk_ess / (2 × diagnostic_chains). + pub avg_ess_per_split_chain: Option, + /// Integrated autocorrelation time τ. + pub tau: Option, + /// Per-coordinate status. + pub status: RankDiagnosticStatus, +} + +/// Aggregate rank/mixing convergence diagnostics from independent prior-drawn chains. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct RankMixingDiagnostics { + /// Number of diagnostic chains drawn from the installed prior (Cd). + pub diagnostic_chains: usize, + /// Number of draws retained per diagnostic chain (post-warmup). + pub draws_per_chain: usize, + /// Number of original fit chains (Cf), for LRV scaling. + pub original_chains: usize, + /// Per-coordinate mixing diagnostics. + pub traces: Vec, + /// Index-preserving per-chain LRV matrices. A failed configured chain is + /// retained as `None` at its original diagnostic-chain index. + pub lrv_per_chain: Vec>>>, + /// Per-chain LRV matrix status classification. + pub lrv_chain_statuses: Vec, + /// Diagnostic-mean LRV: Σ(LRV_i) / Cd² (full matrix). + pub diagnostic_mean_lrv: Option>>, + /// Operational LRV: Σ(LRV_i) / (Cd × Cf) (full matrix); used for Xi/n_avg. + pub operational_lrv: Option>>, + /// Configured maximum trace storage bytes (from config). + pub max_trace_bytes: usize, + /// Conservative deterministic upper bound on simultaneously requested + /// trace-buffer bytes, including nested `Vec` headers/capacities and the + /// largest score/rank transient workspace. + pub accounted_peak_trace_bytes_required: usize, + /// Accounted trace-buffer peak reached; zero when rejected before trace + /// allocation. This is requested-capacity accounting, not allocator metadata. + pub accounted_peak_trace_bytes_used: usize, + /// Worst complete maximum of rank-normalized and folded split-R̂ across + /// coordinates that have both valid component values. + pub worst_rhat: Option, + /// Minimum bulk ESS across all coordinates. + pub min_bulk_ess: Option, + /// Minimum average ESS per split chain across all coordinates. + pub min_avg_ess_per_split_chain: Option, + /// Assumptions text recorded at diagnostic construction. + pub assumptions: String, + /// Aggregate status: the worst status across all coordinates. + pub status: RankDiagnosticStatus, +} +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct SaemEstimatorMetadata { + pub policy: SaemEstimatorPolicy, + pub average_applied: bool, + pub averaging_start_cycle: Option, + pub averaged_iterations: usize, +} + +impl Default for SaemEstimatorMetadata { + fn default() -> Self { + Self { + policy: SaemEstimatorPolicy::TerminalIterate, + average_applied: false, + averaging_start_cycle: None, + averaged_iterations: 0, + } + } +} + +/// Final-chain posterior mean of a subject-level η vector. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct SubjectEtaEstimate { + pub subject_id: String, + pub values: Vec, +} + +/// Final-chain posterior mean of an occasion-level κ vector. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct OccasionKappaEstimate { + pub subject_id: String, + pub occasion_index: usize, + pub values: Vec, +} + +/// Joint posthoc conditional mode for one subject. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct SubjectConditionalMode { + pub subject_id: String, + pub eta: Vec, + pub kappas: Vec, + pub parameters: Vec, + pub objective: f64, + pub converged: bool, + pub iterations: u64, + pub termination: String, + /// One strict joint eta/kappa curvature, reused by opt-in importance sampling. + pub uncertainty: ConditionalCurvatureDiagnostics, +} + +#[derive(Debug, Clone)] pub struct ParametricResult { - _phantom: std::marker::PhantomData, + pub(crate) equation: E, + pub(crate) data: Data, + pub(crate) config: SaemConfig, + pub(crate) effective_n_chains: usize, + pub(crate) objective_function: f64, + pub(crate) converged: bool, + pub(crate) termination_reason: Option, + pub(crate) iterations: usize, + pub(crate) subject_count: usize, + pub(crate) observation_count: usize, + pub(crate) parameter_names: Vec, + pub(crate) parameter_scales: Vec, + pub(crate) estimated_parameters: Vec, + pub(crate) population_initial: Vec, + pub(crate) population_estimates: Vec, + pub(crate) random_effect_indices: Vec, + pub(crate) random_effect_names: Vec, + pub(crate) omega: Array2, + pub(crate) omega_structural_mask: Array2, + pub(crate) omega_estimated_mask: Array2, + pub(crate) omega_initial: Array2, + pub(crate) iov_effect_indices: Vec, + pub(crate) iov_effect_names: Vec, + pub(crate) omega_iov: Option>, + pub(crate) omega_iov_structural_mask: Option>, + pub(crate) omega_iov_estimated_mask: Option>, + pub(crate) omega_iov_initial: Option>, + pub(crate) residual_sigmas: Vec, + pub(crate) residual_error_estimates: Vec, + pub(crate) residual_initial_values: Vec>, + pub(crate) residual_initial_estimated: Vec>, + pub(crate) eta_chain_means: Vec, + pub(crate) kappa_chain_means: Vec, + pub(crate) conditional_modes: Vec, + pub(crate) shrinkage: ShrinkageDiagnostics, + pub(crate) cycle_diagnostics: Vec, + pub(crate) warnings: Vec, + pub(crate) information_diagnostics: InformationDiagnostics, + pub(crate) population_uncertainty: PopulationUncertaintyDiagnostics, + pub(crate) markov_simulation_variance: MarkovSimulationVarianceDiagnostics, + pub(crate) operational_diagnostics: OperationalConvergenceDiagnostics, + pub(crate) marginal_likelihood: Option, + pub(crate) information_criteria: InformationCriteriaDiagnostics, + pub(crate) estimator_metadata: SaemEstimatorMetadata, + pub(crate) individual_estimates: Vec<(String, Vec)>, + pub(crate) covariate_model: Option, +} + +impl ParametricResult { + /// Structural model retained for prediction and follow-up runs. + pub fn equation(&self) -> &E { + &self.equation + } + + /// Original estimation dataset. + pub fn data(&self) -> &Data { + &self.data + } + + /// Original validated requested SAEM configuration. + /// + /// In particular, [`SaemConfig::n_chains`] remains the requested chain + /// count and is not replaced by the effective auto-scaled count. + pub fn config(&self) -> &SaemConfig { + &self.config + } + + /// Effective chain count used by the fit after automatic scaling. + pub fn effective_n_chains(&self) -> usize { + self.effective_n_chains + } + + pub fn iterations(&self) -> usize { + self.iterations + } + + /// Complete-data observed-information diagnostic accumulated during SAEM. + /// + /// This does not provide standard errors, a marginal likelihood, or + /// convergence evidence. Persistent-MCMC assumptions remain unverified. + pub fn information_diagnostics(&self) -> &InformationDiagnostics { + &self.information_diagnostics + } + + /// Free-coordinate population uncertainty from the observed information. + /// + /// Returns covariance, standard errors, and spectral condition number in + /// estimation (φ) space. Natural-scale (ψ) parameter standard errors are + /// available through [`PopulationSummary`] when uncertainty is available. + pub fn population_uncertainty(&self) -> &PopulationUncertaintyDiagnostics { + &self.population_uncertainty + } + + /// Optional frozen-kernel Markov diagnostics, including LRV simulation- + /// variance matrices and rank-normalized/folded mixing diagnostics. + /// + /// These diagnostics remain non-proof: they do not establish stationarity, + /// mathematical convergence, model correctness, or valid uncertainty. + pub fn markov_simulation_variance(&self) -> &MarkovSimulationVarianceDiagnostics { + &self.markov_simulation_variance + } + + pub fn operational_convergence(&self) -> &OperationalConvergenceDiagnostics { + &self.operational_diagnostics + } + + /// Immutable operational convergence lifecycle diagnostics. + /// + /// Empty when no operational convergence criteria were configured. + pub fn operational_diagnostics(&self) -> &OperationalConvergenceDiagnostics { + &self.operational_diagnostics + } + + /// Rank-normalized/folded split-Rhat and bulk-ESS mixing diagnostics. + /// + /// These detect unreliable frozen diagnostic chains but do not prove + /// convergence or alter the fit termination reason. + pub fn rank_mixing_diagnostics(&self) -> &RankMixingDiagnostics { + &self.markov_simulation_variance.rank_diagnostics + } + + /// Final-estimate policy and smoothing-average application details. + pub fn estimator_metadata(&self) -> &SaemEstimatorMetadata { + &self.estimator_metadata + } + + /// Why the parametric fit stopped. + /// + /// A completed fixed SAEM schedule reports [`StopReason::MaxCycles`]; this + /// must not be interpreted as statistical convergence. + pub fn termination_reason(&self) -> Option<&StopReason> { + self.termination_reason.as_ref() + } + + pub fn parameter_names(&self) -> &[String] { + &self.parameter_names + } + + pub fn parameter_scales(&self) -> &[ParameterScale] { + &self.parameter_scales + } + + pub fn estimated_parameters(&self) -> &[bool] { + &self.estimated_parameters + } + + pub fn population_parameters(&self) -> &[f64] { + &self.population_estimates + } + + /// Generate predictions at the final population parameters. + /// + /// The retained data are cloned and expanded for this call; predictions are + /// not cached on the result. + pub fn population_predictions( + &self, + idelta: f64, + tad: f64, + ) -> anyhow::Result> + where + E: pharmsol::equation::EquationTypes

, + { + self.validate_prediction_metadata()?; + let expanded = self.data.clone().expand(idelta, tad); + self.validate_expanded_subjects(&expanded)?; + let population_phi = self + .covariate_model + .as_ref() + .map(|_| population_phi(&self.population_estimates, &self.parameter_scales)) + .transpose()?; + let subject_population = self + .covariate_model + .as_ref() + .zip(population_phi.as_ref()) + .map(|(model, phi)| model.subject_population_parameters(phi, &self.parameter_scales)) + .transpose()?; + expanded + .subjects() + .iter() + .enumerate() + .map(|(subject_index, subject)| { + let parameters = subject_population + .as_ref() + .map(|rows| rows[subject_index].psi()) + .unwrap_or(&self.population_estimates); + self.equation + .estimate_predictions_dense(subject, parameters) + .with_context(|| { + format!( + "population prediction failed for subject '{}'", + subject.id() + ) + }) + }) + .collect() + } + + /// Generate predictions at each subject's final posthoc conditional mode. + /// + /// For IOV fits, each retained occasion is simulated independently using + /// its reconstructed eta/kappa parameter vector. + pub fn conditional_predictions( + &self, + idelta: f64, + tad: f64, + ) -> anyhow::Result> + where + E: pharmsol::equation::EquationTypes

, + { + self.validate_prediction_metadata()?; + if self.random_effect_indices.is_empty() && self.iov_effect_indices.is_empty() { + return self.population_predictions(idelta, tad); + } + if self.conditional_modes.is_empty() { + bail!( + "conditional predictions require conditional modes; rerun with compute_map(true)" + ); + } + + let expanded = self.data.clone().expand(idelta, tad); + self.validate_expanded_subjects(&expanded)?; + if self.conditional_modes.len() != expanded.subjects().len() { + bail!( + "conditional mode count {} does not match subject count {}", + self.conditional_modes.len(), + expanded.subjects().len() + ); + } + + let population_phi = self + .covariate_model + .as_ref() + .map(|_| population_phi(&self.population_estimates, &self.parameter_scales)) + .transpose()?; + let subject_population = self + .covariate_model + .as_ref() + .zip(population_phi.as_ref()) + .map(|(model, phi)| model.subject_population_parameters(phi, &self.parameter_scales)) + .transpose()?; + expanded + .subjects() + .iter() + .zip(&self.conditional_modes) + .enumerate() + .map(|(subject_index, (subject, mode))| { + if mode.subject_id != *subject.id() { + bail!( + "conditional mode subject '{}' does not match retained subject '{}'", + mode.subject_id, + subject.id() + ); + } + if mode.parameters.len() != self.population_estimates.len() { + bail!( + "conditional mode for subject '{}' has parameter width {} but expected {}", + subject.id(), + mode.parameters.len(), + self.population_estimates.len() + ); + } + if mode.eta.len() != self.random_effect_indices.len() { + bail!( + "conditional mode for subject '{}' has eta width {} but expected {}", + subject.id(), + mode.eta.len(), + self.random_effect_indices.len() + ); + } + + if self.iov_effect_indices.is_empty() { + if !mode.kappas.is_empty() { + bail!( + "conditional mode for non-IOV subject '{}' unexpectedly has kappas", + subject.id() + ); + } + return self + .equation + .estimate_predictions_dense(subject, &mode.parameters) + .with_context(|| { + format!( + "conditional prediction failed for subject '{}'", + subject.id() + ) + }); + } + + if mode.kappas.len() != subject.occasions().len() { + bail!( + "conditional mode for subject '{}' has {} occasions but retained data have {}", + subject.id(), + mode.kappas.len(), + subject.occasions().len() + ); + } + let mut combined = Vec::new(); + for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { + if kappa.subject_id != *subject.id() { + bail!( + "conditional kappa subject '{}' does not match retained subject '{}'", + kappa.subject_id, + subject.id() + ); + } + if kappa.occasion_index != occasion.index() { + bail!( + "conditional kappa occasion {} does not match retained occasion {} for subject '{}'", + kappa.occasion_index, + occasion.index(), + subject.id() + ); + } + if kappa.values.len() != self.iov_effect_indices.len() { + bail!( + "conditional kappa for subject '{}' occasion {} has width {} but expected {}", + subject.id(), + occasion.index(), + kappa.values.len(), + self.iov_effect_indices.len() + ); + } + let parameters = match subject_population.as_ref() { + Some(rows) => occasion_psi_from_subject_mean( + rows[subject_index].phi(), + &self.parameter_scales, + &self.random_effect_indices, + &mode.eta, + &self.iov_effect_indices, + &kappa.values, + ), + None => occasion_psi( + &self.population_estimates, + &self.parameter_scales, + &self.random_effect_indices, + &mode.eta, + &self.iov_effect_indices, + &kappa.values, + ), + }?; + let occasion_subject = + Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); + let predictions = self + .equation + .estimate_predictions_dense(&occasion_subject, ¶meters) + .with_context(|| { + format!( + "conditional prediction failed for subject '{}' occasion {}", + subject.id(), + occasion.index() + ) + })?; + let expected_points = occasion + .events() + .iter() + .filter(|event| matches!(event, Event::Observation(_))) + .count(); + if predictions.predictions().len() != expected_points { + bail!( + "conditional prediction shape mismatch for subject '{}' occasion {}: generated {} points but expected {}", + subject.id(), + occasion.index(), + predictions.predictions().len(), + expected_points + ); + } + for mut prediction in predictions.predictions().iter().cloned() { + *prediction.mut_occasion() = occasion.index(); + combined.push(prediction); + } + } + Ok(SubjectPredictions::from(combined)) + }) + .collect() + } + + fn validate_prediction_metadata(&self) -> anyhow::Result<()> { + if self.population_estimates.len() != self.parameter_names.len() { + bail!( + "population parameter width {} does not match parameter-name width {}", + self.population_estimates.len(), + self.parameter_names.len() + ); + } + if self.parameter_scales.len() != self.population_estimates.len() { + bail!( + "parameter-scale width {} does not match population parameter width {}", + self.parameter_scales.len(), + self.population_estimates.len() + ); + } + Ok(()) + } + + fn validate_expanded_subjects(&self, expanded: &Data) -> anyhow::Result<()> { + if expanded.subjects().len() != self.data.subjects().len() { + bail!( + "expanded subject count {} does not match retained subject count {}", + expanded.subjects().len(), + self.data.subjects().len() + ); + } + for (retained, expanded) in self.data.subjects().iter().zip(expanded.subjects()) { + if retained.id() != expanded.id() { + bail!( + "expanded subject '{}' does not match retained subject '{}'", + expanded.id(), + retained.id() + ); + } + } + Ok(()) + } + + pub fn random_effect_indices(&self) -> &[usize] { + &self.random_effect_indices + } + + pub fn random_effect_names(&self) -> &[String] { + &self.random_effect_names + } + + pub fn omega(&self) -> &Array2 { + &self.omega + } + + pub fn omega_structural_mask(&self) -> &Array2 { + &self.omega_structural_mask + } + + pub fn omega_estimated_mask(&self) -> &Array2 { + &self.omega_estimated_mask + } + + pub fn iov_effect_indices(&self) -> &[usize] { + &self.iov_effect_indices + } + + pub fn iov_effect_names(&self) -> &[String] { + &self.iov_effect_names + } + + pub fn omega_iov(&self) -> Option<&Array2> { + self.omega_iov.as_ref() + } + + pub fn omega_iov_structural_mask(&self) -> Option<&Array2> { + self.omega_iov_structural_mask.as_ref() + } + + pub fn omega_iov_estimated_mask(&self) -> Option<&Array2> { + self.omega_iov_estimated_mask.as_ref() + } + + /// Legacy positional primary sigma values. + /// + /// Prefer [`Self::residual_error_estimates`] when output names, complete + /// error-model parameters, or fixed/estimated status are needed. + pub fn residual_sigmas(&self) -> &[f64] { + &self.residual_sigmas + } + + /// Named residual-error models and their estimation status. + pub fn residual_error_estimates(&self) -> &[ResidualErrorEstimate] { + &self.residual_error_estimates + } + + /// Final residual-error estimate for a named model output. + pub fn residual_error_estimate(&self, output: &str) -> Option<&ResidualErrorEstimate> { + self.residual_error_estimates + .iter() + .find(|estimate| estimate.output == output) + } + + /// Final conditional negative log-likelihood at the retained latent chains. + /// + /// This is not a population marginal likelihood or a validated marginal OFV. + pub fn conditional_negative_log_likelihood(&self) -> f64 { + self.objective_function / 2.0 + } + + /// Twice the final conditional negative log-likelihood. + /// + /// This is the value currently returned through [`FitResult::objf`] for API + /// compatibility; it must not be interpreted as a marginal population OFV. + pub fn conditional_n2ll(&self) -> f64 { + self.objective_function + } + + /// Complete post-fit marginal-likelihood diagnostics, when requested. + pub fn marginal_likelihood_diagnostics(&self) -> Option<&MarginalLikelihoodDiagnostics> { + self.marginal_likelihood.as_ref() + } + + /// Population marginal log likelihood, absent when disabled or unavailable. + pub fn marginal_log_likelihood(&self) -> Option { + self.marginal_likelihood + .as_ref() + .and_then(|diagnostics| diagnostics.log_marginal_likelihood) + } + + /// Population marginal negative twice log likelihood, absent when unavailable. + pub fn marginal_n2ll(&self) -> Option { + self.marginal_likelihood + .as_ref() + .and_then(|diagnostics| diagnostics.n2ll) + } + + /// Delta-method Monte Carlo standard error of the marginal N2LL. + pub fn marginal_n2ll_mcse(&self) -> Option { + self.marginal_likelihood + .as_ref() + .and_then(|diagnostics| diagnostics.n2ll_mcse) + } + + /// Typed population marginal-likelihood status, when requested. + pub fn marginal_likelihood_status(&self) -> Option<&MarginalLikelihoodStatus> { + self.marginal_likelihood + .as_ref() + .map(|diagnostics| &diagnostics.status) + } + + /// Immutable AIC/BIC diagnostics derived only from population marginal N2LL. + pub fn information_criteria(&self) -> &InformationCriteriaDiagnostics { + &self.information_criteria + } + + /// Akaike information criterion, absent unless marginal N2LL is available. + pub fn aic(&self) -> Option { + self.information_criteria.aic + } + + /// Bayesian information criterion using independent subjects as sample size. + pub fn bic(&self) -> Option { + self.information_criteria.bic + } + + /// AIC Monte Carlo standard error, exactly the source marginal N2LL MCSE. + pub fn aic_mcse(&self) -> Option { + self.information_criteria.aic_mcse + } + + /// BIC Monte Carlo standard error, exactly the source marginal N2LL MCSE. + pub fn bic_mcse(&self) -> Option { + self.information_criteria.bic_mcse + } + + /// Number of free population-level coordinates used in both penalties. + pub fn free_parameter_count(&self) -> usize { + self.information_criteria.parameter_count.total + } + + /// Subject-level η values averaged across final SAEM chains. + /// + /// These are posterior chain summaries, not posthoc conditional-mode EBEs. + pub fn eta_chain_means(&self) -> &[SubjectEtaEstimate] { + &self.eta_chain_means + } + + /// Final-chain η mean for a named subject. + pub fn eta_chain_mean(&self, subject_id: &str) -> Option<&SubjectEtaEstimate> { + self.eta_chain_means + .iter() + .find(|estimate| estimate.subject_id == subject_id) + } + + /// Occasion-level κ values averaged across final SAEM chains. + /// + /// These are posterior chain summaries, not posthoc conditional-mode EBEs. + pub fn kappa_chain_means(&self) -> &[OccasionKappaEstimate] { + &self.kappa_chain_means + } + + /// Final-chain κ mean for a named subject and occasion index. + pub fn kappa_chain_mean( + &self, + subject_id: &str, + occasion_index: usize, + ) -> Option<&OccasionKappaEstimate> { + self.kappa_chain_means.iter().find(|estimate| { + estimate.subject_id == subject_id && estimate.occasion_index == occasion_index + }) + } + + /// Joint η/κ posthoc conditional modes under the final population fit. + pub fn conditional_modes(&self) -> &[SubjectConditionalMode] { + &self.conditional_modes + } + + /// Joint posthoc conditional mode for a named subject. + pub fn conditional_mode(&self, subject_id: &str) -> Option<&SubjectConditionalMode> { + self.conditional_modes + .iter() + .find(|mode| mode.subject_id == subject_id) + } + + /// Source-explicit eta/kappa posterior-mean and MAP shrinkage diagnostics. + pub fn shrinkage(&self) -> &ShrinkageDiagnostics { + &self.shrinkage + } + + /// Immutable per-cycle SAEM MCMC and covariance diagnostics. + pub fn cycle_diagnostics(&self) -> &[SaemCycleDiagnostics] { + &self.cycle_diagnostics + } + + /// Structured fit warnings aggregated from cycle-level diagnostics. + pub fn warnings(&self) -> &[ParametricWarning] { + &self.warnings + } + + // ─── Covariate result accessors ───────────────────────────────────── + + /// Fully validated subject-static covariate population model, when declared. + pub fn covariates(&self) -> Option<&CovariateModel> { + self.covariate_model.as_ref() + } + + /// Ordered covariate-effect declarations. + pub fn covariate_declarations(&self) -> Option<&[CovariateEffect]> { + self.covariate_model + .as_ref() + .map(|model| model.declarations()) + } + + /// Ordered covariate coefficient estimates. + pub fn covariate_estimates(&self) -> Option<&[CovariateEstimate]> { + self.covariate_model.as_ref().map(|model| model.estimates()) + } + + /// Subject-level covariate values extracted from the estimation dataset. + pub fn covariate_subject_values(&self) -> Option<&[SubjectCovariateValue]> { + self.covariate_model + .as_ref() + .map(|model| model.subject_values()) + } + + /// Subject-level design-matrix rows used by the population M-step. + pub fn covariate_subject_design(&self) -> Option<&[SubjectCovariateDesign]> { + self.covariate_model + .as_ref() + .map(|model| model.subject_design()) + } + + /// Subject-specific transformed (phi) and execution-space (psi) population + /// parameters incorporating covariate effects. + /// + /// This derives from [`CovariateModel::subject_population_parameters`] + /// using the retained population estimates and parameter scales. + pub fn covariate_subject_population_parameters( + &self, + ) -> Result>, CovariateMstepError> { + match self.covariate_model.as_ref() { + Some(model) => { + let phi = population_phi(&self.population_estimates, &self.parameter_scales) + .map_err(|_error| CovariateMstepError::NonFiniteSolution)?; + model + .subject_population_parameters(&phi, &self.parameter_scales) + .map(Some) + } + None => Ok(None), + } + } } impl FitResult for ParametricResult { fn objf(&self) -> f64 { - unimplemented!("Parametric result not yet implemented") + self.objective_function } + fn converged(&self) -> bool { - unimplemented!() + self.converged } + fn summary(&self) -> FitSummary { - unimplemented!() + FitSummary { + objective_function: self.objective_function, + converged: self.converged, + iterations: self.iterations, + subject_count: self.subject_count, + observation_count: self.observation_count, + parameter_count: self.parameter_names.len(), + marginal_log_likelihood: self.marginal_log_likelihood(), + marginal_n2ll: self.marginal_n2ll(), + marginal_n2ll_mcse: self.marginal_n2ll_mcse(), + marginal_likelihood_status: self.marginal_likelihood_status().cloned(), + information_criteria: Some(self.information_criteria.clone()), + } } + fn population_summary(&self) -> PopulationSummary { - unimplemented!() + let free_standard_errors: Option<&[f64]> = match &self.population_uncertainty.status { + PopulationUncertaintyStatus::Available => { + self.population_uncertainty.free_standard_errors.as_deref() + } + _ => None, + }; + let parameters: Vec = self + .parameter_names + .iter() + .enumerate() + .map(|(param_index, name)| { + let estimate = self.population_estimates[param_index]; + let scale = self.parameter_scales[param_index]; + let is_estimated = self.estimated_parameters[param_index]; + let (sd, cv_percent) = if is_estimated { + if let Some(ses) = free_standard_errors { + // Find the population coordinate matching this parameter. + let coordinate_se = self + .population_uncertainty + .coordinates + .iter() + .find_map(|coord| match &coord.kind { + InformationCoordinateKind::Population { parameter_index } + if *parameter_index == param_index => + { + ses.get(coord.index) + } + _ => None, + }) + .copied(); + if let Some(phi_se) = coordinate_se { + let phi = psi_to_phi(estimate, scale); + let abs_deriv = phi_to_psi_derivative(phi, scale).abs(); + let psi_sd = phi_se * abs_deriv; + let sd = psi_sd.is_finite().then_some(psi_sd); + let cv_percent = sd.and_then(|sd| { + if estimate.is_finite() && estimate != 0.0 { + let cv = 100.0 * sd / estimate.abs(); + cv.is_finite().then_some(cv) + } else { + None + } + }); + (sd, cv_percent) + } else { + (None, None) + } + } else { + (None, None) + } + } else { + (None, None) + }; + ParameterSummary { + name: name.clone(), + estimate, + mean: None, + median: None, + sd, + cv_percent, + } + }) + .collect(); + PopulationSummary { + parameters, + information_criteria: Some(self.information_criteria.clone()), + population_uncertainty: Some(self.population_uncertainty.clone()), + shrinkage: Some(self.shrinkage.clone()), + } } + fn individual_summaries(&self) -> Vec { - unimplemented!() + self.individual_estimates + .iter() + .map(|(id, estimates)| IndividualSummary { + id: id.clone(), + parameter_names: self.parameter_names.clone(), + estimates: estimates.clone(), + standard_errors: None, + conditional_uncertainty: self + .conditional_mode(id) + .map(|mode| mode.uncertainty.clone()), + }) + .collect() } } @@ -41,11 +1484,11 @@ use crate::estimation::nonparametric; impl FitResult for NonParametricResult { fn objf(&self) -> f64 { - self.objf() // Assuming the struct has this native method + self.objf() } fn converged(&self) -> bool { - self.converged() // Assuming the struct has this native method + self.converged() } fn summary(&self) -> FitSummary { @@ -60,3 +1503,152 @@ impl FitResult for NonParametricResult { nonparametric::individual_summaries(self) } } + +// ─── Operational convergence result types ──────────────────────────────── + +/// Exact evaluation status of one operational convergence criterion. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "detail", rename_all = "snake_case")] +pub enum OperationalConvergenceCriterionStatus { + Satisfied, + NotSatisfied, + Unavailable(String), +} + +/// One evaluated operational convergence criterion with its retained values. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct OperationalConvergenceCriterion { + /// Short deterministic criterion name. + pub name: String, + /// Observed value, present only when the criterion could be evaluated. + pub observed: Option, + /// Configured threshold against which the observed value is compared. + pub threshold: f64, + /// Per-criterion evaluation status. + pub status: OperationalConvergenceCriterionStatus, +} + +/// Joint outcome of one operational convergence checkpoint. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "outcome", content = "detail", rename_all = "snake_case")] +pub enum OperationalConvergenceOutcome { + Passed, + Failed { criteria: Vec }, + Ineligible { reasons: Vec }, +} + +/// Immutable record of one operational convergence checkpoint. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct OperationalConvergenceCheck { + /// SAEM cycle at which the checkpoint was evaluated. + pub iteration: usize, + /// Number of averaged iterates contributing to the candidate. + pub averaged_iterations: usize, + /// Whether this checkpoint was triggered by the periodic schedule. + pub scheduled: bool, + /// Whether this checkpoint was the mandatory final evaluation. + pub mandatory_final: bool, + /// Deterministic per-checkpoint diagnostic seed; `None` when no frozen + /// diagnostic configuration exists or the candidate was unavailable. + pub checkpoint_seed: Option, + /// Two-sided standard normal quantile for the configured confidence level. + pub z_quantile: Option, + /// Gong/Flegal implied minimum ESS `4*z²/epsilon²`; metadata only and + /// never a substitute for rank ESS. + pub implied_minimum_ess: Option, + /// Averaged-candidate values in deterministic free-coordinate order; + /// empty when the candidate coordinate mapping was unavailable. + pub candidate_free_coordinates: Vec, + /// Exact observed-information snapshot used by this checkpoint. + pub information: Option, + /// Ordered per-criterion evaluations. + pub criteria: Vec, + /// Joint checkpoint outcome. + pub outcome: OperationalConvergenceOutcome, + /// Immutable frozen-kernel diagnostics evaluated at the averaged + /// candidate; `None` when the candidate itself was unavailable. + pub markov: Option, +} + +impl OperationalConvergenceCheck { + /// Per-trace rank/mixing diagnostics collected across all coordinates + /// of the frozen-kernel evaluation (if available). + pub fn per_trace_diagnostics(&self) -> &[RankMixingDiagnostic] { + self.markov + .as_ref() + .map(|markov| markov.rank_diagnostics.traces.as_slice()) + .unwrap_or(&[]) + } + + /// Worst rank-normalized/folded split-Rhat across all coordinates + /// (if available). + pub fn worst_rhat(&self) -> Option { + self.markov + .as_ref() + .and_then(|markov| markov.rank_diagnostics.worst_rhat) + } + + /// Minimum bulk ESS across all coordinates (if available). + pub fn min_bulk_ess(&self) -> Option { + self.markov + .as_ref() + .and_then(|markov| markov.rank_diagnostics.min_bulk_ess) + } +} + +/// Immutable operational convergence lifecycle record for one fit. +/// +/// Retains every scheduled and mandatory-final checkpoint evaluation, +/// accompanied by exact policy-wording summary diagnostics. When no +/// operational convergence configuration was present, `checks` is empty and +/// `used_for_termination` is false. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Default)] +pub struct OperationalConvergenceDiagnostics { + pub config: Option, + /// Ordered checkpoint evaluations, one per scheduled or mandatory-final + /// check. Empty when no criteria were configured or no checkpoint was + /// reachable. + pub checks: Vec, + /// Whether the last mandatory final checkpoint reused an identical + /// earlier check (defensive caching marker). + pub final_check_reused: bool, + /// Whether any checkpoint passed and supplied the final stop reason. + pub used_for_termination: bool, + pub final_status: Option, + pub worst_rhat: Option, + pub min_bulk_ess: Option, + pub fixed_width_ratio: Option, + pub fixed_width_epsilon: Option, + pub implied_minimum_ess: Option, + pub newton_displacement: Option, + pub newton_displacement_mc_sd: Option, +} + +impl OperationalConvergenceDiagnostics { + /// Exact policy-wording summary for this diagnostics record. + /// + /// Returns one or more lines describing the lifecycle state. The wording + /// is public API and must not be rewritten without updating external + /// consumers. + pub fn warnings(&self) -> Vec { + match (&self.config, &self.final_status, self.used_for_termination) { + (_, Some(OperationalConvergenceOutcome::Passed), true) => vec![ + "PMcore operational convergence criteria passed; this is not proof of mathematical convergence, stationarity, model correctness, or valid uncertainty; run an independent doubled-budget fit." + .to_string(), + ], + (Some(_), Some(OperationalConvergenceOutcome::Failed { .. }), false) => vec![ + "PMcore operational convergence criteria were evaluated but not satisfied; finite schedule completion remains MaxCycles and operational convergence was not established." + .to_string(), + ], + (Some(_), Some(OperationalConvergenceOutcome::Ineligible { .. }), false) => vec![ + "PMcore operational convergence criteria were evaluated but were ineligible; finite schedule completion remains MaxCycles and operational convergence was not established." + .to_string(), + ], + (Some(_), None, false) => vec![ + "PMcore operational convergence was configured, but no checkpoint was evaluated; operational convergence was not evaluated or established." + .to_string(), + ], + _ => Vec::new(), + } + } +} diff --git a/src/results/information_criteria.rs b/src/results/information_criteria.rs new file mode 100644 index 000000000..318f7ed01 --- /dev/null +++ b/src/results/information_criteria.rs @@ -0,0 +1,665 @@ +use std::collections::HashSet; + +use serde::{Deserialize, Serialize}; + +use crate::estimation::parametric::marginal_likelihood::{ + MarginalLikelihoodDiagnostics, MarginalLikelihoodStatus, +}; + +use super::{InformationCoordinate, InformationCoordinateKind}; + +/// Availability of post-fit information criteria derived from population marginal N2LL. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "status", content = "detail", rename_all = "snake_case")] +pub enum InformationCriteriaStatus { + NotRequested, + Available, + AvailableWithNonconvergedModes { + subjects: Vec, + }, + Unavailable { + reason: InformationCriteriaUnavailableReason, + }, +} + +/// Typed reason that AIC/BIC cannot be derived. +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +#[serde(tag = "reason", content = "detail", rename_all = "snake_case")] +pub enum InformationCriteriaUnavailableReason { + ZeroSubjects, + SubjectCountNotExactlyRepresentable { subject_count: usize }, + ParameterCountOverflow, + ParameterCountNotExactlyRepresentable { parameter_count: usize }, + InconsistentCoordinateIndices, + DuplicateCoordinateSource, + NoncanonicalCoordinateOrder, + InvalidCovarianceCoordinate { row: usize, column: usize }, + UnknownResidualComponent { component: String }, + SourceMarginalLikelihoodUnavailable, + ImpossibleMarginalLikelihoodState, + NonFiniteMarginalN2ll, + NonFiniteMarginalN2llMcse, + NegativeMarginalN2llMcse, + NonFinitePenalty, + NonFiniteCriterion, +} + +/// Deterministic free-population-coordinate count used by both penalties. +#[derive(Debug, Clone, Copy, Default, PartialEq, Eq, Serialize, Deserialize)] +pub struct InformationCriteriaParameterCount { + pub population: usize, + pub covariate: usize, + pub omega: usize, + pub omega_iov: usize, + pub residual: usize, + pub total: usize, +} + +/// Sample-size convention used by BIC. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub enum InformationCriteriaSampleSizeConvention { + IndependentSubjects, +} + +/// Immutable AIC/BIC diagnostics derived only from population marginal N2LL. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct InformationCriteriaDiagnostics { + pub status: InformationCriteriaStatus, + pub parameter_count: InformationCriteriaParameterCount, + pub sample_size_convention: InformationCriteriaSampleSizeConvention, + pub subject_count: usize, + pub source_marginal_n2ll: Option, + pub source_marginal_n2ll_mcse: Option, + pub aic: Option, + pub bic: Option, + pub aic_mcse: Option, + pub bic_mcse: Option, +} + +const MAX_EXACT_INTEGER_F64: usize = 1usize << f64::MANTISSA_DIGITS; + +pub(crate) fn derive_information_criteria( + marginal: Option<&MarginalLikelihoodDiagnostics>, + coordinates: &[InformationCoordinate], + subject_count: usize, +) -> InformationCriteriaDiagnostics { + let counts = match count_parameters(coordinates) { + Ok(counts) => counts, + Err(reason) => { + return unavailable( + reason, + InformationCriteriaParameterCount::default(), + subject_count, + ) + } + }; + if subject_count == 0 { + return unavailable( + InformationCriteriaUnavailableReason::ZeroSubjects, + counts, + subject_count, + ); + } + if subject_count > MAX_EXACT_INTEGER_F64 { + return unavailable( + InformationCriteriaUnavailableReason::SubjectCountNotExactlyRepresentable { + subject_count, + }, + counts, + subject_count, + ); + } + if counts.total > MAX_EXACT_INTEGER_F64 { + return unavailable( + InformationCriteriaUnavailableReason::ParameterCountNotExactlyRepresentable { + parameter_count: counts.total, + }, + counts, + subject_count, + ); + } + + let Some(marginal) = marginal else { + return InformationCriteriaDiagnostics { + status: InformationCriteriaStatus::NotRequested, + parameter_count: counts, + sample_size_convention: InformationCriteriaSampleSizeConvention::IndependentSubjects, + subject_count, + source_marginal_n2ll: None, + source_marginal_n2ll_mcse: None, + aic: None, + bic: None, + aic_mcse: None, + bic_mcse: None, + }; + }; + + match &marginal.status { + MarginalLikelihoodStatus::Unavailable { .. } => { + if marginal.n2ll.is_some() || marginal.n2ll_mcse.is_some() { + unavailable( + InformationCriteriaUnavailableReason::ImpossibleMarginalLikelihoodState, + counts, + subject_count, + ) + } else { + unavailable( + InformationCriteriaUnavailableReason::SourceMarginalLikelihoodUnavailable, + counts, + subject_count, + ) + } + } + MarginalLikelihoodStatus::Available + | MarginalLikelihoodStatus::AvailableWithNonconvergedModes { .. } => { + let (Some(n2ll), Some(mcse)) = (marginal.n2ll, marginal.n2ll_mcse) else { + return unavailable( + InformationCriteriaUnavailableReason::ImpossibleMarginalLikelihoodState, + counts, + subject_count, + ); + }; + if !n2ll.is_finite() { + return unavailable( + InformationCriteriaUnavailableReason::NonFiniteMarginalN2ll, + counts, + subject_count, + ); + } + if !mcse.is_finite() { + return unavailable( + InformationCriteriaUnavailableReason::NonFiniteMarginalN2llMcse, + counts, + subject_count, + ); + } + if mcse < 0.0 { + return unavailable( + InformationCriteriaUnavailableReason::NegativeMarginalN2llMcse, + counts, + subject_count, + ); + } + let (aic, bic) = + match calculate_criteria(n2ll, counts.total as f64, subject_count as f64) { + Ok(values) => values, + Err(reason) => return unavailable(reason, counts, subject_count), + }; + let status = match &marginal.status { + MarginalLikelihoodStatus::Available => InformationCriteriaStatus::Available, + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects } => { + if subjects.is_empty() { + return unavailable( + InformationCriteriaUnavailableReason::ImpossibleMarginalLikelihoodState, + counts, + subject_count, + ); + } + InformationCriteriaStatus::AvailableWithNonconvergedModes { + subjects: subjects.clone(), + } + } + MarginalLikelihoodStatus::Unavailable { .. } => unreachable!(), + }; + InformationCriteriaDiagnostics { + status, + parameter_count: counts, + sample_size_convention: + InformationCriteriaSampleSizeConvention::IndependentSubjects, + subject_count, + source_marginal_n2ll: Some(n2ll), + source_marginal_n2ll_mcse: Some(mcse), + aic: Some(aic), + bic: Some(bic), + aic_mcse: Some(mcse), + bic_mcse: Some(mcse), + } + } + } +} + +fn calculate_criteria( + n2ll: f64, + parameter_count: f64, + subject_count: f64, +) -> Result<(f64, f64), InformationCriteriaUnavailableReason> { + let aic_penalty = 2.0 * parameter_count; + let bic_penalty = subject_count.ln() * parameter_count; + if !aic_penalty.is_finite() || !bic_penalty.is_finite() { + return Err(InformationCriteriaUnavailableReason::NonFinitePenalty); + } + let aic = n2ll + aic_penalty; + let bic = n2ll + bic_penalty; + if !aic.is_finite() || !bic.is_finite() { + return Err(InformationCriteriaUnavailableReason::NonFiniteCriterion); + } + Ok((aic, bic)) +} + +fn count_parameters( + coordinates: &[InformationCoordinate], +) -> Result { + let mut population_sources = HashSet::new(); + let mut covariate_sources = HashSet::new(); + let mut omega_sources = HashSet::new(); + let mut omega_iov_sources = HashSet::new(); + let mut residual_sources = HashSet::new(); + let mut count = InformationCriteriaParameterCount::default(); + let mut previous_order_key = None; + for (expected_index, coordinate) in coordinates.iter().enumerate() { + if coordinate.index != expected_index { + return Err(InformationCriteriaUnavailableReason::InconsistentCoordinateIndices); + } + let order_key = match &coordinate.kind { + InformationCoordinateKind::Population { parameter_index } => { + if !population_sources.insert(*parameter_index) { + return Err(InformationCriteriaUnavailableReason::DuplicateCoordinateSource); + } + count.population = count + .population + .checked_add(1) + .ok_or(InformationCriteriaUnavailableReason::ParameterCountOverflow)?; + (0, *parameter_index, 0, String::new()) + } + InformationCoordinateKind::CovariateEffect { effect_index } => { + if !covariate_sources.insert(*effect_index) { + return Err(InformationCriteriaUnavailableReason::DuplicateCoordinateSource); + } + count.covariate = count + .covariate + .checked_add(1) + .ok_or(InformationCriteriaUnavailableReason::ParameterCountOverflow)?; + (1, *effect_index, 0, String::new()) + } + InformationCoordinateKind::Omega { row, column } => { + if column > row { + return Err( + InformationCriteriaUnavailableReason::InvalidCovarianceCoordinate { + row: *row, + column: *column, + }, + ); + } + if !omega_sources.insert((*row, *column)) { + return Err(InformationCriteriaUnavailableReason::DuplicateCoordinateSource); + } + count.omega = count + .omega + .checked_add(1) + .ok_or(InformationCriteriaUnavailableReason::ParameterCountOverflow)?; + (2, *row, *column, String::new()) + } + InformationCoordinateKind::OmegaIov { row, column } => { + if column > row { + return Err( + InformationCriteriaUnavailableReason::InvalidCovarianceCoordinate { + row: *row, + column: *column, + }, + ); + } + if !omega_iov_sources.insert((*row, *column)) { + return Err(InformationCriteriaUnavailableReason::DuplicateCoordinateSource); + } + count.omega_iov = count + .omega_iov + .checked_add(1) + .ok_or(InformationCriteriaUnavailableReason::ParameterCountOverflow)?; + (3, *row, *column, String::new()) + } + InformationCoordinateKind::Residual { + output_index, + component, + } => { + if !residual_sources.insert((*output_index, component.as_str())) { + return Err(InformationCriteriaUnavailableReason::DuplicateCoordinateSource); + } + count.residual = count + .residual + .checked_add(1) + .ok_or(InformationCriteriaUnavailableReason::ParameterCountOverflow)?; + let component_order = match component.as_str() { + "sigma" | "additive" => 0, + "proportional" => 1, + "correlation" => 2, + _ => { + return Err( + InformationCriteriaUnavailableReason::UnknownResidualComponent { + component: component.clone(), + }, + ) + } + }; + (4, *output_index, component_order, component.clone()) + } + }; + if previous_order_key + .as_ref() + .is_some_and(|previous| previous >= &order_key) + { + return Err(InformationCriteriaUnavailableReason::NoncanonicalCoordinateOrder); + } + previous_order_key = Some(order_key); + } + count.total = count + .population + .checked_add(count.covariate) + .and_then(|value| value.checked_add(count.omega)) + .and_then(|value| value.checked_add(count.omega_iov)) + .and_then(|value| value.checked_add(count.residual)) + .ok_or(InformationCriteriaUnavailableReason::ParameterCountOverflow)?; + Ok(count) +} + +fn unavailable( + reason: InformationCriteriaUnavailableReason, + parameter_count: InformationCriteriaParameterCount, + subject_count: usize, +) -> InformationCriteriaDiagnostics { + InformationCriteriaDiagnostics { + status: InformationCriteriaStatus::Unavailable { reason }, + parameter_count, + sample_size_convention: InformationCriteriaSampleSizeConvention::IndependentSubjects, + subject_count, + source_marginal_n2ll: None, + source_marginal_n2ll_mcse: None, + aic: None, + bic: None, + aic_mcse: None, + bic_mcse: None, + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::estimation::parametric::marginal_likelihood::{ + MarginalLikelihoodConfig, MarginalLikelihoodFailureReason, MarginalLikelihoodSubjectFailure, + }; + + fn marginal( + status: MarginalLikelihoodStatus, + n2ll: Option, + mcse: Option, + ) -> MarginalLikelihoodDiagnostics { + MarginalLikelihoodDiagnostics { + config: MarginalLikelihoodConfig::new(32, 17, 5, 1.5), + status, + log_marginal_likelihood: n2ll.map(|value| -value / 2.0), + n2ll, + n2ll_mcse: mcse, + subjects: Vec::new(), + } + } + + fn mixed_coordinates() -> Vec { + vec![ + InformationCoordinate { + index: 0, + name: "phi:CL".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }, + InformationCoordinate { + index: 1, + name: "beta:CL:WT".into(), + kind: InformationCoordinateKind::CovariateEffect { effect_index: 0 }, + }, + InformationCoordinate { + index: 2, + name: "omega:CL:CL".into(), + kind: InformationCoordinateKind::Omega { row: 0, column: 0 }, + }, + InformationCoordinate { + index: 3, + name: "omega_iov:V:V".into(), + kind: InformationCoordinateKind::OmegaIov { row: 0, column: 0 }, + }, + InformationCoordinate { + index: 4, + name: "residual:central:sigma".into(), + kind: InformationCoordinateKind::Residual { + output_index: 0, + component: "sigma".into(), + }, + }, + InformationCoordinate { + index: 5, + name: "residual:peripheral:proportional".into(), + kind: InformationCoordinateKind::Residual { + output_index: 1, + component: "proportional".into(), + }, + }, + ] + } + + #[test] + fn information_criteria_formula_and_mcse_are_exact() { + let source = marginal( + MarginalLikelihoodStatus::Available, + Some(100.0), + Some(0.375), + ); + let result = derive_information_criteria(Some(&source), &mixed_coordinates(), 20); + assert_eq!(result.status, InformationCriteriaStatus::Available); + assert_eq!( + result.parameter_count, + InformationCriteriaParameterCount { + population: 1, + covariate: 1, + omega: 1, + omega_iov: 1, + residual: 2, + total: 6, + } + ); + assert!((result.aic.unwrap() - 112.0).abs() <= 1e-12); + assert!((result.bic.unwrap() - (100.0 + 6.0 * 20.0_f64.ln())).abs() <= 1e-12); + assert_eq!(result.aic_mcse.unwrap().to_bits(), 0.375_f64.to_bits()); + assert_eq!(result.bic_mcse.unwrap().to_bits(), 0.375_f64.to_bits()); + } + + #[test] + fn correlated_residual_coordinates_count_in_canonical_component_order() { + let coordinates = ["additive", "proportional", "correlation"] + .into_iter() + .enumerate() + .map(|(index, component)| InformationCoordinate { + index, + name: format!("residual:cp:{component}"), + kind: InformationCoordinateKind::Residual { + output_index: 0, + component: component.to_string(), + }, + }) + .collect::>(); + let count = count_parameters(&coordinates).unwrap(); + assert_eq!(count.residual, 3); + assert_eq!(count.total, 3); + + let mut malformed = coordinates; + malformed[2].kind = InformationCoordinateKind::Residual { + output_index: 0, + component: "unknown".to_string(), + }; + assert_eq!( + count_parameters(&malformed), + Err( + InformationCriteriaUnavailableReason::UnknownResidualComponent { + component: "unknown".to_string() + } + ) + ); + } + + #[test] + fn information_criteria_propagate_all_source_statuses_without_fallback() { + let subjects = vec!["S2".to_string(), "S7".to_string()]; + let source = marginal( + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { + subjects: subjects.clone(), + }, + Some(42.0), + Some(0.2), + ); + let result = derive_information_criteria(Some(&source), &[], 2); + assert_eq!( + result.status, + InformationCriteriaStatus::AvailableWithNonconvergedModes { subjects } + ); + assert_eq!(result.aic, Some(42.0)); + assert_eq!(result.bic, Some(42.0)); + + let unavailable_source = marginal( + MarginalLikelihoodStatus::Unavailable { + failures: vec![MarginalLikelihoodSubjectFailure { + subject_id: "S1".into(), + reason: MarginalLikelihoodFailureReason::AllZeroEffectiveWeights, + }], + }, + None, + None, + ); + let result = derive_information_criteria(Some(&unavailable_source), &[], 1); + assert!(matches!( + result.status, + InformationCriteriaStatus::Unavailable { + reason: InformationCriteriaUnavailableReason::SourceMarginalLikelihoodUnavailable + } + )); + assert_eq!((result.aic, result.bic), (None, None)); + + let result = derive_information_criteria(None, &[], 1); + assert_eq!(result.status, InformationCriteriaStatus::NotRequested); + assert_eq!((result.aic, result.bic), (None, None)); + } + + #[test] + fn information_criteria_accept_zero_parameters_and_one_subject() { + let source = marginal(MarginalLikelihoodStatus::Available, Some(12.5), Some(0.0)); + let result = derive_information_criteria(Some(&source), &[], 1); + assert_eq!(result.parameter_count.total, 0); + assert_eq!(result.aic, Some(12.5)); + assert_eq!(result.bic, Some(12.5)); + } + + #[test] + fn information_criteria_fail_closed_for_invalid_inputs() { + let source = marginal(MarginalLikelihoodStatus::Available, Some(12.5), Some(0.1)); + let assert_unavailable_without_values = |result: InformationCriteriaDiagnostics| { + assert!(matches!( + result.status, + InformationCriteriaStatus::Unavailable { .. } + )); + assert_eq!( + (result.aic, result.bic, result.aic_mcse, result.bic_mcse), + (None, None, None, None) + ); + }; + assert_unavailable_without_values(derive_information_criteria(Some(&source), &[], 0)); + assert_unavailable_without_values(derive_information_criteria( + Some(&source), + &[], + MAX_EXACT_INTEGER_F64 + 1, + )); + + let mut coordinates = mixed_coordinates(); + coordinates[5].index = 4; + assert_unavailable_without_values(derive_information_criteria( + Some(&source), + &coordinates, + 2, + )); + + for invalid_source in [ + marginal( + MarginalLikelihoodStatus::Available, + Some(f64::NAN), + Some(0.1), + ), + marginal( + MarginalLikelihoodStatus::Available, + Some(12.5), + Some(f64::INFINITY), + ), + marginal(MarginalLikelihoodStatus::Available, Some(12.5), Some(-0.1)), + marginal( + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects: vec![] }, + Some(12.5), + Some(0.1), + ), + marginal( + MarginalLikelihoodStatus::Unavailable { failures: vec![] }, + None, + None, + ), + ] { + assert_unavailable_without_values(derive_information_criteria( + Some(&invalid_source), + &[], + 1, + )); + } + + assert_eq!( + calculate_criteria(0.0, f64::MAX, 2.0), + Err(InformationCriteriaUnavailableReason::NonFinitePenalty) + ); + assert_eq!( + calculate_criteria(f64::MAX, f64::MAX / 4.0, 1.0), + Err(InformationCriteriaUnavailableReason::NonFiniteCriterion) + ); + + let mut upper_triangle = mixed_coordinates(); + upper_triangle[2].kind = InformationCoordinateKind::Omega { row: 0, column: 1 }; + assert_unavailable_without_values(derive_information_criteria( + Some(&source), + &upper_triangle, + 2, + )); + + for duplicate_kind in [ + InformationCoordinateKind::Population { parameter_index: 0 }, + InformationCoordinateKind::CovariateEffect { effect_index: 0 }, + InformationCoordinateKind::Omega { row: 0, column: 0 }, + InformationCoordinateKind::OmegaIov { row: 0, column: 0 }, + InformationCoordinateKind::Residual { + output_index: 0, + component: "sigma".into(), + }, + ] { + let duplicate_sources = vec![ + InformationCoordinate { + index: 0, + name: "first".into(), + kind: duplicate_kind.clone(), + }, + InformationCoordinate { + index: 1, + name: "second".into(), + kind: duplicate_kind, + }, + ]; + assert_unavailable_without_values(derive_information_criteria( + Some(&source), + &duplicate_sources, + 2, + )); + } + + let mut reordered = mixed_coordinates(); + reordered.swap(0, 1); + for (index, coordinate) in reordered.iter_mut().enumerate() { + coordinate.index = index; + } + assert_unavailable_without_values(derive_information_criteria( + Some(&source), + &reordered, + 2, + )); + + let impossible = marginal(MarginalLikelihoodStatus::Available, None, None); + assert_unavailable_without_values(derive_information_criteria(Some(&impossible), &[], 1)); + } +} diff --git a/src/results/mod.rs b/src/results/mod.rs index 5a0bed71e..6546600ef 100644 --- a/src/results/mod.rs +++ b/src/results/mod.rs @@ -1,7 +1,36 @@ mod fit_result; - +mod information_criteria; +pub(crate) mod parametric_output; mod summary; -pub use fit_result::{FitResult, ParametricResult}; +pub use fit_result::{ + CovarianceCycleUpdateDiagnostics, CovarianceCycleUpdateOutcome, CovarianceTrialRejectionReason, + CovarianceUpdateNotAttemptedReason, CovarianceUpdateRejectionReason, DiagnosticTraceCoordinate, + FitResult, InformationCoordinate, InformationCoordinateKind, InformationDiagnostics, + InformationStatus, MarkovSimulationVarianceChainDiagnostics, + MarkovSimulationVarianceDiagnostics, MarkovSimulationVarianceStatus, OccasionKappaEstimate, + OperationalConvergenceCheck, OperationalConvergenceCriterion, + OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, + OperationalConvergenceOutcome, ParametricResult, ParametricWarning, + PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, + PopulationUncertaintyStatus, PopulationUncertaintyUnavailableReason, RankDiagnosticStatus, + RankMixingDiagnostic, RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, + SaemCycleDiagnostics, SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, + SubjectEtaEstimate, +}; +pub(crate) use information_criteria::derive_information_criteria; +pub use information_criteria::{ + InformationCriteriaDiagnostics, InformationCriteriaParameterCount, + InformationCriteriaSampleSizeConvention, InformationCriteriaStatus, + InformationCriteriaUnavailableReason, +}; +pub use parametric_output::{ + IndividualEffectRow, IndividualParameterRow, InformationCriteriaRow, IterationRow, + MarginalLikelihoodRow, OmegaRow, ParametricResultRecord, ParametricResultTables, + ParametricSourceCovariance, ParametricSourceEffect, ParametricSourceMetadata, + ParametricSourceParameter, ParametricSourceResidual, ParametricWarningRecord, + PopulationParameterRow, PredictionRow, ResidualErrorRow, StatisticRow, + PARAMETRIC_RESULT_SCHEMA_VERSION, +}; pub use summary::{FitSummary, IndividualSummary, ParameterSummary, PopulationSummary}; diff --git a/src/results/parametric_output.rs b/src/results/parametric_output.rs new file mode 100644 index 000000000..125246f9a --- /dev/null +++ b/src/results/parametric_output.rs @@ -0,0 +1,7322 @@ +use std::fs::File; +use std::path::Path; + +use anyhow::{bail, Context, Result}; +use pharmsol::simulator::prediction::{Prediction, SubjectPredictions}; +use pharmsol::{Censor, Data, Equation}; +use serde::{Deserialize, Serialize}; + +use crate::algorithms::parametric::SaemConfig; +use crate::algorithms::StopReason; +use crate::estimation::parametric::{ + covariates::{CovariateEffect, CovariateEffectFamily}, + individual::{ + individual_psi, individual_psi_from_subject_mean, occasion_psi, + occasion_psi_from_subject_mean, + }, + marginal_likelihood::{ + MarginalLikelihoodDiagnostics, MarginalLikelihoodMethod, MarginalLikelihoodStatus, + ProposalScaleSource, + }, + shrinkage::{ + derive_eta_map_shrinkage, derive_eta_posterior_mean_shrinkage, derive_kappa_map_shrinkage, + derive_kappa_posterior_mean_shrinkage, ShrinkageDiagnostics, ShrinkageValue, + }, + transforms::{phi_to_psi, psi_to_phi}, +}; +use crate::estimation::{EstimationProblem, Iov, Omega, Parametric, ParametricErrorModel}; +use crate::model::{EquationMetadataSource, ParameterScale, UnboundedParameter}; +use crate::results::{ + derive_information_criteria, DiagnosticTraceCoordinate, InformationCoordinate, + InformationCoordinateKind, InformationCriteriaDiagnostics, + InformationCriteriaSampleSizeConvention, InformationCriteriaStatus, InformationDiagnostics, + MarkovSimulationVarianceDiagnostics, MarkovSimulationVarianceStatus, + OperationalConvergenceDiagnostics, OperationalConvergenceOutcome, ParametricResult, + ParametricWarning, PopulationUncertaintyDiagnostics, RankDiagnosticStatus, + SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, +}; +use crate::ResidualErrorModel; + +pub const PARAMETRIC_RESULT_SCHEMA_VERSION: u32 = 9; + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct PopulationParameterRow { + pub name: String, + pub estimate: f64, + pub scale: String, + pub estimated: bool, + pub iiv: bool, + pub iov: bool, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct OmegaRow { + pub row: String, + pub column: String, + pub estimate: f64, + pub structural: bool, + pub estimated: bool, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ResidualErrorRow { + pub output: String, + pub output_index: usize, + pub family: String, + pub component: String, + pub estimate: f64, + pub estimated: bool, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct IndividualEffectRow { + pub subject: String, + pub source: String, + pub effect_kind: String, + pub parameter: String, + pub occasion: Option, + pub value: f64, + pub mode_converged: Option, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct IndividualParameterRow { + pub subject: String, + pub occasion: Option, + pub parameter: String, + pub value: f64, + pub source: String, + pub mode_converged: Option, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct IterationRow { + pub cycle: usize, + pub phase: String, + pub conditional_n2ll: f64, + pub sa_step: f64, + pub covariance_step: f64, + pub eta_proposals: usize, + pub eta_accepted: usize, + pub eta_rejected: usize, + pub eta_nonfinite: usize, + pub eta_block_proposals: usize, + pub eta_block_accepted: usize, + pub eta_block_rejected: usize, + pub eta_block_nonfinite: usize, + pub kappa_proposals: usize, + pub kappa_accepted: usize, + pub kappa_rejected: usize, + pub kappa_nonfinite: usize, + pub omega_update_rejected: bool, + pub omega_iov_update_rejected: bool, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct StatisticRow { + pub cycle: usize, + pub kind: String, + pub name: String, + pub row: Option, + pub column: Option, + pub output_index: Option, + pub component: Option, + pub value: Option, + /// Availability of the corresponding information diagnostic. Ordinary + /// cycle statistics leave this empty. + pub status: Option, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct MarginalLikelihoodRow { + pub scope: String, + pub subject: Option, + pub method: String, + pub status: String, + pub samples_per_subject: usize, + pub seed: Option, + pub degrees_of_freedom: u32, + pub covariance_scale_multiplier: f64, + pub proposal_scale_source: String, + pub dimension: usize, + pub occasion_indices: String, + pub mode: String, + pub mode_converged: Option, + pub log_marginal_likelihood: Option, + pub n2ll: Option, + pub n2ll_mcse: Option, + pub effective_sample_size: Option, + pub effective_sample_fraction: Option, + pub zero_weight_count: usize, + pub failure: Option, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct InformationCriteriaRow { + pub status: String, + pub sample_size_convention: String, + pub subject_count: usize, + pub population_parameter_count: usize, + pub covariate_parameter_count: usize, + pub omega_parameter_count: usize, + pub omega_iov_parameter_count: usize, + pub residual_parameter_count: usize, + pub free_parameter_count: usize, + pub source_marginal_n2ll: Option, + pub source_marginal_n2ll_mcse: Option, + pub aic: Option, + pub bic: Option, + pub aic_mcse: Option, + pub bic_mcse: Option, + pub failure_reason: Option, +} + +/// Legacy CSV projection used only for fits without covariate effects. +#[derive(Serialize)] +struct NoEffectInformationCriteriaRow { + status: String, + sample_size_convention: String, + subject_count: usize, + population_parameter_count: usize, + omega_parameter_count: usize, + omega_iov_parameter_count: usize, + residual_parameter_count: usize, + free_parameter_count: usize, + source_marginal_n2ll: Option, + source_marginal_n2ll_mcse: Option, + aic: Option, + bic: Option, + aic_mcse: Option, + bic_mcse: Option, + failure_reason: Option, +} + +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct PredictionRow { + pub subject: String, + pub time: f64, + pub output_index: usize, + pub block: usize, + pub observation: Option, + pub censoring: String, + pub population_prediction: f64, + pub conditional_prediction: Option, + pub conditional_source: Option, +} + +/// One canonical transformed-space covariate coefficient. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct CovariateEffectRow { + pub order: usize, + pub name: String, + pub family: String, + pub parameter: String, + pub parameter_index: usize, + pub covariate: String, + pub center: Option, + pub reference: Option, + pub level: Option, + pub initial: f64, + pub estimate: f64, + pub estimated: bool, +} + +/// One exact subject-static covariate value. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct SubjectCovariateRow { + pub subject: String, + pub subject_index: usize, + pub covariate: String, + pub value: f64, +} + +/// One subject/parameter population mean in transformed and execution space. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct SubjectPopulationParameterRow { + pub subject: String, + pub subject_index: usize, + pub parameter: String, + pub parameter_index: usize, + pub phi: f64, + pub psi: f64, +} + +/// Parseable, equation-free tables for a parametric fit. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricResultTables { + pub population: Vec, + pub omega: Vec, + pub omega_iov: Option>, + pub residual_error: Vec, + pub individual_effects: Vec, + pub individual_parameters: Vec, + pub iterations: Vec, + pub statistics: Vec, + pub marginal_likelihood: Vec, + pub information_criteria: Vec, + pub predictions: Vec, + /// Required in schema 7, including an explicit empty vector for no-effect fits. + pub covariate_effects: Vec, + /// Required in schema 7, including an explicit empty vector for no-effect fits. + pub subject_covariates: Vec, + /// Required in schema 7, including an explicit empty vector for no-effect fits. + pub subject_population_parameters: Vec, +} + +impl ParametricResultTables { + pub fn write_population(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.population, + &["name", "estimate", "scale", "estimated", "iiv", "iov"], + ) + } + + pub fn write_omega(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.omega, + &["row", "column", "estimate", "structural", "estimated"], + ) + } + + pub fn write_omega_iov(&self, path: impl AsRef) -> Result<()> { + let rows = self + .omega_iov + .as_ref() + .context("Omega_IOV is not present in this result")?; + write_csv( + path.as_ref(), + rows, + &["row", "column", "estimate", "structural", "estimated"], + ) + } + + pub fn write_residual_error(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.residual_error, + &[ + "output", + "output_index", + "family", + "component", + "estimate", + "estimated", + ], + ) + } + + pub fn write_individual_effects(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.individual_effects, + &[ + "subject", + "source", + "effect_kind", + "parameter", + "occasion", + "value", + "mode_converged", + ], + ) + } + + pub fn write_individual_parameters(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.individual_parameters, + &[ + "subject", + "occasion", + "parameter", + "value", + "source", + "mode_converged", + ], + ) + } + + pub fn write_iterations(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.iterations, + &[ + "cycle", + "phase", + "conditional_n2ll", + "sa_step", + "covariance_step", + "eta_proposals", + "eta_accepted", + "eta_rejected", + "eta_nonfinite", + "eta_block_proposals", + "eta_block_accepted", + "eta_block_rejected", + "eta_block_nonfinite", + "kappa_proposals", + "kappa_accepted", + "kappa_rejected", + "kappa_nonfinite", + "omega_update_rejected", + "omega_iov_update_rejected", + ], + ) + } + + pub fn write_statistics(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.statistics, + &[ + "cycle", + "kind", + "name", + "row", + "column", + "output_index", + "component", + "value", + "status", + ], + ) + } + + pub fn write_marginal_likelihood(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.marginal_likelihood, + &[ + "scope", + "subject", + "method", + "status", + "samples_per_subject", + "seed", + "degrees_of_freedom", + "covariance_scale_multiplier", + "proposal_scale_source", + "dimension", + "occasion_indices", + "mode", + "mode_converged", + "log_marginal_likelihood", + "n2ll", + "n2ll_mcse", + "effective_sample_size", + "effective_sample_fraction", + "zero_weight_count", + "failure", + ], + ) + } + + pub fn write_information_criteria(&self, path: impl AsRef) -> Result<()> { + let headers = [ + "status", + "sample_size_convention", + "subject_count", + "population_parameter_count", + "omega_parameter_count", + "omega_iov_parameter_count", + "residual_parameter_count", + "free_parameter_count", + "source_marginal_n2ll", + "source_marginal_n2ll_mcse", + "aic", + "bic", + "aic_mcse", + "bic_mcse", + "failure_reason", + ]; + if self + .information_criteria + .iter() + .all(|row| row.covariate_parameter_count == 0) + { + let rows = self + .information_criteria + .iter() + .map(|row| NoEffectInformationCriteriaRow { + status: row.status.clone(), + sample_size_convention: row.sample_size_convention.clone(), + subject_count: row.subject_count, + population_parameter_count: row.population_parameter_count, + omega_parameter_count: row.omega_parameter_count, + omega_iov_parameter_count: row.omega_iov_parameter_count, + residual_parameter_count: row.residual_parameter_count, + free_parameter_count: row.free_parameter_count, + source_marginal_n2ll: row.source_marginal_n2ll, + source_marginal_n2ll_mcse: row.source_marginal_n2ll_mcse, + aic: row.aic, + bic: row.bic, + aic_mcse: row.aic_mcse, + bic_mcse: row.bic_mcse, + failure_reason: row.failure_reason.clone(), + }) + .collect::>(); + write_csv(path.as_ref(), &rows, &headers) + } else { + write_csv( + path.as_ref(), + &self.information_criteria, + &[ + "status", + "sample_size_convention", + "subject_count", + "population_parameter_count", + "covariate_parameter_count", + "omega_parameter_count", + "omega_iov_parameter_count", + "residual_parameter_count", + "free_parameter_count", + "source_marginal_n2ll", + "source_marginal_n2ll_mcse", + "aic", + "bic", + "aic_mcse", + "bic_mcse", + "failure_reason", + ], + ) + } + } + + pub fn write_predictions(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.predictions, + &[ + "subject", + "time", + "output_index", + "block", + "observation", + "censoring", + "population_prediction", + "conditional_prediction", + "conditional_source", + ], + ) + } + + pub fn write_covariate_effects(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.covariate_effects, + &[ + "order", + "name", + "family", + "parameter", + "parameter_index", + "covariate", + "center", + "reference", + "level", + "initial", + "estimate", + "estimated", + ], + ) + } + + pub fn write_subject_covariates(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.subject_covariates, + &["subject", "subject_index", "covariate", "value"], + ) + } + + pub fn write_subject_population_parameters(&self, path: impl AsRef) -> Result<()> { + write_csv( + path.as_ref(), + &self.subject_population_parameters, + &[ + "subject", + "subject_index", + "parameter", + "parameter_index", + "phi", + "psi", + ], + ) + } +} + +#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] +pub struct ParametricWarningRecord { + pub kind: String, + pub output: Option, + pub first_cycle: usize, + pub count: usize, + pub subjects: Option>, +} + +/// Ordered population declaration retained independently of output tables. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceParameter { + pub name: String, + pub parameter_index: usize, + pub scale: String, + /// Immutable natural-scale value declared before the first SAEM cycle. + pub initial: f64, + pub estimate: f64, + pub estimated: bool, +} + +/// Ordered population parameter selected for an IIV or IOV effect. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceEffect { + pub parameter_index: usize, + pub parameter_name: String, +} + +/// Canonical covariance declaration and final value snapshot. +/// +/// Names and the complete ordered matrix deliberately duplicate the result +/// table so schema-9 readers can bind and validate this source independently. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceCovariance { + pub dimension: usize, + pub names: Vec, + pub values: Vec>, + pub structural_mask: Vec>, + pub estimated_mask: Vec>, + /// Immutable initial covariance as declared before the first SAEM cycle. + /// Fixed entries must equal their final values; free entries must be finite + /// but may differ. + pub initial_values: Vec>, +} + +/// Ordered residual declaration and its exact final component snapshot. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceResidual { + pub output: String, + pub output_index: usize, + pub family: String, + pub components: Vec, + pub values: Vec, + pub estimated_mask: Vec, + /// Immutable initial component values as declared before the first SAEM cycle. + /// Fixed components must equal their final values; free components must be + /// finite but may differ. + pub initial_values: Vec, + /// Immutable initial estimated mask as declared before the first SAEM cycle. + pub initial_estimated_mask: Vec, +} + +/// One ordered covariate declaration and its final coefficient estimate. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceCovariateEffect { + pub order: usize, + pub name: String, + pub family: String, + pub parameter: String, + pub parameter_index: usize, + pub covariate: String, + pub center: Option, + pub reference: Option, + pub level: Option, + pub initial: f64, + pub estimate: f64, + pub estimated: bool, +} + +/// One subject design row retained independently of output tables. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceSubjectDesign { + pub subject: String, + pub subject_index: usize, + pub values: Vec, +} + +/// Canonical scientific declarations and resolved schema-9 snapshot. +/// +/// This snapshot is generated directly from immutable [`ParametricResult`] +/// metadata. It is independent schema consistency evidence, not a +/// cryptographic signature and not continuation state. +#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)] +pub struct ParametricSourceMetadata { + pub parameters: Vec, + pub random_effects: Vec, + pub omega: ParametricSourceCovariance, + pub iov_effects: Vec, + pub omega_iov: Option, + pub residual_outputs: Vec, + /// Required explicit empty fields for no-effect schema-9 records. + pub covariate_effects: Vec, + pub subject_covariates: Vec, + pub subject_design: Vec, + pub subject_population_parameters: Vec, +} + +/// Versioned, equation-free persisted parametric result. +#[derive(Debug, Clone, Serialize, Deserialize)] +pub struct ParametricResultRecord { + pub schema_version: u32, + pub fit_family: String, + pub algorithm: String, + pub config: SaemConfig, + pub effective_n_chains: usize, + pub termination: Option, + pub objective_kind: String, + pub conditional_n2ll: f64, + pub subject_count: usize, + pub marginal_likelihood: Option, + pub information_criteria: InformationCriteriaDiagnostics, + pub source_metadata: ParametricSourceMetadata, + pub warnings: Vec, + pub tables: ParametricResultTables, + pub information_diagnostics: InformationDiagnostics, + pub population_uncertainty: PopulationUncertaintyDiagnostics, + pub conditional_modes: Vec, + pub shrinkage: ShrinkageDiagnostics, + pub markov_simulation_variance: MarkovSimulationVarianceDiagnostics, + pub operational_convergence: OperationalConvergenceDiagnostics, + pub estimator_metadata: SaemEstimatorMetadata, +} + +fn equal_with_roundoff(left: f64, right: f64) -> bool { + left.is_finite() + && right.is_finite() + && (left - right).abs() <= 64.0 * f64::EPSILON * left.abs().max(right.abs()).max(1.0) +} + +fn stable_number(value: f64) -> String { + if value == 0.0 { + "0".to_string() + } else { + value.to_string() + } +} + +fn validate_persisted_marginal_likelihood(record: &ParametricResultRecord) -> Result<()> { + match ( + record.config.marginal_likelihood, + record.marginal_likelihood.as_ref(), + ) { + (None, None) => { + if record.tables.marginal_likelihood != marginal_likelihood_rows(None) { + bail!("persisted disabled marginal_likelihood table is inconsistent"); + } + let actual_statistics = record + .tables + .statistics + .iter() + .filter(|row| row.kind.starts_with("marginal_likelihood")) + .cloned() + .collect::>(); + let mut expected_statistics = Vec::new(); + marginal_likelihood_statistics( + record.tables.iterations.len(), + None, + &mut expected_statistics, + ); + if actual_statistics != expected_statistics { + bail!("persisted disabled marginal-likelihood statistics are inconsistent"); + } + } + (None, Some(_)) => bail!("persisted N2 diagnostics are present while N2 is disabled"), + (Some(_), None) => bail!("persisted N2 diagnostics are required when N2 is configured"), + (Some(config), Some(diagnostics)) => { + if diagnostics.config != config { + bail!("persisted N2 configuration does not match the retained fit configuration"); + } + let mut expected_subjects = Vec::::new(); + for row in &record.tables.individual_parameters { + if expected_subjects.last() != Some(&row.subject) + && !expected_subjects.contains(&row.subject) + { + expected_subjects.push(row.subject.clone()); + } + } + let actual_subjects = diagnostics + .subjects + .iter() + .map(|subject| subject.subject_id.clone()) + .collect::>(); + if actual_subjects != expected_subjects { + bail!("persisted N2 subject ordering does not match result subject ordering"); + } + let available = matches!( + diagnostics.status, + MarginalLikelihoodStatus::Available + | MarginalLikelihoodStatus::AvailableWithNonconvergedModes { .. } + ); + let totals = [ + diagnostics.log_marginal_likelihood, + diagnostics.n2ll, + diagnostics.n2ll_mcse, + ]; + if available { + if !totals.iter().all(Option::is_some) { + bail!("persisted available N2 requires every population value"); + } + } else if totals.iter().any(Option::is_some) { + bail!("persisted unavailable N2 must omit every population value"); + } + if totals.into_iter().flatten().any(|value| !value.is_finite()) { + bail!("persisted N2 population values must be finite when present"); + } + if let MarginalLikelihoodStatus::Unavailable { failures } = &diagnostics.status { + if failures.is_empty() { + bail!("persisted unavailable N2 status must retain failures"); + } + } + let eta_dimension = record + .tables + .omega + .iter() + .filter(|row| row.row == row.column) + .count(); + let kappa_dimension = record + .tables + .omega_iov + .as_ref() + .map(|rows| rows.iter().filter(|row| row.row == row.column).count()) + .unwrap_or(0); + for (subject_index, subject) in diagnostics.subjects.iter().enumerate() { + let mut expected_occasions = Vec::new(); + for effect in record.tables.individual_effects.iter().filter(|effect| { + effect.subject == subject.subject_id && effect.effect_kind == "kappa" + }) { + if let Some(occasion) = effect.occasion { + if !expected_occasions.contains(&occasion) { + expected_occasions.push(occasion); + } + } + } + let expected_dimension = eta_dimension + expected_occasions.len() * kappa_dimension; + if subject.occasion_indices != expected_occasions + || subject.dimension != expected_dimension + || subject.mode.iter().any(|value| !value.is_finite()) + { + bail!( + "persisted N2 metadata for subject '{}' is malformed", + subject.subject_id + ); + } + let subject_available = subject.failure.is_none(); + let subject_values = [ + subject.log_marginal_likelihood, + subject.n2ll, + subject.effective_sample_size, + subject.effective_sample_fraction, + subject.var_log, + subject.n2ll_mcse, + ]; + if subject_available { + if subject.mode.len() != expected_dimension + || ![ + subject.log_marginal_likelihood, + subject.n2ll, + subject.var_log, + subject.n2ll_mcse, + ] + .iter() + .all(Option::is_some) + { + bail!("persisted available N2 subject is incomplete"); + } + } else if subject_values.iter().any(Option::is_some) { + bail!("persisted failed N2 subject must omit every numerical result"); + } + if subject_values + .into_iter() + .flatten() + .any(|value| !value.is_finite()) + || subject.zero_weight_count > subject.samples + { + bail!("persisted N2 subject diagnostics contain invalid numeric values"); + } + if subject_available { + let (Some(log_likelihood), Some(n2ll), Some(variance), Some(mcse)) = ( + subject.log_marginal_likelihood, + subject.n2ll, + subject.var_log, + subject.n2ll_mcse, + ) else { + bail!("persisted available N2 subject is incomplete"); + }; + if variance < 0.0 + || mcse < 0.0 + || !equal_with_roundoff(n2ll, -2.0 * log_likelihood) + || !equal_with_roundoff(mcse * mcse, 4.0 * variance) + { + bail!("persisted N2 subject algebra is inconsistent"); + } + } + match subject.method { + MarginalLikelihoodMethod::ExactNoLatent => { + if subject.dimension != 0 + || !subject.occasion_indices.is_empty() + || !subject.mode.is_empty() + || subject.mode_converged.is_some() + || subject.samples != 0 + || subject.seed.is_some() + || subject.proposal_scale_source + != ProposalScaleSource::NotApplicableNoLatent + || subject.effective_sample_size.is_some() + || subject.effective_sample_fraction.is_some() + || subject.zero_weight_count != 0 + || (subject_available + && (subject.var_log != Some(0.0) || subject.n2ll_mcse != Some(0.0))) + { + bail!("persisted exact no-latent N2 diagnostics are inconsistent"); + } + } + MarginalLikelihoodMethod::StudentTImportanceSampling => { + let expected_seed = crate::estimation::parametric::marginal_likelihood::marginal_likelihood_subject_seed( + config.seed, + subject_index, + ); + let mode_unavailable = matches!( + subject.failure.as_ref(), + Some( + crate::estimation::parametric::marginal_likelihood::MarginalLikelihoodFailureReason::MissingConditionalMode + | crate::estimation::parametric::marginal_likelihood::MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed(_) + ) + ); + let mode_metadata_inconsistent = if mode_unavailable { + !subject.mode.is_empty() || subject.mode_converged.is_some() + } else { + subject.mode_converged.is_none() + }; + let expected_proposal_source = match config.proposal { + crate::estimation::parametric::MarginalLikelihoodProposal::FinalRawOmegaBlocks => { + ProposalScaleSource::FinalRawOmegaBlocks + } + crate::estimation::parametric::MarginalLikelihoodProposal::ConditionalModeCurvature => { + ProposalScaleSource::ConditionalModeCurvature + } + }; + if subject.dimension == 0 + || subject.samples != config.samples_per_subject + || subject.seed != Some(expected_seed) + || subject.proposal_scale_source != expected_proposal_source + || mode_metadata_inconsistent + { + bail!("persisted stochastic N2 diagnostics have inconsistent proposal metadata"); + } + if subject_available { + let ess = subject.effective_sample_size.ok_or_else(|| { + anyhow::anyhow!( + "persisted available stochastic N2 diagnostics require ESS" + ) + })?; + let fraction = subject.effective_sample_fraction.ok_or_else(|| { + anyhow::anyhow!("persisted available stochastic N2 diagnostics require ESS fraction") + })?; + if ess <= 0.0 + || ess > subject.samples as f64 + || fraction <= 0.0 + || fraction > 1.0 + || !equal_with_roundoff(fraction, ess / subject.samples as f64) + { + bail!("persisted available stochastic N2 ESS is out of range or inconsistent"); + } + } + } + } + } + if available { + let mut summed_log = 0.0; + let mut summed_variance = 0.0; + for subject in &diagnostics.subjects { + summed_log += subject.log_marginal_likelihood.ok_or_else(|| { + anyhow::anyhow!("persisted available N2 subject is incomplete") + })?; + summed_variance += subject.var_log.ok_or_else(|| { + anyhow::anyhow!("persisted available N2 subject is incomplete") + })?; + } + let expected_n2ll = -2.0 * summed_log; + let expected_mcse = 2.0 * summed_variance.sqrt(); + let persisted_log = diagnostics.log_marginal_likelihood.ok_or_else(|| { + anyhow::anyhow!("persisted available N2 population is incomplete") + })?; + let persisted_n2ll = diagnostics.n2ll.ok_or_else(|| { + anyhow::anyhow!("persisted available N2 population is incomplete") + })?; + let persisted_mcse = diagnostics.n2ll_mcse.ok_or_else(|| { + anyhow::anyhow!("persisted available N2 population is incomplete") + })?; + if !summed_log.is_finite() + || !summed_variance.is_finite() + || !expected_n2ll.is_finite() + || !expected_mcse.is_finite() + || !equal_with_roundoff(persisted_log, summed_log) + || !equal_with_roundoff(persisted_n2ll, expected_n2ll) + || !equal_with_roundoff(persisted_mcse, expected_mcse) + { + bail!("persisted N2 population algebra is inconsistent"); + } + } + let failed_subjects = diagnostics + .subjects + .iter() + .filter_map(|subject| { + subject.failure.clone().map(|reason| { + crate::estimation::parametric::marginal_likelihood::MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.clone(), + reason, + } + }) + }) + .collect::>(); + let nonconverged_subjects = diagnostics + .subjects + .iter() + .filter(|subject| { + subject.mode_converged == Some(false) && subject.failure.is_none() + }) + .map(|subject| subject.subject_id.clone()) + .collect::>(); + match &diagnostics.status { + MarginalLikelihoodStatus::Available => { + if !failed_subjects.is_empty() || !nonconverged_subjects.is_empty() { + bail!("persisted available N2 status conflicts with subject statuses"); + } + } + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects } => { + if !failed_subjects.is_empty() || *subjects != nonconverged_subjects { + bail!( + "persisted nonconverged-mode N2 status conflicts with subject statuses" + ); + } + } + MarginalLikelihoodStatus::Unavailable { failures } => { + if *failures != failed_subjects { + bail!("persisted unavailable N2 failures conflict with subject failures"); + } + } + } + let expected_rows = marginal_likelihood_rows(record.marginal_likelihood.as_ref()); + if record.tables.marginal_likelihood.len() != expected_rows.len() + || record + .tables + .marginal_likelihood + .iter() + .zip(&expected_rows) + .any(|(actual, expected)| !marginal_likelihood_row_equal(actual, expected)) + { + bail!("persisted marginal_likelihood table does not match N2 diagnostics"); + } + let actual_statistics = record + .tables + .statistics + .iter() + .filter(|row| row.kind.starts_with("marginal_likelihood")) + .cloned() + .collect::>(); + let mut expected_statistics = Vec::new(); + marginal_likelihood_statistics( + record.tables.iterations.len(), + record.marginal_likelihood.as_ref(), + &mut expected_statistics, + ); + if actual_statistics.len() != expected_statistics.len() + || actual_statistics + .iter() + .zip(&expected_statistics) + .any(|(actual, expected)| !statistic_row_equal(actual, expected)) + { + bail!("persisted marginal-likelihood statistics do not match N2 diagnostics"); + } + } + } + Ok(()) +} + +fn validate_persisted_covariate_statistics(record: &ParametricResultRecord) -> Result<()> { + let rows = record + .tables + .statistics + .iter() + .filter(|row| row.kind == "covariate_effect") + .collect::>(); + let final_rows = record + .tables + .statistics + .iter() + .filter(|row| row.kind == "covariate_effect_final") + .collect::>(); + let effects = &record.source_metadata.covariate_effects; + if effects.is_empty() { + if !rows.is_empty() || !final_rows.is_empty() { + bail!("no-effect record contains covariate coefficient statistics"); + } + return Ok(()); + } + if rows.len() != record.tables.iterations.len() * effects.len() { + bail!("persisted covariate coefficient statistics have the wrong shape"); + } + for (cycle_index, iteration) in record.tables.iterations.iter().enumerate() { + for (effect_index, effect) in effects.iter().enumerate() { + let row = rows[cycle_index * effects.len() + effect_index]; + if row.cycle != iteration.cycle + || row.name != effect.name + || row.row.is_some() + || row.column.is_some() + || row.output_index.is_some() + || row.component.is_some() + || row.status.as_deref() + != Some(if effect.estimated { + "estimated" + } else { + "fixed" + }) + || row.value.is_none_or(|value| !value.is_finite()) + { + bail!("persisted covariate coefficient statistic is malformed or reordered"); + } + if !effect.estimated && row.value != Some(effect.initial) { + bail!("persisted fixed covariate coefficient changed during a cycle"); + } + } + } + if final_rows.len() != effects.len() { + bail!("persisted final covariate coefficient statistics have the wrong shape"); + } + for (row, effect) in final_rows.iter().zip(effects) { + if row.cycle + != record + .tables + .iterations + .last() + .map_or(0, |iteration| iteration.cycle) + || row.name != effect.name + || row.row.is_some() + || row.column.is_some() + || row.output_index.is_some() + || row.component.is_some() + || row.status.as_deref() + != Some(if effect.estimated { + "estimated" + } else { + "fixed" + }) + || row + .value + .is_none_or(|value| !equal_with_roundoff(value, effect.estimate)) + { + bail!("persisted final covariate coefficient statistic disagrees with its estimate"); + } + } + Ok(()) +} + +fn validate_persisted_information_criteria(record: &ParametricResultRecord) -> Result<()> { + validate_source_snapshot(&record.source_metadata)?; + validate_persisted_source_metadata(&record.tables, &record.source_metadata)?; + validate_persisted_information_shape(&record.information_diagnostics)?; + if !record.source_metadata.covariate_effects.is_empty() + && record.source_metadata.subject_design.len() != record.subject_count + { + bail!("persisted covariate subject order/count does not match subject_count"); + } + validate_persisted_covariate_statistics(record)?; + validate_final_source_statistics(record)?; + if let Some(marginal) = record.marginal_likelihood.as_ref() { + if marginal.subjects.len() != record.subject_count { + bail!( + "persisted marginal-likelihood subject count does not match persisted data subject count" + ); + } + } + let expected_coordinates = expected_information_coordinates(&record.source_metadata)?; + if record.information_diagnostics.coordinates != expected_coordinates { + bail!("persisted information coordinates do not match persisted free-parameter metadata"); + } + let expected = derive_information_criteria( + record.marginal_likelihood.as_ref(), + &record.information_diagnostics.coordinates, + record.subject_count, + ); + if !information_criteria_equal(&record.information_criteria, &expected) { + bail!("persisted information criteria do not match their N2, coordinate, and subject-count sources"); + } + let expected_rows = information_criteria_rows(&expected); + if record.tables.information_criteria.len() != 1 + || !information_criteria_row_equal( + &record.tables.information_criteria[0], + &expected_rows[0], + ) + { + bail!("persisted information-criteria table does not match diagnostics"); + } + let actual_statistics = record + .tables + .statistics + .iter() + .filter(|row| { + row.kind == "information_criteria_status" + || row.kind == "information_criteria" + || row.kind == "information_criteria_metadata" + }) + .collect::>(); + let mut expected_statistics = Vec::new(); + information_criteria_statistics( + record.tables.iterations.len(), + &expected, + &mut expected_statistics, + ); + if actual_statistics.len() != expected_statistics.len() + || actual_statistics + .iter() + .zip(&expected_statistics) + .any(|(actual, expected)| !statistic_row_equal(actual, expected)) + { + bail!("persisted information-criteria statistics do not match diagnostics"); + } + Ok(()) +} + +fn validate_final_source_statistics(record: &ParametricResultRecord) -> Result<()> { + let final_cycle = record + .tables + .iterations + .last() + .map(|row| row.cycle) + .unwrap_or_default(); + let final_rows = record + .tables + .statistics + .iter() + .filter(|row| row.cycle == final_cycle) + .collect::>(); + let suffix = if record.estimator_metadata.averaged_iterations > 0 { + "_final" + } else { + "" + }; + for parameter in &record.source_metadata.parameters { + let matches = final_rows + .iter() + .filter(|row| row.kind == format!("theta{suffix}") && row.name == parameter.name) + .collect::>(); + if matches.len() != 1 + || !matches[0] + .value + .is_some_and(|value| equal_with_roundoff(value, parameter.estimate)) + { + bail!("persisted final theta statistic disagrees with source snapshot"); + } + } + for (kind, covariance) in std::iter::once(("omega", &record.source_metadata.omega)).chain( + record + .source_metadata + .omega_iov + .as_ref() + .map(|covariance| ("omega_iov", covariance)), + ) { + for row in 0..covariance.dimension { + for column in 0..=row { + let matches = final_rows + .iter() + .filter(|entry| { + entry.kind == format!("{kind}{suffix}") + && entry.row.as_deref() == Some(covariance.names[row].as_str()) + && entry.column.as_deref() == Some(covariance.names[column].as_str()) + }) + .collect::>(); + if matches.len() != 1 + || !matches[0].value.is_some_and(|value| { + equal_with_roundoff(value, covariance.values[row][column]) + }) + { + bail!("persisted final {kind} statistic disagrees with source snapshot"); + } + } + } + } + for residual in &record.source_metadata.residual_outputs { + for (component, expected) in residual.components.iter().zip(&residual.values) { + let matches = final_rows + .iter() + .filter(|row| { + row.kind == format!("residual{suffix}") + && row.name == residual.output + && row.output_index == Some(residual.output_index) + && row.component.as_deref() == Some(component.as_str()) + }) + .collect::>(); + if matches.len() != 1 + || !matches[0] + .value + .is_some_and(|value| equal_with_roundoff(value, *expected)) + { + bail!("persisted final residual statistic disagrees with source snapshot"); + } + } + } + Ok(()) +} + +fn validate_source_snapshot(source: &ParametricSourceMetadata) -> Result<()> { + let mut parameter_names = std::collections::HashSet::new(); + for (parameter_index, parameter) in source.parameters.iter().enumerate() { + if parameter.name.is_empty() + || parameter.parameter_index != parameter_index + || !parameter.initial.is_finite() + || !parameter.estimate.is_finite() + || (!parameter.estimated && !equal_with_roundoff(parameter.initial, parameter.estimate)) + || !parameter_names.insert(parameter.name.as_str()) + { + bail!("source population parameter declarations must be finite, ordered, non-empty, unique, and preserve fixed initial values"); + } + let scale = parse_scale(¶meter.scale)?; + if !psi_to_phi(parameter.initial, scale).is_finite() + || !psi_to_phi(parameter.estimate, scale).is_finite() + { + bail!("source population parameter initial/final value violates its scale domain"); + } + } + validate_source_effects(&source.random_effects, &source.parameters, "IIV")?; + validate_source_covariance( + &source.omega, + &source + .random_effects + .iter() + .map(|effect| effect.parameter_name.as_str()) + .collect::>(), + "Omega", + )?; + validate_source_effects(&source.iov_effects, &source.parameters, "IOV")?; + match (&source.omega_iov, source.iov_effects.is_empty()) { + (None, true) => {} + (Some(covariance), false) => validate_source_covariance( + covariance, + &source + .iov_effects + .iter() + .map(|effect| effect.parameter_name.as_str()) + .collect::>(), + "Omega_IOV", + )?, + (Some(_), true) => bail!("source Omega_IOV exists without IOV effects"), + (None, false) => bail!("source IOV effects require Omega_IOV"), + } + let mut output_names = std::collections::HashSet::new(); + let mut output_indices = std::collections::HashSet::new(); + let mut previous_index = None; + for residual in &source.residual_outputs { + if residual.output.is_empty() + || !output_names.insert(residual.output.as_str()) + || !output_indices.insert(residual.output_index) + || previous_index.is_some_and(|index| index >= residual.output_index) + { + bail!("source residual outputs must be unique and in output-index order"); + } + previous_index = Some(residual.output_index); + let expected = residual_component_names(&residual.family)?; + if residual.components != expected + || residual.values.len() != expected.len() + || residual.estimated_mask.len() != expected.len() + || residual.values.iter().any(|value| !value.is_finite()) + { + bail!("source residual component declaration/value snapshot is malformed"); + } + validate_residual_values(&residual.family, &residual.values, &residual.estimated_mask)?; + // Validate immutable initial residual declarations. + if residual.initial_values.len() != expected.len() + || residual.initial_estimated_mask.len() != expected.len() + || residual + .initial_values + .iter() + .any(|value| !value.is_finite()) + { + bail!("source residual initial values/mask for '{}' must match the component width and be finite", residual.output); + } + // The estimation status is immutable across the fit: initial and + // current masks must agree. + if residual.initial_estimated_mask != residual.estimated_mask { + bail!( + "source residual initial and final estimated masks for '{}' disagree", + residual.output + ); + } + // Fixed components must preserve their declared initial value; free + // components must have finite initials (may differ from finals). + for (component_index, (initial, estimated)) in residual + .initial_values + .iter() + .zip(&residual.estimated_mask) + .enumerate() + { + if !estimated && !equal_with_roundoff(*initial, residual.values[component_index]) { + bail!( + "source residual '{}' fixed component '{}' initial ({}) does not match final ({})", + residual.output, + residual.components[component_index], + initial, + residual.values[component_index], + ); + } + } + validate_residual_values( + &residual.family, + &residual.initial_values, + &residual.initial_estimated_mask, + )?; + } + validate_source_covariates(source) +} + +fn validate_source_covariates(source: &ParametricSourceMetadata) -> Result<()> { + if source.covariate_effects.is_empty() { + if !source.subject_covariates.is_empty() + || !source.subject_design.is_empty() + || !source.subject_population_parameters.is_empty() + { + bail!("no-effect source metadata requires explicit empty N5 fields"); + } + return Ok(()); + } + let mut effect_names = std::collections::HashSet::new(); + let mut covariate_families = std::collections::HashMap::<&str, (&str, Option)>::new(); + for (order, effect) in source.covariate_effects.iter().enumerate() { + if effect.order != order + || effect.parameter_index >= source.parameters.len() + || effect.parameter != source.parameters[effect.parameter_index].name + || effect.name.is_empty() + || effect.covariate.is_empty() + || !effect.initial.is_finite() + || !effect.estimate.is_finite() + || (!effect.estimated && !equal_with_roundoff(effect.initial, effect.estimate)) + { + bail!("source covariate-effect declaration is malformed or out of order"); + } + let expected_name = match effect.family.as_str() { + "continuous" + if effect.center.is_some_and(f64::is_finite) + && effect.reference.is_none() + && effect.level.is_none() => + { + format!("beta:{}:{}", effect.parameter, effect.covariate) + } + "categorical" + if effect.center.is_none() + && effect.reference.is_some_and(f64::is_finite) + && effect.level.is_some_and(f64::is_finite) + && effect.reference != effect.level => + { + format!( + "beta:{}:{}:{}", + effect.parameter, + effect.covariate, + stable_number(effect.level.unwrap()) + ) + } + _ => bail!("source covariate-effect family metadata is malformed"), + }; + if effect.name != expected_name || !effect_names.insert(effect.name.as_str()) { + bail!("source covariate-effect canonical name is inconsistent or duplicated"); + } + let family_key = if effect.family == "continuous" { + ("continuous", effect.center) + } else { + ("categorical", effect.reference) + }; + if covariate_families + .insert(effect.covariate.as_str(), family_key) + .is_some_and(|prior| prior != family_key) + { + bail!("source covariate family/center/reference declarations are inconsistent"); + } + } + let subject_count = source.subject_design.len(); + if subject_count == 0 { + bail!("covariate source metadata requires subject design rows"); + } + let parameter_count = source.parameters.len(); + if source.subject_population_parameters.len() != subject_count * parameter_count { + bail!("source subject population parameter table has the wrong shape"); + } + let mut covariate_names = source + .covariate_effects + .iter() + .map(|effect| effect.covariate.as_str()) + .collect::>(); + covariate_names.sort_unstable(); + covariate_names.dedup(); + if source.subject_covariates.len() != subject_count * covariate_names.len() { + bail!("source subject covariate table has the wrong shape"); + } + for (subject_index, design) in source.subject_design.iter().enumerate() { + if design.subject.is_empty() + || design.subject_index != subject_index + || design.values.len() != source.covariate_effects.len() + { + bail!("source subject design order or width is malformed"); + } + let subject_rows = &source.subject_covariates + [subject_index * covariate_names.len()..(subject_index + 1) * covariate_names.len()]; + for (row, covariate) in subject_rows.iter().zip(&covariate_names) { + if row.subject != design.subject + || row.subject_index != subject_index + || row.covariate != **covariate + || !row.value.is_finite() + { + bail!("source subject covariate order/value metadata is malformed"); + } + } + let values = subject_rows + .iter() + .map(|row| (row.covariate.as_str(), row.value)) + .collect::>(); + for (effect_index, effect) in source.covariate_effects.iter().enumerate() { + let value = *values + .get(effect.covariate.as_str()) + .context("source subject covariate value is missing")?; + let expected = match effect.family.as_str() { + "continuous" => value - effect.center.unwrap(), + "categorical" => { + if value == effect.level.unwrap() { + 1.0 + } else if value == effect.reference.unwrap() + || source.covariate_effects.iter().any(|candidate| { + candidate.parameter == effect.parameter + && candidate.covariate == effect.covariate + && candidate.level == Some(value) + }) + { + 0.0 + } else { + bail!("source subject has an unknown or incompletely declared categorical level"); + } + } + _ => unreachable!(), + }; + if !value.is_finite() || design.values[effect_index] != expected { + bail!("source subject design does not reconstruct exactly from static values"); + } + } + let mut phi = source + .parameters + .iter() + .map(|parameter| { + parse_scale(¶meter.scale).map(|scale| psi_to_phi(parameter.estimate, scale)) + }) + .collect::>>()?; + for (effect, design_value) in source.covariate_effects.iter().zip(&design.values) { + phi[effect.parameter_index] += effect.estimate * design_value; + } + for (parameter_index, phi_value) in phi.iter().copied().enumerate() { + let row = &source.subject_population_parameters + [subject_index * parameter_count + parameter_index]; + let scale = parse_scale(&source.parameters[parameter_index].scale)?; + if row.subject != design.subject + || row.subject_index != subject_index + || row.parameter_index != parameter_index + || row.parameter != source.parameters[parameter_index].name + || !equal_with_roundoff(row.phi, phi_value) + || !equal_with_roundoff(row.psi, phi_to_psi(phi_value, scale)) + { + bail!("source subject population phi/psi rows are inconsistent"); + } + } + } + Ok(()) +} + +fn validate_source_effects( + effects: &[ParametricSourceEffect], + parameters: &[ParametricSourceParameter], + label: &str, +) -> Result<()> { + let mut seen = std::collections::HashSet::new(); + for effect in effects { + if effect.parameter_index >= parameters.len() + || !seen.insert(effect.parameter_index) + || effect.parameter_name != parameters[effect.parameter_index].name + { + bail!("source {label} indices/names are out of bounds, duplicated, or inconsistent"); + } + } + Ok(()) +} + +fn validate_source_covariance( + covariance: &ParametricSourceCovariance, + expected_names: &[&str], + label: &str, +) -> Result<()> { + let dimension = expected_names.len(); + if covariance.dimension != dimension + || covariance.names.len() != dimension + || covariance.values.len() != dimension + || covariance.structural_mask.len() != dimension + || covariance.estimated_mask.len() != dimension + || covariance.values.iter().any(|row| row.len() != dimension) + || covariance + .structural_mask + .iter() + .chain(&covariance.estimated_mask) + .any(|row| row.len() != dimension) + || covariance + .names + .iter() + .map(String::as_str) + .ne(expected_names.iter().copied()) + || covariance.names.iter().any(|name| name.is_empty()) + { + bail!("source {label} names, values, or masks do not match their declared dimension/order"); + } + for row in 0..dimension { + for column in 0..dimension { + let value = covariance.values[row][column]; + if !value.is_finite() + || value != covariance.values[column][row] + || (row == column && !covariance.structural_mask[row][column]) + || covariance.structural_mask[row][column] + != covariance.structural_mask[column][row] + || covariance.estimated_mask[row][column] != covariance.estimated_mask[column][row] + || (covariance.estimated_mask[row][column] + && !covariance.structural_mask[row][column]) + || (!covariance.structural_mask[row][column] && value != 0.0) + { + bail!("source {label} values/masks must be finite, symmetric, structurally consistent, and estimated only where structural"); + } + } + } + // Strict Cholesky: a nonempty persisted covariance is accepted only when + // every pivot is finite and positive, without jitter or matrix repair. + let mut lower = vec![vec![0.0; dimension]; dimension]; + for row in 0..dimension { + for column in 0..=row { + let mut value = covariance.values[row][column]; + for (row_value, column_value) in + lower[row][..column].iter().zip(&lower[column][..column]) + { + value -= row_value * column_value; + } + if row == column { + if !value.is_finite() || value <= 0.0 { + bail!("source {label} final covariance must be strictly positive definite"); + } + lower[row][column] = value.sqrt(); + } else { + lower[row][column] = value / lower[column][column]; + if !lower[row][column].is_finite() { + bail!("source {label} final covariance must be strictly positive definite"); + } + } + } + } + // Immutable initial covariance is validated independently and without + // repair: exact finite symmetry, exact declared structural zeros, positive + // diagonals, and strict Cholesky SPD are all required. + if covariance.initial_values.len() != dimension + || covariance + .initial_values + .iter() + .any(|row| row.len() != dimension) + { + bail!("source {label} initial_values do not match the declared dimension"); + } + for row in 0..dimension { + for column in 0..dimension { + let initial = covariance.initial_values[row][column]; + if !initial.is_finite() { + bail!("source {label} initial_values must be finite"); + } + if initial != covariance.initial_values[column][row] { + bail!("source {label} initial_values must be exactly symmetric"); + } + if !covariance.structural_mask[row][column] && initial != 0.0 { + bail!("source {label} initial structural-zero entries must be exactly zero"); + } + if row == column && initial <= 0.0 { + bail!("source {label} initial variances must be strictly positive"); + } + if !covariance.estimated_mask[row][column] + && !equal_with_roundoff(initial, covariance.values[row][column]) + { + bail!("source {label} fixed entry ({row},{column}) initial does not match final value"); + } + } + } + let mut initial_lower = vec![vec![0.0; dimension]; dimension]; + for row in 0..dimension { + for column in 0..=row { + let mut value = covariance.initial_values[row][column]; + for (row_value, column_value) in initial_lower[row][..column] + .iter() + .zip(&initial_lower[column][..column]) + { + value -= row_value * column_value; + } + if row == column { + if !value.is_finite() || value <= 0.0 { + bail!("source {label} initial covariance must be strictly positive definite"); + } + initial_lower[row][column] = value.sqrt(); + } else { + initial_lower[row][column] = value / initial_lower[column][column]; + if !initial_lower[row][column].is_finite() { + bail!("source {label} initial covariance must be strictly positive definite"); + } + } + } + } + Ok(()) +} + +fn validate_persisted_source_metadata( + tables: &ParametricResultTables, + source: &ParametricSourceMetadata, +) -> Result<()> { + if tables.population.len() != source.parameters.len() { + bail!("persisted population table width does not match source metadata"); + } + for (index, (parameter, declaration)) in + tables.population.iter().zip(&source.parameters).enumerate() + { + if parameter.name != declaration.name + || declaration.parameter_index != index + || parameter.scale != declaration.scale + || !declaration.initial.is_finite() + || !equal_with_roundoff(parameter.estimate, declaration.estimate) + || parameter.estimated != declaration.estimated + || parameter.iiv + != source + .random_effects + .iter() + .any(|effect| effect.parameter_index == index) + || parameter.iov + != source + .iov_effects + .iter() + .any(|effect| effect.parameter_index == index) + || !parameter.estimate.is_finite() + { + bail!("persisted population table disagrees with source metadata"); + } + parse_scale(¶meter.scale)?; + } + validate_covariance_table( + &tables.omega, + &source.random_effects, + &source.omega, + "Omega", + )?; + match (&tables.omega_iov, &source.omega_iov) { + (None, None) => {} + (Some(rows), Some(covariance)) => { + validate_covariance_table(rows, &source.iov_effects, covariance, "Omega_IOV")? + } + _ => bail!("persisted Omega_IOV table disagrees with source metadata"), + } + validate_persisted_residual_metadata(&tables.residual_error)?; + let expected_rows = source + .residual_outputs + .iter() + .flat_map(|residual| { + residual + .components + .iter() + .zip(&residual.values) + .zip(&residual.estimated_mask) + .map(move |((component, value), estimated)| { + ( + residual.output.as_str(), + residual.output_index, + residual.family.as_str(), + component.as_str(), + *value, + *estimated, + ) + }) + }) + .collect::>(); + if tables.residual_error.len() != expected_rows.len() + || tables + .residual_error + .iter() + .zip(expected_rows) + .any(|(row, expected)| { + ( + row.output.as_str(), + row.output_index, + row.family.as_str(), + row.component.as_str(), + row.estimated, + ) != (expected.0, expected.1, expected.2, expected.3, expected.5) + || !equal_with_roundoff(row.estimate, expected.4) + }) + { + bail!("persisted residual table disagrees with source metadata"); + } + let expected_effect_rows = source + .covariate_effects + .iter() + .map(|row| CovariateEffectRow { + order: row.order, + name: row.name.clone(), + family: row.family.clone(), + parameter: row.parameter.clone(), + parameter_index: row.parameter_index, + covariate: row.covariate.clone(), + center: row.center, + reference: row.reference, + level: row.level, + initial: row.initial, + estimate: row.estimate, + estimated: row.estimated, + }) + .collect::>(); + if tables.covariate_effects != expected_effect_rows + || tables.subject_covariates != source.subject_covariates + || tables.subject_population_parameters != source.subject_population_parameters + { + bail!("persisted N5 tables disagree with the independent source snapshot"); + } + Ok(()) +} + +fn validate_covariance_table( + rows: &[OmegaRow], + effects: &[ParametricSourceEffect], + covariance: &ParametricSourceCovariance, + label: &str, +) -> Result<()> { + let expected_len = covariance.dimension * (covariance.dimension + 1) / 2; + if rows.len() != expected_len { + bail!("persisted {label} table has the wrong lower-triangle width"); + } + let mut offset = 0; + for row in 0..covariance.dimension { + for column in 0..=row { + let entry = &rows[offset]; + if entry.row != effects[row].parameter_name + || entry.column != effects[column].parameter_name + || entry.structural != covariance.structural_mask[row][column] + || entry.estimated != covariance.estimated_mask[row][column] + || !equal_with_roundoff(entry.estimate, covariance.values[row][column]) + { + bail!("persisted {label} table disagrees with source metadata"); + } + offset += 1; + } + } + let names = effects + .iter() + .map(|effect| effect.parameter_name.clone()) + .collect::>(); + covariance_declaration(rows, &names, label)?; + Ok(()) +} + +fn residual_component_names(family: &str) -> Result> { + match family { + "constant" | "exponential" => Ok(vec!["sigma".to_string()]), + "proportional" => Ok(vec!["proportional".to_string()]), + "combined" => Ok(vec!["additive".to_string(), "proportional".to_string()]), + "correlated_combined" => Ok(vec![ + "additive".to_string(), + "proportional".to_string(), + "correlation".to_string(), + ]), + family => bail!("unknown source residual-error family '{family}'"), + } +} + +fn validate_residual_values(family: &str, values: &[f64], estimated: &[bool]) -> Result<()> { + if values.iter().any(|value| !value.is_finite()) || values.len() != estimated.len() { + bail!("source residual values/fixed status are malformed"); + } + match family { + "constant" | "proportional" | "exponential" if values == [values[0]] => { + if values[0] <= 0.0 { + bail!("source residual scale must be strictly positive"); + } + } + "combined" if values.len() == 2 => { + if values.iter().any(|value| *value < 0.0) + || values.iter().all(|value| *value == 0.0) + || values + .iter() + .zip(estimated) + .any(|(value, estimated)| *estimated && *value <= 0.0) + { + bail!("source combined residual components are invalid"); + } + } + "correlated_combined" if values.len() == 3 => { + if values[0] <= 0.0 || values[1] <= 0.0 || values[2] <= -1.0 || values[2] >= 1.0 { + bail!("source correlated-combined residual components are invalid"); + } + } + _ => bail!("source residual family/value width is malformed"), + } + Ok(()) +} + +fn validate_persisted_residual_metadata(rows: &[ResidualErrorRow]) -> Result<()> { + let mut seen_outputs = std::collections::HashSet::new(); + let mut previous_output_index = None; + let mut offset = 0; + while offset < rows.len() { + let first = &rows[offset]; + if first.output.is_empty() + || !seen_outputs.insert((first.output_index, first.output.as_str())) + { + bail!("persisted residual outputs must be non-empty and unique"); + } + if previous_output_index.is_some_and(|previous| previous >= first.output_index) { + bail!("persisted residual outputs are not in canonical output-index order"); + } + previous_output_index = Some(first.output_index); + let expected_components: &[&str] = match first.family.as_str() { + "constant" | "exponential" => &["sigma"], + "proportional" => &["proportional"], + "combined" => &["additive", "proportional"], + "correlated_combined" => &["additive", "proportional", "correlation"], + family => bail!("unknown persisted residual-error family '{family}'"), + }; + if offset + expected_components.len() > rows.len() { + bail!("persisted residual-error family has missing components"); + } + let group = &rows[offset..offset + expected_components.len()]; + for (row, expected_component) in group.iter().zip(expected_components) { + if row.output_index != first.output_index + || row.output != first.output + || row.family != first.family + || row.component != *expected_component + || !row.estimate.is_finite() + { + bail!("persisted residual metadata is duplicated, reordered, or malformed"); + } + } + match first.family.as_str() { + "constant" | "proportional" | "exponential" => { + if group[0].estimate <= 0.0 { + bail!("persisted residual scale must be strictly positive"); + } + } + "combined" => { + if group.iter().any(|row| row.estimate < 0.0) + || group.iter().all(|row| row.estimate == 0.0) + || group.iter().any(|row| row.estimated && row.estimate <= 0.0) + { + bail!("persisted combined residual components are invalid"); + } + } + "correlated_combined" => { + if group[0].estimate <= 0.0 + || group[1].estimate <= 0.0 + || group[2].estimate <= -1.0 + || group[2].estimate >= 1.0 + { + bail!("persisted correlated-combined residual components are invalid"); + } + } + _ => unreachable!(), + } + offset += expected_components.len(); + } + Ok(()) +} + +fn validate_persisted_information_shape(information: &InformationDiagnostics) -> Result<()> { + let p = information.coordinates.len(); + let square = |matrix: &[Vec]| matrix.len() == p && matrix.iter().all(|row| row.len() == p); + if information.delta.len() != p + || !square(&information.g) + || !square(&information.expected_complete_hessian) + || !square(&information.observed_hessian) + || !square(&information.observed_information) + { + bail!("persisted information diagnostics do not have exact p-dimensional shapes"); + } + if information + .coordinates + .iter() + .enumerate() + .any(|(index, coordinate)| coordinate.index != index) + { + bail!("persisted information coordinate indices are not contiguous"); + } + if matches!( + information.status, + crate::results::InformationStatus::Available + ) && p == 0 + { + bail!("persisted available information diagnostics require free coordinates"); + } + if matches!( + information.status, + crate::results::InformationStatus::NoFreeCoordinates + ) && p != 0 + { + bail!("persisted no-free-coordinate information status has nonzero width"); + } + if !matches!( + information.status, + crate::results::InformationStatus::NonFinite + ) { + let finite = information.delta.iter().all(|value| value.is_finite()) + && information + .g + .iter() + .chain(&information.expected_complete_hessian) + .chain(&information.observed_hessian) + .chain(&information.observed_information) + .flatten() + .all(|value| value.is_finite()); + if !finite { + bail!("persisted information diagnostics retain nonfinite values for their status"); + } + } + Ok(()) +} + +fn expected_information_coordinates( + source: &ParametricSourceMetadata, +) -> Result> { + let mut coordinates = Vec::new(); + let push = |coordinates: &mut Vec, name, kind| { + coordinates.push(InformationCoordinate { + index: coordinates.len(), + name, + kind, + }); + }; + for (parameter_index, parameter) in source.parameters.iter().enumerate() { + if parameter.estimated { + push( + &mut coordinates, + format!("phi:{}", parameter.name), + InformationCoordinateKind::Population { parameter_index }, + ); + } + } + for effect in &source.covariate_effects { + if effect.estimated { + push( + &mut coordinates, + effect.name.clone(), + InformationCoordinateKind::CovariateEffect { + effect_index: effect.order, + }, + ); + } + } + append_source_covariance_coordinates( + &mut coordinates, + &source.random_effects, + &source.omega, + false, + ); + if let Some(covariance) = &source.omega_iov { + append_source_covariance_coordinates( + &mut coordinates, + &source.iov_effects, + covariance, + true, + ); + } + for residual in &source.residual_outputs { + for (component, estimated) in residual.components.iter().zip(&residual.estimated_mask) { + if *estimated { + push( + &mut coordinates, + format!("residual:{}:{component}", residual.output), + InformationCoordinateKind::Residual { + output_index: residual.output_index, + component: component.clone(), + }, + ); + } + } + } + Ok(coordinates) +} + +fn append_source_covariance_coordinates( + coordinates: &mut Vec, + effects: &[ParametricSourceEffect], + covariance: &ParametricSourceCovariance, + iov: bool, +) { + for row in 0..covariance.dimension { + for column in 0..=row { + if !covariance.estimated_mask[row][column] { + continue; + } + let prefix = if iov { "omega_iov" } else { "omega" }; + coordinates.push(InformationCoordinate { + index: coordinates.len(), + name: format!( + "{prefix}:{}:{}", + effects[row].parameter_name, effects[column].parameter_name + ), + kind: if iov { + InformationCoordinateKind::OmegaIov { row, column } + } else { + InformationCoordinateKind::Omega { row, column } + }, + }); + } + } +} + +fn information_criteria_equal( + actual: &InformationCriteriaDiagnostics, + expected: &InformationCriteriaDiagnostics, +) -> bool { + actual.status == expected.status + && actual.parameter_count == expected.parameter_count + && actual.sample_size_convention == expected.sample_size_convention + && actual.subject_count == expected.subject_count + && optional_float_equal(actual.source_marginal_n2ll, expected.source_marginal_n2ll) + && optional_float_equal( + actual.source_marginal_n2ll_mcse, + expected.source_marginal_n2ll_mcse, + ) + && optional_float_equal(actual.aic, expected.aic) + && optional_float_equal(actual.bic, expected.bic) + && optional_float_equal(actual.aic_mcse, expected.aic_mcse) + && optional_float_equal(actual.bic_mcse, expected.bic_mcse) +} + +fn information_criteria_row_equal( + actual: &InformationCriteriaRow, + expected: &InformationCriteriaRow, +) -> bool { + actual.status == expected.status + && actual.sample_size_convention == expected.sample_size_convention + && actual.subject_count == expected.subject_count + && actual.population_parameter_count == expected.population_parameter_count + && actual.covariate_parameter_count == expected.covariate_parameter_count + && actual.omega_parameter_count == expected.omega_parameter_count + && actual.omega_iov_parameter_count == expected.omega_iov_parameter_count + && actual.residual_parameter_count == expected.residual_parameter_count + && actual.free_parameter_count == expected.free_parameter_count + && optional_float_equal(actual.source_marginal_n2ll, expected.source_marginal_n2ll) + && optional_float_equal( + actual.source_marginal_n2ll_mcse, + expected.source_marginal_n2ll_mcse, + ) + && optional_float_equal(actual.aic, expected.aic) + && optional_float_equal(actual.bic, expected.bic) + && optional_float_equal(actual.aic_mcse, expected.aic_mcse) + && optional_float_equal(actual.bic_mcse, expected.bic_mcse) + && actual.failure_reason == expected.failure_reason +} + +fn marginal_likelihood_row_equal( + actual: &MarginalLikelihoodRow, + expected: &MarginalLikelihoodRow, +) -> bool { + actual.scope == expected.scope + && actual.subject == expected.subject + && actual.method == expected.method + && actual.status == expected.status + && actual.samples_per_subject == expected.samples_per_subject + && actual.seed == expected.seed + && actual.degrees_of_freedom == expected.degrees_of_freedom + && equal_with_roundoff( + actual.covariance_scale_multiplier, + expected.covariance_scale_multiplier, + ) + && actual.proposal_scale_source == expected.proposal_scale_source + && actual.dimension == expected.dimension + && actual.occasion_indices == expected.occasion_indices + && json_float_vec_equal(&actual.mode, &expected.mode) + && actual.mode_converged == expected.mode_converged + && optional_float_equal( + actual.log_marginal_likelihood, + expected.log_marginal_likelihood, + ) + && optional_float_equal(actual.n2ll, expected.n2ll) + && optional_float_equal(actual.n2ll_mcse, expected.n2ll_mcse) + && optional_float_equal(actual.effective_sample_size, expected.effective_sample_size) + && optional_float_equal( + actual.effective_sample_fraction, + expected.effective_sample_fraction, + ) + && actual.zero_weight_count == expected.zero_weight_count + && actual.failure == expected.failure +} + +fn json_float_vec_equal(actual: &str, expected: &str) -> bool { + let Ok(actual) = serde_json::from_str::>(actual) else { + return false; + }; + let Ok(expected) = serde_json::from_str::>(expected) else { + return false; + }; + actual.len() == expected.len() + && actual + .iter() + .zip(expected) + .all(|(actual, expected)| equal_with_roundoff(*actual, expected)) +} + +fn statistic_row_equal(actual: &StatisticRow, expected: &StatisticRow) -> bool { + actual.cycle == expected.cycle + && actual.kind == expected.kind + && actual.name == expected.name + && actual.row == expected.row + && actual.column == expected.column + && actual.output_index == expected.output_index + && actual.component == expected.component + && optional_float_equal(actual.value, expected.value) + && actual.status == expected.status +} + +fn optional_float_equal(actual: Option, expected: Option) -> bool { + match (actual, expected) { + (Some(actual), Some(expected)) => equal_with_roundoff(actual, expected), + (None, None) => true, + _ => false, + } +} + +fn persisted_effect_matrix( + rows: &[IndividualEffectRow], + source: &str, + effect_kind: &str, + effect_names: &[String], +) -> Result>> { + let mut grouped = + std::collections::BTreeMap::<(String, Option), Vec>>::new(); + for row in rows + .iter() + .filter(|row| row.source == source && row.effect_kind == effect_kind) + { + let effect_index = effect_names + .iter() + .position(|name| name == &row.parameter) + .context("persisted individual-effect row has an unknown effect name")?; + let values = grouped + .entry((row.subject.clone(), row.occasion)) + .or_insert_with(|| vec![None; effect_names.len()]); + if values[effect_index].replace(row.value).is_some() { + bail!("persisted individual-effect rows contain a duplicate effect"); + } + } + grouped + .into_values() + .map(|values| { + values + .into_iter() + .collect::>>() + .context("persisted individual-effect row is missing an effect") + }) + .collect() +} + +fn shrinkage_value_equal(actual: &ShrinkageValue, expected: &ShrinkageValue) -> bool { + match (actual, expected) { + ( + ShrinkageValue::Available { + value: actual_value, + unit_count: actual_count, + denominator_documentation: actual_documentation, + }, + ShrinkageValue::Available { + value: expected_value, + unit_count: expected_count, + denominator_documentation: expected_documentation, + }, + ) => { + equal_with_roundoff(*actual_value, *expected_value) + && actual_count == expected_count + && actual_documentation == expected_documentation + } + ( + ShrinkageValue::Unavailable { + reason: actual_reason, + }, + ShrinkageValue::Unavailable { + reason: expected_reason, + }, + ) => actual_reason == expected_reason, + _ => false, + } +} + +fn is_n6_statistic(kind: &str) -> bool { + kind.starts_with("population_uncertainty_") + || kind.starts_with("conditional_curvature_") + || matches!( + kind, + "conditional_latent_se" + | "conditional_hessian" + | "conditional_latent_covariance" + | "eta_shrinkage_posterior_mean" + | "eta_shrinkage_map" + | "kappa_shrinkage_posterior_mean" + | "kappa_shrinkage_map" + ) +} + +fn validate_persisted_n6(record: &ParametricResultRecord) -> Result<()> { + let expected_population = + crate::estimation::parametric::information::derive_population_uncertainty( + &record.information_diagnostics, + ); + if record.population_uncertainty != expected_population { + bail!("persisted population uncertainty is inconsistent with observed information"); + } + let population_status = serde_json::to_string(&record.population_uncertainty.status)?; + let status_rows = record + .tables + .statistics + .iter() + .filter(|row| row.kind == "population_uncertainty_status") + .collect::>(); + if status_rows.len() != 1 + || status_rows[0].value.is_some() + || status_rows[0].status.as_deref() != Some(population_status.as_str()) + { + bail!("persisted population uncertainty status row is inconsistent"); + } + let regularization_rows = record + .tables + .statistics + .iter() + .filter(|row| row.kind == "population_uncertainty_regularization") + .collect::>(); + if regularization_rows.len() != 1 + || regularization_rows[0].value.is_some() + || regularization_rows[0].status.as_deref() != Some("none") + { + bail!("persisted population uncertainty regularization row is inconsistent"); + } + let covariance_rows = record + .tables + .statistics + .iter() + .filter(|row| row.kind == "population_uncertainty_covariance_phi") + .collect::>(); + match record.population_uncertainty.free_covariance.as_ref() { + Some(covariance) => { + let width = record.population_uncertainty.coordinates.len(); + if covariance_rows.len() != width * width { + bail!("persisted population uncertainty covariance rows are incomplete"); + } + for (row_index, values) in covariance.iter().enumerate() { + for (column_index, expected) in values.iter().enumerate() { + let offset = row_index * width + column_index; + let row = covariance_rows[offset]; + if row.row.as_deref() + != Some( + record.population_uncertainty.coordinates[row_index] + .name + .as_str(), + ) + || row.column.as_deref() + != Some( + record.population_uncertainty.coordinates[column_index] + .name + .as_str(), + ) + || row + .value + .is_none_or(|actual| !equal_with_roundoff(actual, *expected)) + { + bail!("persisted population uncertainty covariance row is inconsistent"); + } + } + } + } + None if !covariance_rows.is_empty() => { + bail!("persisted unavailable population uncertainty contains covariance rows"); + } + None => {} + } + + if !record.conditional_modes.is_empty() + && record.conditional_modes.len() != record.subject_count + { + bail!("persisted conditional-mode count does not match subject count"); + } + let eta_width = record.source_metadata.random_effects.len(); + let kappa_width = record.source_metadata.iov_effects.len(); + for mode in &record.conditional_modes { + let dimension = eta_width + mode.kappas.len() * kappa_width; + let diagnostics = &mode.uncertainty; + if diagnostics.coordinates.len() != dimension + || diagnostics + .coordinates + .iter() + .enumerate() + .any(|(index, coordinate)| coordinate.index != index) + || diagnostics.mode_metadata.converged != mode.converged + || diagnostics.mode_metadata.iterations != mode.iterations + || diagnostics.mode_metadata.termination_message != mode.termination + || !equal_with_roundoff(diagnostics.mode_metadata.objective_value, mode.objective) + { + bail!("persisted conditional curvature metadata is inconsistent"); + } + if mode.eta.len() != eta_width + || mode.kappas.iter().any(|kappa| { + kappa.subject_id != mode.subject_id || kappa.values.len() != kappa_width + }) + { + bail!("persisted conditional mode latent widths are inconsistent"); + } + for (effect_index, effect) in record.source_metadata.random_effects.iter().enumerate() { + let coordinate = &diagnostics.coordinates[effect_index]; + let expected_sd = + record.source_metadata.omega.values[effect_index][effect_index].sqrt(); + if coordinate.name != format!("eta:{}", effect.parameter_name) + || !matches!( + coordinate.kind, + crate::estimation::parametric::JointLatentCoordinateKind::Eta { + parameter_index + } if parameter_index == effect.parameter_index + ) + || !equal_with_roundoff(coordinate.prior_sd, expected_sd) + { + bail!("persisted eta curvature coordinate metadata is inconsistent"); + } + } + let omega_iov = record.source_metadata.omega_iov.as_ref(); + for (occasion_position, kappa) in mode.kappas.iter().enumerate() { + for (effect_index, effect) in record.source_metadata.iov_effects.iter().enumerate() { + let coordinate_index = eta_width + occasion_position * kappa_width + effect_index; + let coordinate = &diagnostics.coordinates[coordinate_index]; + let expected_sd = omega_iov + .and_then(|omega| omega.values.get(effect_index)) + .and_then(|row| row.get(effect_index)) + .copied() + .context("persisted kappa coordinate lacks an Omega_IOV variance")? + .sqrt(); + if coordinate.name + != format!("kappa:{}:{}", kappa.occasion_index, effect.parameter_name) + || !matches!( + coordinate.kind, + crate::estimation::parametric::JointLatentCoordinateKind::Kappa { + occasion_index, + effect_index: actual_effect_index, + parameter_index, + } if occasion_index == kappa.occasion_index + && actual_effect_index == effect_index + && parameter_index == effect.parameter_index + ) + || !equal_with_roundoff(coordinate.prior_sd, expected_sd) + { + bail!("persisted kappa curvature coordinate metadata is inconsistent"); + } + } + } + let expected_status = serde_json::to_string(&diagnostics.status)?; + let status_rows = record + .tables + .statistics + .iter() + .filter(|row| row.kind == "conditional_curvature_status" && row.name == mode.subject_id) + .collect::>(); + if status_rows.len() != 1 + || status_rows[0].value.is_some() + || status_rows[0].status.as_deref() != Some(expected_status.as_str()) + { + bail!("persisted conditional curvature status row is inconsistent"); + } + let regularization_rows = record + .tables + .statistics + .iter() + .filter(|row| { + row.kind == "conditional_curvature_regularization" && row.name == mode.subject_id + }) + .collect::>(); + if regularization_rows.len() != 1 + || regularization_rows[0].value.is_some() + || regularization_rows[0].status.as_deref() != Some("none") + { + bail!("persisted conditional curvature regularization row is inconsistent"); + } + match diagnostics.status { + crate::estimation::parametric::ConditionalCurvatureStatus::Available => { + let square = |matrix: &[Vec]| { + matrix.len() == dimension + && matrix.iter().all(|row| { + row.len() == dimension && row.iter().all(|value| value.is_finite()) + }) + }; + if diagnostics + .hessian + .as_deref() + .is_none_or(|matrix| !square(matrix)) + || diagnostics + .latent_covariance + .as_deref() + .is_none_or(|matrix| !square(matrix)) + || diagnostics + .latent_standard_errors + .as_ref() + .is_none_or(|values| { + values.len() != dimension + || values + .iter() + .any(|value| !value.is_finite() || *value <= 0.0) + }) + || diagnostics.finite_difference_steps.len() != dimension + || diagnostics + .finite_difference_steps + .iter() + .any(|value| !value.is_finite() || *value <= 0.0) + || diagnostics + .spectral_condition_number + .is_none_or(|value| !value.is_finite() || value < 1.0) + { + bail!("persisted available conditional curvature has malformed numerics"); + } + } + crate::estimation::parametric::ConditionalCurvatureStatus::Unavailable(_) => { + if diagnostics.hessian.is_some() + || diagnostics.latent_covariance.is_some() + || diagnostics.latent_standard_errors.is_some() + || diagnostics.spectral_condition_number.is_some() + { + bail!("persisted unavailable conditional curvature contains numerics"); + } + } + } + } + + let eta_names = record + .source_metadata + .random_effects + .iter() + .map(|effect| effect.parameter_name.clone()) + .collect::>(); + let kappa_names = record + .source_metadata + .iov_effects + .iter() + .map(|effect| effect.parameter_name.clone()) + .collect::>(); + if record.shrinkage.eta_posterior_mean.len() != eta_names.len() + || record.shrinkage.eta_map.len() != eta_names.len() + || record.shrinkage.kappa_posterior_mean.len() != kappa_names.len() + || record.shrinkage.kappa_map.len() != kappa_names.len() + || record + .shrinkage + .eta_posterior_mean + .iter() + .zip(&eta_names) + .any(|(value, name)| value.effect != *name) + || record + .shrinkage + .eta_map + .iter() + .zip(&eta_names) + .any(|(value, name)| value.effect != *name) + || record + .shrinkage + .kappa_posterior_mean + .iter() + .zip(&kappa_names) + .any(|(value, name)| value.effect != *name) + || record + .shrinkage + .kappa_map + .iter() + .zip(&kappa_names) + .any(|(value, name)| value.effect != *name) + { + bail!("persisted shrinkage effect ordering is inconsistent"); + } + + let eta_variances = (0..eta_names.len()) + .map(|index| record.source_metadata.omega.values[index][index]) + .collect::>(); + let kappa_variances = record + .source_metadata + .omega_iov + .as_ref() + .map(|omega| { + (0..kappa_names.len()) + .map(|index| omega.values[index][index]) + .collect::>() + }) + .unwrap_or_default(); + let eta_posterior_rows = persisted_effect_matrix( + &record.tables.individual_effects, + "chain_mean", + "eta", + &eta_names, + )?; + let eta_map_rows = persisted_effect_matrix( + &record.tables.individual_effects, + "conditional_mode", + "eta", + &eta_names, + )?; + let kappa_posterior_rows = persisted_effect_matrix( + &record.tables.individual_effects, + "chain_mean", + "kappa", + &kappa_names, + )?; + let kappa_map_rows = persisted_effect_matrix( + &record.tables.individual_effects, + "conditional_mode", + "kappa", + &kappa_names, + )?; + let expected_shrinkage = ShrinkageDiagnostics { + eta_posterior_mean: derive_eta_posterior_mean_shrinkage( + &eta_names, + &eta_variances, + &eta_posterior_rows, + ), + eta_map: derive_eta_map_shrinkage( + &eta_names, + &eta_variances, + (!record.conditional_modes.is_empty()).then_some(eta_map_rows.as_slice()), + ), + kappa_posterior_mean: derive_kappa_posterior_mean_shrinkage( + &kappa_names, + &kappa_variances, + &kappa_posterior_rows, + ), + kappa_map: derive_kappa_map_shrinkage( + &kappa_names, + &kappa_variances, + (!record.conditional_modes.is_empty()).then_some(kappa_map_rows.as_slice()), + ), + }; + macro_rules! validate_shrinkage_group { + ($actual:expr, $expected:expr) => { + if $actual.len() != $expected.len() + || $actual.iter().zip($expected).any(|(actual, expected)| { + actual.effect != expected.effect + || !shrinkage_value_equal(&actual.shrinkage, &expected.shrinkage) + }) + { + bail!("persisted shrinkage values are inconsistent with retained effects"); + } + }; + } + validate_shrinkage_group!( + &record.shrinkage.eta_posterior_mean, + &expected_shrinkage.eta_posterior_mean + ); + validate_shrinkage_group!(&record.shrinkage.eta_map, &expected_shrinkage.eta_map); + validate_shrinkage_group!( + &record.shrinkage.kappa_posterior_mean, + &expected_shrinkage.kappa_posterior_mean + ); + validate_shrinkage_group!(&record.shrinkage.kappa_map, &expected_shrinkage.kappa_map); + + let cycle = record + .tables + .iterations + .last() + .map_or(0, |iteration| iteration.cycle); + let expected_statistics = n6_uncertainty_statistic_rows( + cycle, + &record.population_uncertainty, + &record.conditional_modes, + &record.shrinkage, + ); + let actual_statistics = record + .tables + .statistics + .iter() + .filter(|row| is_n6_statistic(&row.kind)) + .collect::>(); + if actual_statistics.len() != expected_statistics.len() + || actual_statistics + .iter() + .zip(&expected_statistics) + .any(|(actual, expected)| !statistic_row_equal(actual, expected)) + { + bail!("persisted N6 statistic rows are inconsistent"); + } + Ok(()) +} + +impl ParametricResultRecord { + pub fn read_json(path: impl AsRef) -> Result { + let path = path.as_ref(); + let file = File::open(path) + .with_context(|| format!("failed to open parametric result '{}'", path.display()))?; + let raw: serde_json::Value = serde_json::from_reader(file) + .with_context(|| format!("failed to parse parametric result '{}'", path.display()))?; + let object = raw + .as_object() + .context("parametric result JSON must be an object")?; + for required in [ + "marginal_likelihood", + "information_criteria", + "subject_count", + "population_uncertainty", + "conditional_modes", + "shrinkage", + ] { + if !object.contains_key(required) { + bail!("schema-9 parametric result requires the {required} field"); + } + } + object + .get("source_metadata") + .and_then(serde_json::Value::as_object) + .context("schema-9 parametric result requires an object source_metadata field")?; + let config = object + .get("config") + .and_then(serde_json::Value::as_object) + .context("schema-9 parametric result requires an object config field")?; + if !config.contains_key("marginal_likelihood") { + bail!("schema-9 parametric result requires config.marginal_likelihood"); + } + let record: Self = serde_json::from_value(raw) + .with_context(|| format!("failed to parse parametric result '{}'", path.display()))?; + if record.schema_version != PARAMETRIC_RESULT_SCHEMA_VERSION { + bail!( + "unsupported parametric result schema version {}", + record.schema_version + ); + } + if record.fit_family != "parametric" || record.algorithm != "saem" { + bail!("JSON record is not a parametric SAEM result"); + } + if record.objective_kind != "conditional_n2ll" { + bail!( + "unsupported parametric objective kind '{}'", + record.objective_kind + ); + } + record + .config + .validate() + .context("schema-9 parametric result contains invalid retained SAEM configuration")?; + validate_persisted_marginal_likelihood(&record)?; + validate_persisted_information_criteria(&record)?; + validate_persisted_n6(&record)?; + Ok(record) + } + + /// Reconstruct a typed problem initialized from this persisted fit. + /// + /// This starts a new run from the final scientific estimates. Sampler, + /// adaptation, cycle, and random-number-generator state are not persisted + /// or continued. The retained [`SaemConfig`] describes the parent run only. + pub fn warm_start_problem( + &self, + equation: E, + data: Data, + ) -> Result> + where + E: Equation + EquationMetadataSource, + { + self.validate_warm_start_header()?; + problem_from_tables(equation, data, &self.tables) + } + + fn validate_warm_start_header(&self) -> Result<()> { + if self.schema_version != PARAMETRIC_RESULT_SCHEMA_VERSION { + bail!( + "unsupported parametric result schema version {}", + self.schema_version + ); + } + if self.fit_family != "parametric" { + bail!("result fit family '{}' is not parametric", self.fit_family); + } + if self.algorithm != "saem" { + bail!("result algorithm '{}' is not SAEM", self.algorithm); + } + self.config.validate()?; + validate_persisted_marginal_likelihood(self)?; + validate_persisted_information_criteria(self)?; + validate_persisted_n6(self)?; + Ok(()) + } + + fn write_json(&self, path: &Path) -> Result<()> { + create_parent_dir(path)?; + let file = + File::create(path).with_context(|| format!("failed to create '{}'", path.display()))?; + serde_json::to_writer_pretty(file, self) + .with_context(|| format!("failed to write '{}'", path.display())) + } +} + +#[derive(Serialize)] +struct ParametricManifest { + schema_version: u32, + fit_family: &'static str, + algorithm: &'static str, + objective_kind: &'static str, + termination: Option, + operational_convergence: OperationalConvergenceDiagnostics, + estimator_metadata: SaemEstimatorMetadata, + marginal_likelihood: Option, + information_criteria: InformationCriteriaDiagnostics, + files: Vec, +} + +impl ParametricResult { + /// Build a new typed problem initialized from this fit's final estimates. + /// + /// The parent result is borrowed and remains unchanged. This is a new run, + /// not an exact continuation: latent chains, proposal adaptation, cycle + /// state, and random-number-generator streams are not carried forward. + pub fn warm_start_problem(&self) -> Result> + where + E: Clone + EquationMetadataSource, + { + problem_from_tables( + self.equation.clone(), + self.data.clone(), + &self.base_tables()?, + ) + } + + /// Fit a new SAEM run initialized from this result's final estimates. + /// + /// `config`, including its seed and schedule, belongs entirely to the new + /// run. The parent configuration and sampler state are not reused. + pub fn fit_next(&self, config: SaemConfig) -> Result> + where + E: Clone + EquationMetadataSource + Send + 'static, + { + self.warm_start_problem()?.fit_with(config) + } + + pub fn tables(&self, idelta: f64, tad: f64) -> Result + where + E: pharmsol::equation::EquationTypes

, + { + let mut tables = self.base_tables()?; + tables.predictions = self.prediction_rows(idelta, tad)?; + Ok(tables) + } + + pub fn write_json(&self, path: impl AsRef, idelta: f64, tad: f64) -> Result<()> + where + E: pharmsol::equation::EquationTypes

, + { + self.record(self.tables(idelta, tad)?)? + .write_json(path.as_ref()) + } + + pub fn write_outputs(&self, directory: impl AsRef, idelta: f64, tad: f64) -> Result<()> + where + E: pharmsol::equation::EquationTypes

, + { + let directory = directory.as_ref(); + std::fs::create_dir_all(directory).with_context(|| { + format!( + "failed to create output directory '{}'", + directory.display() + ) + })?; + let tables = self.tables(idelta, tad)?; + let mut files = vec![ + "population.csv".to_string(), + "omega.csv".to_string(), + "residual_error.csv".to_string(), + "individual_effects.csv".to_string(), + "individual_parameters.csv".to_string(), + "iterations.csv".to_string(), + "statistics.csv".to_string(), + "marginal_likelihood.csv".to_string(), + "information_criteria.csv".to_string(), + "predictions.csv".to_string(), + "covariate_effects.csv".to_string(), + "subject_covariates.csv".to_string(), + "subject_population_parameters.csv".to_string(), + ]; + tables.write_population(directory.join("population.csv"))?; + tables.write_omega(directory.join("omega.csv"))?; + if tables.omega_iov.is_some() { + tables.write_omega_iov(directory.join("omega_iov.csv"))?; + files.push("omega_iov.csv".to_string()); + } + tables.write_residual_error(directory.join("residual_error.csv"))?; + tables.write_individual_effects(directory.join("individual_effects.csv"))?; + tables.write_individual_parameters(directory.join("individual_parameters.csv"))?; + tables.write_iterations(directory.join("iterations.csv"))?; + tables.write_statistics(directory.join("statistics.csv"))?; + tables.write_marginal_likelihood(directory.join("marginal_likelihood.csv"))?; + tables.write_information_criteria(directory.join("information_criteria.csv"))?; + tables.write_predictions(directory.join("predictions.csv"))?; + tables.write_covariate_effects(directory.join("covariate_effects.csv"))?; + tables.write_subject_covariates(directory.join("subject_covariates.csv"))?; + tables.write_subject_population_parameters( + directory.join("subject_population_parameters.csv"), + )?; + + let record = self.record(tables)?; + record.write_json(&directory.join("result.json"))?; + files.push("result.json".to_string()); + files.push("manifest.json".to_string()); + let manifest = ParametricManifest { + schema_version: PARAMETRIC_RESULT_SCHEMA_VERSION, + fit_family: "parametric", + algorithm: "saem", + objective_kind: "conditional_n2ll", + termination: self.termination_reason().cloned(), + operational_convergence: self.operational_convergence().clone(), + estimator_metadata: self.estimator_metadata().clone(), + marginal_likelihood: self.marginal_likelihood_diagnostics().cloned(), + information_criteria: self.information_criteria().clone(), + files, + }; + let manifest_path = directory.join("manifest.json"); + let file = File::create(&manifest_path) + .with_context(|| format!("failed to create '{}'", manifest_path.display()))?; + serde_json::to_writer_pretty(file, &manifest) + .with_context(|| format!("failed to write '{}'", manifest_path.display()))?; + tracing::info!(directory = %directory.display(), "wrote parametric result outputs"); + Ok(()) + } + + fn record(&self, tables: ParametricResultTables) -> Result { + let source_metadata = self.source_metadata(&tables)?; + validate_source_snapshot(&source_metadata)?; + validate_persisted_source_metadata(&tables, &source_metadata)?; + Ok(ParametricResultRecord { + schema_version: PARAMETRIC_RESULT_SCHEMA_VERSION, + fit_family: "parametric".to_string(), + algorithm: "saem".to_string(), + config: self.config.clone(), + effective_n_chains: self.effective_n_chains, + termination: self.termination_reason.clone(), + objective_kind: "conditional_n2ll".to_string(), + conditional_n2ll: self.conditional_n2ll(), + subject_count: self.data.subjects().len(), + marginal_likelihood: self.marginal_likelihood_diagnostics().cloned(), + information_criteria: self.information_criteria().clone(), + source_metadata, + warnings: self.warnings.iter().map(warning_record).collect(), + tables, + information_diagnostics: self.information_diagnostics().clone(), + population_uncertainty: self.population_uncertainty().clone(), + conditional_modes: self.conditional_modes().to_vec(), + shrinkage: self.shrinkage().clone(), + markov_simulation_variance: self.markov_simulation_variance().clone(), + operational_convergence: self.operational_diagnostics().clone(), + estimator_metadata: self.estimator_metadata().clone(), + }) + } + + fn source_metadata(&self, tables: &ParametricResultTables) -> Result { + let effects = |indices: &[usize], names: &[String]| { + indices + .iter() + .zip(names) + .map(|(parameter_index, parameter_name)| ParametricSourceEffect { + parameter_index: *parameter_index, + parameter_name: parameter_name.clone(), + }) + .collect() + }; + let covariance = + |names: &[String], + values: &ndarray::Array2, + structural: &ndarray::Array2, + estimated: &ndarray::Array2, + initial: &ndarray::Array2| ParametricSourceCovariance { + dimension: values.nrows(), + names: names.to_vec(), + values: numeric_matrix_rows(values), + structural_mask: boolean_mask_rows(structural), + estimated_mask: boolean_mask_rows(estimated), + initial_values: numeric_matrix_rows(initial), + }; + if self.population_initial.len() != tables.population.len() { + bail!("result population initial declarations do not match the parameter width"); + } + if self.residual_initial_values.len() != self.residual_error_estimates.len() + || self.residual_initial_estimated.len() != self.residual_error_estimates.len() + { + bail!("result residual initial declarations do not match the output width"); + } + let omega_iov = match ( + self.omega_iov.as_ref(), + self.omega_iov_structural_mask.as_ref(), + self.omega_iov_estimated_mask.as_ref(), + self.omega_iov_initial.as_ref(), + ) { + (None, None, None, None) => None, + (Some(values), Some(structural), Some(estimated), Some(initial)) => Some(covariance( + &self.iov_effect_names, + values, + structural, + estimated, + initial, + )), + _ => bail!("result Omega_IOV final and immutable initial declarations are incomplete"), + }; + Ok(ParametricSourceMetadata { + parameters: tables + .population + .iter() + .enumerate() + .map(|(parameter_index, row)| ParametricSourceParameter { + name: row.name.clone(), + parameter_index, + scale: row.scale.clone(), + initial: self.population_initial[parameter_index], + estimate: row.estimate, + estimated: row.estimated, + }) + .collect(), + random_effects: effects(&self.random_effect_indices, &self.random_effect_names), + omega: covariance( + &self.random_effect_names, + &self.omega, + &self.omega_structural_mask, + &self.omega_estimated_mask, + &self.omega_initial, + ), + iov_effects: effects(&self.iov_effect_indices, &self.iov_effect_names), + omega_iov, + residual_outputs: self + .residual_error_estimates + .iter() + .enumerate() + .map(|(estimate_index, estimate)| { + let components = residual_components( + estimate.model, + estimate.estimated, + estimate.combined_additive_estimated, + estimate.combined_proportional_estimated, + estimate.correlation_estimated, + ); + let initial_values = self.residual_initial_values[estimate_index].clone(); + let initial_estimated = self.residual_initial_estimated[estimate_index].clone(); + ParametricSourceResidual { + output: estimate.output.clone(), + output_index: estimate.output_index, + family: residual_family(estimate.model).to_string(), + components: components + .iter() + .map(|(name, _, _)| (*name).to_string()) + .collect(), + values: components.iter().map(|(_, value, _)| *value).collect(), + estimated_mask: components + .iter() + .map(|(_, _, estimated)| *estimated) + .collect(), + initial_values, + initial_estimated_mask: initial_estimated, + } + }) + .collect(), + covariate_effects: tables + .covariate_effects + .iter() + .map(|row| ParametricSourceCovariateEffect { + order: row.order, + name: row.name.clone(), + family: row.family.clone(), + parameter: row.parameter.clone(), + parameter_index: row.parameter_index, + covariate: row.covariate.clone(), + center: row.center, + reference: row.reference, + level: row.level, + initial: row.initial, + estimate: row.estimate, + estimated: row.estimated, + }) + .collect(), + subject_covariates: tables.subject_covariates.clone(), + subject_design: self + .covariates() + .map(|model| { + model + .subject_design() + .iter() + .enumerate() + .map(|(subject_index, row)| ParametricSourceSubjectDesign { + subject: row.subject().to_string(), + subject_index, + values: row.values().to_vec(), + }) + .collect() + }) + .unwrap_or_default(), + subject_population_parameters: tables.subject_population_parameters.clone(), + }) + } + + fn base_tables(&self) -> Result { + self.validate_output_metadata()?; + let population = self + .parameter_names + .iter() + .enumerate() + .map(|(index, name)| PopulationParameterRow { + name: name.clone(), + estimate: self.population_estimates[index], + scale: scale_text(self.parameter_scales[index]), + estimated: self.estimated_parameters[index], + iiv: self.random_effect_indices.contains(&index), + iov: self.iov_effect_indices.contains(&index), + }) + .collect(); + let omega = covariance_rows( + &self.random_effect_names, + &self.omega, + &self.omega_structural_mask, + &self.omega_estimated_mask, + "Omega", + )?; + let omega_iov = match ( + self.omega_iov.as_ref(), + self.omega_iov_structural_mask.as_ref(), + self.omega_iov_estimated_mask.as_ref(), + ) { + (Some(matrix), Some(structural), Some(estimated)) => Some(covariance_rows( + &self.iov_effect_names, + matrix, + structural, + estimated, + "Omega_IOV", + )?), + (None, None, None) => None, + _ => bail!("Omega_IOV matrix and masks must either all be present or all be absent"), + }; + let residual_error = residual_rows(&self.residual_error_estimates); + let individual_effects = self.individual_effect_rows()?; + let individual_parameters = self.individual_parameter_rows()?; + let iterations = self.iteration_rows(); + let mut statistics = self.statistic_rows()?; + if self.estimator_metadata().averaged_iterations > 0 { + for (name, value) in self.parameter_names.iter().zip(&self.population_estimates) { + statistics.push(statistic( + self.iterations, + "theta_final", + name, + None, + None, + None, + None, + *value, + )); + } + append_covariance_statistics( + &mut statistics, + self.iterations, + "omega_final", + &self.random_effect_names, + &self.omega, + )?; + if let Some(matrix) = self.omega_iov.as_ref() { + append_covariance_statistics( + &mut statistics, + self.iterations, + "omega_iov_final", + &self.iov_effect_names, + matrix, + )?; + } + for residual in &self.residual_error_estimates { + for component in residual_components( + residual.model, + residual.estimated, + residual.combined_additive_estimated, + residual.combined_proportional_estimated, + residual.correlation_estimated, + ) { + statistics.push(statistic( + self.iterations, + "residual_final", + &residual.output, + None, + None, + Some(residual.output_index), + Some(component.0), + component.1, + )); + } + } + } + marginal_likelihood_statistics( + self.iterations, + self.marginal_likelihood_diagnostics(), + &mut statistics, + ); + information_statistics( + self.iterations, + self.information_diagnostics(), + &mut statistics, + ); + n6_uncertainty_statistics(self, &mut statistics); + information_criteria_statistics( + self.iterations, + self.information_criteria(), + &mut statistics, + ); + markov_variance_statistics( + self.iterations, + self.markov_simulation_variance(), + &mut statistics, + ); + operational_convergence_statistics( + self.iterations, + self.operational_diagnostics(), + &mut statistics, + ); + let covariate_effects = self.covariate_effect_rows()?; + for effect in &covariate_effects { + statistics.push(StatisticRow { + cycle: self.iterations, + kind: "covariate_effect_final".to_string(), + name: effect.name.clone(), + row: None, + column: None, + output_index: None, + component: None, + value: Some(effect.estimate), + status: Some( + if effect.estimated { + "estimated" + } else { + "fixed" + } + .to_string(), + ), + }); + } + let subject_covariates = self.subject_covariate_rows(); + let subject_population_parameters = self.subject_population_parameter_rows()?; + Ok(ParametricResultTables { + population, + omega, + omega_iov, + residual_error, + individual_effects, + individual_parameters, + iterations, + statistics, + marginal_likelihood: marginal_likelihood_rows(self.marginal_likelihood_diagnostics()), + information_criteria: information_criteria_rows(self.information_criteria()), + predictions: Vec::new(), + covariate_effects, + subject_covariates, + subject_population_parameters, + }) + } + + fn covariate_effect_rows(&self) -> Result> { + let Some(model) = self.covariates() else { + return Ok(Vec::new()); + }; + model + .declarations() + .iter() + .zip(model.estimates()) + .enumerate() + .map(|(order, (declaration, estimate))| { + let (family, center, reference, level) = match declaration.family() { + CovariateEffectFamily::Continuous { center } => { + ("continuous", Some(center), None, None) + } + CovariateEffectFamily::Categorical { reference, level } => { + ("categorical", None, Some(reference), Some(level)) + } + }; + Ok(CovariateEffectRow { + order, + name: declaration.name(), + family: family.to_string(), + parameter: declaration.parameter().to_string(), + parameter_index: model.parameter_indices()[order], + covariate: declaration.covariate().to_string(), + center, + reference, + level, + initial: declaration + .initial() + .context("validated covariate declaration lacks initial coefficient")?, + estimate: estimate.estimate(), + estimated: estimate.estimated(), + }) + }) + .collect() + } + + fn subject_covariate_rows(&self) -> Vec { + let Some(model) = self.covariates() else { + return Vec::new(); + }; + let subject_indices = self + .data + .subjects() + .iter() + .enumerate() + .map(|(index, subject)| (subject.id().as_str(), index)) + .collect::>(); + model + .subject_values() + .iter() + .map(|row| SubjectCovariateRow { + subject: row.subject().to_string(), + subject_index: subject_indices[row.subject()], + covariate: row.covariate().to_string(), + value: row.value(), + }) + .collect() + } + + fn subject_population_parameter_rows(&self) -> Result> { + let Some(rows) = self + .covariate_subject_population_parameters() + .map_err(|error| anyhow::anyhow!(error))? + else { + return Ok(Vec::new()); + }; + let mut output = Vec::with_capacity(rows.len() * self.parameter_names.len()); + for (subject_index, row) in rows.iter().enumerate() { + for parameter_index in 0..self.parameter_names.len() { + output.push(SubjectPopulationParameterRow { + subject: row.subject().to_string(), + subject_index, + parameter: self.parameter_names[parameter_index].clone(), + parameter_index, + phi: row.phi()[parameter_index], + psi: row.psi()[parameter_index], + }); + } + } + Ok(output) + } + + fn validate_output_metadata(&self) -> Result<()> { + let width = self.parameter_names.len(); + if self.population_estimates.len() != width + || self.parameter_scales.len() != width + || self.estimated_parameters.len() != width + { + bail!("population parameter names, estimates, scales, and estimated flags must have equal lengths"); + } + validate_effect_indices( + &self.random_effect_indices, + &self.random_effect_names, + width, + "IIV", + )?; + validate_effect_indices( + &self.iov_effect_indices, + &self.iov_effect_names, + width, + "IOV", + )?; + if self.cycle_diagnostics.len() != self.iterations { + bail!( + "cycle diagnostic count {} does not match reported iteration count {}", + self.cycle_diagnostics.len(), + self.iterations + ); + } + Ok(()) + } + + fn individual_effect_rows(&self) -> Result> { + let mut rows = Vec::new(); + for eta in &self.eta_chain_means { + if eta.values.len() != self.random_effect_names.len() { + bail!( + "eta width for subject '{}' does not match IIV names", + eta.subject_id + ); + } + for (parameter, value) in self.random_effect_names.iter().zip(&eta.values) { + rows.push(IndividualEffectRow { + subject: eta.subject_id.clone(), + source: "chain_mean".to_string(), + effect_kind: "eta".to_string(), + parameter: parameter.clone(), + occasion: None, + value: *value, + mode_converged: None, + }); + } + } + for kappa in &self.kappa_chain_means { + if kappa.values.len() != self.iov_effect_names.len() { + bail!( + "kappa width for subject '{}' occasion {} does not match IOV names", + kappa.subject_id, + kappa.occasion_index + ); + } + for (parameter, value) in self.iov_effect_names.iter().zip(&kappa.values) { + rows.push(IndividualEffectRow { + subject: kappa.subject_id.clone(), + source: "chain_mean".to_string(), + effect_kind: "kappa".to_string(), + parameter: parameter.clone(), + occasion: Some(kappa.occasion_index), + value: *value, + mode_converged: None, + }); + } + } + for mode in &self.conditional_modes { + if mode.eta.len() != self.random_effect_names.len() { + bail!( + "conditional eta width for subject '{}' does not match IIV names", + mode.subject_id + ); + } + for (parameter, value) in self.random_effect_names.iter().zip(&mode.eta) { + rows.push(IndividualEffectRow { + subject: mode.subject_id.clone(), + source: "conditional_mode".to_string(), + effect_kind: "eta".to_string(), + parameter: parameter.clone(), + occasion: None, + value: *value, + mode_converged: Some(mode.converged), + }); + } + for kappa in &mode.kappas { + if kappa.subject_id != mode.subject_id + || kappa.values.len() != self.iov_effect_names.len() + { + bail!( + "conditional kappa metadata for subject '{}' is inconsistent", + mode.subject_id + ); + } + for (parameter, value) in self.iov_effect_names.iter().zip(&kappa.values) { + rows.push(IndividualEffectRow { + subject: mode.subject_id.clone(), + source: "conditional_mode".to_string(), + effect_kind: "kappa".to_string(), + parameter: parameter.clone(), + occasion: Some(kappa.occasion_index), + value: *value, + mode_converged: Some(mode.converged), + }); + } + } + } + Ok(rows) + } + + fn individual_parameter_rows(&self) -> Result> { + let subjects = self.data.subjects(); + if self.eta_chain_means.len() != subjects.len() { + bail!( + "eta chain-mean count {} does not match subject count {}", + self.eta_chain_means.len(), + subjects.len() + ); + } + + let subject_means = self + .covariate_subject_population_parameters() + .map_err(|error| anyhow::anyhow!(error))?; + if let Some(means) = &subject_means { + if means.len() != subjects.len() { + bail!( + "resolved subject population mean count {} does not match subject count {}", + means.len(), + subjects.len() + ); + } + } + + let mut rows = Vec::new(); + let mut chain_kappas = self.kappa_chain_means.iter(); + for (subject_index, (subject, eta)) in + subjects.iter().zip(&self.eta_chain_means).enumerate() + { + let subject_mu_phi = subject_means.as_ref().map(|means| &means[subject_index]); + if let Some(mean) = subject_mu_phi { + if mean.subject() != subject.id() { + bail!( + "resolved subject population mean '{}' does not match subject '{}'", + mean.subject(), + subject.id() + ); + } + } + validate_eta( + subject.id(), + eta.subject_id.as_str(), + eta.values.len(), + self.random_effect_indices.len(), + "chain mean", + )?; + if self.iov_effect_indices.is_empty() { + let values = if let Some(mean) = subject_mu_phi { + individual_psi_from_subject_mean( + mean.phi(), + &self.parameter_scales, + &self.random_effect_indices, + &eta.values, + )? + } else { + individual_psi( + &self.population_estimates, + &self.parameter_scales, + &self.random_effect_indices, + &eta.values, + )? + }; + push_parameter_rows( + &mut rows, + subject.id(), + None, + &self.parameter_names, + &values, + "chain_mean", + None, + )?; + } else { + for occasion in subject.occasions() { + let kappa = chain_kappas.next().with_context(|| { + format!( + "missing chain-mean kappa for subject '{}' occasion {}", + subject.id(), + occasion.index() + ) + })?; + validate_kappa( + subject.id(), + occasion.index(), + kappa, + self.iov_effect_indices.len(), + "chain mean", + )?; + let values = if let Some(mean) = subject_mu_phi { + occasion_psi_from_subject_mean( + mean.phi(), + &self.parameter_scales, + &self.random_effect_indices, + &eta.values, + &self.iov_effect_indices, + &kappa.values, + )? + } else { + occasion_psi( + &self.population_estimates, + &self.parameter_scales, + &self.random_effect_indices, + &eta.values, + &self.iov_effect_indices, + &kappa.values, + )? + }; + push_parameter_rows( + &mut rows, + subject.id(), + Some(occasion.index()), + &self.parameter_names, + &values, + "chain_mean", + None, + )?; + } + } + } + if let Some(extra) = chain_kappas.next() { + bail!( + "unexpected chain-mean kappa for subject '{}' occasion {}", + extra.subject_id, + extra.occasion_index + ); + } + + if !self.conditional_modes.is_empty() && self.conditional_modes.len() != subjects.len() { + bail!( + "conditional mode count {} does not match subject count {}", + self.conditional_modes.len(), + subjects.len() + ); + } + for (subject_index, (subject, mode)) in + subjects.iter().zip(&self.conditional_modes).enumerate() + { + let subject_mu_phi = subject_means.as_ref().map(|means| &means[subject_index]); + validate_eta( + subject.id(), + mode.subject_id.as_str(), + mode.eta.len(), + self.random_effect_indices.len(), + "conditional mode", + )?; + if self.iov_effect_indices.is_empty() { + if !mode.kappas.is_empty() { + bail!( + "conditional mode for non-IOV subject '{}' unexpectedly has kappas", + subject.id() + ); + } + let values = if let Some(mean) = subject_mu_phi { + individual_psi_from_subject_mean( + mean.phi(), + &self.parameter_scales, + &self.random_effect_indices, + &mode.eta, + )? + } else { + mode.parameters.clone() + }; + push_parameter_rows( + &mut rows, + subject.id(), + None, + &self.parameter_names, + &values, + "conditional_mode", + Some(mode.converged), + )?; + } else { + if mode.kappas.len() != subject.occasions().len() { + bail!( + "conditional mode for subject '{}' has {} kappas but retained data have {} occasions", + subject.id(), + mode.kappas.len(), + subject.occasions().len() + ); + } + for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { + validate_kappa( + subject.id(), + occasion.index(), + kappa, + self.iov_effect_indices.len(), + "conditional mode", + )?; + let values = if let Some(mean) = subject_mu_phi { + occasion_psi_from_subject_mean( + mean.phi(), + &self.parameter_scales, + &self.random_effect_indices, + &mode.eta, + &self.iov_effect_indices, + &kappa.values, + )? + } else { + occasion_psi( + &self.population_estimates, + &self.parameter_scales, + &self.random_effect_indices, + &mode.eta, + &self.iov_effect_indices, + &kappa.values, + )? + }; + push_parameter_rows( + &mut rows, + subject.id(), + Some(occasion.index()), + &self.parameter_names, + &values, + "conditional_mode", + Some(mode.converged), + )?; + } + } + } + Ok(rows) + } + + fn iteration_rows(&self) -> Vec { + self.cycle_diagnostics + .iter() + .map(|cycle| IterationRow { + cycle: cycle.iteration, + phase: phase_text(cycle.phase).to_string(), + conditional_n2ll: 2.0 * cycle.conditional_negative_log_likelihood, + sa_step: cycle.stochastic_approximation_step, + covariance_step: cycle.covariance_step, + eta_proposals: cycle.eta_proposals, + eta_accepted: cycle.eta_accepted, + eta_rejected: cycle.eta_rejected, + eta_nonfinite: cycle.eta_non_finite, + eta_block_proposals: cycle.eta_block_proposals, + eta_block_accepted: cycle.eta_block_accepted, + eta_block_rejected: cycle.eta_block_rejected, + eta_block_nonfinite: cycle.eta_block_non_finite, + kappa_proposals: cycle.kappa_proposals, + kappa_accepted: cycle.kappa_accepted, + kappa_rejected: cycle.kappa_rejected, + kappa_nonfinite: cycle.kappa_non_finite, + omega_update_rejected: cycle.omega_update_rejected, + omega_iov_update_rejected: cycle.omega_iov_update_rejected, + }) + .collect() + } + + fn statistic_rows(&self) -> Result> { + let mut rows = Vec::new(); + for cycle in &self.cycle_diagnostics { + if cycle.population_parameters.len() != self.parameter_names.len() { + bail!( + "population width in cycle {} is inconsistent", + cycle.iteration + ); + } + for (name, value) in self + .parameter_names + .iter() + .zip(&cycle.population_parameters) + { + rows.push(statistic( + cycle.iteration, + "theta", + name, + None, + None, + None, + None, + *value, + )); + } + match (&cycle.covariate_betas, &cycle.covariate_beta_estimated) { + (None, None) if self.covariates().is_none() => {} + (Some(values), Some(estimated)) + if values.len() == estimated.len() + && values.len() + == self.covariates().map_or(0, |model| model.estimates().len()) => + { + for (effect_index, value) in values.iter().enumerate() { + rows.push(StatisticRow { + cycle: cycle.iteration, + kind: "covariate_effect".to_string(), + name: self + .covariates() + .expect("covariate cycle metadata validated") + .estimates()[effect_index] + .name() + .to_string(), + row: None, + column: None, + output_index: None, + component: None, + value: Some(*value), + status: Some( + if estimated[effect_index] { + "estimated" + } else { + "fixed" + } + .to_string(), + ), + }); + } + } + _ => bail!( + "covariate coefficient diagnostics in cycle {} are inconsistent", + cycle.iteration + ), + } + append_covariance_statistics( + &mut rows, + cycle.iteration, + "omega", + &self.random_effect_names, + &cycle.omega, + )?; + if let Some(margin) = cycle.omega_relative_spd_margin { + rows.push(statistic( + cycle.iteration, + "covariance_stability", + "omega_relative_spd_margin", + None, + None, + None, + None, + margin, + )); + } + if let Some(matrix) = cycle.omega_iov.as_ref() { + append_covariance_statistics( + &mut rows, + cycle.iteration, + "omega_iov", + &self.iov_effect_names, + matrix, + )?; + } + if let Some(margin) = cycle.omega_iov_relative_spd_margin { + rows.push(statistic( + cycle.iteration, + "covariance_stability", + "omega_iov_relative_spd_margin", + None, + None, + None, + None, + margin, + )); + } + for residual in &cycle.residual_error_estimates { + for component in residual_components( + residual.model, + residual.estimated, + residual.combined_additive_estimated, + residual.combined_proportional_estimated, + residual.correlation_estimated, + ) { + rows.push(statistic( + cycle.iteration, + "residual", + &residual.output, + None, + None, + Some(residual.output_index), + Some(component.0), + component.1, + )); + } + } + } + Ok(rows) + } + + fn prediction_rows(&self, idelta: f64, tad: f64) -> Result> + where + E: pharmsol::equation::EquationTypes

, + { + let population = self.population_predictions(idelta, tad)?; + let conditional = + if self.random_effect_indices.is_empty() && self.iov_effect_indices.is_empty() { + Some((population.clone(), "population")) + } else if self.conditional_modes.is_empty() { + None + } else { + Some(( + self.conditional_predictions(idelta, tad)?, + "conditional_mode", + )) + }; + if population.len() != self.data.subjects().len() { + bail!("population prediction subject count does not match retained data"); + } + if let Some((conditional, _)) = conditional.as_ref() { + if conditional.len() != population.len() { + bail!("conditional and population prediction subject counts differ"); + } + } + let subjects = self.data.subjects(); + let mut rows = Vec::new(); + for (subject_index, predictions) in population.iter().enumerate() { + let subject = subjects + .get(subject_index) + .context("prediction subject index exceeds retained data")?; + let conditional_predictions = conditional.as_ref().map(|value| &value.0[subject_index]); + if let Some(other) = conditional_predictions { + if other.predictions().len() != predictions.predictions().len() { + bail!("prediction count mismatch for subject '{}'", subject.id()); + } + } + for (point_index, point) in predictions.predictions().iter().enumerate() { + let conditional_point = + conditional_predictions.map(|values| &values.predictions()[point_index]); + if let Some(other) = conditional_point { + validate_prediction_pair(subject.id(), point, other)?; + } + rows.push(PredictionRow { + subject: subject.id().clone(), + time: point.time(), + output_index: point.outeq(), + block: point.occasion(), + observation: point.observation(), + censoring: censor_text(point.censoring()).to_string(), + population_prediction: point.prediction(), + conditional_prediction: conditional_point.map(Prediction::prediction), + conditional_source: conditional.as_ref().map(|value| value.1.to_string()), + }); + } + } + Ok(rows) + } +} + +fn validate_effect_indices( + indices: &[usize], + names: &[String], + width: usize, + label: &str, +) -> Result<()> { + if indices.len() != names.len() { + bail!("{label} indices and names have different lengths"); + } + let mut seen = vec![false; width]; + for index in indices { + if *index >= width { + bail!("{label} parameter index {index} exceeds parameter width {width}"); + } + if seen[*index] { + bail!("{label} parameter index {index} is duplicated"); + } + seen[*index] = true; + } + Ok(()) +} + +fn boolean_mask_rows(mask: &ndarray::Array2) -> Vec> { + (0..mask.nrows()) + .map(|row| { + (0..mask.ncols()) + .map(|column| mask[[row, column]]) + .collect() + }) + .collect() +} + +fn numeric_matrix_rows(matrix: &ndarray::Array2) -> Vec> { + (0..matrix.nrows()) + .map(|row| { + (0..matrix.ncols()) + .map(|column| matrix[[row, column]]) + .collect() + }) + .collect() +} + +fn covariance_rows( + names: &[String], + matrix: &ndarray::Array2, + structural: &ndarray::Array2, + estimated: &ndarray::Array2, + label: &str, +) -> Result> { + let n = names.len(); + if matrix.dim() != (n, n) || structural.dim() != (n, n) || estimated.dim() != (n, n) { + bail!("{label} matrix and masks must be square with width {n}"); + } + let mut rows = Vec::new(); + for row in 0..n { + for column in 0..=row { + rows.push(OmegaRow { + row: names[row].clone(), + column: names[column].clone(), + estimate: matrix[[row, column]], + structural: structural[[row, column]], + estimated: estimated[[row, column]], + }); + } + } + Ok(rows) +} + +fn residual_rows(estimates: &[crate::results::ResidualErrorEstimate]) -> Vec { + let mut rows = Vec::new(); + for estimate in estimates { + let family = residual_family(estimate.model).to_string(); + for (component, value, estimated) in residual_components( + estimate.model, + estimate.estimated, + estimate.combined_additive_estimated, + estimate.combined_proportional_estimated, + estimate.correlation_estimated, + ) { + rows.push(ResidualErrorRow { + output: estimate.output.clone(), + output_index: estimate.output_index, + family: family.clone(), + component: component.to_string(), + estimate: value, + estimated, + }); + } + } + rows +} + +pub(crate) fn residual_components( + model: ResidualErrorModel, + estimated: bool, + additive_estimated: Option, + proportional_estimated: Option, + correlation_estimated: Option, +) -> Vec<(&'static str, f64, bool)> { + match model { + ResidualErrorModel::Constant { a } => vec![("sigma", a, estimated)], + ResidualErrorModel::Proportional { b } => vec![("proportional", b, estimated)], + ResidualErrorModel::Combined { a, b } => vec![ + ("additive", a, additive_estimated.unwrap_or(estimated)), + ( + "proportional", + b, + proportional_estimated.unwrap_or(estimated), + ), + ], + ResidualErrorModel::CorrelatedCombined { a, b, rho } => vec![ + ("additive", a, additive_estimated.unwrap_or(estimated)), + ( + "proportional", + b, + proportional_estimated.unwrap_or(estimated), + ), + ( + "correlation", + rho, + correlation_estimated.unwrap_or(estimated), + ), + ], + ResidualErrorModel::Exponential { sigma } => vec![("sigma", sigma, estimated)], + } +} + +fn residual_family(model: ResidualErrorModel) -> &'static str { + match model { + ResidualErrorModel::Constant { .. } => "constant", + ResidualErrorModel::Proportional { .. } => "proportional", + ResidualErrorModel::Combined { .. } => "combined", + ResidualErrorModel::CorrelatedCombined { .. } => "correlated_combined", + ResidualErrorModel::Exponential { .. } => "exponential", + } +} + +fn problem_from_tables( + equation: E, + data: Data, + tables: &ParametricResultTables, +) -> Result> +where + E: Equation + EquationMetadataSource, +{ + let mut names = std::collections::HashSet::new(); + let mut parameters = Vec::with_capacity(tables.population.len()); + let mut random_effect_names = Vec::new(); + let mut iov_membership_names = Vec::new(); + for row in &tables.population { + if row.name.is_empty() || !names.insert(row.name.as_str()) { + bail!("population parameter names must be non-empty and unique"); + } + if !row.estimate.is_finite() { + bail!("population estimate for '{}' must be finite", row.name); + } + let parameter = UnboundedParameter::new(row.name.clone(), parse_scale(&row.scale)?) + .with_initial(row.estimate) + .with_estimate(row.estimated) + .with_random_effect(row.iiv); + if row.iiv { + random_effect_names.push(row.name.clone()); + } + if row.iov { + iov_membership_names.push(row.name.clone()); + } + parameters.push(parameter); + } + if parameters.is_empty() { + bail!("warm-start population table must contain at least one parameter"); + } + + let omega = covariance_declaration(&tables.omega, &random_effect_names, "Omega")?; + let iov = match (&tables.omega_iov, iov_membership_names.is_empty()) { + (None, true) => None, + (Some(_), true) => bail!("Omega_IOV rows are present but no population parameter has IOV"), + (None, false) => bail!("IOV parameters are present but Omega_IOV rows are missing"), + (Some(rows), false) => { + let ordered_names = covariance_names(rows, &iov_membership_names, "Omega_IOV")?; + Some(iov_declaration(rows, &ordered_names)?) + } + }; + let residuals = residual_declarations(&tables.residual_error, &equation)?; + let covariate_effects = tables + .covariate_effects + .iter() + .enumerate() + .map(|(order, row)| { + if row.order != order + || !row.estimate.is_finite() + || row.parameter_index >= tables.population.len() + || row.parameter != tables.population[row.parameter_index].name + { + bail!("warm-start covariate effect metadata is malformed"); + } + let effect = match row.family.as_str() { + "continuous" => CovariateEffect::continuous( + row.parameter.clone(), + row.covariate.clone(), + row.center + .context("continuous covariate effect requires a center")?, + ), + "categorical" => CovariateEffect::categorical( + row.parameter.clone(), + row.covariate.clone(), + row.reference + .context("categorical covariate effect requires a reference")?, + row.level + .context("categorical covariate effect requires a level")?, + ), + family => bail!("unknown warm-start covariate effect family '{family}'"), + } + .with_initial(row.estimate); + Ok(if row.estimated { + effect + } else { + effect.fixed() + }) + }) + .collect::>>()?; + + let mut builder = EstimationProblem::parametric(equation, data) + .parameters(parameters) + .omega(omega) + .covariate_effects(covariate_effects); + if let Some(iov) = iov { + builder = builder.iov(iov); + } + for (output, declaration) in residuals { + builder = builder.error_model(output, declaration); + } + builder.build() +} + +fn covariance_names(rows: &[OmegaRow], membership: &[String], label: &str) -> Result> { + let expected_len = membership.len() * (membership.len() + 1) / 2; + if rows.len() != expected_len { + bail!( + "{label} lower-triangle row count {} does not match expected {expected_len}", + rows.len() + ); + } + let mut names = Vec::with_capacity(membership.len()); + let mut offset = 0; + for row_index in 0..membership.len() { + let diagonal = &rows[offset + row_index]; + if diagonal.row != diagonal.column + || !membership.iter().any(|name| name == &diagonal.row) + || names.iter().any(|name| name == &diagonal.row) + { + bail!( + "{label} diagonal row {} must name one unique declared effect", + offset + row_index + ); + } + names.push(diagonal.row.clone()); + offset += row_index + 1; + } + Ok(names) +} + +fn covariance_declaration(rows: &[OmegaRow], names: &[String], label: &str) -> Result { + let expected_len = names.len() * (names.len() + 1) / 2; + if rows.len() != expected_len { + bail!( + "{label} lower-triangle row count {} does not match expected {expected_len}", + rows.len() + ); + } + let mut omega = Omega::new(); + let mut offset = 0; + for row_index in 0..names.len() { + for column_index in 0..=row_index { + let row = &rows[offset]; + offset += 1; + if row.row != names[row_index] || row.column != names[column_index] { + bail!( + "{label} row {} must be lower-triangle entry ('{}', '{}'), found ('{}', '{}')", + offset - 1, + names[row_index], + names[column_index], + row.row, + row.column + ); + } + if !row.estimate.is_finite() { + bail!( + "{label} entry ('{}', '{}') must be finite", + row.row, + row.column + ); + } + if !row.structural { + if row.estimated || row.estimate != 0.0 { + bail!( + "non-structural {label} entry ('{}', '{}') must be zero and not estimated", + row.row, + row.column + ); + } + continue; + } + if row_index == column_index && row.estimate <= 0.0 { + bail!("{label} variance for '{}' must be positive", row.row); + } + omega = match (row_index == column_index, row.estimated) { + (true, true) => omega.variance(row.row.clone(), row.estimate), + (true, false) => omega.fixed_variance(row.row.clone(), row.estimate), + (false, true) => { + omega.covariance(row.row.clone(), row.column.clone(), row.estimate) + } + (false, false) => { + omega.fixed_covariance(row.row.clone(), row.column.clone(), row.estimate) + } + }; + } + } + Ok(omega) +} + +fn iov_declaration(rows: &[OmegaRow], names: &[String]) -> Result { + covariance_declaration(rows, names, "Omega_IOV")?; + let mut iov = Iov::new(); + for row in rows.iter().filter(|row| row.structural) { + let diagonal = row.row == row.column; + iov = match (diagonal, row.estimated) { + (true, true) => iov.variance(row.row.clone(), row.estimate), + (true, false) => iov.fixed_variance(row.row.clone(), row.estimate), + (false, true) => iov.covariance(row.row.clone(), row.column.clone(), row.estimate), + (false, false) => { + iov.fixed_covariance(row.row.clone(), row.column.clone(), row.estimate) + } + }; + } + Ok(iov) +} + +fn residual_declarations( + rows: &[ResidualErrorRow], + equation: &E, +) -> Result> +where + E: Equation + EquationMetadataSource, +{ + if rows.is_empty() { + bail!("warm-start residual-error table must not be empty"); + } + let metadata = equation + .equation_metadata() + .context("warm-start equation must provide output metadata")?; + let mut declarations = Vec::new(); + let mut output_indices = std::collections::HashSet::new(); + let mut offset = 0; + while offset < rows.len() { + let first = &rows[offset]; + if first.output.is_empty() { + bail!("residual output {} has an empty name", first.output_index); + } + if !output_indices.insert(first.output_index) { + bail!( + "residual output index {} is declared more than once", + first.output_index + ); + } + let family = first.family.as_str(); + let component_count = match family { + "combined" => 2, + "correlated_combined" => 3, + _ => 1, + }; + if offset + component_count > rows.len() { + bail!("residual output '{}' has missing components", first.output); + } + let group = &rows[offset..offset + component_count]; + for row in group { + let expected_output = metadata.outputs().get(row.output_index).with_context(|| { + format!( + "residual output index {} exceeds equation output metadata", + row.output_index + ) + })?; + if expected_output.name() != row.output { + bail!( + "residual output index {} is named '{}' in the table but '{}' in equation metadata", + row.output_index, + row.output, + expected_output.name() + ); + } + } + if group.iter().any(|row| { + row.output_index != first.output_index + || row.output != first.output + || row.family != first.family + || !row.estimate.is_finite() + }) { + bail!( + "residual output '{}' must have one coherent family with finite components", + first.output + ); + } + let declaration = match family { + "constant" if group[0].component == "sigma" && group[0].estimate > 0.0 => { + ParametricErrorModel::new(ResidualErrorModel::constant(group[0].estimate)) + .with_estimate(group[0].estimated) + } + "proportional" + if group[0].component == "proportional" && group[0].estimate > 0.0 => + { + ParametricErrorModel::new(ResidualErrorModel::proportional(group[0].estimate)) + .with_estimate(group[0].estimated) + } + "exponential" if group[0].component == "sigma" && group[0].estimate > 0.0 => { + ParametricErrorModel::new(ResidualErrorModel::exponential(group[0].estimate)) + .with_estimate(group[0].estimated) + } + "combined" + if group[0].component == "additive" + && group[1].component == "proportional" + && group.iter().all(|row| row.estimate >= 0.0) + && group.iter().all(|row| !row.estimated || row.estimate > 0.0) + && group.iter().any(|row| row.estimate > 0.0) => + { + ParametricErrorModel::new(ResidualErrorModel::combined( + group[0].estimate, + group[1].estimate, + )) + .with_combined_additive_estimate(group[0].estimated) + .with_combined_proportional_estimate(group[1].estimated) + } + "correlated_combined" + if group[0].component == "additive" + && group[1].component == "proportional" + && group[2].component == "correlation" + && group[0].estimate > 0.0 + && group[1].estimate > 0.0 + && group[2].estimate > -1.0 + && group[2].estimate < 1.0 => + { + ParametricErrorModel::new(ResidualErrorModel::correlated_combined( + group[0].estimate, + group[1].estimate, + group[2].estimate, + )) + .with_correlated_combined_additive_estimate(group[0].estimated) + .with_correlated_combined_proportional_estimate(group[1].estimated) + .with_correlated_combined_correlation_estimate(group[2].estimated) + } + "constant" | "proportional" | "exponential" => bail!( + "residual output '{}' requires a finite strictly positive component for family '{}'", + first.output, + family + ), + "combined" => bail!( + "residual output '{}' requires finite non-negative combined components, estimated components must be positive, and both components cannot be zero", + first.output + ), + "correlated_combined" => bail!( + "residual output '{}' requires finite positive additive/proportional components and correlation strictly inside (-1, 1)", + first.output + ), + _ => bail!("unknown residual-error family '{family}'"), + }; + declarations.push((first.output.clone(), declaration)); + offset += component_count; + } + Ok(declarations) +} + +fn parse_scale(text: &str) -> Result { + match text { + "identity" => Ok(ParameterScale::Identity), + "log" => Ok(ParameterScale::Log), + _ => { + let (scale, bounds) = text + .split_once('(') + .and_then(|(scale, rest)| rest.strip_suffix(')').map(|bounds| (scale, bounds))) + .ok_or_else(|| anyhow::anyhow!("unknown parameter scale '{text}'"))?; + let (lower, upper) = bounds + .split_once(',') + .ok_or_else(|| anyhow::anyhow!("invalid parameter scale '{text}'"))?; + let lower = lower + .parse::() + .with_context(|| format!("invalid lower bound in parameter scale '{text}'"))?; + let upper = upper + .parse::() + .with_context(|| format!("invalid upper bound in parameter scale '{text}'"))?; + if !lower.is_finite() || !upper.is_finite() || lower >= upper { + bail!("invalid finite ordered bounds in parameter scale '{text}'"); + } + match scale { + "logit" => Ok(ParameterScale::Logit { lower, upper }), + "probit" => Ok(ParameterScale::Probit { lower, upper }), + _ => bail!("unknown parameter scale '{text}'"), + } + } + } +} + +fn validate_eta( + expected_subject: &str, + actual_subject: &str, + actual_width: usize, + expected_width: usize, + source: &str, +) -> Result<()> { + if actual_subject != expected_subject { + bail!( + "{source} eta subject '{}' does not match retained subject '{}'", + actual_subject, + expected_subject + ); + } + if actual_width != expected_width { + bail!( + "{source} eta for subject '{}' has width {} but expected {}", + expected_subject, + actual_width, + expected_width + ); + } + Ok(()) +} + +fn validate_kappa( + expected_subject: &str, + expected_occasion: usize, + kappa: &crate::results::OccasionKappaEstimate, + expected_width: usize, + source: &str, +) -> Result<()> { + if kappa.subject_id != expected_subject { + bail!( + "{source} kappa subject '{}' does not match retained subject '{}'", + kappa.subject_id, + expected_subject + ); + } + if kappa.occasion_index != expected_occasion { + bail!( + "{source} kappa occasion {} does not match retained occasion {} for subject '{}'", + kappa.occasion_index, + expected_occasion, + expected_subject + ); + } + if kappa.values.len() != expected_width { + bail!( + "{source} kappa for subject '{}' occasion {} has width {} but expected {}", + expected_subject, + expected_occasion, + kappa.values.len(), + expected_width + ); + } + Ok(()) +} + +fn push_parameter_rows( + rows: &mut Vec, + subject: &str, + occasion: Option, + names: &[String], + values: &[f64], + source: &str, + converged: Option, +) -> Result<()> { + if names.len() != values.len() { + bail!("individual parameter width for subject '{subject}' does not match parameter names"); + } + for (parameter, value) in names.iter().zip(values) { + rows.push(IndividualParameterRow { + subject: subject.to_string(), + occasion, + parameter: parameter.clone(), + value: *value, + source: source.to_string(), + mode_converged: converged, + }); + } + Ok(()) +} + +fn append_covariance_statistics( + rows: &mut Vec, + cycle: usize, + kind: &str, + names: &[String], + matrix: &ndarray::Array2, +) -> Result<()> { + let n = names.len(); + if matrix.dim() != (n, n) { + bail!("{kind} width in cycle {cycle} is inconsistent"); + } + for row in 0..n { + for column in 0..=row { + rows.push(statistic( + cycle, + kind, + "", + Some(names[row].clone()), + Some(names[column].clone()), + None, + None, + matrix[[row, column]], + )); + } + } + Ok(()) +} + +fn marginal_likelihood_rows( + diagnostics: Option<&MarginalLikelihoodDiagnostics>, +) -> Vec { + let Some(diagnostics) = diagnostics else { + return vec![MarginalLikelihoodRow { + scope: "total".to_string(), + subject: None, + method: "disabled".to_string(), + status: "disabled".to_string(), + samples_per_subject: 0, + seed: None, + degrees_of_freedom: 0, + covariance_scale_multiplier: 0.0, + proposal_scale_source: "not_applicable".to_string(), + dimension: 0, + occasion_indices: "[]".to_string(), + mode: "[]".to_string(), + mode_converged: None, + log_marginal_likelihood: None, + n2ll: None, + n2ll_mcse: None, + effective_sample_size: None, + effective_sample_fraction: None, + zero_weight_count: 0, + failure: None, + }]; + }; + let status = marginal_status_text(&diagnostics.status); + let mut rows = vec![MarginalLikelihoodRow { + scope: "total".to_string(), + subject: None, + method: "population".to_string(), + status: status.clone(), + samples_per_subject: diagnostics.config.samples_per_subject, + seed: Some(diagnostics.config.seed), + degrees_of_freedom: diagnostics.config.degrees_of_freedom, + covariance_scale_multiplier: diagnostics.config.covariance_scale_multiplier, + proposal_scale_source: "mixed_by_subject".to_string(), + dimension: diagnostics + .subjects + .iter() + .map(|subject| subject.dimension) + .sum(), + occasion_indices: "[]".to_string(), + mode: "[]".to_string(), + mode_converged: None, + log_marginal_likelihood: diagnostics.log_marginal_likelihood, + n2ll: diagnostics.n2ll, + n2ll_mcse: diagnostics.n2ll_mcse, + effective_sample_size: None, + effective_sample_fraction: None, + zero_weight_count: diagnostics + .subjects + .iter() + .map(|subject| subject.zero_weight_count) + .sum(), + failure: match &diagnostics.status { + MarginalLikelihoodStatus::Unavailable { failures } => { + serde_json::to_string(failures).ok() + } + _ => None, + }, + }]; + rows.extend(diagnostics.subjects.iter().map(|subject| { + MarginalLikelihoodRow { + scope: "subject".to_string(), + subject: Some(subject.subject_id.clone()), + method: marginal_method_text(subject.method).to_string(), + status: if subject.failure.is_some() { + "unavailable".to_string() + } else if subject.mode_converged == Some(false) { + "available_with_nonconverged_mode".to_string() + } else { + "available".to_string() + }, + samples_per_subject: subject.samples, + seed: subject.seed, + degrees_of_freedom: diagnostics.config.degrees_of_freedom, + covariance_scale_multiplier: diagnostics.config.covariance_scale_multiplier, + proposal_scale_source: proposal_scale_source_text(subject.proposal_scale_source) + .to_string(), + dimension: subject.dimension, + occasion_indices: serde_json::to_string(&subject.occasion_indices) + .expect("occasion indices serialize"), + mode: serde_json::to_string(&subject.mode).expect("mode coordinates serialize"), + mode_converged: subject.mode_converged, + log_marginal_likelihood: subject.log_marginal_likelihood, + n2ll: subject.n2ll, + n2ll_mcse: subject.n2ll_mcse, + effective_sample_size: subject.effective_sample_size, + effective_sample_fraction: subject.effective_sample_fraction, + zero_weight_count: subject.zero_weight_count, + failure: subject + .failure + .as_ref() + .and_then(|reason| serde_json::to_string(reason).ok()), + } + })); + rows +} + +fn marginal_likelihood_statistics( + iterations: usize, + diagnostics: Option<&MarginalLikelihoodDiagnostics>, + rows: &mut Vec, +) { + let status = diagnostics + .map(|diagnostics| marginal_status_text(&diagnostics.status)) + .unwrap_or_else(|| "disabled".to_string()); + rows.push(StatisticRow { + cycle: iterations, + kind: "marginal_likelihood_status".to_string(), + name: "population".to_string(), + row: None, + column: None, + output_index: None, + component: None, + value: None, + status: Some(status.clone()), + }); + let Some(diagnostics) = diagnostics else { + return; + }; + for (name, value) in [ + ( + "log_marginal_likelihood", + diagnostics.log_marginal_likelihood, + ), + ("marginal_n2ll", diagnostics.n2ll), + ("marginal_n2ll_mcse", diagnostics.n2ll_mcse), + ] { + rows.push(StatisticRow { + cycle: iterations, + kind: "marginal_likelihood".to_string(), + name: name.to_string(), + row: None, + column: None, + output_index: None, + component: None, + value, + status: Some(status.clone()), + }); + } + for subject in &diagnostics.subjects { + rows.push(StatisticRow { + cycle: iterations, + kind: "marginal_likelihood_subject_status".to_string(), + name: subject.subject_id.clone(), + row: None, + column: None, + output_index: None, + component: None, + value: subject.n2ll, + status: Some(if subject.failure.is_some() { + "unavailable".to_string() + } else { + "available".to_string() + }), + }); + } +} + +fn marginal_status_text(status: &MarginalLikelihoodStatus) -> String { + match status { + MarginalLikelihoodStatus::Available => "available", + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { .. } => { + "available_with_nonconverged_modes" + } + MarginalLikelihoodStatus::Unavailable { .. } => "unavailable", + } + .to_string() +} + +fn marginal_method_text(method: MarginalLikelihoodMethod) -> &'static str { + match method { + MarginalLikelihoodMethod::ExactNoLatent => "exact_no_latent", + MarginalLikelihoodMethod::StudentTImportanceSampling => "student_t_importance_sampling", + } +} + +fn proposal_scale_source_text(source: ProposalScaleSource) -> &'static str { + match source { + ProposalScaleSource::FinalRawOmegaBlocks => "final_raw_omega_blocks", + ProposalScaleSource::ConditionalModeCurvature => "conditional_mode_curvature", + ProposalScaleSource::NotApplicableNoLatent => "not_applicable_no_latent", + } +} + +fn information_criteria_rows( + diagnostics: &InformationCriteriaDiagnostics, +) -> Vec { + vec![InformationCriteriaRow { + status: information_criteria_status_text(&diagnostics.status).to_string(), + sample_size_convention: information_criteria_convention_text( + diagnostics.sample_size_convention, + ) + .to_string(), + subject_count: diagnostics.subject_count, + population_parameter_count: diagnostics.parameter_count.population, + covariate_parameter_count: diagnostics.parameter_count.covariate, + omega_parameter_count: diagnostics.parameter_count.omega, + omega_iov_parameter_count: diagnostics.parameter_count.omega_iov, + residual_parameter_count: diagnostics.parameter_count.residual, + free_parameter_count: diagnostics.parameter_count.total, + source_marginal_n2ll: diagnostics.source_marginal_n2ll, + source_marginal_n2ll_mcse: diagnostics.source_marginal_n2ll_mcse, + aic: diagnostics.aic, + bic: diagnostics.bic, + aic_mcse: diagnostics.aic_mcse, + bic_mcse: diagnostics.bic_mcse, + failure_reason: match &diagnostics.status { + InformationCriteriaStatus::Unavailable { reason } => serde_json::to_string(reason).ok(), + _ => None, + }, + }] +} + +fn information_criteria_statistics( + iterations: usize, + diagnostics: &InformationCriteriaDiagnostics, + rows: &mut Vec, +) { + let status = information_criteria_status_text(&diagnostics.status).to_string(); + rows.push(StatisticRow { + cycle: iterations, + kind: "information_criteria_status".to_string(), + name: "population".to_string(), + row: None, + column: None, + output_index: None, + component: None, + value: None, + status: Some(status.clone()), + }); + for (name, value) in [ + ("source_marginal_n2ll", diagnostics.source_marginal_n2ll), + ( + "source_marginal_n2ll_mcse", + diagnostics.source_marginal_n2ll_mcse, + ), + ("aic", diagnostics.aic), + ("bic", diagnostics.bic), + ("aic_mcse", diagnostics.aic_mcse), + ("bic_mcse", diagnostics.bic_mcse), + ] { + rows.push(StatisticRow { + cycle: iterations, + kind: "information_criteria".to_string(), + name: name.to_string(), + row: None, + column: None, + output_index: None, + component: None, + value, + status: Some(status.clone()), + }); + } + for (name, value) in [ + ( + "population_parameter_count", + diagnostics.parameter_count.population, + ), + ("omega_parameter_count", diagnostics.parameter_count.omega), + ( + "omega_iov_parameter_count", + diagnostics.parameter_count.omega_iov, + ), + ( + "residual_parameter_count", + diagnostics.parameter_count.residual, + ), + ("free_parameter_count", diagnostics.parameter_count.total), + ("independent_subject_count", diagnostics.subject_count), + ] { + rows.push(StatisticRow { + cycle: iterations, + kind: "information_criteria_metadata".to_string(), + name: name.to_string(), + row: None, + column: None, + output_index: None, + component: None, + value: Some(value as f64), + status: Some(status.clone()), + }); + } +} + +fn information_criteria_status_text(status: &InformationCriteriaStatus) -> &'static str { + match status { + InformationCriteriaStatus::NotRequested => "not_requested", + InformationCriteriaStatus::Available => "available", + InformationCriteriaStatus::AvailableWithNonconvergedModes { .. } => { + "available_with_nonconverged_modes" + } + InformationCriteriaStatus::Unavailable { .. } => "unavailable", + } +} + +fn information_criteria_convention_text( + convention: InformationCriteriaSampleSizeConvention, +) -> &'static str { + match convention { + InformationCriteriaSampleSizeConvention::IndependentSubjects => "independent_subjects", + } +} + +fn n6_uncertainty_statistic_rows( + cycle: usize, + population: &PopulationUncertaintyDiagnostics, + conditional_modes: &[SubjectConditionalMode], + shrinkage: &ShrinkageDiagnostics, +) -> Vec { + let mut rows = Vec::new(); + let population_status = serde_json::to_string(&population.status) + .unwrap_or_else(|_| "population_uncertainty_status_unserializable".to_string()); + rows.push(StatisticRow { + cycle, + kind: "population_uncertainty_status".into(), + name: "observed_information_inverse".into(), + row: None, + column: None, + output_index: None, + component: None, + value: None, + status: Some(population_status.clone()), + }); + rows.push(StatisticRow { + cycle, + kind: "population_uncertainty_regularization".into(), + name: "regularization".into(), + row: None, + column: None, + output_index: None, + component: None, + value: None, + status: Some("none".into()), + }); + if let Some(covariance) = population.free_covariance.as_ref() { + for (row_index, row_values) in covariance.iter().enumerate() { + for (column_index, value) in row_values.iter().enumerate() { + rows.push(StatisticRow { + cycle, + kind: "population_uncertainty_covariance_phi".into(), + name: "free_coordinate_covariance".into(), + row: population + .coordinates + .get(row_index) + .map(|coordinate| coordinate.name.clone()), + column: population + .coordinates + .get(column_index) + .map(|coordinate| coordinate.name.clone()), + output_index: None, + component: None, + value: Some(*value), + status: Some(population_status.clone()), + }); + } + } + } + if let Some(condition) = population.spectral_condition_number { + rows.push(StatisticRow { + cycle, + kind: "population_uncertainty_condition".into(), + name: "spectral_condition_number".into(), + row: None, + column: None, + output_index: None, + component: None, + value: Some(condition), + status: Some(population_status.clone()), + }); + } + if let Some(standard_errors) = population.free_standard_errors.as_ref() { + for (coordinate, standard_error) in population.coordinates.iter().zip(standard_errors) { + rows.push(StatisticRow { + cycle, + kind: "population_uncertainty_se_phi".into(), + name: coordinate.name.clone(), + row: None, + column: None, + output_index: None, + component: None, + value: Some(*standard_error), + status: Some(population_status.clone()), + }); + } + } + + for mode in conditional_modes { + let diagnostics = &mode.uncertainty; + let status = serde_json::to_string(&diagnostics.status) + .unwrap_or_else(|_| "conditional_curvature_status_unserializable".to_string()); + rows.push(StatisticRow { + cycle, + kind: "conditional_curvature_status".into(), + name: mode.subject_id.clone(), + row: None, + column: None, + output_index: None, + component: None, + value: None, + status: Some(status.clone()), + }); + rows.push(StatisticRow { + cycle, + kind: "conditional_curvature_regularization".into(), + name: mode.subject_id.clone(), + row: None, + column: None, + output_index: None, + component: None, + value: None, + status: Some("none".into()), + }); + for (coordinate, step) in diagnostics + .coordinates + .iter() + .zip(&diagnostics.finite_difference_steps) + { + rows.push(StatisticRow { + cycle, + kind: "conditional_curvature_step".into(), + name: mode.subject_id.clone(), + row: Some(coordinate.name.clone()), + column: None, + output_index: None, + component: None, + value: Some(*step), + status: Some(status.clone()), + }); + } + if let Some(standard_errors) = diagnostics.latent_standard_errors.as_ref() { + for (coordinate, standard_error) in diagnostics.coordinates.iter().zip(standard_errors) + { + rows.push(StatisticRow { + cycle, + kind: "conditional_latent_se".into(), + name: mode.subject_id.clone(), + row: Some(coordinate.name.clone()), + column: None, + output_index: None, + component: None, + value: Some(*standard_error), + status: Some(status.clone()), + }); + } + } + for (kind, matrix) in [ + ("conditional_hessian", diagnostics.hessian.as_ref()), + ( + "conditional_latent_covariance", + diagnostics.latent_covariance.as_ref(), + ), + ] { + if let Some(matrix) = matrix { + for (row_index, row_values) in matrix.iter().enumerate() { + for (column_index, value) in row_values.iter().enumerate() { + rows.push(StatisticRow { + cycle, + kind: kind.into(), + name: mode.subject_id.clone(), + row: diagnostics + .coordinates + .get(row_index) + .map(|coordinate| coordinate.name.clone()), + column: diagnostics + .coordinates + .get(column_index) + .map(|coordinate| coordinate.name.clone()), + output_index: None, + component: None, + value: Some(*value), + status: Some(status.clone()), + }); + } + } + } + } + if let Some(condition) = diagnostics.spectral_condition_number { + rows.push(StatisticRow { + cycle, + kind: "conditional_curvature_condition".into(), + name: mode.subject_id.clone(), + row: None, + column: None, + output_index: None, + component: None, + value: Some(condition), + status: Some(status), + }); + } + } + + let mut push_shrinkage = + |kind: &str, effect: &str, value: &crate::estimation::parametric::ShrinkageValue| { + let (numeric, status) = match value { + crate::estimation::parametric::ShrinkageValue::Available { value, .. } => { + (Some(*value), "available".to_string()) + } + crate::estimation::parametric::ShrinkageValue::Unavailable { reason } => ( + None, + serde_json::to_string(reason) + .unwrap_or_else(|_| "shrinkage_status_unserializable".to_string()), + ), + }; + rows.push(StatisticRow { + cycle, + kind: kind.into(), + name: effect.into(), + row: None, + column: None, + output_index: None, + component: None, + value: numeric, + status: Some(status), + }); + }; + for value in &shrinkage.eta_posterior_mean { + push_shrinkage( + "eta_shrinkage_posterior_mean", + &value.effect, + &value.shrinkage, + ); + } + for value in &shrinkage.eta_map { + push_shrinkage("eta_shrinkage_map", &value.effect, &value.shrinkage); + } + for value in &shrinkage.kappa_posterior_mean { + push_shrinkage( + "kappa_shrinkage_posterior_mean", + &value.effect, + &value.shrinkage, + ); + } + for value in &shrinkage.kappa_map { + push_shrinkage("kappa_shrinkage_map", &value.effect, &value.shrinkage); + } + rows +} + +fn n6_uncertainty_statistics( + result: &ParametricResult, + rows: &mut Vec, +) { + rows.extend(n6_uncertainty_statistic_rows( + result.iterations(), + result.population_uncertainty(), + result.conditional_modes(), + result.shrinkage(), + )); +} + +fn information_statistics( + iterations: usize, + diagnostics: &InformationDiagnostics, + rows: &mut Vec, +) { + let status = information_status_text(&diagnostics.status); + for (index, value) in diagnostics.delta.iter().enumerate() { + let name = diagnostics + .coordinates + .get(index) + .map(|coordinate| coordinate.name.as_str()) + .unwrap_or(""); + let mut row = statistic( + iterations, + "information_delta", + name, + None, + None, + None, + None, + *value, + ); + row.status = Some(status.clone()); + rows.push(row); + } + for (kind, matrix) in [ + ("information_g", &diagnostics.g), + ( + "information_complete_hessian", + &diagnostics.expected_complete_hessian, + ), + ( + "information_observed_hessian", + &diagnostics.observed_hessian, + ), + ("observed_information", &diagnostics.observed_information), + ] { + for (row_index, values) in matrix.iter().enumerate() { + for (column_index, value) in values.iter().enumerate() { + let row_name = diagnostics + .coordinates + .get(row_index) + .map(|coordinate| coordinate.name.clone()); + let column_name = diagnostics + .coordinates + .get(column_index) + .map(|coordinate| coordinate.name.clone()); + let mut row = statistic( + iterations, + kind, + "", + row_name, + column_name, + None, + None, + *value, + ); + row.status = Some(status.clone()); + rows.push(row); + } + } + } +} + +fn markov_variance_statistics( + iterations: usize, + diagnostics: &MarkovSimulationVarianceDiagnostics, + rows: &mut Vec, +) { + push_markov_metadata( + iterations, + "markov_status", + "aggregate", + 0.0, + &diagnostics.status, + rows, + ); + push_markov_metadata( + iterations, + "markov_chain_count", + "retained_chains", + diagnostics.chain_count as f64, + &diagnostics.status, + rows, + ); + push_markov_metadata( + iterations, + "markov_n_avg", + "averaged_iterations", + diagnostics.n_avg as f64, + &diagnostics.status, + rows, + ); + if let Some(config) = diagnostics.config { + for (name, value) in [ + ("seed", config.seed as f64), + ("warmup_transitions", config.warmup_transitions as f64), + ("draws_per_chain", config.draws_per_chain as f64), + ("batch_size", config.batch_size as f64), + ("lugsail_r", config.lugsail.r as f64), + ("lugsail_c", config.lugsail.c), + ("diagnostic_chains", config.diagnostic_chains as f64), + ("max_trace_bytes", config.max_trace_bytes as f64), + ] { + push_markov_metadata( + iterations, + "markov_config", + name, + value, + &diagnostics.status, + rows, + ); + } + } + for chain in &diagnostics.chains { + let chain_name = format!("chain_{}", chain.chain); + for (name, value) in [ + ("retained_proposals", chain.proposals), + ("retained_accepts", chain.accepts), + ("retained_state_changes", chain.state_changes), + ] { + push_markov_metadata( + iterations, + "markov_chain_count", + &format!("{chain_name}_{name}"), + value as f64, + &chain.status, + rows, + ); + } + let chain_status = markov_variance_status_text(&chain.status); + for (kind, matrix) in [ + ("markov_bm_batch", &chain.bm_batch), + ("markov_bm_batch_over_r", &chain.bm_batch_over_r), + ("markov_lugsail_lrv", &chain.lugsail_lrv), + ] { + append_markov_matrix( + iterations, + kind, + &chain_name, + matrix, + &diagnostics.coordinates, + &chain_status, + rows, + ); + } + } + for (kind, matrix, matrix_status) in [ + ( + "markov_combined_lambda", + &diagnostics.lambda, + &diagnostics.lambda_status, + ), + ("markov_xi", &diagnostics.xi, &diagnostics.xi_status), + ( + "markov_xi_over_n_avg", + &diagnostics.simulation_covariance, + &diagnostics.simulation_covariance_status, + ), + ] { + append_markov_matrix( + iterations, + kind, + "", + matrix, + &diagnostics.coordinates, + &markov_variance_status_text(matrix_status), + rows, + ); + } + + // ── Rank/mixing diagnostic statistics ── + { + let rank = &diagnostics.rank_diagnostics; + // Always emit aggregate metadata even when disabled. + let rank_status_text = rank_diagnostic_status_text(&rank.status); + for (name, value) in [ + ("rank_status", 0.0), + ("diagnostic_chains", rank.diagnostic_chains as f64), + ("fit_chains", rank.original_chains as f64), + ("draws_per_chain", rank.draws_per_chain as f64), + ("max_trace_bytes", rank.max_trace_bytes as f64), + ( + "accounted_peak_trace_bytes_required", + rank.accounted_peak_trace_bytes_required as f64, + ), + ( + "accounted_peak_trace_bytes_used", + rank.accounted_peak_trace_bytes_used as f64, + ), + ] { + push_rank_metadata( + iterations, + "markov_rank_status", + name, + value, + &rank.status, + rows, + ); + } + for (name, value) in [ + ("worst_rhat", rank.worst_rhat), + ("min_bulk_ess", rank.min_bulk_ess), + ( + "min_avg_ess_per_split_chain", + rank.min_avg_ess_per_split_chain, + ), + ] { + if let Some(value) = value { + push_rank_metadata( + iterations, + "markov_rank_status", + name, + value, + &rank.status, + rows, + ); + } + } + + // Per-chain LRV status is always emitted at the configured chain + // index; matrix rows follow only when that same chain has a matrix. + for chain_idx in 0..rank.diagnostic_chains { + let chain_name = format!("diagnostic_chain_{chain_idx}"); + let chain_status = rank + .lrv_chain_statuses + .get(chain_idx) + .cloned() + .unwrap_or(RankDiagnosticStatus::Unavailable); + push_rank_metadata( + iterations, + "markov_rank_lrv_chain_status", + &chain_name, + 0.0, + &chain_status, + rows, + ); + if let Some(Some(lrv_matrix)) = rank.lrv_per_chain.get(chain_idx) { + append_markov_matrix( + iterations, + "markov_rank_lrv_per_chain", + &chain_name, + lrv_matrix, + &diagnostics.coordinates, + rank_diagnostic_status_text(&chain_status), + rows, + ); + } + } + + // Diagnostic-mean LRV matrix. + if let Some(ref lrv) = rank.diagnostic_mean_lrv { + append_markov_matrix( + iterations, + "markov_rank_lrv_diagnostic_mean", + "", + lrv, + &diagnostics.coordinates, + rank_status_text, + rows, + ); + } + // Operational LRV matrix. + if let Some(ref lrv) = rank.operational_lrv { + append_markov_matrix( + iterations, + "markov_rank_lrv_operational", + "", + lrv, + &diagnostics.coordinates, + rank_status_text, + rows, + ); + } + + // Per-coordinate rank diagnostics. Use each trace's actual status. + for trace in &rank.traces { + let coord_status = rank_diagnostic_status_text(&trace.status); + let label = diagnostic_trace_label(&trace.trace); + for (statistic_name, statistic_status) in [ + ("rank_rhat", &trace.rank_rhat_status), + ("folded_rhat", &trace.folded_rhat_status), + ("max_rhat", &trace.max_rhat_status), + ("bulk_ess", &trace.bulk_ess_status), + ] { + push_rank_metadata( + iterations, + "markov_rank_statistic_status", + &format!("{statistic_name}:{label}"), + 0.0, + statistic_status, + rows, + ); + } + if let Some(value) = trace.rank_rhat { + let mut row = statistic( + iterations, + "markov_rank_rhat", + &format!("rank_rhat:{label}"), + None, + None, + None, + None, + value, + ); + row.status = Some(rank_diagnostic_status_text(&trace.rank_rhat_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.folded_rhat { + let mut row = statistic( + iterations, + "markov_rank_rhat", + &format!("folded_rhat:{label}"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&trace.folded_rhat_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.max_rhat { + let mut row = statistic( + iterations, + "markov_rank_rhat", + &format!("max_rhat:{label}"), + None, + None, + None, + None, + value, + ); + row.status = Some(rank_diagnostic_status_text(&trace.max_rhat_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.bulk_ess { + let mut row = statistic( + iterations, + "markov_rank_ess", + &format!("bulk_ess:{label}"), + None, + None, + None, + None, + value, + ); + row.status = Some(rank_diagnostic_status_text(&trace.bulk_ess_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.avg_ess_per_split_chain { + let mut row = statistic( + iterations, + "markov_rank_ess", + &format!("avg_ess_per_split_chain:{label}"), + None, + None, + None, + None, + value, + ); + row.status = Some(rank_diagnostic_status_text(&trace.bulk_ess_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.tau { + let mut row = statistic( + iterations, + "markov_rank_ess", + &format!("tau:{label}"), + None, + None, + None, + None, + value, + ); + row.status = Some(rank_diagnostic_status_text(&trace.bulk_ess_status).to_string()); + rows.push(row); + } + // Per-coordinate aggregate status (PartialAvailability when only a + // subset of rank Rhat, folded Rhat, and ESS succeeded). + let mut row = statistic( + iterations, + "markov_rank_coord_status", + &format!("status:{label}"), + None, + None, + None, + None, + 0.0, + ); + row.status = Some(coord_status.to_string()); + rows.push(row); + } + } +} + +fn operational_convergence_statistics( + iterations: usize, + diagnostics: &OperationalConvergenceDiagnostics, + rows: &mut Vec, +) { + // Always emit lifecycle metadata. When `checks` is empty the diagnostics + // are explicitly absent. + push_operational_metadata( + iterations, + "operational_convergence_checkpoints", + "total_checkpoints", + diagnostics.checks.len() as f64, + rows, + ); + let flag = if diagnostics.used_for_termination { + 1.0 + } else { + 0.0 + }; + push_operational_metadata( + iterations, + "operational_convergence_flag", + "used_for_termination", + flag, + rows, + ); + let flag = if diagnostics.final_check_reused { + 1.0 + } else { + 0.0 + }; + push_operational_metadata( + iterations, + "operational_convergence_flag", + "final_check_reused", + flag, + rows, + ); + + for (check_index, check) in diagnostics.checks.iter().enumerate() { + let row_start = rows.len(); + let prefix = format!("check_{check_index}"); + + // Checkpoint-level metadata. + push_operational_metadata( + iterations, + "operational_convergence_check", + &format!("{prefix}:iteration"), + check.iteration as f64, + rows, + ); + push_operational_metadata( + iterations, + "operational_convergence_check", + &format!("{prefix}:averaged_iterations"), + check.averaged_iterations as f64, + rows, + ); + let flag = if check.scheduled { 1.0 } else { 0.0 }; + push_operational_metadata( + iterations, + "operational_convergence_flag", + &format!("{prefix}:scheduled"), + flag, + rows, + ); + let flag = if check.mandatory_final { 1.0 } else { 0.0 }; + push_operational_metadata( + iterations, + "operational_convergence_flag", + &format!("{prefix}:mandatory_final"), + flag, + rows, + ); + let outcome_value = match &check.outcome { + OperationalConvergenceOutcome::Passed => 1.0, + OperationalConvergenceOutcome::Failed { .. } => 0.0, + OperationalConvergenceOutcome::Ineligible { .. } => -1.0, + }; + push_operational_metadata( + iterations, + "operational_convergence_outcome", + &format!("{prefix}:outcome"), + outcome_value, + rows, + ); + // Numeric checkpoint fields. Absent values (None) are not emitted; + // only finite available values produce rows. + if let Some(z) = check.z_quantile { + if z.is_finite() { + push_operational_metadata( + iterations, + "operational_convergence_check", + &format!("{prefix}:z_quantile"), + z, + rows, + ); + } + } + if let Some(imp) = check.implied_minimum_ess { + if imp.is_finite() { + push_operational_metadata( + iterations, + "operational_convergence_check", + &format!("{prefix}:implied_minimum_ess"), + imp, + rows, + ); + } + } + if let Some(seed) = check.checkpoint_seed { + push_operational_metadata( + iterations, + "operational_convergence_check", + &format!("{prefix}:checkpoint_seed_high_u32"), + (seed >> 32) as u32 as f64, + rows, + ); + push_operational_metadata( + iterations, + "operational_convergence_check", + &format!("{prefix}:checkpoint_seed_low_u32"), + seed as u32 as f64, + rows, + ); + } + // Averaged-candidate free coordinates. Retain only finite values. + for (coord_index, value) in check.candidate_free_coordinates.iter().enumerate() { + if value.is_finite() { + push_operational_metadata( + iterations, + "operational_convergence_coordinate", + &format!("{prefix}:free_coordinate_{coord_index}"), + *value, + rows, + ); + } + } + // Per-criterion evaluations. + for (criterion_index, criterion) in check.criteria.iter().enumerate() { + let crit_prefix = format!("{prefix}:crit_{criterion_index}"); + let status_value = match criterion.status { + crate::results::OperationalConvergenceCriterionStatus::Satisfied => 1.0, + crate::results::OperationalConvergenceCriterionStatus::NotSatisfied => 0.0, + crate::results::OperationalConvergenceCriterionStatus::Unavailable(_) => -1.0, + }; + push_operational_metadata( + iterations, + "operational_convergence_criterion_status", + &format!("{crit_prefix}:{}:status", criterion.name), + status_value, + rows, + ); + if let Some(row) = rows.last_mut() { + row.status = Some(match &criterion.status { + crate::results::OperationalConvergenceCriterionStatus::Satisfied => { + "satisfied".to_string() + } + crate::results::OperationalConvergenceCriterionStatus::NotSatisfied => { + "not_satisfied".to_string() + } + crate::results::OperationalConvergenceCriterionStatus::Unavailable(reason) => { + format!("unavailable: {reason}") + } + }); + } + if let Some(observed) = criterion.observed { + if observed.is_finite() { + push_operational_metadata( + iterations, + "operational_convergence_criterion", + &format!("{crit_prefix}:observed"), + observed, + rows, + ); + } + } + if criterion.threshold.is_finite() { + push_operational_metadata( + iterations, + "operational_convergence_criterion", + &format!("{crit_prefix}:threshold"), + criterion.threshold, + rows, + ); + } + } + // Per-trace rank diagnostics and matrix availability from the exact + // frozen-kernel object serialized in JSON. Status rows are emitted even + // when an optional numeric or matrix value is absent; their zero value + // is an availability code, never a fabricated diagnostic value. + if let Some(ref markov) = check.markov { + let rank = &markov.rank_diagnostics; + for trace in &rank.traces { + let label = diagnostic_trace_label(&trace.trace); + for (name, status) in [ + ("rank_rhat", &trace.rank_rhat_status), + ("folded_rhat", &trace.folded_rhat_status), + ("max_rhat", &trace.max_rhat_status), + ("bulk_ess", &trace.bulk_ess_status), + ("coordinate_aggregate", &trace.status), + ] { + push_rank_metadata( + iterations, + "operational_convergence_trace_status", + &format!("{prefix}:{name}:{label}"), + 0.0, + status, + rows, + ); + } + if let Some(value) = trace.rank_rhat { + let mut row = statistic( + iterations, + "operational_convergence_rhat", + &format!("{prefix}:rank_rhat:{label}"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&trace.rank_rhat_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.folded_rhat { + let mut row = statistic( + iterations, + "operational_convergence_rhat", + &format!("{prefix}:folded_rhat:{label}"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&trace.folded_rhat_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.max_rhat { + let mut row = statistic( + iterations, + "operational_convergence_rhat", + &format!("{prefix}:max_rhat:{label}"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&trace.max_rhat_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.bulk_ess { + let mut row = statistic( + iterations, + "operational_convergence_ess", + &format!("{prefix}:bulk_ess:{label}"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&trace.bulk_ess_status).to_string()); + rows.push(row); + } + if let Some(value) = trace.avg_ess_per_split_chain { + let mut row = statistic( + iterations, + "operational_convergence_ess", + &format!("{prefix}:avg_ess_per_split_chain:{label}"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&trace.bulk_ess_status).to_string()); + rows.push(row); + } + } + // Aggregate rank values each receive an explicit status row, + // regardless of whether the corresponding numeric exists. + for name in ["worst_rhat", "min_bulk_ess", "min_avg_ess_per_split_chain"] { + push_rank_metadata( + iterations, + "operational_convergence_rank_aggregate_status", + &format!("{prefix}:{name}"), + 0.0, + &rank.status, + rows, + ); + } + + // Every configured rank LRV chain retains its JSON index and status. + for chain_index in 0..rank.diagnostic_chains { + let chain_status = rank + .lrv_chain_statuses + .get(chain_index) + .cloned() + .unwrap_or(RankDiagnosticStatus::Unavailable); + let chain_name = format!("{prefix}:diagnostic_chain_{chain_index}"); + push_rank_metadata( + iterations, + "operational_convergence_lrv_chain_status", + &chain_name, + 0.0, + &chain_status, + rows, + ); + if let Some(Some(matrix)) = rank.lrv_per_chain.get(chain_index) { + append_markov_matrix( + iterations, + "operational_convergence_lrv_per_chain", + &chain_name, + matrix, + &markov.coordinates, + rank_diagnostic_status_text(&chain_status), + rows, + ); + } + } + for (name, matrix) in [ + ("diagnostic_mean", rank.diagnostic_mean_lrv.as_ref()), + ("operational", rank.operational_lrv.as_ref()), + ] { + push_rank_metadata( + iterations, + "operational_convergence_lrv_aggregate_status", + &format!("{prefix}:{name}"), + 0.0, + &rank.status, + rows, + ); + if let Some(matrix) = matrix { + append_markov_matrix( + iterations, + "operational_convergence_lrv_aggregate", + &format!("{prefix}:{name}"), + matrix, + &markov.coordinates, + rank_diagnostic_status_text(&rank.status), + rows, + ); + } + } + + // Information-mapped matrices used by eligibility mirror the JSON + // status exactly, whether or not matrix cells exist. + for (name, matrix, status) in [ + ("lambda", &markov.lambda, &markov.lambda_status), + ("xi", &markov.xi, &markov.xi_status), + ( + "simulation_covariance", + &markov.simulation_covariance, + &markov.simulation_covariance_status, + ), + ] { + push_markov_metadata( + iterations, + "operational_convergence_matrix_status", + &format!("{prefix}:{name}"), + 0.0, + status, + rows, + ); + append_markov_matrix( + iterations, + "operational_convergence_matrix", + &format!("{prefix}:{name}"), + matrix, + &markov.coordinates, + &markov_variance_status_text(status), + rows, + ); + } + + // Checkpoint worst/meta from markov. + if let Some(value) = rank.worst_rhat { + let mut row = statistic( + iterations, + "operational_convergence_rhat", + &format!("{prefix}:worst_rhat"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&markov.rank_diagnostics.status).to_string()); + rows.push(row); + } + if let Some(value) = rank.min_bulk_ess { + let mut row = statistic( + iterations, + "operational_convergence_ess", + &format!("{prefix}:min_bulk_ess"), + None, + None, + None, + None, + value, + ); + row.status = + Some(rank_diagnostic_status_text(&markov.rank_diagnostics.status).to_string()); + rows.push(row); + } + if let Some(value) = rank.min_avg_ess_per_split_chain { + let mut row = statistic( + iterations, + "operational_convergence_ess", + &format!("{prefix}:min_avg_ess_per_split_chain"), + None, + None, + None, + None, + value, + ); + row.status = Some(rank_diagnostic_status_text(&rank.status).to_string()); + rows.push(row); + } + } else { + for name in ["lambda", "xi", "simulation_covariance"] { + let mut row = statistic( + iterations, + "operational_convergence_matrix_status", + &format!("{prefix}:{name}"), + None, + None, + None, + None, + 0.0, + ); + row.status = Some("unavailable: no frozen-kernel diagnostic".to_string()); + rows.push(row); + } + for name in ["diagnostic_mean", "operational"] { + let mut row = statistic( + iterations, + "operational_convergence_lrv_aggregate_status", + &format!("{prefix}:{name}"), + None, + None, + None, + None, + 0.0, + ); + row.status = Some("unavailable: no frozen-kernel diagnostic".to_string()); + rows.push(row); + } + } + for row in &mut rows[row_start..] { + row.cycle = check.iteration; + } + } +} + +fn push_operational_metadata( + iterations: usize, + kind: &str, + name: &str, + value: f64, + rows: &mut Vec, +) { + rows.push(statistic( + iterations, kind, name, None, None, None, None, value, + )); +} + +fn push_markov_metadata( + iterations: usize, + kind: &str, + name: &str, + value: f64, + status: &MarkovSimulationVarianceStatus, + rows: &mut Vec, +) { + let mut row = statistic(iterations, kind, name, None, None, None, None, value); + row.status = Some(markov_variance_status_text(status)); + rows.push(row); +} + +fn push_rank_metadata( + iterations: usize, + kind: &str, + name: &str, + value: f64, + status: &RankDiagnosticStatus, + rows: &mut Vec, +) { + let mut row = statistic(iterations, kind, name, None, None, None, None, value); + row.status = Some(rank_diagnostic_status_text(status).to_string()); + rows.push(row); +} + +fn append_markov_matrix( + iterations: usize, + kind: &str, + name: &str, + matrix: &[Vec], + coordinates: &[crate::results::InformationCoordinate], + status: &str, + rows: &mut Vec, +) { + for (row_index, values) in matrix.iter().enumerate() { + for (column_index, value) in values.iter().enumerate() { + let mut row = statistic( + iterations, + kind, + name, + coordinates.get(row_index).map(|value| value.name.clone()), + coordinates + .get(column_index) + .map(|value| value.name.clone()), + None, + None, + *value, + ); + row.status = Some(status.to_string()); + rows.push(row); + } + } +} + +fn markov_variance_status_text(status: &MarkovSimulationVarianceStatus) -> String { + match status { + MarkovSimulationVarianceStatus::Disabled => "disabled".into(), + MarkovSimulationVarianceStatus::AverageNotApplied => "average_not_applied".into(), + MarkovSimulationVarianceStatus::NoFreeCoordinates => "no_free_coordinates".into(), + MarkovSimulationVarianceStatus::ExactZeroNoLatentState => { + "exact_zero_no_latent_state".into() + } + MarkovSimulationVarianceStatus::InformationUnavailable(reason) => { + format!("information_unavailable: {reason}") + } + MarkovSimulationVarianceStatus::InvalidConfiguration(reason) => { + format!("invalid_configuration: {reason}") + } + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow => { + "trace_memory_accounting_overflow".into() + } + MarkovSimulationVarianceStatus::CoordinateMismatch => "coordinate_mismatch".into(), + MarkovSimulationVarianceStatus::UnsupportedScore(reason) => { + format!("unsupported_score: {reason}") + } + MarkovSimulationVarianceStatus::NonFinite => "non_finite".into(), + MarkovSimulationVarianceStatus::NonSymmetric => "non_symmetric".into(), + MarkovSimulationVarianceStatus::Indefinite => "indefinite".into(), + MarkovSimulationVarianceStatus::StuckChain { chain } => format!("stuck_chain: {chain}"), + MarkovSimulationVarianceStatus::AssumptionsUnverified => "assumptions_unverified".into(), + } +} + +fn information_status_text(status: &crate::results::InformationStatus) -> String { + use crate::results::InformationStatus; + + match status { + InformationStatus::Available => "available".to_string(), + InformationStatus::NoFreeCoordinates => "no_free_coordinates".to_string(), + InformationStatus::NonFinite => "non_finite".to_string(), + InformationStatus::ObservedInformationNotPositiveDefinite => { + "observed_information_not_positive_definite".to_string() + } + InformationStatus::Unsupported(reason) => format!("unsupported: {reason}"), + InformationStatus::Ineligible(reason) => format!("ineligible: {reason}"), + } +} + +fn rank_diagnostic_status_text(status: &RankDiagnosticStatus) -> &str { + match status { + RankDiagnosticStatus::Disabled => "disabled", + RankDiagnosticStatus::NoLatent => "no_latent", + RankDiagnosticStatus::ScoreUnavailable => "score_unavailable", + RankDiagnosticStatus::Unavailable => "unavailable", + RankDiagnosticStatus::PartialAvailability => "partial_availability", + RankDiagnosticStatus::NoChains => "no_chains", + RankDiagnosticStatus::TooFewChains => "too_few_chains", + RankDiagnosticStatus::UnequalChainLengths => "unequal_chain_lengths", + RankDiagnosticStatus::TooFewDraws => "too_few_draws", + RankDiagnosticStatus::OddDraws => "odd_draws", + RankDiagnosticStatus::TraceByteCapExceeded => "trace_byte_cap_exceeded", + RankDiagnosticStatus::TraceMemoryAccountingOverflow => "trace_memory_accounting_overflow", + RankDiagnosticStatus::NonFiniteDraws => "non_finite_draws", + RankDiagnosticStatus::ConstantDraws => "constant_draws", + RankDiagnosticStatus::InvalidVariance => "invalid_variance", + RankDiagnosticStatus::NonPositiveTau => "non_positive_tau", + RankDiagnosticStatus::Available => "available", + } +} + +fn diagnostic_trace_label(trace: &DiagnosticTraceCoordinate) -> String { + match trace { + DiagnosticTraceCoordinate::Score { index, name, .. } => { + format!("score[{index}]:{name}") + } + DiagnosticTraceCoordinate::Eta { + subject, + effect_index, + effect_name, + } => { + format!("eta:{subject}:{effect_index}:{effect_name}") + } + DiagnosticTraceCoordinate::Kappa { + subject, + occasion_index, + effect_index, + effect_name, + } => { + format!("kappa:{subject}:{occasion_index}:{effect_index}:{effect_name}") + } + } +} + +#[allow(clippy::too_many_arguments)] +fn statistic( + cycle: usize, + kind: &str, + name: &str, + row: Option, + column: Option, + output_index: Option, + component: Option<&str>, + value: f64, +) -> StatisticRow { + StatisticRow { + cycle, + kind: kind.to_string(), + name: name.to_string(), + row, + column, + output_index, + component: component.map(str::to_string), + value: Some(value), + status: None, + } +} + +fn validate_prediction_pair( + subject: &str, + population: &Prediction, + conditional: &Prediction, +) -> Result<()> { + if population.time() != conditional.time() + || population.outeq() != conditional.outeq() + || population.occasion() != conditional.occasion() + || population.observation() != conditional.observation() + || population.censoring() != conditional.censoring() + { + bail!("population and conditional prediction metadata mismatch for subject '{subject}' at time {}", population.time()); + } + Ok(()) +} + +fn scale_text(scale: ParameterScale) -> String { + match scale { + ParameterScale::Identity => "identity".to_string(), + ParameterScale::Log => "log".to_string(), + ParameterScale::Logit { lower, upper } => format!("logit({lower},{upper})"), + ParameterScale::Probit { lower, upper } => format!("probit({lower},{upper})"), + } +} + +fn phase_text(phase: SaemPhase) -> &'static str { + match phase { + SaemPhase::BurnIn => "burn_in", + SaemPhase::Exploration => "exploration", + SaemPhase::Smoothing => "smoothing", + } +} + +fn censor_text(censor: Censor) -> &'static str { + match censor { + Censor::None => "none", + Censor::BLOQ => "bloq", + Censor::ALOQ => "aloq", + } +} + +fn warning_record(warning: &ParametricWarning) -> ParametricWarningRecord { + match warning { + ParametricWarning::OmegaUpdateRejected { + first_iteration, + cycles, + } => warning_values("omega_update_rejected", None, *first_iteration, *cycles), + ParametricWarning::OmegaIovUpdateRejected { + first_iteration, + cycles, + } => warning_values("omega_iov_update_rejected", None, *first_iteration, *cycles), + ParametricWarning::OmegaBoundaryRejection { + first_iteration, + longest_run, + } => warning_values( + "omega_boundary_rejection_run", + None, + *first_iteration, + *longest_run, + ), + ParametricWarning::OmegaIovBoundaryRejection { + first_iteration, + longest_run, + } => warning_values( + "omega_iov_boundary_rejection_run", + None, + *first_iteration, + *longest_run, + ), + ParametricWarning::EtaNonFiniteProposals { + first_iteration, + count, + } => warning_values("eta_nonfinite_proposals", None, *first_iteration, *count), + ParametricWarning::EtaBlockNonFiniteProposals { + first_iteration, + count, + } => warning_values( + "eta_block_nonfinite_proposals", + None, + *first_iteration, + *count, + ), + ParametricWarning::KappaNonFiniteProposals { + first_iteration, + count, + } => warning_values("kappa_nonfinite_proposals", None, *first_iteration, *count), + ParametricWarning::ResidualUpdateRejected { + output, + first_iteration, + cycles, + } => warning_values( + "residual_update_rejected", + Some(output.clone()), + *first_iteration, + *cycles, + ), + ParametricWarning::ProportionalPredictionFloor { + output, + first_iteration, + count, + } => warning_values( + "proportional_prediction_floor", + Some(output.clone()), + *first_iteration, + *count, + ), + ParametricWarning::NonFiniteResidualPrediction { + output, + first_iteration, + count, + } => warning_values( + "nonfinite_residual_prediction", + Some(output.clone()), + *first_iteration, + *count, + ), + ParametricWarning::ExponentialDomainViolation { + output, + first_iteration, + count, + } => warning_values( + "exponential_domain_violation", + Some(output.clone()), + *first_iteration, + *count, + ), + ParametricWarning::CombinedAdditiveCollapse { + output, + first_iteration, + cycles, + } => warning_values( + "combined_additive_collapse", + Some(output.clone()), + *first_iteration, + *cycles, + ), + ParametricWarning::ResidualOptimizerNotConverged { + output, + first_iteration, + cycles, + } => warning_values( + "residual_optimizer_not_converged", + Some(output.clone()), + *first_iteration, + *cycles, + ), + ParametricWarning::MarginalLikelihoodUnavailable { subjects } => ParametricWarningRecord { + kind: "marginal_likelihood_unavailable".to_string(), + output: None, + first_cycle: 0, + count: subjects.len(), + subjects: Some(subjects.clone()), + }, + ParametricWarning::MarginalLikelihoodNonconvergedModes { subjects } => { + ParametricWarningRecord { + kind: "marginal_likelihood_nonconverged_modes".to_string(), + output: None, + first_cycle: 0, + count: subjects.len(), + subjects: Some(subjects.clone()), + } + } + } +} + +fn warning_values( + kind: &str, + output: Option, + first_cycle: usize, + count: usize, +) -> ParametricWarningRecord { + ParametricWarningRecord { + kind: kind.to_string(), + output, + first_cycle, + count, + subjects: None, + } +} + +fn write_csv(path: &Path, rows: &[T], headers: &[&str]) -> Result<()> { + create_parent_dir(path)?; + let mut writer = csv::Writer::from_path(path) + .with_context(|| format!("failed to create '{}'", path.display()))?; + if rows.is_empty() { + writer + .write_record(headers) + .with_context(|| format!("failed to write headers to '{}'", path.display()))?; + } else { + for row in rows { + writer + .serialize(row) + .with_context(|| format!("failed to serialize row to '{}'", path.display()))?; + } + } + writer + .flush() + .with_context(|| format!("failed to flush '{}'", path.display())) +} + +fn create_parent_dir(path: &Path) -> Result<()> { + if let Some(parent) = path.parent() { + if !parent.as_os_str().is_empty() { + std::fs::create_dir_all(parent) + .with_context(|| format!("failed to create directory '{}'", parent.display()))?; + } + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn partial_rank_and_index_preserving_lrv_rows_keep_exact_statuses() { + use crate::results::RankMixingDiagnostic; + + let mut diagnostics = MarkovSimulationVarianceDiagnostics::disabled(); + diagnostics.rank_diagnostics.diagnostic_chains = 2; + diagnostics.rank_diagnostics.status = RankDiagnosticStatus::PartialAvailability; + diagnostics.rank_diagnostics.lrv_per_chain = vec![None, Some(vec![vec![2.0]])]; + diagnostics.rank_diagnostics.lrv_chain_statuses = vec![ + RankDiagnosticStatus::ScoreUnavailable, + RankDiagnosticStatus::Available, + ]; + diagnostics.rank_diagnostics.traces = vec![RankMixingDiagnostic { + trace: DiagnosticTraceCoordinate::Eta { + subject: "subject-1".into(), + effect_index: 0, + effect_name: "CL".into(), + }, + rank_rhat: Some(1.01), + rank_rhat_status: RankDiagnosticStatus::Available, + folded_rhat: None, + folded_rhat_status: RankDiagnosticStatus::ConstantDraws, + max_rhat: None, + max_rhat_status: RankDiagnosticStatus::ConstantDraws, + bulk_ess: None, + bulk_ess_status: RankDiagnosticStatus::NonPositiveTau, + avg_ess_per_split_chain: None, + tau: None, + status: RankDiagnosticStatus::PartialAvailability, + }]; + + let json = serde_json::to_string(&diagnostics).unwrap(); + let decoded: MarkovSimulationVarianceDiagnostics = serde_json::from_str(&json).unwrap(); + assert_eq!(decoded.rank_diagnostics.lrv_per_chain.len(), 2); + assert!(decoded.rank_diagnostics.lrv_per_chain[0].is_none()); + assert_eq!( + decoded.rank_diagnostics.lrv_per_chain[1], + Some(vec![vec![2.0]]) + ); + assert_eq!( + decoded.rank_diagnostics.lrv_chain_statuses, + vec![ + RankDiagnosticStatus::ScoreUnavailable, + RankDiagnosticStatus::Available, + ] + ); + + let mut rows = Vec::new(); + markov_variance_statistics(7, &decoded, &mut rows); + let status_rows = rows + .iter() + .filter(|row| row.kind == "markov_rank_lrv_chain_status") + .collect::>(); + assert_eq!(status_rows.len(), 2); + assert_eq!(status_rows[0].name, "diagnostic_chain_0"); + assert_eq!(status_rows[0].status.as_deref(), Some("score_unavailable")); + assert_eq!(status_rows[1].name, "diagnostic_chain_1"); + assert_eq!(status_rows[1].status.as_deref(), Some("available")); + let matrix_rows = rows + .iter() + .filter(|row| row.kind == "markov_rank_lrv_per_chain") + .collect::>(); + assert_eq!(matrix_rows.len(), 1); + assert_eq!(matrix_rows[0].name, "diagnostic_chain_1"); + + let statistic_status = |name: &str| { + rows.iter() + .find(|row| { + row.kind == "markov_rank_statistic_status" && row.name.starts_with(name) + }) + .and_then(|row| row.status.as_deref()) + }; + assert_eq!(statistic_status("rank_rhat:"), Some("available")); + assert_eq!(statistic_status("folded_rhat:"), Some("constant_draws")); + assert_eq!(statistic_status("max_rhat:"), Some("constant_draws")); + assert_eq!(statistic_status("bulk_ess:"), Some("non_positive_tau")); + let value_rows = rows + .iter() + .filter(|row| row.kind == "markov_rank_rhat" && row.name.starts_with("rank_rhat:")) + .collect::>(); + assert_eq!(value_rows.len(), 1); + assert_eq!(value_rows[0].value, Some(1.01)); + assert_eq!(value_rows[0].status.as_deref(), Some("available")); + assert!(!rows + .iter() + .any(|row| { row.kind == "markov_rank_rhat" && row.name.starts_with("max_rhat:") })); + assert!(!rows + .iter() + .any(|row| { row.kind == "markov_rank_ess" && row.name.starts_with("bulk_ess:") })); + + let csv_path = std::env::temp_dir().join(format!( + "pmcore-failed-chain-{}-statistics.csv", + std::process::id() + )); + write_csv( + &csv_path, + &rows, + &[ + "cycle", + "kind", + "name", + "row", + "column", + "output_index", + "component", + "value", + "status", + ], + ) + .unwrap(); + let csv_rows = csv::Reader::from_path(&csv_path) + .unwrap() + .deserialize::() + .collect::, _>>() + .unwrap(); + std::fs::remove_file(&csv_path).unwrap(); + let csv_status_rows = csv_rows + .iter() + .filter(|row| row.kind == "markov_rank_lrv_chain_status") + .collect::>(); + assert_eq!(csv_status_rows.len(), 2); + assert_eq!(csv_status_rows[0].name, "diagnostic_chain_0"); + assert_eq!( + csv_status_rows[0].status.as_deref(), + Some("score_unavailable") + ); + assert_eq!(csv_status_rows[1].name, "diagnostic_chain_1"); + assert_eq!(csv_status_rows[1].status.as_deref(), Some("available")); + let csv_matrix_rows = csv_rows + .iter() + .filter(|row| row.kind == "markov_rank_lrv_per_chain") + .collect::>(); + assert_eq!(csv_matrix_rows.len(), 1); + assert_eq!(csv_matrix_rows[0].name, "diagnostic_chain_1"); + assert_eq!(csv_matrix_rows[0].value, Some(2.0)); + } + + #[test] + fn every_markov_outcome_has_an_explicit_neutral_status_row() { + let outcomes = vec![ + MarkovSimulationVarianceStatus::Disabled, + MarkovSimulationVarianceStatus::AverageNotApplied, + MarkovSimulationVarianceStatus::NoFreeCoordinates, + MarkovSimulationVarianceStatus::InformationUnavailable("test".into()), + MarkovSimulationVarianceStatus::InvalidConfiguration("test".into()), + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, + MarkovSimulationVarianceStatus::CoordinateMismatch, + MarkovSimulationVarianceStatus::UnsupportedScore("test".into()), + MarkovSimulationVarianceStatus::NonFinite, + MarkovSimulationVarianceStatus::NonSymmetric, + MarkovSimulationVarianceStatus::Indefinite, + MarkovSimulationVarianceStatus::StuckChain { chain: 2 }, + MarkovSimulationVarianceStatus::ExactZeroNoLatentState, + MarkovSimulationVarianceStatus::AssumptionsUnverified, + ]; + for outcome in outcomes { + let mut rows = Vec::new(); + push_markov_metadata(9, "markov_status", "aggregate", 0.0, &outcome, &mut rows); + assert_eq!(rows.len(), 1); + assert_eq!(rows[0].kind, "markov_status"); + assert_eq!(rows[0].value, Some(0.0)); + assert_eq!( + rows[0].status.as_deref(), + Some(markov_variance_status_text(&outcome).as_str()) + ); + } + } + + #[test] + fn operational_convergence_schema_roundtrip_preserves_all_fields() { + use crate::results::{ + OperationalConvergenceCheck, OperationalConvergenceCriterion, + OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, + OperationalConvergenceOutcome, + }; + + let diagnostic = OperationalConvergenceDiagnostics { + checks: vec![OperationalConvergenceCheck { + iteration: 42, + averaged_iterations: 10, + scheduled: true, + mandatory_final: false, + checkpoint_seed: Some(12345), + z_quantile: Some(1.96), + implied_minimum_ess: Some(100.0), + candidate_free_coordinates: vec![0.5, -0.3], + information: None, + criteria: vec![ + OperationalConvergenceCriterion { + name: "max_rhat".into(), + observed: Some(1.005), + threshold: 1.01, + status: OperationalConvergenceCriterionStatus::Satisfied, + }, + OperationalConvergenceCriterion { + name: "min_bulk_ess".into(), + observed: Some(350.0), + threshold: 400.0, + status: OperationalConvergenceCriterionStatus::NotSatisfied, + }, + OperationalConvergenceCriterion { + name: "fixed_width_ratio".into(), + observed: None, + threshold: 0.1, + status: OperationalConvergenceCriterionStatus::Unavailable( + "no diagnostic chains configured".into(), + ), + }, + ], + outcome: OperationalConvergenceOutcome::Failed { + criteria: vec!["min_bulk_ess".into()], + }, + markov: None, + }], + final_check_reused: false, + used_for_termination: false, + ..OperationalConvergenceDiagnostics::default() + }; + + let json = serde_json::to_string(&diagnostic).unwrap(); + let decoded: OperationalConvergenceDiagnostics = serde_json::from_str(&json).unwrap(); + + assert_eq!(decoded.checks.len(), 1); + let check = &decoded.checks[0]; + assert_eq!(check.iteration, 42); + assert_eq!(check.averaged_iterations, 10); + assert!(check.scheduled); + assert!(!check.mandatory_final); + assert_eq!(check.checkpoint_seed, Some(12345)); + assert_eq!(check.z_quantile, Some(1.96)); + assert_eq!(check.implied_minimum_ess, Some(100.0)); + assert_eq!(check.candidate_free_coordinates, vec![0.5, -0.3]); + assert_eq!(check.criteria.len(), 3); + + // Criterion 0: satisfied + assert_eq!(check.criteria[0].name, "max_rhat"); + assert_eq!(check.criteria[0].observed, Some(1.005)); + assert_eq!(check.criteria[0].threshold, 1.01); + assert!(matches!( + check.criteria[0].status, + OperationalConvergenceCriterionStatus::Satisfied + )); + + // Criterion 1: not satisfied + assert_eq!(check.criteria[1].name, "min_bulk_ess"); + assert_eq!(check.criteria[1].observed, Some(350.0)); + assert_eq!(check.criteria[1].threshold, 400.0); + assert!(matches!( + check.criteria[1].status, + OperationalConvergenceCriterionStatus::NotSatisfied + )); + + // Criterion 2: unavailable + assert_eq!(check.criteria[2].name, "fixed_width_ratio"); + assert_eq!(check.criteria[2].observed, None); + assert_eq!(check.criteria[2].threshold, 0.1); + assert!(matches!( + &check.criteria[2].status, + OperationalConvergenceCriterionStatus::Unavailable(reason) if reason == "no diagnostic chains configured" + )); + + assert!(matches!( + check.outcome, + OperationalConvergenceOutcome::Failed { .. } + )); + assert!(!decoded.final_check_reused); + assert!(!decoded.used_for_termination); + } + + #[test] + fn operational_convergence_stat_rows_omit_absent_numerics() { + use crate::results::{ + OperationalConvergenceCheck, OperationalConvergenceCriterion, + OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, + OperationalConvergenceOutcome, + }; + + let diagnostic = OperationalConvergenceDiagnostics { + checks: vec![OperationalConvergenceCheck { + iteration: 5, + averaged_iterations: 2, + scheduled: false, + mandatory_final: true, + checkpoint_seed: Some(42), + z_quantile: None, + implied_minimum_ess: None, + candidate_free_coordinates: vec![f64::NAN, 1.0], + information: None, + criteria: vec![OperationalConvergenceCriterion { + name: "max_rhat".into(), + observed: None, + threshold: 1.01, + status: OperationalConvergenceCriterionStatus::Unavailable( + "no frozen diagnostic".into(), + ), + }], + outcome: OperationalConvergenceOutcome::Ineligible { + reasons: vec!["no frozen diagnostic".into()], + }, + markov: None, + }], + final_check_reused: true, + used_for_termination: false, + ..OperationalConvergenceDiagnostics::default() + }; + + let mut rows = Vec::new(); + operational_convergence_statistics(10, &diagnostic, &mut rows); + + // Filter rows for interest. + let by_kind = |kind: &str| -> Vec<&StatisticRow> { + rows.iter().filter(|row| row.kind == kind).collect() + }; + + // Checkpoint count. + let checkpoints = by_kind("operational_convergence_checkpoints"); + assert_eq!(checkpoints.len(), 1); + assert_eq!(checkpoints[0].value, Some(1.0)); + + // Flags. + let flags = by_kind("operational_convergence_flag"); + let used_termination = flags.iter().find(|row| row.name == "used_for_termination"); + assert!(used_termination.is_some()); + assert_eq!(used_termination.unwrap().value, Some(0.0)); + let reused = flags.iter().find(|row| row.name == "final_check_reused"); + assert_eq!(reused.unwrap().value, Some(1.0)); + let sched = flags.iter().find(|row| row.name == "check_0:scheduled"); + assert_eq!(sched.unwrap().value, Some(0.0)); + let final_flag = flags + .iter() + .find(|row| row.name == "check_0:mandatory_final"); + assert_eq!(final_flag.unwrap().value, Some(1.0)); + + // Check metadata. + let checks = by_kind("operational_convergence_check"); + let iter_row = checks.iter().find(|row| row.name == "check_0:iteration"); + assert_eq!(iter_row.unwrap().value, Some(5.0)); + let seed_high = checks + .iter() + .find(|row| row.name == "check_0:checkpoint_seed_high_u32"); + let seed_low = checks + .iter() + .find(|row| row.name == "check_0:checkpoint_seed_low_u32"); + assert_eq!(seed_high.unwrap().value, Some(0.0)); + assert_eq!(seed_low.unwrap().value, Some(42.0)); + // z_quantile and implied were None: absent. + assert!(checks + .iter() + .find(|row| row.name == "check_0:z_quantile") + .is_none()); + assert!(checks + .iter() + .find(|row| row.name == "check_0:implied_minimum_ess") + .is_none()); + + // Free coordinates: only the finite one (1.0) appears. + let coords = by_kind("operational_convergence_coordinate"); + assert_eq!(coords.len(), 1); + assert_eq!(coords[0].name, "check_0:free_coordinate_1"); + assert_eq!(coords[0].value, Some(1.0)); + + // Criterion status row. + let crit_status = by_kind("operational_convergence_criterion_status"); + assert_eq!(crit_status.len(), 1); + assert_eq!(crit_status[0].name, "check_0:crit_0:max_rhat:status"); + assert_eq!(crit_status[0].value, Some(-1.0)); // Unavailable + + // Criterion observed absent, threshold present. + let crit_values = by_kind("operational_convergence_criterion"); + assert_eq!(crit_values.len(), 1); + assert_eq!(crit_values[0].name, "check_0:crit_0:threshold"); + assert_eq!(crit_values[0].value, Some(1.01)); + + // Outcome. + let outcomes = by_kind("operational_convergence_outcome"); + assert_eq!(outcomes.len(), 1); + assert_eq!(outcomes[0].name, "check_0:outcome"); + assert_eq!(outcomes[0].value, Some(-1.0)); + } + + #[test] + fn empty_operational_convergence_emits_only_metadata() { + let diagnostic = OperationalConvergenceDiagnostics::default(); + let mut rows = Vec::new(); + operational_convergence_statistics(10, &diagnostic, &mut rows); + + // Only metadata rows: checkpoints count and two flags. + let by_kind = |kind: &str| -> Vec<&StatisticRow> { + rows.iter().filter(|row| row.kind == kind).collect() + }; + assert_eq!(by_kind("operational_convergence_checkpoints").len(), 1); + assert_eq!(by_kind("operational_convergence_flag").len(), 2); + assert_eq!(rows.len(), 3); + } + + #[test] + fn unavailable_marginal_rows_and_statistics_omit_numerics() { + use crate::estimation::parametric::marginal_likelihood::{ + MarginalLikelihoodConfig, MarginalLikelihoodFailureReason, + MarginalLikelihoodSubjectFailure, SubjectMarginalLikelihoodDiagnostics, + }; + + let failure = MarginalLikelihoodFailureReason::ScoringFailure("posthoc failed".into()); + let diagnostics = MarginalLikelihoodDiagnostics { + config: MarginalLikelihoodConfig::new(16, 7, 5, 1.5), + status: MarginalLikelihoodStatus::Unavailable { + failures: vec![MarginalLikelihoodSubjectFailure { + subject_id: "subject".into(), + reason: failure.clone(), + }], + }, + log_marginal_likelihood: None, + n2ll: None, + n2ll_mcse: None, + subjects: vec![SubjectMarginalLikelihoodDiagnostics { + subject_id: "subject".into(), + method: MarginalLikelihoodMethod::StudentTImportanceSampling, + proposal_scale_source: ProposalScaleSource::FinalRawOmegaBlocks, + seed: Some( + crate::estimation::parametric::marginal_likelihood::marginal_likelihood_subject_seed( + 7, 0, + ), + ), + dimension: 1, + occasion_indices: vec![], + mode: vec![], + mode_converged: Some(false), + samples: 16, + log_marginal_likelihood: None, + n2ll: None, + effective_sample_size: None, + effective_sample_fraction: None, + zero_weight_count: 0, + var_log: None, + n2ll_mcse: None, + failure: Some(failure), + }], + }; + let rows = marginal_likelihood_rows(Some(&diagnostics)); + assert!(rows.iter().all(|row| { + row.log_marginal_likelihood.is_none() + && row.n2ll.is_none() + && row.n2ll_mcse.is_none() + && row.effective_sample_size.is_none() + && row.effective_sample_fraction.is_none() + })); + let mut statistics = Vec::new(); + marginal_likelihood_statistics(3, Some(&diagnostics), &mut statistics); + assert!(statistics.iter().all(|row| row.value.is_none())); + } + + #[test] + fn covariance_boundary_warnings_have_stable_output_kinds() { + let omega = warning_record(&ParametricWarning::OmegaBoundaryRejection { + first_iteration: 12, + longest_run: 7, + }); + assert_eq!(omega.kind, "omega_boundary_rejection_run"); + assert_eq!(omega.first_cycle, 12); + assert_eq!(omega.count, 7); + + let omega_iov = warning_record(&ParametricWarning::OmegaIovBoundaryRejection { + first_iteration: 21, + longest_run: 4, + }); + assert_eq!(omega_iov.kind, "omega_iov_boundary_rejection_run"); + assert_eq!(omega_iov.first_cycle, 21); + assert_eq!(omega_iov.count, 4); + } + + #[test] + fn operational_convergence_warnings_have_exact_policy_wording() { + use crate::algorithms::parametric::OperationalConvergenceConfig; + + assert!(OperationalConvergenceDiagnostics::default() + .warnings() + .is_empty()); + let configured = OperationalConvergenceDiagnostics { + config: Some(OperationalConvergenceConfig::literature_guided( + 1, 1, 0.05, 0.95, 0.1, 0.02, + )), + ..OperationalConvergenceDiagnostics::default() + }; + assert!(configured.warnings()[0].contains("no checkpoint was evaluated")); + + let failed = OperationalConvergenceDiagnostics { + final_status: Some(OperationalConvergenceOutcome::Failed { + criteria: vec!["max_rhat".into()], + }), + ..configured.clone() + }; + assert!(failed.warnings()[0].contains("evaluated but not satisfied")); + + let ineligible = OperationalConvergenceDiagnostics { + final_status: Some(OperationalConvergenceOutcome::Ineligible { + reasons: vec!["constant draws".into()], + }), + ..configured.clone() + }; + assert!(ineligible.warnings()[0].contains("evaluated but were ineligible")); + + let passed = OperationalConvergenceDiagnostics { + final_status: Some(OperationalConvergenceOutcome::Passed), + used_for_termination: true, + ..configured + }; + let warning = &passed.warnings()[0]; + assert!(warning.contains("PMcore operational convergence criteria passed")); + assert!(warning.contains("not proof of mathematical convergence")); + assert!(warning.contains("independent doubled-budget fit")); + } + + // ─── Schema-7 immutable declaration tests ──────────────────────── + + #[test] + fn source_covariance_initial_values_are_validated() { + // Fixed diagonal entry must have initial == final. + let covariance = ParametricSourceCovariance { + dimension: 1, + names: vec!["ke".to_string()], + values: vec![vec![0.5]], + structural_mask: vec![vec![true]], + estimated_mask: vec![vec![false]], + initial_values: vec![vec![0.5]], + }; + assert!(validate_source_covariance(&covariance, &["ke"], "Omega").is_ok()); + + // Mismatched fixed entry. + let bad_covariance = ParametricSourceCovariance { + initial_values: vec![vec![0.3]], + ..covariance.clone() + }; + let err = validate_source_covariance(&bad_covariance, &["ke"], "Omega").unwrap_err(); + assert!( + err.to_string().contains("initial does not match final"), + "unexpected error: {err}" + ); + + // Free entry: initial may differ from final, but must be finite. + let free_covariance = ParametricSourceCovariance { + dimension: 1, + names: vec!["ke".to_string()], + values: vec![vec![0.6]], + structural_mask: vec![vec![true]], + estimated_mask: vec![vec![true]], + initial_values: vec![vec![0.4]], + }; + assert!(validate_source_covariance(&free_covariance, &["ke"], "Omega").is_ok()); + + // Non-finite initial value is rejected. + let nonfinite_covariance = ParametricSourceCovariance { + initial_values: vec![vec![f64::NAN]], + ..free_covariance.clone() + }; + let err = validate_source_covariance(&nonfinite_covariance, &["ke"], "Omega").unwrap_err(); + assert!( + err.to_string().contains("must be finite"), + "unexpected error: {err}" + ); + + // Wrong dimension. + let wrong_dim = ParametricSourceCovariance { + initial_values: vec![vec![0.4, 0.0], vec![0.0, 0.5]], + ..free_covariance + }; + let err = validate_source_covariance(&wrong_dim, &["ke"], "Omega").unwrap_err(); + assert!( + err.to_string() + .contains("do not match the declared dimension"), + "unexpected error: {err}" + ); + + let structural_zero = ParametricSourceCovariance { + dimension: 2, + names: vec!["ke".to_string(), "v".to_string()], + values: vec![vec![0.5, 0.0], vec![0.0, 0.6]], + structural_mask: vec![vec![true, false], vec![false, true]], + estimated_mask: vec![vec![true, false], vec![false, true]], + initial_values: vec![vec![0.4, 0.01], vec![0.01, 0.7]], + }; + let error = + validate_source_covariance(&structural_zero, &["ke", "v"], "Omega").unwrap_err(); + assert!(error.to_string().contains("structural-zero")); + + let non_spd = ParametricSourceCovariance { + initial_values: vec![vec![0.4, 0.8], vec![0.8, 0.7]], + structural_mask: vec![vec![true, true], vec![true, true]], + estimated_mask: vec![vec![true, true], vec![true, true]], + ..structural_zero + }; + let error = validate_source_covariance(&non_spd, &["ke", "v"], "Omega").unwrap_err(); + assert!(error.to_string().contains("strictly positive definite")); + } + + #[test] + fn source_covariance_two_by_two_initial_validation() { + // Mixed fixed/free two-dimensional Omega. + let covariance = ParametricSourceCovariance { + dimension: 2, + names: vec!["ke".to_string(), "v".to_string()], + values: vec![vec![0.25, 0.05], vec![0.05, 0.30]], + structural_mask: vec![vec![true, true], vec![true, true]], + estimated_mask: vec![vec![true, true], vec![true, false]], + initial_values: vec![vec![0.20, 0.03], vec![0.03, 0.30]], + }; + // The covariance and ke variance are free; only the v variance is + // fixed, so its immutable initial and final values are both 0.30. + assert!(validate_source_covariance(&covariance, &["ke", "v"], "Omega").is_ok()); + } + + #[test] + fn source_residual_initial_values_are_validated() { + // Fixed component: initial == final. + let residual = ParametricSourceResidual { + output: "cp".to_string(), + output_index: 0, + family: "constant".to_string(), + components: vec!["sigma".to_string()], + values: vec![0.5], + estimated_mask: vec![false], + initial_values: vec![0.5], + initial_estimated_mask: vec![false], + }; + let metadata = ParametricSourceMetadata { + parameters: vec![], + random_effects: vec![], + omega: ParametricSourceCovariance { + dimension: 0, + names: vec![], + values: vec![], + structural_mask: vec![], + estimated_mask: vec![], + initial_values: vec![], + }, + iov_effects: vec![], + omega_iov: None, + residual_outputs: vec![residual.clone()], + covariate_effects: vec![], + subject_covariates: vec![], + subject_design: vec![], + subject_population_parameters: vec![], + }; + assert!(validate_source_snapshot(&metadata).is_ok()); + + // Mismatched fixed component: change initial but leave final same. + let bad_residual = ParametricSourceResidual { + initial_values: vec![0.3], + ..residual.clone() + }; + let bad_metadata = ParametricSourceMetadata { + residual_outputs: vec![bad_residual], + ..metadata.clone() + }; + let err = validate_source_snapshot(&bad_metadata).unwrap_err(); + let msg = err.to_string(); + assert!( + msg.contains("initial"), + "error should mention initial, got: {msg}" + ); + assert!( + msg.contains("final") || msg.contains("match"), + "error should reject mismatch, got: {msg}" + ); + + // Initial estimated mask disagrees. + let mask_residual = ParametricSourceResidual { + estimated_mask: vec![false], + initial_estimated_mask: vec![true], + ..residual.clone() + }; + let mask_metadata = ParametricSourceMetadata { + residual_outputs: vec![mask_residual], + ..metadata.clone() + }; + let err = validate_source_snapshot(&mask_metadata).unwrap_err(); + assert!( + err.to_string() + .contains("initial and final estimated masks"), + "unexpected error: {err}" + ); + + // Non-finite initial value. + let nonfinite_residual = ParametricSourceResidual { + initial_values: vec![f64::NAN], + ..residual + }; + let nf_metadata = ParametricSourceMetadata { + residual_outputs: vec![nonfinite_residual], + ..metadata + }; + let err = validate_source_snapshot(&nf_metadata).unwrap_err(); + assert!( + err.to_string() + .contains("must match the component width and be finite"), + "unexpected error: {err}" + ); + } + + #[test] + fn source_residual_free_component_initial_may_differ_from_final() { + let residual = ParametricSourceResidual { + output: "cp".to_string(), + output_index: 0, + family: "constant".to_string(), + components: vec!["sigma".to_string()], + values: vec![0.8], + estimated_mask: vec![true], + initial_values: vec![0.5], + initial_estimated_mask: vec![true], + }; + let metadata = ParametricSourceMetadata { + parameters: vec![], + random_effects: vec![], + omega: ParametricSourceCovariance { + dimension: 0, + names: vec![], + values: vec![], + structural_mask: vec![], + estimated_mask: vec![], + initial_values: vec![], + }, + iov_effects: vec![], + omega_iov: None, + residual_outputs: vec![residual], + covariate_effects: vec![], + subject_covariates: vec![], + subject_design: vec![], + subject_population_parameters: vec![], + }; + assert!(validate_source_snapshot(&metadata).is_ok()); + } + + #[test] + fn source_covariance_initial_symmetry_is_checked() { + let asymmetric = ParametricSourceCovariance { + dimension: 2, + names: vec!["ke".to_string(), "v".to_string()], + values: vec![vec![0.25, 0.05], vec![0.05, 0.30]], + structural_mask: vec![vec![true, true], vec![true, true]], + estimated_mask: vec![vec![true, true], vec![true, true]], + initial_values: vec![vec![0.20, 0.03], vec![0.04, 0.30]], + }; + let err = validate_source_covariance(&asymmetric, &["ke", "v"], "Omega").unwrap_err(); + assert!( + err.to_string().contains("symmetric"), + "unexpected error: {err}" + ); + } + + /// Run a real SAEM fit and verify that the source snapshot preserves + /// immutable initial declarations: fixed residual components have + /// initial == final, free omega variances have finite initials that + /// may differ from finals. + #[test] + fn real_fit_preserves_immutable_initial_declarations() { + use crate::estimation::{EstimationProblem, Omega}; + use crate::model::Parameter; + use pharmsol::prelude::*; + + let equation = analytical! { + name: "immutable_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .build(), + ]); + + let omega = Omega::new().variance("ke", 0.04).fixed_variance("v", 0.09); + + let result = EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.30)) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .omega(omega) + .error_model( + "cp", + crate::estimation::ParametricErrorModel::new(ResidualErrorModel::constant(0.5)) + .fixed(), + ) + .build() + .unwrap() + .fit_with( + SaemConfig::default() + .seed(42) + .burn_in(20) + .k1_iterations(30) + .k2_iterations(30) + .n_chains(2), + ) + .unwrap(); + + // Extract source metadata. + let tables = result.tables(0.1, 1.0).unwrap(); + let record = result.record(tables).unwrap(); + let source = &record.source_metadata; + + assert_eq!(source.parameters[0].initial, 0.30); + assert_eq!(source.parameters[1].initial, 20.0); + assert!(!source.parameters[1].estimated); + assert!(equal_with_roundoff( + source.parameters[1].initial, + source.parameters[1].estimate + )); + + // Omega: ke-ke is free, v-v is fixed. + assert_eq!(source.omega.dimension, 2); + assert!(source.omega.estimated_mask[0][0]); + assert!(!source.omega.estimated_mask[1][1]); + // Fixed entry must have initial == final. + assert!( + (source.omega.values[1][1] - source.omega.initial_values[1][1]).abs() + <= 64.0 + * f64::EPSILON + * source.omega.initial_values[1][1] + .abs() + .max(source.omega.values[1][1].abs()) + .max(1.0), + "fixed v variance: initial {} != final {}", + source.omega.initial_values[1][1], + source.omega.values[1][1], + ); + // Free entry: initial must be finite. + assert!(source.omega.initial_values[0][0].is_finite()); + assert!(source.omega.initial_values[0][0] > 0.0); + + // Residual: sigma is fixed, must have initial == final. + let residual = &source.residual_outputs[0]; + assert!(!residual.estimated_mask[0]); + assert!( + (residual.values[0] - residual.initial_values[0]).abs() + <= 64.0 + * f64::EPSILON + * residual.initial_values[0] + .abs() + .max(residual.values[0].abs()) + .max(1.0), + "fixed sigma: initial {} != final {}", + residual.initial_values[0], + residual.values[0], + ); + } + + /// Tamper a persisted JSON record: change a fixed residual value. + /// The reader must reject it. + #[test] + fn persisted_fixed_residual_tampering_is_rejected() { + use crate::estimation::EstimationProblem; + use crate::model::Parameter; + use pharmsol::prelude::*; + + let equation = analytical! { + name: "tamper_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .build(), + ]); + + let result = EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.30).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .error_model( + "cp", + crate::estimation::ParametricErrorModel::new(ResidualErrorModel::constant(0.5)) + .fixed(), + ) + .build() + .unwrap() + .fit_with( + SaemConfig::default() + .seed(42) + .burn_in(10) + .k1_iterations(10) + .k2_iterations(10) + .n_chains(2), + ) + .unwrap(); + + let tables = result.tables(0.1, 1.0).unwrap(); + let record = result.record(tables).unwrap(); + let mut json = serde_json::to_value(&record).expect("valid record should serialize"); + + // Coordinate the mutable source/table/statistic snapshots while + // leaving the immutable constant-error declaration unchanged. + let original_final = json["source_metadata"]["residual_outputs"][0]["values"][0] + .as_f64() + .unwrap(); + let changed = original_final + 0.1; + json["source_metadata"]["residual_outputs"][0]["values"][0] = serde_json::json!(changed); + json["tables"]["residual_error"][0]["estimate"] = serde_json::json!(changed); + for row in json["tables"]["statistics"].as_array_mut().unwrap() { + if row["kind"] == "residual" && row["name"] == "cp" && row["component"] == "sigma" { + row["value"] = serde_json::json!(changed); + } + } + + let tampered: Result = serde_json::from_value(json.clone()); + assert!(tampered.is_ok(), "deserialization should succeed"); + let err = ParametricResultRecord::read_json_from_value(json).unwrap_err(); + assert!( + err.to_string().contains("initial") + && err.to_string().contains("final") + && err.to_string().contains("sigma"), + "unexpected error: {err}" + ); + } + + /// Tamper a persisted JSON record: change a fixed Omega entry. + /// The reader must reject it. + #[test] + fn persisted_fixed_omega_tampering_is_rejected() { + use crate::estimation::{EstimationProblem, Omega}; + use crate::model::Parameter; + use pharmsol::prelude::*; + + let equation = analytical! { + name: "tamper_omega_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .build(), + ]); + + let omega = Omega::new().variance("ke", 0.04).fixed_variance("v", 0.09); + + let result = EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.30)) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .omega(omega) + .error_model( + "cp", + crate::estimation::ParametricErrorModel::new(ResidualErrorModel::constant(0.5)) + .fixed(), + ) + .build() + .unwrap() + .fit_with( + SaemConfig::default() + .seed(42) + .burn_in(10) + .k1_iterations(10) + .k2_iterations(10) + .n_chains(2), + ) + .unwrap(); + + let tables = result.tables(0.1, 1.0).unwrap(); + let record = result.record(tables).unwrap(); + let mut json = serde_json::to_value(&record).expect("valid record should serialize"); + + // Tamper: change a fixed Omega entry final value. + let omega = &mut json["source_metadata"]["omega"]; + let original = omega["values"][1][1].as_f64().unwrap(); + omega["values"][1][1] = + serde_json::Value::Number(serde_json::Number::from_f64(original + 0.05).unwrap()); + + let tampered: Result = serde_json::from_value(json.clone()); + assert!(tampered.is_ok(), "deserialization should succeed"); + let err = ParametricResultRecord::read_json_from_value(json).unwrap_err(); + assert!( + err.to_string().contains("initial") && err.to_string().contains("final"), + "unexpected error: {err}" + ); + } +} + +/// Helper: read a ParametricResultRecord from a JSON value, performing +/// all the same validation as read_json without touching a file. +#[allow(clippy::items_after_test_module)] +#[cfg(test)] +impl ParametricResultRecord { + fn read_json_from_value(raw: serde_json::Value) -> Result { + let object = raw + .as_object() + .context("parametric result JSON must be an object")?; + for required in [ + "marginal_likelihood", + "information_criteria", + "subject_count", + "population_uncertainty", + "conditional_modes", + "shrinkage", + ] { + if !object.contains_key(required) { + bail!("schema-9 parametric result requires the {required} field"); + } + } + object + .get("source_metadata") + .and_then(serde_json::Value::as_object) + .context("schema-9 parametric result requires an object source_metadata field")?; + let config = object + .get("config") + .and_then(serde_json::Value::as_object) + .context("schema-9 parametric result requires an object config field")?; + if !config.contains_key("marginal_likelihood") { + bail!("schema-9 parametric result requires config.marginal_likelihood"); + } + let record: Self = + serde_json::from_value(raw).context("failed to parse parametric result")?; + if record.schema_version != PARAMETRIC_RESULT_SCHEMA_VERSION { + bail!( + "unsupported parametric result schema version {}", + record.schema_version + ); + } + if record.fit_family != "parametric" || record.algorithm != "saem" { + bail!("JSON record is not a parametric SAEM result"); + } + if record.objective_kind != "conditional_n2ll" { + bail!( + "unsupported parametric objective kind '{}'", + record.objective_kind + ); + } + record + .config + .validate() + .context("schema-9 parametric result contains invalid retained SAEM configuration")?; + validate_persisted_marginal_likelihood(&record)?; + validate_persisted_information_criteria(&record)?; + validate_persisted_n6(&record)?; + Ok(record) + } +} diff --git a/src/results/summary.rs b/src/results/summary.rs index c6769f38e..577f2f61f 100644 --- a/src/results/summary.rs +++ b/src/results/summary.rs @@ -1,5 +1,9 @@ use serde::{Deserialize, Serialize}; +use crate::estimation::parametric::{ConditionalCurvatureDiagnostics, ShrinkageDiagnostics}; +use crate::estimation::MarginalLikelihoodStatus; +use crate::results::{InformationCriteriaDiagnostics, PopulationUncertaintyDiagnostics}; + #[derive(Debug, Clone, Serialize, Deserialize, PartialEq)] pub struct FitSummary { pub objective_function: f64, @@ -8,20 +12,31 @@ pub struct FitSummary { pub subject_count: usize, pub observation_count: usize, pub parameter_count: usize, + pub marginal_log_likelihood: Option, + pub marginal_n2ll: Option, + pub marginal_n2ll_mcse: Option, + pub marginal_likelihood_status: Option, + pub information_criteria: Option, } #[derive(Debug, Clone, Serialize, Deserialize, PartialEq)] pub struct PopulationSummary { pub parameters: Vec, + pub information_criteria: Option, + /// Parametric observed-information covariance and conditioning diagnostics. + pub population_uncertainty: Option, + /// Source-explicit eta/kappa posterior-mean and MAP shrinkage. + pub shrinkage: Option, } #[derive(Debug, Clone, Serialize, Deserialize, PartialEq)] pub struct ParameterSummary { pub name: String, - pub mean: f64, - pub median: f64, - pub sd: f64, - pub cv_percent: f64, + pub estimate: f64, + pub mean: Option, + pub median: Option, + pub sd: Option, + pub cv_percent: Option, } #[derive(Debug, Clone, Serialize, Deserialize, PartialEq)] @@ -30,4 +45,6 @@ pub struct IndividualSummary { pub parameter_names: Vec, pub estimates: Vec, pub standard_errors: Option>, + /// Joint eta/kappa curvature in exact flattened subject/occasion order. + pub conditional_uncertainty: Option, } diff --git a/tests/fixtures/combined_residual.csv b/tests/fixtures/combined_residual.csv new file mode 100644 index 000000000..6ac2663b8 --- /dev/null +++ b/tests/fixtures/combined_residual.csv @@ -0,0 +1,401 @@ +ID,TIME,EVID,DOSE,DUR,ADDL,II,INPUT,OUT,OUTEQ,CENS,C0,C1,C2,C3 +combined-001,0,1,50,0.5,.,.,iv,.,.,.,.,.,.,. +combined-001,0.5,0,.,.,.,.,.,3.16322519358134668,cp,.,.,.,.,. +combined-001,1,0,.,.,.,.,.,2.41852502947601433,cp,.,.,.,.,. +combined-001,2,0,.,.,.,.,.,2.00066023986848407,cp,.,.,.,.,. +combined-001,4,0,.,.,.,.,.,0.87394133683132613,cp,.,.,.,.,. +combined-002,0,1,100,0.5,.,.,iv,.,.,.,.,.,.,. +combined-002,0.5,0,.,.,.,.,.,5.01258618132855904,cp,.,.,.,.,. +combined-002,1,0,.,.,.,.,.,4.16636843001983603,cp,.,.,.,.,. +combined-002,2,0,.,.,.,.,.,2.98931222320249868,cp,.,.,.,.,. +combined-002,4,0,.,.,.,.,.,1.56666354216395831,cp,.,.,.,.,. +combined-003,0,1,150,0.5,.,.,iv,.,.,.,.,.,.,. 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+iov-048,1,0.5,4.07025179226894718,. +iov-048,1,1,3.01746182997021783,. +iov-048,1,2,2.64560303879768188,. diff --git a/tests/iov_diffusion_optimizer.rs b/tests/iov_diffusion_optimizer.rs new file mode 100644 index 000000000..bccf055d1 --- /dev/null +++ b/tests/iov_diffusion_optimizer.rs @@ -0,0 +1,311 @@ +use faer::Mat; +use pharmsol::equation::{metadata, ModelKind, Route}; +use pharmsol::{fa, lag, Data, SubjectBuilderExt, SDE}; +use pmcore::iov::{DiffusionConfig, DiffusionOptimize}; +use pmcore::prelude::{BoundedParameter, ParameterSpace, Posterior, Theta}; +use pmcore::{AssayErrorModel, AssayErrorModels, ErrorPoly}; + +fn model() -> SDE { + SDE::new( + |x, p, _t, dx, _rateiv, _cov| dx[0] = -p[0] * x[0], + |p, diffusion| diffusion[0] = p[1], + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, _p, _t, _cov, y| y[0] = x[0], + 64, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata( + metadata::new("iov_diffusion_optimizer") + .kind(ModelKind::Sde) + .parameters(["ke", "diff"]) + .states(["central"]) + .outputs(["cp"]) + .route( + Route::bolus("dose") + .to_state("central") + .inject_input_to_destination(), + ) + .particles(64), + ) + .unwrap() +} + +fn data() -> Data { + Data::new(vec![pharmsol::Subject::builder("id") + .bolus(0.0, 10.0, "dose") + .observation(0.5, 6.0, "cp") + .observation(1.0, 3.7, "cp") + .build()]) +} + +fn theta() -> Theta { + theta_with_diffusion(0.001, 0.1, &[0.01]) +} + +fn theta_with_diffusion(lower: f64, upper: f64, values: &[f64]) -> Theta { + let parameters = ParameterSpace::::new() + .add("ke", 0.5, 1.5) + .add("diff", lower, upper); + Theta::from_parts( + Mat::from_fn( + values.len(), + 2, + |row, column| { + if column == 0 { + 1.0 + } else { + values[row] + } + }, + ), + parameters, + ) + .unwrap() +} + +fn error_models() -> AssayErrorModels { + AssayErrorModels::new() + .add( + "cp", + AssayErrorModel::additive(ErrorPoly::new(0.5, 0.0, 0.0, 0.0), 0.0), + ) + .unwrap() +} + +#[test] +fn diffusion_optimizer_mutates_selected_column_and_returns_per_point_results() { + let sde = model(); + let data = data(); + let mut theta = theta(); + let errors = error_models(); + let result = sde + .optimize_diffusion( + &data, + &mut theta, + &["diff".to_string()], + &errors, + None, + DiffusionConfig { + max_iter: 2, + resampling_samples: 1, + ..DiffusionConfig::default() + }, + ) + .unwrap(); + + assert_eq!(result.per_point_likelihood.len(), 1); + assert_eq!(result.per_point_iterations.len(), 1); + assert_eq!(result.per_point_converged.len(), 1); + assert_eq!(theta.matrix()[(0, 0)], 1.0); + assert!((0.001..=0.1).contains(&theta.matrix()[(0, 1)])); + assert!(result.per_point_likelihood[0].is_finite()); +} + +#[test] +fn diffusion_optimizer_rejects_invalid_public_config_without_mutating_theta() { + let sde = model(); + let data = data(); + let errors = error_models(); + let invalid_configs = [ + DiffusionConfig { + max_iter: 0, + ..DiffusionConfig::default() + }, + DiffusionConfig { + sd_tolerance: f64::NAN, + ..DiffusionConfig::default() + }, + DiffusionConfig { + sd_tolerance: 0.0, + ..DiffusionConfig::default() + }, + DiffusionConfig { + initial_perturbation: 0.0, + ..DiffusionConfig::default() + }, + DiffusionConfig { + initial_perturbation: 1.01, + ..DiffusionConfig::default() + }, + DiffusionConfig { + resampling_samples: 0, + ..DiffusionConfig::default() + }, + ]; + + for config in invalid_configs { + let mut theta = theta(); + let before = theta.matrix().clone(); + assert!(sde + .optimize_diffusion( + &data, + &mut theta, + &["diff".to_string()], + &errors, + None, + config, + ) + .is_err()); + assert_eq!(theta.matrix(), &before); + } +} + +#[test] +fn diffusion_optimizer_rejects_duplicate_diffusion_parameter() { + let sde = model(); + let data = data(); + let mut theta = theta(); + let before = theta.matrix().clone(); + let error = sde + .optimize_diffusion( + &data, + &mut theta, + &["diff".to_string(), "diff".to_string()], + &error_models(), + None, + DiffusionConfig::default(), + ) + .unwrap_err(); + + assert!(error.to_string().contains("duplicate diffusion parameter")); + assert_eq!(theta.matrix(), &before); +} + +#[test] +fn diffusion_optimizer_rejects_negative_diffusion_bounds_without_mutating_theta() { + let sde = model(); + for (lower, upper, initial) in [(-1.0, -0.1, -0.5), (-0.1, 0.1, 0.0)] { + let mut theta = theta_with_diffusion(lower, upper, &[initial]); + let before = theta.matrix().clone(); + let error = sde + .optimize_diffusion( + &data(), + &mut theta, + &["diff".to_string()], + &error_models(), + None, + DiffusionConfig::default(), + ) + .unwrap_err(); + + assert!(error.to_string().contains("nonnegative inclusive bounds")); + assert_eq!(theta.matrix(), &before); + } +} + +#[test] +fn diffusion_optimizer_rejects_invalid_initial_values_without_mutating_theta() { + let sde = model(); + for initial in [f64::NAN, -0.001, 0.101] { + let mut theta = theta_with_diffusion(0.0, 0.1, &[initial]); + let before = theta.matrix().clone(); + let error = sde + .optimize_diffusion( + &data(), + &mut theta, + &["diff".to_string()], + &error_models(), + None, + DiffusionConfig::default(), + ) + .unwrap_err(); + + assert!(error.to_string().contains("initial diffusion parameter")); + assert_eq!(theta.matrix()[(0, 0)].to_bits(), before[(0, 0)].to_bits()); + assert_eq!(theta.matrix()[(0, 1)].to_bits(), before[(0, 1)].to_bits()); + } +} + +#[test] +fn diffusion_optimizer_rejects_posterior_row_mismatch_without_mutating_theta() { + let sde = model(); + let mut theta = theta(); + let before = theta.matrix().clone(); + let posterior = Posterior::from(Mat::from_fn(2, 1, |_, _| 1.0)); + let error = sde + .optimize_diffusion( + &data(), + &mut theta, + &["diff".to_string()], + &error_models(), + Some(&posterior), + DiffusionConfig::default(), + ) + .unwrap_err(); + + assert!(error.to_string().contains("posterior row count")); + assert_eq!(theta.matrix(), &before); +} + +#[test] +fn diffusion_optimizer_rejects_posterior_column_mismatch_without_mutating_theta() { + let sde = model(); + let mut theta = theta(); + let before = theta.matrix().clone(); + let posterior = Posterior::from(Mat::from_fn(1, 2, |_, _| 0.5)); + let error = sde + .optimize_diffusion( + &data(), + &mut theta, + &["diff".to_string()], + &error_models(), + Some(&posterior), + DiffusionConfig::default(), + ) + .unwrap_err(); + + assert!(error.to_string().contains("posterior column count")); + assert_eq!(theta.matrix(), &before); +} + +#[test] +fn diffusion_optimizer_rejects_non_finite_posterior_without_mutating_theta() { + let sde = model(); + let mut theta = theta(); + let before = theta.matrix().clone(); + let posterior = Posterior::from(Mat::from_fn(1, 1, |_, _| f64::NAN)); + let error = sde + .optimize_diffusion( + &data(), + &mut theta, + &["diff".to_string()], + &error_models(), + Some(&posterior), + DiffusionConfig::default(), + ) + .unwrap_err(); + + assert!(error.to_string().contains("posterior value")); + assert_eq!(theta.matrix(), &before); +} + +#[test] +fn diffusion_optimizer_propagates_particle_filter_failure_without_mutating_theta() { + let sde = model(); + let data = data(); + let mut theta = theta(); + let before = theta.matrix().clone(); + let error = sde + .optimize_diffusion( + &data, + &mut theta, + &["diff".to_string()], + &AssayErrorModels::new(), + None, + DiffusionConfig { + max_iter: 1, + resampling_samples: 1, + ..DiffusionConfig::default() + }, + ) + .unwrap_err(); + + assert!(error + .to_string() + .contains("diffusion optimization failed for support point 0")); + assert_eq!(theta.matrix(), &before); +} diff --git a/tests/ode_scoring_parity.rs b/tests/ode_scoring_parity.rs new file mode 100644 index 000000000..b4b63d71d --- /dev/null +++ b/tests/ode_scoring_parity.rs @@ -0,0 +1,179 @@ +use pharmsol::equation::{metadata, AnalyticalKernel, ModelKind, Route}; +use pharmsol::prelude::models::one_compartment; +use pharmsol::{fa, fetch_params, lag, Analytical, Equation, Parameters, SubjectBuilderExt, ODE}; +use pmcore::{ + AssayErrorModel, AssayErrorModels, AssayLikelihoodError, ErrorModelError, ErrorPoly, + NormalDistributionError, +}; + +/// Scientific check that analytical and ODE predictions score to matching +/// likelihoods. +#[test] +fn likelihood_calculation_matches_analytical() { + let subject = pharmsol::Subject::builder("likelihood") + .bolus(0.0, 100.0, "iv_bolus") + .observation(1.0, 1.8, "cp") + .observation(2.0, 1.6, "cp") + .observation(4.0, 1.3, "cp") + .observation(8.0, 0.8, "cp") + .build(); + + let analytical = Analytical::new( + one_compartment, + |_p, _t, _cov| {}, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, _ke, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_nout(1) + .with_ndrugs(1) + .with_metadata( + metadata::new("likelihood_calculation") + .kind(ModelKind::Analytical) + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["cp"]) + .routes([ + Route::bolus("iv_bolus").to_state("central"), + Route::infusion("iv").to_state("central"), + ]) + .analytical_kernel(AnalyticalKernel::OneCompartment), + ) + .unwrap(); + + let ode = ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke, _v); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, _ke, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_nout(1) + .with_ndrugs(1) + .with_metadata( + metadata::new("likelihood_calculation") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["cp"]) + .routes([ + Route::bolus("iv_bolus") + .to_state("central") + .expect_explicit_input(), + Route::infusion("iv") + .to_state("central") + .expect_explicit_input(), + ]), + ) + .unwrap(); + + let error_models = AssayErrorModels::from(vec![AssayErrorModel::additive( + ErrorPoly::new(0.0, 0.1, 0.0, 0.0), + 0.0, + )]); + let analytical_params = Parameters::with_model(&analytical, [("ke", 0.1), ("v", 50.0)]) + .expect("analytical parameters should validate"); + let ode_params = Parameters::with_model(&ode, [("ke", 0.1), ("v", 50.0)]) + .expect("ODE parameters should validate"); + + let analytical_predictions = analytical + .estimate_predictions(&subject, &analytical_params) + .expect("analytical predictions"); + let ode_predictions = ode + .estimate_predictions(&subject, &ode_params) + .expect("ODE predictions"); + + let dense_log_likelihood = error_models + .log_likelihood(&analytical_predictions) + .expect("PMcore dense analytical likelihood"); + let named_error_models = AssayErrorModels::new() + .add( + "cp", + AssayErrorModel::additive(ErrorPoly::new(0.0, 0.1, 0.0, 0.0), 0.0), + ) + .expect("named cp assay model"); + let canonical_outputs = analytical + .metadata() + .expect("analytical model metadata") + .outputs() + .iter() + .map(|output| output.name()); + let bound_log_likelihood = named_error_models + .bind_outputs(canonical_outputs.clone()) + .expect("bind canonical analytical outputs") + .log_likelihood(&analytical_predictions) + .expect("score bound named assay model"); + let convenience_log_likelihood = named_error_models + .log_likelihood_for_outputs(&analytical_predictions, canonical_outputs) + .expect("bind and score named assay model"); + assert_eq!(bound_log_likelihood, dense_log_likelihood); + assert_eq!(convenience_log_likelihood, dense_log_likelihood); + + let unbound_multi_output = named_error_models + .clone() + .add( + "effect", + AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 0.0), + ) + .expect("second named assay model"); + assert!(matches!( + unbound_multi_output.log_likelihood(&analytical_predictions), + Err(AssayLikelihoodError::ErrorModel( + ErrorModelError::UnboundOutputModels { outputs } + )) if outputs == ["cp", "effect"] + )); + + let ll_analytical = dense_log_likelihood.exp(); + let ll_ode = error_models + .log_likelihood(&ode_predictions) + .expect("PMcore ODE likelihood") + .exp(); + + let ll_diff = (ll_analytical - ll_ode).abs(); + let ll_rel_diff = ll_diff / ll_analytical.abs().max(1e-10); + + assert!( + ll_rel_diff < 0.01, // Within 1% + "Likelihoods should match: analytical={:.6}, ode={:.6}, rel_diff={:.2e}", + ll_analytical, + ll_ode, + ll_rel_diff + ); + + let zero_sigma_models = AssayErrorModels::from(vec![AssayErrorModel::additive( + ErrorPoly::new(0.0, 0.0, 0.0, 0.0), + 0.0, + )]); + assert!(matches!( + zero_sigma_models.log_likelihood(&analytical_predictions), + Err(AssayLikelihoodError::Distribution( + NormalDistributionError::InvalidSigma(sigma) + )) if sigma == 0.0 + )); + + let impossible_subject = pharmsol::Subject::builder("impossible_likelihood") + .observation(1.0, 1e308, "cp") + .build(); + let impossible_predictions = analytical + .estimate_predictions(&impossible_subject, &analytical_params) + .expect("analytical impossible-score predictions"); + let constant_sigma_models = AssayErrorModels::from(vec![AssayErrorModel::additive( + ErrorPoly::new(1.0, 0.0, 0.0, 0.0), + 0.0, + )]); + assert!(matches!( + constant_sigma_models.log_likelihood(&impossible_predictions), + Err(AssayLikelihoodError::Impossible) + )); +} diff --git a/tests/ode_solver_profile.rs b/tests/ode_solver_profile.rs new file mode 100644 index 000000000..8d7508830 --- /dev/null +++ b/tests/ode_solver_profile.rs @@ -0,0 +1,240 @@ +use pharmsol::prelude::*; +use pharmsol::Predictions; + +const RELEASE_RTOL: f64 = 1e-8; +const RELEASE_ATOL: f64 = 1e-10; + +fn configure(model: equation::ODE) -> equation::ODE { + model + .with_solver(OdeSolver::Bdf) + .with_tolerances(RELEASE_RTOL, RELEASE_ATOL) +} + +fn one_compartment() -> equation::ODE { + configure(ode! { + name: "ode_solver_profile_one_compartment", + params: [ke], + states: [central], + outputs: [amount], + routes: [ + bolus(iv_bolus) -> central, + infusion(iv_infusion) -> central, + ], + diffeq: |x, _t, dx| { + dx[central] = -ke * x[central]; + }, + out: |x, _t, y| { + y[amount] = x[central]; + }, + }) +} + +fn two_compartment() -> equation::ODE { + configure(ode! { + name: "ode_solver_profile_two_compartment", + params: [k10, k12, k21], + states: [central, peripheral], + outputs: [central_amount, peripheral_amount], + routes: [ + bolus(iv_bolus) -> central, + ], + diffeq: |x, _t, dx| { + dx[central] = -(k10 + k12) * x[central] + k21 * x[peripheral]; + dx[peripheral] = k12 * x[central] - k21 * x[peripheral]; + }, + out: |x, _t, y| { + y[central_amount] = x[central]; + y[peripheral_amount] = x[peripheral]; + }, + }) +} + +fn within_d1(actual: f64, expected: f64) -> bool { + let absolute = (actual - expected).abs(); + let relative = absolute / expected.abs().max(f64::MIN_POSITIVE); + absolute <= 1e-6 || relative <= 1e-4 +} + +fn assert_d1(label: &str, actual: f64, expected: f64) { + assert!(actual.is_finite(), "{label}: actual is non-finite"); + assert!(expected.is_finite(), "{label}: oracle is non-finite"); + assert!( + within_d1(actual, expected), + "{label}: actual={actual:.16e}, expected={expected:.16e}, absolute={:.6e}, relative={:.6e}", + (actual - expected).abs(), + (actual - expected).abs() / expected.abs().max(f64::MIN_POSITIVE), + ); +} + +fn predictions( + label: &str, + model: &equation::ODE, + subject: &Subject, + parameters: &[f64], +) -> Vec { + model + .estimate_predictions_dense(subject, parameters) + .unwrap_or_else(|error| panic!("{label}: solver-profile prediction failed: {error}")) + .get_predictions() +} + +#[test] +fn one_compartment_scale_and_time_panel_meets_d1() { + let cases: &[(f64, f64, &[f64])] = &[ + (1e-4, 1e-4, &[0.01, 1.0, 100.0, 10_000.0]), + (100.0, 0.1, &[0.001, 0.1, 1.0, 10.0, 100.0]), + (1e6, 20.0, &[1e-5, 1e-4, 0.001, 0.01, 0.1, 1.0]), + ]; + + for (case_index, &(dose, ke, times)) in cases.iter().enumerate() { + let mut builder = Subject::builder(format!("s1-{case_index}")).bolus(0.0, dose, "iv_bolus"); + for &time in times { + builder = builder.observation(time, 0.0, "amount"); + } + let subject = builder.build(); + let release = predictions( + &format!("S1 case {case_index} release"), + &one_compartment(), + &subject, + &[ke], + ); + assert_eq!(release.len(), times.len()); + + for (index, (release_point, &time)) in release.iter().zip(times).enumerate() { + let expected = dose * (-ke * time).exp(); + assert_eq!(release_point.time().to_bits(), time.to_bits()); + assert_eq!(release_point.outeq(), 0); + assert_eq!(release_point.state().len(), 1); + assert_d1( + &format!("S1 case {case_index} point {index} release output"), + release_point.prediction(), + expected, + ); + assert_d1( + &format!("S1 case {case_index} point {index} release state"), + release_point.state()[0], + expected, + ); + } + } +} + +fn bolus_contribution(amount: f64, event_time: f64, time: f64, ke: f64) -> f64 { + if time <= event_time { + 0.0 + } else { + amount * (-ke * (time - event_time)).exp() + } +} + +fn infusion_contribution(amount: f64, start: f64, duration: f64, time: f64, ke: f64) -> f64 { + if time <= start { + return 0.0; + } + let rate = amount / duration; + let elapsed_infusion = (time - start).min(duration); + let amount_at_elapsed = rate / ke * (1.0 - (-ke * elapsed_infusion).exp()); + if time <= start + duration { + amount_at_elapsed + } else { + amount_at_elapsed * (-ke * (time - start - duration)).exp() + } +} + +#[test] +fn event_driven_bolus_and_infusion_panel_meets_d1() { + let ke = 0.17; + let times = [0.5, 3.5, 6.5, 7.5, 9.5, 20.0]; + let mut builder = Subject::builder("s2") + .bolus(0.0, 100.0, "iv_bolus") + .infusion(3.0, 80.0, "iv_infusion", 4.0) + .bolus(9.0, 25.0, "iv_bolus"); + for time in times { + builder = builder.observation(time, 0.0, "amount"); + } + let subject = builder.build(); + let release = predictions("S2 release", &one_compartment(), &subject, &[ke]); + assert_eq!(release.len(), times.len()); + + for (index, (release_point, time)) in release.iter().zip(times).enumerate() { + let expected = bolus_contribution(100.0, 0.0, time, ke) + + infusion_contribution(80.0, 3.0, 4.0, time, ke) + + bolus_contribution(25.0, 9.0, time, ke); + assert_eq!(release_point.time().to_bits(), time.to_bits()); + assert_eq!(release_point.outeq(), 0); + assert_eq!(release_point.state().len(), 1); + assert_d1( + &format!("S2 point {index} release output"), + release_point.prediction(), + expected, + ); + assert_d1( + &format!("S2 point {index} release state"), + release_point.state()[0], + expected, + ); + } +} + +fn two_compartment_closed_form(dose: f64, k10: f64, k12: f64, k21: f64, time: f64) -> [f64; 2] { + let sum = k10 + k12 + k21; + let discriminant = (sum * sum - 4.0 * k10 * k21).sqrt(); + let alpha = 0.5 * (sum + discriminant); + let beta = 0.5 * (sum - discriminant); + let denominator = alpha - beta; + let fast = (-alpha * time).exp(); + let slow = (-beta * time).exp(); + let central = dose * ((alpha - k21) * fast + (k21 - beta) * slow) / denominator; + let peripheral = dose * k12 * (slow - fast) / denominator; + [central, peripheral] +} + +#[test] +fn two_compartment_stiffness_panel_meets_d1() { + let cases: &[(f64, f64, f64, &[f64])] = &[ + (0.1, 0.2, 0.15, &[0.001, 0.1, 1.0, 5.0, 20.0, 100.0]), + (0.05, 25.0, 0.1, &[1e-5, 1e-4, 0.001, 0.01, 0.1, 1.0, 20.0]), + (2.0, 0.02, 30.0, &[1e-5, 1e-4, 0.001, 0.01, 0.1, 1.0, 5.0]), + ]; + let dose = 100.0; + + for (case_index, &(k10, k12, k21, times)) in cases.iter().enumerate() { + let mut builder = Subject::builder(format!("s3-{case_index}")).bolus(0.0, dose, "iv_bolus"); + for &time in times { + builder = builder + .observation(time, 0.0, "central_amount") + .observation(time, 0.0, "peripheral_amount"); + } + let subject = builder.build(); + let parameters = [k10, k12, k21]; + let release = predictions( + &format!("S3 case {case_index} release"), + &two_compartment(), + &subject, + ¶meters, + ); + assert_eq!(release.len(), 2 * times.len()); + + for (index, release_point) in release.iter().enumerate() { + let time_index = index / 2; + let output_index = index % 2; + let time = times[time_index]; + let expected = two_compartment_closed_form(dose, k10, k12, k21, time); + assert_eq!(release_point.time().to_bits(), time.to_bits()); + assert_eq!(release_point.outeq(), output_index); + assert_eq!(release_point.state().len(), 2); + assert_d1( + &format!("S3 case {case_index} point {index} release output"), + release_point.prediction(), + expected[output_index], + ); + for (state_index, expected_state) in expected.iter().copied().enumerate() { + assert_d1( + &format!("S3 case {case_index} point {index} release state {state_index}"), + release_point.state()[state_index], + expected_state, + ); + } + } + } +} diff --git a/tests/onecomp.rs b/tests/onecomp.rs index c63509b52..072292bac 100644 --- a/tests/onecomp.rs +++ b/tests/onecomp.rs @@ -130,9 +130,15 @@ fn test_one_compartment_npod() -> Result<()> { let result = EstimationProblem::nonparametric(eq, data, prior, error_models)? .fit_with(NonParametricAlgorithm::npod())?; - // Check the results - assert_eq!(result.cycles(), 11); - assert!(result.objf() - 565.7749 < 0.01); + // Convergence and the final objective are stable; the exact number of + // optimization cycles may vary with numerically equivalent support points. + assert!(result.converged()); + let objective = result.objf(); + assert!(objective.is_finite()); + assert!( + objective <= 85.13, + "NPOD objective exceeded the regression bound: {objective}" + ); Ok(()) } diff --git a/tests/particle_filter_scientific.rs b/tests/particle_filter_scientific.rs new file mode 100644 index 000000000..93d2bc285 --- /dev/null +++ b/tests/particle_filter_scientific.rs @@ -0,0 +1,74 @@ +use pharmsol::equation::{metadata, ModelKind, Route}; +use pharmsol::{fa, lag, Parameters, SubjectBuilderExt, SDE}; +use pmcore::{AssayErrorModel, AssayErrorModels, ErrorPoly, SdeParticleConfig, SdeParticleFilter}; + +/// Scientific check that the SDE particle-filter likelihood stays finite. +#[test] +fn test_particle_filter_likelihood() { + let subject = pharmsol::Subject::builder("id1") + .bolus(0.0, 20.0, "dose") + .observation(0.2, 16.6434, "cp") + .observation(0.4, 14.3233, "cp") + .observation(0.6, 9.8468, "cp") + .observation(0.8, 9.4177, "cp") + .observation(1.0, 7.5170, "cp") + .build(); + + let sde = SDE::new( + |x, p, _t, dx, _rateiv, _cov| { + dx[0] = -x[0] * x[1]; + dx[1] = -x[1] + p[0]; + }, + |_p, diffusion| { + diffusion[0] = 1.0; + diffusion[1] = 0.01; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, x| x[1] = 1.0, + |x, _p, _t, _cov, y| y[0] = x[0], + 10_000, + ) + .with_nstates(2) + .with_ndrugs(1) + .with_nout(1) + .with_metadata( + metadata::new("particle_filter_test") + .kind(ModelKind::Sde) + .parameters(["ke0"]) + .states(["central", "ke_latent"]) + .outputs(["cp"]) + .route( + Route::bolus("dose") + .to_state("central") + .inject_input_to_destination(), + ) + .particles(10_000), + ) + .expect("particle filter metadata should validate"); + + let error_models = AssayErrorModels::new() + .add( + "cp", + AssayErrorModel::additive(ErrorPoly::new(0.5, 0.0, 0.0, 0.0), 0.0), + ) + .unwrap(); + + const NUM_RUNS: usize = 10; + let mut likelihoods = Vec::with_capacity(NUM_RUNS); + for run in 0..NUM_RUNS { + let parameters = Parameters::with_model(&sde, [("ke0", 1.0)]).unwrap(); + let config = SdeParticleConfig::new(10_000) + .with_process_seed(run as u64) + .with_resampling_seed(10_000 + run as u64); + likelihoods.push( + sde.particle_filter(&subject, ¶meters, &error_models, &config) + .unwrap() + .log_value + .exp(), + ); + } + + let mean = likelihoods.iter().sum::() / NUM_RUNS as f64; + assert!(mean.is_finite(), "Mean likelihood should be finite"); +} diff --git a/tests/results_summary_tests.rs b/tests/results_summary_tests.rs index 88363e4b9..2875210cb 100644 --- a/tests/results_summary_tests.rs +++ b/tests/results_summary_tests.rs @@ -1,5 +1,4 @@ use anyhow::Result; -use pharmsol::{AssayErrorModel, ErrorPoly}; use pmcore::prelude::*; fn simple_equation() -> equation::ODE { @@ -56,7 +55,17 @@ fn test_nonparametric_fit_result_summary_surface() -> Result<()> { assert_eq!(summary.parameter_count, 2); assert_eq!(summary.subject_count, 1); assert_eq!(summary.observation_count, 2); - assert_eq!(result.population_summary().parameters.len(), 2); + let population = result.population_summary(); + assert_eq!(population.parameters.len(), 2); + for parameter in population.parameters { + assert_eq!( + parameter.estimate, + parameter.mean.expect("mean should be available") + ); + assert!(parameter.median.is_some()); + assert!(parameter.sd.is_some()); + assert!(parameter.cv_percent.is_some()); + } assert_eq!(result.individual_summaries().len(), 1); Ok(()) diff --git a/tests/saem_correlated_residual.rs b/tests/saem_correlated_residual.rs new file mode 100644 index 000000000..7f6466403 --- /dev/null +++ b/tests/saem_correlated_residual.rs @@ -0,0 +1,321 @@ +use std::fs; +use std::time::{SystemTime, UNIX_EPOCH}; + +use pharmsol::prelude::*; +use pmcore::prelude::*; +use pmcore::results::{CovarianceCycleUpdateOutcome, InformationCoordinateKind}; + +fn equation() -> pharmsol::equation::Analytical { + analytical! { + name: "n8_correlated_residual_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + } +} + +fn data() -> Data { + Data::new(vec![ + Subject::builder("n8-1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.0, "cp") + .observation(6.0, 1.5, "cp") + .reset() + .infusion(12.0, 120.0, "iv", 0.5) + .observation(13.0, 5.7, "cp") + .observation(15.0, 3.5, "cp") + .observation(18.0, 1.8, "cp") + .build(), + Subject::builder("n8-2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.2, "cp") + .observation(3.0, 3.3, "cp") + .observation(6.0, 1.7, "cp") + .reset() + .infusion(12.0, 120.0, "iv", 0.5) + .observation(13.0, 6.1, "cp") + .observation(15.0, 3.8, "cp") + .observation(18.0, 2.0, "cp") + .build(), + Subject::builder("n8-3") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.5, "cp") + .observation(3.0, 2.8, "cp") + .observation(6.0, 1.4, "cp") + .reset() + .infusion(12.0, 120.0, "iv", 0.5) + .observation(13.0, 5.4, "cp") + .observation(15.0, 3.3, "cp") + .observation(18.0, 1.6, "cp") + .build(), + Subject::builder("n8-4") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.0, "cp") + .observation(3.0, 3.1, "cp") + .observation(6.0, 1.6, "cp") + .reset() + .infusion(12.0, 120.0, "iv", 0.5) + .observation(13.0, 5.9, "cp") + .observation(15.0, 3.6, "cp") + .observation(18.0, 1.9, "cp") + .build(), + ]) +} + +fn problem() -> EstimationProblem { + EstimationProblem::parametric(equation(), data()) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::diagonal([("ke", 0.05)])) + .iov(Iov::diagonal([("ke", 0.03)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::correlated_combined(0.3, 0.12, 0.1)) + .fixed_correlated_combined_additive(), + ) + .build() + .expect("N8 analytical problem") +} + +fn config() -> SaemConfig { + SaemConfig::new() + .seed(0x6e38_2026) + .n_chains(2) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(2) + .k2_iterations(1) + .compute_map(false) +} + +fn scalar_nll(residual: f64, variance: f64) -> f64 { + 0.5 * (variance.ln() + residual * residual / variance) +} + +fn pair_nll(residual: [f64; 2], omega: f64, variances: [f64; 2]) -> f64 { + let a = omega + variances[0]; + let b = omega; + let d = omega + variances[1]; + let determinant = a * d - b * b; + let quadratic = (d * residual[0] * residual[0] - 2.0 * b * residual[0] * residual[1] + + a * residual[1] * residual[1]) + / determinant; + 0.5 * (determinant.ln() + quadratic) +} + +#[test] +fn declaration_domains_are_strict_and_fixed_free_controls_are_independent() { + for model in [ + ResidualErrorModel::correlated_combined(0.0, 0.1, 0.0), + ResidualErrorModel::correlated_combined(0.1, 0.0, 0.0), + ResidualErrorModel::correlated_combined(0.1, 0.1, -1.0), + ResidualErrorModel::correlated_combined(0.1, 0.1, 1.0), + ResidualErrorModel::correlated_combined(0.1, 0.1, f64::NAN), + ] { + let built = EstimationProblem::parametric(equation(), data()) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.05)) + .error_model("cp", model) + .build(); + assert!( + built.is_err(), + "invalid declaration was accepted: {model:?}" + ); + } + + for free in [ + [true, true, true], + [false, true, true], + [true, false, true], + [true, true, false], + [false, false, true], + [false, true, false], + [true, false, false], + [false, false, false], + ] { + let declaration = + ParametricErrorModel::new(ResidualErrorModel::correlated_combined(0.3, 0.12, -0.2)) + .with_correlated_combined_additive_estimate(free[0]) + .with_correlated_combined_proportional_estimate(free[1]) + .with_correlated_combined_correlation_estimate(free[2]); + let built = EstimationProblem::parametric(equation(), data()) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.05)) + .error_model("cp", declaration) + .build(); + assert!(built.is_ok(), "fixed/free mask {free:?}"); + } +} + +#[test] +fn iov_and_residual_identifiability_is_design_dependent() { + let residual = 0.4; + assert_eq!( + scalar_nll(residual, 0.2 + 0.5), + scalar_nll(residual, 0.3 + 0.4) + ); + + let repeated_residual = [0.4, -0.2]; + let first = pair_nll(repeated_residual, 0.2, [0.5, 0.5]); + let second = pair_nll(repeated_residual, 0.3, [0.4, 0.4]); + assert!((first - second).abs() > 1e-6); + + let truth = [0.7_f64, 0.25_f64, -0.3_f64]; + let one_prediction = 2.0_f64; + let one_variance = truth[0].powi(2) + + 2.0 * truth[2] * truth[0] * truth[1] * one_prediction + + truth[1].powi(2) * one_prediction.powi(2); + let alternative_a = 0.5_f64; + let alternative_b = 0.3_f64; + let alternative_rho = + (one_variance - alternative_a.powi(2) - alternative_b.powi(2) * one_prediction.powi(2)) + / (2.0 * alternative_a * alternative_b * one_prediction); + assert!(alternative_rho > -1.0 && alternative_rho < 1.0); + let alternative_variance = alternative_a.powi(2) + + 2.0 * alternative_rho * alternative_a * alternative_b * one_prediction + + alternative_b.powi(2) * one_prediction.powi(2); + assert!((one_variance - alternative_variance).abs() < 1e-14); + + let levels = [-1.0_f64, 0.0, 2.0]; + let design_determinant = + (levels[1] - levels[0]) * (levels[2] - levels[0]) * (levels[2] - levels[1]); + assert!(design_determinant.abs() > 0.0); + assert!(levels.into_iter().any(|prediction| { + let true_variance = truth[0].powi(2) + + 2.0 * truth[2] * truth[0] * truth[1] * prediction + + truth[1].powi(2) * prediction.powi(2); + let other_variance = alternative_a.powi(2) + + 2.0 * alternative_rho * alternative_a * alternative_b * prediction + + alternative_b.powi(2) * prediction.powi(2); + (true_variance - other_variance).abs() > 1e-6 + })); +} + +#[test] +fn short_iiv_iov_fit_routes_components_and_schema_nine_roundtrips() { + let result = problem().fit_with(config()).expect("N8 short fit"); + let residual = &result.residual_error_estimates()[0]; + let ResidualErrorModel::CorrelatedCombined { a, b, rho } = residual.model else { + panic!("correlated-combined family was not retained") + }; + assert_eq!(a, 0.3); + assert!(b.is_finite() && b > 0.0); + assert!(rho.is_finite() && rho > -1.0 && rho < 1.0); + assert_eq!(residual.combined_additive_estimated, Some(false)); + assert_eq!(residual.combined_proportional_estimated, Some(true)); + assert_eq!(residual.correlation_estimated, Some(true)); + assert!(result.omega().iter().all(|value| value.is_finite())); + assert!(result + .omega_iov() + .expect("IOV covariance") + .iter() + .all(|value| value.is_finite())); + + for cycle in result.cycle_diagnostics() { + assert!(!matches!( + cycle.omega_update.outcome, + CovarianceCycleUpdateOutcome::NotAttempted { .. } + )); + assert!(!matches!( + cycle.omega_iov_update.outcome, + CovarianceCycleUpdateOutcome::NotAttempted { .. } + )); + let diagnostic = cycle.residual_diagnostic("cp").unwrap(); + assert_eq!(diagnostic.prediction_evaluation_count, 48); + assert!(diagnostic.optimizer_objective.is_some()); + assert!(diagnostic.optimizer_iterations.is_some()); + assert!(!diagnostic.update_rejected); + } + + let tables = result.tables(0.0, 0.0).unwrap(); + assert_eq!( + tables + .residual_error + .iter() + .map(|row| (row.component.as_str(), row.estimated)) + .collect::>(), + [ + ("additive", false), + ("proportional", true), + ("correlation", true) + ] + ); + assert!(result + .information_diagnostics() + .coordinates + .iter() + .any(|coordinate| { + matches!( + &coordinate.kind, + InformationCoordinateKind::Residual { + component, + .. + } if component == "correlation" + ) + })); + + let unique = SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos(); + let directory = std::env::temp_dir().join(format!("pmcore-n8-{unique}")); + result.write_outputs(&directory, 0.0, 0.0).unwrap(); + let path = directory.join("result.json"); + let record = ParametricResultRecord::read_json(&path).unwrap(); + assert_eq!(record.schema_version, 9); + assert_eq!(record.tables.residual_error, tables.residual_error); + + let live_warm = result + .warm_start_problem() + .unwrap() + .fit_with(config()) + .unwrap(); + let persisted_warm = record + .warm_start_problem(equation(), data()) + .unwrap() + .fit_with(config()) + .unwrap(); + for warm in [&live_warm, &persisted_warm] { + let ResidualErrorModel::CorrelatedCombined { a, b, rho } = + warm.residual_error_estimates()[0].model + else { + panic!("warm start changed residual family") + }; + assert_eq!(a, 0.3); + assert!(b.is_finite() && b > 0.0); + assert!(rho.is_finite() && rho > -1.0 && rho < 1.0); + } + + let mut tampered: serde_json::Value = + serde_json::from_reader(fs::File::open(&path).unwrap()).unwrap(); + tampered["source_metadata"]["residual_outputs"][0]["values"][2] = serde_json::json!(1.0); + let tampered_path = directory.join("tampered.json"); + fs::write( + &tampered_path, + serde_json::to_vec_pretty(&tampered).unwrap(), + ) + .unwrap(); + assert!(ParametricResultRecord::read_json(&tampered_path).is_err()); + fs::remove_dir_all(directory).unwrap(); +} diff --git a/tests/saem_covariates.rs b/tests/saem_covariates.rs new file mode 100644 index 000000000..f74e31dce --- /dev/null +++ b/tests/saem_covariates.rs @@ -0,0 +1,296 @@ +use pmcore::prelude::*; + +fn analytical_model() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_covariate_integration", + params: [ke, v, bio, frac], + states: [central], + outputs: [cp], + routes: [bolus(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = bio * frac * x[central] / v; }, + } +} + +fn ode_model() -> pharmsol::equation::ODE { + ode! { + name: "saem_covariate_integration", + params: [ke, v, bio, frac], + states: [central], + outputs: [cp], + routes: [bolus(iv) -> central], + diffeq: |x, _t, dx| { dx[central] = -ke * x[central]; }, + out: |x, _t, y| { y[cp] = bio * frac * x[central] / v; }, + } +} + +fn data() -> Data { + let weights = [55.0, 74.0, 83.0, 61.0, 79.0, 68.0]; + Data::new( + (0..6) + .map(|index| { + let wt = weights[index]; + let group = (index % 3) as f64; + let group_effect = if group == 1.0 { + 0.18 + } else if group == 2.0 { + -0.14 + } else { + 0.0 + }; + let ke = (0.12_f64.ln() + 0.012 * (wt - 70.0) + group_effect).exp(); + let frac_phi = ((0.65_f64 - 0.2) / (0.9 - 0.65)).ln() + 0.004 * (wt - 70.0); + let frac = 0.2 + 0.7 / (1.0 + (-frac_phi).exp()); + let mut subject = Subject::builder(format!("covariate_{index}")) + .covariate("wt", 0.0, wt) + .covariate("group", 0.0, group) + .bolus(0.0, 100.0, "iv"); + for time in [0.5, 1.0, 2.0, 4.0, 8.0, 12.0] { + subject = + subject.observation(time, frac * 100.0 * (-ke * time).exp() / 45.0, "cp"); + } + subject.build() + }) + .collect(), + ) +} + +fn problem(model: E) -> anyhow::Result> +where + E: pharmsol::Equation + EquationMetadataSource, +{ + EstimationProblem::parametric(model, data()) + .parameter(Parameter::log("ke").with_initial(0.14)) + .parameter(Parameter::log("v").with_initial(45.0).fixed()) + .parameter(Parameter::probit("bio", 0.5, 1.5).with_initial(1.0).fixed()) + .parameter( + Parameter::logit("frac", 0.2, 0.9) + .with_initial(0.65) + .fixed() + .without_random_effect(), + ) + .omega( + Omega::new() + .variance("ke", 0.08) + .fixed_variance("v", 0.04) + .fixed_variance("bio", 0.03) + .fixed_covariance("ke", "v", 0.012), + ) + .covariate_effect(CovariateEffect::continuous("ke", "wt", 70.0).with_initial(0.006)) + .covariate_effect(CovariateEffect::categorical("ke", "group", 0.0, 1.0).with_initial(0.10)) + .covariate_effect(CovariateEffect::categorical("ke", "group", 0.0, 2.0).with_initial(-0.08)) + .covariate_effect( + CovariateEffect::continuous("frac", "wt", 70.0) + .with_initial(0.004) + .fixed(), + ) + .error_model("cp", ResidualErrorModel::constant(0.12)) + .build() +} + +fn signed_zero_builder( + effects: impl IntoIterator, + observed: f64, +) -> anyhow::Result> { + let data = Data::new(vec![Subject::builder("signed_zero") + .covariate("group", 0.0, observed) + .bolus(0.0, 100.0, "iv") + .observation(1.0, 1.0, "cp") + .build()]); + let mut builder = EstimationProblem::parametric(analytical_model(), data) + .parameter(Parameter::log("ke").with_initial(0.12).fixed()) + .parameter( + Parameter::log("v") + .with_initial(45.0) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::probit("bio", 0.5, 1.5) + .with_initial(1.0) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::logit("frac", 0.2, 0.9) + .with_initial(0.65) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.08)); + for effect in effects { + builder = builder.covariate_effect(effect); + } + builder + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.12)).fixed(), + ) + .build() +} + +fn config(seed: u64) -> SaemConfig { + SaemConfig::new() + .seed(seed) + .n_chains(1) + .mcmc_iterations(1) + .eta_block_iterations(0) + .burn_in(1) + // Include one actual exploration cycle: k1 counts burn-in plus + // exploration, so k1 == burn_in would skip exploration entirely. + .k1_iterations(2) + .k2_iterations(1) + .compute_map(false) +} + +fn assert_result(result: &ParametricResult) { + let cycles = result.cycle_diagnostics(); + assert_eq!(cycles.len(), 3); + assert_eq!(cycles[0].phase, SaemPhase::BurnIn); + assert_eq!(cycles[1].phase, SaemPhase::Exploration); + assert_eq!(cycles[2].phase, SaemPhase::Smoothing); + assert_eq!(cycles[0].stochastic_approximation_step, 0.0); + assert_eq!(cycles[1].stochastic_approximation_step, 1.0); + assert_eq!(cycles[2].stochastic_approximation_step, 1.0); + assert!(matches!( + cycles[0].omega_update.outcome, + CovarianceCycleUpdateOutcome::NotAttempted { + reason: CovarianceUpdateNotAttemptedReason::BurnIn + } + )); + assert!(cycles[0].omega_update.proposal.is_none()); + assert!(cycles[0].omega_update.solved_target.is_none()); + for cycle in cycles { + assert!(matches!( + cycle.omega_iov_update.outcome, + CovarianceCycleUpdateOutcome::NotAttempted { + reason: CovarianceUpdateNotAttemptedReason::NotConfigured + } + )); + assert!(cycle.omega_iov_update.proposal.is_none()); + assert!(cycle.omega_iov_update.solved_target.is_none()); + } + for cycle in &cycles[1..] { + assert!(cycle.omega_update.proposal.is_some()); + assert!(cycle.omega_update.solved_target.is_some()); + assert!(!matches!( + cycle.omega_update.outcome, + CovarianceCycleUpdateOutcome::NotAttempted { .. } + )); + } + assert!(cycles[1] + .omega_update + .attempted_fractions + .first() + .is_some_and(|fraction| *fraction <= 0.1)); + assert_eq!( + cycles[2].omega_update.attempted_fractions.first(), + Some(&1.0) + ); + + for (actual, expected) in result.population_parameters()[1..] + .iter() + .zip([45.0, 1.0, 0.65]) + { + assert!((actual - expected).abs() <= 1e-12); + } + let effects = result.covariate_estimates().unwrap(); + assert_eq!(effects.len(), 4); + assert_eq!(effects[0].name(), "beta:ke:wt"); + assert_eq!(effects[1].name(), "beta:ke:group:1"); + assert_eq!(effects[2].name(), "beta:ke:group:2"); + assert_eq!(effects[3].name(), "beta:frac:wt"); + assert_eq!(effects[3].estimate(), 0.004); + assert!(result.population_parameters().iter().all(|x| x.is_finite())); + assert!(result + .covariate_subject_population_parameters() + .unwrap() + .unwrap() + .iter() + .all(|row| { + row.phi().iter().all(|x| x.is_finite()) + && row.psi()[2] > 0.5 + && row.psi()[2] < 1.5 + && row.psi()[3] > 0.2 + && row.psi()[3] < 0.9 + })); + assert_eq!(result.omega()[[0, 1]], 0.012); + assert_eq!(result.omega()[[0, 2]], 0.0); + assert_eq!(result.omega()[[1, 2]], 0.0); +} + +#[test] +fn analytical_model_exercises_covariate_and_covariance_declarations() { + let result = problem(analytical_model()) + .unwrap() + .fit_with(config(9001)) + .unwrap(); + assert_result(&result); +} + +#[test] +fn ode_model_exercises_covariate_and_covariance_declarations() { + let result = problem(ode_model()) + .unwrap() + .fit_with(config(9002)) + .unwrap(); + assert_result(&result); +} + +#[test] +fn signed_zero_category_levels_use_one_canonical_identity() { + let duplicate = signed_zero_builder( + [ + CovariateEffect::categorical("ke", "group", 1.0, 0.0) + .with_initial(0.1) + .fixed(), + CovariateEffect::categorical("ke", "group", 1.0, -0.0) + .with_initial(0.2) + .fixed(), + ], + -0.0, + ) + .unwrap_err() + .to_string(); + assert!(duplicate.contains("duplicate"), "{duplicate}"); + + let collision = signed_zero_builder( + [CovariateEffect::categorical("ke", "group", -0.0, 0.0) + .with_initial(0.1) + .fixed()], + 0.0, + ) + .unwrap_err() + .to_string(); + assert!(collision.contains("reference"), "{collision}"); + + let accepted = signed_zero_builder( + [CovariateEffect::categorical("ke", "group", 1.0, 0.0) + .with_initial(0.1) + .fixed()], + -0.0, + ) + .unwrap(); + let model = accepted.covariates().unwrap(); + assert_eq!( + model.subject_values()[0].value().to_bits(), + 0.0f64.to_bits() + ); + assert_eq!(model.subject_design()[0].values(), &[1.0]); +} + +#[test] +fn nonlinear_constraint_is_explicitly_rejected() { + let error = EstimationProblem::parametric(analytical_model(), data()) + .parameter(Parameter::log("ke").with_initial(0.12)) + .parameter(Parameter::log("v").with_initial(45.0)) + .parameter(Parameter::probit("bio", 0.5, 1.5).with_initial(1.0)) + .parameter(Parameter::logit("frac", 0.2, 0.9).with_initial(0.65)) + .constraint(ParametricConstraint::nonlinear("ke * v <= 10")) + .error_model("cp", ResidualErrorModel::constant(0.12)) + .build() + .unwrap_err(); + assert!(error + .to_string() + .contains("unsupported nonlinear parametric constraint")); +} diff --git a/tests/saem_information_criteria.rs b/tests/saem_information_criteria.rs new file mode 100644 index 000000000..d6f3c1c2f --- /dev/null +++ b/tests/saem_information_criteria.rs @@ -0,0 +1,840 @@ +use std::fs; +use std::sync::atomic::{AtomicUsize, Ordering}; +use std::time::{SystemTime, UNIX_EPOCH}; + +use pharmsol::prelude::*; +use pmcore::prelude::*; + +static EQUATION_CALLS: AtomicUsize = AtomicUsize::new(0); + +fn exact_problem() -> EstimationProblem { + let equation = analytical! { + name: "n3_exact_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + exact_problem_from_equation(equation) +} + +fn instrumented_exact_problem() -> EstimationProblem { + let equation = analytical! { + name: "n3_instrumented_exact_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + EQUATION_CALLS.fetch_add(1, Ordering::SeqCst); + y[cp] = x[central] / v; + }, + }; + exact_problem_from_equation(equation) +} + +fn exact_problem_from_equation( + equation: pharmsol::equation::Analytical, +) -> EstimationProblem { + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new()) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.35)).fixed(), + ) + .build() + .expect("exact N3 fixture") +} + +fn mixed_problem() -> EstimationProblem { + let equation = analytical! { + name: "n3_mixed_count_fixture", + params: [ke, v, bio], + states: [central], + outputs: [cp, amount], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = bio * x[central] / v; + y[amount] = x[central]; + }, + }; + let subject = |id: &str, shift: f64| { + Subject::builder(id) + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.9 + shift, "cp") + .observation(1.0, 98.0 + shift, "amount") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.7 + shift, "cp") + .observation(13.0, 96.0 + shift, "amount") + .build() + }; + EstimationProblem::parametric( + equation, + Data::new(vec![subject("one", 0.0), subject("two", 0.2)]), + ) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .parameter( + Parameter::log("bio") + .with_initial(1.0) + .fixed() + .without_random_effect(), + ) + .omega( + Omega::new() + .variance("ke", 0.09) + .fixed_variance("v", 0.16) + .covariance("ke", "v", 0.02), + ) + .iov( + Iov::new() + .variance("ke", 0.04) + .fixed_variance("v", 0.09) + .fixed_covariance("ke", "v", 0.01), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::combined(0.3, 0.05)) + .fixed_combined_proportional(), + ) + .error_model( + "amount", + ParametricErrorModel::new(ResidualErrorModel::proportional(0.05)).fixed(), + ) + .build() + .expect("mixed N3 count fixture") +} + +fn config(seed: u64) -> SaemConfig { + SaemConfig::new() + .seed(seed) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(true) +} + +fn n2(seed: u64) -> MarginalLikelihoodConfig { + MarginalLikelihoodConfig::new(64, seed, 5, 1.5) +} + +fn temp_dir(label: &str) -> std::path::PathBuf { + std::env::temp_dir().join(format!( + "pmcore-n3-{label}-{}-{}", + std::process::id(), + SystemTime::now() + .duration_since(UNIX_EPOCH) + .expect("clock") + .as_nanos() + )) +} + +fn replace_json_string(value: &mut serde_json::Value, old: &str, new: &str) { + match value { + serde_json::Value::String(text) if text == old => *text = new.to_string(), + serde_json::Value::Array(values) => { + for value in values { + replace_json_string(value, old, new); + } + } + serde_json::Value::Object(values) => { + for value in values.values_mut() { + replace_json_string(value, old, new); + } + } + _ => {} + } +} + +#[test] +fn exact_n2_drives_aic_bic_and_not_requested_is_explicit() { + let result = exact_problem() + .fit_with(config(91).marginal_likelihood(n2(901))) + .expect("exact N3 fit"); + let n2ll = result.marginal_n2ll().expect("exact marginal N2LL"); + let mcse = result.marginal_n2ll_mcse().expect("exact N2LL MCSE"); + assert_eq!(result.free_parameter_count(), 0); + assert_eq!(result.aic(), Some(n2ll)); + assert_eq!(result.bic(), Some(n2ll)); + assert_eq!(result.aic_mcse().unwrap().to_bits(), mcse.to_bits()); + assert_eq!(result.bic_mcse().unwrap().to_bits(), mcse.to_bits()); + assert_eq!( + result.information_criteria().status, + InformationCriteriaStatus::Available + ); + assert_eq!( + result.information_criteria().sample_size_convention, + InformationCriteriaSampleSizeConvention::IndependentSubjects + ); + assert_eq!(result.information_criteria().subject_count, 2); + assert_eq!( + result.summary().information_criteria, + Some(result.information_criteria().clone()) + ); + assert_eq!( + result.population_summary().information_criteria, + Some(result.information_criteria().clone()) + ); + + let disabled = exact_problem() + .fit_with(config(92)) + .expect("N2-disabled N3 fit"); + assert_eq!( + disabled.information_criteria().status, + InformationCriteriaStatus::NotRequested + ); + assert_eq!((disabled.aic(), disabled.bic()), (None, None)); + assert!(disabled.marginal_likelihood_diagnostics().is_none()); +} + +#[test] +fn mixed_real_metadata_counts_each_free_coordinate_once() { + let result = mixed_problem() + .fit_with(config(93).marginal_likelihood(n2(903))) + .expect("mixed count N3 fit"); + assert_eq!( + result.information_criteria().parameter_count, + InformationCriteriaParameterCount { + population: 1, + covariate: 0, + omega: 2, + omega_iov: 1, + residual: 1, + total: 5, + } + ); + let source_subjects = match result.marginal_likelihood_status().unwrap() { + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects } => subjects.clone(), + status => panic!("expected nonconverged-mode source status, got {status:?}"), + }; + assert_eq!( + result.information_criteria().status, + InformationCriteriaStatus::AvailableWithNonconvergedModes { + subjects: source_subjects, + } + ); + let n2ll = result.marginal_n2ll().expect("mixed marginal N2LL"); + assert!((result.aic().unwrap() - (n2ll + 10.0)).abs() <= 1e-12); + assert!((result.bic().unwrap() - (n2ll + 5.0 * 2.0_f64.ln())).abs() <= 1e-12); +} + +#[test] +fn criteria_access_is_pure_and_seeded_fit_boundary_is_bit_exact() { + // The criteria API takes neither an equation nor an RNG. Repeating the + // complete seeded fit/N2 boundary bit-exactly is the observable RNG- + // isolation regression; no production RNG hook is needed. + EQUATION_CALLS.store(0, Ordering::SeqCst); + let first = instrumented_exact_problem() + .fit_with(config(94).marginal_likelihood(n2(904))) + .expect("first instrumented N3 fit"); + let first_fit_calls = EQUATION_CALLS.load(Ordering::SeqCst); + assert!(first_fit_calls > 0); + let first_tables = first.tables(0.0, 0.0).expect("first tables"); + for _ in 0..10 { + let _ = first.information_criteria(); + let _ = ( + first.aic(), + first.bic(), + first.aic_mcse(), + first.bic_mcse(), + first.summary(), + first.population_summary(), + ); + } + let first_directory = temp_dir("isolation-first"); + first + .write_outputs(&first_directory, 0.0, 0.0) + .expect("write first complete outputs"); + let first_record = ParametricResultRecord::read_json(first_directory.join("result.json")) + .expect("read first schema-six output"); + let _ = ( + &first_record.information_criteria, + &first_record.tables.information_criteria, + ); + assert_eq!(EQUATION_CALLS.load(Ordering::SeqCst), first_fit_calls); + + EQUATION_CALLS.store(0, Ordering::SeqCst); + let second = instrumented_exact_problem() + .fit_with(config(94).marginal_likelihood(n2(904))) + .expect("repeated instrumented N3 fit"); + let second_fit_calls = EQUATION_CALLS.load(Ordering::SeqCst); + assert_eq!(second_fit_calls, first_fit_calls); + assert!(second_fit_calls > 0); + let second_tables = second.tables(0.0, 0.0).expect("second tables"); + let second_directory = temp_dir("isolation-second"); + second + .write_outputs(&second_directory, 0.0, 0.0) + .expect("write second complete outputs"); + let second_record = ParametricResultRecord::read_json(second_directory.join("result.json")) + .expect("read second schema-six output"); + let _ = &second_record.information_criteria; + assert_eq!(EQUATION_CALLS.load(Ordering::SeqCst), second_fit_calls); + + assert_eq!( + second.conditional_n2ll().to_bits(), + first.conditional_n2ll().to_bits() + ); + assert_eq!( + second.population_parameters().len(), + first.population_parameters().len() + ); + for (second_estimate, first_estimate) in second + .population_parameters() + .iter() + .zip(first.population_parameters()) + { + assert_eq!(second_estimate.to_bits(), first_estimate.to_bits()); + } + assert_eq!( + serde_json::to_vec(second.cycle_diagnostics()).unwrap(), + serde_json::to_vec(first.cycle_diagnostics()).unwrap() + ); + assert_eq!( + serde_json::to_vec(&second.marginal_likelihood_diagnostics()).unwrap(), + serde_json::to_vec(&first.marginal_likelihood_diagnostics()).unwrap() + ); + assert_eq!( + serde_json::to_vec(second.information_criteria()).unwrap(), + serde_json::to_vec(first.information_criteria()).unwrap() + ); + assert_eq!( + serde_json::to_vec(&second_tables).unwrap(), + serde_json::to_vec(&first_tables).unwrap() + ); + assert_eq!( + fs::read(second_directory.join("result.json")).unwrap(), + fs::read(first_directory.join("result.json")).unwrap() + ); + for file in [ + "population.csv", + "omega.csv", + "residual_error.csv", + "individual_effects.csv", + "individual_parameters.csv", + "iterations.csv", + "statistics.csv", + "marginal_likelihood.csv", + "information_criteria.csv", + "predictions.csv", + "covariate_effects.csv", + "subject_covariates.csv", + "subject_population_parameters.csv", + "manifest.json", + ] { + assert_eq!( + fs::read(second_directory.join(file)).unwrap(), + fs::read(first_directory.join(file)).unwrap(), + "deterministic output mismatch in {file}" + ); + } + fs::remove_dir_all(first_directory).expect("remove first isolation directory"); + fs::remove_dir_all(second_directory).expect("remove second isolation directory"); +} + +#[test] +fn schema_six_outputs_validate_and_reject_tampering_and_old_schemas() { + let result = mixed_problem() + .fit_with(config(95).marginal_likelihood(n2(905))) + .expect("schema N3 fit"); + let directory = temp_dir("schema"); + result + .write_outputs(&directory, 0.0, 0.0) + .expect("write schema-six outputs"); + let result_path = directory.join("result.json"); + let record = ParametricResultRecord::read_json(&result_path).expect("load schema six"); + assert_eq!(record.schema_version, 9); + assert_eq!( + record.information_criteria.status, + result.information_criteria().status + ); + assert_eq!( + record.information_criteria.parameter_count, + result.information_criteria().parameter_count + ); + let persisted_bic = record.information_criteria.bic.unwrap(); + let in_memory_bic = result.information_criteria().bic.unwrap(); + assert!( + (persisted_bic - in_memory_bic).abs() + <= 64.0 * f64::EPSILON * persisted_bic.abs().max(in_memory_bic.abs()).max(1.0) + ); + assert_eq!(record.tables.information_criteria.len(), 1); + let csv = fs::read_to_string(directory.join("information_criteria.csv")) + .expect("read information criteria CSV"); + let mut reader = csv::Reader::from_reader(csv.as_bytes()); + let headers = reader + .headers() + .expect("information criteria headers") + .clone(); + let rows = reader + .records() + .collect::, _>>() + .expect("parse information criteria CSV"); + assert_eq!(rows.len(), 1); + let column = |name: &str| headers.iter().position(|header| header == name).unwrap(); + assert_eq!( + &rows[0][column("status")], + record.tables.information_criteria[0].status + ); + assert_eq!( + rows[0][column("free_parameter_count")] + .parse::() + .unwrap(), + record.tables.information_criteria[0].free_parameter_count + ); + let csv_bic = rows[0][column("bic")].parse::().unwrap(); + let json_bic = record.tables.information_criteria[0].bic.unwrap(); + assert!( + (csv_bic - json_bic).abs() + <= 64.0 * f64::EPSILON * csv_bic.abs().max(json_bic.abs()).max(1.0) + ); + let manifest: serde_json::Value = + serde_json::from_reader(fs::File::open(directory.join("manifest.json")).expect("manifest")) + .expect("parse manifest"); + assert_eq!(manifest["schema_version"], 9); + assert!(manifest["files"] + .as_array() + .unwrap() + .contains(&serde_json::json!("information_criteria.csv"))); + + let original: serde_json::Value = + serde_json::from_reader(fs::File::open(&result_path).unwrap()).unwrap(); + assert_eq!(record.source_metadata.parameters.len(), 3); + assert_eq!(record.source_metadata.random_effects.len(), 2); + assert_eq!(record.source_metadata.omega.dimension, 2); + assert_eq!(record.source_metadata.iov_effects.len(), 2); + assert_eq!( + record.source_metadata.omega_iov.as_ref().unwrap().dimension, + 2 + ); + assert_eq!(record.source_metadata.residual_outputs.len(), 2); + + let mut missing = original.clone(); + missing + .as_object_mut() + .unwrap() + .remove("information_criteria"); + fs::write(&result_path, serde_json::to_vec_pretty(&missing).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&result_path).is_err()); + + let mut tampered = original.clone(); + tampered["information_criteria"]["aic"] = serde_json::json!(123.0); + fs::write(&result_path, serde_json::to_vec_pretty(&tampered).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&result_path).is_err()); + + let reject = |value: &serde_json::Value| { + fs::write(&result_path, serde_json::to_vec_pretty(value).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&result_path).is_err()); + }; + + // Coordinated final/table/statistic changes still cannot alter immutable + // fixed declarations captured before cycle one. + let mut fixed_theta = original.clone(); + let changed_theta = fixed_theta["source_metadata"]["parameters"][1]["estimate"] + .as_f64() + .unwrap() + + 1.0; + fixed_theta["source_metadata"]["parameters"][1]["estimate"] = serde_json::json!(changed_theta); + fixed_theta["tables"]["population"][1]["estimate"] = serde_json::json!(changed_theta); + for row in fixed_theta["tables"]["statistics"].as_array_mut().unwrap() { + if row["kind"] == "theta" && row["name"] == "v" { + row["value"] = serde_json::json!(changed_theta); + } + } + reject(&fixed_theta); + + let mut fixed_omega = original.clone(); + let changed_omega = fixed_omega["source_metadata"]["omega"]["values"][1][1] + .as_f64() + .unwrap() + + 0.01; + fixed_omega["source_metadata"]["omega"]["values"][1][1] = serde_json::json!(changed_omega); + fixed_omega["tables"]["omega"][2]["estimate"] = serde_json::json!(changed_omega); + for row in fixed_omega["tables"]["statistics"].as_array_mut().unwrap() { + if row["kind"] == "omega" && row["row"] == "v" && row["column"] == "v" { + row["value"] = serde_json::json!(changed_omega); + } + } + reject(&fixed_omega); + + let mut fixed_omega_iov = original.clone(); + let changed_omega_iov = fixed_omega_iov["source_metadata"]["omega_iov"]["values"][1][1] + .as_f64() + .unwrap() + + 0.01; + fixed_omega_iov["source_metadata"]["omega_iov"]["values"][1][1] = + serde_json::json!(changed_omega_iov); + fixed_omega_iov["tables"]["omega_iov"][2]["estimate"] = serde_json::json!(changed_omega_iov); + for row in fixed_omega_iov["tables"]["statistics"] + .as_array_mut() + .unwrap() + { + if row["kind"] == "omega_iov" && row["row"] == "v" && row["column"] == "v" { + row["value"] = serde_json::json!(changed_omega_iov); + } + } + reject(&fixed_omega_iov); + + let mut fixed_combined_residual = original.clone(); + let changed_proportional = fixed_combined_residual["source_metadata"]["residual_outputs"][0] + ["values"][1] + .as_f64() + .unwrap() + + 0.01; + fixed_combined_residual["source_metadata"]["residual_outputs"][0]["values"][1] = + serde_json::json!(changed_proportional); + fixed_combined_residual["tables"]["residual_error"][1]["estimate"] = + serde_json::json!(changed_proportional); + for row in fixed_combined_residual["tables"]["statistics"] + .as_array_mut() + .unwrap() + { + if row["kind"] == "residual" && row["name"] == "cp" && row["component"] == "proportional" { + row["value"] = serde_json::json!(changed_proportional); + } + } + reject(&fixed_combined_residual); + + for field in ["initial_values", "initial_estimated_mask"] { + let mut missing_residual_initial = original.clone(); + missing_residual_initial["source_metadata"]["residual_outputs"][0] + .as_object_mut() + .unwrap() + .remove(field); + reject(&missing_residual_initial); + } + + let mut non_spd_initial = original.clone(); + non_spd_initial["source_metadata"]["omega"]["initial_values"][0][1] = serde_json::json!(10.0); + non_spd_initial["source_metadata"]["omega"]["initial_values"][1][0] = serde_json::json!(10.0); + reject(&non_spd_initial); + + let mut nonzero_initial_structural_zero = original.clone(); + for (row, column) in [(0, 1), (1, 0)] { + nonzero_initial_structural_zero["source_metadata"]["omega"]["structural_mask"][row] + [column] = serde_json::json!(false); + nonzero_initial_structural_zero["source_metadata"]["omega"]["estimated_mask"][row] + [column] = serde_json::json!(false); + nonzero_initial_structural_zero["source_metadata"]["omega"]["values"][row][column] = + serde_json::json!(0.0); + } + nonzero_initial_structural_zero["tables"]["omega"][1]["structural"] = serde_json::json!(false); + nonzero_initial_structural_zero["tables"]["omega"][1]["estimated"] = serde_json::json!(false); + nonzero_initial_structural_zero["tables"]["omega"][1]["estimate"] = serde_json::json!(0.0); + reject(&nonzero_initial_structural_zero); + + let mut missing_source = original.clone(); + missing_source + .as_object_mut() + .unwrap() + .remove("source_metadata"); + reject(&missing_source); + + let mut malformed_source = original.clone(); + malformed_source["source_metadata"]["omega"]["estimated_mask"][0] + .as_array_mut() + .unwrap() + .pop(); + reject(&malformed_source); + + let mut asymmetric_source = original.clone(); + asymmetric_source["source_metadata"]["omega"]["estimated_mask"][0][1] = + serde_json::json!(false); + asymmetric_source["source_metadata"]["omega"]["estimated_mask"][1][0] = serde_json::json!(true); + reject(&asymmetric_source); + + let mut unordered_source = original.clone(); + unordered_source["source_metadata"]["random_effects"][1]["parameter_index"] = + serde_json::json!(0); + reject(&unordered_source); + + let mut inconsistent_iov_source = original.clone(); + inconsistent_iov_source["source_metadata"]["omega_iov"] = serde_json::Value::Null; + reject(&inconsistent_iov_source); + + let mut malformed_residual_source = original.clone(); + malformed_residual_source["source_metadata"]["residual_outputs"][0]["estimated_mask"] = + serde_json::json!([]); + reject(&malformed_residual_source); + + for pointer in [ + "/source_metadata/omega/values", + "/source_metadata/omega/names", + "/source_metadata/residual_outputs/0/values", + ] { + let mut missing_value_snapshot = original.clone(); + let (parent, field) = pointer.rsplit_once('/').unwrap(); + missing_value_snapshot + .pointer_mut(parent) + .unwrap() + .as_object_mut() + .unwrap() + .remove(field); + reject(&missing_value_snapshot); + } + + let mut nonsymmetric_covariance = original.clone(); + nonsymmetric_covariance["source_metadata"]["omega"]["values"][0][1] = + serde_json::json!(0.012345); + reject(&nonsymmetric_covariance); + + let mut non_spd_covariance = original.clone(); + non_spd_covariance["source_metadata"]["omega"]["values"][0][1] = serde_json::json!(10.0); + non_spd_covariance["source_metadata"]["omega"]["values"][1][0] = serde_json::json!(10.0); + reject(&non_spd_covariance); + + let mut residual_value_mismatch = original.clone(); + residual_value_mismatch["source_metadata"]["residual_outputs"][0]["values"][0] = + serde_json::json!(0.123456); + reject(&residual_value_mismatch); + + // Finite coordinated table/statistic tampering cannot replace the + // independently bound covariance source snapshot. + let mut table_and_statistics = original.clone(); + let changed = table_and_statistics["tables"]["omega"][0]["estimate"] + .as_f64() + .unwrap() + + 0.001; + table_and_statistics["tables"]["omega"][0]["estimate"] = serde_json::json!(changed); + for row in table_and_statistics["tables"]["statistics"] + .as_array_mut() + .unwrap() + { + if row["kind"] == "omega" && row["row"] == "ke" && row["column"] == "ke" { + row["value"] = serde_json::json!(changed); + } + } + reject(&table_and_statistics); + + // Conversely, coordinated source/statistic tampering cannot replace the + // unchanged structured covariance table. + let mut source_and_statistics = original.clone(); + source_and_statistics["source_metadata"]["omega"]["values"][0][0] = serde_json::json!(changed); + for row in source_and_statistics["tables"]["statistics"] + .as_array_mut() + .unwrap() + { + if row["kind"] == "omega" && row["row"] == "ke" && row["column"] == "ke" { + row["value"] = serde_json::json!(changed); + } + } + reject(&source_and_statistics); + + // Coordinated mutable population derivatives are changed together: the + // population free flag moves to a fixed covariance diagonal, coordinates + // and criteria count families follow, while canonical source metadata is + // deliberately untouched. + let mut coordinated_population = original.clone(); + coordinated_population["tables"]["population"][0]["estimated"] = serde_json::json!(false); + coordinated_population["tables"]["omega"][2]["estimated"] = serde_json::json!(true); + let coordinates = coordinated_population["information_diagnostics"]["coordinates"] + .as_array_mut() + .unwrap(); + coordinates.remove(0); + coordinates.insert( + 2, + serde_json::json!({ + "index": 2, + "name": "omega:v:v", + "kind": {"Omega": {"row": 1, "column": 1}} + }), + ); + for (index, coordinate) in coordinates.iter_mut().enumerate() { + coordinate["index"] = serde_json::json!(index); + } + coordinated_population["information_criteria"]["parameter_count"]["population"] = + serde_json::json!(0); + coordinated_population["information_criteria"]["parameter_count"]["omega"] = + serde_json::json!(3); + coordinated_population["tables"]["information_criteria"][0]["population_parameter_count"] = + serde_json::json!(0); + coordinated_population["tables"]["information_criteria"][0]["omega_parameter_count"] = + serde_json::json!(3); + for row in coordinated_population["tables"]["statistics"] + .as_array_mut() + .unwrap() + { + match row["name"].as_str() { + Some("population_parameter_count") => row["value"] = serde_json::json!(0.0), + Some("omega_parameter_count") => row["value"] = serde_json::json!(3.0), + _ => {} + } + } + reject(&coordinated_population); + + // Covariance structure/free status and every mutable coordinate derivative + // move together; the independent source masks still reject the record. + let mut coordinated_covariance = original.clone(); + coordinated_covariance["tables"]["omega"][1]["structural"] = serde_json::json!(false); + coordinated_covariance["tables"]["omega"][1]["estimated"] = serde_json::json!(false); + coordinated_covariance["tables"]["omega"][1]["estimate"] = serde_json::json!(0.0); + coordinated_covariance["tables"]["omega"][2]["estimated"] = serde_json::json!(true); + coordinated_covariance["information_diagnostics"]["coordinates"][2] = serde_json::json!({ + "index": 2, + "name": "omega:v:v", + "kind": {"Omega": {"row": 1, "column": 1}} + }); + replace_json_string( + &mut coordinated_covariance["tables"]["statistics"], + "omega:v:ke", + "omega:v:v", + ); + reject(&coordinated_covariance); + + // Residual family/component declarations and all mutable labels are + // synchronized, but the untouched canonical family/component mask wins. + let mut coordinated_residual = original.clone(); + coordinated_residual["tables"]["residual_error"] + .as_array_mut() + .unwrap() + .remove(1); + coordinated_residual["tables"]["residual_error"][0]["family"] = serde_json::json!("constant"); + coordinated_residual["tables"]["residual_error"][0]["component"] = serde_json::json!("sigma"); + coordinated_residual["information_diagnostics"]["coordinates"][4]["name"] = + serde_json::json!("residual:cp:sigma"); + coordinated_residual["information_diagnostics"]["coordinates"][4]["kind"]["Residual"] + ["component"] = serde_json::json!("sigma"); + replace_json_string( + &mut coordinated_residual["tables"]["statistics"], + "additive", + "sigma", + ); + replace_json_string( + &mut coordinated_residual["tables"]["statistics"], + "residual:cp:additive", + "residual:cp:sigma", + ); + reject(&coordinated_residual); + + let mut coordinate = original.clone(); + coordinate["information_diagnostics"]["coordinates"][0]["index"] = serde_json::json!(7); + reject(&coordinate); + + let mut duplicate_population = original.clone(); + duplicate_population["tables"]["population"][1]["name"] = + duplicate_population["tables"]["population"][0]["name"].clone(); + reject(&duplicate_population); + + let mut reordered_population = original.clone(); + reordered_population["tables"]["population"] + .as_array_mut() + .unwrap() + .swap(0, 1); + reject(&reordered_population); + + let mut upper_triangle = original.clone(); + let lower_row = upper_triangle["tables"]["omega"][1]["row"].clone(); + let lower_column = upper_triangle["tables"]["omega"][1]["column"].clone(); + upper_triangle["tables"]["omega"][1]["row"] = lower_column; + upper_triangle["tables"]["omega"][1]["column"] = lower_row; + reject(&upper_triangle); + + let mut duplicate_covariance = original.clone(); + duplicate_covariance["tables"]["omega"][1] = duplicate_covariance["tables"]["omega"][0].clone(); + reject(&duplicate_covariance); + + let mut fixed_source = original.clone(); + fixed_source["tables"]["population"][0]["estimated"] = serde_json::json!(false); + reject(&fixed_source); + + let mut free_structural_zero = original.clone(); + free_structural_zero["tables"]["omega"][1]["structural"] = serde_json::json!(false); + free_structural_zero["tables"]["omega"][1]["estimated"] = serde_json::json!(true); + reject(&free_structural_zero); + + let mut unknown_component = original.clone(); + unknown_component["tables"]["residual_error"][0]["component"] = serde_json::json!("unknown"); + unknown_component["information_diagnostics"]["coordinates"][4]["kind"]["component"] = + serde_json::json!("unknown"); + reject(&unknown_component); + + let mut duplicate_residual = original.clone(); + let residual = duplicate_residual["tables"]["residual_error"][0].clone(); + duplicate_residual["tables"]["residual_error"] + .as_array_mut() + .unwrap() + .push(residual); + reject(&duplicate_residual); + + for field in [ + "delta", + "g", + "expected_complete_hessian", + "observed_hessian", + "observed_information", + ] { + let mut malformed_shape = original.clone(); + malformed_shape["information_diagnostics"][field] + .as_array_mut() + .unwrap() + .pop(); + reject(&malformed_shape); + } + + for schema in 1..=8 { + let mut old = original.clone(); + old["schema_version"] = serde_json::json!(schema); + fs::write(&result_path, serde_json::to_vec_pretty(&old).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&result_path).is_err()); + } + fs::remove_dir_all(directory).expect("remove N3 schema directory"); +} + +#[test] +fn fit_next_recomputes_criteria_from_child_configuration() { + let parent = exact_problem() + .fit_with(config(96).marginal_likelihood(n2(906))) + .expect("parent N3 fit"); + let disabled = parent.fit_next(config(97)).expect("disabled child"); + assert_eq!( + disabled.information_criteria().status, + InformationCriteriaStatus::NotRequested + ); + let enabled = parent + .fit_next(config(98).marginal_likelihood(n2(908))) + .expect("enabled child"); + assert_eq!( + enabled.information_criteria().status, + InformationCriteriaStatus::Available + ); + assert_ne!( + enabled + .marginal_likelihood_diagnostics() + .unwrap() + .config + .seed, + parent + .marginal_likelihood_diagnostics() + .unwrap() + .config + .seed + ); +} diff --git a/tests/saem_kernel.rs b/tests/saem_kernel.rs new file mode 100644 index 000000000..84573b93f --- /dev/null +++ b/tests/saem_kernel.rs @@ -0,0 +1,204 @@ +#[path = "../src/estimation/parametric/rank_diagnostics.rs"] +mod rank_diagnostics; + +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; +use rand_distr::{Distribution, StandardNormal}; +use rank_diagnostics::{bulk_ess, folded_split_rhat, rank_normalized_split_rhat}; + +const RHO: f64 = 0.995; +const COMPONENT_SCALE: f64 = 0.2; +const BLOCK_SCALE: f64 = 1.0; +const WARMUP: usize = 250; +const RETAINED: usize = 1_500; + +#[derive(Default)] +struct Counts { + block_proposals: usize, + block_accepts: usize, + component_proposals: usize, + component_accepts: usize, +} + +fn log_target(eta: [f64; 2]) -> f64 { + -0.5 / (1.0 - RHO * RHO) * (eta[0] * eta[0] - 2.0 * RHO * eta[0] * eta[1] + eta[1] * eta[1]) +} + +fn accept(current: [f64; 2], proposal: [f64; 2], uniform: f64) -> bool { + let log_ratio = log_target(proposal) - log_target(current); + log_ratio >= 0.0 || uniform.ln() < log_ratio +} + +fn block_proposal(current: [f64; 2], z: [f64; 2], scale: f64) -> [f64; 2] { + let conditional_sd = (1.0 - RHO * RHO).sqrt(); + [ + current[0] + scale * z[0], + current[1] + scale * (RHO * z[0] + conditional_sd * z[1]), + ] +} + +fn component_sweep(state: &mut [f64; 2], rng: &mut StdRng, counts: &mut Counts) { + for coordinate in 0..2 { + let z: f64 = StandardNormal.sample(rng); + let mut proposal = *state; + proposal[coordinate] += COMPONENT_SCALE * z; + counts.component_proposals += 1; + if accept(*state, proposal, rng.random()) { + *state = proposal; + counts.component_accepts += 1; + } + } +} + +fn transition(state: &mut [f64; 2], rng: &mut StdRng, block: bool, counts: &mut Counts) { + if block { + let proposal = block_proposal( + *state, + [StandardNormal.sample(rng), StandardNormal.sample(rng)], + BLOCK_SCALE, + ); + counts.block_proposals += 1; + if accept(*state, proposal, rng.random()) { + *state = proposal; + counts.block_accepts += 1; + } + } + component_sweep(state, rng, counts); +} + +fn run_chains(block: bool) -> (Vec>, Counts) { + let starts = [[-5.0, -5.0], [-2.0, -2.0], [2.0, 2.0], [5.0, 5.0]]; + let mut retained = Vec::with_capacity(4); + let mut total = Counts::default(); + for (chain, start) in starts.into_iter().enumerate() { + let mut state = start; + let mut rng = StdRng::seed_from_u64(0x4e37_2026 + chain as u64); + let mut counts = Counts::default(); + for _ in 0..WARMUP { + transition(&mut state, &mut rng, block, &mut counts); + } + let mut draws = Vec::with_capacity(RETAINED); + for _ in 0..RETAINED { + transition(&mut state, &mut rng, block, &mut counts); + draws.push(state); + } + total.block_proposals += counts.block_proposals; + total.block_accepts += counts.block_accepts; + total.component_proposals += counts.component_proposals; + total.component_accepts += counts.component_accepts; + retained.push(draws); + } + (retained, total) +} + +fn diagnostics(chains: &[Vec<[f64; 2]>]) -> (f64, f64) { + let features = [ + chains + .iter() + .map(|chain| chain.iter().map(|eta| eta[0]).collect::>()) + .collect::>(), + chains + .iter() + .map(|chain| chain.iter().map(|eta| eta[1]).collect::>()) + .collect::>(), + chains + .iter() + .map(|chain| { + chain + .iter() + .map(|eta| (eta[0] + eta[1]) / (2.0 * (1.0 + RHO)).sqrt()) + .collect::>() + }) + .collect::>(), + ]; + let mut max_rhat = 0.0_f64; + let mut min_ess = f64::INFINITY; + for feature in features { + max_rhat = max_rhat + .max(rank_normalized_split_rhat(&feature).unwrap()) + .max(folded_split_rhat(&feature).unwrap()); + min_ess = min_ess.min(bulk_ess(&feature).unwrap().0); + } + (max_rhat, min_ess) +} + +#[test] +fn fixed_reference_trace_has_symmetric_posterior_ratio_and_declared_adaptation() { + let lower = [[1.0, 0.0], [0.8, 0.6]]; + let normals = [[0.5, -1.0], [-0.25, 0.75], [1.2, 0.1], [-0.8, -0.4]]; + let uniforms = [0.2_f64, 0.9, 0.4, 0.7]; + let expected_trace = [ + [0.65, -0.3], + [0.525, -0.175], + [0.525, -0.175], + [0.525, -0.175], + ]; + let expected_ratios = [ + -0.9451955782312924, + 0.6944515306122447, + -2.211747363945578, + -0.4124850340136057, + ]; + let expected_scales = [0.55, 0.495]; + let log_posterior = |eta: [f64; 2]| { + let likelihood = + -0.5 * ((eta[0] - 0.3) / 0.5).powi(2) - 0.5 * ((eta[1] + 0.1) / 0.7).powi(2); + let prior = -0.5 / (1.0 - 0.8_f64.powi(2)) + * (eta[0].powi(2) - 1.6 * eta[0] * eta[1] + eta[1].powi(2)); + likelihood + prior + }; + + let mut eta = [0.4, -0.2]; + let mut scale = 0.5; + let mut accepted = 0; + for (step, (z, uniform)) in normals.iter().zip(uniforms).enumerate() { + let proposal = [ + eta[0] + scale * lower[0][0] * z[0], + eta[1] + scale * (lower[1][0] * z[0] + lower[1][1] * z[1]), + ]; + let ratio = log_posterior(proposal) - log_posterior(eta); + assert!((ratio - expected_ratios[step]).abs() < 1e-12); + let accepted_step = ratio >= 0.0 || uniform.ln() < ratio; + if accepted_step { + eta = proposal; + accepted += 1; + } + assert!((eta[0] - expected_trace[step][0]).abs() < 1e-12); + assert!((eta[1] - expected_trace[step][1]).abs() < 1e-12); + if (step + 1) % 2 == 0 { + scale = if accepted as f64 / 2.0 > 0.40 { + (scale * 1.1).min(5.0) + } else { + (scale * 0.9).max(1e-6) + }; + assert!((scale - expected_scales[step / 2]).abs() < 1e-12); + accepted = 0; + } + } +} + +#[test] +fn correlated_gaussian_mixing_comparison_is_characterization_not_tuning() { + let (component_chains, component_counts) = run_chains(false); + let (compound_chains, compound_counts) = run_chains(true); + let (component_rhat, component_ess) = diagnostics(&component_chains); + let (compound_rhat, compound_ess) = diagnostics(&compound_chains); + let component_acceptance = + component_counts.component_accepts as f64 / component_counts.component_proposals as f64; + let compound_component_acceptance = + compound_counts.component_accepts as f64 / compound_counts.component_proposals as f64; + let compound_block_acceptance = + compound_counts.block_accepts as f64 / compound_counts.block_proposals as f64; + + println!( + "N7 rho={RHO:.3} retained={RETAINED} component[accept={component_acceptance:.3},max_rhat={component_rhat:.4},min_bulk_ess={component_ess:.1}] compound[block_accept={compound_block_acceptance:.3},component_accept={compound_component_acceptance:.3},max_rhat={compound_rhat:.4},min_bulk_ess={compound_ess:.1}]" + ); + + assert!(component_rhat.is_finite() && component_ess.is_finite()); + assert!(compound_rhat.is_finite() && compound_ess.is_finite()); + assert!(compound_rhat < 1.05); + assert!(compound_ess > component_ess); + assert!((0.0..=1.0).contains(&component_acceptance)); + assert!((0.0..=1.0).contains(&compound_component_acceptance)); + assert!((0.0..=1.0).contains(&compound_block_acceptance)); +} diff --git a/tests/saem_lifecycle.rs b/tests/saem_lifecycle.rs new file mode 100644 index 000000000..647c77925 --- /dev/null +++ b/tests/saem_lifecycle.rs @@ -0,0 +1,326 @@ +use std::fs; +use std::path::Path; +use std::sync::Mutex; + +use pharmsol::prelude::*; +use pmcore::algorithms::{Status, StopReason}; +use pmcore::prelude::*; + +static STOP_FILE_LOCK: Mutex<()> = Mutex::new(()); + +struct StopFileCleanup; + +impl Drop for StopFileCleanup { + fn drop(&mut self) { + let _ = fs::remove_file("stop"); + } +} + +fn lifecycle_problem() -> EstimationProblem { + let equation = analytical! { + name: "saem_lifecycle_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + }, + }; + let data = Data::new(vec![Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .build()]); + + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.3).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("lifecycle fixture should build") +} + +fn tiny_config(total_cycles: usize) -> SaemConfig { + SaemConfig::new() + .seed(7) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(total_cycles) + .k2_iterations(0) + .compute_map(false) +} + +fn averaged_config() -> SaemConfig { + SaemConfig::new() + .seed(8) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(3) + .averaged_iterates(0.75) + .compute_map(false) +} + +#[test] +fn snapshot_tracks_initial_and_completed_cycle_state() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let mut controller = lifecycle_problem() + .fit_controller(tiny_config(2)) + .expect("controller should build"); + + let initial = controller.snapshot(); + assert_eq!(initial.cycle, 0); + assert_eq!(initial.total_cycles, 2); + assert_eq!(initial.status, Status::Continue); + assert_eq!(initial.progress(), 0.0); + assert!(!initial.is_terminal()); + assert_eq!(initial.conditional_n2ll, controller.n2ll()); + assert_eq!( + initial.population_parameters, + controller.population_parameters() + ); + assert_eq!(initial.omega.as_ref(), controller.omega()); + assert_eq!(initial.omega_iov.as_ref(), controller.omega_iov()); + assert_eq!(initial.residual_sigmas, controller.residual_sigmas()); + assert!(initial.latest_cycle_diagnostics.is_none()); + assert_eq!(controller.total_cycles(), 2); + assert_eq!(controller.progress(), 0.0); + + assert_eq!( + controller.step().expect("first cycle should run"), + Status::Continue + ); + let completed = controller.snapshot(); + assert_eq!(completed.cycle, 1); + assert_eq!(completed.total_cycles, 2); + assert_eq!(completed.status, Status::Continue); + assert_eq!(completed.progress(), 0.5); + assert_eq!(controller.progress(), 0.5); + assert_eq!( + completed.latest_cycle_diagnostics.as_ref(), + controller.cycle_diagnostics().last() + ); + assert_eq!( + completed + .latest_cycle_diagnostics + .as_ref() + .expect("completed-cycle diagnostics") + .iteration, + completed.cycle + ); + + let zero_total = ParametricFitSnapshot { + total_cycles: 0, + ..completed + }; + assert_eq!(zero_total.progress(), 0.0); +} + +#[test] +fn observer_receives_matching_snapshots_including_final_cycle() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let mut snapshots = Vec::new(); + let result = lifecycle_problem() + .fit_with_observer(tiny_config(2), |controller: &ParametricFitController<_>| { + snapshots.push(controller.snapshot()); + ParametricCycleFlow::Continue + }) + .expect("observed fit should complete"); + + assert_eq!(snapshots.len(), 2); + assert_eq!( + snapshots + .iter() + .map(|snapshot| snapshot.cycle) + .collect::>(), + vec![1, 2] + ); + assert_eq!(snapshots[0].progress(), 0.5); + assert_eq!(snapshots[1].progress(), 1.0); + assert!(snapshots[1].is_terminal()); + assert_eq!(snapshots[1].status, Status::Stop(StopReason::MaxCycles)); + assert_eq!( + snapshots[1].latest_cycle_diagnostics.as_ref(), + result.cycle_diagnostics().last() + ); +} + +#[test] +fn observer_stop_is_aborted_only_while_fit_is_running() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let result = lifecycle_problem() + .fit_with_observer( + tiny_config(3), + |_controller: &ParametricFitController<_>| ParametricCycleFlow::Stop, + ) + .expect("observer-aborted fit should return its completed cycle"); + + assert_eq!(result.cycle_diagnostics().len(), 1); + assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); +} + +#[test] +fn averaged_observer_abort_before_smoothing_retains_terminal_estimator() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let result = lifecycle_problem() + .fit_with_observer( + averaged_config(), + |_controller: &ParametricFitController<_>| ParametricCycleFlow::Stop, + ) + .expect("pre-smoothing observer abort should produce a result"); + + assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); + assert!(!result.converged()); + assert_eq!(result.cycle_diagnostics().len(), 1); + assert!(!result.estimator_metadata().average_applied); + assert_eq!(result.estimator_metadata().averaging_start_cycle, None); + assert_eq!(result.estimator_metadata().averaged_iterations, 0); +} + +#[test] +fn averaged_observer_abort_after_smoothing_installs_completed_average() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let result = lifecycle_problem() + .fit_with_observer( + averaged_config(), + |controller: &ParametricFitController<_>| { + if controller.cycle() == 2 { + ParametricCycleFlow::Stop + } else { + ParametricCycleFlow::Continue + } + }, + ) + .expect("post-smoothing observer abort should produce a result"); + + assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); + assert!(!result.converged()); + assert_eq!(result.cycle_diagnostics().len(), 2); + assert!(result.estimator_metadata().average_applied); + assert_eq!(result.estimator_metadata().averaging_start_cycle, Some(2)); + assert_eq!(result.estimator_metadata().averaged_iterations, 1); +} + +#[test] +fn algorithm_terminal_reason_wins_over_observer_and_user_stop() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let result = lifecycle_problem() + .fit_with_observer( + tiny_config(1), + |_controller: &ParametricFitController<_>| ParametricCycleFlow::Stop, + ) + .expect("final observer callback should not replace MaxCycles"); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + + let mut controller = lifecycle_problem() + .fit_controller(tiny_config(1)) + .expect("controller should build"); + assert_eq!( + controller.step().expect("final cycle should run"), + Status::Stop(StopReason::MaxCycles) + ); + controller.request_stop(); + assert_eq!(controller.status(), &Status::Stop(StopReason::MaxCycles)); +} + +#[test] +fn stale_stop_file_is_removed_when_controller_is_constructed() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + fs::write("stop", "stale").expect("stale stop file should be writable"); + + let controller = lifecycle_problem() + .fit_controller(tiny_config(2)) + .expect("controller should remove stale stop file"); + + assert!(!Path::new("stop").exists()); + assert_eq!(controller.status(), &Status::Continue); +} + +#[test] +fn averaged_stop_file_before_smoothing_retains_terminal_estimator() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let mut controller = lifecycle_problem() + .fit_controller(averaged_config()) + .expect("controller should build"); + fs::write("stop", "stop").expect("stop file should be writable"); + assert_eq!( + controller + .step() + .expect("exploration cycle should complete"), + Status::Stop(StopReason::StopFile) + ); + let result = controller.into_result().expect("stop-file result"); + + assert_eq!(result.termination_reason(), Some(&StopReason::StopFile)); + assert!(!result.converged()); + assert!(!result.estimator_metadata().average_applied); + assert_eq!(result.estimator_metadata().averaging_start_cycle, None); + assert_eq!(result.estimator_metadata().averaged_iterations, 0); +} + +#[test] +fn averaged_stop_file_after_smoothing_installs_completed_average() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let mut controller = lifecycle_problem() + .fit_controller(averaged_config()) + .expect("controller should build"); + assert_eq!( + controller.step().expect("exploration cycle"), + Status::Continue + ); + fs::write("stop", "stop").expect("stop file should be writable"); + assert_eq!( + controller.step().expect("smoothing cycle should complete"), + Status::Stop(StopReason::StopFile) + ); + let result = controller.into_result().expect("stop-file result"); + + assert_eq!(result.termination_reason(), Some(&StopReason::StopFile)); + assert!(!result.converged()); + assert!(result.estimator_metadata().average_applied); + assert_eq!(result.estimator_metadata().averaging_start_cycle, Some(2)); + assert_eq!(result.estimator_metadata().averaged_iterations, 1); +} + +#[test] +fn stop_file_after_a_completed_cycle_preserves_cycle_and_reason() { + let _lock = STOP_FILE_LOCK.lock().expect("stop-file test lock"); + let _cleanup = StopFileCleanup; + let mut controller = lifecycle_problem() + .fit_controller(tiny_config(2)) + .expect("controller should build"); + fs::write("stop", "stop").expect("stop file should be writable"); + + let status = controller + .step() + .expect("completed cycle should be retained"); + assert_eq!(status, Status::Stop(StopReason::StopFile)); + assert_eq!(controller.cycle(), 1); + assert_eq!(controller.cycle_diagnostics().len(), 1); + assert_eq!( + controller.snapshot().status, + Status::Stop(StopReason::StopFile) + ); + assert!(Path::new("stop").exists()); + + let result = controller + .into_result() + .expect("stop-file termination should produce a completed fit result"); + assert_eq!(result.termination_reason(), Some(&StopReason::StopFile)); + assert_eq!(result.cycle_diagnostics().len(), 1); +} diff --git a/tests/saem_marginal_likelihood.rs b/tests/saem_marginal_likelihood.rs new file mode 100644 index 000000000..3f72d1be6 --- /dev/null +++ b/tests/saem_marginal_likelihood.rs @@ -0,0 +1,968 @@ +use std::fs; +use std::sync::atomic::{AtomicUsize, Ordering}; +use std::time::{SystemTime, UNIX_EPOCH}; + +static N2_EQUATION_CALLS: AtomicUsize = AtomicUsize::new(0); + +use pharmsol::prelude::*; +use pharmsol::Cache; +use pmcore::prelude::*; + +fn latent_problem() -> EstimationProblem { + let equation = analytical! { + name: "n2_latent_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.1, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.2, "cp") + .observation(3.0, 3.6, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.09)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.35)).fixed(), + ) + .build() + .expect("latent N2 fixture") +} + +fn joint_iiv_iov_problem() -> EstimationProblem { + let equation = analytical! { + name: "n2_joint_iiv_iov_fixture", + params: [ke, v], + states: [central], + outputs: [cp, amount], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + y[amount] = x[central]; + }, + }; + let one = Subject::builder("one") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.9, "cp") + .observation(1.0, 98.0, "amount") + .build(); + let two = Subject::builder("two") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .observation(1.0, 101.0, "amount") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.7, "cp") + .observation(13.0, 96.0, "amount") + .build(); + let three = Subject::builder("three") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.0, "cp") + .observation(1.0, 100.0, "amount") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 5.2, "cp") + .observation(13.0, 103.0, "amount") + .reset() + .infusion(24.0, 100.0, "iv", 0.5) + .observation(25.0, 4.8, "cp") + .observation(25.0, 97.0, "amount") + .build(); + EstimationProblem::parametric(equation, Data::new(vec![one, two, three])) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .omega( + Omega::new() + .fixed_variance("ke", 0.09) + .fixed_variance("v", 0.16) + .fixed_covariance("ke", "v", 0.03), + ) + .iov( + Iov::new() + .fixed_variance("ke", 0.04) + .fixed_variance("v", 0.09) + .fixed_covariance("ke", "v", 0.01), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.3)).fixed(), + ) + .error_model( + "amount", + ParametricErrorModel::new(ResidualErrorModel::constant(4.0)).fixed(), + ) + .build() + .expect("joint IIV+IOV N2 fixture") +} + +fn no_latent_problem() -> EstimationProblem { + let equation = analytical! { + name: "n2_no_latent_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new()) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.35)).fixed(), + ) + .build() + .expect("no-latent N2 fixture") +} + +fn instrumented_no_latent_problem() -> EstimationProblem +{ + let equation = analytical! { + name: "n2_instrumented_no_latent_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + N2_EQUATION_CALLS.fetch_add(1, Ordering::SeqCst); + y[cp] = x[central] / v; + }, + }; + let equation = equation.disable_cache(); + let data = Data::new(vec![ + Subject::builder("counter-1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .build(), + Subject::builder("counter-2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.1, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new()) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.35)).fixed(), + ) + .build() + .expect("instrumented no-latent N2 fixture") +} + +fn fit_config() -> SaemConfig { + SaemConfig::new() + .seed(7001) + .n_chains(2) + .mcmc_iterations(2) + .burn_in(0) + .k1_iterations(2) + .k2_iterations(1) + .compute_map(true) +} + +fn n2(seed: u64) -> MarginalLikelihoodConfig { + MarginalLikelihoodConfig::new(1024, seed, 5, 1.5) +} + +fn write_record(path: &std::path::Path, record: &ParametricResultRecord) { + fs::write(path, serde_json::to_vec_pretty(record).unwrap()).unwrap(); +} + +fn clear_subject_n2_numerics(subject: &mut SubjectMarginalLikelihoodDiagnostics) { + subject.log_marginal_likelihood = None; + subject.n2ll = None; + subject.effective_sample_size = None; + subject.effective_sample_fraction = None; + subject.var_log = None; + subject.n2ll_mcse = None; + subject.zero_weight_count = 0; +} + +fn synchronize_unavailable_n2_tables(record: &mut ParametricResultRecord) { + let diagnostics = record.marginal_likelihood.as_ref().unwrap(); + let failures = diagnostics + .subjects + .iter() + .filter_map(|subject| { + subject + .failure + .clone() + .map(|reason| MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.clone(), + reason, + }) + }) + .collect::>(); + record.marginal_likelihood.as_mut().unwrap().status = MarginalLikelihoodStatus::Unavailable { + failures: failures.clone(), + }; + let diagnostics = record.marginal_likelihood.as_ref().unwrap(); + let total = record + .tables + .marginal_likelihood + .iter_mut() + .find(|row| row.scope == "total") + .unwrap(); + total.status = "unavailable".to_string(); + total.log_marginal_likelihood = None; + total.n2ll = None; + total.n2ll_mcse = None; + total.zero_weight_count = diagnostics + .subjects + .iter() + .map(|subject| subject.zero_weight_count) + .sum(); + total.failure = Some(serde_json::to_string(&failures).unwrap()); + for subject in &diagnostics.subjects { + let row = record + .tables + .marginal_likelihood + .iter_mut() + .find(|row| row.subject.as_deref() == Some(subject.subject_id.as_str())) + .unwrap(); + row.status = if subject.failure.is_some() { + "unavailable".to_string() + } else if subject.mode_converged == Some(false) { + "available_with_nonconverged_mode".to_string() + } else { + "available".to_string() + }; + row.mode = serde_json::to_string(&subject.mode).unwrap(); + row.mode_converged = subject.mode_converged; + row.log_marginal_likelihood = subject.log_marginal_likelihood; + row.n2ll = subject.n2ll; + row.n2ll_mcse = subject.n2ll_mcse; + row.effective_sample_size = subject.effective_sample_size; + row.effective_sample_fraction = subject.effective_sample_fraction; + row.zero_weight_count = subject.zero_weight_count; + row.failure = subject + .failure + .as_ref() + .map(|reason| serde_json::to_string(reason).unwrap()); + } + for row in record + .tables + .statistics + .iter_mut() + .filter(|row| row.kind.starts_with("marginal_likelihood")) + { + if row.kind == "marginal_likelihood_status" || row.kind == "marginal_likelihood" { + row.status = Some("unavailable".to_string()); + } + if row.kind == "marginal_likelihood" { + row.value = None; + } else if row.kind == "marginal_likelihood_subject_status" { + let subject = diagnostics + .subjects + .iter() + .find(|subject| subject.subject_id == row.name) + .unwrap(); + row.value = subject.n2ll; + row.status = Some(if subject.failure.is_some() { + "unavailable".to_string() + } else { + "available".to_string() + }); + } + } + + let reason = InformationCriteriaUnavailableReason::SourceMarginalLikelihoodUnavailable; + record.information_criteria.status = InformationCriteriaStatus::Unavailable { + reason: reason.clone(), + }; + record.information_criteria.source_marginal_n2ll = None; + record.information_criteria.source_marginal_n2ll_mcse = None; + record.information_criteria.aic = None; + record.information_criteria.bic = None; + record.information_criteria.aic_mcse = None; + record.information_criteria.bic_mcse = None; + let criteria = record.tables.information_criteria.first_mut().unwrap(); + criteria.status = "unavailable".to_string(); + criteria.source_marginal_n2ll = None; + criteria.source_marginal_n2ll_mcse = None; + criteria.aic = None; + criteria.bic = None; + criteria.aic_mcse = None; + criteria.bic_mcse = None; + criteria.failure_reason = Some(serde_json::to_string(&reason).unwrap()); + for row in record + .tables + .statistics + .iter_mut() + .filter(|row| row.kind.starts_with("information_criteria")) + { + row.status = Some("unavailable".to_string()); + if row.kind == "information_criteria" { + row.value = None; + } + } +} + +fn coordinate_retained_n2_config( + record: &mut ParametricResultRecord, + config: MarginalLikelihoodConfig, +) { + record.config.marginal_likelihood = Some(config); + let diagnostics = record.marginal_likelihood.as_mut().unwrap(); + diagnostics.config = config; + for subject in &mut diagnostics.subjects { + if subject.method == MarginalLikelihoodMethod::StudentTImportanceSampling { + subject.samples = config.samples_per_subject; + if config.samples_per_subject == 1 && subject.failure.is_none() { + subject.effective_sample_size = Some(1.0); + subject.effective_sample_fraction = Some(1.0); + } + } + } + for row in &mut record.tables.marginal_likelihood { + row.samples_per_subject = if row.scope == "total" { + config.samples_per_subject + } else { + diagnostics + .subjects + .iter() + .find(|subject| subject.subject_id == row.subject.clone().unwrap()) + .unwrap() + .samples + }; + row.degrees_of_freedom = config.degrees_of_freedom; + row.covariance_scale_multiplier = config.covariance_scale_multiplier; + if let Some(subject_id) = row.subject.as_deref() { + let subject = diagnostics + .subjects + .iter() + .find(|subject| subject.subject_id == subject_id) + .unwrap(); + row.effective_sample_size = subject.effective_sample_size; + row.effective_sample_fraction = subject.effective_sample_fraction; + } + } +} + +#[test] +fn no_latent_n2_is_exact_without_map_or_fabricated_ess() { + let result = no_latent_problem() + .fit_with( + fit_config() + .compute_map(false) + .marginal_likelihood(n2(8001)), + ) + .expect("exact N2 fit"); + let diagnostics = result + .marginal_likelihood_diagnostics() + .expect("N2 diagnostics"); + assert!(matches!( + diagnostics.status, + MarginalLikelihoodStatus::Available + )); + assert!((result.marginal_n2ll().unwrap() - result.conditional_n2ll()).abs() <= 1e-10); + assert_eq!(result.marginal_n2ll_mcse(), Some(0.0)); + for subject in &diagnostics.subjects { + assert_eq!(subject.method, MarginalLikelihoodMethod::ExactNoLatent); + assert_eq!(subject.samples, 0); + assert_eq!(subject.n2ll_mcse, Some(0.0)); + assert_eq!(subject.effective_sample_size, None); + assert_eq!(subject.effective_sample_fraction, None); + } +} + +#[test] +fn enabled_exact_n2_performs_only_the_explicit_post_fit_scoring_calls() { + N2_EQUATION_CALLS.store(0, Ordering::SeqCst); + let disabled = instrumented_no_latent_problem() + .fit_with(fit_config().compute_map(false)) + .expect("instrumented disabled fit"); + let disabled_calls = N2_EQUATION_CALLS.load(Ordering::SeqCst); + + N2_EQUATION_CALLS.store(0, Ordering::SeqCst); + let enabled = instrumented_no_latent_problem() + .fit_with( + fit_config() + .compute_map(false) + .marginal_likelihood(n2(8051)), + ) + .expect("instrumented enabled fit"); + let enabled_calls = N2_EQUATION_CALLS.load(Ordering::SeqCst); + + assert_eq!(disabled.cycle_diagnostics(), enabled.cycle_diagnostics()); + assert_eq!( + disabled.population_parameters(), + enabled.population_parameters() + ); + assert_eq!(disabled.conditional_n2ll(), enabled.conditional_n2ll()); + assert_eq!( + enabled_calls - disabled_calls, + enabled.data().subjects().len(), + "exact no-latent N2 must add one post-fit scoring call per subject" + ); +} + +#[test] +fn n2_stream_is_reproducible_and_does_not_change_canonical_fit() { + let disabled = latent_problem() + .fit_with(fit_config()) + .expect("disabled fit"); + let first = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8101))) + .expect("first N2 fit"); + let repeated = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8101))) + .expect("repeated N2 fit"); + let changed = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8102))) + .expect("changed-seed N2 fit"); + + assert_eq!(disabled.cycle_diagnostics(), first.cycle_diagnostics()); + assert_eq!( + disabled.population_parameters(), + first.population_parameters() + ); + assert_eq!(disabled.conditional_n2ll(), first.conditional_n2ll()); + assert_eq!( + first.marginal_likelihood_diagnostics(), + repeated.marginal_likelihood_diagnostics() + ); + assert_ne!( + first.marginal_likelihood_diagnostics(), + changed.marginal_likelihood_diagnostics() + ); + assert_eq!(first.cycle_diagnostics(), changed.cycle_diagnostics()); + assert_eq!( + first.population_parameters(), + changed.population_parameters() + ); + assert_eq!(first.conditional_n2ll(), changed.conditional_n2ll()); + assert!(first.marginal_n2ll().is_some()); + assert!(first.marginal_n2ll_mcse().is_some()); + assert_eq!(first.summary().marginal_n2ll, first.marginal_n2ll()); +} + +#[test] +fn fourfold_sample_budget_reduces_median_reported_mcse_at_frozen_rate() { + let mut low = Vec::new(); + let mut high = Vec::new(); + for seed in 8501..8511 { + let low_result = latent_problem() + .fit_with( + fit_config().marginal_likelihood(MarginalLikelihoodConfig::new(4096, seed, 5, 1.5)), + ) + .expect("low-budget N2 fit"); + let high_result = latent_problem() + .fit_with( + fit_config() + .marginal_likelihood(MarginalLikelihoodConfig::new(16384, seed, 5, 1.5)), + ) + .expect("high-budget N2 fit"); + low.push(low_result.marginal_n2ll_mcse().expect("low MCSE")); + high.push(high_result.marginal_n2ll_mcse().expect("high MCSE")); + } + low.sort_by(f64::total_cmp); + high.sort_by(f64::total_cmp); + let low_median = (low[4] + low[5]) / 2.0; + let high_median = (high[4] + high[5]) / 2.0; + eprintln!( + "N2 MCSE medians: K4096={low_median:.17}, K16384={high_median:.17}, ratio={:.17}", + high_median / low_median + ); + assert!( + high_median <= 0.65 * low_median, + "high median {high_median} exceeds frozen 0.65 ratio of low median {low_median}" + ); +} + +#[test] +fn joint_iiv_iov_uses_correlated_blocks_and_actual_uneven_occasion_order() { + let result = joint_iiv_iov_problem() + .fit_with( + fit_config().marginal_likelihood(MarginalLikelihoodConfig::new(512, 8151, 5, 1.5)), + ) + .expect("joint IIV+IOV N2 fit"); + let diagnostics = result + .marginal_likelihood_diagnostics() + .expect("joint diagnostics"); + assert!(result.marginal_n2ll().is_some()); + assert!(result.marginal_n2ll_mcse().is_some()); + assert_eq!(diagnostics.subjects.len(), 3); + for (subject, data_subject) in diagnostics.subjects.iter().zip(result.data().subjects()) { + let expected = data_subject + .occasions() + .iter() + .map(|occasion| occasion.index()) + .collect::>(); + assert_eq!(subject.occasion_indices, expected); + assert_eq!(subject.dimension, 2 + 2 * expected.len()); + assert_eq!( + subject.proposal_scale_source, + ProposalScaleSource::FinalRawOmegaBlocks + ); + assert!(subject.effective_sample_size.is_some()); + assert!(subject.effective_sample_fraction.is_some()); + } +} + +#[test] +fn latent_n2_without_map_fails_before_fit() { + let error = latent_problem() + .fit_with( + fit_config() + .compute_map(false) + .marginal_likelihood(n2(8201)), + ) + .expect_err("latent N2 without MAP must fail"); + assert!(format!("{error:#}").contains("requires compute_map=true")); +} + +#[test] +fn schema_six_persists_complete_n2_and_warm_start_recomputes_only_on_request() { + let parent = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8301))) + .expect("parent N2 fit"); + let directory = std::env::temp_dir().join(format!( + "pmcore-n2-{}-{}", + std::process::id(), + SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos() + )); + parent + .write_outputs(&directory, 0.0, 0.0) + .expect("write N2 outputs"); + let record = ParametricResultRecord::read_json(directory.join("result.json")) + .expect("read schema-six N2 result"); + assert_eq!(record.schema_version, 9); + assert_eq!( + record.marginal_likelihood.as_ref(), + parent.marginal_likelihood_diagnostics() + ); + assert_eq!( + record.tables.marginal_likelihood.len(), + parent.data().subjects().len() + 1 + ); + let csv = fs::read_to_string(directory.join("marginal_likelihood.csv")) + .expect("read marginal likelihood CSV"); + assert_eq!(csv.lines().count(), parent.data().subjects().len() + 2); + let csv_rows = csv::Reader::from_reader(csv.as_bytes()) + .deserialize::() + .collect::, _>>() + .expect("deserialize marginal-likelihood CSV rows"); + assert_eq!(csv_rows, record.tables.marginal_likelihood); + let diagnostics = record.marginal_likelihood.as_ref().unwrap(); + assert_eq!( + record.tables.marginal_likelihood[0].log_marginal_likelihood, + diagnostics.log_marginal_likelihood + ); + assert_eq!(record.tables.marginal_likelihood[0].n2ll, diagnostics.n2ll); + assert_eq!( + record.tables.marginal_likelihood[0].n2ll_mcse, + diagnostics.n2ll_mcse + ); + for (row, subject) in record.tables.marginal_likelihood[1..] + .iter() + .zip(&diagnostics.subjects) + { + assert_eq!(row.subject.as_deref(), Some(subject.subject_id.as_str())); + assert_eq!(row.log_marginal_likelihood, subject.log_marginal_likelihood); + assert_eq!(row.n2ll, subject.n2ll); + assert_eq!(row.n2ll_mcse, subject.n2ll_mcse); + assert_eq!(row.effective_sample_size, subject.effective_sample_size); + assert_eq!( + row.effective_sample_fraction, + subject.effective_sample_fraction + ); + } + for (name, expected) in [ + ( + "log_marginal_likelihood", + diagnostics.log_marginal_likelihood, + ), + ("marginal_n2ll", diagnostics.n2ll), + ("marginal_n2ll_mcse", diagnostics.n2ll_mcse), + ] { + assert_eq!( + record + .tables + .statistics + .iter() + .find(|row| row.kind == "marginal_likelihood" && row.name == name) + .and_then(|row| row.value), + expected + ); + } + + let child_disabled = parent + .fit_next(fit_config().seed(7002)) + .expect("disabled warm-start child"); + assert!(child_disabled.marginal_likelihood_diagnostics().is_none()); + let child_enabled = parent + .fit_next(fit_config().seed(7002).marginal_likelihood(n2(8302))) + .expect("enabled warm-start child"); + assert_eq!(child_enabled.config().marginal_likelihood, Some(n2(8302))); + assert_eq!(parent.config().marginal_likelihood, Some(n2(8301))); + fs::remove_dir_all(directory).expect("remove N2 output directory"); +} + +#[test] +fn schema_six_round_trips_global_posthoc_failure_without_fabricated_modes() { + let result = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8351))) + .expect("global failure persistence fixture"); + let path = std::env::temp_dir().join(format!( + "pmcore-n2-global-posthoc-failure-{}.json", + std::process::id() + )); + result.write_json(&path, 0.0, 0.0).expect("write fixture"); + let mut record = ParametricResultRecord::read_json(&path).expect("read fixture"); + let reason = MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed( + "global conditional mode calculation failed: optimizer fixture".to_string(), + ); + let diagnostics = record.marginal_likelihood.as_mut().unwrap(); + diagnostics.log_marginal_likelihood = None; + diagnostics.n2ll = None; + diagnostics.n2ll_mcse = None; + for subject in &mut diagnostics.subjects { + subject.mode.clear(); + subject.mode_converged = None; + clear_subject_n2_numerics(subject); + subject.failure = Some(reason.clone()); + } + synchronize_unavailable_n2_tables(&mut record); + write_record(&path, &record); + + let round_trip = + ParametricResultRecord::read_json(&path).expect("global posthoc failure should round trip"); + let diagnostics = round_trip.marginal_likelihood.unwrap(); + let expected_failures = diagnostics + .subjects + .iter() + .map(|subject| MarginalLikelihoodSubjectFailure { + subject_id: subject.subject_id.clone(), + reason: reason.clone(), + }) + .collect::>(); + assert_eq!( + diagnostics.status, + MarginalLikelihoodStatus::Unavailable { + failures: expected_failures + } + ); + assert_eq!(diagnostics.log_marginal_likelihood, None); + assert_eq!(diagnostics.n2ll, None); + assert_eq!(diagnostics.n2ll_mcse, None); + for (index, subject) in diagnostics.subjects.iter().enumerate() { + assert_eq!(subject.dimension, 1); + assert!(subject.occasion_indices.is_empty()); + assert_eq!(subject.samples, 1024); + assert_eq!( + subject.seed, + Some(pmcore::estimation::parametric::marginal_likelihood_subject_seed(8351, index,)) + ); + assert!(subject.mode.is_empty()); + assert_eq!(subject.mode_converged, None); + assert_eq!(subject.failure, Some(reason.clone())); + assert!(subject.log_marginal_likelihood.is_none()); + assert!(subject.n2ll.is_none()); + assert!(subject.effective_sample_size.is_none()); + assert!(subject.effective_sample_fraction.is_none()); + assert!(subject.var_log.is_none()); + assert!(subject.n2ll_mcse.is_none()); + } + fs::remove_file(path).expect("remove global failure fixture"); +} + +#[test] +fn schema_six_round_trips_missing_mode_for_non_first_subject() { + let result = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8352))) + .expect("missing mode persistence fixture"); + let path = std::env::temp_dir().join(format!( + "pmcore-n2-missing-mode-{}.json", + std::process::id() + )); + result.write_json(&path, 0.0, 0.0).expect("write fixture"); + let mut record = ParametricResultRecord::read_json(&path).expect("read fixture"); + let diagnostics = record.marginal_likelihood.as_mut().unwrap(); + diagnostics.log_marginal_likelihood = None; + diagnostics.n2ll = None; + diagnostics.n2ll_mcse = None; + let missing = diagnostics.subjects.get_mut(1).expect("second subject"); + missing.mode.clear(); + missing.mode_converged = None; + clear_subject_n2_numerics(missing); + missing.failure = Some(MarginalLikelihoodFailureReason::MissingConditionalMode); + synchronize_unavailable_n2_tables(&mut record); + write_record(&path, &record); + + let round_trip = + ParametricResultRecord::read_json(&path).expect("non-first missing mode should round trip"); + let diagnostics = round_trip.marginal_likelihood.unwrap(); + assert_eq!( + diagnostics.status, + MarginalLikelihoodStatus::Unavailable { + failures: vec![MarginalLikelihoodSubjectFailure { + subject_id: "s2".to_string(), + reason: MarginalLikelihoodFailureReason::MissingConditionalMode, + }] + } + ); + assert!(diagnostics.log_marginal_likelihood.is_none()); + assert!(diagnostics.n2ll.is_none()); + assert!(diagnostics.n2ll_mcse.is_none()); + assert_eq!(diagnostics.subjects[0].mode_converged, Some(true)); + assert_eq!(diagnostics.subjects[0].mode.len(), 1); + assert!(diagnostics.subjects[0].failure.is_none()); + let missing = &diagnostics.subjects[1]; + assert_eq!(missing.subject_id, "s2"); + assert_eq!(missing.dimension, 1); + assert_eq!(missing.samples, 1024); + assert_eq!( + missing.seed, + Some(pmcore::estimation::parametric::marginal_likelihood_subject_seed(8352, 1,)) + ); + assert!(missing.mode.is_empty()); + assert_eq!(missing.mode_converged, None); + assert_eq!( + missing.failure, + Some(MarginalLikelihoodFailureReason::MissingConditionalMode) + ); + assert!(missing.log_marginal_likelihood.is_none()); + assert!(missing.n2ll.is_none()); + assert!(missing.effective_sample_size.is_none()); + assert!(missing.effective_sample_fraction.is_none()); + assert!(missing.var_log.is_none()); + assert!(missing.n2ll_mcse.is_none()); + fs::remove_file(path).expect("remove missing mode fixture"); +} + +#[test] +fn schema_six_rejects_absent_or_fabricated_mode_metadata_for_other_failures() { + let result = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8354))) + .expect("mode metadata validation fixture"); + let path = std::env::temp_dir().join(format!( + "pmcore-n2-invalid-mode-metadata-{}.json", + std::process::id() + )); + result.write_json(&path, 0.0, 0.0).expect("write fixture"); + let original = ParametricResultRecord::read_json(&path).expect("read fixture"); + + let mut absent_finite_mode_status = original.clone(); + let diagnostics = absent_finite_mode_status + .marginal_likelihood + .as_mut() + .unwrap(); + diagnostics.log_marginal_likelihood = None; + diagnostics.n2ll = None; + diagnostics.n2ll_mcse = None; + let failed = &mut diagnostics.subjects[1]; + failed.mode_converged = None; + clear_subject_n2_numerics(failed); + failed.failure = Some(MarginalLikelihoodFailureReason::ScoringFailure( + "finite mode scoring fixture".to_string(), + )); + synchronize_unavailable_n2_tables(&mut absent_finite_mode_status); + write_record(&path, &absent_finite_mode_status); + let error = ParametricResultRecord::read_json(&path) + .expect_err("a finite mode failure must retain convergence status"); + assert!(format!("{error:#}").contains("inconsistent proposal metadata")); + + let mut fabricated_missing_mode = original; + let diagnostics = fabricated_missing_mode + .marginal_likelihood + .as_mut() + .unwrap(); + diagnostics.log_marginal_likelihood = None; + diagnostics.n2ll = None; + diagnostics.n2ll_mcse = None; + let failed = &mut diagnostics.subjects[1]; + failed.mode_converged = None; + clear_subject_n2_numerics(failed); + failed.failure = Some(MarginalLikelihoodFailureReason::MissingConditionalMode); + assert!(!failed.mode.is_empty()); + synchronize_unavailable_n2_tables(&mut fabricated_missing_mode); + write_record(&path, &fabricated_missing_mode); + let error = ParametricResultRecord::read_json(&path) + .expect_err("a missing-mode failure must not fabricate coordinates"); + assert!(format!("{error:#}").contains("inconsistent proposal metadata")); + fs::remove_file(path).expect("remove mode metadata fixture"); +} + +#[test] +fn schema_six_rejects_each_invalid_retained_n2_configuration() { + let result = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8353))) + .expect("invalid retained config fixture"); + let path = std::env::temp_dir().join(format!( + "pmcore-n2-invalid-retained-config-{}.json", + std::process::id() + )); + result.write_json(&path, 0.0, 0.0).expect("write fixture"); + let original = ParametricResultRecord::read_json(&path).expect("read fixture"); + + for (label, config, expected) in [ + ( + "samples", + MarginalLikelihoodConfig::new(1, 8353, 5, 1.5), + "N2 samples_per_subject must be at least 2", + ), + ( + "degrees-of-freedom", + MarginalLikelihoodConfig::new(1024, 8353, 2, 1.5), + "N2 degrees_of_freedom must be at least 3", + ), + ( + "zero-scale", + MarginalLikelihoodConfig::new(1024, 8353, 5, 0.0), + "N2 covariance_scale_multiplier must be finite and positive", + ), + ( + "negative-scale", + MarginalLikelihoodConfig::new(1024, 8353, 5, -1.0), + "N2 covariance_scale_multiplier must be finite and positive", + ), + ] { + let mut malformed = original.clone(); + coordinate_retained_n2_config(&mut malformed, config); + write_record(&path, &malformed); + let error = ParametricResultRecord::read_json(&path) + .expect_err(&format!("{label} config must be rejected")); + let message = format!("{error:#}"); + assert!( + message.contains("invalid retained SAEM configuration") && message.contains(expected), + "{label} should fail for its retained configuration, got: {message}" + ); + } + fs::remove_file(path).expect("remove invalid retained config fixture"); +} + +#[test] +fn schema_six_rejects_missing_malformed_and_reordered_n2_diagnostics() { + let result = latent_problem() + .fit_with(fit_config().marginal_likelihood(n2(8401))) + .expect("N2 persistence fixture"); + let path = + std::env::temp_dir().join(format!("pmcore-n2-malformed-{}.json", std::process::id())); + result.write_json(&path, 0.0, 0.0).expect("write N2 JSON"); + let original: serde_json::Value = + serde_json::from_reader(fs::File::open(&path).expect("open N2 JSON")) + .expect("parse N2 JSON"); + + let mut missing = original.clone(); + missing + .as_object_mut() + .unwrap() + .remove("marginal_likelihood"); + fs::write(&path, serde_json::to_vec_pretty(&missing).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut missing_nested = original.clone(); + missing_nested["config"] + .as_object_mut() + .unwrap() + .remove("marginal_likelihood"); + fs::write(&path, serde_json::to_vec_pretty(&missing_nested).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut inconsistent = original.clone(); + inconsistent["marginal_likelihood"]["n2ll"] = serde_json::Value::Null; + fs::write(&path, serde_json::to_vec_pretty(&inconsistent).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut malformed_mode = original.clone(); + malformed_mode["marginal_likelihood"]["subjects"][0]["mode"] = serde_json::json!([null]); + fs::write(&path, serde_json::to_vec_pretty(&malformed_mode).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut invalid_algebra = original.clone(); + invalid_algebra["marginal_likelihood"]["subjects"][0]["n2ll"] = serde_json::json!(123.0); + fs::write(&path, serde_json::to_vec_pretty(&invalid_algebra).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut invalid_table = original.clone(); + invalid_table["tables"]["marginal_likelihood"][0]["n2ll"] = serde_json::json!(123.0); + fs::write(&path, serde_json::to_vec_pretty(&invalid_table).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut invalid_statistics = original.clone(); + let marginal_stat = invalid_statistics["tables"]["statistics"] + .as_array_mut() + .unwrap() + .iter_mut() + .find(|row| row["kind"] == "marginal_likelihood") + .unwrap(); + marginal_stat["value"] = serde_json::json!(123.0); + fs::write( + &path, + serde_json::to_vec_pretty(&invalid_statistics).unwrap(), + ) + .unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut reordered = original.clone(); + reordered["marginal_likelihood"]["subjects"] + .as_array_mut() + .unwrap() + .swap(0, 1); + fs::write(&path, serde_json::to_vec_pretty(&reordered).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + + let mut old_schema = original; + old_schema["schema_version"] = serde_json::json!(4); + fs::write(&path, serde_json::to_vec_pretty(&old_schema).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&path).is_err()); + fs::remove_file(path).expect("remove malformed N2 JSON"); +} diff --git a/tests/saem_no_iiv.rs b/tests/saem_no_iiv.rs new file mode 100644 index 000000000..314b17f53 --- /dev/null +++ b/tests/saem_no_iiv.rs @@ -0,0 +1,309 @@ +use std::{ + fs, + time::{SystemTime, UNIX_EPOCH}, +}; + +use pharmsol::prelude::*; +use pmcore::prelude::*; +use pmcore::results::{InformationStatus, ParametricResultRecord}; + +fn direct_equation() -> pharmsol::equation::Analytical { + analytical! { + name: "n8b_direct", + params: [ke, v], states: [central], outputs: [cp], + routes: [infusion(iv) -> central], structure: one_compartment, + out: |_x, _p, _t, _cov, y| { y[cp] = v; }, + } +} + +fn direct_data(center: f64) -> Data { + Data::new(vec![Subject::builder("direct") + .observation(1.0, center - 1.0, "cp") + .observation(1.0, center + 1.0, "cp") + .build()]) +} + +fn fixed_error() -> ParametricErrorModel { + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed() +} + +fn direct_problem(initial: f64) -> EstimationProblem { + EstimationProblem::parametric(direct_equation(), direct_data(20.0)) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(initial) + .without_random_effect(), + ) + .error_model("cp", fixed_error()) + .build() + .unwrap() +} + +fn config() -> SaemConfig { + SaemConfig::new() + .seed(0x6e38_b026) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false) +} + +#[test] +fn activation_waits_for_annealing_boundary_and_applies_one_gain() { + let mut delayed = config().burn_in(1).k1_iterations(2).k2_iterations(3); + delayed.sa_iterations = 4; + let result = direct_problem(40.0).fit_with(delayed).unwrap(); + let cycles = result.cycle_diagnostics(); + assert_eq!(cycles.len(), 5); + for cycle in &cycles[..3] { + assert!((cycle.population_parameters[1] - 40.0).abs() < 1e-12); + } + assert_eq!(cycles[3].iteration, 4); + assert_eq!(cycles[3].stochastic_approximation_step, 0.5); + let expected = (40.0_f64 * 20.0).sqrt(); + assert!((cycles[3].population_parameters[1] - expected).abs() < 1e-3); +} + +#[test] +fn tiny_no_iiv_fixture_uses_pre_update_residual_evidence_then_next_cycle_state() { + let result = EstimationProblem::parametric(direct_equation(), direct_data(20.0)) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::real("v") + .with_initial(40.0) + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::constant(5.0)) + .build() + .unwrap() + .fit_with(config().k1_iterations(2)) + .unwrap(); + let cycles = result.cycle_diagnostics(); + assert_eq!(cycles.len(), 2); + assert!((cycles[0].population_parameters[1] - 20.0).abs() < 1e-3); + let ResidualErrorModel::Constant { a: first_sigma } = + cycles[0].residual_error_estimates[0].model + else { + panic!("constant residual model was not retained") + }; + assert!((first_sigma - 401.0_f64.sqrt()).abs() < 1e-6); + let ResidualErrorModel::Constant { a: second_sigma } = + cycles[1].residual_error_estimates[0].model + else { + panic!("constant residual model was not retained") + }; + assert!((second_sigma - 1.0).abs() < 1e-6); + assert!((result.population_parameters()[1] - 20.0).abs() < 1e-3); +} + +fn mixed_equation() -> pharmsol::equation::Analytical { + analytical! { + name: "n8b_mixed", + params: [ke, v], states: [central], outputs: [cp], + routes: [infusion(iv) -> central], structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + } +} + +#[test] +fn mixed_iiv_and_no_iiv_coordinates_follow_separate_paths() { + let data = Data::new(vec![ + Subject::builder("m1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.0, "cp") + .build(), + Subject::builder("m2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.2, "cp") + .observation(3.0, 3.4, "cp") + .build(), + ]); + let result = EstimationProblem::parametric(mixed_equation(), data) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter( + Parameter::log("v") + .with_initial(30.0) + .without_random_effect(), + ) + .omega(Omega::diagonal_variances([("ke", 0.05)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.3)).fixed(), + ) + .build() + .unwrap() + .fit_with(config()) + .unwrap(); + assert_eq!(result.random_effect_names(), ["ke"]); + assert_eq!(result.omega().dim(), (1, 1)); + assert!(result + .eta_chain_means() + .iter() + .all(|eta| eta.values.len() == 1)); + assert!(result + .population_parameters() + .iter() + .all(|value| value.is_finite())); + assert!(matches!(result.information_diagnostics().status, + InformationStatus::Unsupported(ref reason) if reason.contains("estimated non-IIV"))); +} + +#[test] +fn zero_iiv_covariate_effect_is_estimated_jointly_with_its_intercept() { + let subjects = [-1.0_f64, 0.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + let prediction = (20.0_f64.ln() + 0.2 * wt).exp(); + Subject::builder(format!("c{index}")) + .covariate("wt", 0.0, wt) + .observation(1.0, prediction - 0.5, "cp") + .observation(1.0, prediction + 0.5, "cp") + .build() + }) + .collect(); + let result = EstimationProblem::parametric(direct_equation(), Data::new(subjects)) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(18.0) + .without_random_effect(), + ) + .covariate_effect(CovariateEffect::continuous("v", "wt", 0.0).with_initial(0.0)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.5)).fixed(), + ) + .build() + .unwrap() + .fit_with(config()) + .unwrap(); + assert!((result.population_parameters()[1] - 20.0).abs() < 1e-3); + assert!((result.covariates().unwrap().estimates()[0].estimate() - 0.2).abs() < 1e-3); + assert!(result.random_effect_names().is_empty()); +} + +fn saturation_equation() -> pharmsol::equation::Analytical { + analytical! { + name: "n8b_invalid_trial", + params: [ke, v], states: [central], outputs: [cp], + routes: [infusion(iv) -> central], structure: one_compartment, + out: |_x, _p, _t, _cov, y| { y[cp] = 1.0 + 0.0 * v; }, + } +} + +#[test] +fn invalid_optimizer_trials_are_penalized_without_aborting_the_fit() { + let result = EstimationProblem::parametric(saturation_equation(), direct_data(1.0)) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(f64::MAX / 2.0) + .without_random_effect(), + ) + .error_model("cp", fixed_error()) + .build() + .unwrap() + .fit_with(config()) + .unwrap(); + assert!(result.population_parameters()[1].is_finite()); + assert!(result.population_parameters()[1] > 0.0); + assert_eq!(result.cycle_diagnostics().len(), 1); +} + +#[test] +fn schema_nine_lifecycle_count_warm_start_and_information_are_honest() { + let result = direct_problem(30.0) + .fit_with(config().marginal_likelihood(MarginalLikelihoodConfig::new(16, 88, 5, 1.5))) + .unwrap(); + assert_eq!(result.free_parameter_count(), 1); + assert!(result.aic().unwrap().is_finite() && result.bic().unwrap().is_finite()); + assert!(matches!(result.information_diagnostics().status, + InformationStatus::Unsupported(ref reason) if reason.contains("estimated non-IIV"))); + + let path = std::env::temp_dir().join(format!( + "pmcore-n8b-{}-{}.json", + std::process::id(), + SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos() + )); + result.write_json(&path, 0.0, 0.0).unwrap(); + let record = ParametricResultRecord::read_json(&path).unwrap(); + assert_eq!(record.schema_version, 9); + let warm = record + .warm_start_problem(direct_equation(), direct_data(20.0)) + .unwrap(); + assert_eq!( + warm.parameters().items[1].initial.unwrap().to_bits(), + result.population_parameters()[1].to_bits() + ); + fs::remove_file(path).unwrap(); +} + +#[test] +fn variance_and_sd_diagonal_constructors_are_explicit_and_fail_closed() { + fn build( + omega: Omega, + iov: Iov, + ) -> anyhow::Result> { + EstimationProblem::parametric(direct_equation(), direct_data(20.0)) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(omega) + .iov(iov) + .error_model("cp", fixed_error()) + .build() + } + let variance = build( + Omega::diagonal_variances([("ke", 0.09)]), + Iov::diagonal_variances([("v", 0.16)]), + ) + .unwrap(); + let sd = build( + Omega::diagonal_standard_deviations([("ke", 0.3)]), + Iov::diagonal_standard_deviations([("v", 0.4)]), + ) + .unwrap(); + assert!((variance.omega()[(0, 0)] - sd.omega()[(0, 0)]).abs() < 1e-15); + assert!( + (variance.omega_iov().unwrap()[(0, 0)] - sd.omega_iov().unwrap()[(0, 0)]).abs() < 1e-15 + ); + for invalid in [0.0, -1.0, f64::NAN, f64::MAX] { + assert!(build( + Omega::diagonal_standard_deviations([("ke", invalid)]), + Iov::diagonal_variances([("v", 0.16)]) + ) + .is_err()); + } +} diff --git a/tests/saem_operational_convergence.rs b/tests/saem_operational_convergence.rs new file mode 100644 index 000000000..43d50de2b --- /dev/null +++ b/tests/saem_operational_convergence.rs @@ -0,0 +1,379 @@ +use std::io::{self, Write}; +use std::sync::{Arc, Mutex}; + +use pharmsol::prelude::*; +use pmcore::algorithms::{Status, StopReason}; +use pmcore::prelude::*; +use pmcore::results::ParametricResultRecord; +use tracing::Level; +use tracing_subscriber::fmt::MakeWriter; + +#[derive(Clone, Default)] +struct LogBuffer(Arc>>); + +impl Write for LogBuffer { + fn write(&mut self, bytes: &[u8]) -> io::Result { + self.0.lock().expect("log lock").extend_from_slice(bytes); + Ok(bytes.len()) + } + + fn flush(&mut self) -> io::Result<()> { + Ok(()) + } +} + +impl<'a> MakeWriter<'a> for LogBuffer { + type Writer = Self; + + fn make_writer(&'a self) -> Self::Writer { + self.clone() + } +} + +fn identifiable_latent_problem() -> EstimationProblem { + let equation = analytical! { + name: "saem_operational_convergence_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [bolus(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + }, + }; + let data = Data::new(vec![ + Subject::builder("s1") + .bolus(0.0, 100.0, "iv") + .observation(0.5, 7.8, "cp") + .observation(2.0, 5.0, "cp") + .observation(6.0, 1.7, "cp") + .build(), + Subject::builder("s2") + .bolus(0.0, 100.0, "iv") + .observation(0.5, 7.2, "cp") + .observation(2.0, 4.3, "cp") + .observation(6.0, 1.2, "cp") + .build(), + Subject::builder("s3") + .bolus(0.0, 100.0, "iv") + .observation(0.5, 8.2, "cp") + .observation(2.0, 5.5, "cp") + .observation(6.0, 2.0, "cp") + .build(), + Subject::builder("s4") + .bolus(0.0, 100.0, "iv") + .observation(0.5, 7.5, "cp") + .observation(2.0, 4.7, "cp") + .observation(6.0, 1.5, "cp") + .build(), + ]); + + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(12.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.4)).fixed(), + ) + .build() + .expect("identifiable analytical path-test fixture") +} + +fn path_test_config() -> SaemConfig { + // These deliberately generous fixed-width/Newton limits are declared path-test + // thresholds before execution. They are not production recommendations. The + // literature rank gates remain Rhat < 1.01, bulk ESS > 400, and average ESS + // per split chain >= 50. + let path_test_policy = + OperationalConvergenceConfig::literature_guided(4, 20, 10.0, 0.95, 100.0, 100.0); + let frozen_diagnostics = MarkovSimulationVarianceConfig::new( + 0x51a7_2026, + 1024, + 1152, + 96, + LugsailConfig::over_lugsail_bartlett(), + 4, + 64 * 1024 * 1024, + ); + SaemConfig::new() + .seed(0x51a7_0001) + .n_chains(2) + .mcmc_iterations(2) + .burn_in(8) + .k1_iterations(8) + .k2_iterations(8) + .averaged_iterates(0.75) + .markov_simulation_variance(frozen_diagnostics) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(path_test_policy) + .compute_map(false) +} + +#[test] +fn genuine_operational_pass_sets_converged() { + let writer = LogBuffer::default(); + let subscriber = tracing_subscriber::fmt() + .with_ansi(false) + .without_time() + .with_max_level(Level::INFO) + .with_writer(writer.clone()) + .finish(); + let mut snapshots = Vec::new(); + let result = tracing::subscriber::with_default(subscriber, || { + identifiable_latent_problem().fit_with_observer( + path_test_config(), + |controller: &ParametricFitController<_>| { + snapshots.push(controller.snapshot()); + ParametricCycleFlow::Continue + }, + ) + }) + .expect("operational path-test fit"); + let diagnostics = result.operational_diagnostics(); + let final_check = diagnostics.checks.last().expect("operational checkpoint"); + assert_eq!( + result.termination_reason(), + Some(&StopReason::Converged), + "outcome={:?}; criteria={:?}; information={:?}; markov={:?}", + final_check.outcome, + final_check.criteria, + final_check.information.as_ref().map(|value| &value.status), + final_check.markov.as_ref().map(|value| ( + &value.status, + &value.lambda_status, + &value.xi_status, + &value.simulation_covariance_status + )) + ); + assert!(diagnostics.used_for_termination); + assert!(matches!( + final_check.outcome, + OperationalConvergenceOutcome::Passed + )); + assert!(final_check.worst_rhat().expect("worst Rhat") < 1.01); + assert!(final_check.min_bulk_ess().expect("minimum bulk ESS") > 400.0); + assert!(diagnostics.warnings()[0].contains("PMcore operational convergence criteria passed")); + assert!(diagnostics.warnings()[0].contains("not proof of mathematical convergence")); + + let final_snapshot = snapshots.last().expect("observer final snapshot"); + assert_eq!(final_snapshot.status, Status::Stop(StopReason::Converged)); + assert_eq!(final_snapshot.cycle, result.iterations()); + let logs = String::from_utf8(writer.0.lock().expect("log lock").clone()) + .expect("UTF-8 tracing output"); + assert!( + logs.contains("PMcore operational convergence criteria passed"), + "{logs}" + ); + assert!( + logs.contains("does not prove mathematical convergence"), + "{logs}" + ); + + let tables = result.tables(0.0, 0.0).expect("path-test tables"); + let outcome_row = tables + .statistics + .iter() + .find(|row| row.kind == "operational_convergence_outcome" && row.name.ends_with(":outcome")) + .expect("statistics operational outcome"); + assert_eq!(outcome_row.value, Some(1.0)); + let count_kind = |kind: &str| { + tables + .statistics + .iter() + .filter(|row| row.kind == kind) + .count() + }; + assert!(result + .cycle_diagnostics() + .iter() + .all(|cycle| cycle.omega_relative_spd_margin.is_some())); + assert_eq!( + count_kind("covariance_stability"), + result.cycle_diagnostics().len() + ); + assert_eq!( + count_kind("operational_convergence_criterion_status"), + final_check.criteria.len() + ); + assert_eq!( + count_kind("operational_convergence_trace_status"), + final_check.per_trace_diagnostics().len() * 5 + ); + assert_eq!(count_kind("operational_convergence_lrv_chain_status"), 4); + assert_eq!( + count_kind("operational_convergence_lrv_aggregate_status"), + 2 + ); + assert_eq!(count_kind("operational_convergence_matrix_status"), 3); + + let json_path = std::env::temp_dir().join(format!( + "pmcore-operational-convergence-{}.json", + std::process::id() + )); + result + .write_json(&json_path, 0.0, 0.0) + .expect("write path-test JSON"); + let record = ParametricResultRecord::read_json(&json_path).expect("read path-test JSON"); + std::fs::remove_file(&json_path).expect("remove path-test JSON"); + assert_eq!(record.termination, Some(StopReason::Converged)); + assert_eq!(record.operational_convergence.config, diagnostics.config); + assert_eq!( + record.operational_convergence.final_status, + diagnostics.final_status + ); + assert_eq!( + record.operational_convergence.used_for_termination, + diagnostics.used_for_termination + ); + assert_eq!( + record.operational_convergence.checks.len(), + diagnostics.checks.len() + ); +} + +fn failed_path_test_config() -> SaemConfig { + let mut config = path_test_config(); + config.operational_convergence = Some(OperationalConvergenceConfig::literature_guided( + 4, 20, 1.0e-6, 0.95, 100.0, 100.0, + )); + config +} + +#[test] +fn valid_diagnostics_that_miss_predeclared_precision_end_max_cycles() { + let result = identifiable_latent_problem() + .fit_with(failed_path_test_config()) + .expect("failed operational control"); + let diagnostics = result.operational_diagnostics(); + let check = diagnostics.checks.last().expect("failed checkpoint"); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert!(matches!( + check.outcome, + OperationalConvergenceOutcome::Failed { .. } + )); + assert!(check.criteria.iter().all(|criterion| !matches!( + criterion.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + ))); + let fixed_width = check + .criteria + .iter() + .find(|criterion| criterion.name == "relative_fixed_width") + .expect("fixed-width criterion"); + assert!(matches!( + fixed_width.status, + OperationalConvergenceCriterionStatus::NotSatisfied + )); + assert!(diagnostics.warnings()[0].contains("evaluated but not satisfied")); +} + +#[test] +fn operational_diagnostic_rng_does_not_change_fit_trajectory_or_results() { + let checked = identifiable_latent_problem() + .fit_with(failed_path_test_config()) + .expect("checked fit"); + let mut unchecked_config = failed_path_test_config(); + unchecked_config.operational_convergence = None; + let unchecked = identifiable_latent_problem() + .fit_with(unchecked_config) + .expect("otherwise-identical unchecked fit"); + + assert_eq!(checked.termination_reason(), Some(&StopReason::MaxCycles)); + assert_eq!(unchecked.termination_reason(), Some(&StopReason::MaxCycles)); + assert_eq!(checked.cycle_diagnostics(), unchecked.cycle_diagnostics()); + assert_eq!( + checked.population_parameters(), + unchecked.population_parameters() + ); + assert_eq!(checked.omega(), unchecked.omega()); + assert_eq!(checked.omega_iov(), unchecked.omega_iov()); + assert_eq!(checked.residual_sigmas(), unchecked.residual_sigmas()); + assert_eq!(checked.conditional_n2ll(), unchecked.conditional_n2ll()); + assert_eq!(checked.eta_chain_means(), unchecked.eta_chain_means()); + assert_eq!(checked.kappa_chain_means(), unchecked.kappa_chain_means()); + assert_eq!(checked.conditional_modes(), unchecked.conditional_modes()); + assert_eq!( + checked + .tables(0.0, 0.0) + .expect("checked tables") + .predictions, + unchecked + .tables(0.0, 0.0) + .expect("unchecked tables") + .predictions + ); +} + +fn no_latent_problem() -> EstimationProblem { + let equation = analytical! { + name: "saem_operational_no_latent_control", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [bolus(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![Subject::builder("fixed") + .bolus(0.0, 100.0, "iv") + .observation(1.0, 6.5, "cp") + .build()]); + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.25) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(12.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.4)).fixed(), + ) + .build() + .expect("no-latent control") +} + +#[test] +fn no_latent_control_is_ineligible_and_ends_max_cycles() { + let result = no_latent_problem() + .fit_with(path_test_config()) + .expect("ineligible operational control"); + let diagnostics = result.operational_diagnostics(); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert!(matches!( + diagnostics.final_status, + Some(OperationalConvergenceOutcome::Ineligible { .. }) + )); + assert!(diagnostics.warnings()[0].contains("evaluated but were ineligible")); +} + +#[test] +fn configured_but_aborted_before_a_checkpoint_is_neutral() { + let result = identifiable_latent_problem() + .fit_with_observer( + path_test_config(), + |_controller: &ParametricFitController<_>| ParametricCycleFlow::Stop, + ) + .expect("pre-checkpoint abort result"); + let diagnostics = result.operational_diagnostics(); + assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); + assert!(diagnostics.checks.is_empty()); + assert!(diagnostics.final_status.is_none()); + let warning = &diagnostics.warnings()[0]; + assert!(warning.contains("no checkpoint was evaluated")); + assert!(warning.contains("not evaluated or established")); + assert!(!warning.contains("failed")); + assert!(!warning.contains("ineligible")); +} diff --git a/tests/saem_outputs.rs b/tests/saem_outputs.rs new file mode 100644 index 000000000..a62e9fccb --- /dev/null +++ b/tests/saem_outputs.rs @@ -0,0 +1,1498 @@ +use std::fs; +use std::time::{SystemTime, UNIX_EPOCH}; + +use pharmsol::prelude::*; +use pmcore::algorithms::StopReason; +use pmcore::prelude::*; +use pmcore::results::{InformationCoordinateKind, InformationStatus}; + +fn output_problem() -> EstimationProblem { + let equation = analytical! { + name: "saem_output_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .observation(2.0, 3.5, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.5, "cp") + .observation(2.0, 3.8, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.3)) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::diagonal([("ke", 0.09)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("output fixture should build") +} + +fn fit(compute_map: bool) -> ParametricResult { + output_problem() + .fit_with( + SaemConfig::new() + .seed(19) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(compute_map), + ) + .expect("output fixture should fit") +} + +fn fit_iov() -> ParametricResult { + let equation = analytical! { + name: "saem_output_iov_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.2, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.4, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.1, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.3).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.0225)) + .iov(Iov::new().fixed_variance("ke", 0.04)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("IOV output fixture should build") + .fit_with( + SaemConfig::new() + .seed(23) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(true), + ) + .expect("IOV output fixture should fit") +} + +fn markov_output_equation() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_markov_output_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + } +} + +fn markov_output_data() -> Data { + Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.2, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.1, "cp") + .observation(15.0, 2.5, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.3, "cp") + .observation(3.0, 3.8, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.6, "cp") + .observation(15.0, 3.0, "cp") + .build(), + ]) +} + +fn markov_output_problem() -> EstimationProblem { + EstimationProblem::parametric(markov_output_equation(), markov_output_data()) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.09)) + .iov(Iov::new().fixed_variance("ke", 0.04)) + .error_model("cp", ResidualErrorModel::constant(0.35)) + .build() + .expect("Markov output fixture should build") +} + +fn averaged_iov_problem() -> EstimationProblem { + let equation = analytical! { + name: "saem_averaged_output_iov_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.2, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.1, "cp") + .observation(15.0, 2.5, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.3, "cp") + .observation(3.0, 3.8, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.6, "cp") + .observation(15.0, 3.0, "cp") + .build(), + ]); + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::diagonal([("ke", 0.09)])) + .iov(Iov::new().variance("ke", 0.04)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.35)), + ) + .build() + .expect("averaged IOV output fixture should build") +} + +fn averaged_iov_config(policy: SaemEstimatorPolicy) -> SaemConfig { + SaemConfig::new() + .seed(71_105) + .n_chains(2) + .mcmc_iterations(2) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(4) + .compute_map(true) + .estimator_policy(policy) +} + +fn assert_float_slice_close(actual: &[f64], expected: &[f64]) { + assert_eq!(actual.len(), expected.len()); + assert!(actual + .iter() + .zip(expected) + .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); +} + +fn assert_json_close(actual: &serde_json::Value, expected: &serde_json::Value) { + match (actual, expected) { + (serde_json::Value::Number(actual), serde_json::Value::Number(expected)) => { + let actual = actual.as_f64().expect("numeric JSON value"); + let expected = expected.as_f64().expect("numeric JSON value"); + assert!((actual - expected).abs() < 1e-12, "{actual} != {expected}"); + } + (serde_json::Value::Array(actual), serde_json::Value::Array(expected)) => { + assert_eq!(actual.len(), expected.len()); + for (actual, expected) in actual.iter().zip(expected) { + assert_json_close(actual, expected); + } + } + (serde_json::Value::Object(actual), serde_json::Value::Object(expected)) => { + assert_eq!(actual.len(), expected.len()); + for (key, expected) in expected { + assert_json_close(&actual[key], expected); + } + } + _ => assert_eq!(actual, expected), + } +} + +fn assert_errorpoly_close( + actual: Option, + expected: Option, +) { + match (actual, expected) { + (Some(actual), Some(expected)) => { + let actual = actual.coefficients(); + let expected = expected.coefficients(); + for (actual, expected) in [actual.0, actual.1, actual.2, actual.3] + .into_iter() + .zip([expected.0, expected.1, expected.2, expected.3]) + { + assert!((actual - expected).abs() < 1e-12); + } + } + (None, None) => {} + _ => panic!("prediction error-polynomial presence differs"), + } +} + +fn constant_mode_objective( + result: &ParametricResult, + mode: &pmcore::results::SubjectConditionalMode, + population: &[f64], + omega: f64, + omega_iov: f64, + sigma: f64, +) -> f64 { + let log_normal = |value: f64, variance: f64| { + -0.5 * ((2.0 * std::f64::consts::PI * variance).ln() + value * value / variance) + }; + let subjects = result.data().subjects(); + let subject = subjects + .into_iter() + .find(|subject| subject.id() == &mode.subject_id) + .expect("mode subject should exist"); + let mut log_posterior = log_normal(mode.eta[0], omega); + for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { + assert_eq!(occasion.index(), kappa.occasion_index); + log_posterior += log_normal(kappa.values[0], omega_iov); + let parameters = [ + population[0] * (mode.eta[0] + kappa.values[0]).exp(), + population[1], + ]; + let occasion_subject = + Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); + let predictions = result + .equation() + .estimate_predictions_dense(&occasion_subject, ¶meters) + .expect("fresh occasion prediction should succeed"); + for prediction in predictions.predictions() { + if let Some(observation) = prediction.observation() { + let residual = observation - prediction.prediction(); + log_posterior += log_normal(residual, sigma * sigma); + } + } + } + -log_posterior +} + +fn fit_without_random_effects() -> ParametricResult { + let equation = analytical! { + name: "saem_output_no_random_effect_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .build()]); + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.3) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("no-random-effect output fixture should build") + .fit_with( + SaemConfig::new() + .seed(29) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("no-random-effect output fixture should fit") +} + +fn fit_covariate_individual_output(iov: bool) -> ParametricResult { + let equation = analytical! { + name: "saem_covariate_individual_output", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [bolus(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let subject = |id: &str, wt: f64, group: f64| { + let mut builder = Subject::builder(id) + .covariate("wt", 0.0, wt) + .covariate("group", 0.0, group) + .bolus(0.0, 100.0, "iv") + .observation(1.0, 3.0, "cp"); + if iov { + builder = builder + .reset() + .covariate("wt", 12.0, wt) + .covariate("group", 12.0, group) + .bolus(12.0, 100.0, "iv") + .observation(13.0, 3.0, "cp"); + } + builder.build() + }; + let data = Data::new(vec![subject("s1", 0.0, 0.0), subject("s2", 10.0, 1.0)]); + let builder = EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .with_random_effect(iov), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .covariate_effect( + CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.01) + .fixed(), + ) + .covariate_effect( + CovariateEffect::categorical("ke", "group", 0.0, 1.0) + .with_initial(0.2) + .fixed(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ); + let problem = if iov { + builder + .omega(Omega::new().fixed_variance("ke", 0.04)) + .iov(Iov::new().fixed_variance("ke", 0.09)) + .build() + } else { + builder.build() + } + .expect("covariate individual-output fixture should build"); + problem + .fit_with( + SaemConfig::new() + .seed(30_031) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("covariate individual-output fixture should fit") +} + +fn fit_structured_omega() -> ParametricResult { + let equation = analytical! { + name: "saem_output_structured_omega_fixture", + params: [ke, v, bio], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = bio * x[central] / v; }, + }; + let data = Data::new(vec![Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .observation(2.0, 3.5, "cp") + .build()]); + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.3).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .parameter(Parameter::log("bio").with_initial(1.0).fixed()) + .omega( + Omega::new() + .variance("ke", 0.09) + .fixed_variance("v", 1.0) + .variance("bio", 0.04) + .fixed_covariance("ke", "v", 0.01) + .covariance("v", "bio", 0.02), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("structured Omega fixture should build") + .fit_with( + SaemConfig::new() + .seed(31) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("structured Omega fixture should fit") +} + +fn fit_residual_output( + residual: ParametricErrorModel, + seed: u64, +) -> ParametricResult { + let equation = analytical! { + name: "saem_output_residual_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .observation(2.0, 3.5, "cp") + .observation(4.0, 2.5, "cp") + .build()]); + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.3) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .error_model("cp", residual) + .build() + .expect("combined output fixture should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("residual output fixture should fit") +} + +fn fit_combined_residual(fixed_additive: bool) -> ParametricResult { + let residual = ParametricErrorModel::new(ResidualErrorModel::combined(0.25, 0.05)); + let residual = if fixed_additive { + residual.fixed_combined_additive() + } else { + residual.fixed_combined_proportional() + }; + fit_residual_output(residual, 37) +} + +fn temp_output_dir(label: &str) -> std::path::PathBuf { + let nonce = SystemTime::now() + .duration_since(UNIX_EPOCH) + .expect("system clock should follow epoch") + .as_nanos(); + std::path::Path::new("target") + .join("saem-output-tests") + .join(format!("{label}-{nonce}")) +} + +#[test] +fn tables_preserve_order_masks_and_optional_conditionals() { + let result = fit(false); + let tables = result.tables(0.0, 0.0).expect("tables should build"); + let summary = result.population_summary(); + assert_eq!(summary.parameters.len(), 2); + for (parameter, estimate) in summary + .parameters + .iter() + .zip(result.population_parameters()) + { + assert_eq!(parameter.estimate, *estimate); + assert_eq!(parameter.mean, None); + assert_eq!(parameter.median, None); + assert_eq!(parameter.sd, None); + assert_eq!(parameter.cv_percent, None); + } + + assert_eq!(tables.population.len(), 2); + assert_eq!(tables.population[0].name, "ke"); + assert!(tables.population[0].estimated); + assert!(tables.population[0].iiv); + assert_eq!(tables.population[1].name, "v"); + assert!(!tables.population[1].estimated); + assert!(!tables.population[1].iiv); + assert_eq!(tables.omega.len(), 1); + assert!(tables.omega[0].structural); + assert!(tables.omega[0].estimated); + assert!(tables.omega_iov.is_none()); + assert_eq!(tables.residual_error.len(), 1); + assert_eq!(tables.residual_error[0].component, "sigma"); + assert!(!tables.residual_error[0].estimated); + assert_eq!(tables.individual_effects.len(), 2); + assert_eq!(tables.individual_effects[0].subject, "s1"); + assert_eq!(tables.individual_effects[0].parameter, "ke"); + assert_eq!(tables.individual_effects[0].source, "chain_mean"); + assert_eq!(tables.individual_parameters.len(), 4); + assert_eq!(tables.individual_parameters[0].subject, "s1"); + assert_eq!(tables.individual_parameters[0].occasion, None); + assert_eq!(tables.individual_parameters[0].parameter, "ke"); + assert_eq!(tables.individual_parameters[1].parameter, "v"); + assert_eq!(tables.individual_parameters[2].subject, "s2"); + assert_eq!(tables.iterations.len(), 1); + assert_eq!(tables.iterations[0].cycle, 1); + assert!(tables.statistics.iter().any(|row| row.kind == "theta")); + assert!(tables.statistics.iter().any(|row| row.kind == "omega")); + assert!(tables.statistics.iter().any(|row| row.kind == "residual")); + assert!(!tables.predictions.is_empty()); + assert!(tables + .predictions + .iter() + .all(|row| row.conditional_prediction.is_none() && row.conditional_source.is_none())); +} + +#[test] +fn iov_tables_include_lower_triangle_and_ordered_kappas() { + let result = fit_iov(); + let tables = result.tables(0.0, 0.0).expect("IOV tables should build"); + let omega_iov = tables.omega_iov.expect("IOV table should be present"); + assert_eq!(omega_iov.len(), 1); + assert_eq!(omega_iov[0].row, "ke"); + assert_eq!(omega_iov[0].column, "ke"); + assert!(omega_iov[0].structural); + assert!(!omega_iov[0].estimated); + let kappas: Vec<_> = tables + .individual_effects + .iter() + .filter(|row| row.effect_kind == "kappa" && row.source == "chain_mean") + .collect(); + assert_eq!(kappas.len(), 4); + assert!(kappas.iter().all(|row| row.occasion.is_some())); + + for source in ["chain_mean", "conditional_mode"] { + let parameters: Vec<_> = tables + .individual_parameters + .iter() + .filter(|row| row.source == source) + .collect(); + assert_eq!(parameters.len(), 8); + let keys: Vec<_> = parameters + .iter() + .map(|row| (row.subject.as_str(), row.occasion, row.parameter.as_str())) + .collect(); + assert_eq!( + keys, + vec![ + ("s1", Some(0), "ke"), + ("s1", Some(0), "v"), + ("s1", Some(1), "ke"), + ("s1", Some(1), "v"), + ("s2", Some(0), "ke"), + ("s2", Some(0), "v"), + ("s2", Some(1), "ke"), + ("s2", Some(1), "v"), + ] + ); + assert_ne!(parameters[0].value, parameters[2].value); + assert!((parameters[1].value - 20.0).abs() < 1e-12); + assert!((parameters[3].value - 20.0).abs() < 1e-12); + for row in parameters.iter().filter(|row| row.parameter == "ke") { + let occasion = row.occasion.expect("IOV row should name an occasion"); + let (eta, kappa) = if source == "chain_mean" { + let eta = result + .eta_chain_mean(&row.subject) + .expect("chain eta should exist") + .values[0]; + let kappa = result + .kappa_chain_mean(&row.subject, occasion) + .expect("chain kappa should exist") + .values[0]; + (eta, kappa) + } else { + let mode = result + .conditional_mode(&row.subject) + .expect("conditional mode should exist"); + let kappa = mode + .kappas + .iter() + .find(|value| value.occasion_index == occasion) + .expect("mode kappa should exist") + .values[0]; + (mode.eta[0], kappa) + }; + let expected = result.population_parameters()[0] * (eta + kappa).exp(); + assert!((row.value - expected).abs() < 1e-12); + } + } + + let population = result + .population_predictions(0.25, 0.0) + .expect("dense population predictions should build"); + let conditional = result + .conditional_predictions(0.25, 0.0) + .expect("dense conditional predictions should build"); + for (population_subject, conditional_subject) in population.iter().zip(&conditional) { + assert_eq!( + population_subject.predictions().len(), + conditional_subject.predictions().len() + ); + for (population_point, conditional_point) in population_subject + .predictions() + .iter() + .zip(conditional_subject.predictions()) + { + assert_eq!(population_point.time(), conditional_point.time()); + assert_eq!(population_point.outeq(), conditional_point.outeq()); + assert_eq!(population_point.occasion(), conditional_point.occasion()); + assert_eq!( + population_point.observation(), + conditional_point.observation() + ); + assert_eq!(population_point.censoring(), conditional_point.censoring()); + } + } + + let dense = result + .tables(0.25, 0.0) + .expect("dense IOV tables should build"); + assert!(dense.predictions.len() > 4); + assert!(dense.predictions.iter().all(|row| { + row.conditional_prediction.is_some() + && row.conditional_source.as_deref() == Some("conditional_mode") + && row.block <= 1 + })); +} + +#[test] +fn averaged_iov_result_rebuilds_all_deterministic_outputs_from_canonical_state() { + let averaged = averaged_iov_problem() + .fit_with(averaged_iov_config(SaemEstimatorPolicy::AveragedIterates { + alpha: 0.75, + })) + .expect("averaged IOV output fixture should fit"); + assert_eq!(averaged.termination_reason(), Some(&StopReason::MaxCycles)); + assert!(!averaged.converged()); + assert!(averaged.estimator_metadata().average_applied); + assert_eq!(averaged.estimator_metadata().averaging_start_cycle, Some(2)); + assert_eq!(averaged.estimator_metadata().averaged_iterations, 4); + assert_eq!(averaged.cycle_diagnostics().len(), 5); + let terminal_cycle = averaged + .cycle_diagnostics() + .last() + .expect("terminal cycle diagnostic"); + assert_eq!(terminal_cycle.iteration, 5); + assert_eq!(terminal_cycle.phase, pmcore::results::SaemPhase::Smoothing); + + let installed_terminal_population_gap = averaged + .population_parameters() + .iter() + .zip(&terminal_cycle.population_parameters) + .map(|(average, terminal)| (average - terminal).abs()) + .fold(0.0_f64, f64::max); + assert!(installed_terminal_population_gap > 1e-4); + assert!((averaged.omega()[[0, 0]] - terminal_cycle.omega[[0, 0]]).abs() > 1e-6); + assert!( + (averaged.omega_iov().unwrap()[[0, 0]] + - terminal_cycle.omega_iov.as_ref().unwrap()[[0, 0]]) + .abs() + > 1e-6 + ); + let averaged_sigma = averaged.residual_error_estimates()[0].model.sigma(1.0); + let terminal_sigma = terminal_cycle.residual_error_estimates[0].model.sigma(1.0); + assert!((averaged_sigma - terminal_sigma).abs() > 1e-5); + + assert_eq!(averaged.conditional_modes().len(), 2); + for mode in averaged.conditional_modes() { + let fresh_objective = constant_mode_objective( + &averaged, + mode, + averaged.population_parameters(), + averaged.omega()[[0, 0]], + averaged.omega_iov().unwrap()[[0, 0]], + averaged_sigma, + ); + assert!((mode.objective - fresh_objective).abs() < 1e-9); + let terminal_state_objective = constant_mode_objective( + &averaged, + mode, + &terminal_cycle.population_parameters, + terminal_cycle.omega[[0, 0]], + terminal_cycle.omega_iov.as_ref().unwrap()[[0, 0]], + terminal_sigma, + ); + assert!((mode.objective - terminal_state_objective).abs() > 1e-4); + + assert!( + (mode.parameters[0] - averaged.population_parameters()[0] * mode.eta[0].exp()).abs() + < 1e-12 + ); + assert!((mode.parameters[1] - averaged.population_parameters()[1]).abs() < 1e-12); + } + + let tables = averaged.tables(0.25, 0.0).expect("averaged tables"); + let mode_rows: Vec<_> = tables + .individual_parameters + .iter() + .filter(|row| row.source == "conditional_mode") + .collect(); + assert_eq!(mode_rows.len(), 8); + for row in mode_rows { + let mode = averaged.conditional_mode(&row.subject).unwrap(); + let occasion = row.occasion.expect("IOV mode row should name an occasion"); + let kappa = mode + .kappas + .iter() + .find(|kappa| kappa.occasion_index == occasion) + .unwrap(); + let expected = match row.parameter.as_str() { + "ke" => averaged.population_parameters()[0] * (mode.eta[0] + kappa.values[0]).exp(), + "v" => averaged.population_parameters()[1], + parameter => panic!("unexpected parameter row {parameter}"), + }; + assert!((row.value - expected).abs() < 1e-12); + } + + let expanded = averaged.data().clone().expand(0.25, 0.0); + let population = averaged.population_predictions(0.25, 0.0).unwrap(); + let mut differs_from_terminal_prediction = false; + for (subject, actual) in expanded.subjects().iter().zip(&population) { + let expected = averaged + .equation() + .estimate_predictions_dense(subject, averaged.population_parameters()) + .unwrap(); + let terminal_expected = averaged + .equation() + .estimate_predictions_dense(subject, &terminal_cycle.population_parameters) + .unwrap(); + assert_eq!(actual.predictions().len(), expected.predictions().len()); + for ((actual, expected), terminal_expected) in actual + .predictions() + .iter() + .zip(expected.predictions()) + .zip(terminal_expected.predictions()) + { + assert_eq!(actual.time(), expected.time()); + assert!((actual.prediction() - expected.prediction()).abs() < 1e-12); + assert_eq!(actual.observation(), expected.observation()); + assert_eq!(actual.outeq(), expected.outeq()); + assert_errorpoly_close(actual.errorpoly(), expected.errorpoly()); + assert_float_slice_close(actual.state(), expected.state()); + assert_eq!(actual.occasion(), expected.occasion()); + assert_eq!(actual.censoring(), expected.censoring()); + differs_from_terminal_prediction |= + (actual.prediction() - terminal_expected.prediction()).abs() > 1e-5; + } + } + assert!(differs_from_terminal_prediction); + + let conditional = averaged.conditional_predictions(0.25, 0.0).unwrap(); + for (subject, actual) in expanded.subjects().iter().zip(&conditional) { + let mode = averaged.conditional_mode(subject.id()).unwrap(); + let mut expected = Vec::new(); + for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { + assert_eq!(occasion.index(), kappa.occasion_index); + let parameters = [ + averaged.population_parameters()[0] * (mode.eta[0] + kappa.values[0]).exp(), + averaged.population_parameters()[1], + ]; + let occasion_subject = + Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); + expected.extend( + averaged + .equation() + .estimate_predictions_dense(&occasion_subject, ¶meters) + .unwrap() + .predictions() + .iter() + .cloned(), + ); + } + assert_eq!(actual.predictions().len(), expected.len()); + for (actual, expected) in actual.predictions().iter().zip(&expected) { + assert_eq!(actual.time(), expected.time()); + assert!((actual.prediction() - expected.prediction()).abs() < 1e-12); + assert_eq!(actual.observation(), expected.observation()); + assert_eq!(actual.outeq(), expected.outeq()); + assert_errorpoly_close(actual.errorpoly(), expected.errorpoly()); + assert_float_slice_close(actual.state(), expected.state()); + assert_eq!(actual.occasion(), expected.occasion()); + assert_eq!(actual.censoring(), expected.censoring()); + } + } +} + +#[test] +fn covariance_rows_use_named_lower_triangle_order_and_masks() { + let tables = fit_structured_omega() + .tables(0.0, 0.0) + .expect("structured tables should build"); + let actual: Vec<_> = tables + .omega + .iter() + .map(|row| { + ( + row.row.as_str(), + row.column.as_str(), + row.structural, + row.estimated, + ) + }) + .collect(); + assert_eq!( + actual, + vec![ + ("ke", "ke", true, true), + ("v", "ke", true, false), + ("v", "v", true, false), + ("bio", "ke", false, false), + ("bio", "v", true, true), + ("bio", "bio", true, true), + ] + ); +} + +#[test] +fn standalone_proportional_coordinate_joins_persisted_residual_row() { + let result = fit_residual_output( + ParametricErrorModel::new(ResidualErrorModel::proportional(0.05)), + 39, + ); + let row = result + .tables(0.0, 0.0) + .unwrap() + .residual_error + .into_iter() + .next() + .unwrap(); + assert_eq!(row.component, "proportional"); + assert!(result + .information_diagnostics() + .coordinates + .iter() + .any(|coordinate| matches!( + &coordinate.kind, + InformationCoordinateKind::Residual { + output_index: 0, + component, + } if component == "proportional" + ) && coordinate.name == "residual:cp:proportional")); +} + +#[test] +fn combined_residual_rows_preserve_independent_component_masks() { + for (fixed_additive, expected) in [ + (true, [("additive", false), ("proportional", true)]), + (false, [("additive", true), ("proportional", false)]), + ] { + let tables = fit_combined_residual(fixed_additive) + .tables(0.0, 0.0) + .expect("combined tables should build"); + let actual: Vec<_> = tables + .residual_error + .iter() + .map(|row| (row.component.as_str(), row.estimated)) + .collect(); + assert_eq!(actual, expected); + + let result = fit_combined_residual(fixed_additive); + let coordinate_components: Vec<_> = result + .information_diagnostics() + .coordinates + .iter() + .filter_map(|coordinate| match &coordinate.kind { + InformationCoordinateKind::Residual { + output_index, + component, + } => Some((*output_index, component.as_str())), + _ => None, + }) + .collect(); + for row in result.tables(0.0, 0.0).unwrap().residual_error { + if row.estimated { + assert!(coordinate_components.contains(&(row.output_index, row.component.as_str()))); + } + } + } +} + +#[test] +fn nondifferentiable_information_floor_does_not_fail_a_valid_fit() { + let equation = analytical! { + name: "saem_information_floor_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .build()]); + let result = EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.3) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(f64::EPSILON.sqrt())).fixed(), + ) + .build() + .unwrap() + .fit_with( + SaemConfig::new() + .seed(41) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("the fit remains scientifically valid when only information is unavailable"); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + assert_eq!( + result.information_diagnostics().status, + InformationStatus::Ineligible( + "constant residual scale is exactly at the nondifferentiable likelihood floor boundary" + .to_string() + ) + ); +} + +#[test] +fn zero_random_effect_predictions_are_available_without_conditional_modes() { + let result = fit_without_random_effects(); + assert!(result.conditional_modes().is_empty()); + let tables = result.tables(0.25, 0.0).expect("tables should build"); + assert_eq!(tables.individual_parameters.len(), 2); + assert!(tables + .individual_parameters + .iter() + .all(|row| row.occasion.is_none() && row.source == "chain_mean")); + assert!(!tables.predictions.is_empty()); + assert!(tables.predictions.iter().all(|row| { + row.conditional_prediction == Some(row.population_prediction) + && row.conditional_source.as_deref() == Some("population") + })); + let directory = temp_output_dir("zero-random-schema-seven"); + result.write_outputs(&directory, 0.25, 0.0).unwrap(); + let persisted = ParametricResultRecord::read_json(directory.join("result.json")).unwrap(); + assert_eq!(persisted.source_metadata.omega.dimension, 0); + assert!(persisted.source_metadata.omega.names.is_empty()); + assert!(persisted.source_metadata.omega.values.is_empty()); + assert!(persisted.source_metadata.omega.structural_mask.is_empty()); + assert!(persisted.source_metadata.omega.estimated_mask.is_empty()); + assert!(persisted.source_metadata.covariate_effects.is_empty()); + assert!(persisted.source_metadata.subject_covariates.is_empty()); + assert!(persisted.source_metadata.subject_design.is_empty()); + assert!(persisted + .source_metadata + .subject_population_parameters + .is_empty()); + assert_eq!(persisted.source_metadata.residual_outputs[0].values, [0.25]); + fs::remove_dir_all(directory).unwrap(); +} + +#[test] +fn covariate_subject_means_drive_no_iov_individual_and_prediction_outputs() { + let result = fit_covariate_individual_output(false); + assert!(result + .eta_chain_means() + .iter() + .all(|eta| eta.values.is_empty())); + let tables = result.tables(0.0, 0.0).unwrap(); + let ke = |subject: &str, rows: &[IndividualParameterRow]| { + rows.iter() + .find(|row| row.subject == subject && row.parameter == "ke") + .unwrap() + .value + }; + let expected_s1 = 0.2; + let expected_s2 = 0.2 * 0.3f64.exp(); + assert!((ke("s1", &tables.individual_parameters) - expected_s1).abs() <= 1e-12); + assert!((ke("s2", &tables.individual_parameters) - expected_s2).abs() <= 1e-12); + assert_ne!(expected_s1, expected_s2); + for (subject, expected_ke) in [("s1", expected_s1), ("s2", expected_s2)] { + let prediction = tables + .predictions + .iter() + .find(|row| row.subject == subject && row.time == 1.0) + .unwrap(); + let expected_prediction = 100.0 * (-expected_ke).exp() / 20.0; + assert!((prediction.population_prediction - expected_prediction).abs() <= 1e-12); + } + + let directory = temp_output_dir("covariate-no-iov-individual"); + result.write_outputs(&directory, 0.0, 0.0).unwrap(); + let csv = fs::read_to_string(directory.join("individual_parameters.csv")).unwrap(); + assert!(csv.contains("s1,,ke,0.2,chain_mean,")); + assert!(csv.contains("s2,,ke,")); + let persisted = ParametricResultRecord::read_json(directory.join("result.json")).unwrap(); + assert_eq!( + persisted.tables.individual_parameters, + tables.individual_parameters + ); + fs::remove_dir_all(directory).unwrap(); +} + +#[test] +fn schema_seven_reader_rejects_coordinated_fixed_beta_tampering() { + let result = fit_covariate_individual_output(false); + let directory = temp_output_dir("fixed-beta-tampering"); + result.write_outputs(&directory, 0.0, 0.0).unwrap(); + let result_path = directory.join("result.json"); + let mut raw: serde_json::Value = + serde_json::from_reader(fs::File::open(&result_path).unwrap()).unwrap(); + let changed = raw["source_metadata"]["covariate_effects"][0]["estimate"] + .as_f64() + .unwrap() + + 0.01; + raw["source_metadata"]["covariate_effects"][0]["estimate"] = serde_json::json!(changed); + raw["tables"]["covariate_effects"][0]["estimate"] = serde_json::json!(changed); + for row in raw["tables"]["statistics"].as_array_mut().unwrap() { + if row["kind"] == "covariate_effect_final" && row["name"] == "beta:ke:wt" { + row["value"] = serde_json::json!(changed); + } + } + fs::write(&result_path, serde_json::to_vec_pretty(&raw).unwrap()).unwrap(); + assert!(ParametricResultRecord::read_json(&result_path).is_err()); + fs::remove_dir_all(directory).unwrap(); +} + +#[test] +fn covariate_subject_means_drive_iov_occasion_individual_outputs() { + let result = fit_covariate_individual_output(true); + let tables = result.tables(0.0, 0.0).unwrap(); + for subject in ["s1", "s2"] { + let mu = if subject == "s1" { + 0.2 + } else { + 0.2 * 0.3f64.exp() + }; + let eta = result.eta_chain_mean(subject).unwrap().values[0]; + for occasion in 0..2 { + let kappa = result.kappa_chain_mean(subject, occasion).unwrap().values[0]; + let row = tables + .individual_parameters + .iter() + .find(|row| { + row.subject == subject + && row.occasion == Some(occasion) + && row.parameter == "ke" + && row.source == "chain_mean" + }) + .unwrap(); + let expected = (mu.ln() + eta + kappa).exp(); + assert!((row.value - expected).abs() <= 1e-12); + let global_fallback = (0.2f64.ln() + eta + kappa).exp(); + if subject == "s2" { + assert!((row.value - global_fallback).abs() > 1e-6); + } + } + } + let directory = temp_output_dir("covariate-iov-individual"); + result.write_outputs(&directory, 0.0, 0.0).unwrap(); + let persisted = ParametricResultRecord::read_json(directory.join("result.json")).unwrap(); + assert_eq!( + persisted.tables.individual_parameters, + tables.individual_parameters + ); + fs::remove_dir_all(directory).unwrap(); +} + +#[test] +fn output_bundle_has_exact_files_headers_and_loadable_json() { + let result = fit(true); + let directory = temp_output_dir("bundle"); + result + .write_outputs(&directory, 0.0, 0.0) + .expect("output bundle should write"); + + let required = [ + "population.csv", + "omega.csv", + "residual_error.csv", + "individual_effects.csv", + "individual_parameters.csv", + "iterations.csv", + "statistics.csv", + "marginal_likelihood.csv", + "information_criteria.csv", + "predictions.csv", + "covariate_effects.csv", + "subject_covariates.csv", + "subject_population_parameters.csv", + "result.json", + "manifest.json", + ]; + let mut entries: Vec<_> = fs::read_dir(&directory) + .expect("output directory should read") + .map(|entry| { + entry + .expect("output entry should read") + .file_name() + .into_string() + .expect("output filename should be UTF-8") + }) + .collect(); + entries.sort(); + let mut expected_entries = required.map(str::to_string).to_vec(); + expected_entries.sort(); + assert_eq!(entries, expected_entries); + + let population = + fs::read_to_string(directory.join("population.csv")).expect("population CSV should read"); + assert_eq!( + population.lines().next(), + Some("name,estimate,scale,estimated,iiv,iov") + ); + let predictions = + fs::read_to_string(directory.join("predictions.csv")).expect("predictions CSV should read"); + assert_eq!( + predictions.lines().next(), + Some("subject,time,output_index,block,observation,censoring,population_prediction,conditional_prediction,conditional_source") + ); + + let statistics = + fs::read_to_string(directory.join("statistics.csv")).expect("statistics CSV should read"); + assert_eq!( + statistics.lines().next(), + Some("cycle,kind,name,row,column,output_index,component,value,status") + ); + + let marginal = fs::read_to_string(directory.join("marginal_likelihood.csv")) + .expect("marginal likelihood CSV should read"); + assert!(marginal.lines().next().is_some_and(|header| { + header.starts_with("scope,subject,method,status,samples_per_subject,seed") + })); + + let individual_parameters = fs::read_to_string(directory.join("individual_parameters.csv")) + .expect("individual parameter CSV should read"); + assert_eq!( + individual_parameters.lines().next(), + Some("subject,occasion,parameter,value,source,mode_converged") + ); + + let manifest: serde_json::Value = serde_json::from_reader( + fs::File::open(directory.join("manifest.json")).expect("manifest should open"), + ) + .expect("manifest should parse"); + assert_eq!( + manifest, + serde_json::json!({ + "schema_version": 9, + "fit_family": "parametric", + "algorithm": "saem", + "objective_kind": "conditional_n2ll", + "termination": "MaxCycles", + "operational_convergence": { + "config": null, + "checks": [], + "final_check_reused": false, + "used_for_termination": false, + "final_status": null, + "worst_rhat": null, + "min_bulk_ess": null, + "fixed_width_ratio": null, + "fixed_width_epsilon": null, + "implied_minimum_ess": null, + "newton_displacement": null, + "newton_displacement_mc_sd": null + }, + "estimator_metadata": { + "policy": "TerminalIterate", + "average_applied": false, + "averaging_start_cycle": null, + "averaged_iterations": 0 + }, + "marginal_likelihood": null, + "information_criteria": result.information_criteria(), + "files": [ + "population.csv", + "omega.csv", + "residual_error.csv", + "individual_effects.csv", + "individual_parameters.csv", + "iterations.csv", + "statistics.csv", + "marginal_likelihood.csv", + "information_criteria.csv", + "predictions.csv", + "covariate_effects.csv", + "subject_covariates.csv", + "subject_population_parameters.csv", + "result.json", + "manifest.json" + ] + }) + ); + + let expected_tables = result.tables(0.0, 0.0).expect("tables should rebuild"); + let expected_config_seed = result.config().seed; + let expected_termination = result.termination_reason().cloned(); + drop(result); + + let record = ParametricResultRecord::read_json(directory.join("result.json")) + .expect("equation-free result should load"); + assert_eq!(record.schema_version, 9); + assert_eq!(record.fit_family, "parametric"); + assert_eq!(record.algorithm, "saem"); + assert_eq!(record.objective_kind, "conditional_n2ll"); + assert_eq!(record.config.seed, expected_config_seed); + assert_eq!(record.termination, expected_termination); + assert_eq!(record.tables, expected_tables); + let information = &record.information_diagnostics; + assert!(!information.coordinates.is_empty()); + assert!(information + .observed_information + .iter() + .flatten() + .all(|value| value.is_finite())); + for row in 0..information.observed_information.len() { + for column in 0..row { + assert!( + (information.observed_information[row][column] + - information.observed_information[column][row]) + .abs() + < 1e-12 + ); + } + } + let information_rows: Vec<_> = record + .tables + .statistics + .iter() + .filter(|row| { + (row.kind.starts_with("information_") && !row.kind.starts_with("information_criteria")) + || row.kind == "observed_information" + }) + .collect(); + assert!(!information_rows.is_empty()); + let expected_status = match &information.status { + InformationStatus::Available => "available".to_string(), + InformationStatus::NoFreeCoordinates => "no_free_coordinates".to_string(), + InformationStatus::NonFinite => "non_finite".to_string(), + InformationStatus::ObservedInformationNotPositiveDefinite => { + "observed_information_not_positive_definite".to_string() + } + InformationStatus::Unsupported(reason) => format!("unsupported: {reason}"), + InformationStatus::Ineligible(reason) => format!("ineligible: {reason}"), + }; + assert!(information_rows + .iter() + .all(|row| row.status.as_deref() == Some(expected_status.as_str()))); + let markov_rows: Vec<_> = record + .tables + .statistics + .iter() + .filter(|row| row.kind.starts_with("markov_")) + .collect(); + assert!(!markov_rows.is_empty()); + assert!(markov_rows.iter().all(|row| row.status.is_some())); + assert!(record + .tables + .statistics + .iter() + .filter(|row| { + !row.kind.starts_with("information_") + && row.kind != "observed_information" + && !row.kind.starts_with("markov_") + && !row.kind.starts_with("marginal_likelihood") + && !row.kind.starts_with("population_uncertainty") + && !row.kind.starts_with("conditional_") + && !row.kind.contains("shrinkage") + }) + .all(|row| row.status.is_none())); + assert!(record + .tables + .predictions + .iter() + .all(|row| row.conditional_prediction.is_some() + && row.conditional_source.as_deref() == Some("conditional_mode"))); + + fs::remove_dir_all(directory).expect("temporary output directory should be removable"); +} + +#[test] +fn enabled_schema_six_preserves_markov_config_status_raw_matrices_and_warm_start() { + use pmcore::algorithms::parametric::{ + LugsailConfig, MarkovSimulationVarianceConfig, SaemEstimatorPolicy, + }; + + let diagnostic_config = MarkovSimulationVarianceConfig::new( + 80_401, + 1, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let config = SaemConfig::new() + .seed(80_400) + .n_chains(2) + .mcmc_iterations(1) + .eta_block_iterations(1) + .burn_in(0) + .k1_iterations(2) + .k2_iterations(4) + .compute_map(true) + .averaged_iterates(0.75) + .markov_simulation_variance(diagnostic_config); + let result = markov_output_problem() + .fit_with(config) + .expect("enabled Markov output fixture should fit"); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert!(!result.conditional_modes().is_empty()); + let diagnostic = result.markov_simulation_variance(); + assert_eq!(diagnostic.config, Some(diagnostic_config)); + assert!(!diagnostic.chains.is_empty()); + assert!(!diagnostic.lambda.is_empty()); + assert!(!diagnostic.xi.is_empty()); + assert!(!diagnostic.simulation_covariance.is_empty()); + + let directory = temp_output_dir("markov-schema-six"); + result + .write_outputs(&directory, 0.0, 0.0) + .expect("enabled Markov output should write"); + let record = ParametricResultRecord::read_json(directory.join("result.json")) + .expect("enabled schema-eight result should load"); + assert_eq!(record.schema_version, 9); + assert_eq!( + record.config.estimator_policy, + SaemEstimatorPolicy::AveragedIterates { alpha: 0.75 } + ); + assert_eq!( + record.config.markov_simulation_variance, + Some(diagnostic_config) + ); + let loaded = &record.markov_simulation_variance; + assert_eq!(loaded.config, diagnostic.config); + assert_eq!(loaded.coordinates, diagnostic.coordinates); + assert_eq!(loaded.chain_count, diagnostic.chain_count); + assert_eq!(loaded.n_avg, diagnostic.n_avg); + assert_json_close( + &serde_json::to_value(&loaded.rank_diagnostics).expect("serialize loaded rank diagnostics"), + &serde_json::to_value(&diagnostic.rank_diagnostics) + .expect("serialize in-memory rank diagnostics"), + ); + assert_eq!(loaded.status, diagnostic.status); + assert_eq!(loaded.lambda_status, diagnostic.lambda_status); + assert_eq!(loaded.xi_status, diagnostic.xi_status); + assert_eq!( + loaded.simulation_covariance_status, + diagnostic.simulation_covariance_status + ); + let assert_matrix_close = |actual: &[Vec], expected: &[Vec]| { + assert_eq!(actual.len(), expected.len()); + for (actual, expected) in actual.iter().zip(expected) { + assert_float_slice_close(actual, expected); + } + }; + assert_matrix_close(&loaded.lambda, &diagnostic.lambda); + assert_matrix_close(&loaded.xi, &diagnostic.xi); + assert_matrix_close( + &loaded.simulation_covariance, + &diagnostic.simulation_covariance, + ); + assert_eq!(loaded.chains.len(), diagnostic.chains.len()); + for (actual, expected) in loaded.chains.iter().zip(&diagnostic.chains) { + assert_eq!(actual.chain, expected.chain); + assert_eq!(actual.status, expected.status); + assert_eq!(actual.proposals, expected.proposals); + assert_eq!(actual.accepts, expected.accepts); + assert_eq!(actual.state_changes, expected.state_changes); + assert_matrix_close(&actual.bm_batch, &expected.bm_batch); + assert_matrix_close(&actual.bm_batch_over_r, &expected.bm_batch_over_r); + assert_matrix_close(&actual.lugsail_lrv, &expected.lugsail_lrv); + } + let rows: Vec<_> = record + .tables + .statistics + .iter() + .filter(|row| row.kind.starts_with("markov_")) + .collect(); + assert!(rows.iter().any(|row| row.kind == "markov_status")); + assert!(rows.iter().any(|row| row.kind == "markov_config")); + assert!(rows.iter().any(|row| row.kind == "markov_lugsail_lrv")); + assert!(rows.iter().all(|row| row + .status + .as_deref() + .is_some_and(|status| !status.is_empty()))); + + record + .warm_start_problem(markov_output_equation(), markov_output_data()) + .expect("enabled schema-six result should warm start"); + fs::remove_dir_all(directory).expect("temporary output directory should be removable"); +} diff --git a/tests/saem_prediction_parity.rs b/tests/saem_prediction_parity.rs new file mode 100644 index 000000000..d73ab6b93 --- /dev/null +++ b/tests/saem_prediction_parity.rs @@ -0,0 +1,341 @@ +use pharmsol::{prelude::Prediction, Predictions}; +use pmcore::prelude::*; + +const EXPECTED_PREDICTIONS: [f64; 5] = [ + 1.902_458_849_001_428, + 1.809_674_836_071_919, + 1.637_461_506_155_963_6, + 1.340_640_092_071_278_7, + 0.898_657_928_234_443_1, +]; +const EXPECTED_CONDITIONAL_NLL: f64 = -2.233_675_005_800_264; +const EXPECTED_N2LL: f64 = -4.467_350_011_600_528; + +fn analytical_model() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_v07_one_compartment", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [ + bolus(iv_bolus) -> central, + ], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + }, + } +} + +fn raw_ode_model() -> pharmsol::equation::ODE { + ode! { + name: "saem_v07_one_compartment", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [ + bolus(iv_bolus) -> central, + ], + diffeq: |x, _t, dx| { + dx[central] = -ke * x[central]; + }, + out: |x, _t, y| { + y[cp] = x[central] / v; + }, + } +} + +fn ode_model() -> pharmsol::equation::ODE { + raw_ode_model() + .with_solver(pharmsol::equation::OdeSolver::Bdf) + .with_tolerances(1e-8, 1e-10) +} + +fn data() -> Data { + Data::new(vec![Subject::builder("v07") + .bolus(0.0, 100.0, "iv_bolus") + .observation(0.5, 1.9, "cp") + .observation(1.0, 1.8, "cp") + .observation(2.0, 1.6, "cp") + .observation(4.0, 1.3, "cp") + .observation(8.0, 0.8, "cp") + .build()]) +} + +fn config() -> SaemConfig { + SaemConfig::new() + .seed(20_260_707) + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false) +} + +fn within_d1(actual: f64, expected: f64) -> bool { + let absolute = (actual - expected).abs(); + let relative = absolute / expected.abs().max(f64::MIN_POSITIVE); + absolute <= 1e-6 || relative <= 1e-4 +} + +#[test] +fn explicit_bdf_estimation_profiles_meet_analytical_oracle_bounds() { + let prediction_data = data(); + let subject = &prediction_data.subjects()[0]; + let analytical = analytical_model() + .estimate_predictions_dense(subject, &[0.1, 50.0]) + .expect("analytical predictions should succeed") + .get_predictions(); + let release_profile = ode_model() + .estimate_predictions_dense(subject, &[0.1, 50.0]) + .expect("release-profile ODE predictions should succeed") + .get_predictions(); + let oracle_profile = raw_ode_model() + .with_solver(pharmsol::equation::OdeSolver::Bdf) + .with_tolerances(1e-10, 1e-12) + .estimate_predictions_dense(subject, &[0.1, 50.0]) + .expect("oracle-profile ODE predictions should succeed") + .get_predictions(); + + assert_eq!(release_profile.len(), analytical.len()); + assert_eq!(oracle_profile.len(), analytical.len()); + let maximum_error = |predictions: &[Prediction]| { + predictions + .iter() + .zip(&analytical) + .map(|(numerical, exact)| (numerical.prediction() - exact.prediction()).abs()) + .fold(0.0_f64, f64::max) + }; + let release_error = maximum_error(&release_profile); + let oracle_error = maximum_error(&oracle_profile); + + assert!( + release_error <= 1e-7, + "release-profile error={release_error}" + ); + assert!(oracle_error <= 1e-8, "oracle-profile error={oracle_error}"); +} + +#[test] +fn deterministic_analytical_and_ode_predictions_and_objectives_match() { + let analytical = analytical_model(); + let ode = ode_model(); + let prediction_data = data(); + let subject = &prediction_data.subjects()[0]; + + let analytical_predictions = analytical + .estimate_predictions_dense(subject, &[0.1, 50.0]) + .expect("analytical predictions should succeed") + .get_predictions(); + let ode_predictions = ode + .estimate_predictions_dense(subject, &[0.1, 50.0]) + .expect("ODE predictions should succeed") + .get_predictions(); + + assert_eq!(analytical_predictions.len(), EXPECTED_PREDICTIONS.len()); + assert_eq!(ode_predictions.len(), EXPECTED_PREDICTIONS.len()); + for ((analytical_prediction, ode_prediction), expected) in analytical_predictions + .iter() + .zip(&ode_predictions) + .zip(EXPECTED_PREDICTIONS) + { + let analytical_value = analytical_prediction.prediction(); + let ode_value = ode_prediction.prediction(); + assert!( + (analytical_value - expected).abs() <= 1e-10, + "analytical={analytical_value:.16}, expected={expected:.16}", + ); + assert!( + within_d1(ode_value, expected), + "ODE={ode_value:.16}, expected={expected:.16}", + ); + assert!( + (ode_value - analytical_value).abs() <= 1e-7, + "release-profile ODE={ode_value:.16}, analytical={analytical_value:.16}", + ); + assert!(within_d1(ode_value, analytical_value)); + } + + let analytical_result = EstimationProblem::parametric(analytical, data()) + .parameter( + Parameter::log("ke") + .with_initial(0.1) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(50.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("analytical problem should build") + .fit_with(config()) + .expect("analytical fit should complete"); + let ode_result = EstimationProblem::parametric(ode, data()) + .parameter( + Parameter::log("ke") + .with_initial(0.1) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(50.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("ODE problem should build") + .fit_with(config()) + .expect("ODE fit should complete"); + + assert!( + (analytical_result.conditional_negative_log_likelihood() - EXPECTED_CONDITIONAL_NLL).abs() + <= 1e-10 + ); + assert!((analytical_result.conditional_n2ll() - EXPECTED_N2LL).abs() <= 1e-10); + assert!(within_d1( + ode_result.conditional_negative_log_likelihood(), + EXPECTED_CONDITIONAL_NLL, + )); + assert!(within_d1(ode_result.conditional_n2ll(), EXPECTED_N2LL)); + assert!(within_d1( + ode_result.conditional_negative_log_likelihood(), + analytical_result.conditional_negative_log_likelihood(), + )); + assert!(within_d1( + ode_result.conditional_n2ll(), + analytical_result.conditional_n2ll(), + )); + assert_eq!( + analytical_result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles), + ); + assert_eq!( + ode_result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles), + ); +} + +fn assert_finite_symmetric_information(result: &ParametricResult) { + let information = result.information_diagnostics(); + assert_eq!(information.recursion_cycles, 1); + assert!(!information.coordinates.is_empty()); + for matrix in [ + &information.g, + &information.expected_complete_hessian, + &information.observed_hessian, + &information.observed_information, + ] { + assert!(matrix.iter().flatten().all(|value| value.is_finite())); + for (row_index, row) in matrix.iter().enumerate() { + for (column_index, column) in matrix.iter().take(row_index).enumerate() { + assert!((row[column_index] - column[row_index]).abs() < 1e-12); + } + } + } + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); +} + +#[test] +fn analytical_and_ode_fits_produce_finite_symmetric_information_diagnostics() { + let diagnostic_config = SaemConfig::new() + .seed(20_260_707) + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(0) + .compute_map(false); + let analytical = EstimationProblem::parametric(analytical_model(), data()) + .parameter( + Parameter::log("ke") + .with_initial(0.1) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(50.0) + .fixed() + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::constant(0.25)) + .build() + .unwrap() + .fit_with(diagnostic_config.clone()) + .unwrap(); + let ode = EstimationProblem::parametric(ode_model(), data()) + .parameter( + Parameter::log("ke") + .with_initial(0.1) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(50.0) + .fixed() + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::constant(0.25)) + .build() + .unwrap() + .fit_with(diagnostic_config) + .unwrap(); + + assert_finite_symmetric_information(&analytical); + assert_finite_symmetric_information(&ode); + + let analytical_information = analytical.information_diagnostics(); + let ode_information = ode.information_diagnostics(); + assert_eq!( + analytical_information.coordinates, ode_information.coordinates, + "analytical and ODE information coordinates must have identical order" + ); + assert_eq!(analytical_information.status, ode_information.status); + for (analytical_value, ode_value) in analytical_information + .delta + .iter() + .zip(&ode_information.delta) + { + assert!(within_d1(*analytical_value, *ode_value)); + } + for (analytical_matrix, ode_matrix) in [ + (&analytical_information.g, &ode_information.g), + ( + &analytical_information.expected_complete_hessian, + &ode_information.expected_complete_hessian, + ), + ( + &analytical_information.observed_hessian, + &ode_information.observed_hessian, + ), + ( + &analytical_information.observed_information, + &ode_information.observed_information, + ), + ] { + for (analytical_value, ode_value) in analytical_matrix + .iter() + .flatten() + .zip(ode_matrix.iter().flatten()) + { + assert!(within_d1(*analytical_value, *ode_value)); + } + } +} diff --git a/tests/saem_regressions.rs b/tests/saem_regressions.rs new file mode 100644 index 000000000..778fb6976 --- /dev/null +++ b/tests/saem_regressions.rs @@ -0,0 +1,1742 @@ +use pharmsol::Predictions; +use pmcore::prelude::*; +use rand::{rngs::StdRng, RngExt, SeedableRng}; +use std::collections::BTreeMap; + +fn analytical_one_compartment() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_reproducible_analytical_one_cmt", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [ + infusion(iv) -> central, + ], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + }, + } +} + +fn analytical_one_compartment_with_scale() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_correlated_subset_iiv", + params: [ke, v, scale], + states: [central], + outputs: [cp], + routes: [ + infusion(iv) -> central, + ], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = scale * x[central] / v; + }, + } +} + +fn analytical_one_compartment_two_outputs() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_multi_output_residuals", + params: [ke, v], + states: [central], + outputs: [cp, doubled], + routes: [ + infusion(iv) -> central, + ], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + y[doubled] = 2.0 * x[central] / v; + }, + } +} + +fn validation_data() -> Data { + Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(0.5, 4.70, "cp") + .observation(1.0, 4.15, "cp") + .observation(2.0, 3.15, "cp") + .observation(4.0, 1.75, "cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 120.0, "iv", 0.5) + .observation(0.5, 4.65, "cp") + .observation(1.0, 4.25, "cp") + .observation(2.0, 3.45, "cp") + .observation(4.0, 2.15, "cp") + .build(), + Subject::builder("s3") + .infusion(0.0, 80.0, "iv", 0.5) + .observation(0.5, 4.45, "cp") + .observation(1.0, 3.75, "cp") + .observation(2.0, 2.65, "cp") + .observation(4.0, 1.20, "cp") + .build(), + Subject::builder("s4") + .infusion(0.0, 110.0, "iv", 0.5) + .observation(0.5, 4.55, "cp") + .observation(1.0, 4.10, "cp") + .observation(2.0, 3.25, "cp") + .observation(4.0, 1.95, "cp") + .build(), + ]) +} + +fn validation_problem() -> EstimationProblem { + EstimationProblem::parametric(analytical_one_compartment(), validation_data()) + .parameter(Parameter::log("ke").with_initial(0.30)) + .parameter(Parameter::log("v").with_initial(20.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("V01 analytical validation problem should build") +} + +fn validation_config(seed: u64) -> SaemConfig { + SaemConfig::new() + .seed(seed) + .n_chains(3) + .mcmc_iterations(2) + .burn_in(2) + .k1_iterations(8) + .k2_iterations(4) + .map_max_iterations(100) +} + +#[test] +fn analytical_same_seed_is_exactly_reproducible() { + let first = validation_problem() + .fit_with(validation_config(20_260_710)) + .expect("first V01 fit should complete"); + let second = validation_problem() + .fit_with(validation_config(20_260_710)) + .expect("second V01 fit should complete"); + + assert_eq!(first.objf().to_bits(), second.objf().to_bits()); + assert_eq!(first.iterations(), second.iterations()); + assert_eq!( + first.population_parameters(), + second.population_parameters() + ); + assert_eq!(first.omega(), second.omega()); + assert_eq!(first.residual_sigmas(), second.residual_sigmas()); + assert_eq!(first.residual_sigmas(), &[0.25]); + assert_eq!(first.residual_error_estimates().len(), 1); + assert_eq!(first.residual_error_estimates()[0].output, "cp"); + assert_eq!(first.residual_error_estimates()[0].output_index, 0); + assert_eq!( + first.residual_error_estimates()[0].model, + ResidualErrorModel::constant(0.25) + ); + assert!(!first.residual_error_estimates()[0].estimated); + assert_eq!(first.eta_chain_means(), second.eta_chain_means()); + assert_eq!(first.kappa_chain_means(), second.kappa_chain_means()); + assert_eq!(first.conditional_modes(), second.conditional_modes()); + assert_eq!(first.individual_summaries(), second.individual_summaries()); + + assert!(first.objf().is_finite()); + assert!(first + .population_parameters() + .iter() + .all(|value| value.is_finite() && *value > 0.0)); + assert!(first.omega().iter().all(|value| value.is_finite())); +} + +fn standard_normal(rng: &mut StdRng) -> f64 { + let u1 = rng.random::().max(f64::MIN_POSITIVE); + let u2 = rng.random::(); + (-2.0 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() +} + +fn one_compartment_infusion_concentration( + time: f64, + dose: f64, + duration: f64, + ke: f64, + volume: f64, +) -> f64 { + let rate = dose / duration; + let amount_at_end = rate * (-ke * duration).exp_m1().abs() / ke; + let amount = if time <= duration { + rate * (-ke * time).exp_m1().abs() / ke + } else { + amount_at_end * (-ke * (time - duration)).exp() + }; + amount / volume +} + +#[test] +fn closed_form_generator_matches_model_predictions() { + const TIMES: [f64; 6] = [0.25, 0.5, 1.0, 2.0, 4.0, 8.0]; + let dose = 100.0; + let duration = 0.5; + let ke = 0.3; + let volume = 20.0; + let mut builder = Subject::builder("formula-check").infusion(0.0, dose, "iv", duration); + for time in TIMES { + builder = builder.observation(time, 0.0, "cp"); + } + let subject = builder.build(); + let predictions = analytical_one_compartment() + .estimate_predictions_dense(&subject, &[ke, volume]) + .expect("analytical prediction should succeed") + .get_predictions(); + + assert_eq!(predictions.len(), TIMES.len()); + for (prediction, time) in predictions.iter().zip(TIMES) { + let expected = one_compartment_infusion_concentration(time, dose, duration, ke, volume); + assert!((prediction.prediction() - expected).abs() <= 1e-12); + } +} + +#[test] +fn constant_sigma_fixture_matches_seeded_generator() { + const TIMES: [f64; 6] = [0.5, 1.0, 2.0, 4.0, 6.0, 8.0]; + let mut lines = include_str!("fixtures/constant_sigma.csv").lines(); + assert_eq!(lines.next(), Some("ID,TIME,DV,EVID,AMT,CMT,RATE,MDV")); + let mut rng = StdRng::seed_from_u64(20_260_711); + for index in 0..48 { + let dose = 80.0 + 10.0 * (index % 5) as f64; + let ke = 0.30 * (0.20 * standard_normal(&mut rng)).exp(); + let volume = 20.0 * (0.20 * standard_normal(&mut rng)).exp(); + let dose_record = lines.next().expect("dose record should be present"); + assert!(dose_record.starts_with(&format!("v02_{index:03},0,.,1,{dose}"))); + for time in TIMES { + let expected = one_compartment_infusion_concentration(time, dose, 0.5, ke, volume) + + 0.25 * standard_normal(&mut rng); + let record = lines.next().expect("observation record should be present"); + let fields = record.split(',').collect::>(); + assert_eq!(fields[0], format!("v02_{index:03}")); + assert_eq!(fields[1].parse::().unwrap(), time); + assert_eq!( + fields[2].parse::().unwrap().to_bits(), + expected.to_bits() + ); + } + } + assert_eq!(lines.next(), None); +} + +fn seeded_constant_sigma_data(seed: u64, subject_count: usize) -> Data { + const POPULATION_KE: f64 = 0.30; + const POPULATION_V: f64 = 20.0; + const ETA_SD: f64 = 0.20; + const SIGMA: f64 = 0.25; + const DURATION: f64 = 0.5; + const TIMES: [f64; 6] = [0.5, 1.0, 2.0, 4.0, 6.0, 8.0]; + + let mut rng = StdRng::seed_from_u64(seed); + let subjects = (0..subject_count) + .map(|index| { + let dose = 80.0 + 10.0 * (index % 5) as f64; + let ke = POPULATION_KE * (ETA_SD * standard_normal(&mut rng)).exp(); + let volume = POPULATION_V * (ETA_SD * standard_normal(&mut rng)).exp(); + let mut builder = + Subject::builder(format!("v02_{index:03}")).infusion(0.0, dose, "iv", DURATION); + for time in TIMES { + let prediction = + one_compartment_infusion_concentration(time, dose, DURATION, ke, volume); + let observation = prediction + SIGMA * standard_normal(&mut rng); + builder = builder.observation(time, observation, "cp"); + } + builder.build() + }) + .collect(); + Data::new(subjects) +} + +fn constant_sigma_problem( + data_seed: u64, +) -> EstimationProblem { + EstimationProblem::parametric( + analytical_one_compartment(), + seeded_constant_sigma_data(data_seed, 48), + ) + .parameter(Parameter::log("ke").with_initial(0.22)) + .parameter(Parameter::log("v").with_initial(25.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::constant(0.50)) + .build() + .expect("V02 analytical known-truth problem should build") +} + +fn constant_sigma_config(seed: u64) -> SaemConfig { + SaemConfig::new() + .seed(seed) + .n_chains(3) + .mcmc_iterations(3) + .burn_in(20) + .k1_iterations(100) + .k2_iterations(80) + .compute_map(false) +} + +#[test] +fn estimated_constant_sigma_recovers_seeded_known_truth() { + const TRUE_THETA: [f64; 2] = [0.30, 20.0]; + const TRUE_OMEGA_DIAGONAL: [f64; 2] = [0.04, 0.04]; + const TRUE_SIGMA: f64 = 0.25; + + let first = constant_sigma_problem(20_260_711) + .fit_with(constant_sigma_config(20_260_712)) + .expect("first V02 known-truth fit should complete"); + let second = constant_sigma_problem(20_260_711) + .fit_with(constant_sigma_config(20_260_712)) + .expect("second V02 known-truth fit should complete"); + + assert_eq!( + first.population_parameters(), + second.population_parameters() + ); + assert_eq!(first.omega(), second.omega()); + assert_eq!(first.residual_sigmas(), second.residual_sigmas()); + assert_eq!( + first.residual_error_estimates(), + second.residual_error_estimates() + ); + assert!(first.objf().is_finite()); + assert_eq!(first.conditional_n2ll(), first.objf()); + assert_eq!( + first.conditional_negative_log_likelihood() * 2.0, + first.conditional_n2ll() + ); + let final_cycle = first + .cycle_diagnostics() + .last() + .expect("completed V02 fit should retain cycle diagnostics"); + assert_eq!( + final_cycle.conditional_negative_log_likelihood, + first.conditional_negative_log_likelihood() + ); + assert_eq!( + final_cycle.population_parameters, + first.population_parameters() + ); + assert_eq!(&final_cycle.omega, first.omega()); + assert_eq!( + final_cycle.residual_error_estimates, + first.residual_error_estimates() + ); + assert_eq!(first.residual_sigmas().len(), 1); + assert_eq!(first.residual_error_estimates().len(), 1); + assert_eq!(first.residual_error_estimates()[0].output, "cp"); + assert_eq!(first.residual_error_estimates()[0].output_index, 0); + assert_eq!( + first.residual_error_estimates()[0].model, + ResidualErrorModel::constant(first.residual_sigmas()[0]) + ); + assert!(first.residual_error_estimates()[0].estimated); + + let relative_error = |estimate: f64, truth: f64| (estimate - truth).abs() / truth; + assert!(relative_error(first.population_parameters()[0], TRUE_THETA[0]) <= 0.10); + assert!(relative_error(first.population_parameters()[1], TRUE_THETA[1]) <= 0.10); + assert!(relative_error(first.omega()[[0, 0]], TRUE_OMEGA_DIAGONAL[0]) <= 0.50); + assert!(relative_error(first.omega()[[1, 1]], TRUE_OMEGA_DIAGONAL[1]) <= 0.50); + assert!(relative_error(first.residual_sigmas()[0], TRUE_SIGMA) <= 0.15); +} + +fn residual_fixture(path: &str) -> Data { + data::read_pmetrics(path).expect("residual validation fixture should parse") +} + +fn iov_fixture(path: &str) -> Data { + let mut rows = BTreeMap::, Option)>>::new(); + for line in std::fs::read_to_string(path) + .expect("IOV validation fixture should be readable") + .lines() + .skip(1) + { + let columns = line.split(',').collect::>(); + let optional = |value: &str| { + (value != ".") + .then(|| value.parse()) + .transpose() + .expect("IOV numeric field should parse") + }; + rows.entry(columns[0].to_owned()).or_default().push(( + columns[1].parse().expect("occasion should parse"), + columns[2].parse().expect("time should parse"), + optional(columns[3]), + optional(columns[4]), + )); + } + Data::new( + rows.into_iter() + .map(|(id, rows)| { + let mut builder = Subject::builder(id); + let mut current_occasion = None; + for (occasion, time, observation, dose) in rows { + if current_occasion.is_some_and(|current| current != occasion) { + builder = builder.reset(); + } + current_occasion = Some(occasion); + if let Some(dose) = dose { + builder = builder.infusion(time, dose, "iv", 0.5); + } + if let Some(observation) = observation { + builder = builder.observation(time, observation, "cp"); + } + } + builder.build() + }) + .collect(), + ) +} + +fn relative_error(estimate: f64, truth: f64) -> f64 { + (estimate - truth).abs() / truth +} + +#[test] +fn proportional_fit_recovers_population_and_residual_scales() { + const TRUE_KE: f64 = 0.30; + const TRUE_V: f64 = 20.0; + const TRUE_OMEGA: f64 = 0.04; + const TRUE_PROPORTIONAL_SD: f64 = 0.10; + + for seed in [20_260_741, 20_260_742, 20_260_743] { + let result = EstimationProblem::parametric( + analytical_one_compartment(), + residual_fixture("tests/fixtures/proportional_residual.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::proportional(0.20)) + .build() + .expect("V03 proportional problem should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(4) + .mcmc_iterations(4) + .eta_block_iterations(1) + .burn_in(100) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("V03 proportional fit should complete"); + + assert!(relative_error(result.population_parameters()[0], TRUE_KE) < 0.10); + assert!(relative_error(result.population_parameters()[1], TRUE_V) < 0.10); + assert!(relative_error(result.omega()[[0, 0]], TRUE_OMEGA) < 0.50); + assert!(relative_error(result.omega()[[1, 1]], TRUE_OMEGA) < 0.50); + assert!(relative_error(result.residual_sigmas()[0], TRUE_PROPORTIONAL_SD) < 0.15); + assert_eq!( + result.residual_error_estimates()[0].model, + ResidualErrorModel::proportional(result.residual_sigmas()[0]) + ); + assert!(result.residual_error_estimates()[0].estimated); + assert!(result.cycle_diagnostics()[..100] + .iter() + .all(|cycle| cycle.residual_diagnostics.is_empty())); + assert!(result.cycle_diagnostics()[100..].iter().all(|cycle| { + cycle.residual_diagnostics.len() == 1 + && cycle.residual_diagnostics[0].output == "cp" + && cycle.residual_diagnostics[0].prediction_evaluation_count == 64 * 4 * 4 + && cycle.residual_diagnostics[0].proportional_floor_count == 0 + && cycle.residual_diagnostics[0].non_finite_prediction_count == 0 + && !cycle.residual_diagnostics[0].update_rejected + })); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + } +} + +fn exponential_residual_data(seed: u64, subject_count: usize) -> Data { + const KE: f64 = 0.30; + const V: f64 = 20.0; + const EXPONENTIAL_SD: f64 = 0.15; + let error_model = ResidualErrorModel::exponential(EXPONENTIAL_SD); + let mut rng = StdRng::seed_from_u64(seed); + let times = [0.5, 1.0, 2.0, 4.0]; + + Data::new( + (0..subject_count) + .map(|subject_index| { + let individual_ke = KE; + let individual_v = V; + let mut subject = Subject::builder(format!("v03-exp-{}", subject_index + 1)) + .infusion(0.0, 100.0, "iv", 0.5); + for time in times { + let prediction = one_compartment_infusion_concentration( + time, + 100.0, + 0.5, + individual_ke, + individual_v, + ); + let observation = error_model + .simulate_with_standard_normal(prediction, standard_normal(&mut rng)) + .expect( + "positive analytical prediction should support lognormal simulation", + ); + subject = subject.observation(time, observation, "cp"); + } + subject.build() + }) + .collect(), + ) +} + +#[test] +fn exponential_sigma_recovers_log_scale_coefficient() { + const TRUE_EXPONENTIAL_SD: f64 = 0.15; + let problem = EstimationProblem::parametric( + analytical_one_compartment(), + exponential_residual_data(20_260_713, 64), + ) + .parameter( + Parameter::log("ke") + .with_initial(0.30) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::exponential(0.30)) + .build() + .expect("V03 exponential problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_714) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("V03 exponential fit should complete"); + + let estimate = result + .residual_error_estimate("cp") + .expect("named exponential estimate should exist"); + let ResidualErrorModel::Exponential { sigma } = estimate.model else { + panic!("expected exponential residual estimate"); + }; + assert!( + (sigma - TRUE_EXPONENTIAL_SD).abs() / TRUE_EXPONENTIAL_SD < 0.30, + "estimated exponential sigma {sigma} should recover truth {TRUE_EXPONENTIAL_SD}" + ); + assert!(estimate.estimated); + assert!(result + .population_parameters() + .iter() + .all(|value| value.is_finite())); + assert!(result.omega().is_empty()); + assert!(result.conditional_negative_log_likelihood().is_finite()); + assert!(result.cycle_diagnostics()[20..].iter().all(|cycle| { + cycle.residual_diagnostics.len() == 1 + && cycle.residual_diagnostics[0].output == "cp" + && !cycle.residual_diagnostics[0].update_rejected + && cycle.residual_diagnostics[0].non_finite_prediction_count == 0 + && cycle.residual_diagnostics[0].exponential_domain_violation_count == 0 + })); +} + +#[test] +fn exponential_fit_recovers_population_and_residual_scales() { + const TRUE_KE: f64 = 0.30; + const TRUE_V: f64 = 20.0; + const TRUE_OMEGA: f64 = 0.04; + const TRUE_EXPONENTIAL_SD: f64 = 0.15; + + for seed in [20_260_714, 20_260_741, 20_260_742] { + let result = EstimationProblem::parametric( + analytical_one_compartment(), + residual_fixture("tests/fixtures/exponential_residual.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::exponential(0.30)) + .build() + .expect("V03 exponential full-fit problem should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(100) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("V03 exponential full fit should complete"); + + assert!(relative_error(result.population_parameters()[0], TRUE_KE) < 0.10); + assert!(relative_error(result.population_parameters()[1], TRUE_V) < 0.10); + assert!(relative_error(result.omega()[[0, 0]], TRUE_OMEGA) < 0.35); + assert!(relative_error(result.omega()[[1, 1]], TRUE_OMEGA) < 0.35); + assert!(relative_error(result.residual_sigmas()[0], TRUE_EXPONENTIAL_SD) < 0.10); + assert!(result.conditional_negative_log_likelihood().is_finite()); + assert!(result.cycle_diagnostics()[100..].iter().all(|cycle| { + cycle.residual_diagnostics.len() == 1 + && !cycle.residual_diagnostics[0].update_rejected + && cycle.residual_diagnostics[0].non_finite_prediction_count == 0 + && cycle.residual_diagnostics[0].exponential_domain_violation_count == 0 + })); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + } +} + +#[test] +fn exponential_residual_fit_rejects_nonpositive_observation_domain() { + let data = Data::new(vec![Subject::builder("invalid-exp") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 0.0, "cp") + .build()]); + let problem = EstimationProblem::parametric(analytical_one_compartment(), data) + .parameter( + Parameter::log("ke") + .with_initial(0.30) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .error_model("cp", ResidualErrorModel::exponential(0.15)) + .build() + .expect("declaration validation should precede prediction-domain evaluation"); + + let error = problem + .fit_with(SaemConfig::new().compute_map(false)) + .expect_err("zero observation is outside the exponential residual domain"); + let message = error.to_string(); + assert!(message.contains("initial conditional likelihood is non-finite")); + assert!(message.contains("invalid-exp")); + assert!(message.contains("exponential residual model output 'cp'")); + assert!(message.contains("1 non-positive or non-finite observation/prediction pair")); + assert!(message.contains("positive finite observations and predictions")); +} + +fn multi_output_residual_data(seed: u64, subject_count: usize) -> Data { + const KE: f64 = 0.30; + const V: f64 = 20.0; + const ETA_SD: f64 = 0.20; + const CONSTANT_SD: f64 = 0.25; + const PROPORTIONAL_SD: f64 = 0.10; + let mut rng = StdRng::seed_from_u64(seed); + let times = [0.5, 1.0, 2.0, 4.0]; + + Data::new( + (0..subject_count) + .map(|subject_index| { + let individual_ke = KE * (ETA_SD * standard_normal(&mut rng)).exp(); + let individual_v = V * (ETA_SD * standard_normal(&mut rng)).exp(); + let mut subject = Subject::builder(format!("multi-{}", subject_index + 1)) + .infusion(0.0, 100.0, "iv", 0.5); + for time in times { + let cp = one_compartment_infusion_concentration( + time, + 100.0, + 0.5, + individual_ke, + individual_v, + ); + let doubled = 2.0 * cp; + subject = subject + .observation(time, cp + CONSTANT_SD * standard_normal(&mut rng), "cp") + .observation( + time, + doubled + PROPORTIONAL_SD * doubled.abs() * standard_normal(&mut rng), + "doubled", + ); + } + subject.build() + }) + .collect(), + ) +} + +#[test] +fn multi_output_constant_and_proportional_sigmas_update_independently() { + const TRUE_CONSTANT_SD: f64 = 0.25; + const TRUE_PROPORTIONAL_SD: f64 = 0.10; + let problem = EstimationProblem::parametric( + analytical_one_compartment_two_outputs(), + multi_output_residual_data(20_260_720, 64), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::constant(0.50)) + .error_model("doubled", ResidualErrorModel::proportional(0.20)) + .build() + .expect("multi-output residual problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_721) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("multi-output residual fit should complete"); + + assert_eq!(result.residual_error_estimates().len(), 2); + assert_eq!(result.residual_error_estimates()[0].output, "cp"); + assert_eq!(result.residual_error_estimates()[1].output, "doubled"); + assert_eq!( + result.residual_error_estimate("cp"), + Some(&result.residual_error_estimates()[0]) + ); + assert_eq!( + result.residual_error_estimate("doubled"), + Some(&result.residual_error_estimates()[1]) + ); + assert_eq!(result.residual_error_estimate("missing"), None); + assert!((result.residual_sigmas()[0] - TRUE_CONSTANT_SD).abs() / TRUE_CONSTANT_SD < 0.30); + assert!( + (result.residual_sigmas()[1] - TRUE_PROPORTIONAL_SD).abs() / TRUE_PROPORTIONAL_SD < 0.30 + ); + assert_eq!( + result.residual_error_estimates()[0].model, + ResidualErrorModel::constant(result.residual_sigmas()[0]) + ); + assert_eq!( + result.residual_error_estimates()[1].model, + ResidualErrorModel::proportional(result.residual_sigmas()[1]) + ); + assert!(result.cycle_diagnostics()[20..].iter().all(|cycle| { + cycle.residual_diagnostic("cp") == Some(&cycle.residual_diagnostics[0]) + && cycle.residual_diagnostic("doubled") == Some(&cycle.residual_diagnostics[1]) + && cycle.residual_diagnostics.len() == 2 + && cycle.residual_diagnostics[0].output == "cp" + && cycle.residual_diagnostics[1].output == "doubled" + && cycle.residual_diagnostics[0].prediction_evaluation_count == 64 * 4 * 4 + && cycle.residual_diagnostics[1].prediction_evaluation_count == 64 * 4 * 4 + && cycle.residual_diagnostics[0].proportional_floor_count == 0 + && cycle.residual_diagnostics[1].proportional_floor_count == 0 + && !cycle.residual_diagnostics[0].update_rejected + && !cycle.residual_diagnostics[1].update_rejected + })); +} + +fn combined_residual_data(seed: u64, subject_count: usize) -> Data { + const KE: f64 = 0.30; + const V: f64 = 20.0; + const ETA_SD: f64 = 0.20; + const ADDITIVE_SD: f64 = 0.20; + const PROPORTIONAL_SD: f64 = 0.08; + let mut rng = StdRng::seed_from_u64(seed); + let times = [0.5, 1.0, 2.0, 4.0]; + + Data::new( + (0..subject_count) + .map(|subject_index| { + let dose = 50.0 * (1 + subject_index % 4) as f64; + let individual_ke = KE * (ETA_SD * standard_normal(&mut rng)).exp(); + let individual_v = V * (ETA_SD * standard_normal(&mut rng)).exp(); + let mut subject = Subject::builder(format!("v04-{}", subject_index + 1)) + .infusion(0.0, dose, "iv", 0.5); + for time in times { + let prediction = one_compartment_infusion_concentration( + time, + dose, + 0.5, + individual_ke, + individual_v, + ); + let residual_sd = + (ADDITIVE_SD.powi(2) + PROPORTIONAL_SD.powi(2) * prediction.powi(2)).sqrt(); + subject = subject.observation( + time, + prediction + residual_sd * standard_normal(&mut rng), + "cp", + ); + } + subject.build() + }) + .collect(), + ) +} + +#[test] +fn combined_error_jointly_estimates_additive_and_proportional_scales() { + const TRUE_ADDITIVE_SD: f64 = 0.20; + const TRUE_PROPORTIONAL_SD: f64 = 0.08; + let problem = EstimationProblem::parametric( + analytical_one_compartment(), + combined_residual_data(20_260_704, 80), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::combined(0.40, 0.15)) + .build() + .expect("V04 combined residual problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_705) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(100) + .k2_iterations(50) + .residual_optimizer_max_iterations(100) + .compute_map(false), + ) + .expect("V04 combined residual fit should complete"); + + let ResidualErrorModel::Combined { a, b } = result.residual_error_estimates()[0].model else { + panic!("V04 should retain a combined residual model"); + }; + assert!( + (a - TRUE_ADDITIVE_SD).abs() / TRUE_ADDITIVE_SD < 0.40, + "combined additive estimate {a}, proportional estimate {b}" + ); + assert!( + (b - TRUE_PROPORTIONAL_SD).abs() / TRUE_PROPORTIONAL_SD < 0.40, + "combined additive estimate {a}, proportional estimate {b}" + ); + assert_eq!( + result.residual_error_estimates()[0].combined_additive_estimated, + Some(true) + ); + assert_eq!( + result.residual_error_estimates()[0].combined_proportional_estimated, + Some(true) + ); + assert!(result.cycle_diagnostics()[20..].iter().all(|cycle| { + let diagnostics = &cycle.residual_diagnostics[0]; + !diagnostics.update_rejected + && diagnostics.optimizer_objective.is_some_and(f64::is_finite) + && diagnostics.optimizer_converged.is_some() + && diagnostics.optimizer_iterations.is_some() + && diagnostics.optimizer_termination.is_some() + && !diagnostics.combined_additive_collapse_warning + })); + assert!(result.cycle_diagnostics()[20..] + .iter() + .any(|cycle| cycle.residual_diagnostics[0].optimizer_converged == Some(true))); + assert!(!result.warnings().iter().any(|warning| matches!( + warning, + ParametricWarning::CombinedAdditiveCollapse { output, .. } + | ParametricWarning::ResidualUpdateRejected { output, .. } + if output == "cp" + ))); +} + +#[test] +fn combined_fit_recovers_population_and_residual_scales() { + const TRUE_KE: f64 = 0.30; + const TRUE_V: f64 = 20.0; + const TRUE_OMEGA: f64 = 0.04; + const TRUE_ADDITIVE_SD: f64 = 0.20; + const TRUE_PROPORTIONAL_SD: f64 = 0.08; + + for seed in [20_260_741, 20_260_742, 20_260_743] { + let result = EstimationProblem::parametric( + analytical_one_compartment(), + residual_fixture("tests/fixtures/combined_residual.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::combined(0.40, 0.15)) + .build() + .expect("V04 combined panel problem should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(100) + .k1_iterations(100) + .k2_iterations(50) + .residual_optimizer_max_iterations(100) + .compute_map(false), + ) + .expect("V04 combined panel fit should complete"); + + let ResidualErrorModel::Combined { a, b } = result.residual_error_estimates()[0].model + else { + panic!("V04 should retain a combined residual model"); + }; + assert!( + relative_error(result.population_parameters()[0], TRUE_KE) < 0.10, + "seed {seed}: KE {}", + result.population_parameters()[0] + ); + assert!( + relative_error(result.population_parameters()[1], TRUE_V) < 0.10, + "seed {seed}: V {}", + result.population_parameters()[1] + ); + assert!( + relative_error(result.omega()[[0, 0]], TRUE_OMEGA) < 0.25, + "seed {seed}: omega KE {}", + result.omega()[[0, 0]] + ); + assert!( + relative_error(result.omega()[[1, 1]], TRUE_OMEGA) < 0.25, + "seed {seed}: omega V {}", + result.omega()[[1, 1]] + ); + assert!( + relative_error(a, TRUE_ADDITIVE_SD) < 0.35, + "seed {seed}: additive {a}" + ); + assert!( + relative_error(b, TRUE_PROPORTIONAL_SD) < 0.10, + "seed {seed}: proportional {b}" + ); + assert!(result.cycle_diagnostics()[100..].iter().all(|cycle| { + let diagnostics = &cycle.residual_diagnostics[0]; + !diagnostics.update_rejected + && diagnostics.optimizer_objective.is_some_and(f64::is_finite) + && !diagnostics.combined_additive_collapse_warning + })); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + } +} + +#[test] +fn eta_block_mixture_preserves_combined_residual_behavior() { + const TRUE_ADDITIVE_SD: f64 = 0.20; + const TRUE_PROPORTIONAL_SD: f64 = 0.08; + let result = EstimationProblem::parametric( + analytical_one_compartment(), + combined_residual_data(20_260_704, 80), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", ResidualErrorModel::combined(0.40, 0.15)) + .build() + .expect("V04 block-mixture problem should build") + .fit_with( + SaemConfig::new() + .seed(20_260_705) + .n_chains(4) + .mcmc_iterations(4) + .eta_block_iterations(1) + .burn_in(20) + .k1_iterations(100) + .k2_iterations(50) + .residual_optimizer_max_iterations(100) + .compute_map(false), + ) + .expect("V04 block-mixture fit should complete"); + + let ResidualErrorModel::Combined { a, b } = result.residual_error_estimates()[0].model else { + panic!("V04 should retain a combined residual model"); + }; + assert!( + (a - TRUE_ADDITIVE_SD).abs() / TRUE_ADDITIVE_SD < 0.40, + "block-mixture additive estimate {a}, proportional estimate {b}" + ); + assert!( + (b - TRUE_PROPORTIONAL_SD).abs() / TRUE_PROPORTIONAL_SD < 0.40, + "block-mixture additive estimate {a}, proportional estimate {b}" + ); + assert!(result.cycle_diagnostics().iter().all(|cycle| { + cycle.eta_block_proposals == 80 * 4 + && cycle.eta_block_accepted + cycle.eta_block_rejected == cycle.eta_block_proposals + && cycle.eta_block_subject_acceptance_rates.len() == 80 + })); +} + +#[test] +fn combined_error_can_fix_additive_and_estimate_proportional_component() { + const FIXED_ADDITIVE_SD: f64 = 0.20; + const TRUE_PROPORTIONAL_SD: f64 = 0.08; + let problem = EstimationProblem::parametric( + analytical_one_compartment(), + combined_residual_data(20_260_704, 80), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::combined(FIXED_ADDITIVE_SD, 0.15)) + .fixed_combined_additive(), + ) + .build() + .expect("V04 partially fixed combined problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_705) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(100) + .k2_iterations(50) + .residual_optimizer_max_iterations(100) + .compute_map(false), + ) + .expect("V04 partially fixed combined fit should complete"); + + let estimate = &result.residual_error_estimates()[0]; + let ResidualErrorModel::Combined { a, b } = estimate.model else { + panic!("V04 should retain a combined residual model"); + }; + assert_eq!(a, FIXED_ADDITIVE_SD); + assert!((b - TRUE_PROPORTIONAL_SD).abs() / TRUE_PROPORTIONAL_SD < 0.40); + assert_eq!(estimate.combined_additive_estimated, Some(false)); + assert_eq!(estimate.combined_proportional_estimated, Some(true)); + assert!(estimate.estimated); + assert!(result.cycle_diagnostics()[20..] + .iter() + .all(|cycle| { !cycle.residual_diagnostics[0].combined_additive_collapse_warning })); +} + +#[test] +fn correlated_subset_iiv_fit_preserves_structure() { + const TRUE_KE: f64 = 0.30; + const TRUE_V: f64 = 20.0; + const TRUE_OMEGA: f64 = 0.04; + const TRUE_SIGMA: f64 = 0.25; + + for seed in [20_260_741, 20_260_742, 20_260_743] { + let result = EstimationProblem::parametric( + analytical_one_compartment_with_scale(), + residual_fixture("tests/fixtures/correlated_iiv.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .parameter( + Parameter::log("scale") + .with_initial(1.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)]).covariance("ke", "v", 0.03)) + .error_model("cp", ResidualErrorModel::constant(0.50)) + .build() + .expect("V05 correlated subset-IIV problem should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(100) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("V05 correlated subset-IIV fit should complete"); + + assert_eq!(result.population_parameters().len(), 3); + assert_eq!(result.population_parameters()[2], 1.0); + assert_eq!(result.random_effect_names(), ["ke", "v"]); + assert_eq!(result.omega().dim(), (2, 2)); + assert!(relative_error(result.population_parameters()[0], TRUE_KE) < 0.10); + assert!(relative_error(result.population_parameters()[1], TRUE_V) < 0.10); + assert!(relative_error(result.omega()[[0, 0]], TRUE_OMEGA) < 0.35); + assert!(relative_error(result.omega()[[1, 1]], TRUE_OMEGA) < 0.35); + // This finite fixture consistently estimates covariance near 0.029 + // across independent implementations rather than recovering the + // generating-population value exactly. + assert!((0.027..=0.030).contains(&result.omega()[[0, 1]])); + assert_eq!(result.omega()[[0, 1]], result.omega()[[1, 0]]); + assert!( + result.omega()[[0, 0]] * result.omega()[[1, 1]] - result.omega()[[0, 1]].powi(2) > 0.0 + ); + assert!(relative_error(result.residual_sigmas()[0], TRUE_SIGMA) < 0.10); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + } +} + +fn two_occasion_iov_data(seed: u64, subject_count: usize) -> Data { + const KE: f64 = 0.30; + const V: f64 = 20.0; + const ETA_SD: f64 = 0.15; + const KAPPA_SD: f64 = 0.20; + const SIGMA: f64 = 0.25; + let mut rng = StdRng::seed_from_u64(seed); + let times = [0.5, 1.0, 2.0]; + + Data::new( + (0..subject_count) + .map(|subject_index| { + let eta_ke = ETA_SD * standard_normal(&mut rng); + let eta_v = ETA_SD * standard_normal(&mut rng); + let individual_v = V * eta_v.exp(); + let mut subject = Subject::builder(format!("v06-{}", subject_index + 1)); + for occasion in 0..2 { + if occasion > 0 { + subject = subject.reset(); + } + subject = subject.infusion(0.0, 100.0, "iv", 0.5); + let kappa_ke = KAPPA_SD * standard_normal(&mut rng); + let occasion_ke = KE * (eta_ke + kappa_ke).exp(); + for time in times { + let prediction = one_compartment_infusion_concentration( + time, + 100.0, + 0.5, + occasion_ke, + individual_v, + ); + subject = subject.observation( + time, + prediction + SIGMA * standard_normal(&mut rng), + "cp", + ); + } + } + subject.build() + }) + .collect(), + ) +} + +#[test] +fn two_occasion_iov_recovers_distinct_eta_and_kappa_covariances() { + const TRUE_OMEGA_IOV: f64 = 0.04; + let problem = EstimationProblem::parametric( + analytical_one_compartment(), + two_occasion_iov_data(20_260_706, 48), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.05), ("v", 0.05)])) + .iov(Iov::diagonal([("ke", 0.08)])) + .error_model("cp", ResidualErrorModel::constant(0.50)) + .build() + .expect("V06 two-occasion IOV problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_707) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("V06 two-occasion IOV fit should complete"); + + let omega_iov = result + .omega_iov() + .expect("V06 result should retain Omega_IOV"); + assert_eq!(result.iov_effect_names(), ["ke"]); + assert_eq!(omega_iov.dim(), (1, 1)); + assert!(omega_iov[[0, 0]].is_finite()); + assert!(omega_iov[[0, 0]] > 0.0); + assert!((omega_iov[[0, 0]] - TRUE_OMEGA_IOV).abs() / TRUE_OMEGA_IOV < 0.75); + assert_eq!(result.kappa_chain_means().len(), 96); + assert!(result.kappa_chain_means().chunks_exact(2).all(|occasions| { + occasions[0].subject_id == occasions[1].subject_id + && occasions[0].occasion_index == 0 + && occasions[1].occasion_index == 1 + })); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + assert!(result + .cycle_diagnostics() + .iter() + .all(|cycle| cycle.kappa_proposals == 48 * 4 * 4 * 2)); + assert!(result + .cycle_diagnostics() + .iter() + .all(|cycle| cycle.kappa_accepted + cycle.kappa_rejected == cycle.kappa_proposals)); + assert!(result.omega()[[0, 0]].is_finite()); + assert!(result.omega()[[1, 1]].is_finite()); + assert!(result.residual_sigmas()[0].is_finite()); +} + +#[test] +fn iov_fit_recovers_occasion_variance_and_finite_iiv() { + const TRUE_KE: f64 = 0.30; + const TRUE_V: f64 = 20.0; + const TRUE_OMEGA_V: f64 = 0.0225; + const TRUE_OMEGA_IOV: f64 = 0.04; + const TRUE_SIGMA: f64 = 0.25; + + for seed in [20_260_741, 20_260_742, 20_260_743] { + let result = EstimationProblem::parametric( + analytical_one_compartment(), + iov_fixture("tests/fixtures/two_occasion_iov.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.05), ("v", 0.05)])) + .iov(Iov::diagonal([("ke", 0.08)])) + .error_model("cp", ResidualErrorModel::constant(0.50)) + .build() + .expect("V06 shared IOV problem should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(4) + .mcmc_iterations(20) + .burn_in(100) + .k1_iterations(120) + .k2_iterations(80) + .compute_map(false), + ) + .expect("V06 shared IOV fit should complete"); + + let omega_iov = result.omega_iov().expect("V06 should retain Omega_IOV"); + let eta_ke_variance = result.omega()[[0, 0]]; + let total_ke_variance = eta_ke_variance + omega_iov[[0, 0]]; + assert!(relative_error(result.population_parameters()[0], TRUE_KE) < 0.10); + assert!(relative_error(result.population_parameters()[1], TRUE_V) < 0.10); + assert!(eta_ke_variance.is_finite() && (0.01..0.08).contains(&eta_ke_variance)); + assert!(relative_error(result.omega()[[1, 1]], TRUE_OMEGA_V) < 0.25); + assert!(relative_error(omega_iov[[0, 0]], TRUE_OMEGA_IOV) < 0.20); + assert!((0.05..0.10).contains(&total_ke_variance)); + assert!(relative_error(result.residual_sigmas()[0], TRUE_SIGMA) < 0.10); + assert_eq!(result.kappa_chain_means().len(), 96); + assert!(result.cycle_diagnostics().iter().all(|cycle| { + cycle.kappa_proposals == 48 * 4 * 20 * 2 + && cycle.kappa_accepted + cycle.kappa_rejected == cycle.kappa_proposals + })); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + } +} + +fn uneven_correlated_iov_data(seed: u64) -> Data { + const KE: f64 = 0.30; + const V: f64 = 20.0; + const KAPPA_KE_SD: f64 = 0.15; + const KAPPA_V_SD: f64 = 0.10; + const KAPPA_CORRELATION: f64 = 0.40; + const SIGMA: f64 = 0.25; + let mut rng = StdRng::seed_from_u64(seed); + let times = [0.5, 2.0]; + + Data::new( + (0..12) + .map(|subject_index| { + let occasion_count = 1 + subject_index % 3; + let mut subject = Subject::builder(format!("v06-uneven-{}", subject_index + 1)); + for occasion_index in 0..occasion_count { + if occasion_index > 0 { + subject = subject.reset(); + } + subject = subject.infusion(0.0, 100.0, "iv", 0.5); + let z1 = standard_normal(&mut rng); + let z2 = standard_normal(&mut rng); + let kappa_ke = KAPPA_KE_SD * z1; + let kappa_v = KAPPA_V_SD + * (KAPPA_CORRELATION * z1 + (1.0 - KAPPA_CORRELATION.powi(2)).sqrt() * z2); + let occasion_ke = KE * kappa_ke.exp(); + let occasion_v = V * kappa_v.exp(); + for time in times { + let prediction = one_compartment_infusion_concentration( + time, + 100.0, + 0.5, + occasion_ke, + occasion_v, + ); + subject = subject.observation( + time, + prediction + SIGMA * standard_normal(&mut rng), + "cp", + ); + } + } + subject.build() + }) + .collect(), + ) +} + +#[test] +fn two_dimensional_iov_supports_correlation_and_uneven_occasion_counts() { + let problem = EstimationProblem::parametric( + analytical_one_compartment(), + uneven_correlated_iov_data(20_260_724), + ) + .parameter(Parameter::log("ke").with_initial(0.30).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .omega( + Omega::new() + .fixed_variance("ke", 0.01) + .fixed_variance("v", 0.01), + ) + .iov( + Iov::new() + .fixed_variance("ke", 0.0225) + .fixed_variance("v", 0.01) + .fixed_covariance("ke", "v", 0.006), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("correlated two-dimensional IOV problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_725) + .n_chains(2) + .mcmc_iterations(2) + .burn_in(2) + .k1_iterations(8) + .k2_iterations(4) + .compute_map(false), + ) + .expect("uneven two-dimensional IOV fit should complete"); + + assert_eq!(result.iov_effect_names(), ["ke", "v"]); + assert_eq!( + result.omega_iov(), + Some(&ndarray::array![[0.0225, 0.006], [0.006, 0.01]]) + ); + assert_eq!(result.kappa_chain_means().len(), 24); + assert!(result + .kappa_chain_means() + .iter() + .all(|estimate| estimate.values.len() == 2)); + for subject_index in 0..12 { + let subject_id = format!("v06-uneven-{}", subject_index + 1); + let occasion_count = 1 + subject_index % 3; + for occasion_index in 0..occasion_count { + assert!(result + .kappa_chain_mean(&subject_id, occasion_index) + .is_some()); + } + assert!(result + .kappa_chain_mean(&subject_id, occasion_count) + .is_none()); + } + for cycle in result.cycle_diagnostics() { + assert_eq!(cycle.kappa_proposals, 24 * 2 * 2, "cycle {cycle:?}"); + assert_eq!( + cycle.kappa_accepted + cycle.kappa_rejected, + cycle.kappa_proposals, + "cycle {cycle:?}" + ); + assert!(!cycle.omega_update_rejected, "cycle {cycle:?}"); + assert!(!cycle.omega_iov_update_rejected, "cycle {cycle:?}"); + } + assert!(!result.warnings().iter().any(|warning| matches!( + warning, + ParametricWarning::OmegaUpdateRejected { .. } + | ParametricWarning::OmegaIovUpdateRejected { .. } + ))); +} + +fn combined_iov_data(seed: u64, subject_count: usize) -> Data { + const KE: f64 = 0.30; + const V: f64 = 20.0; + const ETA_SD: f64 = 0.15; + const KAPPA_SD: f64 = 0.20; + const ADDITIVE_SD: f64 = 0.50; + const PROPORTIONAL_SD: f64 = 0.08; + let mut rng = StdRng::seed_from_u64(seed); + let times = [0.5, 2.0, 8.0]; + + Data::new( + (0..subject_count) + .map(|subject_index| { + let eta_ke = ETA_SD * standard_normal(&mut rng); + let eta_v = ETA_SD * standard_normal(&mut rng); + let individual_v = V * eta_v.exp(); + let mut subject = Subject::builder(format!("combined-iov-{}", subject_index + 1)); + for occasion in 0..2 { + if occasion > 0 { + subject = subject.reset(); + } + let dose = if occasion == 0 { 25.0 } else { 200.0 }; + subject = subject.infusion(0.0, dose, "iv", 0.5); + let kappa_ke = KAPPA_SD * standard_normal(&mut rng); + let occasion_ke = KE * (eta_ke + kappa_ke).exp(); + for time in times { + let prediction = one_compartment_infusion_concentration( + time, + dose, + 0.5, + occasion_ke, + individual_v, + ); + let residual_sd = (ADDITIVE_SD.powi(2) + + PROPORTIONAL_SD.powi(2) * prediction.powi(2)) + .sqrt(); + subject = subject.observation( + time, + prediction + residual_sd * standard_normal(&mut rng), + "cp", + ); + } + } + subject.build() + }) + .collect(), + ) +} + +#[test] +fn combined_error_updates_from_iiv_and_iov_prediction_pairs() { + const TRUE_ADDITIVE_SD: f64 = 0.50; + const TRUE_PROPORTIONAL_SD: f64 = 0.08; + let problem = EstimationProblem::parametric( + analytical_one_compartment(), + combined_iov_data(20_260_722, 40), + ) + .parameter(Parameter::log("ke").with_initial(0.24)) + .parameter(Parameter::log("v").with_initial(24.0)) + .omega(Omega::diagonal([("ke", 0.05), ("v", 0.05)])) + .iov(Iov::diagonal([("ke", 0.08)])) + .error_model("cp", ResidualErrorModel::combined(0.40, 0.15)) + .build() + .expect("combined IOV problem should build"); + let result = problem + .fit_with( + SaemConfig::new() + .seed(20_260_723) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(100) + .k2_iterations(50) + .residual_optimizer_max_iterations(100) + .compute_map(false), + ) + .expect("combined IOV fit should complete"); + + let ResidualErrorModel::Combined { a, b } = result.residual_error_estimates()[0].model else { + panic!("combined IOV fit should retain its residual family"); + }; + assert!((a - TRUE_ADDITIVE_SD).abs() / TRUE_ADDITIVE_SD < 0.50); + assert!(b.is_finite() && b > 0.0 && b < 0.30); + assert!(b > TRUE_PROPORTIONAL_SD * 0.25); + assert!(result.omega_iov().is_some_and(|omega| omega[[0, 0]] > 0.0)); + assert_eq!(result.kappa_chain_means().len(), 80); + assert!(result.cycle_diagnostics()[20..].iter().all(|cycle| { + let residual = &cycle.residual_diagnostics[0]; + cycle.kappa_proposals == 40 * 4 * 4 * 2 + && residual.prediction_evaluation_count == 40 * 4 * 6 + && residual.optimizer_objective.is_some_and(f64::is_finite) + && !residual.update_rejected + })); +} + +fn sparse_iiv_data() -> Data { + Data::new( + [8.5, 10.0, 11.5, 9.25] + .into_iter() + .enumerate() + .map(|(index, observation)| { + Subject::builder(format!("sparse-{}", index + 1)) + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, observation, "cp") + .build() + }) + .collect(), + ) +} + +#[test] +fn sparse_correlated_iiv_fit_remains_positive_definite() { + for seed in [20_260_708, 20_260_742, 20_260_743] { + let result = EstimationProblem::parametric( + analytical_one_compartment(), + residual_fixture("tests/fixtures/sparse_iiv.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25), ("v", 0.25)]).covariance("ke", "v", 0.20)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .expect("V08 sparse correlated-IIV problem should build") + .fit_with( + SaemConfig::new() + .seed(seed) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(80) + .k2_iterations(40) + .omega_sa_max_step(0.1) + .compute_map(false), + ) + .expect("V08 sparse fit should complete without covariance collapse"); + + let omega = result.omega(); + let determinant = omega[[0, 0]] * omega[[1, 1]] - omega[[0, 1]].powi(2); + let correlation = omega[[0, 1]] / (omega[[0, 0]] * omega[[1, 1]]).sqrt(); + assert!(result + .population_parameters() + .iter() + .all(|value| value.is_finite())); + assert!(omega.iter().all(|value| value.is_finite())); + assert!(omega[[0, 0]] >= 1e-10); + assert!(omega[[1, 1]] >= 1e-10); + assert!(determinant > 0.0); + assert_eq!(result.cycle_diagnostics().len(), 120); + assert!(result.cycle_diagnostics().iter().all(|cycle| { + cycle.eta_proposals == 128 + && cycle.eta_accepted + cycle.eta_rejected == cycle.eta_proposals + && cycle.kappa_proposals == 0 + && cycle.kappa_subject_acceptance_rates.is_empty() + })); + let rejected_omega_cycles = result + .cycle_diagnostics() + .iter() + .filter(|cycle| cycle.omega_update_rejected) + .count(); + let rejection_warning = result.warnings().iter().find_map(|warning| match warning { + ParametricWarning::OmegaUpdateRejected { cycles, .. } => Some(*cycles), + _ => None, + }); + assert_eq!( + rejection_warning, + (rejected_omega_cycles > 0).then_some(rejected_omega_cycles) + ); + // One observation cannot identify two correlated random effects, so a + // near-boundary correlation is statistically possible. Robustness here + // means finite, strictly positive-definite covariance rather than an + // arbitrary correlation shrinkage target. + assert!(correlation.abs() < 1.0); + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); + } +} + +#[test] +fn eta_block_mixture_remains_finite_and_positive_definite() { + let result = EstimationProblem::parametric(analytical_one_compartment(), sparse_iiv_data()) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25), ("v", 0.25)]).covariance("ke", "v", 0.20)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .expect("V08 block-mixture problem should build") + .fit_with( + SaemConfig::new() + .seed(20_260_708) + .n_chains(4) + .mcmc_iterations(4) + .eta_block_iterations(1) + .burn_in(20) + .k1_iterations(80) + .k2_iterations(40) + .omega_sa_max_step(0.1) + .compute_map(false), + ) + .expect("V08 block-mixture fit should complete"); + + let omega = result.omega(); + let determinant = omega[[0, 0]] * omega[[1, 1]] - omega[[0, 1]].powi(2); + assert!(omega.iter().all(|value| value.is_finite())); + assert!(determinant > 0.0); + assert!(result.cycle_diagnostics().iter().all(|cycle| { + cycle.eta_block_proposals == 4 * 4 + && cycle.eta_block_accepted + cycle.eta_block_rejected == cycle.eta_block_proposals + && cycle.eta_proposals == 128 + 16 + && cycle.eta_block_subject_acceptance_rates.len() == 4 + })); +} + +#[test] +fn joint_eta_kappa_conditional_modes_match_fixed_fixture() { + const EXPECTED: [[f64; 5]; 4] = [ + [ + 0.133_062_784_399_915_76, + -0.067_013_580_960_008_37, + 0.001_491_987_806_696_499_3, + 0.235_068_327_315_337_67, + -3.593_568_882_829_558_7, + ], + [ + -0.065_327_837_183_284_7, + -0.020_090_278_178_073_37, + -0.011_648_729_042_780_788, + -0.104_521_845_434_551_87, + -4.100_406_935_081_131, + ], + [ + -0.004_356_546_702_307_954, + 0.068_298_914_413_273_82, + 0.125_753_711_642_636_2, + -0.133_475_798_701_328_86, + -1.584_497_158_120_898_4, + ], + [ + 0.069_515_500_467_549_06, + 0.112_919_976_382_365_78, + 0.017_704_794_919_049_423, + 0.105_901_941_785_214_2, + -2.100_335_072_625_273_6, + ], + ]; + + let result = EstimationProblem::parametric( + analytical_one_compartment(), + iov_fixture("tests/fixtures/conditional_modes.csv"), + ) + .parameter(Parameter::log("ke").with_initial(0.30).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .omega( + Omega::new() + .fixed_variance("ke", 0.0225) + .fixed_variance("v", 0.0225), + ) + .iov(Iov::new().fixed_variance("ke", 0.04)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("V10 fixed conditional-mode problem should build") + .fit_with( + SaemConfig::new() + .seed(20_260_741) + .n_chains(4) + .mcmc_iterations(4) + .burn_in(20) + .k1_iterations(20) + .k2_iterations(10) + .map_max_iterations(500), + ) + .expect("V10 conditional modes should complete"); + + assert_eq!(result.conditional_modes().len(), EXPECTED.len()); + for (mode, expected) in result.conditional_modes().iter().zip(EXPECTED) { + assert_eq!(mode.eta.len(), 2); + assert_eq!(mode.kappas.len(), 2); + assert_eq!(mode.kappas[0].occasion_index, 0); + assert_eq!(mode.kappas[1].occasion_index, 1); + assert!((mode.eta[0] - expected[0]).abs() < 5e-5); + assert!((mode.eta[1] - expected[1]).abs() < 5e-5); + assert!((mode.kappas[0].values[0] - expected[2]).abs() < 5e-5); + assert!((mode.kappas[1].values[0] - expected[3]).abs() < 5e-5); + assert!((mode.objective - expected[4]).abs() < 1e-8); + assert!(mode.converged); + } + assert_eq!( + result.termination_reason(), + Some(&pmcore::algorithms::StopReason::MaxCycles) + ); +} + +fn build_with_residual_model( + model: ParametricErrorModel, +) -> anyhow::Result> { + EstimationProblem::parametric(analytical_one_compartment(), validation_data()) + .parameter(Parameter::log("ke").with_initial(0.30)) + .parameter(Parameter::log("v").with_initial(20.0)) + .omega(Omega::diagonal([("ke", 0.09), ("v", 0.09)])) + .error_model("cp", model) + .build() +} + +#[test] +fn residual_model_declarations_validate_supported_parameter_domains() { + let zero_proportional = build_with_residual_model(ResidualErrorModel::proportional(0.0).into()) + .expect_err("zero proportional SD must fail closed"); + assert!(zero_proportional + .to_string() + .contains("proportional residual SD coefficient")); + + let non_finite_constant = + build_with_residual_model(ResidualErrorModel::constant(f64::NAN).into()) + .expect_err("non-finite constant SD must fail closed"); + assert!(non_finite_constant + .to_string() + .contains("constant residual SD")); + + build_with_residual_model( + ParametricErrorModel::new(ResidualErrorModel::exponential(0.25)).fixed(), + ) + .expect("positive fixed exponential log-scale SD should build"); + let invalid_exponential = + build_with_residual_model(ResidualErrorModel::exponential(0.0).into()) + .expect_err("zero exponential log-scale SD must fail closed"); + assert!(invalid_exponential + .to_string() + .contains("exponential residual log-scale SD")); + + build_with_residual_model(ResidualErrorModel::combined(0.25, 0.10).into()) + .expect("positive estimated combined residual coefficients should build"); + let zero_combined = build_with_residual_model(ResidualErrorModel::combined(0.25, 0.0).into()) + .expect_err("estimated combined components must both start above zero"); + assert!(zero_combined + .to_string() + .contains("estimated combined proportional SD")); + + build_with_residual_model( + ParametricErrorModel::new(ResidualErrorModel::combined(0.25, 0.0)) + .fixed_combined_proportional(), + ) + .expect("a fixed zero combined component should remain valid"); + + build_with_residual_model( + ParametricErrorModel::new(ResidualErrorModel::combined(0.25, 0.0)).fixed(), + ) + .expect("fixed combined residual scoring may fix one component at zero"); +} + +#[test] +fn exponential_residual_fit_uses_positive_domain_and_updates_log_scale_sigma() { + let result = build_with_residual_model(ResidualErrorModel::exponential(0.40).into()) + .expect("positive exponential declaration should build") + .fit_with( + SaemConfig::new() + .seed(20_260_713) + .n_chains(2) + .mcmc_iterations(2) + .burn_in(1) + .k1_iterations(6) + .k2_iterations(2) + .compute_map(false), + ) + .expect("positive-domain exponential fit should complete"); + + let estimate = result + .residual_error_estimate("cp") + .expect("named exponential residual estimate should exist"); + let ResidualErrorModel::Exponential { sigma } = estimate.model else { + panic!("expected exponential residual estimate"); + }; + assert!(estimate.estimated); + assert!(sigma.is_finite() && sigma > 0.0); + assert!(result.conditional_negative_log_likelihood().is_finite()); + assert_eq!(result.cycle_diagnostics().len(), 8); +} diff --git a/tests/saem_tracing.rs b/tests/saem_tracing.rs new file mode 100644 index 000000000..6cc14572d --- /dev/null +++ b/tests/saem_tracing.rs @@ -0,0 +1,97 @@ +use std::io::{self, Write}; +use std::sync::{Arc, Mutex}; + +use pharmsol::prelude::*; +use pmcore::prelude::*; +use tracing::Level; +use tracing_subscriber::fmt::MakeWriter; + +#[derive(Clone, Default)] +struct LogBuffer(Arc>>); + +impl Write for LogBuffer { + fn write(&mut self, bytes: &[u8]) -> io::Result { + self.0 + .lock() + .expect("log buffer lock") + .extend_from_slice(bytes); + Ok(bytes.len()) + } + + fn flush(&mut self) -> io::Result<()> { + Ok(()) + } +} + +impl<'a> MakeWriter<'a> for LogBuffer { + type Writer = Self; + + fn make_writer(&'a self) -> Self::Writer { + self.clone() + } +} + +fn short_problem() -> EstimationProblem { + let equation = analytical! { + name: "saem_tracing_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + y[cp] = x[central] / v; + }, + }; + let data = Data::new(vec![Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.0, "cp") + .build()]); + + EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.3).fixed()) + .parameter(Parameter::log("v").with_initial(20.0).fixed()) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("tracing fixture should build") +} + +#[test] +fn fixed_schedule_fit_emits_honest_lifecycle_logs() { + let writer = LogBuffer::default(); + let subscriber = tracing_subscriber::fmt() + .with_ansi(false) + .without_time() + .with_max_level(Level::DEBUG) + .with_writer(writer.clone()) + .finish(); + + tracing::subscriber::with_default(subscriber, || { + short_problem() + .fit_with( + SaemConfig::new() + .seed(7) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("short SAEM fit should complete"); + }); + + let bytes = writer.0.lock().expect("log buffer lock").clone(); + let logs = String::from_utf8(bytes).expect("tracing output should be UTF-8"); + assert!(logs.contains("Starting SAEM fit"), "{logs}"); + assert!(logs.contains("Cycle 1"), "{logs}"); + assert!(logs.contains("Conditional N2LL ="), "{logs}"); + assert!( + logs.contains("Maximum SAEM cycles reached; this is not statistical convergence"), + "{logs}" + ); + assert!(!logs.contains("Objective function ="), "{logs}"); +} diff --git a/tests/saem_uncertainty.rs b/tests/saem_uncertainty.rs new file mode 100644 index 000000000..d7ecc75ee --- /dev/null +++ b/tests/saem_uncertainty.rs @@ -0,0 +1,320 @@ +use std::fs; +use std::time::{SystemTime, UNIX_EPOCH}; + +use pharmsol::prelude::*; +use pmcore::prelude::*; + +fn analytical_problem( + with_iov: bool, +) -> EstimationProblem { + let equation = analytical! { + name: "n6_joint_uncertainty_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let subjects = vec![ + Subject::builder("n6-1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.9, "cp") + .observation(3.0, 3.1, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 4.7, "cp") + .observation(15.0, 2.9, "cp") + .build(), + Subject::builder("n6-2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 5.2, "cp") + .observation(3.0, 3.5, "cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 5.0, "cp") + .observation(15.0, 3.2, "cp") + .build(), + ]; + let problem = EstimationProblem::parametric(equation, Data::new(subjects)) + .parameter(Parameter::log("ke").with_initial(0.25).fixed()) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.09)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.35)).fixed(), + ); + let problem = if with_iov { + problem.iov(Iov::new().fixed_variance("ke", 0.04)) + } else { + problem + }; + problem.build().expect("N6 analytical fixture") +} + +fn fit_config() -> SaemConfig { + SaemConfig::new() + .seed(0x6e36_2026) + .n_chains(2) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(2) + .k2_iterations(1) + .compute_map(true) +} + +fn n2(curvature: bool) -> MarginalLikelihoodConfig { + let config = MarginalLikelihoodConfig::new(128, 0x6e32_2026, 5, 1.5); + if curvature { + config.conditional_mode_curvature_proposal() + } else { + config + } +} + +fn close(left: f64, right: f64) -> bool { + (left - right).abs() <= 1e-12 * left.abs().max(right.abs()).max(1.0) +} + +fn available_shrinkage(value: &ShrinkageValue, expected_count: usize) { + match value { + ShrinkageValue::Available { + value, unit_count, .. + } => { + assert!(value.is_finite()); + assert_eq!(*unit_count, expected_count); + } + ShrinkageValue::Unavailable { reason } => { + panic!("expected available shrinkage, got {reason:?}") + } + } +} + +#[test] +fn analytical_iiv_conditional_uncertainty_and_fixed_summary_are_wired() { + let result = analytical_problem(false).fit_with(fit_config()).unwrap(); + assert_eq!(result.conditional_modes().len(), 2); + for mode in result.conditional_modes() { + assert!(mode.uncertainty.mode_metadata.objective_value.is_finite()); + assert_eq!(mode.uncertainty.coordinates.len(), 1); + assert!(matches!( + mode.uncertainty.coordinates[0].kind, + JointLatentCoordinateKind::Eta { parameter_index: 0 } + )); + assert_eq!( + mode.uncertainty.status, + ConditionalCurvatureStatus::Available + ); + assert_eq!( + mode.uncertainty.regularization, + ConditionalCurvatureRegularization::None + ); + } + available_shrinkage(&result.shrinkage().eta_posterior_mean[0].shrinkage, 2); + available_shrinkage(&result.shrinkage().eta_map[0].shrinkage, 2); + + let summary = result.population_summary(); + assert!(summary + .parameters + .iter() + .all(|parameter| { parameter.sd.is_none() && parameter.cv_percent.is_none() })); + assert!(result + .individual_summaries() + .iter() + .all(|summary| summary.conditional_uncertainty.is_some())); +} + +#[test] +fn analytical_two_occasion_iov_curvature_proposal_reuses_joint_covariance_and_roundtrips() { + let default = analytical_problem(true) + .fit_with(fit_config().marginal_likelihood(n2(false))) + .unwrap(); + let curvature = analytical_problem(true) + .fit_with(fit_config().marginal_likelihood(n2(true))) + .unwrap(); + + assert_eq!( + default.population_parameters(), + curvature.population_parameters() + ); + assert_eq!(default.objf().to_bits(), curvature.objf().to_bits()); + assert_eq!(default.cycle_diagnostics(), curvature.cycle_diagnostics()); + assert_eq!(default.eta_chain_means(), curvature.eta_chain_means()); + assert_eq!(default.kappa_chain_means(), curvature.kappa_chain_means()); + + for mode in curvature.conditional_modes() { + assert_eq!( + mode.uncertainty.status, + ConditionalCurvatureStatus::Available + ); + assert_eq!(mode.uncertainty.coordinates.len(), 3); + assert!(matches!( + mode.uncertainty.coordinates[0].kind, + JointLatentCoordinateKind::Eta { parameter_index: 0 } + )); + for (position, coordinate) in mode.uncertainty.coordinates[1..].iter().enumerate() { + assert!(matches!( + coordinate.kind, + JointLatentCoordinateKind::Kappa { + occasion_index, + effect_index: 0, + parameter_index: 0, + } if occasion_index == position + )); + } + let covariance = mode.uncertainty.latent_covariance.as_ref().unwrap(); + for (row, values) in covariance.iter().enumerate() { + for (column, value) in values.iter().enumerate().take(row) { + assert_eq!(value.to_bits(), covariance[column][row].to_bits()); + } + } + } + + let default_n2 = default.marginal_likelihood_diagnostics().unwrap(); + assert!(default_n2.subjects.iter().all(|subject| { + subject.failure.is_none() + && subject.proposal_scale_source == ProposalScaleSource::FinalRawOmegaBlocks + })); + let curvature_n2 = curvature.marginal_likelihood_diagnostics().unwrap(); + assert!(curvature_n2.subjects.iter().all(|subject| { + subject.failure.is_none() + && subject.proposal_scale_source == ProposalScaleSource::ConditionalModeCurvature + })); + + available_shrinkage(&curvature.shrinkage().eta_posterior_mean[0].shrinkage, 2); + available_shrinkage(&curvature.shrinkage().eta_map[0].shrinkage, 2); + available_shrinkage(&curvature.shrinkage().kappa_posterior_mean[0].shrinkage, 4); + available_shrinkage(&curvature.shrinkage().kappa_map[0].shrinkage, 4); + + let unique = SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos(); + let directory = std::env::temp_dir().join(format!("pmcore-n6-{unique}")); + curvature.write_outputs(&directory, 0.0, 0.0).unwrap(); + let record = ParametricResultRecord::read_json(directory.join("result.json")).unwrap(); + assert_eq!(record.schema_version, 9); + assert_eq!( + record.population_uncertainty.status, + curvature.population_uncertainty().status + ); + assert_eq!( + record.population_uncertainty.coordinates, + curvature.population_uncertainty().coordinates + ); + assert_eq!( + record.conditional_modes.len(), + curvature.conditional_modes().len() + ); + for (persisted, live) in record + .conditional_modes + .iter() + .zip(curvature.conditional_modes()) + { + assert_eq!(persisted.subject_id, live.subject_id); + assert!(persisted + .eta + .iter() + .zip(&live.eta) + .all(|(left, right)| close(*left, *right))); + assert_eq!(persisted.kappas.len(), live.kappas.len()); + for (persisted_kappa, live_kappa) in persisted.kappas.iter().zip(&live.kappas) { + assert_eq!(persisted_kappa.subject_id, live_kappa.subject_id); + assert_eq!(persisted_kappa.occasion_index, live_kappa.occasion_index); + assert!(persisted_kappa + .values + .iter() + .zip(&live_kappa.values) + .all(|(left, right)| close(*left, *right))); + } + assert!(persisted + .parameters + .iter() + .zip(&live.parameters) + .all(|(left, right)| close(*left, *right))); + assert!(close(persisted.objective, live.objective)); + assert_eq!(persisted.converged, live.converged); + assert_eq!(persisted.iterations, live.iterations); + assert_eq!(persisted.termination, live.termination); + assert_eq!(persisted.uncertainty.status, live.uncertainty.status); + assert_eq!( + persisted.uncertainty.coordinates, + live.uncertainty.coordinates + ); + let persisted_covariance = persisted.uncertainty.latent_covariance.as_ref().unwrap(); + let live_covariance = live.uncertainty.latent_covariance.as_ref().unwrap(); + assert!(persisted_covariance + .iter() + .flatten() + .zip(live_covariance.iter().flatten()) + .all(|(left, right)| close(*left, *right))); + } + assert_eq!(record.shrinkage.eta_posterior_mean.len(), 1); + assert_eq!(record.shrinkage.eta_map.len(), 1); + assert_eq!(record.shrinkage.kappa_posterior_mean.len(), 1); + assert_eq!(record.shrinkage.kappa_map.len(), 1); + available_shrinkage(&record.shrinkage.eta_posterior_mean[0].shrinkage, 2); + available_shrinkage(&record.shrinkage.eta_map[0].shrinkage, 2); + available_shrinkage(&record.shrinkage.kappa_posterior_mean[0].shrinkage, 4); + available_shrinkage(&record.shrinkage.kappa_map[0].shrinkage, 4); + assert_eq!( + record + .tables + .statistics + .iter() + .filter(|row| row.kind == "conditional_curvature_status") + .count(), + 2 + ); + assert!(record + .tables + .statistics + .iter() + .any(|row| row.kind == "population_uncertainty_status")); + + let original: serde_json::Value = + serde_json::from_reader(fs::File::open(directory.join("result.json")).unwrap()).unwrap(); + let tampered_path = directory.join("tampered.json"); + + let mut tampered_coordinate = original.clone(); + tampered_coordinate["conditional_modes"][0]["uncertainty"]["coordinates"][1]["effect_index"] = + serde_json::json!(1); + fs::write( + &tampered_path, + serde_json::to_vec_pretty(&tampered_coordinate).unwrap(), + ) + .unwrap(); + assert!(ParametricResultRecord::read_json(&tampered_path).is_err()); + + let mut tampered_shrinkage = original.clone(); + tampered_shrinkage["shrinkage"]["eta_posterior_mean"][0]["shrinkage"]["value"] = + serde_json::json!(999.0); + fs::write( + &tampered_path, + serde_json::to_vec_pretty(&tampered_shrinkage).unwrap(), + ) + .unwrap(); + assert!(ParametricResultRecord::read_json(&tampered_path).is_err()); + + let mut tampered_status = original; + let status_row = tampered_status["tables"]["statistics"] + .as_array_mut() + .unwrap() + .iter_mut() + .find(|row| row["kind"] == "population_uncertainty_status") + .unwrap(); + status_row["status"] = serde_json::json!("tampered"); + fs::write( + &tampered_path, + serde_json::to_vec_pretty(&tampered_status).unwrap(), + ) + .unwrap(); + assert!(ParametricResultRecord::read_json(&tampered_path).is_err()); + + fs::remove_dir_all(directory).unwrap(); +} diff --git a/tests/saem_warm_start.rs b/tests/saem_warm_start.rs new file mode 100644 index 000000000..7faa256f4 --- /dev/null +++ b/tests/saem_warm_start.rs @@ -0,0 +1,745 @@ +use std::fs; +use std::time::{SystemTime, UNIX_EPOCH}; + +use pharmsol::prelude::*; +use pmcore::prelude::*; + +fn equation() -> pharmsol::equation::Analytical { + analytical! { + name: "saem_warm_start_fixture", + params: [ke, volume, fraction, bio], + states: [central], + outputs: [cp, prop_cp, log_cp, mixed_cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { + let concentration = x[central] / volume; + y[cp] = ke + fraction * concentration; + y[prop_cp] = concentration; + y[log_cp] = bio * concentration; + y[mixed_cp] = (fraction + bio) * concentration; + }, + } +} + +fn data() -> Data { + Data::new(vec![ + Subject::builder("s1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 2.4, "cp") + .observation(1.0, 2.1, "prop_cp") + .observation(1.0, 1.8, "log_cp") + .observation(1.0, 3.7, "mixed_cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 2.5, "cp") + .observation(13.0, 2.2, "prop_cp") + .observation(13.0, 1.9, "log_cp") + .observation(13.0, 3.8, "mixed_cp") + .build(), + Subject::builder("s2") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 2.2, "cp") + .observation(1.0, 2.0, "prop_cp") + .observation(1.0, 1.7, "log_cp") + .observation(1.0, 3.5, "mixed_cp") + .reset() + .infusion(12.0, 100.0, "iv", 0.5) + .observation(13.0, 2.3, "cp") + .observation(13.0, 2.1, "prop_cp") + .observation(13.0, 1.8, "log_cp") + .observation(13.0, 3.6, "mixed_cp") + .build(), + ]) +} + +fn initial_problem() -> EstimationProblem { + EstimationProblem::parametric(equation(), data()) + .parameter(Parameter::real("ke").with_initial(0.2)) + .parameter(Parameter::log("volume").with_initial(20.0).fixed()) + .parameter(Parameter::logit("fraction", 0.0, 1.0).with_initial(0.55)) + .parameter( + Parameter::probit("bio", 0.0, 1.0) + .with_initial(0.45) + .fixed(), + ) + .omega( + Omega::new() + .variance("ke", 0.04) + .fixed_variance("volume", 0.09) + .variance("fraction", 0.03) + .fixed_variance("bio", 0.02) + .covariance("ke", "volume", 0.01) + .fixed_covariance("fraction", "bio", 0.005), + ) + .iov( + Iov::new() + .fixed_variance("volume", 0.025) + .variance("bio", 0.015) + .covariance("volume", "bio", 0.004) + .fixed_variance("fraction", 0.02), + ) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.2)).fixed(), + ) + .error_model( + "prop_cp", + ParametricErrorModel::new(ResidualErrorModel::proportional(0.12)), + ) + .error_model( + "log_cp", + ParametricErrorModel::new(ResidualErrorModel::exponential(0.15)), + ) + .error_model( + "mixed_cp", + ParametricErrorModel::new(ResidualErrorModel::combined(0.2, 0.1)) + .fixed_combined_additive(), + ) + .build() + .expect("warm-start fixture should build") +} + +fn short_config(seed: u64) -> SaemConfig { + SaemConfig::new() + .seed(seed) + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false) +} + +fn fitted() -> ParametricResult { + initial_problem() + .fit_with(short_config(41)) + .expect("parent fit should complete") +} + +fn averaged_fitted() -> ParametricResult { + initial_problem() + .fit_with( + short_config(45) + .k1_iterations(1) + .k2_iterations(3) + .averaged_iterates(0.75), + ) + .expect("averaged parent fit should complete") +} + +fn fixed_parameters( + builder: pmcore::estimation::ParametricBuilder, +) -> pmcore::estimation::ParametricBuilder { + builder + .parameter(Parameter::real("ke").with_initial(0.2).fixed()) + .parameter(Parameter::log("volume").with_initial(20.0).fixed()) + .parameter( + Parameter::logit("fraction", 0.0, 1.0) + .with_initial(0.55) + .fixed(), + ) + .parameter( + Parameter::probit("bio", 0.0, 1.0) + .with_initial(0.45) + .fixed(), + ) +} + +fn output_data(output: &str) -> Data { + Data::new(vec![Subject::builder("sparse") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 3.5, output) + .observation(2.0, 2.8, output) + .build()]) +} + +fn sparse_second_output_fitted() -> ParametricResult { + fixed_parameters(EstimationProblem::parametric( + equation(), + output_data("prop_cp"), + )) + .error_model( + "prop_cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.2)).fixed(), + ) + .build() + .expect("sparse second-output problem") + .fit_with(short_config(42)) + .expect("sparse second-output fit") +} + +fn fixed_zero_combined_fitted( + additive_zero: bool, +) -> ParametricResult { + let residual = if additive_zero { + ParametricErrorModel::new(ResidualErrorModel::combined(0.0, 0.1)).fixed_combined_additive() + } else { + ParametricErrorModel::new(ResidualErrorModel::combined(0.2, 0.0)) + .fixed_combined_proportional() + }; + fixed_parameters(EstimationProblem::parametric( + equation(), + output_data("mixed_cp"), + )) + .error_model("mixed_cp", residual) + .build() + .expect("fixed-zero combined problem") + .fit_with( + short_config(if additive_zero { 43 } else { 44 }) + .k2_iterations(2) + .averaged_iterates(0.75), + ) + .expect("fixed-zero combined fit") +} + +fn assert_matrix_close(actual: &ndarray::Array2, expected: &ndarray::Array2) { + assert_eq!(actual.dim(), expected.dim()); + for (actual, expected) in actual.iter().zip(expected) { + assert_roundoff(*actual, *expected); + } +} + +fn assert_roundoff(actual: f64, expected: f64) { + assert!( + (actual - expected).abs() + <= 64.0 * f64::EPSILON * actual.abs().max(expected.abs()).max(1.0), + "{actual} != {expected}" + ); +} + +fn assert_residual_roundoff(actual: &ResidualErrorModel, expected: &ResidualErrorModel) { + match (actual, expected) { + (ResidualErrorModel::Constant { a }, ResidualErrorModel::Constant { a: expected }) + | ( + ResidualErrorModel::Proportional { b: a }, + ResidualErrorModel::Proportional { b: expected }, + ) + | ( + ResidualErrorModel::Exponential { sigma: a }, + ResidualErrorModel::Exponential { sigma: expected }, + ) => assert_roundoff(*a, *expected), + ( + ResidualErrorModel::Combined { a, b }, + ResidualErrorModel::Combined { + a: expected_a, + b: expected_b, + }, + ) => { + assert_roundoff(*a, *expected_a); + assert_roundoff(*b, *expected_b); + } + _ => panic!("warm-start residual family differs from parent"), + } +} + +fn assert_problem_matches( + parent: &ParametricResult, + problem: &EstimationProblem, +) { + let parameters: Vec<_> = problem.parameters().iter().collect(); + assert_eq!(parameters.len(), parent.population_parameters().len()); + for (index, parameter) in parameters.iter().enumerate() { + assert_eq!(parameter.name, parent.parameter_names()[index]); + assert_eq!(parameter.scale, parent.parameter_scales()[index]); + assert_roundoff( + parameter.initial.expect("warm-start initial is required"), + parent.population_parameters()[index], + ); + assert_eq!(parameter.estimate, parent.estimated_parameters()[index]); + assert_eq!( + parameter.random_effect, + parent.random_effect_indices().contains(&index) + ); + } + assert_eq!(problem.random_effect_names(), parent.random_effect_names()); + assert_matrix_close(problem.omega(), parent.omega()); + let expected_iov_names = + (!parent.iov_effect_names().is_empty()).then_some(parent.iov_effect_names()); + assert_eq!(problem.iov_effect_names(), expected_iov_names); + match (problem.omega_iov(), parent.omega_iov()) { + (Some(actual), Some(expected)) => assert_matrix_close(actual, expected), + (None, None) => {} + _ => panic!("warm-start IOV presence differs from parent"), + } + + let errors = problem.residual_error_models(); + for estimate in parent.residual_error_estimates() { + assert_eq!( + errors.output_name(estimate.output_index), + Some(estimate.output.as_str()) + ); + assert_residual_roundoff(errors.get(estimate.output_index).unwrap(), &estimate.model); + assert_eq!( + errors.is_estimated(estimate.output_index), + estimate.estimated + ); + let expected = [ + estimate.combined_additive_estimated.unwrap_or(false), + estimate.combined_proportional_estimated.unwrap_or(false), + ]; + if estimate.model.is_combined() { + assert_eq!( + errors.combined_component_estimated(estimate.output_index), + expected + ); + } + } +} + +#[test] +fn schema_six_averaged_roundtrip_and_warm_start_use_canonical_iov_multi_output_state() { + let parent = averaged_fitted(); + assert_eq!( + parent.config().estimator_policy, + SaemEstimatorPolicy::AveragedIterates { alpha: 0.75 } + ); + assert!(parent.estimator_metadata().average_applied); + assert_eq!(parent.estimator_metadata().averaging_start_cycle, Some(2)); + assert_eq!(parent.estimator_metadata().averaged_iterations, 3); + + let directory = std::env::temp_dir().join(format!( + "pmcore-saem-averaged-roundtrip-{}-{}", + std::process::id(), + SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos() + )); + parent + .write_outputs(&directory, 0.0, 0.0) + .expect("write averaged outputs"); + let record = ParametricResultRecord::read_json(directory.join("result.json")) + .expect("read averaged result"); + assert_eq!(record.schema_version, 9); + assert_eq!( + record.config.estimator_policy, + parent.config().estimator_policy + ); + assert_eq!(record.estimator_metadata, *parent.estimator_metadata()); + let manifest: serde_json::Value = serde_json::from_reader( + fs::File::open(directory.join("manifest.json")).expect("open averaged manifest"), + ) + .expect("parse averaged manifest"); + assert_eq!(manifest["schema_version"], 9); + assert_eq!( + manifest["estimator_metadata"]["policy"]["AveragedIterates"]["alpha"], + 0.75 + ); + assert_eq!(manifest["estimator_metadata"]["average_applied"], true); + assert_eq!(manifest["estimator_metadata"]["averaging_start_cycle"], 2); + assert_eq!(manifest["estimator_metadata"]["averaged_iterations"], 3); + + let warm = record + .warm_start_problem(equation(), data()) + .expect("averaged persisted warm start"); + assert_problem_matches(&parent, &warm); + fs::remove_dir_all(directory).expect("remove averaged output directory"); +} + +#[test] +fn live_and_json_warm_starts_preserve_typed_scientific_initialization() { + let parent = fitted(); + let parent_tables = parent.tables(0.0, 0.0).expect("parent tables"); + assert_eq!( + parent.parameter_scales(), + [ + ParameterScale::Identity, + ParameterScale::Log, + ParameterScale::Logit { + lower: 0.0, + upper: 1.0, + }, + ParameterScale::Probit { + lower: 0.0, + upper: 1.0, + }, + ] + ); + + let live = parent.warm_start_problem().expect("live warm start"); + assert_problem_matches(&parent, &live); + assert_eq!( + parent.tables(0.0, 0.0).expect("unchanged parent"), + parent_tables + ); + + let path = std::env::temp_dir().join(format!( + "pmcore-saem-warm-start-{}-{}.json", + std::process::id(), + SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos() + )); + parent.write_json(&path, 0.0, 0.0).expect("write result"); + let record = ParametricResultRecord::read_json(&path).expect("read result"); + assert_eq!(record.tables.population, parent_tables.population); + assert_eq!(record.tables.omega.len(), parent_tables.omega.len()); + for (actual, expected) in record.tables.omega.iter().zip(&parent_tables.omega) { + assert_eq!(actual.row, expected.row); + assert_eq!(actual.column, expected.column); + assert_eq!(actual.structural, expected.structural); + assert_eq!(actual.estimated, expected.estimated); + assert_roundoff(actual.estimate, expected.estimate); + } + match (&record.tables.omega_iov, &parent_tables.omega_iov) { + (Some(actual), Some(expected)) => { + assert_eq!(actual.len(), expected.len()); + for (actual, expected) in actual.iter().zip(expected) { + assert_eq!(actual.row, expected.row); + assert_eq!(actual.column, expected.column); + assert_eq!(actual.structural, expected.structural); + assert_eq!(actual.estimated, expected.estimated); + assert_roundoff(actual.estimate, expected.estimate); + } + } + (None, None) => {} + _ => panic!("persisted Omega_IOV presence changed"), + } + assert_eq!( + record.tables.residual_error.len(), + parent_tables.residual_error.len() + ); + for (actual, expected) in record + .tables + .residual_error + .iter() + .zip(&parent_tables.residual_error) + { + assert_eq!(actual.output, expected.output); + assert_eq!(actual.output_index, expected.output_index); + assert_eq!(actual.family, expected.family); + assert_eq!(actual.component, expected.component); + assert_eq!(actual.estimated, expected.estimated); + assert_roundoff(actual.estimate, expected.estimate); + } + let persisted = record + .warm_start_problem(equation(), data()) + .expect("persisted warm start"); + assert_problem_matches(&parent, &persisted); + fs::remove_file(path).expect("remove temporary result"); +} + +#[test] +fn sparse_second_output_warm_starts_preserve_index_and_name() { + let parent = sparse_second_output_fitted(); + let rows = &parent.tables(0.0, 0.0).expect("tables").residual_error; + assert_eq!(rows.len(), 1); + assert_eq!( + (rows[0].output_index, rows[0].output.as_str()), + (1, "prop_cp") + ); + + let live = parent.warm_start_problem().expect("live sparse warm start"); + assert!(live.residual_error_models().get(0).is_none()); + assert_eq!(live.residual_error_models().output_name(1), Some("prop_cp")); + + let path = std::env::temp_dir().join(format!( + "pmcore-saem-warm-start-sparse-{}.json", + std::process::id() + )); + parent.write_json(&path, 0.0, 0.0).expect("write result"); + let record = ParametricResultRecord::read_json(&path).expect("read result"); + let persisted = record + .warm_start_problem(equation(), output_data("prop_cp")) + .expect("JSON sparse warm start"); + assert!(persisted.residual_error_models().get(0).is_none()); + assert_eq!( + persisted.residual_error_models().output_name(1), + Some("prop_cp") + ); + fs::remove_file(path).expect("remove temporary result"); +} + +fn assert_fixed_zero_combined_warm_starts(additive_zero: bool) { + let parent = fixed_zero_combined_fitted(additive_zero); + assert!(parent.estimator_metadata().average_applied); + assert_eq!(parent.estimator_metadata().averaging_start_cycle, Some(2)); + assert_eq!(parent.estimator_metadata().averaged_iterations, 2); + let expected_mask = if additive_zero { + [false, true] + } else { + [true, false] + }; + let expected_free = parent.cycle_diagnostics()[1..] + .iter() + .map(|cycle| match cycle.residual_error_estimates[0].model { + ResidualErrorModel::Combined { a, b } => { + if additive_zero { + b + } else { + a + } + } + _ => panic!("expected combined cycle residual model"), + }) + .sum::() + / 2.0; + match parent.residual_error_estimates()[0].model { + ResidualErrorModel::Combined { a, b } => { + assert!((if additive_zero { b } else { a } - expected_free).abs() < 1e-12); + } + _ => panic!("expected combined averaged residual model"), + } + let assert_problem = |problem: &EstimationProblem<_, Parametric>| { + let errors = problem.residual_error_models(); + assert_eq!(errors.output_name(3), Some("mixed_cp")); + assert_eq!(errors.combined_component_estimated(3), expected_mask); + match errors.get(3).expect("combined model") { + ResidualErrorModel::Combined { a, b } => { + assert_eq!((*a == 0.0, *b == 0.0), (additive_zero, !additive_zero)); + } + other => panic!("expected combined model, found {other:?}"), + } + }; + + let live = parent + .warm_start_problem() + .expect("live fixed-zero warm start"); + assert_problem(&live); + + let path = std::env::temp_dir().join(format!( + "pmcore-saem-warm-start-fixed-zero-{}-{}.json", + additive_zero, + std::process::id() + )); + parent.write_json(&path, 0.0, 0.0).expect("write result"); + let record = ParametricResultRecord::read_json(&path).expect("read result"); + let persisted = record + .warm_start_problem(equation(), output_data("mixed_cp")) + .expect("JSON fixed-zero warm start"); + assert_problem(&persisted); + fs::remove_file(path).expect("remove temporary result"); +} + +#[test] +fn combined_fixed_zero_components_survive_live_and_json_warm_starts() { + assert_fixed_zero_combined_warm_starts(true); + assert_fixed_zero_combined_warm_starts(false); +} + +#[test] +fn fit_next_uses_caller_configuration_without_mutating_parent() { + let parent = fitted(); + let parent_tables = parent.tables(0.0, 0.0).expect("parent tables"); + let child_config = short_config(9_991).k1_iterations(2); + let child = parent.fit_next(child_config.clone()).expect("child fit"); + assert_eq!( + serde_json::to_value(child.config()).expect("serialize child config"), + serde_json::to_value(&child_config).expect("serialize requested config") + ); + assert_eq!(child.config().seed, 9_991); + assert_eq!(parent.config().seed, 41); + assert_eq!( + parent.tables(0.0, 0.0).expect("unchanged parent"), + parent_tables + ); + assert_eq!(child.iterations(), 2); +} + +#[test] +fn iov_warm_start_preserves_multidimensional_structure_and_masks() { + let parent = fitted(); + assert_eq!(parent.iov_effect_names(), ["volume", "bio", "fraction"]); + + let tables = parent.tables(0.0, 0.0).expect("parent tables"); + let omega_masks: Vec<_> = tables + .omega + .iter() + .map(|row| (row.structural, row.estimated)) + .collect(); + assert_eq!( + omega_masks, + [ + (true, true), + (true, true), + (true, false), + (false, false), + (false, false), + (true, true), + (false, false), + (false, false), + (true, false), + (true, false), + ] + ); + let omega_iov_masks: Vec<_> = tables + .omega_iov + .as_ref() + .expect("IOV table") + .iter() + .map(|row| (row.structural, row.estimated)) + .collect(); + assert_eq!( + omega_iov_masks, + [ + (true, false), + (true, true), + (true, true), + (false, false), + (false, false), + (true, false), + ] + ); + + let child = parent + .warm_start_problem() + .expect("IOV warm start") + .fit_with(short_config(812)) + .expect("IOV child fit"); + assert_eq!(child.iov_effect_names(), parent.iov_effect_names()); + assert_eq!( + child.omega_structural_mask(), + parent.omega_structural_mask() + ); + assert_eq!(child.omega_estimated_mask(), parent.omega_estimated_mask()); + assert_eq!( + child.omega_iov_structural_mask(), + parent.omega_iov_structural_mask() + ); + assert_eq!( + child.omega_iov_estimated_mask(), + parent.omega_iov_estimated_mask() + ); +} + +#[test] +fn combined_components_reconstruct_with_independent_masks() { + let parent = fitted(); + let path = std::env::temp_dir().join(format!( + "pmcore-saem-warm-start-combined-{}.json", + std::process::id() + )); + parent.write_json(&path, 0.0, 0.0).expect("write result"); + let mut record = ParametricResultRecord::read_json(&path).expect("read result"); + let mut combined: Vec<_> = record + .tables + .residual_error + .iter_mut() + .filter(|row| row.family == "combined") + .collect(); + assert_eq!(combined.len(), 2); + combined[0].estimated = true; + combined[1].estimated = false; + + assert!(record.warm_start_problem(equation(), data()).is_err()); + fs::remove_file(path).expect("remove temporary result"); +} + +fn assert_warm_start_rejected(record: &ParametricResultRecord) { + assert!(record.warm_start_problem(equation(), data()).is_err()); +} + +#[test] +fn schemas_one_through_eight_and_missing_current_diagnostics_are_rejected() { + let parent = fitted(); + let path = std::env::temp_dir().join(format!( + "pmcore-saem-schema-four-required-{}.json", + std::process::id() + )); + parent.write_json(&path, 0.0, 0.0).expect("write result"); + let current: serde_json::Value = + serde_json::from_reader(fs::File::open(&path).expect("open result")).expect("parse result"); + + for schema in 1..=8 { + let mut legacy = current.clone(); + legacy["schema_version"] = serde_json::json!(schema); + serde_json::to_writer_pretty(fs::File::create(&path).expect("rewrite"), &legacy) + .expect("write legacy version"); + assert!(ParametricResultRecord::read_json(&path).is_err()); + } + for required in [ + "information_diagnostics", + "markov_simulation_variance", + "population_uncertainty", + "conditional_modes", + "shrinkage", + ] { + let mut missing = current.clone(); + missing + .as_object_mut() + .expect("record object") + .remove(required); + serde_json::to_writer_pretty(fs::File::create(&path).expect("rewrite"), &missing) + .expect("write missing field"); + assert!(ParametricResultRecord::read_json(&path).is_err()); + } + fs::remove_file(path).expect("remove temporary result"); +} + +#[test] +fn malformed_persisted_headers_and_tables_are_rejected() { + let parent = fitted(); + let path = std::env::temp_dir().join(format!( + "pmcore-saem-warm-start-malformed-{}.json", + std::process::id() + )); + parent.write_json(&path, 0.0, 0.0).expect("write result"); + let record = ParametricResultRecord::read_json(&path).expect("read result"); + + for schema_version in 1..=8 { + let mut bad = record.clone(); + bad.schema_version = schema_version; + assert_warm_start_rejected(&bad); + } + let mut bad = record.clone(); + bad.fit_family = "nonparametric".to_string(); + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.algorithm = "other".to_string(); + assert_warm_start_rejected(&bad); + + let mut bad = record.clone(); + bad.tables.population[1].name = bad.tables.population[0].name.clone(); + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.population[0].scale = "logit(1,1)".to_string(); + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.omega.swap(0, 1); + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.omega[0].estimate = f64::NAN; + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.omega[3].structural = false; + bad.tables.omega[3].estimated = true; + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.omega_iov.as_mut().expect("IOV table").swap(0, 1); + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.omega_iov.as_mut().expect("IOV table")[3].estimate = 0.01; + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.residual_error[0].family = "unknown".to_string(); + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.residual_error[1].output_index = 7; + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.residual_error[1].output = "cp".to_string(); + assert_warm_start_rejected(&bad); + + let combined_start = record.tables.residual_error.len() - 2; + let mut bad = record.clone(); + bad.tables.residual_error[combined_start].estimate = 0.0; + bad.tables.residual_error[combined_start].estimated = true; + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.residual_error[combined_start].estimate = 0.0; + bad.tables.residual_error[combined_start].estimated = false; + bad.tables.residual_error[combined_start + 1].estimate = 0.0; + bad.tables.residual_error[combined_start + 1].estimated = false; + assert_warm_start_rejected(&bad); + let mut bad = record.clone(); + bad.tables.residual_error[combined_start].estimate = -0.1; + bad.tables.residual_error[combined_start].estimated = false; + assert_warm_start_rejected(&bad); + let mut bad = record; + bad.tables.residual_error[combined_start].estimate = f64::INFINITY; + assert_warm_start_rejected(&bad); + + fs::remove_file(path).expect("remove temporary result"); +} diff --git a/tests/sde_particle_filter.rs b/tests/sde_particle_filter.rs new file mode 100644 index 000000000..c97f7fbfb --- /dev/null +++ b/tests/sde_particle_filter.rs @@ -0,0 +1,325 @@ +use pharmsol::equation::{metadata, ModelKind, Route, SdeSessionError}; +use pharmsol::{fa, lag, Censor, Parameters, Subject, SubjectBuilderExt, SDE}; +use pmcore::{ + AssayErrorModel, AssayErrorModels, ErrorPoly, SdeParticleConfig, SdeParticleError, + SdeParticleFilter, +}; +use rand::rngs::StdRng; +use rand::SeedableRng; + +const N: usize = 128; + +fn model() -> SDE { + SDE::new( + |x, p, _t, dx, _rateiv, _cov| { + dx[0] = -x[0] * x[1]; + dx[1] = -x[1] + p[0]; + }, + |_p, diffusion| { + diffusion[0] = 1.0; + diffusion[1] = 0.05; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, x| x[1] = 1.0, + |x, _p, _t, _cov, y| y[0] = x[0], + N, + ) + .with_nstates(2) + .with_ndrugs(1) + .with_nout(1) + .with_metadata( + metadata::new("sde_filter_test") + .kind(ModelKind::Sde) + .parameters(["ke0"]) + .states(["central", "ke_latent"]) + .outputs(["cp"]) + .route( + Route::bolus("dose") + .to_state("central") + .inject_input_to_destination(), + ) + .particles(N), + ) + .unwrap() +} + +fn parameters(model: &SDE) -> Parameters { + Parameters::with_model(model, [("ke0", 1.0)]).unwrap() +} + +fn assay(sigma: f64) -> AssayErrorModels { + AssayErrorModels::new() + .add( + "cp", + AssayErrorModel::additive(ErrorPoly::new(sigma, 0.0, 0.0, 0.0), 0.0), + ) + .unwrap() +} + +fn config(threshold: f64) -> SdeParticleConfig { + SdeParticleConfig::new(N) + .with_ess_threshold(threshold) + .with_process_seed(7123) + .with_resampling_seed(991) +} + +fn observed_subject() -> Subject { + Subject::builder("id1") + .bolus(0.0, 20.0, "dose") + .observation(0.2, 16.6434, "cp") + .observation(0.4, 14.3233, "cp") + .observation(0.6, 9.8468, "cp") + .observation(0.8, 9.4177, "cp") + .observation(1.0, 7.5170, "cp") + .build() +} + +#[test] +fn original_particle_filter_scientific_intent_remains_finite() { + let model = model(); + let result = model + .particle_filter( + &observed_subject(), + ¶meters(&model), + &assay(0.5), + &config(0.5), + ) + .unwrap(); + + assert!(result.log_value.is_finite()); + assert_eq!(result.records.len(), 5); +} + +#[test] +fn same_seeds_are_exactly_reproducible() { + let model = model(); + let parameters = parameters(&model); + let subject = observed_subject(); + let first = model + .particle_filter(&subject, ¶meters, &assay(0.5), &config(0.8)) + .unwrap(); + let second = model + .particle_filter(&subject, ¶meters, &assay(0.5), &config(0.8)) + .unwrap(); + + assert_eq!(first, second); +} + +#[test] +fn session_enforces_boundary_and_validates_ancestors() { + let model = model(); + let parameters = parameters(&model); + let subject = Subject::builder("barrier") + .missing_observation(0.2, "cp") + .missing_observation(0.4, "cp") + .build(); + let mut rng = StdRng::seed_from_u64(9); + let mut session = model + .particle_session(&subject, ¶meters, N, &mut rng) + .unwrap(); + + session.next_observation().unwrap().unwrap(); + assert!(matches!( + session.next_observation(), + Err(SdeSessionError::BoundaryPending) + )); + assert!(matches!( + session.select_ancestors(&[0]), + Err(SdeSessionError::AncestorCount { .. }) + )); + let mut invalid = vec![0; N]; + invalid[N - 1] = N; + assert!(matches!( + session.select_ancestors(&invalid), + Err(SdeSessionError::AncestorOutOfRange { .. }) + )); + session.select_ancestors(&vec![0; N]).unwrap(); + assert!(session.next_observation().unwrap().is_some()); +} + +#[test] +fn ancestry_selection_changes_later_particle_states() { + let model = model(); + let parameters = parameters(&model); + let subject = Subject::builder("state") + .bolus(0.0, 20.0, "dose") + .missing_observation(0.2, "cp") + .missing_observation(0.8, "cp") + .build(); + let mut left_rng = StdRng::seed_from_u64(44); + let mut right_rng = StdRng::seed_from_u64(44); + let mut selected = model + .particle_session(&subject, ¶meters, N, &mut left_rng) + .unwrap(); + let mut retained = model + .particle_session(&subject, ¶meters, N, &mut right_rng) + .unwrap(); + + let first = selected.next_observation().unwrap().unwrap(); + let ancestor = first + .predictions() + .iter() + .enumerate() + .min_by(|(_, a), (_, b)| a.prediction().total_cmp(&b.prediction())) + .unwrap() + .0; + selected.select_ancestors(&vec![ancestor; N]).unwrap(); + retained.next_observation().unwrap(); + retained.retain_particles().unwrap(); + + let selected_mean = selected + .next_observation() + .unwrap() + .unwrap() + .predictions() + .iter() + .map(|prediction| prediction.prediction()) + .sum::() + / N as f64; + let retained_mean = retained + .next_observation() + .unwrap() + .unwrap() + .predictions() + .iter() + .map(|prediction| prediction.prediction()) + .sum::() + / N as f64; + assert_ne!(selected_mean, retained_mean); +} + +#[test] +fn tiny_densities_remain_finite_and_all_impossible_is_typed() { + let model = model(); + let parameters = parameters(&model); + let tiny = Subject::builder("tiny") + .bolus(0.0, 20.0, "dose") + .observation(0.2, 100.0, "cp") + .build(); + let result = model + .particle_filter(&tiny, ¶meters, &assay(0.01), &config(0.01)) + .unwrap(); + assert!(result.log_value.is_finite()); + + let impossible = Subject::builder("impossible") + .bolus(0.0, 20.0, "dose") + .observation(0.2, 1e308, "cp") + .build(); + assert!(matches!( + model.particle_filter(&impossible, ¶meters, &assay(0.5), &config(0.01)), + Err(SdeParticleError::ImpossibleObservation { .. }) + )); +} + +#[test] +fn uncensored_zero_sigma_is_typed() { + let model = model(); + let parameters = parameters(&model); + let subject = Subject::builder("zero-sigma") + .bolus(0.0, 20.0, "dose") + .observation(0.2, 16.0, "cp") + .build(); + + assert!(matches!( + model.particle_filter(&subject, ¶meters, &assay(0.0), &config(0.01)), + Err(SdeParticleError::InvalidSigma { sigma: 0.0, .. }) + )); +} + +#[test] +fn ess_threshold_controls_resampling_and_no_resampling_preserves_weights() { + let model = model(); + let parameters = parameters(&model); + let subject = observed_subject(); + let never = model + .particle_filter(&subject, ¶meters, &assay(0.2), &config(1e-9)) + .unwrap(); + assert!(never.records.iter().all(|record| !record.resampled)); + assert!(never.records[0] + .normalized_weights + .windows(2) + .any(|pair| pair[0] != pair[1])); + assert_eq!( + never.final_normalized_weights, + never.records.last().unwrap().normalized_weights + ); + + let aggressive = model + .particle_filter(&subject, ¶meters, &assay(0.2), &config(1.0)) + .unwrap(); + assert!(aggressive.records.iter().any(|record| record.resampled)); + assert!(aggressive + .records + .iter() + .filter(|record| record.resampled) + .all(|record| record.ancestors.as_ref().unwrap().len() == N)); + + for record in aggressive.records.iter().filter(|record| record.resampled) { + let recorded_ess = 1.0 + / record + .normalized_weights + .iter() + .map(|weight| weight * weight) + .sum::(); + assert!((recorded_ess - record.effective_sample_size).abs() < 1e-10); + assert!(record.effective_sample_size <= N as f64); + } + assert!(aggressive + .final_normalized_weights + .iter() + .all(|weight| (*weight - 1.0 / N as f64).abs() < 1e-12)); +} + +#[test] +fn censoring_and_missing_observations_follow_sequential_rules() { + let model = model(); + let parameters = parameters(&model); + for censor in [Censor::BLOQ, Censor::ALOQ] { + let subject = Subject::builder("censored") + .bolus(0.0, 20.0, "dose") + .censored_observation(0.2, 18.0, "cp", censor) + .missing_observation(0.4, "cp") + .build(); + let result = model + .particle_filter(&subject, ¶meters, &assay(0.5), &config(0.01)) + .unwrap(); + assert!(result.log_value.is_finite()); + assert_eq!(result.records[1].log_increment, 0.0); + assert_eq!( + result.records[0].normalized_weights, + result.records[1].normalized_weights + ); + } +} + +#[test] +fn result_uses_predictive_mixture_not_final_mean_particle() { + let model = model(); + let parameters = parameters(&model); + let subject = Subject::builder("mixture") + .bolus(0.0, 20.0, "dose") + .observation(0.5, 10.0, "cp") + .build(); + let result = model + .particle_filter(&subject, ¶meters, &assay(0.5), &config(1e-9)) + .unwrap(); + + let mut rng = StdRng::seed_from_u64(7123); + let mut session = model + .particle_session(&subject, ¶meters, N, &mut rng) + .unwrap(); + let boundary = session.next_observation().unwrap().unwrap(); + let mean = boundary + .predictions() + .iter() + .map(|prediction| prediction.prediction()) + .sum::() + / N as f64; + let sigma: f64 = 0.5; + let final_mean_approximation = -0.5 * (2.0 * std::f64::consts::PI).ln() + - sigma.ln() + - (10.0 - mean).powi(2) / (2.0 * sigma * sigma); + + assert_ne!(result.log_value, final_mean_approximation); +} From d88f918a423525b9cfe74c411eb382eaa0879d0c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juli=C3=A1n=20D=2E=20Ot=C3=A1lvaro?= Date: Tue, 21 Jul 2026 22:06:39 +0100 Subject: [PATCH 2/5] Use pharmsol feature branch --- Cargo.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Cargo.toml b/Cargo.toml index 8c451270c..f3c67fda7 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -65,7 +65,7 @@ tracing-subscriber = { version = "0.3.19", features = [ "time", ] } faer = "0.24.0" -pharmsol = { path = "../pharmsol-likelihood-ownership", version = "=0.28.2" } +pharmsol = { git = "https://github.com/LAPKB/pharmsol", branch = "feature/likelihood-extraction", version = "=0.28.2" } anyhow = "1.0.100" statrs = "0.18.0" rayon = "1.10.0" From 2f2e680240cf6d8b4014eee66711e39aafe75db1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juli=C3=A1n=20D=2E=20Ot=C3=A1lvaro?= Date: Wed, 29 Jul 2026 18:08:29 +0100 Subject: [PATCH 3/5] Complete pharmsol API migration --- Cargo.toml | 2 +- src/algorithms/mod.rs | 10 +- src/algorithms/parametric/saem.rs | 10584 ---------------- src/algorithms/parametric/saem/mod.rs | 652 + .../parametric/saem/state/diagnostics.rs | 1641 +++ src/algorithms/parametric/saem/state/mod.rs | 2301 ++++ .../parametric/saem/state/runner.rs | 465 + .../parametric/saem/state/support.rs | 1118 ++ .../parametric/saem/state/tests/controller.rs | 582 + .../saem/state/tests/diagnostics.rs | 717 ++ .../parametric/saem/state/tests/estimation.rs | 421 + .../parametric/saem/state/tests/mod.rs | 497 + .../parametric/saem/state/tests/results.rs | 694 + .../parametric/saem/state/tests/schedule.rs | 659 + .../saem/state/tests/state_and_iov.rs | 888 ++ src/bestdose/cost.rs | 66 +- src/estimation/assay_error.rs | 140 +- src/estimation/error_models.rs | 7 + src/estimation/likelihood/batch.rs | 31 +- src/estimation/likelihood/matrix.rs | 4 +- src/estimation/likelihood/objective.rs | 8 +- src/estimation/likelihood/residual.rs | 14 +- src/estimation/nonparametric/predictions.rs | 47 +- src/estimation/nonparametric/result.rs | 86 +- src/estimation/parametric/information.rs | 14 +- src/estimation/parametric/residual.rs | 10 +- src/estimation/problem.rs | 49 +- src/estimation/sde_particle.rs | 4 +- src/iov/mod.rs | 3 +- src/iov/optimizer.rs | 3 +- src/lib.rs | 3 +- src/model/mod.rs | 4 +- src/results/fit_result.rs | 14 +- src/results/parametric_output.rs | 49 +- tests/bestdose_tests.rs | 10 +- tests/iov_diffusion_optimizer.rs | 4 +- tests/ode_scoring_parity.rs | 21 +- tests/ode_solver_profile.rs | 29 +- tests/particle_filter_scientific.rs | 4 +- tests/saem_outputs.rs | 66 +- tests/sde_particle_filter.rs | 4 +- 41 files changed, 11119 insertions(+), 10806 deletions(-) delete mode 100644 src/algorithms/parametric/saem.rs create mode 100644 src/algorithms/parametric/saem/mod.rs create mode 100644 src/algorithms/parametric/saem/state/diagnostics.rs create mode 100644 src/algorithms/parametric/saem/state/mod.rs create mode 100644 src/algorithms/parametric/saem/state/runner.rs create mode 100644 src/algorithms/parametric/saem/state/support.rs create mode 100644 src/algorithms/parametric/saem/state/tests/controller.rs create mode 100644 src/algorithms/parametric/saem/state/tests/diagnostics.rs create mode 100644 src/algorithms/parametric/saem/state/tests/estimation.rs create mode 100644 src/algorithms/parametric/saem/state/tests/mod.rs create mode 100644 src/algorithms/parametric/saem/state/tests/results.rs create mode 100644 src/algorithms/parametric/saem/state/tests/schedule.rs create mode 100644 src/algorithms/parametric/saem/state/tests/state_and_iov.rs diff --git a/Cargo.toml b/Cargo.toml index f3c67fda7..082636da8 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -65,7 +65,7 @@ tracing-subscriber = { version = "0.3.19", features = [ "time", ] } faer = "0.24.0" -pharmsol = { git = "https://github.com/LAPKB/pharmsol", branch = "feature/likelihood-extraction", version = "=0.28.2" } +pharmsol = { git = "https://github.com/LAPKB/pharmsol", branch = "feature/likelihood-extraction" } anyhow = "1.0.100" statrs = "0.18.0" rayon = "1.10.0" diff --git a/src/algorithms/mod.rs b/src/algorithms/mod.rs index deceee8c1..4232e478c 100644 --- a/src/algorithms/mod.rs +++ b/src/algorithms/mod.rs @@ -185,15 +185,11 @@ pub trait NonParametricRunner: Sync + Send + 'stat preds.iter().map(|x| x.prediction()).collect::>() ); tracing::debug!( - "\t\tOuteqs: {:?}", - preds.iter().map(|x| x.outeq()).collect::>() - ); - tracing::debug!( - "\t\tStates: {:?}", + "\t\tOutputs: {:?}", preds .iter() - .map(|x| x.state().to_vec()) - .collect::>>() + .map(|x| x.output().to_string()) + .collect::>() ); } tracing::debug!("====================="); diff --git a/src/algorithms/parametric/saem.rs b/src/algorithms/parametric/saem.rs deleted file mode 100644 index ac5ad87e9..000000000 --- a/src/algorithms/parametric/saem.rs +++ /dev/null @@ -1,10584 +0,0 @@ -use std::collections::BTreeMap; - -use anyhow::{anyhow, Result}; -use argmin::{ - core::{CostFunction, Error as ArgminError, Executor}, - solver::neldermead::NelderMead, -}; -use ndarray::Array2; -use pharmsol::{Data, Equation, Event, Subject}; -use rand::rngs::StdRng; -use rand::{RngExt, SeedableRng}; - -use crate::algorithms::{Status, StopReason}; -use crate::estimation::likelihood::batch::{ - parametric_occasion_log_likelihood, parametric_subject_log_likelihood, -}; -use crate::estimation::likelihood::objective::parametric_subject_log_likelihoods; -use crate::estimation::parametric::conditional_uncertainty::{ - conditional_mode_curvature, ConditionalModeMetadata, JointLatentCoordinate, - JointLatentCoordinateKind, -}; -use crate::estimation::parametric::covariance::{ - cholesky_lower, relative_spd_margin, worst_contrast, -}; -use crate::estimation::parametric::covariates::{ - rebase_eta, solve_covariate_gls, subject_centered_omega, CovariateGlsProblem, CovariateModel, -}; -use crate::estimation::parametric::individual::{ - individual_phi, individual_phi_from_subject_mean, individual_psi, - individual_psi_from_subject_mean, occasion_psi, occasion_psi_from_subject_mean, population_phi, - population_psi, -}; -use crate::estimation::parametric::information::{ - derive_population_uncertainty, CompleteDerivative, InformationLayout, InformationRecursion, -}; -use crate::estimation::parametric::marginal_likelihood::{ - calculate_population_marginal_likelihood, unavailable_population_marginal_likelihood, - MarginalLikelihoodDiagnostics, MarginalLikelihoodFailureReason, MarginalLikelihoodStatus, - MarginalSubject, -}; -use crate::estimation::parametric::markov_variance::{ - classify_psd, lugsail_batch_means, rows, scale_lrv_sum, transform_simulation_variance, - MatrixClassification, -}; -use crate::estimation::parametric::posterior::{ - eta_log_prior_from_omega, eta_log_priors, SubjectPosteriorScore, -}; -use crate::estimation::parametric::posthoc::optimize_conditional_mode; -use crate::estimation::parametric::prior::CovarianceUpdateResult; -use crate::estimation::parametric::rank_diagnostics::{ - bulk_ess, folded_split_rhat, rank_normalized_split_rhat, RankDiagnosticError, -}; -use crate::estimation::parametric::residual::{ - combined_additive_sigma_collapsed, optimize_combined_residual, - optimize_correlated_combined_residual, primary_sigma_parameter, primary_sigma_parameters, - residual_statistics_for_subject, update_estimated_combined_residual_model, - update_estimated_correlated_combined_residual_model, - update_estimated_simple_residual_model_with_sigma, ResidualSufficientStatistics, -}; -use crate::estimation::parametric::shrinkage::{ - derive_eta_map_shrinkage, derive_eta_posterior_mean_shrinkage, derive_kappa_map_shrinkage, - derive_kappa_posterior_mean_shrinkage, ShrinkageDiagnostics, -}; -use crate::estimation::parametric::sufficient::{ - CovariateSufficientStatistics, PhiSufficientStatistics, -}; -use crate::estimation::parametric::{CovarianceUpdateStatus, ResolvedOmega}; -use crate::estimation::{EstimationProblem, Parametric, ParametricErrorModels}; -use crate::model::{ParameterScale, UnboundedParameter}; -use crate::ResidualErrorModel; - -use crate::results::{ - derive_information_criteria, CovarianceCycleUpdateDiagnostics, CovarianceCycleUpdateOutcome, - CovarianceUpdateNotAttemptedReason, DiagnosticTraceCoordinate, InformationCoordinateKind, - InformationDiagnostics, InformationStatus, MarkovSimulationVarianceChainDiagnostics, - MarkovSimulationVarianceDiagnostics, MarkovSimulationVarianceStatus, OccasionKappaEstimate, - OperationalConvergenceCheck, OperationalConvergenceCriterion, - OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, - OperationalConvergenceOutcome, ParametricResult, ParametricWarning, RankDiagnosticStatus, - RankMixingDiagnostic, RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, - SaemCycleDiagnostics, SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, - SubjectEtaEstimate, -}; - -use super::{ - CovarianceStabilityConfig, NumericalFailure, NumericalFailurePhase, - OperationalConvergenceConfig, ParametricRunner, SaemConfig, SaemEstimatorPolicy, -}; - -fn pending_covariance_update_diagnostics( - phase: SaemPhase, - configured: bool, - has_estimated_entries: bool, -) -> CovarianceCycleUpdateDiagnostics { - let reason = if !configured { - CovarianceUpdateNotAttemptedReason::NotConfigured - } else if !has_estimated_entries { - CovarianceUpdateNotAttemptedReason::NoEstimatedEntries - } else if phase == SaemPhase::BurnIn { - CovarianceUpdateNotAttemptedReason::BurnIn - } else { - CovarianceUpdateNotAttemptedReason::UpdateInactive - }; - CovarianceCycleUpdateDiagnostics::not_attempted(reason) -} - -fn completed_covariance_update_diagnostics( - proposal: &Array2, - update: &CovarianceUpdateResult, -) -> Result { - let outcome = match update.status { - CovarianceUpdateStatus::Accepted => CovarianceCycleUpdateOutcome::Accepted, - CovarianceUpdateStatus::NoOp => CovarianceCycleUpdateOutcome::NoOp, - CovarianceUpdateStatus::Rejected => CovarianceCycleUpdateOutcome::Rejected { - reason: update.rejection_reason.ok_or_else(|| { - anyhow!("rejected covariance update lacks a typed diagnostic reason") - })?, - }, - }; - Ok(CovarianceCycleUpdateDiagnostics { - proposal: Some(proposal.clone()), - solved_target: update.solved_target.clone(), - outcome, - accepted_fraction: update.accepted_fraction, - attempted_fractions: update.attempted_fractions.clone(), - trial_rejections: update.trial_rejections.clone(), - }) -} - -const COMPONENT_TARGET_ACCEPTANCE: f64 = 0.44; -const ETA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; -const KAPPA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; -const PROPOSAL_SCALE_INCREASE: f64 = 1.1; -const MARKOV_VARIANCE_ASSUMPTIONS: &str = concat!( - "diagnostic only: prior draws at frozen averaged Omega/Omega_IOV; ", - "per-chain seed = config.seed.wrapping_add(i).wrapping_mul(0x9E3779B97F4A7C15); ", - "frozen-kernel stationarity, adequate mixing, the Poisson equation, and the ", - "controlled-Markov averaged-SA CLT are unverified; lugsail batch means alone is not a ", - "mixing diagnostic; failure detection (non-finite, ", - "constant, stuck, byte overflow, non-positive tau) is not a convergence claim; ", - "literature recommendations for R̂ and ESS are referenced but no threshold " -); - -#[derive(Clone)] -struct FrozenDiagnosticState { - etas: Vec>>, - kappas: Vec>>>, -} - -struct DiagnosticCandidate { - population_parameters: Vec, - covariate_model: Option, - omega: Array2, - omega_iov: Option>, - error_models: ParametricErrorModels, -} - -#[derive(Debug, Clone)] -struct NonIivCoordinateLayout { - population_indices: Vec, - covariate_indices: Vec, -} - -impl NonIivCoordinateLayout { - fn len(&self) -> usize { - self.population_indices.len() + self.covariate_indices.len() - } - - fn is_empty(&self) -> bool { - self.len() == 0 - } -} - -type NonIivCandidateComponents = (Vec, Option, Option>>); - -fn parameters_are_strictly_in_domain(values: &[f64], scales: &[ParameterScale]) -> bool { - values.len() == scales.len() - && values.iter().zip(scales).all(|(value, scale)| { - value.is_finite() - && match scale { - ParameterScale::Identity => true, - ParameterScale::Log => *value > 0.0, - ParameterScale::Logit { lower, upper } - | ParameterScale::Probit { lower, upper } => *value > *lower && *value < *upper, - } - }) -} - -fn non_iiv_candidate_improves(current: f64, candidate: f64) -> bool { - candidate.is_finite() && candidate < current -} - -struct NonIivPopulationCost<'a, E: Equation> { - state: &'a SaemState, - layout: &'a NonIivCoordinateLayout, -} - -impl CostFunction for NonIivPopulationCost<'_, E> { - type Param = Vec; - type Output = f64; - - fn cost(&self, coordinates: &Self::Param) -> std::result::Result { - Ok(self - .state - .non_iiv_observation_nll(self.layout, coordinates) - .unwrap_or(NON_IIV_OPTIMIZER_PENALTY)) - } -} - -const NON_IIV_OPTIMIZER_MAX_ITERATIONS: u64 = 100; -const NON_IIV_OPTIMIZER_PENALTY: f64 = 1e100; -const NON_IIV_OPTIMIZER_SD_TOLERANCE: f64 = 1e-8; -const PROPOSAL_SCALE_DECREASE: f64 = 0.9; -const MIN_PROPOSAL_SCALE: f64 = 1e-6; -const MAX_PROPOSAL_SCALE: f64 = 5.0; - -/// SAEM iteration schedule derived from [`SaemConfig`]. -/// -/// This uses the established high-level split: a pure burn-in -/// region, an exploration region with full stochastic approximation updates, -/// then a smoothing region with decreasing step size. -#[derive(Debug, Clone, PartialEq)] -pub(crate) struct SaemSchedule { - pub(crate) pure_burn_in: usize, - pub(crate) exploration_iterations: usize, - pub(crate) smoothing_iterations: usize, - pub(crate) total_iterations: usize, - pub(crate) variance_floor_iterations: usize, - pub(crate) annealing_alpha: f64, - pub(crate) omega_sa_max_step: f64, - pub(crate) minimum_variance: f64, - pub(crate) minimum_iov_variance: f64, - pub(crate) minimum_residual_sigma: f64, - pub(crate) averaging_alpha: Option, -} - -impl SaemSchedule { - pub(crate) fn from_config(config: &SaemConfig) -> Self { - let pure_burn_in = config.burn_in; - let exploration_iterations = config.k1_iterations.saturating_sub(pure_burn_in); - let smoothing_iterations = config.k2_iterations; - let total_iterations = config.k1_iterations + config.k2_iterations; - let variance_floor_iterations = if config.sa_iterations > 0 { - config.sa_iterations - } else { - config.k1_iterations / 2 - }; - - Self { - pure_burn_in, - exploration_iterations, - smoothing_iterations, - total_iterations, - variance_floor_iterations, - annealing_alpha: config.sa_cooling_factor, - omega_sa_max_step: config.omega_sa_max_step, - minimum_variance: config.omega_min_variance, - minimum_iov_variance: config.omega_iov_min_variance, - minimum_residual_sigma: config.residual_min_sigma, - averaging_alpha: match config.estimator_policy { - SaemEstimatorPolicy::TerminalIterate => None, - SaemEstimatorPolicy::AveragedIterates { alpha } => Some(alpha), - }, - } - } - - pub(crate) fn stochastic_approximation_step(&self, iteration: usize) -> f64 { - if iteration <= self.pure_burn_in { - 0.0 - } else if iteration <= self.pure_burn_in + self.exploration_iterations { - 1.0 - } else { - let smoothing_iteration = iteration - .saturating_sub(self.pure_burn_in + self.exploration_iterations) - .max(1); - match self.averaging_alpha { - Some(alpha) => (smoothing_iteration as f64).powf(-alpha), - None => 1.0 / smoothing_iteration as f64, - } - } - } - - /// Stochastic-approximation step for Ω/Ω_IOV sufficient statistics. - /// - /// Covariance learning is damped during both pure chain - /// warm-up and exploration so one un-equilibrated draw cannot overwrite a - /// correlated covariance. The cap is lifted in smoothing. - pub(crate) fn covariance_step(&self, iteration: usize) -> f64 { - if iteration <= self.pure_burn_in + self.exploration_iterations { - self.omega_sa_max_step.min(1.0) - } else { - self.stochastic_approximation_step(iteration) - } - } - - pub(crate) fn covariance_update_active(&self, iteration: usize) -> bool { - iteration > self.pure_burn_in - } - - pub(crate) fn phase(&self, iteration: usize) -> SaemPhase { - if iteration <= self.pure_burn_in { - SaemPhase::BurnIn - } else if iteration <= self.pure_burn_in + self.exploration_iterations { - SaemPhase::Exploration - } else { - SaemPhase::Smoothing - } - } - - /// Guard an estimated residual SD against early collapse. - /// - /// During simulated annealing, PMcore cools the previous residual SD by - /// `alpha.sa` and takes the larger of that value and the M-step candidate. - /// The configured residual floor always applies. Fixed residual models are - /// left untouched. - pub(crate) fn guarded_residual_sigma( - &self, - iteration: usize, - previous: f64, - candidate: f64, - ) -> f64 { - let mut guarded = candidate.max(self.minimum_residual_sigma); - if iteration <= self.variance_floor_iterations { - guarded = guarded.max(previous * self.annealing_alpha); - } - guarded - } -} - -fn covariate_omega_update_maximum_fraction( - has_covariates: bool, - phase: SaemPhase, - covariance_step: f64, -) -> f64 { - if has_covariates && phase == SaemPhase::Exploration { - covariance_step - } else { - 1.0 - } -} - -fn applied_combined_residual_component( - schedule: &SaemSchedule, - iteration: usize, - previous: f64, - candidate: f64, - estimated: bool, -) -> f64 { - if !estimated { - return previous; - } - let guarded_candidate = candidate.max(schedule.minimum_residual_sigma); - if iteration <= schedule.variance_floor_iterations { - return guarded_candidate.max(previous * schedule.annealing_alpha); - } - if schedule.phase(iteration) != SaemPhase::Smoothing { - return guarded_candidate; - } - let gamma = schedule.stochastic_approximation_step(iteration); - previous + gamma * (guarded_candidate - previous) -} - -/// Immutable SAEM setup computed once before the iterations begin. -/// -/// Parameter metadata, random/IOV effect indices, the resolved omega -/// specification, and initial subject-conditioned log-likelihoods are all -/// resolved here so the runner state only carries mutable estimation state. -#[derive(Debug, Clone)] -pub(crate) struct SaemInitialization { - pub(crate) schedule: SaemSchedule, - pub(crate) n_chains: usize, - pub(crate) parameter_names: Vec, - pub(crate) parameter_scales: Vec, - pub(crate) estimated_parameters: Vec, - pub(crate) random_effect_indices: Vec, - pub(crate) random_effect_names: Vec, - pub(crate) omega: ResolvedOmega, - pub(crate) iov_effect_indices: Vec, - pub(crate) iov_effect_names: Vec, - pub(crate) omega_iov: Option, - pub(crate) occasion_counts: Vec, - pub(crate) subject_ids: Vec, - pub(crate) observation_count: usize, - pub(crate) initial_population_parameters: Vec, - pub(crate) initial_subject_log_likelihoods: Vec, - pub(crate) initial_negative_log_likelihood: f64, - pub(crate) covariate_model: Option, - pub(crate) initial_subject_mu_phi: Option>>, - pub(crate) initial_residual_values: Vec>, - pub(crate) initial_residual_estimated: Vec>, -} - -fn applied_correlated_residual_correlation( - schedule: &SaemSchedule, - iteration: usize, - previous: f64, - candidate: f64, - estimated: bool, -) -> f64 { - if !estimated { - return previous; - } - if schedule.phase(iteration) != SaemPhase::Smoothing { - return candidate; - } - let gamma = schedule.stochastic_approximation_step(iteration); - previous + gamma * (candidate - previous) -} - -fn validate_initial_estimated_variance_floor( - covariance_name: &str, - floor_name: &str, - omega: &ResolvedOmega, - minimum_variance: f64, -) -> Result<()> { - for (index, effect_name) in omega.names().iter().enumerate() { - let initial_variance = omega.initial()[[index, index]]; - if omega.estimated_mask()[[index, index]] && initial_variance < minimum_variance { - anyhow::bail!( - "SAEM initial {covariance_name} variance for estimated effect '{effect_name}' ({initial_variance}) is below configured {floor_name} ({minimum_variance})" - ); - } - } - Ok(()) -} - -impl SaemInitialization { - pub(crate) fn create( - problem: &EstimationProblem, - config: &SaemConfig, - ) -> Result - where - E: Equation, - { - config.validate()?; - let omega = problem.prior.resolved_omega().clone(); - let n_subjects = problem.data.subjects().len(); - let initial_row = initial_parameter_row(problem.parameters().iter()); - let random_effect_indices = problem - .parameters() - .iter() - .enumerate() - .filter_map(|(index, parameter)| parameter.random_effect.then_some(index)) - .collect::>(); - let random_effect_names = random_effect_indices - .iter() - .map(|index| problem.parameters().items[*index].name.clone()) - .collect(); - let (iov_effect_indices, iov_effect_names, omega_iov) = problem - .prior - .resolved_iov() - .map(|iov| { - ( - iov.parameter_indices().to_vec(), - iov.omega().names().to_vec(), - Some(iov.omega().clone()), - ) - }) - .unwrap_or_else(|| (Vec::new(), Vec::new(), None)); - validate_initial_estimated_variance_floor( - "Omega", - "omega_min_variance", - &omega, - config.omega_min_variance, - )?; - if let Some(omega_iov) = omega_iov.as_ref() { - validate_initial_estimated_variance_floor( - "Omega_IOV", - "omega_iov_min_variance", - omega_iov, - config.omega_iov_min_variance, - )?; - } - if config.marginal_likelihood.is_some() - && (!random_effect_indices.is_empty() || !iov_effect_indices.is_empty()) - && !config.compute_map - { - anyhow::bail!( - "N2 with latent dimensions requires compute_map=true; conditional modes are not enabled" - ); - } - let covariate_model = problem.covariates().cloned(); - let initial_population_phi = population_phi( - &initial_row, - &problem - .parameters() - .iter() - .map(|parameter| parameter.scale) - .collect::>(), - )?; - let initial_subject_population = covariate_model - .as_ref() - .map(|model| { - model.subject_population_parameters( - &initial_population_phi, - &problem - .parameters() - .iter() - .map(|parameter| parameter.scale) - .collect::>(), - ) - }) - .transpose()?; - let initial_subject_mu_phi = initial_subject_population.as_ref().map(|rows| { - rows.iter() - .map(|row| row.phi().to_vec()) - .collect::>() - }); - let initial_individual_parameters = match initial_subject_population.as_ref() { - Some(rows) => { - Array2::from_shape_fn((n_subjects, initial_row.len()), |(i, j)| rows[i].psi()[j]) - } - None => Array2::from_shape_fn((n_subjects, initial_row.len()), |(_, j)| initial_row[j]), - }; - let initial_subject_log_likelihoods = - parametric_subject_log_likelihoods(problem, &initial_individual_parameters)?; - if let Some((subject_index, _)) = initial_subject_log_likelihoods - .iter() - .enumerate() - .find(|(_, value)| !value.is_finite()) - { - let subject = problem.data.subjects()[subject_index]; - if let Ok(statistics) = residual_statistics_for_subject( - &problem.model.equation, - subject, - &initial_row, - problem.error_models.models(), - ) { - for (output_index, _) in problem.error_models.models().iter() { - let Some(statistic) = statistics.output(output_index) else { - continue; - }; - if statistic.exponential_domain_violation_count > 0 { - let output = problem - .error_models - .output_name(output_index) - .map(str::to_owned) - .unwrap_or_else(|| format!("output_{output_index}")); - anyhow::bail!( - "initial conditional likelihood is non-finite for subject '{}' because exponential residual model output '{}' has {} non-positive or non-finite observation/prediction pair(s); exponential errors require positive finite observations and predictions", - subject.id(), - output, - statistic.exponential_domain_violation_count - ); - } - } - } - anyhow::bail!( - "initial conditional likelihood is non-finite for subject '{}'; verify parameter values, predictions, observations, and residual-model domain", - subject.id() - ); - } - let initial_negative_log_likelihood = - negative_log_likelihood(&initial_subject_log_likelihoods); - Ok(Self { - schedule: SaemSchedule::from_config(config), - n_chains: n_chains(config, n_subjects), - parameter_names: problem.parameters().names(), - parameter_scales: problem - .parameters() - .iter() - .map(|parameter| parameter.scale) - .collect(), - estimated_parameters: problem - .parameters() - .iter() - .map(|parameter| parameter.estimate) - .collect(), - random_effect_indices, - random_effect_names, - omega, - iov_effect_indices, - iov_effect_names, - omega_iov, - occasion_counts: problem - .data - .subjects() - .iter() - .map(|subject| subject.occasions().len()) - .collect(), - subject_ids: problem - .data - .subjects() - .iter() - .map(|subject| subject.id().clone()) - .collect(), - observation_count: count_observations(&problem.data), - initial_population_parameters: initial_row, - initial_subject_log_likelihoods, - initial_negative_log_likelihood, - covariate_model, - initial_subject_mu_phi, - initial_residual_values: Vec::new(), - initial_residual_estimated: Vec::new(), - }) - } -} - -#[derive(Debug, Clone)] -struct SaemIterateAverage { - population_phi: Vec, - covariate_betas: Option>, - omega: Array2, - omega_iov: Option>, - residual_model_width: usize, - residual_models: Vec<(usize, ResidualErrorModel)>, - start_cycle: usize, - count: usize, -} - -// ─── Operational convergence lifecycle ──────────────────────────────────── -// -// Result types live in `crate::results::fit_result`. -// `OperationalConvergenceConfig` is the source of truth for settings. - -/// Domain-separation constant for deterministic per-checkpoint seeds. -/// -/// Its fixed bytes are combined with the SAEM seed via wrapping addition. -const OPERATIONAL_CHECKPOINT_SEED_DOMAIN: u64 = 0x4E31_4F50_4352_4954; - -/// Per-cycle SAEM estimation state. -/// -/// MCMC chains, stochastic-approximation sufficient statistics, and the -/// current population / omega / sigma estimates are updated in-place. -#[derive(Debug)] -pub(crate) struct SaemState { - equation: E, - data: Data, - error_models: ParametricErrorModels, - config: SaemConfig, - pub(crate) initialization: SaemInitialization, - cycle: usize, - status: Status, - numerical_failure: Option, - etas: Vec>>, - kappas: Vec>>>, - population_parameters: Vec, - omega: Array2, - omega_iov: Option>, - iiv_second_moment: Array2, - iov_second_moment: Option>, - sufficient_statistics: PhiSufficientStatistics, - covariate_statistics: Option, - subject_mu_phi: Option>>, - covariate_model: Option, - residual_statistics: ResidualSufficientStatistics, - residual_sigmas: Vec, - information: InformationRecursion, - proposal_step_sizes: Vec, - eta_block_step_sizes: Vec, - kappa_proposal_step_sizes: Vec, - mcmc_iterations: usize, - eta_block_iterations: usize, - adapt_interval: usize, - residual_optimizer_max_iterations: usize, - compute_map: bool, - map_max_iterations: usize, - map_sd_tolerance: f64, - map_initial_step: f64, - steps_since_adapt: usize, - adaptation_accept_counts: Vec, - adaptation_proposal_counts: Vec, - eta_block_adaptation_accept_counts: Vec, - eta_block_adaptation_proposal_counts: Vec, - kappa_adaptation_accept_counts: Vec, - kappa_adaptation_proposal_counts: Vec, - rng: StdRng, - subject_log_likelihoods: Vec, - subject_log_priors: Vec, - subject_kappa_log_priors: Vec, - last_log_acceptance_ratios: Vec, - last_acceptance_rate: Option, - last_eta_block_acceptance_rate: Option, - last_kappa_acceptance_rate: Option, - last_rejected_proposals: Option, - last_non_finite_proposals: Option, - last_parameter_acceptance_rates: Vec, - cycle_diagnostics: Vec, - negative_log_likelihood: f64, - iterate_average: Option, - operational_settings: Option, - operational_diagnostics: OperationalConvergenceDiagnostics, -} - -impl SaemState { - pub(crate) fn from_problem( - problem: EstimationProblem, - config: &SaemConfig, - ) -> Result { - let mut initialization = SaemInitialization::create(&problem, config)?; - let EstimationProblem { - model, - data, - error_models, - .. - } = problem; - // Capture immutable initial residual values and estimated masks before - // any SAEM cycle modifies them. - let mut initial_residual_values = Vec::new(); - let mut initial_residual_estimated = Vec::new(); - for (outeq, model) in error_models.models().iter() { - let estimate = error_models.is_estimated(outeq); - let combined = error_models.combined_component_estimated(outeq); - let correlated = error_models.correlated_combined_component_estimated(outeq); - let (additive, proportional, correlation) = - if matches!(model, ResidualErrorModel::CorrelatedCombined { .. }) { - (correlated[0], correlated[1], Some(correlated[2])) - } else { - (combined[0], combined[1], None) - }; - let components = crate::results::parametric_output::residual_components( - *model, - estimate, - Some(additive), - Some(proportional), - correlation, - ); - initial_residual_values.push(components.iter().map(|c| c.1).collect()); - initial_residual_estimated.push(components.iter().map(|c| c.2).collect()); - } - initialization.initial_residual_values = initial_residual_values; - initialization.initial_residual_estimated = initial_residual_estimated; - Ok(Self::new( - model.equation, - data, - error_models, - initialization, - config, - )) - } - - pub(crate) fn new( - equation: E, - data: Data, - error_models: ParametricErrorModels, - initialization: SaemInitialization, - config: &SaemConfig, - ) -> Self { - let n_random_effects = initialization.random_effect_indices.len(); - let etas = zero_etas( - initialization.subject_ids.len(), - initialization.n_chains, - n_random_effects, - ); - let kappas = zero_kappas( - &initialization.occasion_counts, - initialization.n_chains, - initialization.iov_effect_indices.len(), - ); - let population_parameters = initialization.initial_population_parameters.clone(); - let omega = initialization.omega.initial().clone(); - let iiv_second_moment = omega.clone(); - let omega_iov = initialization - .omega_iov - .as_ref() - .map(|omega| omega.initial().clone()); - let iov_second_moment = omega_iov.clone(); - let initial_subject_phi = zero_eta_subject_phi(&population_parameters, &initialization) - .expect("initial population parameters should produce valid phi statistics"); - let mut sufficient_statistics = - PhiSufficientStatistics::from_subject_phi(&initial_subject_phi) - .expect("initial phi statistics should be valid"); - for (eta_row, parameter_row) in initialization.random_effect_indices.iter().enumerate() { - for (eta_col, parameter_col) in initialization.random_effect_indices.iter().enumerate() - { - sufficient_statistics.second_moment[[*parameter_row, *parameter_col]] += - omega[[eta_row, eta_col]]; - } - } - let subject_mu_phi = initialization.initial_subject_mu_phi.clone(); - let covariate_model = initialization.covariate_model.clone(); - let covariate_statistics = subject_mu_phi.as_ref().map(|means| { - let expected_phi = means - .iter() - .map(|mean| { - initialization - .random_effect_indices - .iter() - .map(|index| mean[*index]) - .collect::>() - }) - .collect::>(); - let mut global_second_moment = Array2::zeros((n_random_effects, n_random_effects)); - for mean in &expected_phi { - for row in 0..n_random_effects { - for column in 0..n_random_effects { - global_second_moment[[row, column]] += - mean[row] * mean[column] / expected_phi.len() as f64; - } - } - } - global_second_moment += ω - CovariateSufficientStatistics { - expected_phi, - global_second_moment, - } - }); - let subject_log_priors = eta_log_priors(&etas, &omega, 0) - .expect("validated initial omega should produce finite eta priors"); - let subject_kappa_log_priors = omega_iov - .as_ref() - .map(|omega| { - kappas - .iter() - .map(|subject_chains| { - subject_chains[0] - .iter() - .map(|kappa| eta_log_prior_from_omega(kappa, omega)) - .collect::>>() - .map(|priors| priors.into_iter().sum()) - }) - .collect::>>() - .expect("validated initial omega_iov should produce finite kappa priors") - }) - .unwrap_or_else(|| vec![0.0; initialization.subject_ids.len()]); - let residual_statistics = ResidualSufficientStatistics::zero(error_models.models().len()); - let residual_sigmas = primary_sigma_parameters(error_models.models()); - let proposal_step_sizes = initial_proposal_step_sizes(&omega, config.rw_init); - let eta_block_step_sizes = if config.eta_block_iterations > 0 { - vec![config.rw_init; initialization.subject_ids.len()] - } else { - Vec::new() - }; - let kappa_proposal_step_sizes = omega_iov - .as_ref() - .map(|_| vec![config.rw_init; initialization.subject_ids.len()]) - .unwrap_or_default(); - let mcmc_iterations = config.mcmc_iterations; - let eta_block_iterations = config.eta_block_iterations; - let adapt_interval = config.adapt_interval; - let steps_since_adapt = 0; - let adaptation_accept_counts = vec![0; n_random_effects]; - let adaptation_proposal_counts = vec![0; n_random_effects]; - let eta_block_adaptation_accept_counts = vec![0; eta_block_step_sizes.len()]; - let eta_block_adaptation_proposal_counts = vec![0; eta_block_step_sizes.len()]; - let kappa_adaptation_accept_counts = vec![0; initialization.subject_ids.len()]; - let kappa_adaptation_proposal_counts = vec![0; initialization.subject_ids.len()]; - let rng = StdRng::seed_from_u64(config.seed); - let last_log_acceptance_ratios = vec![0.0; initialization.subject_ids.len()]; - let last_acceptance_rate = None; - let last_parameter_acceptance_rates = vec![0.0; n_random_effects]; - let covariate_effect_names = covariate_model - .as_ref() - .map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.name().to_string()) - .collect::>() - }) - .unwrap_or_default(); - let covariate_estimated = covariate_model - .as_ref() - .map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimated()) - .collect::>() - }) - .unwrap_or_default(); - let information_layout = InformationLayout::new( - &initialization.parameter_names, - &initialization.estimated_parameters, - &covariate_effect_names, - &covariate_estimated, - &initialization.random_effect_names, - initialization.omega.structural_mask(), - initialization.omega.estimated_mask(), - &initialization.iov_effect_names, - initialization - .omega_iov - .as_ref() - .map(|omega| omega.structural_mask()), - initialization - .omega_iov - .as_ref() - .map(|omega| omega.estimated_mask()), - &error_models, - ) - .expect("validated SAEM metadata must produce an information layout"); - let mut information = InformationRecursion::new(information_layout); - let has_non_iiv_population = - initialization - .estimated_parameters - .iter() - .enumerate() - .any(|(index, estimated)| { - *estimated && !initialization.random_effect_indices.contains(&index) - }); - let has_non_iiv_covariate = covariate_model.as_ref().is_some_and(|model| { - model - .estimates() - .iter() - .enumerate() - .any(|(index, estimate)| { - estimate.estimated() - && !initialization - .random_effect_indices - .contains(&model.parameter_indices()[index]) - }) - }); - if has_non_iiv_population || has_non_iiv_covariate { - information.mark_unavailable(InformationStatus::Unsupported( - "structural observation sensitivities are unavailable for estimated non-IIV population or covariate coordinates" - .to_string(), - )); - } - - Self { - equation, - data, - error_models, - config: config.clone(), - etas, - kappas, - population_parameters, - omega, - omega_iov, - iiv_second_moment, - iov_second_moment, - sufficient_statistics, - covariate_statistics, - subject_mu_phi, - covariate_model, - residual_statistics, - residual_sigmas, - information, - proposal_step_sizes, - eta_block_step_sizes, - kappa_proposal_step_sizes, - mcmc_iterations, - eta_block_iterations, - adapt_interval, - residual_optimizer_max_iterations: config.residual_optimizer_max_iterations, - compute_map: config.compute_map, - map_max_iterations: config.map_max_iterations, - map_sd_tolerance: config.map_sd_tolerance, - map_initial_step: config.map_initial_step, - steps_since_adapt, - adaptation_accept_counts, - adaptation_proposal_counts, - eta_block_adaptation_accept_counts, - eta_block_adaptation_proposal_counts, - kappa_adaptation_accept_counts, - kappa_adaptation_proposal_counts, - rng, - subject_log_likelihoods: initialization.initial_subject_log_likelihoods.clone(), - subject_log_priors, - subject_kappa_log_priors, - last_log_acceptance_ratios, - last_acceptance_rate, - last_eta_block_acceptance_rate: None, - last_kappa_acceptance_rate: None, - last_rejected_proposals: None, - last_non_finite_proposals: None, - last_parameter_acceptance_rates, - cycle_diagnostics: Vec::with_capacity(initialization.schedule.total_iterations), - negative_log_likelihood: initialization.initial_negative_log_likelihood, - iterate_average: None, - operational_settings: config.operational_convergence, - operational_diagnostics: OperationalConvergenceDiagnostics { - config: config.operational_convergence, - ..OperationalConvergenceDiagnostics::default() - }, - initialization, - cycle: 0, - status: Status::Continue, - numerical_failure: None, - } - } - - fn e_step(&mut self) -> Result<()> { - let mut eta_accepted = 0usize; - let mut eta_rejected = 0usize; - let mut eta_non_finite = 0usize; - let mut eta_proposed = 0usize; - let mut eta_block_accepted = 0usize; - let mut eta_block_rejected = 0usize; - let mut eta_block_non_finite = 0usize; - let mut eta_block_proposed = 0usize; - let mut kappa_accepted = 0usize; - let mut kappa_rejected = 0usize; - let mut kappa_non_finite = 0usize; - let mut kappa_proposed = 0usize; - let eta_step_sizes_before = self.proposal_step_sizes.clone(); - let eta_block_step_sizes_before = self.eta_block_step_sizes.clone(); - let kappa_step_sizes_before = self.kappa_proposal_step_sizes.clone(); - let kappa_subject_count = if self.omega_iov.is_some() { - self.initialization.subject_ids.len() - } else { - 0 - }; - let mut kappa_subject_accept_counts = vec![0usize; kappa_subject_count]; - let mut kappa_subject_proposal_counts = vec![0usize; kappa_subject_count]; - let eta_block_subject_count = if self.eta_block_iterations > 0 { - self.initialization.subject_ids.len() - } else { - 0 - }; - let mut eta_block_subject_accept_counts = vec![0usize; eta_block_subject_count]; - let mut eta_block_subject_proposal_counts = vec![0usize; eta_block_subject_count]; - let n_parameters = self.initialization.random_effect_indices.len(); - let mut subject_log_acceptance_sums = vec![0.0; self.initialization.subject_ids.len()]; - let mut subject_proposal_counts = vec![0usize; self.initialization.subject_ids.len()]; - let mut parameter_accept_counts = vec![0usize; n_parameters]; - let mut parameter_proposal_counts = vec![0usize; n_parameters]; - - // Compound-kernel order: Omega-scaled eta blocks first, followed by - // component eta walks and occasion-level kappa blocks. Eta blocks are - // opt-in. - for _ in 0..self.eta_block_iterations { - for subject_index in 0..self.initialization.subject_ids.len() { - for chain_index in 0..self.initialization.n_chains { - let current_eta = self.etas[subject_index][chain_index].clone(); - let proposed_eta = self.block_random_walk_eta(¤t_eta, subject_index)?; - let log_acceptance_ratio = self.proposal_log_acceptance_ratio( - subject_index, - chain_index, - &proposed_eta, - )?; - subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; - subject_proposal_counts[subject_index] += 1; - eta_block_subject_proposal_counts[subject_index] += 1; - self.eta_block_adaptation_proposal_counts[subject_index] += 1; - eta_block_proposed += 1; - eta_proposed += 1; - if !log_acceptance_ratio.is_finite() { - eta_block_non_finite += 1; - eta_non_finite += 1; - } - if self.accept_proposal(log_acceptance_ratio) { - self.etas[subject_index][chain_index] = proposed_eta; - eta_block_subject_accept_counts[subject_index] += 1; - self.eta_block_adaptation_accept_counts[subject_index] += 1; - eta_block_accepted += 1; - eta_accepted += 1; - } else { - eta_block_rejected += 1; - eta_rejected += 1; - } - } - } - } - - for _ in 0..self.mcmc_iterations { - for subject_index in 0..self.initialization.subject_ids.len() { - for chain_index in 0..self.initialization.n_chains { - for parameter_index in 0..n_parameters { - let current_eta = self.etas[subject_index][chain_index].clone(); - let proposed_eta = - self.component_random_walk_eta(¤t_eta, parameter_index); - let log_acceptance_ratio = self.proposal_log_acceptance_ratio( - subject_index, - chain_index, - &proposed_eta, - )?; - subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; - subject_proposal_counts[subject_index] += 1; - parameter_proposal_counts[parameter_index] += 1; - eta_proposed += 1; - if !log_acceptance_ratio.is_finite() { - eta_non_finite += 1; - } - if self.accept_proposal(log_acceptance_ratio) { - self.etas[subject_index][chain_index] = proposed_eta; - parameter_accept_counts[parameter_index] += 1; - eta_accepted += 1; - } else { - eta_rejected += 1; - } - } - - // Gibbs sweep over occasion-specific κ blocks. Every - // proposal is evaluated against the full subject posterior, - // keeping η and all other occasions fixed. - if self.omega_iov.is_some() { - for occasion_index in 0..self.kappas[subject_index][chain_index].len() { - let current_kappa = - self.kappas[subject_index][chain_index][occasion_index].clone(); - let proposed_kappa = - self.block_random_walk_kappa(¤t_kappa, subject_index)?; - let log_acceptance_ratio = self.kappa_proposal_log_acceptance_ratio( - subject_index, - chain_index, - occasion_index, - &proposed_kappa, - )?; - subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; - subject_proposal_counts[subject_index] += 1; - kappa_proposed += 1; - kappa_subject_proposal_counts[subject_index] += 1; - self.kappa_adaptation_proposal_counts[subject_index] += 1; - if !log_acceptance_ratio.is_finite() { - kappa_non_finite += 1; - } - if self.accept_proposal(log_acceptance_ratio) { - self.kappas[subject_index][chain_index][occasion_index] = - proposed_kappa; - kappa_accepted += 1; - kappa_subject_accept_counts[subject_index] += 1; - self.kappa_adaptation_accept_counts[subject_index] += 1; - } else { - kappa_rejected += 1; - } - } - } - } - } - } - - self.refresh_subject_scores_from_chains()?; - self.last_log_acceptance_ratios = subject_log_acceptance_sums - .into_iter() - .zip(subject_proposal_counts) - .map(|(sum, count)| if count > 0 { sum / count as f64 } else { 0.0 }) - .collect(); - let proposed = eta_proposed + kappa_proposed; - let accepted = eta_accepted + kappa_accepted; - self.last_acceptance_rate = if proposed > 0 { - Some(accepted as f64 / proposed as f64) - } else { - None - }; - self.last_eta_block_acceptance_rate = if self.eta_block_iterations > 0 { - Some(eta_block_accepted as f64 / eta_block_proposed.max(1) as f64) - } else { - None - }; - self.last_kappa_acceptance_rate = if self.omega_iov.is_some() { - Some(kappa_accepted as f64 / kappa_proposed.max(1) as f64) - } else { - None - }; - self.last_rejected_proposals = Some(eta_rejected + kappa_rejected); - self.last_non_finite_proposals = Some(eta_non_finite + kappa_non_finite); - self.last_parameter_acceptance_rates = parameter_accept_counts - .iter() - .zip(parameter_proposal_counts.iter()) - .map(|(accepted, proposed)| { - if *proposed > 0 { - *accepted as f64 / *proposed as f64 - } else { - 0.0 - } - }) - .collect(); - for parameter_index in 0..n_parameters { - self.adaptation_accept_counts[parameter_index] += - parameter_accept_counts[parameter_index]; - self.adaptation_proposal_counts[parameter_index] += - parameter_proposal_counts[parameter_index]; - } - self.steps_since_adapt += 1; - self.adapt_proposal_step_sizes(); - let phase = self.initialization.schedule.phase(self.cycle); - let omega_update = pending_covariance_update_diagnostics( - phase, - true, - self.initialization.omega.has_estimated_entries(), - ); - let omega_iov_update = pending_covariance_update_diagnostics( - phase, - self.initialization.omega_iov.is_some(), - self.initialization - .omega_iov - .as_ref() - .is_some_and(ResolvedOmega::has_estimated_entries), - ); - self.cycle_diagnostics.push(SaemCycleDiagnostics { - iteration: self.cycle, - phase, - stochastic_approximation_step: self - .initialization - .schedule - .stochastic_approximation_step(self.cycle), - covariance_step: self.initialization.schedule.covariance_step(self.cycle), - eta_proposals: eta_proposed, - eta_accepted, - eta_rejected, - eta_non_finite, - eta_parameter_acceptance_rates: self.last_parameter_acceptance_rates.clone(), - eta_proposal_step_sizes_before_adaptation: eta_step_sizes_before, - eta_proposal_step_sizes_after_adaptation: self.proposal_step_sizes.clone(), - eta_block_proposals: eta_block_proposed, - eta_block_accepted, - eta_block_rejected, - eta_block_non_finite, - eta_block_subject_acceptance_rates: eta_block_subject_accept_counts - .iter() - .zip(eta_block_subject_proposal_counts.iter()) - .map(|(accepted, proposed)| { - if *proposed > 0 { - *accepted as f64 / *proposed as f64 - } else { - 0.0 - } - }) - .collect(), - eta_block_step_sizes_before_adaptation: eta_block_step_sizes_before, - eta_block_step_sizes_after_adaptation: self.eta_block_step_sizes.clone(), - kappa_proposals: kappa_proposed, - kappa_accepted, - kappa_rejected, - kappa_non_finite, - kappa_subject_acceptance_rates: kappa_subject_accept_counts - .iter() - .zip(kappa_subject_proposal_counts.iter()) - .map(|(accepted, proposed)| { - if *proposed > 0 { - *accepted as f64 / *proposed as f64 - } else { - 0.0 - } - }) - .collect(), - kappa_proposal_step_sizes_before_adaptation: kappa_step_sizes_before, - kappa_proposal_step_sizes_after_adaptation: self.kappa_proposal_step_sizes.clone(), - simulated_annealing_active: self.cycle - <= self.initialization.schedule.variance_floor_iterations, - population_parameters: self.population_parameters.clone(), - omega: self.omega.clone(), - omega_iov: self.omega_iov.clone(), - residual_error_estimates: self.residual_error_estimates(), - residual_diagnostics: Vec::new(), - conditional_negative_log_likelihood: self.negative_log_likelihood, - eta_log_prior: self.subject_log_priors.iter().sum(), - kappa_log_prior: self.subject_kappa_log_priors.iter().sum(), - omega_update_rejected: false, - omega_iov_update_rejected: false, - omega_update, - omega_iov_update, - omega_relative_spd_margin: None, - omega_iov_relative_spd_margin: None, - covariate_betas: self.covariate_model.as_ref().map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect() - }), - covariate_beta_estimated: self.covariate_model.as_ref().map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimated()) - .collect() - }), - }); - self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); - Ok(()) - } - - fn m_step(&mut self) -> Result<()> { - let parameter_step = self - .initialization - .schedule - .stochastic_approximation_step(self.cycle); - let covariance_step = self.initialization.schedule.covariance_step(self.cycle); - if self.covariate_model.is_some() { - let observed = self.current_covariate_statistics()?; - self.covariate_statistics - .as_mut() - .expect("covariate model has initialized statistics") - .stochastic_update(&observed, parameter_step)?; - } else { - let observed_statistics = self.current_phi_statistics()?; - self.sufficient_statistics.stochastic_update_with_steps( - &observed_statistics, - parameter_step, - covariance_step, - )?; - } - - if let Some(second_moment) = self.iov_second_moment.as_mut() { - let observed_second_moment = covariance_from_kappas(&self.kappas)?; - *second_moment = - &*second_moment + &((&observed_second_moment - &*second_moment) * covariance_step); - } - - // Pure burn-in warms the latent chains and their centered covariance - // statistics while theta, Omega, Omega_IOV, and sigma remain fixed. Raw - // covariate phi moments remain unchanged, matching their zero SA gain. - if parameter_step == 0.0 { - let observed_second_moment = second_moment_from_etas(&self.etas)?; - self.iiv_second_moment = &self.iiv_second_moment - + &((&observed_second_moment - &self.iiv_second_moment) * covariance_step); - self.finalize_cycle_diagnostics()?; - return Ok(()); - } - - let pre_update_residual_evidence = self.current_residual_statistics_and_information()?; - if self.covariate_model.is_some() { - // The raw first and second phi moments already share the SAEM gain. - // Keep their centered covariance candidate coherent; exploration - // robustness is applied later to the accepted Omega iterate rather - // than introducing a second sufficient-statistic recursion. - self.iiv_second_moment = self.update_covariate_population_and_recenter_etas()?; - } else { - self.update_population_and_recenter_etas()?; - let observed_second_moment = second_moment_from_etas(&self.etas)?; - self.iiv_second_moment = &self.iiv_second_moment - + &((&observed_second_moment - &self.iiv_second_moment) * covariance_step); - } - - self.update_non_iiv_population(parameter_step)?; - let (observed_residual_statistics, information_replicates) = pre_update_residual_evidence; - match information_replicates { - Ok(replicates) => self.information.update(&replicates, parameter_step), - Err(reason) => self - .information - .mark_unavailable(information_failure_status(reason)), - } - let mut residual_diagnostics = self - .error_models - .models() - .iter() - .map(|(output_index, _)| { - let statistic = observed_residual_statistics - .output(output_index) - .unwrap_or_default(); - ResidualCycleDiagnostics { - output: self - .error_models - .output_name(output_index) - .map(str::to_owned) - .unwrap_or_else(|| format!("output_{output_index}")), - output_index, - prediction_evaluation_count: statistic.observation_count, - proportional_floor_count: statistic.proportional_floor_count, - non_finite_prediction_count: statistic.non_finite_prediction_count, - exponential_domain_violation_count: statistic - .exponential_domain_violation_count, - update_rejected: false, - optimizer_objective: None, - optimizer_converged: None, - optimizer_iterations: None, - optimizer_termination: None, - combined_additive_collapse_warning: false, - } - }) - .collect::>(); - let residual_observations = (0..self.error_models.len()) - .map(|output_index| { - observed_residual_statistics - .observations(output_index) - .unwrap_or_default() - .to_vec() - }) - .collect::>(); - self.residual_statistics = self - .residual_statistics - .stochastic_update(observed_residual_statistics, parameter_step); - - if self - .initialization - .schedule - .covariance_update_active(self.cycle) - { - if self.initialization.omega.has_estimated_entries() { - let phase = self.initialization.schedule.phase(self.cycle); - let update = if self.covariate_model.is_some() && phase == SaemPhase::Exploration { - self.initialization - .omega - .update_with_status_and_max_fraction( - &self.omega, - &self.iiv_second_moment, - self.initialization.schedule.minimum_variance, - covariate_omega_update_maximum_fraction(true, phase, covariance_step), - )? - } else { - // Preserve the established floor-after-interpolation path - // for non-covariate IIV and for uncapped covariate smoothing. - self.initialization.omega.update_with_status( - &self.omega, - &self.iiv_second_moment, - self.initialization.schedule.minimum_variance, - )? - }; - let status = update.status; - let update_diagnostics = - completed_covariance_update_diagnostics(&self.iiv_second_moment, &update)?; - self.omega = update.matrix; - if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { - diagnostics.omega_update_rejected = status == CovarianceUpdateStatus::Rejected; - diagnostics.omega_update = update_diagnostics; - } - } - if let (Some(specification), Some(omega_iov), Some(second_moment)) = ( - self.initialization.omega_iov.as_ref(), - self.omega_iov.as_mut(), - self.iov_second_moment.as_ref(), - ) { - if specification.has_estimated_entries() { - let update = specification.update_with_status( - omega_iov, - second_moment, - self.initialization.schedule.minimum_iov_variance, - )?; - let status = update.status; - let update_diagnostics = - completed_covariance_update_diagnostics(second_moment, &update)?; - *omega_iov = update.matrix; - if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { - diagnostics.omega_iov_update_rejected = - status == CovarianceUpdateStatus::Rejected; - diagnostics.omega_iov_update = update_diagnostics; - } - } - } - } - for residual_diagnostic in &mut residual_diagnostics { - let outeq = residual_diagnostic.output_index; - if !self.error_models.is_estimated(outeq) { - continue; - } - let Some(model) = self.error_models.models().get(outeq).copied() else { - residual_diagnostic.update_rejected = true; - continue; - }; - if let ResidualErrorModel::Combined { a, b } = model { - match optimize_combined_residual( - &residual_observations[outeq], - a, - b, - self.error_models.combined_component_estimated(outeq), - self.initialization.schedule.minimum_residual_sigma, - self.residual_optimizer_max_iterations as u64, - ) { - Ok(solution) => { - let component_estimated = - self.error_models.combined_component_estimated(outeq); - let additive_sd = applied_combined_residual_component( - &self.initialization.schedule, - self.cycle, - a, - solution.additive_sd, - component_estimated[0], - ); - let proportional_sd = applied_combined_residual_component( - &self.initialization.schedule, - self.cycle, - b, - solution.proportional_sd, - component_estimated[1], - ); - residual_diagnostic.combined_additive_collapse_warning = - combined_additive_sigma_collapsed(additive_sd, component_estimated[0]); - update_estimated_combined_residual_model( - &mut self.error_models, - outeq, - additive_sd, - proportional_sd, - ); - residual_diagnostic.optimizer_objective = Some(solution.objective); - residual_diagnostic.optimizer_converged = Some(solution.converged); - residual_diagnostic.optimizer_iterations = Some(solution.iterations); - residual_diagnostic.optimizer_termination = Some(solution.termination); - } - Err(error) => { - residual_diagnostic.update_rejected = true; - residual_diagnostic.optimizer_termination = Some(error.to_string()); - } - } - continue; - } - if let ResidualErrorModel::CorrelatedCombined { a, b, rho } = model { - match optimize_correlated_combined_residual( - &residual_observations[outeq], - a, - b, - rho, - self.error_models - .correlated_combined_component_estimated(outeq), - self.initialization.schedule.minimum_residual_sigma, - self.residual_optimizer_max_iterations as u64, - ) { - Ok(solution) => { - let component_estimated = self - .error_models - .correlated_combined_component_estimated(outeq); - let additive_sd = applied_combined_residual_component( - &self.initialization.schedule, - self.cycle, - a, - solution.additive_sd, - component_estimated[0], - ); - let proportional_sd = applied_combined_residual_component( - &self.initialization.schedule, - self.cycle, - b, - solution.proportional_sd, - component_estimated[1], - ); - let correlation = applied_correlated_residual_correlation( - &self.initialization.schedule, - self.cycle, - rho, - solution.correlation, - component_estimated[2], - ); - if !correlation.is_finite() || correlation <= -1.0 || correlation >= 1.0 { - residual_diagnostic.update_rejected = true; - residual_diagnostic.optimizer_termination = Some( - "correlated-combined residual update left (-1, 1)".to_string(), - ); - continue; - } - residual_diagnostic.combined_additive_collapse_warning = - combined_additive_sigma_collapsed(additive_sd, component_estimated[0]); - update_estimated_correlated_combined_residual_model( - &mut self.error_models, - outeq, - additive_sd, - proportional_sd, - correlation, - ); - residual_diagnostic.optimizer_objective = Some(solution.objective); - residual_diagnostic.optimizer_converged = Some(solution.converged); - residual_diagnostic.optimizer_iterations = Some(solution.iterations); - residual_diagnostic.optimizer_termination = Some(solution.termination); - } - Err(error) => { - residual_diagnostic.update_rejected = true; - residual_diagnostic.optimizer_termination = Some(error.to_string()); - } - } - continue; - } - let Some(candidate_sigma) = self - .residual_statistics - .output(outeq) - .and_then(|statistic| statistic.sigma()) - else { - residual_diagnostic.update_rejected = true; - continue; - }; - let previous_sigma = primary_sigma_parameter(&model); - let sigma = self.initialization.schedule.guarded_residual_sigma( - self.cycle, - previous_sigma, - candidate_sigma, - ); - update_estimated_simple_residual_model_with_sigma(&mut self.error_models, outeq, sigma); - } - if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { - diagnostics.residual_diagnostics = residual_diagnostics; - } - self.residual_sigmas = primary_sigma_parameters(self.error_models.models()); - self.refresh_subject_scores_from_chains()?; - self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); - self.update_iterate_average()?; - self.finalize_cycle_diagnostics()?; - Ok(()) - } - - fn update_iterate_average(&mut self) -> Result<()> { - if self.initialization.schedule.phase(self.cycle) != SaemPhase::Smoothing - || !matches!( - self.config.estimator_policy, - SaemEstimatorPolicy::AveragedIterates { .. } - ) - { - return Ok(()); - } - let population_phi = population_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - )?; - let residual_models = self - .error_models - .models() - .iter() - .map(|(output_index, model)| (output_index, *model)) - .collect::>(); - let residual_model_width = self.error_models.models().len(); - let Some(average) = self.iterate_average.as_mut() else { - self.iterate_average = Some(SaemIterateAverage { - population_phi, - covariate_betas: self.covariate_model.as_ref().map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect() - }), - omega: self.omega.clone(), - omega_iov: self.omega_iov.clone(), - residual_model_width, - residual_models, - start_cycle: self.cycle, - count: 1, - }); - return Ok(()); - }; - let next_count = average.count + 1; - for (index, value) in population_phi.iter().copied().enumerate() { - if self.initialization.estimated_parameters[index] { - average.population_phi[index] = - incremental_average(average.population_phi[index], value, next_count); - } - } - if let (Some(average_betas), Some(model)) = ( - average.covariate_betas.as_mut(), - self.covariate_model.as_ref(), - ) { - for (index, estimate) in model.estimates().iter().enumerate() { - if estimate.estimated() { - average_betas[index] = - incremental_average(average_betas[index], estimate.estimate(), next_count); - } - } - } - average_covariance( - &mut average.omega, - &self.omega, - self.initialization.omega.estimated_mask(), - next_count, - ); - if let (Some(average_iov), Some(current_iov), Some(specification)) = ( - average.omega_iov.as_mut(), - self.omega_iov.as_ref(), - self.initialization.omega_iov.as_ref(), - ) { - average_covariance( - average_iov, - current_iov, - specification.estimated_mask(), - next_count, - ); - } - if residual_model_width != average.residual_model_width - || residual_models.len() != average.residual_models.len() - { - anyhow::bail!("residual output declarations changed while accumulating SAEM averages"); - } - for ((average_output_index, previous), (output_index, current)) in - average.residual_models.iter_mut().zip(residual_models) - { - if *average_output_index != output_index { - anyhow::bail!( - "residual output declarations changed while accumulating SAEM averages" - ); - } - let estimated = self.error_models.is_estimated(output_index); - let components = self.error_models.combined_component_estimated(output_index); - let correlated_components = self - .error_models - .correlated_combined_component_estimated(output_index); - *previous = average_residual_model( - *previous, - current, - estimated, - components, - correlated_components, - next_count, - )?; - } - average.count = next_count; - Ok(()) - } - - fn install_iterate_average(&mut self) -> Result { - let policy = self.config.estimator_policy; - let Some(average) = self.iterate_average.clone() else { - tracing::info!("averaged SAEM estimate was not available; retaining terminal iterate"); - return Ok(SaemEstimatorMetadata { - policy, - ..SaemEstimatorMetadata::default() - }); - }; - let terminal_phi = population_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - )?; - validate_average_population(&average.population_phi, &self.initialization)?; - validate_average_covariance(&average.omega, &self.initialization.omega, "Omega")?; - if let (Some(matrix), Some(specification)) = ( - average.omega_iov.as_ref(), - self.initialization.omega_iov.as_ref(), - ) { - validate_average_covariance(matrix, specification, "Omega_IOV")?; - } - validate_average_residuals( - average.residual_model_width, - &average.residual_models, - &self.error_models, - )?; - - self.population_parameters = population_psi( - &average.population_phi, - &self.initialization.parameter_scales, - )?; - if let (Some(model), Some(beta_values), Some(old_means)) = ( - self.covariate_model.as_ref(), - average.covariate_betas.as_ref(), - self.subject_mu_phi.as_ref(), - ) { - let averaged_model = model.with_estimates(beta_values)?; - let new_rows = averaged_model.subject_population_parameters( - &average.population_phi, - &self.initialization.parameter_scales, - )?; - let new_means = new_rows - .iter() - .map(|row| row.phi().to_vec()) - .collect::>(); - for (subject_index, chains) in self.etas.iter_mut().enumerate() { - let old_random = self - .initialization - .random_effect_indices - .iter() - .map(|index| old_means[subject_index][*index]) - .collect::>(); - let new_random = self - .initialization - .random_effect_indices - .iter() - .map(|index| new_means[subject_index][*index]) - .collect::>(); - for eta in chains { - rebase_eta(eta, &old_random, &new_random)?; - } - } - self.covariate_model = Some(averaged_model); - self.subject_mu_phi = Some(new_means); - } else { - for (eta_index, parameter_index) in self - .initialization - .random_effect_indices - .iter() - .copied() - .enumerate() - { - let shift = terminal_phi[parameter_index] - average.population_phi[parameter_index]; - for subject_chains in &mut self.etas { - for eta in subject_chains { - eta[eta_index] += shift; - } - } - } - } - self.omega = average.omega; - self.omega_iov = average.omega_iov; - for (output_index, model) in average.residual_models { - match model { - ResidualErrorModel::Combined { a, b } => update_estimated_combined_residual_model( - &mut self.error_models, - output_index, - a, - b, - ), - ResidualErrorModel::CorrelatedCombined { a, b, rho } => { - update_estimated_correlated_combined_residual_model( - &mut self.error_models, - output_index, - a, - b, - rho, - ) - } - ResidualErrorModel::Constant { .. } - | ResidualErrorModel::Proportional { .. } - | ResidualErrorModel::Exponential { .. } => { - update_estimated_simple_residual_model_with_sigma( - &mut self.error_models, - output_index, - primary_sigma_parameter(&model), - ) - } - } - } - self.residual_sigmas = primary_sigma_parameters(self.error_models.models()); - self.refresh_subject_scores_from_chains()?; - self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); - tracing::info!( - start_cycle = average.start_cycle, - averaged_iterations = average.count, - "installed averaged SAEM estimate" - ); - Ok(SaemEstimatorMetadata { - policy, - average_applied: true, - averaging_start_cycle: Some(average.start_cycle), - averaged_iterations: average.count, - }) - } - - fn residual_error_estimates(&self) -> Vec { - self.error_models - .models() - .iter() - .map(|(output_index, model)| { - let model = *model; - let combined_components = - self.error_models.combined_component_estimated(output_index); - let correlated_components = self - .error_models - .correlated_combined_component_estimated(output_index); - let is_combined = matches!(model, ResidualErrorModel::Combined { .. }); - let is_correlated = matches!(model, ResidualErrorModel::CorrelatedCombined { .. }); - ResidualErrorEstimate { - output: self - .error_models - .output_name(output_index) - .map(str::to_owned) - .expect("declared residual models have output names"), - output_index, - model, - estimated: self.error_models.is_estimated(output_index), - combined_additive_estimated: if is_combined { - Some(combined_components[0]) - } else { - is_correlated.then_some(correlated_components[0]) - }, - combined_proportional_estimated: if is_combined { - Some(combined_components[1]) - } else { - is_correlated.then_some(correlated_components[1]) - }, - correlation_estimated: is_correlated.then_some(correlated_components[2]), - } - }) - .collect() - } - - fn finalize_cycle_diagnostics(&mut self) -> Result<()> { - let population_parameters = self.population_parameters.clone(); - let omega = self.omega.clone(); - let omega_iov = self.omega_iov.clone(); - let residual_error_estimates = self.residual_error_estimates(); - let conditional_negative_log_likelihood = self.negative_log_likelihood; - let eta_log_prior = self.subject_log_priors.iter().sum(); - let kappa_log_prior = self.subject_kappa_log_priors.iter().sum(); - let (omega_relative_spd_margin, omega_iov_relative_spd_margin) = - if self.config.covariance_stability.is_some() { - let initial_omega = self.initialization.omega.initial(); - let omega_margin = (initial_omega.nrows() > 0) - .then(|| relative_spd_margin(&omega, initial_omega)) - .transpose()?; - let omega_iov_margin = - match (self.initialization.omega_iov.as_ref(), omega_iov.as_ref()) { - (Some(specification), Some(matrix)) - if specification.initial().nrows() > 0 => - { - Some(relative_spd_margin(matrix, specification.initial())?) - } - _ => None, - }; - (omega_margin, omega_iov_margin) - } else { - (None, None) - }; - let covariate_betas = self.covariate_model.as_ref().map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect() - }); - let covariate_beta_estimated = self.covariate_model.as_ref().map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimated()) - .collect() - }); - if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { - diagnostics.population_parameters = population_parameters; - diagnostics.omega = omega; - diagnostics.omega_iov = omega_iov; - diagnostics.omega_relative_spd_margin = omega_relative_spd_margin; - diagnostics.omega_iov_relative_spd_margin = omega_iov_relative_spd_margin; - diagnostics.residual_error_estimates = residual_error_estimates; - diagnostics.conditional_negative_log_likelihood = conditional_negative_log_likelihood; - diagnostics.eta_log_prior = eta_log_prior; - diagnostics.kappa_log_prior = kappa_log_prior; - diagnostics.covariate_betas = covariate_betas; - diagnostics.covariate_beta_estimated = covariate_beta_estimated; - } - Ok(()) - } - - fn update_population_and_recenter_etas(&mut self) -> Result> { - let old_population_phi = population_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - )?; - let mut new_population_phi = old_population_phi.clone(); - for (parameter_index, parameter_phi) in new_population_phi.iter_mut().enumerate() { - if self.initialization.estimated_parameters[parameter_index] - && self - .initialization - .random_effect_indices - .contains(¶meter_index) - { - *parameter_phi = self.sufficient_statistics.mean_phi[parameter_index]; - } - } - - for (eta_index, parameter_index) in self - .initialization - .random_effect_indices - .iter() - .copied() - .enumerate() - { - let realized_shift = - new_population_phi[parameter_index] - old_population_phi[parameter_index]; - for subject_chains in &mut self.etas { - for eta in subject_chains { - eta[eta_index] -= realized_shift; - } - } - } - self.population_parameters = - population_psi(&new_population_phi, &self.initialization.parameter_scales)?; - Ok(new_population_phi) - } - - fn update_covariate_population_and_recenter_etas(&mut self) -> Result> { - let model = self - .covariate_model - .as_ref() - .expect("covariate update requires a resolved model") - .clone(); - let statistics = self - .covariate_statistics - .as_ref() - .expect("covariate update requires sufficient statistics") - .clone(); - let q = self.initialization.random_effect_indices.len(); - let old_population_phi = population_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - )?; - let old_subject_mu = self - .subject_mu_phi - .as_ref() - .expect("covariate update requires subject means") - .clone(); - - let free_intercepts = self - .initialization - .random_effect_indices - .iter() - .copied() - .filter(|index| self.initialization.estimated_parameters[*index]) - .collect::>(); - let free_effects = model - .estimates() - .iter() - .enumerate() - .filter_map(|(index, estimate)| { - (estimate.estimated() - && self - .initialization - .random_effect_indices - .contains(&model.parameter_indices()[index])) - .then_some(index) - }) - .collect::>(); - let width = free_intercepts.len() + free_effects.len(); - let random_row = self - .initialization - .random_effect_indices - .iter() - .enumerate() - .map(|(row, parameter)| (*parameter, row)) - .collect::>(); - let mut designs = Vec::with_capacity(model.subject_design().len()); - let mut offsets = Vec::with_capacity(model.subject_design().len()); - for subject in model.subject_design() { - let mut design = Array2::zeros((q, width)); - let mut offset = vec![0.0; q]; - for (row, parameter_index) in self - .initialization - .random_effect_indices - .iter() - .copied() - .enumerate() - { - if let Some(column) = free_intercepts - .iter() - .position(|index| *index == parameter_index) - { - design[[row, column]] = 1.0; - } else { - offset[row] = old_population_phi[parameter_index]; - } - } - for (effect_index, value) in subject.values().iter().copied().enumerate() { - let parameter_index = model.parameter_indices()[effect_index]; - let Some(&row) = random_row.get(¶meter_index) else { - continue; - }; - if let Some(effect_column) = - free_effects.iter().position(|index| *index == effect_index) - { - design[[row, free_intercepts.len() + effect_column]] = value; - } else { - offset[row] += value * model.estimates()[effect_index].estimate(); - } - } - designs.push(design); - offsets.push(offset); - } - - let solution = if width == 0 { - Vec::new() - } else { - solve_covariate_gls(CovariateGlsProblem { - design: &designs, - expected_phi: &statistics.expected_phi, - offset: &offsets, - omega: &self.omega, - })? - }; - let mut new_population_phi = old_population_phi; - for (column, parameter_index) in free_intercepts.iter().copied().enumerate() { - new_population_phi[parameter_index] = solution[column]; - } - let mut beta_values = model - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect::>(); - for (column, effect_index) in free_effects.iter().copied().enumerate() { - beta_values[effect_index] = solution[free_intercepts.len() + column]; - } - let updated_model = model.with_estimates(&beta_values)?; - let subject_population = updated_model.subject_population_parameters( - &new_population_phi, - &self.initialization.parameter_scales, - )?; - let new_subject_mu = subject_population - .iter() - .map(|row| row.phi().to_vec()) - .collect::>(); - for (subject_index, subject_chains) in self.etas.iter_mut().enumerate() { - let old_random = self - .initialization - .random_effect_indices - .iter() - .map(|index| old_subject_mu[subject_index][*index]) - .collect::>(); - let new_random = self - .initialization - .random_effect_indices - .iter() - .map(|index| new_subject_mu[subject_index][*index]) - .collect::>(); - for eta in subject_chains { - rebase_eta(eta, &old_random, &new_random)?; - } - } - let subject_mu_random = new_subject_mu - .iter() - .map(|mean| { - self.initialization - .random_effect_indices - .iter() - .map(|index| mean[*index]) - .collect::>() - }) - .collect::>(); - let candidate = if q == 0 { - Array2::zeros((0, 0)) - } else { - subject_centered_omega( - &statistics.global_second_moment, - &statistics.expected_phi, - &subject_mu_random, - )? - }; - self.population_parameters = - population_psi(&new_population_phi, &self.initialization.parameter_scales)?; - self.subject_mu_phi = Some(new_subject_mu); - self.covariate_model = Some(updated_model); - Ok(candidate) - } - - fn adapt_proposal_step_sizes(&mut self) { - if self.steps_since_adapt < self.adapt_interval { - return; - } - - for parameter_index in 0..self.proposal_step_sizes.len() { - let proposed = self.adaptation_proposal_counts[parameter_index].max(1); - let acceptance_rate = - self.adaptation_accept_counts[parameter_index] as f64 / proposed as f64; - self.proposal_step_sizes[parameter_index] = adapt_component_step_size( - self.proposal_step_sizes[parameter_index], - acceptance_rate, - ); - self.adaptation_accept_counts[parameter_index] = 0; - self.adaptation_proposal_counts[parameter_index] = 0; - } - for subject_index in 0..self.eta_block_step_sizes.len() { - let proposed = self.eta_block_adaptation_proposal_counts[subject_index].max(1); - let acceptance_rate = - self.eta_block_adaptation_accept_counts[subject_index] as f64 / proposed as f64; - self.eta_block_step_sizes[subject_index] = adapt_block_step_size( - self.eta_block_step_sizes[subject_index], - acceptance_rate, - ETA_BLOCK_TARGET_ACCEPTANCE, - ); - self.eta_block_adaptation_accept_counts[subject_index] = 0; - self.eta_block_adaptation_proposal_counts[subject_index] = 0; - } - for subject_index in 0..self.kappa_proposal_step_sizes.len() { - let proposed = self.kappa_adaptation_proposal_counts[subject_index].max(1); - let acceptance_rate = - self.kappa_adaptation_accept_counts[subject_index] as f64 / proposed as f64; - self.kappa_proposal_step_sizes[subject_index] = adapt_block_step_size( - self.kappa_proposal_step_sizes[subject_index], - acceptance_rate, - KAPPA_BLOCK_TARGET_ACCEPTANCE, - ); - self.kappa_adaptation_accept_counts[subject_index] = 0; - self.kappa_adaptation_proposal_counts[subject_index] = 0; - } - self.steps_since_adapt = 0; - } - - fn component_random_walk_eta( - &mut self, - current_eta: &[f64], - parameter_index: usize, - ) -> Vec { - let mut proposed_eta = current_eta.to_vec(); - proposed_eta[parameter_index] += - self.proposal_step_sizes[parameter_index] * self.standard_normal(); - proposed_eta - } - - fn block_random_walk_eta( - &mut self, - current_eta: &[f64], - subject_index: usize, - ) -> Result> { - let lower = cholesky_lower(&self.omega)?; - let standard_normals = (0..current_eta.len()) - .map(|_| self.standard_normal()) - .collect::>(); - correlated_random_walk( - current_eta, - &lower, - &standard_normals, - self.eta_block_step_sizes[subject_index], - ) - } - - fn block_random_walk_kappa( - &mut self, - current_kappa: &[f64], - subject_index: usize, - ) -> Result> { - let omega_iov = self - .omega_iov - .as_ref() - .ok_or_else(|| anyhow::anyhow!("kappa proposal requires configured omega_iov"))?; - let lower = cholesky_lower(omega_iov)?; - let standard_normals = (0..current_kappa.len()) - .map(|_| self.standard_normal()) - .collect::>(); - correlated_random_walk( - current_kappa, - &lower, - &standard_normals, - self.kappa_proposal_step_sizes[subject_index], - ) - } - - fn standard_normal(&mut self) -> f64 { - let u1 = self.rng.random::().max(f64::MIN_POSITIVE); - let u2 = self.rng.random::(); - (-2.0_f64 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() - } - - fn accept_proposal(&mut self, log_acceptance_ratio: f64) -> bool { - if !log_acceptance_ratio.is_finite() { - return false; - } - if log_acceptance_ratio >= 0.0 { - return true; - } - self.rng.random::().max(f64::MIN_POSITIVE).ln() < log_acceptance_ratio - } - - fn individual_parameters(&self, subject_index: usize, chain_index: usize) -> Vec { - self.individual_parameters_from_eta(subject_index, &self.etas[subject_index][chain_index]) - .expect("stored eta should match parameter dimensions") - } - - fn individual_parameters_from_eta( - &self, - subject_index: usize, - eta: &[f64], - ) -> Result> { - match self.subject_mu_phi.as_ref() { - Some(means) => individual_psi_from_subject_mean( - &means[subject_index], - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - ), - None => individual_psi( - &self.population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - ), - } - } - - fn individual_phi(&self, subject_index: usize, chain_index: usize) -> Result> { - match self.subject_mu_phi.as_ref() { - Some(means) => individual_phi_from_subject_mean( - &means[subject_index], - &self.initialization.random_effect_indices, - &self.etas[subject_index][chain_index], - ), - None => individual_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &self.etas[subject_index][chain_index], - ), - } - } - - fn current_phi_statistics(&self) -> Result { - let mut subject_phi = Vec::with_capacity( - self.initialization.subject_ids.len() * self.initialization.n_chains, - ); - for subject_index in 0..self.initialization.subject_ids.len() { - for chain_index in 0..self.initialization.n_chains { - subject_phi.push(self.individual_phi(subject_index, chain_index)?); - } - } - PhiSufficientStatistics::from_subject_phi(&subject_phi) - } - - fn current_covariate_statistics(&self) -> Result { - let mut subjects = Vec::with_capacity(self.initialization.subject_ids.len()); - for subject_index in 0..self.initialization.subject_ids.len() { - let mut chains = Vec::with_capacity(self.initialization.n_chains); - for chain_index in 0..self.initialization.n_chains { - let phi = self.individual_phi(subject_index, chain_index)?; - chains.push( - self.initialization - .random_effect_indices - .iter() - .map(|index| phi[*index]) - .collect(), - ); - } - subjects.push(chains); - } - CovariateSufficientStatistics::from_subject_chains(&subjects) - } - - fn current_residual_statistics_and_information( - &self, - ) -> Result<( - ResidualSufficientStatistics, - std::result::Result, String>, - )> { - let mut total = ResidualSufficientStatistics::zero(self.error_models.len()); - let layout = self.information.layout(); - let mut replicates = (0..self.initialization.n_chains) - .map(|_| CompleteDerivative::zero(layout.len())) - .collect::>(); - let mut information_error = None; - // Preserve the established subject-major/chain-minor prediction and - // accumulation order so this diagnostic cannot alter fit trajectories. - for subject_index in 0..self.initialization.subject_ids.len() { - let subject = self.data.subjects()[subject_index]; - for (chain_index, derivative) in replicates.iter_mut().enumerate() { - if information_error.is_none() { - let derivative_result = match self.covariate_model.as_ref() { - Some(model) => derivative.add_covariate_population_prior( - &self.etas[subject_index][chain_index], - &self.omega, - &self.initialization.random_effect_indices, - model.parameter_indices(), - model.subject_design()[subject_index].values(), - layout, - ), - None => derivative.add_population_prior( - &self.etas[subject_index][chain_index], - &self.omega, - &self.initialization.random_effect_indices, - layout, - ), - }; - if let Err(error) = derivative_result { - information_error = Some(error.to_string()); - } - } - if self.omega_iov.is_none() { - let parameters = self.individual_parameters(subject_index, chain_index); - let predictions = self - .equation - .estimate_predictions_dense(subject, ¶meters)?; - total.add_assign(&ResidualSufficientStatistics::from_predictions( - &predictions, - self.error_models.models(), - )); - if information_error.is_none() { - if let Err(error) = - derivative.add_predictions(&predictions, &self.error_models, layout) - { - information_error = Some(error.to_string()); - } - } - continue; - } - for (occasion, kappa) in subject - .occasions() - .iter() - .zip(&self.kappas[subject_index][chain_index]) - { - if information_error.is_none() { - if let Some(omega_iov) = self.omega_iov.as_ref() { - if let Err(error) = derivative.add_iov_prior(kappa, omega_iov, layout) { - information_error = Some(error.to_string()); - } - } - } - let parameters = match self.subject_mu_phi.as_ref() { - Some(means) => occasion_psi_from_subject_mean( - &means[subject_index], - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &self.etas[subject_index][chain_index], - &self.initialization.iov_effect_indices, - kappa, - ), - None => occasion_psi( - &self.population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &self.etas[subject_index][chain_index], - &self.initialization.iov_effect_indices, - kappa, - ), - }?; - let occasion_subject = - Subject::from_occasions(subject.id().to_owned(), vec![occasion.clone()]); - let predictions = self - .equation - .estimate_predictions_dense(&occasion_subject, ¶meters)?; - total.add_assign(&ResidualSufficientStatistics::from_predictions( - &predictions, - self.error_models.models(), - )); - if information_error.is_none() { - if let Err(error) = - derivative.add_predictions(&predictions, &self.error_models, layout) - { - information_error = Some(error.to_string()); - } - } - } - } - } - Ok(( - total, - match information_error { - Some(error) => Err(error), - None => Ok(replicates), - }, - )) - } - - #[cfg(test)] - fn current_residual_statistics(&self) -> Result { - self.current_residual_statistics_and_information() - .map(|(statistics, _)| statistics) - } - - fn refresh_subject_scores_from_chains(&mut self) -> Result<()> { - let n_chains = self.initialization.n_chains as f64; - let mut subject_log_likelihoods = vec![0.0; self.initialization.subject_ids.len()]; - let mut subject_log_priors = vec![0.0; self.initialization.subject_ids.len()]; - let mut subject_kappa_log_priors = vec![0.0; self.initialization.subject_ids.len()]; - for subject_index in 0..self.initialization.subject_ids.len() { - for chain_index in 0..self.initialization.n_chains { - let score = self.score_subject_latents( - subject_index, - &self.etas[subject_index][chain_index], - &self.kappas[subject_index][chain_index], - )?; - subject_log_likelihoods[subject_index] += score.log_likelihood / n_chains; - subject_log_priors[subject_index] += score.eta_log_prior / n_chains; - subject_kappa_log_priors[subject_index] += score.kappa_log_prior / n_chains; - } - } - self.subject_log_likelihoods = subject_log_likelihoods; - self.subject_log_priors = subject_log_priors; - self.subject_kappa_log_priors = subject_kappa_log_priors; - Ok(()) - } - - fn score_subject_latents( - &self, - subject_index: usize, - eta: &[f64], - kappas: &[Vec], - ) -> Result { - self.score_subject_latents_at(subject_index, eta, kappas, None) - } - - fn non_iiv_coordinate_layout(&self) -> NonIivCoordinateLayout { - let population_indices = self - .initialization - .estimated_parameters - .iter() - .enumerate() - .filter_map(|(index, estimated)| { - (*estimated && !self.initialization.random_effect_indices.contains(&index)) - .then_some(index) - }) - .collect(); - let covariate_indices = self - .covariate_model - .as_ref() - .map(|model| { - model - .estimates() - .iter() - .enumerate() - .filter_map(|(index, estimate)| { - (estimate.estimated() - && !self - .initialization - .random_effect_indices - .contains(&model.parameter_indices()[index])) - .then_some(index) - }) - .collect() - }) - .unwrap_or_default(); - NonIivCoordinateLayout { - population_indices, - covariate_indices, - } - } - - fn non_iiv_population_update_active(&self, parameter_step: f64) -> bool { - let Some(post_burn_start) = self.initialization.schedule.pure_burn_in.checked_add(1) else { - return false; - }; - let first_active_cycle = self - .initialization - .schedule - .variance_floor_iterations - .max(post_burn_start); - parameter_step.is_finite() && parameter_step > 0.0 && self.cycle >= first_active_cycle - } - - fn pack_non_iiv_coordinates(&self, layout: &NonIivCoordinateLayout) -> Result> { - let population = population_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - )?; - let mut coordinates = layout - .population_indices - .iter() - .map(|index| population[*index]) - .collect::>(); - if let Some(model) = self.covariate_model.as_ref() { - coordinates.extend( - layout - .covariate_indices - .iter() - .map(|index| model.estimates()[*index].estimate()), - ); - } - Ok(coordinates) - } - - fn non_iiv_candidate_components( - &self, - layout: &NonIivCoordinateLayout, - coordinates: &[f64], - ) -> Result { - if coordinates.len() != layout.len() || coordinates.iter().any(|value| !value.is_finite()) { - anyhow::bail!("non-IIV population coordinate width or value is invalid"); - } - let mut population = population_phi( - &self.population_parameters, - &self.initialization.parameter_scales, - )?; - for (coordinate, parameter_index) in coordinates - .iter() - .copied() - .zip(layout.population_indices.iter().copied()) - { - population[parameter_index] = coordinate; - } - let population_parameters = - population_psi(&population, &self.initialization.parameter_scales)?; - if !parameters_are_strictly_in_domain( - &population_parameters, - &self.initialization.parameter_scales, - ) { - anyhow::bail!("non-IIV population candidate violates its declared parameter domain"); - } - - let covariate_model = match self.covariate_model.as_ref() { - Some(model) => { - let mut values = model - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect::>(); - for (coordinate, effect_index) in coordinates[layout.population_indices.len()..] - .iter() - .copied() - .zip(layout.covariate_indices.iter().copied()) - { - values[effect_index] = coordinate; - } - Some(model.with_estimates(&values)?) - } - None if layout.covariate_indices.is_empty() => None, - None => anyhow::bail!("non-IIV covariate coordinates lack a covariate model"), - }; - let subject_rows = covariate_model - .as_ref() - .map(|model| { - model.subject_population_parameters( - &population, - &self.initialization.parameter_scales, - ) - }) - .transpose()?; - if subject_rows.as_ref().is_some_and(|rows| { - rows.iter().any(|row| { - !parameters_are_strictly_in_domain(row.psi(), &self.initialization.parameter_scales) - }) - }) { - anyhow::bail!("non-IIV covariate candidate violates a declared parameter domain"); - } - let subject_means = subject_rows.map(|rows| { - rows.into_iter() - .map(|row| row.phi().to_vec()) - .collect::>() - }); - Ok((population_parameters, covariate_model, subject_means)) - } - - fn non_iiv_observation_nll( - &self, - layout: &NonIivCoordinateLayout, - coordinates: &[f64], - ) -> Result { - let (population_parameters, _covariate_model, subject_means) = - self.non_iiv_candidate_components(layout, coordinates)?; - let chain_count = self.initialization.n_chains; - if chain_count == 0 { - anyhow::bail!("non-IIV observation objective requires at least one chain"); - } - let mut objective = 0.0; - for subject_index in 0..self.initialization.subject_ids.len() { - let subject = self.data.subjects()[subject_index]; - let subject_mean = subject_means - .as_ref() - .map(|means| means[subject_index].as_slice()); - for chain_index in 0..chain_count { - let eta = &self.etas[subject_index][chain_index]; - let log_likelihood = if self.omega_iov.is_none() { - let parameters = match subject_mean { - Some(mean) => individual_psi_from_subject_mean( - mean, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - ), - None => individual_psi( - &population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - ), - }?; - parametric_subject_log_likelihood( - &self.equation, - subject, - ¶meters, - self.error_models.models(), - ) - } else { - let kappas = &self.kappas[subject_index][chain_index]; - if kappas.len() != subject.occasions().len() { - anyhow::bail!("non-IIV objective kappa/occasion dimension mismatch"); - } - let mut value = 0.0; - for (occasion, kappa) in subject.occasions().iter().zip(kappas) { - let parameters = match subject_mean { - Some(mean) => occasion_psi_from_subject_mean( - mean, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - &self.initialization.iov_effect_indices, - kappa, - ), - None => occasion_psi( - &population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - &self.initialization.iov_effect_indices, - kappa, - ), - }?; - let occasion_value = parametric_occasion_log_likelihood( - &self.equation, - subject.id(), - occasion, - ¶meters, - self.error_models.models(), - ); - if !occasion_value.is_finite() { - anyhow::bail!("non-IIV observation objective is non-finite"); - } - value += occasion_value; - } - value - }; - if !log_likelihood.is_finite() { - anyhow::bail!("non-IIV observation objective is non-finite"); - } - objective -= log_likelihood / chain_count as f64; - } - } - if !objective.is_finite() { - anyhow::bail!("non-IIV observation objective is non-finite"); - } - Ok(objective) - } - - fn update_non_iiv_population(&mut self, parameter_step: f64) -> Result { - let layout = self.non_iiv_coordinate_layout(); - if layout.is_empty() || !self.non_iiv_population_update_active(parameter_step) { - return Ok(false); - } - - let initial = self.pack_non_iiv_coordinates(&layout)?; - let initial_objective = self.non_iiv_observation_nll(&layout, &initial)?; - if !initial_objective.is_finite() { - anyhow::bail!("current non-IIV observation objective is non-finite"); - } - - let mut simplex = Vec::with_capacity(initial.len() + 1); - simplex.push(initial.clone()); - for coordinate in 0..initial.len() { - let mut point = initial.clone(); - point[coordinate] += 0.1 * initial[coordinate].abs().max(1.0); - simplex.push(point); - } - let solver = NelderMead::new(simplex).with_sd_tolerance(NON_IIV_OPTIMIZER_SD_TOLERANCE)?; - let execution = Executor::new( - NonIivPopulationCost { - state: self, - layout: &layout, - }, - solver, - ) - .configure(|state| state.max_iters(NON_IIV_OPTIMIZER_MAX_ITERATIONS)) - .run(); - let result = match execution { - Ok(result) => result, - Err(error) => { - tracing::warn!( - error = %error, - "Non-IIV population optimizer failed; retaining current state" - ); - return Ok(false); - } - }; - let Some(candidate) = result.state.best_param.as_ref() else { - return Ok(false); - }; - let candidate_objective = match self.non_iiv_observation_nll(&layout, candidate) { - Ok(value) if value.is_finite() => value, - _ => return Ok(false), - }; - if !non_iiv_candidate_improves(initial_objective, candidate_objective) { - return Ok(false); - } - - let applied = initial - .iter() - .zip(candidate) - .map(|(current, target)| current + parameter_step * (target - current)) - .collect::>(); - match self.non_iiv_observation_nll(&layout, &applied) { - Ok(value) if value.is_finite() => {} - _ => return Ok(false), - } - - let (population_parameters, covariate_model, subject_means) = - self.non_iiv_candidate_components(&layout, &applied)?; - self.population_parameters = population_parameters; - self.covariate_model = covariate_model; - self.subject_mu_phi = subject_means; - Ok(true) - } - - fn score_subject_latents_at( - &self, - subject_index: usize, - eta: &[f64], - kappas: &[Vec], - candidate: Option<&DiagnosticCandidate>, - ) -> Result { - if eta.len() != self.initialization.random_effect_indices.len() { - anyhow::bail!( - "eta has {} values but there are {} random effects", - eta.len(), - self.initialization.random_effect_indices.len() - ); - } - - let subject = self.data.subjects()[subject_index]; - let population_parameters = candidate - .map_or(self.population_parameters.as_slice(), |value| { - value.population_parameters.as_slice() - }); - let omega = candidate.map_or(&self.omega, |value| &value.omega); - let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); - let error_models = candidate.map_or(&self.error_models, |value| &value.error_models); - let candidate_covariates = candidate - .and_then(|value| value.covariate_model.as_ref()) - .or(self.covariate_model.as_ref()); - let calculated_subject_mu = if candidate.is_some() { - candidate_covariates - .map(|model| { - let phi = population_phi( - population_parameters, - &self.initialization.parameter_scales, - )?; - Ok::<_, anyhow::Error>( - model.subject_population_parameters( - &phi, - &self.initialization.parameter_scales, - )?[subject_index] - .phi() - .to_vec(), - ) - }) - .transpose()? - } else { - None - }; - let subject_mu = calculated_subject_mu.as_deref().or_else(|| { - self.subject_mu_phi - .as_ref() - .map(|means| means[subject_index].as_slice()) - }); - let eta_log_prior = eta_log_prior_from_omega(eta, omega)?; - if omega_iov.is_none() { - let parameters = match subject_mu { - Some(mean) => individual_psi_from_subject_mean( - mean, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - ), - None => individual_psi( - population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - ), - }?; - return Ok(SubjectPosteriorScore { - log_likelihood: parametric_subject_log_likelihood( - &self.equation, - subject, - ¶meters, - error_models.models(), - ), - eta_log_prior, - kappa_log_prior: 0.0, - }); - } - - if kappas.len() != subject.occasions().len() { - anyhow::bail!( - "subject '{}' has {} occasions but {} kappa states", - subject.id(), - subject.occasions().len(), - kappas.len() - ); - } - let omega_iov = omega_iov.expect("checked above"); - let mut log_likelihood = 0.0; - let mut kappa_log_prior = 0.0; - for (occasion, kappa) in subject.occasions().iter().zip(kappas) { - let parameters = match subject_mu { - Some(mean) => occasion_psi_from_subject_mean( - mean, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - &self.initialization.iov_effect_indices, - kappa, - ), - None => occasion_psi( - population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - eta, - &self.initialization.iov_effect_indices, - kappa, - ), - }?; - let occasion_log_likelihood = parametric_occasion_log_likelihood( - &self.equation, - subject.id(), - occasion, - ¶meters, - error_models.models(), - ); - if !occasion_log_likelihood.is_finite() { - log_likelihood = f64::NEG_INFINITY; - } else if log_likelihood.is_finite() { - log_likelihood += occasion_log_likelihood; - } - kappa_log_prior += eta_log_prior_from_omega(kappa, omega_iov)?; - } - - Ok(SubjectPosteriorScore { - log_likelihood, - eta_log_prior, - kappa_log_prior, - }) - } - - fn proposal_log_acceptance_ratio( - &self, - subject_index: usize, - chain_index: usize, - proposed_eta: &[f64], - ) -> Result { - let current = self.score_subject_latents( - subject_index, - &self.etas[subject_index][chain_index], - &self.kappas[subject_index][chain_index], - )?; - let proposed = self.score_subject_latents( - subject_index, - proposed_eta, - &self.kappas[subject_index][chain_index], - )?; - Ok(current.log_acceptance_ratio(proposed)) - } - - fn kappa_proposal_log_acceptance_ratio( - &self, - subject_index: usize, - chain_index: usize, - occasion_index: usize, - proposed_kappa: &[f64], - ) -> Result { - let current_kappas = &self.kappas[subject_index][chain_index]; - let current = self.score_subject_latents( - subject_index, - &self.etas[subject_index][chain_index], - current_kappas, - )?; - let mut proposed_kappas = current_kappas.clone(); - proposed_kappas[occasion_index] = proposed_kappa.to_vec(); - let proposed = self.score_subject_latents( - subject_index, - &self.etas[subject_index][chain_index], - &proposed_kappas, - )?; - Ok(current.log_acceptance_ratio(proposed)) - } - - fn markov_variance_diagnostics( - &self, - estimator: &SaemEstimatorMetadata, - information: &InformationDiagnostics, - ) -> MarkovSimulationVarianceDiagnostics { - self.markov_variance_diagnostics_with_seed(estimator, information, None, None) - } - - /// Frozen-kernel diagnostic with an optional deterministic seed override. - /// - /// The override gives each operational checkpoint its own deterministic - /// stream; `None` preserves the exact - /// post-fit path seeded by the diagnostic configuration. - fn markov_variance_diagnostics_with_seed( - &self, - estimator: &SaemEstimatorMetadata, - information: &InformationDiagnostics, - seed_override: Option, - candidate: Option<&DiagnosticCandidate>, - ) -> MarkovSimulationVarianceDiagnostics { - let Some(config) = self.config.markov_simulation_variance else { - return MarkovSimulationVarianceDiagnostics::disabled(); - }; - let diagnostic_seed = seed_override.unwrap_or(config.seed); - let cd = config.diagnostic_chains; - let cf = self.initialization.n_chains; - let mut diagnostic = MarkovSimulationVarianceDiagnostics { - config: Some(config), - coordinates: information.coordinates.clone(), - chain_count: cd, - n_avg: estimator.averaged_iterations, - chains: Vec::new(), - grand_score_mean: Vec::new(), - lambda: Vec::new(), - lambda_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, - xi: Vec::new(), - xi_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, - simulation_covariance: Vec::new(), - simulation_covariance_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, - status: MarkovSimulationVarianceStatus::AssumptionsUnverified, - assumptions: MARKOV_VARIANCE_ASSUMPTIONS.into(), - rank_diagnostics: RankMixingDiagnostics { - diagnostic_chains: cd, - draws_per_chain: config.draws_per_chain, - original_chains: cf, - traces: Vec::new(), - lrv_per_chain: Vec::new(), - lrv_chain_statuses: Vec::new(), - diagnostic_mean_lrv: None, - operational_lrv: None, - max_trace_bytes: 0, - accounted_peak_trace_bytes_required: 0, - accounted_peak_trace_bytes_used: 0, - worst_rhat: None, - min_bulk_ess: None, - min_avg_ess_per_split_chain: None, - assumptions: MARKOV_VARIANCE_ASSUMPTIONS.into(), - status: RankDiagnosticStatus::Disabled, - }, - }; - let width = information.coordinates.len(); - let information_eligible = estimator.average_applied - && matches!(information.status, InformationStatus::Available) - && width > 0; - let observed_information = if information_eligible { - match matrix_from_rows(&information.observed_information, width) { - Ok(matrix) => Some(matrix), - Err(_) => { - diagnostic.xi_status = MarkovSimulationVarianceStatus::CoordinateMismatch; - None - } - } - } else { - None - }; - if self.initialization.random_effect_indices.is_empty() - && self.initialization.iov_effect_indices.is_empty() - { - let zero = Array2::zeros((width, width)); - diagnostic.lambda = rows(&zero); - diagnostic.lambda_status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; - diagnostic.xi = rows(&zero); - diagnostic.xi_status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; - diagnostic.simulation_covariance = rows(&zero); - diagnostic.simulation_covariance_status = - MarkovSimulationVarianceStatus::ExactZeroNoLatentState; - diagnostic.status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; - diagnostic.rank_diagnostics.status = RankDiagnosticStatus::NoLatent; - diagnostic - .rank_diagnostics - .lrv_chain_statuses - .fill(RankDiagnosticStatus::NoLatent); - diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; - return diagnostic; - } - - // ── Pre-execution byte-cap check (checked) ─────────────────────── - let trace_shape = self - .initialization - .random_effect_indices - .len() - .checked_mul(self.initialization.subject_ids.len()) - .and_then(|n_eta| { - self.initialization - .occasion_counts - .iter() - .try_fold(0usize, |total, count| total.checked_add(*count)) - .and_then(|occasions| { - occasions - .checked_mul(self.initialization.iov_effect_indices.len()) - .and_then(|n_kappa| width.checked_add(n_eta)?.checked_add(n_kappa)) - }) - }); - let Some(n_traces) = trace_shape else { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::TraceMemoryAccountingOverflow, - MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, - ); - diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; - return diagnostic; - }; - // Deterministic requested-capacity accounting. `traces` is nested - // coordinate-major storage, so its heap-resident Vec headers count in - // addition to every f64 leaf payload. The peak adds the larger of: - // (a) one nested draw-major score view, or (b) a conservative upper - // bound for the live rank/folding/ESS workspaces. The latter is eight - // payload-widths per retained draw (including the 24-byte ranked tuple) - // plus sixteen Vec headers per chain. This upper-bounds all capacities - // explicitly requested by the current rank helpers; allocator metadata - // and allocator size-class rounding are intentionally not claimed. - let vec_header = std::mem::size_of::>(); - let f64_bytes = std::mem::size_of::(); - let accounted = cd - .checked_mul(config.draws_per_chain) - .and_then(|samples_per_coordinate| { - samples_per_coordinate - .checked_mul(n_traces) - .and_then(|values| values.checked_mul(f64_bytes)) - .and_then(|leaf_payload| { - n_traces - .checked_mul(cd) - .and_then(|headers| headers.checked_mul(vec_header)) - .and_then(|leaf_headers| leaf_payload.checked_add(leaf_headers)) - }) - .and_then(|bytes| { - n_traces - .checked_mul(vec_header) - .and_then(|middle_headers| bytes.checked_add(middle_headers)) - }) - .and_then(|persistent_bytes| { - config - .draws_per_chain - .checked_mul(width) - .and_then(|values| values.checked_mul(f64_bytes)) - .and_then(|payload| { - config - .draws_per_chain - .checked_mul(vec_header) - .and_then(|headers| payload.checked_add(headers)) - }) - .and_then(|score_transient_bytes| { - samples_per_coordinate - .checked_mul(8 * f64_bytes) - .and_then(|payload| { - cd.checked_mul(16) - .and_then(|headers| headers.checked_mul(vec_header)) - .and_then(|headers| payload.checked_add(headers)) - }) - .and_then(|rank_transient_bytes| { - persistent_bytes - .checked_add( - score_transient_bytes.max(rank_transient_bytes), - ) - .map(|required_bytes| { - ( - persistent_bytes, - score_transient_bytes, - required_bytes, - ) - }) - }) - }) - }) - }); - let Some((persistent_bytes, score_transient_bytes, required_bytes)) = accounted else { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::TraceMemoryAccountingOverflow, - MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, - ); - diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; - return diagnostic; - }; - diagnostic - .rank_diagnostics - .accounted_peak_trace_bytes_required = required_bytes; - diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; - if required_bytes > config.max_trace_bytes { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::TraceByteCapExceeded, - MarkovSimulationVarianceStatus::InvalidConfiguration(format!( - "diagnostic trace accounted peak requires {required_bytes} bytes, exceeding cap {}", - config.max_trace_bytes - )), - ); - return diagnostic; - } - diagnostic.rank_diagnostics.lrv_per_chain = vec![None; cd]; - diagnostic.rank_diagnostics.lrv_chain_statuses = - vec![RankDiagnosticStatus::Unavailable; cd]; - - // ── Trace coordinate metadata ───────────────────────────────────── - let mut trace_coords: Vec = Vec::with_capacity(n_traces); - for coord in &information.coordinates { - trace_coords.push(DiagnosticTraceCoordinate::Score { - index: coord.index, - name: coord.name.clone(), - kind: coord.kind.clone(), - }); - } - for subject_id in &self.initialization.subject_ids { - for (eff_idx, name) in self.initialization.random_effect_names.iter().enumerate() { - trace_coords.push(DiagnosticTraceCoordinate::Eta { - subject: subject_id.clone(), - effect_index: eff_idx, - effect_name: name.clone(), - }); - } - } - if !self.initialization.iov_effect_indices.is_empty() { - for (subject_idx, subject_id) in self.initialization.subject_ids.iter().enumerate() { - for occasion in self.data.subjects()[subject_idx].occasions() { - for (eff_idx, name) in self.initialization.iov_effect_names.iter().enumerate() { - trace_coords.push(DiagnosticTraceCoordinate::Kappa { - subject: subject_id.clone(), - occasion_index: occasion.index(), - effect_index: eff_idx, - effect_name: name.clone(), - }); - } - } - } - } - - // ── Cd < 2 → still execute frozen chains, LRV, and Xi ──────────── - // Only per-trace rank diagnostics are unavailable (TooFewChains). - let rank_possible = cd >= 2; - - // ── Fresh prior-drawn chains ────────────────────────────────────── - let omega = candidate.map_or(&self.omega, |value| &value.omega); - let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); - let omega_lower = match cholesky_lower(omega) { - Ok(lower) => lower, - Err(_) => { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::InvalidVariance, - MarkovSimulationVarianceStatus::Indefinite, - ); - return diagnostic; - } - }; - let iov_lower = if self.initialization.iov_effect_indices.is_empty() { - None - } else { - match omega_iov.map(cholesky_lower) { - Some(Ok(lower)) => Some(lower), - Some(Err(_)) | None => { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::InvalidVariance, - MarkovSimulationVarianceStatus::Indefinite, - ); - return diagnostic; - } - } - }; - - // Canonical storage: [score_0..score_{w-1}, eta_0.., kappa_0..]. - // A draw-major score view is created one chain at a time for LRV and - // released before the next chain. - let mut traces: Vec>> = (0..n_traces) - .map(|_| vec![Vec::with_capacity(config.draws_per_chain); cd]) - .collect(); - diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = persistent_bytes; - let score_eligible = - width > 0 && matches!(information.status, InformationStatus::Available); - - // Initialize Cd independent diagnostic chains with domain-separated seeds. - // Seed derivation: per-chain seed = base.wrapping_add(i).wrapping_mul(GOLDEN_RATIO) - // where GOLDEN_RATIO = 0x9E3779B97F4A7C15 (2^64 / φ) and base is the - // configured diagnostic seed or the deterministic checkpoint override. - let mut chain_states: Vec = (0..cd) - .map(|chain| { - let chain_seed = diagnostic_seed - .wrapping_add(chain as u64) - .wrapping_mul(0x9E3779B97F4A7C15); - let mut chain_rng = StdRng::seed_from_u64(chain_seed); - FrozenDiagnosticState { - etas: self.draw_prior_etas(&omega_lower, &mut chain_rng), - kappas: self.draw_prior_kappas(iov_lower.as_deref(), &mut chain_rng), - } - }) - .collect(); - // Independent RNG streams for transitions (offset by +1 to separate - // from prior-initialization streams). - let mut chain_rngs: Vec = (0..cd) - .map(|chain| { - let chain_seed = diagnostic_seed - .wrapping_add(chain as u64) - .wrapping_mul(0x9E3779B97F4A7C15) - .wrapping_add(1); - StdRng::seed_from_u64(chain_seed) - }) - .collect(); - let mut chain_counts = vec![(0usize, 0usize, 0usize); cd]; - - // ── Warmup ──────────────────────────────────────────────────────── - for _ in 0..config.warmup_transitions { - for chain in 0..cd { - let mut single = [chain_counts[chain]]; - if self - .frozen_diagnostic_transition( - &mut chain_states[chain], - &mut chain_rngs[chain], - &mut single, - candidate, - ) - .is_err() - { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::Unavailable, - MarkovSimulationVarianceStatus::UnsupportedScore( - "frozen diagnostic warmup transition failed".into(), - ), - ); - return diagnostic; - } - chain_counts[chain] = single[0]; - } - } - begin_retained_transition_accounting(&mut chain_counts); - - // ── Single retained-draw pass: transition → collect traces ────── - for _ in 0..config.draws_per_chain { - for chain in 0..cd { - let mut single = [chain_counts[chain]]; - if self - .frozen_diagnostic_transition( - &mut chain_states[chain], - &mut chain_rngs[chain], - &mut single, - candidate, - ) - .is_err() - { - mark_diagnostic_failure( - &mut diagnostic, - RankDiagnosticStatus::Unavailable, - MarkovSimulationVarianceStatus::UnsupportedScore( - "frozen retained diagnostic transition failed".into(), - ), - ); - return diagnostic; - } - chain_counts[chain] = single[0]; - - // Score failure never discards independently valid latent draws. - let score = if score_eligible { - match self.frozen_complete_score(&chain_states[chain], 0, candidate) { - Ok(values) if values.len() == width => Some(values), - Ok(_) | Err(_) => None, - } - } else { - None - }; - for coord_idx in 0..width { - traces[coord_idx][chain] - .push(score.as_ref().map_or(f64::NAN, |values| values[coord_idx])); - } - - // Collect eta coordinates: subject-major, coordinate-major. - let mut trace_idx = width; - for subject_etas in &chain_states[chain].etas { - for eta_coord in &subject_etas[0] { - traces[trace_idx][chain].push(*eta_coord); - trace_idx += 1; - } - } - - // Collect kappa coordinates. - for subject_kappas in &chain_states[chain].kappas { - for kappa_vec in &subject_kappas[0] { - for kappa_coord in kappa_vec { - traces[trace_idx][chain].push(*kappa_coord); - trace_idx += 1; - } - } - } - } - } - - // Preserve the raw grand complete-score mean used by the invariant - // stationarity diagnostic. Any non-finite score leaves it unavailable. - if score_eligible { - let denominator = (cd * config.draws_per_chain) as f64; - let means = (0..width) - .map(|coordinate| { - traces[coordinate].iter().flatten().copied().sum::() / denominator - }) - .collect::>(); - if means.iter().all(|value| value.is_finite()) { - diagnostic.grand_score_mean = means; - } - } - - // ── Per-chain score LRV from transient draw-major views ───────── - let mut lrv_matrices: Vec>> = Vec::with_capacity(cd); - for chain in 0..cd { - let (proposals, accepts, state_changes) = chain_counts[chain]; - let score_view = (0..config.draws_per_chain) - .map(|draw| (0..width).map(|coord| traces[coord][chain][draw]).collect()) - .collect::>>(); - diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = persistent_bytes - .checked_add(score_transient_bytes) - .unwrap_or(required_bytes); - let lrv_result = if score_eligible { - match lugsail_batch_means(&score_view, config.batch_size, config.lugsail) { - Ok(value) => Some(value), - Err(_) => { - diagnostic.xi_status = MarkovSimulationVarianceStatus::UnsupportedScore( - "per-chain score LRV failed".into(), - ); - None - } - } - } else { - None - }; - if let Some((coarse, fine, lrv)) = lrv_result { - let classification = classify_psd(&lrv); - let lrv_status = markov_matrix_status(classification); - diagnostic - .chains - .push(MarkovSimulationVarianceChainDiagnostics { - chain, - bm_batch: rows(&coarse), - bm_batch_over_r: rows(&fine), - lugsail_lrv: rows(&lrv), - status: lrv_status, - proposals, - accepts, - state_changes, - }); - diagnostic.rank_diagnostics.lrv_per_chain[chain] = Some(rows(&lrv)); - diagnostic.rank_diagnostics.lrv_chain_statuses[chain] = match classification { - MatrixClassification::EligiblePsd => RankDiagnosticStatus::Available, - MatrixClassification::NonFinite => RankDiagnosticStatus::NonFiniteDraws, - MatrixClassification::NonSymmetric | MatrixClassification::Indefinite => { - RankDiagnosticStatus::InvalidVariance - } - }; - lrv_matrices.push(Some(lrv)); - } else { - diagnostic - .chains - .push(MarkovSimulationVarianceChainDiagnostics { - chain, - bm_batch: Vec::new(), - bm_batch_over_r: Vec::new(), - lugsail_lrv: Vec::new(), - status: MarkovSimulationVarianceStatus::UnsupportedScore( - "complete-score trace or information unavailable".into(), - ), - proposals, - accepts, - state_changes, - }); - diagnostic.rank_diagnostics.lrv_per_chain[chain] = None; - diagnostic.rank_diagnostics.lrv_chain_statuses[chain] = - RankDiagnosticStatus::ScoreUnavailable; - lrv_matrices.push(None); - } - } - - let stuck_chain = chain_counts - .iter() - .enumerate() - .find(|(_, count)| count.2 == 0) - .map(|(chain, _)| chain); - - // Aggregate only when every chain has an eligible score LRV. - let all_lrvs_available = lrv_matrices.len() == cd - && lrv_matrices.iter().all(Option::is_some) - && diagnostic - .rank_diagnostics - .lrv_chain_statuses - .iter() - .all(|status| matches!(status, RankDiagnosticStatus::Available)); - if all_lrvs_available { - let mut lrv_sum = Array2::zeros((width, width)); - for lrv in &lrv_matrices { - lrv_sum += lrv - .as_ref() - .expect("all per-chain LRV matrices were checked available"); - } - let (diag_mean, operational) = scale_lrv_sum(&lrv_sum, cd, cf); - diagnostic.rank_diagnostics.diagnostic_mean_lrv = Some(rows(&diag_mean)); - diagnostic.lambda = rows(&diag_mean); - diagnostic.lambda_status = markov_matrix_status(classify_psd(&diag_mean)); - - // Cd != Cf is intentional: operational scale is Σ/(Cd*Cf). - diagnostic.rank_diagnostics.operational_lrv = Some(rows(&operational)); - if let Some(observed_information) = observed_information.as_ref() { - match transform_simulation_variance( - observed_information, - &operational, - estimator.averaged_iterations, - ) { - Ok((xi, covariance)) => { - diagnostic.xi = rows(&xi); - diagnostic.xi_status = markov_matrix_status(classify_psd(&xi)); - diagnostic.simulation_covariance = rows(&covariance); - diagnostic.simulation_covariance_status = - markov_matrix_status(classify_psd(&covariance)); - } - Err(_) => { - diagnostic.xi_status = MarkovSimulationVarianceStatus::NonFinite; - diagnostic.simulation_covariance_status = - MarkovSimulationVarianceStatus::NonFinite; - } - } - } else { - diagnostic.xi_status = MarkovSimulationVarianceStatus::InformationUnavailable( - format!("{:?}", information.status), - ); - diagnostic.simulation_covariance_status = diagnostic.xi_status.clone(); - } - } else { - let failure = if !score_eligible { - MarkovSimulationVarianceStatus::InformationUnavailable(format!( - "{:?}", - information.status - )) - } else { - diagnostic - .chains - .iter() - .map(|chain| &chain.status) - .find(|status| { - !matches!( - status, - MarkovSimulationVarianceStatus::AssumptionsUnverified - ) - }) - .cloned() - .unwrap_or_else(|| { - MarkovSimulationVarianceStatus::UnsupportedScore( - "one or more configured diagnostic-chain score LRVs failed".into(), - ) - }) - }; - diagnostic.lambda_status = failure.clone(); - diagnostic.xi_status = failure.clone(); - diagnostic.simulation_covariance_status = failure; - } - - // ── Rank/mixing diagnostics from traces ───────────────────────── - // The prechecked rank workspace is the accounted peak whenever rank - // diagnostics execute; no allocator-specific byte claim is made. - if rank_possible { - diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = required_bytes; - diagnostic.rank_diagnostics.traces = - self.rank_diagnostics_from_traces(cd, &traces, &trace_coords); - } else { - diagnostic.rank_diagnostics.traces = trace_coords - .iter() - .map(|coord| RankMixingDiagnostic { - trace: coord.clone(), - rank_rhat: None, - rank_rhat_status: RankDiagnosticStatus::TooFewChains, - folded_rhat: None, - folded_rhat_status: RankDiagnosticStatus::TooFewChains, - max_rhat: None, - max_rhat_status: RankDiagnosticStatus::TooFewChains, - bulk_ess: None, - bulk_ess_status: RankDiagnosticStatus::TooFewChains, - avg_ess_per_split_chain: None, - tau: None, - status: RankDiagnosticStatus::TooFewChains, - }) - .collect(); - } - - // ── Aggregate per-coordinate worst/min across traces ──────────── - diagnostic.rank_diagnostics.worst_rhat = - worst_valid_max_rhat(&diagnostic.rank_diagnostics.traces); - diagnostic.rank_diagnostics.min_bulk_ess = diagnostic - .rank_diagnostics - .traces - .iter() - .filter_map(|t| t.bulk_ess) - .min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); - diagnostic.rank_diagnostics.min_avg_ess_per_split_chain = diagnostic - .rank_diagnostics - .traces - .iter() - .filter_map(|t| t.avg_ess_per_split_chain) - .min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); - - // Aggregate status: ineligible if any coordinate or LRV is non-available. - let any_coord_non_available = diagnostic - .rank_diagnostics - .traces - .iter() - .any(|t| !matches!(t.status, RankDiagnosticStatus::Available)); - let any_lrv_non_available = diagnostic - .rank_diagnostics - .lrv_chain_statuses - .iter() - .any(|s| !matches!(s, RankDiagnosticStatus::Available)); - if rank_possible - && (any_coord_non_available || any_lrv_non_available || stuck_chain.is_some()) - { - diagnostic.rank_diagnostics.status = if diagnostic - .rank_diagnostics - .traces - .iter() - .any(|trace| matches!(trace.status, RankDiagnosticStatus::Available)) - { - RankDiagnosticStatus::PartialAvailability - } else { - RankDiagnosticStatus::Unavailable - }; - } else if !rank_possible { - diagnostic.rank_diagnostics.status = RankDiagnosticStatus::TooFewChains; - } else { - diagnostic.rank_diagnostics.status = RankDiagnosticStatus::Available; - } - - // ── Final aggregate markov status ─────────────────────────────── - diagnostic.status = if let Some(chain) = stuck_chain { - MarkovSimulationVarianceStatus::StuckChain { chain } - } else if !estimator.average_applied { - MarkovSimulationVarianceStatus::AverageNotApplied - } else if !matches!(information.status, InformationStatus::Available) { - MarkovSimulationVarianceStatus::InformationUnavailable(format!( - "{:?}", - information.status - )) - } else { - diagnostic - .chains - .iter() - .map(|chain| &chain.status) - .chain([ - &diagnostic.lambda_status, - &diagnostic.xi_status, - &diagnostic.simulation_covariance_status, - ]) - .find(|status| { - !matches!( - status, - MarkovSimulationVarianceStatus::AssumptionsUnverified - ) - }) - .cloned() - .unwrap_or(MarkovSimulationVarianceStatus::AssumptionsUnverified) - }; - diagnostic - } - - /// Build per-coordinate rank/mixing diagnostics from collected trace chains. - fn rank_diagnostics_from_traces( - &self, - cd: usize, - traces: &[Vec>], - trace_coords: &[DiagnosticTraceCoordinate], - ) -> Vec { - trace_coords - .iter() - .enumerate() - .map(|(idx, coord)| { - let chains = &traces[idx]; - let rank_result = rank_normalized_split_rhat(chains); - let folded_result = folded_split_rhat(chains); - let ess_result = bulk_ess(chains); - - let rank_rhat = match rank_result.as_ref() { - Ok(value) => Some(*value), - Err(_) => None, - }; - let folded_rhat = match folded_result.as_ref() { - Ok(value) => Some(*value), - Err(_) => None, - }; - let (bulk_ess, tau) = match ess_result.as_ref() { - Ok((ess, tau)) => (Some(*ess), Some(*tau)), - Err(_) => (None, None), - }; - let avg_ess_per_split_chain = bulk_ess.map(|ess| ess / (2.0 * cd as f64)); - - let score_unavailable = matches!(coord, DiagnosticTraceCoordinate::Score { .. }) - && chains.iter().flatten().any(|draw| !draw.is_finite()); - let statistic_status = |result: Result<(), &RankDiagnosticError>| { - if score_unavailable { - RankDiagnosticStatus::ScoreUnavailable - } else { - result - .map(|()| RankDiagnosticStatus::Available) - .unwrap_or_else(rank_diagnostic_error_status) - } - }; - let rank_rhat_status = statistic_status(rank_result.as_ref().map(|_| ())); - let folded_rhat_status = statistic_status(folded_result.as_ref().map(|_| ())); - let max_rhat = match (rank_rhat, folded_rhat) { - (Some(rank), Some(folded)) => Some(rank.max(folded)), - _ => None, - }; - let max_rhat_status = if matches!(rank_rhat_status, RankDiagnosticStatus::Available) - && matches!(folded_rhat_status, RankDiagnosticStatus::Available) - { - RankDiagnosticStatus::Available - } else if !matches!(rank_rhat_status, RankDiagnosticStatus::Available) { - rank_rhat_status.clone() - } else { - folded_rhat_status.clone() - }; - let bulk_ess_status = statistic_status(ess_result.as_ref().map(|_| ())); - let statuses = [&rank_rhat_status, &folded_rhat_status, &bulk_ess_status]; - let available = statuses - .iter() - .filter(|status| matches!(status, RankDiagnosticStatus::Available)) - .count(); - let status = if available == statuses.len() { - RankDiagnosticStatus::Available - } else if available > 0 { - RankDiagnosticStatus::PartialAvailability - } else if statuses.iter().all(|status| *status == statuses[0]) { - statuses[0].clone() - } else { - RankDiagnosticStatus::Unavailable - }; - - RankMixingDiagnostic { - trace: coord.clone(), - rank_rhat, - rank_rhat_status, - folded_rhat, - folded_rhat_status, - max_rhat, - max_rhat_status, - bulk_ess, - bulk_ess_status, - avg_ess_per_split_chain, - tau, - status, - } - }) - .collect() - } - - /// Draw initial η vectors from N(0, Omega) for fresh diagnostic chains. - fn draw_prior_etas(&self, omega_lower: &[Vec], rng: &mut StdRng) -> Vec>> { - let n_eta = self.initialization.random_effect_indices.len(); - if n_eta == 0 { - return vec![vec![Vec::new(); 1]; self.initialization.subject_ids.len()]; - } - self.initialization - .subject_ids - .iter() - .map(|_| { - let normals: Vec = (0..n_eta) - .map(|_| diagnostic_standard_normal(rng)) - .collect(); - let eta = (0..n_eta) - .map(|row| { - (0..=row) - .map(|col| omega_lower[row][col] * normals[col]) - .sum() - }) - .collect::>(); - vec![eta] - }) - .collect() - } - - /// Draw initial κ vectors from N(0, Omega_IOV) for fresh diagnostic chains. - fn draw_prior_kappas( - &self, - iov_lower: Option<&[Vec]>, - rng: &mut StdRng, - ) -> Vec>>> { - let Some(iov_lower) = iov_lower else { - return vec![vec![Vec::new(); 1]; self.initialization.subject_ids.len()]; - }; - let n_kappa = self.initialization.iov_effect_indices.len(); - self.initialization - .occasion_counts - .iter() - .map(|&n_occasions| { - let kappas: Vec> = (0..n_occasions) - .map(|_| { - let normals: Vec = (0..n_kappa) - .map(|_| diagnostic_standard_normal(rng)) - .collect(); - (0..n_kappa) - .map(|row| { - (0..=row) - .map(|col| iov_lower[row][col] * normals[col]) - .sum() - }) - .collect() - }) - .collect(); - vec![kappas] - }) - .collect() - } - - fn frozen_diagnostic_transition( - &self, - state: &mut FrozenDiagnosticState, - rng: &mut StdRng, - counts: &mut [(usize, usize, usize)], - candidate: Option<&DiagnosticCandidate>, - ) -> std::result::Result<(), String> { - for _ in 0..self.eta_block_iterations { - for subject in 0..self.initialization.subject_ids.len() { - let omega = candidate.map_or(&self.omega, |value| &value.omega); - let lower = cholesky_lower(omega).map_err(|error| error.to_string())?; - for (chain, count) in counts.iter_mut().enumerate() { - let current = state.etas[subject][chain].clone(); - let normals = (0..current.len()) - .map(|_| diagnostic_standard_normal(rng)) - .collect::>(); - let proposed = correlated_random_walk( - ¤t, - &lower, - &normals, - self.eta_block_step_sizes[subject], - ) - .map_err(|error| error.to_string())?; - let current_score = self - .score_subject_latents_at( - subject, - ¤t, - &state.kappas[subject][chain], - candidate, - ) - .map_err(|error| error.to_string())?; - let proposed_score = self - .score_subject_latents_at( - subject, - &proposed, - &state.kappas[subject][chain], - candidate, - ) - .map_err(|error| error.to_string())?; - count.0 += 1; - if diagnostic_accept(rng, current_score.log_acceptance_ratio(proposed_score)) { - count.1 += 1; - if proposed != current { - count.2 += 1; - } - state.etas[subject][chain] = proposed; - } - } - } - } - for _ in 0..self.mcmc_iterations { - for subject in 0..self.initialization.subject_ids.len() { - for (chain, count) in counts.iter_mut().enumerate() { - for parameter in 0..self.initialization.random_effect_indices.len() { - let current = state.etas[subject][chain].clone(); - let mut proposed = current.clone(); - proposed[parameter] += - self.proposal_step_sizes[parameter] * diagnostic_standard_normal(rng); - let current_score = self - .score_subject_latents_at( - subject, - ¤t, - &state.kappas[subject][chain], - candidate, - ) - .map_err(|error| error.to_string())?; - let proposed_score = self - .score_subject_latents_at( - subject, - &proposed, - &state.kappas[subject][chain], - candidate, - ) - .map_err(|error| error.to_string())?; - count.0 += 1; - if diagnostic_accept( - rng, - current_score.log_acceptance_ratio(proposed_score), - ) { - count.1 += 1; - if proposed != current { - count.2 += 1; - } - state.etas[subject][chain] = proposed; - } - } - let omega_iov = - candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); - if let Some(omega_iov) = omega_iov { - let lower = cholesky_lower(omega_iov).map_err(|error| error.to_string())?; - for occasion in 0..state.kappas[subject][chain].len() { - let current = state.kappas[subject][chain][occasion].clone(); - let normals = (0..current.len()) - .map(|_| diagnostic_standard_normal(rng)) - .collect::>(); - let proposed = correlated_random_walk( - ¤t, - &lower, - &normals, - self.kappa_proposal_step_sizes[subject], - ) - .map_err(|error| error.to_string())?; - let current_score = self - .score_subject_latents_at( - subject, - &state.etas[subject][chain], - &state.kappas[subject][chain], - candidate, - ) - .map_err(|error| error.to_string())?; - let mut proposed_kappas = state.kappas[subject][chain].clone(); - proposed_kappas[occasion] = proposed.clone(); - let proposed_score = self - .score_subject_latents_at( - subject, - &state.etas[subject][chain], - &proposed_kappas, - candidate, - ) - .map_err(|error| error.to_string())?; - count.0 += 1; - if diagnostic_accept( - rng, - current_score.log_acceptance_ratio(proposed_score), - ) { - count.1 += 1; - if proposed != current { - count.2 += 1; - } - state.kappas[subject][chain][occasion] = proposed; - } - } - } - } - } - } - Ok(()) - } - - // ─── Operational convergence ───────────────────────────────────────── - - /// Evaluate an operational convergence checkpoint if one is due. - fn evaluate_operational_convergence( - &mut self, - iteration: usize, - scheduled: bool, - mandatory_final: bool, - ) -> Result<()> { - let Some(settings) = self.operational_settings else { - return Ok(()); - }; - // Only check during smoothing, unless this is a mandatory final check. - if !mandatory_final && self.initialization.schedule.phase(iteration) != SaemPhase::Smoothing - { - return Ok(()); - } - let Some(ref average) = self.iterate_average else { - return Ok(()); - }; - let n_averaged = average.count; - if n_averaged < settings.first_eligible_averaged_iteration { - return Ok(()); - } - - // Cadence: periodic checkpoints are evaluated every check_interval - // iterations starting from first_eligible_averaged_iteration. - if scheduled && !mandatory_final { - let smoothing_start = self.initialization.schedule.pure_burn_in - + self.initialization.schedule.exploration_iterations - + 1; - let smoothing_offset = iteration.saturating_sub(smoothing_start) + 1; - if smoothing_offset < settings.first_eligible_averaged_iteration - || !(smoothing_offset - settings.first_eligible_averaged_iteration) - .is_multiple_of(settings.check_interval) - { - return Ok(()); - } - } - - // Defensive caching: if this is a mandatory final check and we already - // evaluated at this iteration, reuse instead of rerunning. - if mandatory_final { - if let Some(last) = self.operational_diagnostics.checks.last() { - if last.iteration == iteration { - self.operational_diagnostics.final_check_reused = true; - return Ok(()); - } - } - } - - // Build the deterministic per-checkpoint seed. - let checkpoint_seed = self - .config - .markov_simulation_variance - .expect("operational policy validation requires Markov diagnostics") - .seed - .wrapping_add(OPERATIONAL_CHECKPOINT_SEED_DOMAIN) - .wrapping_add(iteration as u64); - - // Two-sided standard normal quantile. - let z_quantile = normal_two_sided_z(settings.confidence_level); - - let implied_averaged_iterations = - Some(4.0 * z_quantile * z_quantile / settings.relative_fixed_width_epsilon.powi(2)); - - let info = self.information.diagnostics(); - let avg_psi = match population_psi( - &average.population_phi, - &self.initialization.parameter_scales, - ) { - Ok(psi) => psi, - Err(_) => { - self.record_ineligible_checkpoint( - iteration, - n_averaged, - scheduled, - mandatory_final, - checkpoint_seed, - z_quantile, - implied_averaged_iterations, - Vec::new(), - "averaged population psi conversion failed".to_string(), - ); - return Ok(()); - } - }; - let mut candidate_error_models = self.error_models.clone(); - for (output_index, model) in &average.residual_models { - match *model { - ResidualErrorModel::Combined { a, b } => update_estimated_combined_residual_model( - &mut candidate_error_models, - *output_index, - a, - b, - ), - ResidualErrorModel::CorrelatedCombined { a, b, rho } => { - update_estimated_correlated_combined_residual_model( - &mut candidate_error_models, - *output_index, - a, - b, - rho, - ) - } - ResidualErrorModel::Constant { .. } - | ResidualErrorModel::Proportional { .. } - | ResidualErrorModel::Exponential { .. } => { - update_estimated_simple_residual_model_with_sigma( - &mut candidate_error_models, - *output_index, - primary_sigma_parameter(model), - ) - } - } - } - let candidate_covariate_model = match ( - self.covariate_model.as_ref(), - average.covariate_betas.as_ref(), - ) { - (Some(model), Some(values)) => Some(model.with_estimates(values)?), - (None, None) => None, - _ => anyhow::bail!("averaged covariate metadata dimension mismatch"), - }; - let candidate = DiagnosticCandidate { - population_parameters: avg_psi, - covariate_model: candidate_covariate_model, - omega: average.omega.clone(), - omega_iov: average.omega_iov.clone(), - error_models: candidate_error_models, - }; - let candidate_free_coordinates = match operational_free_coordinates(&info, average) { - Ok(values) if !values.is_empty() => values, - Ok(_) => { - self.record_ineligible_checkpoint( - iteration, - n_averaged, - scheduled, - mandatory_final, - checkpoint_seed, - z_quantile, - implied_averaged_iterations, - Vec::new(), - "no free coordinates".to_string(), - ); - return Ok(()); - } - Err(error) => { - self.record_ineligible_checkpoint( - iteration, - n_averaged, - scheduled, - mandatory_final, - checkpoint_seed, - z_quantile, - implied_averaged_iterations, - Vec::new(), - error.to_string(), - ); - return Ok(()); - } - }; - if self.initialization.random_effect_indices.is_empty() - && self.initialization.iov_effect_indices.is_empty() - { - self.record_ineligible_checkpoint( - iteration, - n_averaged, - scheduled, - mandatory_final, - checkpoint_seed, - z_quantile, - implied_averaged_iterations, - candidate_free_coordinates, - "no latent coordinates".to_string(), - ); - return Ok(()); - } - - let diagnostic_metadata = SaemEstimatorMetadata { - policy: self.config.estimator_policy, - average_applied: true, - averaging_start_cycle: Some(average.start_cycle), - averaged_iterations: n_averaged, - }; - - let markov = self.markov_variance_diagnostics_with_seed( - &diagnostic_metadata, - &info, - Some(checkpoint_seed), - Some(&candidate), - ); - - let rank = &markov.rank_diagnostics; - let simulation_sd_fraction = operational_simulation_sd_fraction(&info, &markov); - let fixed_width = simulation_sd_fraction.map(|fraction| 2.0 * z_quantile * fraction); - let fixed_width_ratio = - fixed_width.map(|width| width / settings.relative_fixed_width_epsilon); - let newton_value = newton_displacement(&info, &markov).filter(|value| value.is_finite()); - let newton_mc_sd = - newton_displacement_mc_sd(&info, &markov).filter(|value| value.is_finite()); - let matrix_valid = matches!(info.status, InformationStatus::Available) - && matches!( - markov.lambda_status, - MarkovSimulationVarianceStatus::AssumptionsUnverified - ) - && matches!( - markov.xi_status, - MarkovSimulationVarianceStatus::AssumptionsUnverified - ) - && matches!( - markov.simulation_covariance_status, - MarkovSimulationVarianceStatus::AssumptionsUnverified - ); - let every_chain_moved = - !markov.chains.is_empty() && markov.chains.iter().all(|chain| chain.state_changes > 0); - let every_trace_valid = !rank.traces.is_empty() - && rank.traces.iter().all(|trace| { - trace.rank_rhat.is_some() - && trace.folded_rhat.is_some() - && trace.max_rhat.is_some() - && trace.bulk_ess.is_some() - && matches!(trace.rank_rhat_status, RankDiagnosticStatus::Available) - && matches!(trace.folded_rhat_status, RankDiagnosticStatus::Available) - && matches!(trace.max_rhat_status, RankDiagnosticStatus::Available) - && matches!(trace.bulk_ess_status, RankDiagnosticStatus::Available) - }); - let covariance_policy = self - .config - .covariance_stability - .expect("operational policy validation requires covariance stability"); - let omega_boundary = covariance_boundary_rejection_summary( - &self.cycle_diagnostics, - covariance_policy, - false, - ); - let omega_iov_boundary = - covariance_boundary_rejection_summary(&self.cycle_diagnostics, covariance_policy, true); - let covariance_active_cycles = - iteration.saturating_sub(self.initialization.schedule.pure_burn_in); - let covariance_window_available = - covariance_active_cycles >= covariance_policy.rejection_window; - let boundary_criterion = |name: &str, longest_run: usize| { - if covariance_window_available { - evaluate_criterion( - name, - Some(longest_run as f64), - covariance_policy.rejection_window as f64, - |observed| observed < covariance_policy.rejection_window as f64, - ) - } else { - OperationalConvergenceCriterion { - name: name.to_string(), - observed: Some(longest_run as f64), - threshold: covariance_policy.rejection_window as f64, - status: OperationalConvergenceCriterionStatus::Unavailable(format!( - "covariance-stability window requires {} active cycles; {covariance_active_cycles} completed", - covariance_policy.rejection_window - )), - } - } - }; - let criteria: Vec = vec![ - evaluate_criterion( - "valid_information_and_matrices", - Some(matrix_valid as u8 as f64), - 1.0, - |value| value == 1.0, - ), - evaluate_criterion( - "every_diagnostic_chain_moved", - Some(every_chain_moved as u8 as f64), - 1.0, - |value| value == 1.0, - ), - evaluate_criterion( - "every_rank_diagnostic_valid", - Some(every_trace_valid as u8 as f64), - 1.0, - |value| value == 1.0, - ), - evaluate_criterion("max_rhat", rank.worst_rhat, settings.max_rhat, |observed| { - observed < settings.max_rhat - }), - evaluate_criterion( - "min_bulk_ess", - rank.min_bulk_ess, - settings.min_bulk_ess, - |observed| observed > settings.min_bulk_ess, - ), - evaluate_criterion( - "min_average_bulk_ess_per_split_chain", - rank.min_avg_ess_per_split_chain, - settings.min_average_bulk_ess_per_split_chain, - |observed| observed >= settings.min_average_bulk_ess_per_split_chain, - ), - evaluate_criterion( - "worst_simulation_sd_fraction", - simulation_sd_fraction, - settings.relative_fixed_width_epsilon / (2.0 * z_quantile), - |observed| 2.0 * z_quantile * observed <= settings.relative_fixed_width_epsilon, - ), - evaluate_criterion( - "relative_fixed_width", - fixed_width, - settings.relative_fixed_width_epsilon, - |observed| observed <= settings.relative_fixed_width_epsilon, - ), - evaluate_criterion( - "newton_displacement", - newton_value, - settings.max_newton_displacement, - |observed| observed <= settings.max_newton_displacement, - ), - evaluate_criterion( - "newton_displacement_mc_sd", - newton_mc_sd, - settings.max_newton_displacement_mc_sd, - |observed| observed <= settings.max_newton_displacement_mc_sd, - ), - boundary_criterion("omega_boundary_rejection_run", omega_boundary.longest_run), - boundary_criterion( - "omega_iov_boundary_rejection_run", - omega_iov_boundary.longest_run, - ), - ]; - - let mut ineligible_reasons = criteria - .iter() - .filter_map(|criterion| match &criterion.status { - OperationalConvergenceCriterionStatus::Unavailable(reason) => { - Some(format!("{}: {reason}", criterion.name)) - } - _ => None, - }) - .collect::>(); - if !matrix_valid { - ineligible_reasons.push("information or matrix validation failed".to_string()); - } - if !every_trace_valid { - ineligible_reasons.push("one or more rank diagnostics unavailable".to_string()); - } - if !every_chain_moved { - ineligible_reasons - .push("one or more retained diagnostic chains did not move".to_string()); - } - let failed_criteria = criteria - .iter() - .filter(|criterion| { - matches!( - criterion.status, - OperationalConvergenceCriterionStatus::NotSatisfied - ) - }) - .map(|criterion| criterion.name.clone()) - .collect::>(); - let outcome = if !ineligible_reasons.is_empty() { - OperationalConvergenceOutcome::Ineligible { - reasons: ineligible_reasons, - } - } else if !failed_criteria.is_empty() { - OperationalConvergenceOutcome::Failed { - criteria: failed_criteria, - } - } else { - OperationalConvergenceOutcome::Passed - }; - - let passed = matches!(outcome, OperationalConvergenceOutcome::Passed); - self.operational_diagnostics.final_status = Some(outcome.clone()); - self.operational_diagnostics.worst_rhat = rank.worst_rhat; - self.operational_diagnostics.min_bulk_ess = rank.min_bulk_ess; - self.operational_diagnostics.fixed_width_ratio = fixed_width_ratio; - self.operational_diagnostics.fixed_width_epsilon = - Some(settings.relative_fixed_width_epsilon); - self.operational_diagnostics.implied_minimum_ess = implied_averaged_iterations; - self.operational_diagnostics.newton_displacement = newton_value; - self.operational_diagnostics.newton_displacement_mc_sd = newton_mc_sd; - let checkpoint = OperationalConvergenceCheck { - iteration, - averaged_iterations: n_averaged, - scheduled, - mandatory_final, - checkpoint_seed: Some(checkpoint_seed), - z_quantile: Some(z_quantile), - implied_minimum_ess: implied_averaged_iterations, - candidate_free_coordinates, - information: Some(info), - criteria, - outcome, - markov: Some(markov), - }; - - self.operational_diagnostics.checks.push(checkpoint); - - // Terminate early if converged and this was a scheduled check. - if passed { - self.operational_diagnostics.used_for_termination = true; - self.status = Status::Stop(StopReason::Converged); - } - - Ok(()) - } - - /// Record an ineligible checkpoint (candidate unavailable). - #[allow(clippy::too_many_arguments)] - fn record_ineligible_checkpoint( - &mut self, - iteration: usize, - averaged_iterations: usize, - scheduled: bool, - mandatory_final: bool, - checkpoint_seed: u64, - z_quantile: f64, - implied_averaged_iterations: Option, - candidate_free_coordinates: Vec, - reason: String, - ) { - let settings = self - .operational_settings - .expect("ineligible operational checkpoint requires configured settings"); - let unavailable = |name: &str, threshold: f64| OperationalConvergenceCriterion { - name: name.to_string(), - observed: None, - threshold, - status: OperationalConvergenceCriterionStatus::Unavailable(reason.clone()), - }; - let criteria = vec![ - unavailable("candidate_available", 1.0), - unavailable("valid_information_and_matrices", 1.0), - unavailable("every_diagnostic_chain_moved", 1.0), - unavailable("every_rank_diagnostic_valid", 1.0), - unavailable("max_rhat", settings.max_rhat), - unavailable("min_bulk_ess", settings.min_bulk_ess), - unavailable( - "min_average_bulk_ess_per_split_chain", - settings.min_average_bulk_ess_per_split_chain, - ), - unavailable( - "worst_simulation_sd_fraction", - settings.relative_fixed_width_epsilon / (2.0 * z_quantile), - ), - unavailable( - "relative_fixed_width", - settings.relative_fixed_width_epsilon, - ), - unavailable("newton_displacement", settings.max_newton_displacement), - unavailable( - "newton_displacement_mc_sd", - settings.max_newton_displacement_mc_sd, - ), - ]; - let outcome = OperationalConvergenceOutcome::Ineligible { - reasons: vec![reason], - }; - self.operational_diagnostics.final_status = Some(outcome.clone()); - self.operational_diagnostics - .checks - .push(OperationalConvergenceCheck { - iteration, - averaged_iterations, - scheduled, - mandatory_final, - checkpoint_seed: Some(checkpoint_seed), - z_quantile: Some(z_quantile), - implied_minimum_ess: implied_averaged_iterations, - candidate_free_coordinates, - information: None, - criteria, - outcome, - markov: None, - }); - } - - fn frozen_complete_score( - &self, - state: &FrozenDiagnosticState, - chain: usize, - candidate: Option<&DiagnosticCandidate>, - ) -> std::result::Result, String> { - let layout = self.information.layout(); - let population_parameters = candidate - .map_or(self.population_parameters.as_slice(), |value| { - value.population_parameters.as_slice() - }); - let omega = candidate.map_or(&self.omega, |value| &value.omega); - let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); - let error_models = candidate.map_or(&self.error_models, |value| &value.error_models); - let mut derivative = CompleteDerivative::zero(layout.len()); - for subject_index in 0..self.initialization.subject_ids.len() { - let covariate_model = candidate - .and_then(|value| value.covariate_model.as_ref()) - .or(self.covariate_model.as_ref()); - match covariate_model { - Some(model) => derivative.add_covariate_population_prior( - &state.etas[subject_index][chain], - omega, - &self.initialization.random_effect_indices, - model.parameter_indices(), - model.subject_design()[subject_index].values(), - layout, - ), - None => derivative.add_population_prior( - &state.etas[subject_index][chain], - omega, - &self.initialization.random_effect_indices, - layout, - ), - } - .map_err(|error| error.to_string())?; - let calculated_mu = if candidate.is_some() { - covariate_model - .map(|model| { - let phi = population_phi( - population_parameters, - &self.initialization.parameter_scales, - )?; - Ok::<_, anyhow::Error>( - model.subject_population_parameters( - &phi, - &self.initialization.parameter_scales, - )?[subject_index] - .phi() - .to_vec(), - ) - }) - .transpose() - .map_err(|error| error.to_string())? - } else { - None - }; - let subject_mu = calculated_mu.as_deref().or_else(|| { - self.subject_mu_phi - .as_ref() - .map(|means| means[subject_index].as_slice()) - }); - let subject = self.data.subjects()[subject_index]; - if let Some(omega_iov) = omega_iov { - let occasions = subject.occasions(); - let kappas = &state.kappas[subject_index][chain]; - if occasions.len() != kappas.len() { - return Err(format!( - "subject {} has {} occasions but {} diagnostic kappa states", - subject.id(), - occasions.len(), - kappas.len() - )); - } - for (occasion, kappa) in occasions.iter().zip(kappas) { - derivative - .add_iov_prior(kappa, omega_iov, layout) - .map_err(|error| error.to_string())?; - let parameters = match subject_mu { - Some(mean) => occasion_psi_from_subject_mean( - mean, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &state.etas[subject_index][chain], - &self.initialization.iov_effect_indices, - kappa, - ), - None => occasion_psi( - population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &state.etas[subject_index][chain], - &self.initialization.iov_effect_indices, - kappa, - ), - } - .map_err(|error| error.to_string())?; - let occasion_subject = - Subject::from_occasions(subject.id().to_owned(), vec![occasion.clone()]); - let predictions = self - .equation - .estimate_predictions_dense(&occasion_subject, ¶meters) - .map_err(|error| error.to_string())?; - derivative - .add_predictions_strict(&predictions, error_models, layout) - .map_err(|error| error.to_string())?; - } - } else { - let parameters = match subject_mu { - Some(mean) => individual_psi_from_subject_mean( - mean, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &state.etas[subject_index][chain], - ), - None => individual_psi( - population_parameters, - &self.initialization.parameter_scales, - &self.initialization.random_effect_indices, - &state.etas[subject_index][chain], - ), - } - .map_err(|error| error.to_string())?; - let predictions = self - .equation - .estimate_predictions_dense(subject, ¶meters) - .map_err(|error| error.to_string())?; - derivative - .add_predictions_strict(&predictions, error_models, layout) - .map_err(|error| error.to_string())?; - } - } - Ok(derivative.score) - } -} - -fn diagnostic_standard_normal(rng: &mut StdRng) -> f64 { - let u1 = rng.random::().max(f64::MIN_POSITIVE); - let u2 = rng.random::(); - (-2.0 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() -} - -fn diagnostic_accept(rng: &mut StdRng, ratio: f64) -> bool { - ratio.is_finite() && (ratio >= 0.0 || rng.random::().max(f64::MIN_POSITIVE).ln() < ratio) -} - -fn begin_retained_transition_accounting(counts: &mut [(usize, usize, usize)]) { - counts.fill((0, 0, 0)); -} - -fn mark_diagnostic_failure( - diagnostic: &mut MarkovSimulationVarianceDiagnostics, - rank_status: RankDiagnosticStatus, - markov_status: MarkovSimulationVarianceStatus, -) { - diagnostic.rank_diagnostics.status = rank_status.clone(); - diagnostic - .rank_diagnostics - .lrv_chain_statuses - .fill(rank_status); - diagnostic.lambda_status = markov_status.clone(); - diagnostic.xi_status = markov_status.clone(); - diagnostic.simulation_covariance_status = markov_status.clone(); - diagnostic.status = markov_status; -} - -fn markov_matrix_status(classification: MatrixClassification) -> MarkovSimulationVarianceStatus { - match classification { - MatrixClassification::EligiblePsd => MarkovSimulationVarianceStatus::AssumptionsUnverified, - MatrixClassification::NonFinite => MarkovSimulationVarianceStatus::NonFinite, - MatrixClassification::NonSymmetric => MarkovSimulationVarianceStatus::NonSymmetric, - MatrixClassification::Indefinite => MarkovSimulationVarianceStatus::Indefinite, - } -} - -fn worst_valid_max_rhat(traces: &[RankMixingDiagnostic]) -> Option { - traces - .iter() - .filter(|trace| matches!(trace.max_rhat_status, RankDiagnosticStatus::Available)) - .filter_map(|trace| trace.max_rhat) - .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) -} - -fn rank_diagnostic_error_status(error: &RankDiagnosticError) -> RankDiagnosticStatus { - match error { - RankDiagnosticError::NoChains => RankDiagnosticStatus::NoChains, - RankDiagnosticError::TooFewChains { .. } => RankDiagnosticStatus::TooFewChains, - RankDiagnosticError::UnequalChainLengths { .. } => { - RankDiagnosticStatus::UnequalChainLengths - } - RankDiagnosticError::OddChainLength { .. } => RankDiagnosticStatus::OddDraws, - RankDiagnosticError::NonFiniteDraw => RankDiagnosticStatus::NonFiniteDraws, - RankDiagnosticError::TooFewDraws { .. } => RankDiagnosticStatus::TooFewDraws, - RankDiagnosticError::ConstantDraws => RankDiagnosticStatus::ConstantDraws, - RankDiagnosticError::InvalidVariance => RankDiagnosticStatus::InvalidVariance, - RankDiagnosticError::NonPositiveTau { .. } => RankDiagnosticStatus::NonPositiveTau, - } -} - -fn matrix_from_rows(values: &[Vec], width: usize) -> Result> { - if values.len() != width || values.iter().any(|row| row.len() != width) { - anyhow::bail!("matrix coordinate width mismatch"); - } - Ok(Array2::from_shape_vec( - (width, width), - values.iter().flatten().copied().collect(), - )?) -} - -impl ParametricRunner for SaemState { - fn step(&mut self) -> Result { - if self.status.is_stop() { - return Ok(self.status.clone()); - } - - if self.cycle >= self.initialization.schedule.total_iterations { - self.status = Status::Stop(StopReason::MaxCycles); - return Ok(self.status.clone()); - } - - self.cycle += 1; - if let Err(error) = self.e_step() { - let failure = NumericalFailure::new( - self.cycle, - NumericalFailurePhase::Expectation, - format!("{error:#}"), - ); - self.status = Status::Stop(StopReason::NumericalFailure); - self.numerical_failure = Some(failure.clone()); - return Err(failure.into()); - } - // m_step also accumulates damped covariance sufficient statistics - // during pure burn-in while leaving theta, Omega, Omega_IOV, and sigma - // unchanged, so it must run in every schedule phase. - if let Err(error) = self.m_step() { - let failure = NumericalFailure::new( - self.cycle, - NumericalFailurePhase::Maximization, - format!("{error:#}"), - ); - self.status = Status::Stop(StopReason::NumericalFailure); - self.numerical_failure = Some(failure.clone()); - return Err(failure.into()); - } - - if self.cycle >= self.initialization.schedule.total_iterations { - self.status = Status::Stop(StopReason::MaxCycles); - let scheduled = self - .operational_settings - .zip(self.iterate_average.as_ref()) - .is_some_and(|(policy, average)| { - average.count >= policy.first_eligible_averaged_iteration - && (average.count - policy.first_eligible_averaged_iteration) - .is_multiple_of(policy.check_interval) - }); - self.evaluate_operational_convergence(self.cycle, scheduled, true)?; - } else { - self.evaluate_operational_convergence(self.cycle, true, false)?; - } - - Ok(self.status.clone()) - } - - fn request_stop(&mut self, reason: StopReason) { - if self.status.is_continue() && self.numerical_failure.is_none() { - self.status = Status::Stop(reason); - } - } - - fn cycle(&self) -> usize { - self.cycle - } - - fn status(&self) -> &Status { - &self.status - } - - fn cycle_diagnostics(&self) -> &[SaemCycleDiagnostics] { - &self.cycle_diagnostics - } - - fn log_likelihood(&self) -> f64 { - self.subject_log_likelihoods.iter().sum() - } - - fn population_parameters(&self) -> &[f64] { - &self.population_parameters - } - - fn covariate_betas(&self) -> Option> { - self.covariate_model.as_ref().map(|model| { - model - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect() - }) - } - - fn random_effect_names(&self) -> &[String] { - &self.initialization.random_effect_names - } - - fn iov_effect_names(&self) -> Option<&[String]> { - (!self.initialization.iov_effect_names.is_empty()) - .then_some(&self.initialization.iov_effect_names) - } - - fn eta_log_prior(&self) -> f64 { - self.subject_log_priors.iter().sum() - } - - fn kappa_log_prior(&self) -> f64 { - self.subject_kappa_log_priors.iter().sum() - } - - fn acceptance_rate(&self) -> Option { - self.last_acceptance_rate - } - - fn eta_block_acceptance_rate(&self) -> Option { - self.last_eta_block_acceptance_rate - } - - fn kappa_acceptance_rate(&self) -> Option { - self.last_kappa_acceptance_rate - } - - fn rejected_proposals(&self) -> Option { - self.last_rejected_proposals - } - - fn non_finite_proposals(&self) -> Option { - self.last_non_finite_proposals - } - - fn parameter_acceptance_rates(&self) -> Option<&[f64]> { - self.last_acceptance_rate - .map(|_| self.last_parameter_acceptance_rates.as_slice()) - } - - fn proposal_step_sizes(&self) -> Option<&[f64]> { - Some(&self.proposal_step_sizes) - } - - fn eta_block_step_sizes(&self) -> Option<&[f64]> { - (self.eta_block_iterations > 0).then_some(self.eta_block_step_sizes.as_slice()) - } - - fn log_acceptance_ratios(&self) -> Option<&[f64]> { - Some(&self.last_log_acceptance_ratios) - } - - fn negative_log_likelihood(&self) -> f64 { - self.negative_log_likelihood - } - - fn n_chains(&self) -> Option { - self.etas - .first() - .map(|subject_chains| subject_chains.len()) - .or(Some(self.initialization.n_chains)) - } - - fn omega(&self) -> Option<&Array2> { - Some(&self.omega) - } - - fn omega_iov(&self) -> Option<&Array2> { - self.omega_iov.as_ref() - } - - fn residual_sigmas(&self) -> &[f64] { - &self.residual_sigmas - } - - fn step_size(&self) -> f64 { - self.initialization - .schedule - .stochastic_approximation_step(self.cycle) - } - - fn total_iterations(&self) -> usize { - self.initialization.schedule.total_iterations - } - - fn into_result(mut self: Box) -> Result> { - if let Some(failure) = self.numerical_failure.as_ref() { - return Err(failure.clone().into()); - } - - let result_cycle = self.cycle; - let estimator_metadata = match self.config.estimator_policy { - SaemEstimatorPolicy::TerminalIterate => SaemEstimatorMetadata::default(), - SaemEstimatorPolicy::AveragedIterates { .. } => { - self.install_iterate_average().map_err(|error| { - NumericalFailure::new( - result_cycle, - NumericalFailurePhase::ResultAssembly, - format!("{error:#}"), - ) - })? - } - }; - let information_diagnostics = self.information.diagnostics(); - let population_uncertainty = derive_population_uncertainty(&information_diagnostics); - let markov_simulation_variance = if self.operational_settings.is_some() { - self.operational_diagnostics - .checks - .last() - .and_then(|check| check.markov.clone()) - .unwrap_or_else(MarkovSimulationVarianceDiagnostics::disabled) - } else { - self.markov_variance_diagnostics(&estimator_metadata, &information_diagnostics) - }; - let (conditional_modes, conditional_mode_error) = match conditional_modes(&self) { - Ok(modes) => (modes, None), - Err(error) if self.config.marginal_likelihood.is_some() => { - (Vec::new(), Some(format!("{error:#}"))) - } - Err(error) => { - return Err(NumericalFailure::new( - result_cycle, - NumericalFailurePhase::ResultAssembly, - format!("{error:#}"), - ) - .into()) - } - }; - let marginal_likelihood = calculate_result_marginal_likelihood( - &self, - &conditional_modes, - conditional_mode_error.as_deref(), - ); - let information_criteria = derive_information_criteria( - marginal_likelihood.as_ref(), - &information_diagnostics.coordinates, - self.initialization.subject_ids.len(), - ); - let eta_chain_means = self - .initialization - .subject_ids - .iter() - .enumerate() - .map(|(subject_index, subject_id)| { - Ok(SubjectEtaEstimate { - subject_id: subject_id.clone(), - values: mean_vectors( - self.etas[subject_index].iter().map(|eta| eta.as_slice()), - )?, - }) - }) - .collect::>>() - .map_err(|error| { - NumericalFailure::new( - result_cycle, - NumericalFailurePhase::ResultAssembly, - format!("{error:#}"), - ) - })?; - let mut kappa_chain_means = Vec::new(); - if self.omega_iov.is_some() { - for (subject_index, subject_id) in self.initialization.subject_ids.iter().enumerate() { - for (occasion_position, occasion) in self.data.subjects()[subject_index] - .occasions() - .iter() - .enumerate() - { - kappa_chain_means.push(OccasionKappaEstimate { - subject_id: subject_id.clone(), - occasion_index: occasion.index(), - values: mean_vectors( - self.kappas[subject_index] - .iter() - .map(|chain| chain[occasion_position].as_slice()), - ) - .map_err(|error| { - NumericalFailure::new( - result_cycle, - NumericalFailurePhase::ResultAssembly, - format!("{error:#}"), - ) - })?, - }); - } - } - } - let eta_variances = (0..self.omega.nrows()) - .map(|index| self.omega[[index, index]]) - .collect::>(); - let eta_posterior_rows = eta_chain_means - .iter() - .map(|estimate| estimate.values.clone()) - .collect::>(); - let eta_map_rows = (!conditional_modes.is_empty()).then(|| { - conditional_modes - .iter() - .map(|mode| mode.eta.clone()) - .collect::>() - }); - let kappa_variances = self - .omega_iov - .as_ref() - .map(|omega| { - (0..omega.nrows()) - .map(|index| omega[[index, index]]) - .collect::>() - }) - .unwrap_or_default(); - let kappa_posterior_rows = kappa_chain_means - .iter() - .map(|estimate| estimate.values.clone()) - .collect::>(); - let kappa_map_rows = (!conditional_modes.is_empty()).then(|| { - conditional_modes - .iter() - .flat_map(|mode| mode.kappas.iter().map(|kappa| kappa.values.clone())) - .collect::>() - }); - let shrinkage = ShrinkageDiagnostics { - eta_posterior_mean: derive_eta_posterior_mean_shrinkage( - &self.initialization.random_effect_names, - &eta_variances, - &eta_posterior_rows, - ), - eta_map: derive_eta_map_shrinkage( - &self.initialization.random_effect_names, - &eta_variances, - eta_map_rows.as_deref(), - ), - kappa_posterior_mean: derive_kappa_posterior_mean_shrinkage( - &self.initialization.iov_effect_names, - &kappa_variances, - &kappa_posterior_rows, - ), - kappa_map: derive_kappa_map_shrinkage( - &self.initialization.iov_effect_names, - &kappa_variances, - kappa_map_rows.as_deref(), - ), - }; - let residual_error_estimates = self.residual_error_estimates(); - let mut warnings = - parametric_warnings(&self.cycle_diagnostics, self.config.covariance_stability); - if let Some(diagnostics) = marginal_likelihood.as_ref() { - match &diagnostics.status { - MarginalLikelihoodStatus::Unavailable { failures } => { - warnings.push(ParametricWarning::MarginalLikelihoodUnavailable { - subjects: failures - .iter() - .map(|failure| failure.subject_id.clone()) - .collect(), - }); - } - MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects } => { - warnings.push(ParametricWarning::MarginalLikelihoodNonconvergedModes { - subjects: subjects.clone(), - }); - } - MarginalLikelihoodStatus::Available => {} - } - } - let omega_structural_mask = self.initialization.omega.structural_mask().clone(); - let omega_estimated_mask = self.initialization.omega.estimated_mask().clone(); - let omega_iov_structural_mask = self - .initialization - .omega_iov - .as_ref() - .map(|omega| omega.structural_mask().clone()); - let omega_iov_estimated_mask = self - .initialization - .omega_iov - .as_ref() - .map(|omega| omega.estimated_mask().clone()); - let individual_estimates = if conditional_modes.is_empty() { - self.initialization - .subject_ids - .iter() - .enumerate() - .map(|(subject_index, subject_id)| { - ( - subject_id.clone(), - self.individual_parameters(subject_index, 0), - ) - }) - .collect() - } else { - conditional_modes - .iter() - .map(|mode| (mode.subject_id.clone(), mode.parameters.clone())) - .collect() - }; - - let SaemState { - equation, - data, - config, - negative_log_likelihood, - initialization, - cycle, - status, - population_parameters, - omega, - omega_iov, - residual_sigmas, - cycle_diagnostics, - operational_diagnostics, - covariate_model, - .. - } = *self; - - Ok(ParametricResult { - equation, - data, - config, - effective_n_chains: initialization.n_chains, - objective_function: 2.0 * negative_log_likelihood, - converged: status.converged(), - termination_reason: status.stop_reason().cloned(), - iterations: cycle, - subject_count: initialization.subject_ids.len(), - observation_count: initialization.observation_count, - parameter_names: initialization.parameter_names, - parameter_scales: initialization.parameter_scales, - estimated_parameters: initialization.estimated_parameters, - population_initial: initialization.initial_population_parameters.clone(), - population_estimates: population_parameters, - random_effect_indices: initialization.random_effect_indices, - random_effect_names: initialization.random_effect_names, - omega, - omega_structural_mask, - omega_estimated_mask, - omega_initial: initialization.omega.initial().clone(), - iov_effect_indices: initialization.iov_effect_indices, - iov_effect_names: initialization.iov_effect_names, - omega_iov, - omega_iov_structural_mask, - omega_iov_estimated_mask, - omega_iov_initial: initialization - .omega_iov - .as_ref() - .map(|omega| omega.initial().clone()), - residual_sigmas, - residual_error_estimates, - residual_initial_values: initialization.initial_residual_values.clone(), - residual_initial_estimated: initialization.initial_residual_estimated.clone(), - eta_chain_means, - kappa_chain_means, - conditional_modes, - shrinkage, - cycle_diagnostics, - warnings, - information_diagnostics, - population_uncertainty, - markov_simulation_variance, - operational_diagnostics, - marginal_likelihood, - information_criteria, - estimator_metadata, - individual_estimates, - covariate_model, - }) - } -} - -// ─── Operational convergence helpers ──────────────────────────────────── - -/// Two-sided standard normal quantile for confidence level `p` ∈ (0, 1). -/// -/// Returns z such that P(|Z| ≤ z) = p, i.e. z = Φ⁻¹(p + (1-p)/2). -fn normal_two_sided_z(p: f64) -> f64 { - use statrs::distribution::{ContinuousCDF, Normal}; - let norm = Normal::new(0.0, 1.0).expect("standard normal parameters are valid"); - let one_sided = p + (1.0 - p) / 2.0; - norm.inverse_cdf(one_sided) -} - -/// Evaluate one operational convergence criterion. -fn evaluate_criterion( - name: &str, - observed: Option, - threshold: f64, - predicate: impl FnOnce(f64) -> bool, -) -> OperationalConvergenceCriterion { - let status = match observed { - Some(value) if value.is_finite() && predicate(value) => { - OperationalConvergenceCriterionStatus::Satisfied - } - Some(value) if value.is_finite() => OperationalConvergenceCriterionStatus::NotSatisfied, - Some(_) => OperationalConvergenceCriterionStatus::Unavailable( - "observed value is non-finite".to_string(), - ), - None => OperationalConvergenceCriterionStatus::Unavailable( - "criterion could not be evaluated".to_string(), - ), - }; - OperationalConvergenceCriterion { - name: name.to_string(), - observed, - threshold, - status, - } -} - -fn operational_free_coordinates( - information: &InformationDiagnostics, - average: &SaemIterateAverage, -) -> Result> { - information - .coordinates - .iter() - .map(|coordinate| match &coordinate.kind { - InformationCoordinateKind::Population { parameter_index } => average - .population_phi - .get(*parameter_index) - .copied() - .ok_or_else(|| anyhow::anyhow!("population coordinate out of range")), - InformationCoordinateKind::CovariateEffect { effect_index } => average - .covariate_betas - .as_ref() - .and_then(|values| values.get(*effect_index)) - .copied() - .ok_or_else(|| anyhow::anyhow!("covariate coordinate out of range")), - InformationCoordinateKind::Omega { row, column } => average - .omega - .get((*row, *column)) - .copied() - .ok_or_else(|| anyhow::anyhow!("Omega coordinate out of range")), - InformationCoordinateKind::OmegaIov { row, column } => average - .omega_iov - .as_ref() - .and_then(|matrix| matrix.get((*row, *column))) - .copied() - .ok_or_else(|| anyhow::anyhow!("Omega_IOV coordinate out of range")), - InformationCoordinateKind::Residual { - output_index, - component, - } => { - let model = average - .residual_models - .iter() - .find(|(index, _)| index == output_index) - .map(|(_, model)| model) - .ok_or_else(|| anyhow::anyhow!("residual coordinate output unavailable"))?; - match (model, component.as_str()) { - (ResidualErrorModel::Constant { a }, "sigma") => Ok(*a), - (ResidualErrorModel::Exponential { sigma }, "sigma") => Ok(*sigma), - (ResidualErrorModel::Proportional { b }, "proportional") => Ok(*b), - (ResidualErrorModel::Combined { a, .. }, "additive") - | (ResidualErrorModel::CorrelatedCombined { a, .. }, "additive") => Ok(*a), - (ResidualErrorModel::Combined { b, .. }, "proportional") - | (ResidualErrorModel::CorrelatedCombined { b, .. }, "proportional") => Ok(*b), - (ResidualErrorModel::CorrelatedCombined { rho, .. }, "correlation") => Ok(*rho), - _ => anyhow::bail!("residual coordinate component mismatch"), - } - } - }) - .collect() -} - -fn operational_simulation_sd_fraction( - information: &InformationDiagnostics, - markov: &MarkovSimulationVarianceDiagnostics, -) -> Option { - let width = information.coordinates.len(); - let observed = matrix_from_rows(&information.observed_information, width).ok()?; - let covariance = matrix_from_rows(&markov.simulation_covariance, width).ok()?; - worst_contrast(&observed, &covariance).ok() -} - -fn solve_spd(matrix: &Array2, rhs: &[f64]) -> Option> { - if matrix.nrows() != matrix.ncols() || matrix.nrows() != rhs.len() { - return None; - } - let lower = cholesky_lower(matrix).ok()?; - let n = rhs.len(); - let mut y = vec![0.0; n]; - for row in 0..n { - let subtotal = (0..row) - .map(|column| lower[row][column] * y[column]) - .sum::(); - y[row] = (rhs[row] - subtotal) / lower[row][row]; - } - let mut result = vec![0.0; n]; - for row in (0..n).rev() { - let subtotal = ((row + 1)..n) - .map(|column| lower[column][row] * result[column]) - .sum::(); - result[row] = (y[row] - subtotal) / lower[row][row]; - } - result - .iter() - .all(|value| value.is_finite()) - .then_some(result) -} - -/// Invariant Newton displacement `sqrt(g^T Iobs^-1 g)`. -fn newton_displacement( - info: &InformationDiagnostics, - markov: &MarkovSimulationVarianceDiagnostics, -) -> Option { - let width = info.coordinates.len(); - if width == 0 || markov.grand_score_mean.len() != width { - return None; - } - let observed = matrix_from_rows(&info.observed_information, width).ok()?; - let displacement = solve_spd(&observed, &markov.grand_score_mean)?; - let squared = markov - .grand_score_mean - .iter() - .zip(&displacement) - .map(|(score, step)| score * step) - .sum::(); - (squared.is_finite() && squared >= 0.0).then(|| squared.sqrt()) -} - -/// Worst-direction Newton-step MC SD from diagnostic-mean LRV/draws. -fn newton_displacement_mc_sd( - info: &InformationDiagnostics, - markov: &MarkovSimulationVarianceDiagnostics, -) -> Option { - let width = info.coordinates.len(); - let draws = markov.config?.draws_per_chain; - if width == 0 || draws == 0 { - return None; - } - let observed = matrix_from_rows(&info.observed_information, width).ok()?; - let mut score_covariance = - matrix_from_rows(markov.rank_diagnostics.diagnostic_mean_lrv.as_ref()?, width).ok()?; - score_covariance /= draws as f64; - let mut inverse = Array2::zeros((width, width)); - for column in 0..width { - let mut unit = vec![0.0; width]; - unit[column] = 1.0; - let solved = solve_spd(&observed, &unit)?; - for row in 0..width { - inverse[[row, column]] = solved[row]; - } - } - let mut mapped = Array2::zeros((width, width)); - for row in 0..width { - for column in 0..=row { - let mut value = 0.0; - for left in 0..width { - for right in 0..width { - value += inverse[[row, left]] - * score_covariance[[left, right]] - * inverse[[column, right]]; - } - } - mapped[[row, column]] = value; - mapped[[column, row]] = value; - } - } - worst_contrast(&observed, &mapped).ok() -} - -fn incremental_average(previous: f64, current: f64, count: usize) -> f64 { - previous + (current - previous) / count as f64 -} - -fn average_covariance( - average: &mut Array2, - current: &Array2, - estimated_mask: &Array2, - count: usize, -) { - for row in 0..average.nrows() { - for col in 0..=row { - if estimated_mask[[row, col]] { - let value = incremental_average(average[[row, col]], current[[row, col]], count); - average[[row, col]] = value; - average[[col, row]] = value; - } - } - } -} - -fn average_residual_model( - previous: ResidualErrorModel, - current: ResidualErrorModel, - estimated: bool, - components: [bool; 2], - correlated_components: [bool; 3], - count: usize, -) -> Result { - let averaged = match (previous, current) { - (ResidualErrorModel::Constant { a }, ResidualErrorModel::Constant { a: current }) => { - ResidualErrorModel::Constant { - a: if estimated { - incremental_average(a, current, count) - } else { - a - }, - } - } - ( - ResidualErrorModel::Proportional { b }, - ResidualErrorModel::Proportional { b: current }, - ) => ResidualErrorModel::Proportional { - b: if estimated { - incremental_average(b, current, count) - } else { - b - }, - }, - ( - ResidualErrorModel::Exponential { sigma }, - ResidualErrorModel::Exponential { sigma: current }, - ) => ResidualErrorModel::Exponential { - sigma: if estimated { - incremental_average(sigma, current, count) - } else { - sigma - }, - }, - ( - ResidualErrorModel::Combined { a, b }, - ResidualErrorModel::Combined { - a: current_a, - b: current_b, - }, - ) => ResidualErrorModel::Combined { - a: if components[0] { - incremental_average(a, current_a, count) - } else { - a - }, - b: if components[1] { - incremental_average(b, current_b, count) - } else { - b - }, - }, - ( - ResidualErrorModel::CorrelatedCombined { a, b, rho }, - ResidualErrorModel::CorrelatedCombined { - a: current_a, - b: current_b, - rho: current_rho, - }, - ) => ResidualErrorModel::CorrelatedCombined { - a: if correlated_components[0] { - incremental_average(a, current_a, count) - } else { - a - }, - b: if correlated_components[1] { - incremental_average(b, current_b, count) - } else { - b - }, - rho: if correlated_components[2] { - incremental_average(rho, current_rho, count) - } else { - rho - }, - }, - _ => anyhow::bail!("residual family changed while accumulating SAEM averages"), - }; - Ok(averaged) -} - -fn validate_average_population(values: &[f64], initialization: &SaemInitialization) -> Result<()> { - let initial = population_phi( - &initialization.initial_population_parameters, - &initialization.parameter_scales, - )?; - if values.len() != initial.len() || values.iter().any(|value| !value.is_finite()) { - anyhow::bail!("averaged population phi values must be finite and retain their width"); - } - for index in 0..values.len() { - if !initialization.estimated_parameters[index] && values[index] != initial[index] { - anyhow::bail!("averaged population phi changed fixed coordinate {index}"); - } - } - Ok(()) -} - -fn validate_average_covariance( - matrix: &Array2, - specification: &ResolvedOmega, - label: &str, -) -> Result<()> { - if matrix.raw_dim() != specification.initial().raw_dim() { - anyhow::bail!("averaged {label} has an invalid shape"); - } - for row in 0..matrix.nrows() { - for col in 0..matrix.ncols() { - let value = matrix[[row, col]]; - if !value.is_finite() || value != matrix[[col, row]] { - anyhow::bail!("averaged {label} must be finite and symmetric"); - } - if !specification.structural_mask()[[row, col]] && value != 0.0 { - anyhow::bail!("averaged {label} changed a structural zero"); - } - if !specification.estimated_mask()[[row, col]] - && value != specification.initial()[[row, col]] - { - anyhow::bail!("averaged {label} changed a fixed entry"); - } - } - } - cholesky_lower(matrix) - .map(|_| ()) - .map_err(|error| anyhow::anyhow!("averaged {label} is not positive definite: {error}")) -} - -fn validate_average_residuals( - original_width: usize, - models: &[(usize, ResidualErrorModel)], - declarations: &ParametricErrorModels, -) -> Result<()> { - if original_width != declarations.models().len() - || models.len() != declarations.models().iter().count() - { - anyhow::bail!("averaged residual output collection changed"); - } - for ((output, model), (declared_output, terminal)) in models.iter().copied().zip( - declarations - .models() - .iter() - .map(|(index, model)| (index, *model)), - ) { - if output != declared_output || output >= original_width { - anyhow::bail!("averaged residual output indices changed"); - } - let output_name = declarations - .output_name(output) - .ok_or_else(|| anyhow::anyhow!("averaged residual output {output} has no name"))?; - let components = declarations.combined_component_estimated(output); - if !declarations.is_estimated(output) && model != terminal { - anyhow::bail!( - "averaged residual model changed fixed output '{output_name}' at index {output}" - ); - } - if let ( - ResidualErrorModel::Combined { a, b }, - ResidualErrorModel::Combined { - a: terminal_a, - b: terminal_b, - }, - ) = (model, terminal) - { - if (!components[0] && a != terminal_a) || (!components[1] && b != terminal_b) { - anyhow::bail!( - "averaged residual model changed a fixed component for output '{output_name}' at index {output}" - ); - } - } - let correlated_components = declarations.correlated_combined_component_estimated(output); - if let ( - ResidualErrorModel::CorrelatedCombined { a, b, rho }, - ResidualErrorModel::CorrelatedCombined { - a: terminal_a, - b: terminal_b, - rho: terminal_rho, - }, - ) = (model, terminal) - { - if (!correlated_components[0] && a != terminal_a) - || (!correlated_components[1] && b != terminal_b) - || (!correlated_components[2] && rho != terminal_rho) - { - anyhow::bail!( - "averaged correlated-combined model changed a fixed component for output '{output_name}' at index {output}" - ); - } - } - let valid = match model { - ResidualErrorModel::Constant { a } => a.is_finite() && a > 0.0, - ResidualErrorModel::Proportional { b } => b.is_finite() && b > 0.0, - ResidualErrorModel::Exponential { sigma } => sigma.is_finite() && sigma > 0.0, - ResidualErrorModel::Combined { a, b } => { - a.is_finite() - && b.is_finite() - && a >= 0.0 - && b >= 0.0 - && (!components[0] || a > 0.0) - && (!components[1] || b > 0.0) - } - ResidualErrorModel::CorrelatedCombined { a, b, rho } => { - a.is_finite() - && a > 0.0 - && b.is_finite() - && b > 0.0 - && rho.is_finite() - && rho > -1.0 - && rho < 1.0 - } - }; - if !valid { - anyhow::bail!( - "averaged residual model for output '{output_name}' at index {output} is outside its domain" - ); - } - } - Ok(()) -} - -#[derive(Debug, Default)] -struct WarningCount { - first_iteration: Option, - cycles: usize, - count: usize, -} - -impl WarningCount { - fn record_cycle(&mut self, iteration: usize) { - self.first_iteration.get_or_insert(iteration); - self.cycles += 1; - } - - fn record_count(&mut self, iteration: usize, count: usize) { - if count == 0 { - return; - } - self.first_iteration.get_or_insert(iteration); - self.count += count; - } -} - -#[derive(Debug, Default, PartialEq, Eq)] -struct CovarianceBoundaryRejectionSummary { - first_iteration: Option, - longest_run: usize, -} - -fn covariance_boundary_rejection_summary( - cycles: &[SaemCycleDiagnostics], - policy: CovarianceStabilityConfig, - iov: bool, -) -> CovarianceBoundaryRejectionSummary { - let mut summary = CovarianceBoundaryRejectionSummary::default(); - let mut current_run = 0usize; - let mut current_start = None; - for cycle in cycles { - let (rejected, margin) = if iov { - ( - cycle.omega_iov_update_rejected, - cycle.omega_iov_relative_spd_margin, - ) - } else { - (cycle.omega_update_rejected, cycle.omega_relative_spd_margin) - }; - if rejected && margin.is_some_and(|value| value <= policy.minimum_relative_spd_margin) { - if current_run == 0 { - current_start = Some(cycle.iteration); - } - current_run += 1; - summary.longest_run = summary.longest_run.max(current_run); - if current_run >= policy.rejection_window && summary.first_iteration.is_none() { - summary.first_iteration = current_start; - } - } else { - current_run = 0; - current_start = None; - } - } - summary -} - -fn parametric_warnings( - cycles: &[SaemCycleDiagnostics], - covariance_stability: Option, -) -> Vec { - let mut omega = WarningCount::default(); - let mut omega_iov = WarningCount::default(); - let mut eta_non_finite = WarningCount::default(); - let mut eta_block_non_finite = WarningCount::default(); - let mut kappa_non_finite = WarningCount::default(); - let mut residual_rejected = BTreeMap::::new(); - let mut proportional_floor = BTreeMap::::new(); - let mut residual_non_finite = BTreeMap::::new(); - let mut exponential_domain = BTreeMap::::new(); - let mut additive_collapse = BTreeMap::::new(); - let mut optimizer_not_converged = BTreeMap::::new(); - - for cycle in cycles { - if cycle.omega_update_rejected { - omega.record_cycle(cycle.iteration); - } - if cycle.omega_iov_update_rejected { - omega_iov.record_cycle(cycle.iteration); - } - eta_non_finite.record_count(cycle.iteration, cycle.eta_non_finite); - eta_block_non_finite.record_count(cycle.iteration, cycle.eta_block_non_finite); - kappa_non_finite.record_count(cycle.iteration, cycle.kappa_non_finite); - for residual in &cycle.residual_diagnostics { - if residual.update_rejected { - residual_rejected - .entry(residual.output.clone()) - .or_default() - .record_cycle(cycle.iteration); - } - proportional_floor - .entry(residual.output.clone()) - .or_default() - .record_count(cycle.iteration, residual.proportional_floor_count); - residual_non_finite - .entry(residual.output.clone()) - .or_default() - .record_count(cycle.iteration, residual.non_finite_prediction_count); - exponential_domain - .entry(residual.output.clone()) - .or_default() - .record_count(cycle.iteration, residual.exponential_domain_violation_count); - if residual.combined_additive_collapse_warning { - additive_collapse - .entry(residual.output.clone()) - .or_default() - .record_cycle(cycle.iteration); - } - if residual.optimizer_converged == Some(false) { - optimizer_not_converged - .entry(residual.output.clone()) - .or_default() - .record_cycle(cycle.iteration); - } - } - } - - let mut warnings = Vec::new(); - if let Some(first_iteration) = omega.first_iteration { - warnings.push(ParametricWarning::OmegaUpdateRejected { - first_iteration, - cycles: omega.cycles, - }); - } - if let Some(first_iteration) = omega_iov.first_iteration { - warnings.push(ParametricWarning::OmegaIovUpdateRejected { - first_iteration, - cycles: omega_iov.cycles, - }); - } - if let Some(policy) = covariance_stability { - let omega_boundary = covariance_boundary_rejection_summary(cycles, policy, false); - if let Some(first_iteration) = omega_boundary.first_iteration { - warnings.push(ParametricWarning::OmegaBoundaryRejection { - first_iteration, - longest_run: omega_boundary.longest_run, - }); - } - let omega_iov_boundary = covariance_boundary_rejection_summary(cycles, policy, true); - if let Some(first_iteration) = omega_iov_boundary.first_iteration { - warnings.push(ParametricWarning::OmegaIovBoundaryRejection { - first_iteration, - longest_run: omega_iov_boundary.longest_run, - }); - } - } - if let Some(first_iteration) = eta_non_finite.first_iteration { - warnings.push(ParametricWarning::EtaNonFiniteProposals { - first_iteration, - count: eta_non_finite.count, - }); - } - if let Some(first_iteration) = eta_block_non_finite.first_iteration { - warnings.push(ParametricWarning::EtaBlockNonFiniteProposals { - first_iteration, - count: eta_block_non_finite.count, - }); - } - if let Some(first_iteration) = kappa_non_finite.first_iteration { - warnings.push(ParametricWarning::KappaNonFiniteProposals { - first_iteration, - count: kappa_non_finite.count, - }); - } - for (output, warning) in residual_rejected { - if let Some(first_iteration) = warning.first_iteration { - warnings.push(ParametricWarning::ResidualUpdateRejected { - output, - first_iteration, - cycles: warning.cycles, - }); - } - } - for (output, warning) in proportional_floor { - if let Some(first_iteration) = warning.first_iteration { - warnings.push(ParametricWarning::ProportionalPredictionFloor { - output, - first_iteration, - count: warning.count, - }); - } - } - for (output, warning) in residual_non_finite { - if let Some(first_iteration) = warning.first_iteration { - warnings.push(ParametricWarning::NonFiniteResidualPrediction { - output, - first_iteration, - count: warning.count, - }); - } - } - for (output, warning) in exponential_domain { - if let Some(first_iteration) = warning.first_iteration { - warnings.push(ParametricWarning::ExponentialDomainViolation { - output, - first_iteration, - count: warning.count, - }); - } - } - for (output, warning) in additive_collapse { - if let Some(first_iteration) = warning.first_iteration { - warnings.push(ParametricWarning::CombinedAdditiveCollapse { - output, - first_iteration, - cycles: warning.cycles, - }); - } - } - for (output, warning) in optimizer_not_converged { - if let Some(first_iteration) = warning.first_iteration { - warnings.push(ParametricWarning::ResidualOptimizerNotConverged { - output, - first_iteration, - cycles: warning.cycles, - }); - } - } - warnings -} - -fn calculate_result_marginal_likelihood( - state: &SaemState, - conditional_modes: &[SubjectConditionalMode], - conditional_mode_error: Option<&str>, -) -> Option { - let config = state.config.marginal_likelihood?; - let n_eta = state.initialization.random_effect_indices.len(); - let n_kappa = state.initialization.iov_effect_indices.len(); - let latent = n_eta > 0 || n_kappa > 0; - let occasion_indices = state - .data - .subjects() - .iter() - .map(|subject| { - if n_kappa == 0 { - Vec::new() - } else { - subject - .occasions() - .iter() - .map(|occasion| occasion.index()) - .collect() - } - }) - .collect::>>(); - let mut flattened_modes = Vec::with_capacity(state.initialization.subject_ids.len()); - let mut converged = Vec::with_capacity(state.initialization.subject_ids.len()); - let mut validation_failures = Vec::with_capacity(state.initialization.subject_ids.len()); - - for (subject_index, subject_id) in state.initialization.subject_ids.iter().enumerate() { - if !latent { - flattened_modes.push(Vec::new()); - converged.push(None); - validation_failures.push(None); - continue; - } - let Some(mode) = conditional_modes.get(subject_index) else { - flattened_modes.push(Vec::new()); - converged.push(None); - validation_failures.push(Some( - MarginalLikelihoodFailureReason::MissingConditionalMode, - )); - continue; - }; - let mut validation_failure = None; - if mode.subject_id != *subject_id { - validation_failure.get_or_insert(MarginalLikelihoodFailureReason::SubjectIdMismatch { - expected: subject_id.clone(), - actual: mode.subject_id.clone(), - }); - } - if mode.eta.len() != n_eta { - validation_failure.get_or_insert(MarginalLikelihoodFailureReason::EtaWidthMismatch { - expected: n_eta, - actual: mode.eta.len(), - }); - } - if mode.kappas.len() != occasion_indices[subject_index].len() { - validation_failure.get_or_insert(MarginalLikelihoodFailureReason::KappaCountMismatch { - expected: occasion_indices[subject_index].len(), - actual: mode.kappas.len(), - }); - } - for (position, kappa) in mode.kappas.iter().enumerate() { - if let Some(expected) = occasion_indices[subject_index].get(position) { - if kappa.occasion_index != *expected { - validation_failure.get_or_insert( - MarginalLikelihoodFailureReason::KappaOccasionMismatch { - position, - expected: *expected, - actual: kappa.occasion_index, - }, - ); - } - } - if kappa.values.len() != n_kappa { - validation_failure.get_or_insert( - MarginalLikelihoodFailureReason::KappaWidthMismatch { - position, - expected: n_kappa, - actual: kappa.values.len(), - }, - ); - } - } - let mut flattened = mode.eta.clone(); - for kappa in &mode.kappas { - flattened.extend_from_slice(&kappa.values); - } - if flattened.iter().any(|value| !value.is_finite()) { - validation_failure - .get_or_insert(MarginalLikelihoodFailureReason::NonFiniteModeCoordinate); - } - flattened_modes.push(flattened); - converged.push(Some(mode.converged)); - validation_failures.push(validation_failure); - } - - let curvature_covariances = conditional_modes - .iter() - .map(|mode| { - mode.uncertainty - .latent_covariance - .as_ref() - .and_then(|rows| matrix_from_rows(rows, rows.len()).ok()) - }) - .collect::>(); - let subjects = state - .initialization - .subject_ids - .iter() - .enumerate() - .map(|(index, subject_id)| MarginalSubject { - subject_id, - occasion_indices: &occasion_indices[index], - mode: &flattened_modes[index], - mode_converged: converged[index], - eta_dimension: n_eta, - kappa_dimension: n_kappa, - validation_failure: validation_failures[index].clone(), - curvature_availability: conditional_modes - .get(index) - .map(|mode| &mode.uncertainty.status), - curvature_covariance: curvature_covariances.get(index).and_then(Option::as_ref), - }) - .collect::>(); - if let Some(error) = conditional_mode_error { - return Some(unavailable_population_marginal_likelihood( - config, - &subjects, - MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed(format!( - "global conditional mode calculation failed: {error}" - )), - )); - } - Some(calculate_population_marginal_likelihood( - config, - &subjects, - &state.omega, - state.omega_iov.as_ref(), - |subject_index, eta, kappas| { - state - .score_subject_latents(subject_index, eta, kappas) - .map(SubjectPosteriorScore::log_posterior) - }, - )) -} - -fn conditional_modes(state: &SaemState) -> Result> { - if !state.compute_map { - return Ok(Vec::new()); - } - - let n_eta = state.initialization.random_effect_indices.len(); - let n_kappa = state.initialization.iov_effect_indices.len(); - if n_eta == 0 && n_kappa == 0 { - return Ok(Vec::new()); - } - let mut modes = Vec::with_capacity(state.initialization.subject_ids.len()); - for (subject_index, subject_id) in state.initialization.subject_ids.iter().enumerate() { - let eta_start = mean_vectors(state.etas[subject_index].iter().map(|eta| eta.as_slice()))?; - let occasion_count = if state.omega_iov.is_some() { - state.data.subjects()[subject_index].occasions().len() - } else { - 0 - }; - let mut kappa_start = Vec::with_capacity(occasion_count); - for occasion_position in 0..occasion_count { - kappa_start.push(mean_vectors( - state.kappas[subject_index] - .iter() - .map(|chain| chain[occasion_position].as_slice()), - )?); - } - let mut initial = eta_start; - for kappa in &kappa_start { - initial.extend_from_slice(kappa); - } - - let step_fraction = state.map_initial_step; - let mut scales = (0..n_eta) - .map(|index| state.omega[[index, index]].sqrt() * step_fraction) - .collect::>(); - if let Some(omega_iov) = state.omega_iov.as_ref() { - for _ in 0..occasion_count { - scales.extend( - (0..n_kappa).map(|index| omega_iov[[index, index]].sqrt() * step_fraction), - ); - } - } - for scale in &mut scales { - *scale = scale.max(1e-6); - } - - let solution = optimize_conditional_mode( - initial, - &scales, - state.map_max_iterations as u64, - state.map_sd_tolerance, - |coordinates| { - let (eta, kappas) = unflatten_latents(coordinates, n_eta, occasion_count, n_kappa); - match state.score_subject_latents(subject_index, eta, &kappas) { - Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), - _ => f64::INFINITY, - } - }, - )?; - let mut coordinates = (0..n_eta) - .map(|index| JointLatentCoordinate { - index, - name: format!("eta:{}", state.initialization.random_effect_names[index]), - kind: JointLatentCoordinateKind::Eta { - parameter_index: state.initialization.random_effect_indices[index], - }, - prior_sd: state.omega[[index, index]].sqrt(), - }) - .collect::>(); - if let Some(omega_iov) = state.omega_iov.as_ref() { - for occasion_position in 0..occasion_count { - let occasion_index = - state.data.subjects()[subject_index].occasions()[occasion_position].index(); - for effect_index in 0..n_kappa { - coordinates.push(JointLatentCoordinate { - index: n_eta + occasion_position * n_kappa + effect_index, - name: format!( - "kappa:{occasion_index}:{}", - state.initialization.iov_effect_names[effect_index] - ), - kind: JointLatentCoordinateKind::Kappa { - occasion_index, - effect_index, - parameter_index: state.initialization.iov_effect_indices[effect_index], - }, - prior_sd: omega_iov[[effect_index, effect_index]].sqrt(), - }); - } - } - } - let prior_sds = coordinates - .iter() - .map(|coordinate| coordinate.prior_sd) - .collect::>(); - let mode_metadata = ConditionalModeMetadata { - converged: solution.converged, - iterations: solution.iterations, - objective_value: solution.objective, - termination_message: solution.termination.clone(), - }; - let uncertainty = conditional_mode_curvature( - &solution.coordinates, - &prior_sds, - &coordinates, - &mode_metadata, - |coordinates| { - let (eta, kappas) = unflatten_latents(coordinates, n_eta, occasion_count, n_kappa); - match state.score_subject_latents(subject_index, eta, &kappas) { - Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), - _ => f64::INFINITY, - } - }, - ); - let (eta, kappas) = - unflatten_latents(&solution.coordinates, n_eta, occasion_count, n_kappa); - let parameters = state.individual_parameters_from_eta(subject_index, eta)?; - let kappa_estimates = kappas - .into_iter() - .enumerate() - .map(|(occasion_position, values)| OccasionKappaEstimate { - subject_id: subject_id.clone(), - occasion_index: state.data.subjects()[subject_index].occasions()[occasion_position] - .index(), - values, - }) - .collect(); - modes.push(SubjectConditionalMode { - subject_id: subject_id.clone(), - eta: eta.to_vec(), - kappas: kappa_estimates, - parameters, - objective: solution.objective, - converged: solution.converged, - iterations: solution.iterations, - termination: solution.termination, - uncertainty, - }); - } - Ok(modes) -} - -fn unflatten_latents( - coordinates: &[f64], - n_eta: usize, - occasion_count: usize, - n_kappa: usize, -) -> (&[f64], Vec>) { - let eta = &coordinates[..n_eta]; - let kappas = (0..occasion_count) - .map(|occasion| { - let start = n_eta + occasion * n_kappa; - coordinates[start..start + n_kappa].to_vec() - }) - .collect(); - (eta, kappas) -} - -fn mean_vectors<'a>(vectors: impl IntoIterator) -> Result> { - let mut vectors = vectors.into_iter(); - let Some(first) = vectors.next() else { - anyhow::bail!("cannot summarize random effects without chains"); - }; - let mut mean = first.to_vec(); - let mut count = 1usize; - for vector in vectors { - if vector.len() != mean.len() { - anyhow::bail!("random-effect chains have inconsistent dimensions"); - } - for (sum, value) in mean.iter_mut().zip(vector) { - *sum += value; - } - count += 1; - } - for value in &mut mean { - *value /= count as f64; - } - Ok(mean) -} - -fn zero_etas(n_subjects: usize, n_chains: usize, n_parameters: usize) -> Vec>> { - vec![vec![vec![0.0; n_parameters]; n_chains]; n_subjects] -} - -fn zero_kappas( - occasion_counts: &[usize], - n_chains: usize, - n_kappa: usize, -) -> Vec>>> { - occasion_counts - .iter() - .map(|&n_occasions| vec![vec![vec![0.0; n_kappa]; n_occasions]; n_chains]) - .collect() -} - -fn second_moment_from_etas(etas: &[Vec>]) -> Result> { - let mut samples = etas.iter().flat_map(|subject_chains| subject_chains.iter()); - let Some(first) = samples.next() else { - anyhow::bail!("cannot update omega without subject-chain samples"); - }; - let dimension = first.len(); - let mut second_moment = Array2::zeros((dimension, dimension)); - let mut count = 0usize; - for eta in std::iter::once(first).chain(samples) { - if eta.len() != dimension { - anyhow::bail!("eta samples have inconsistent dimensions"); - } - for row in 0..dimension { - for col in 0..dimension { - second_moment[[row, col]] += eta[row] * eta[col]; - } - } - count += 1; - } - second_moment.mapv_inplace(|value| value / count as f64); - Ok(second_moment) -} - -fn covariance_from_kappas(kappas: &[Vec>>]) -> Result> { - let mut samples = kappas - .iter() - .flat_map(|subject_chains| subject_chains.iter()) - .flat_map(|chains| chains.iter()); - let Some(first) = samples.next() else { - anyhow::bail!("cannot update omega_iov without occasion samples"); - }; - let dimension = first.len(); - let mut covariance = Array2::zeros((dimension, dimension)); - let mut count = 0usize; - for kappa in std::iter::once(first).chain(samples) { - if kappa.len() != dimension { - anyhow::bail!("kappa samples have inconsistent dimensions"); - } - for row in 0..dimension { - for col in 0..dimension { - covariance[[row, col]] += kappa[row] * kappa[col]; - } - } - count += 1; - } - covariance.mapv_inplace(|value| value / count as f64); - Ok(covariance) -} - -fn correlated_random_walk( - current: &[f64], - lower: &[Vec], - standard_normals: &[f64], - scale: f64, -) -> Result> { - anyhow::ensure!( - lower.len() == current.len() - && standard_normals.len() == current.len() - && lower - .iter() - .enumerate() - .all(|(row, values)| values.len() > row), - "correlated random-walk dimensions do not match" - ); - Ok((0..current.len()) - .map(|row| { - let perturbation = (0..=row) - .map(|column| lower[row][column] * standard_normals[column]) - .sum::(); - current[row] + scale * perturbation - }) - .collect()) -} - -fn initial_proposal_step_sizes(omega: &Array2, rw_init: f64) -> Vec { - (0..omega.nrows()) - .map(|index| omega[[index, index]].max(f64::EPSILON).sqrt() * rw_init) - .collect() -} - -fn adapt_component_step_size(current: f64, acceptance_rate: f64) -> f64 { - adapt_block_step_size(current, acceptance_rate, COMPONENT_TARGET_ACCEPTANCE) -} - -fn adapt_block_step_size(current: f64, acceptance_rate: f64, target: f64) -> f64 { - if acceptance_rate > target { - (current * PROPOSAL_SCALE_INCREASE).min(MAX_PROPOSAL_SCALE) - } else { - (current * PROPOSAL_SCALE_DECREASE).max(MIN_PROPOSAL_SCALE) - } -} - -fn zero_eta_subject_phi( - population_parameters: &[f64], - initialization: &SaemInitialization, -) -> Result>> { - let phi = population_phi(population_parameters, &initialization.parameter_scales)?; - Ok(vec![phi; initialization.subject_ids.len()]) -} - -fn negative_log_likelihood(subject_log_likelihoods: &[f64]) -> f64 { - if subject_log_likelihoods.iter().any(|ll| !ll.is_finite()) { - f64::INFINITY - } else { - -subject_log_likelihoods.iter().sum::() - } -} - -fn count_observations(data: &Data) -> usize { - data.subjects() - .iter() - .flat_map(|subject| subject.occasions()) - .flat_map(|occasion| occasion.events()) - .filter(|event| matches!(event, Event::Observation(_))) - .count() -} - -fn n_chains(config: &SaemConfig, n_subjects: usize) -> usize { - if n_subjects > 0 && n_subjects < 50 && config.n_chains == 1 { - ((50.0 / n_subjects as f64).ceil() as usize).max(1) - } else { - config.n_chains - } -} - -fn initial_parameter_row<'a>( - parameters: impl IntoIterator, -) -> Vec { - parameters - .into_iter() - .map(initial_parameter_value) - .collect() -} - -fn initial_parameter_value(parameter: &UnboundedParameter) -> f64 { - if let Some(initial) = parameter.initial { - return initial; - } - - match parameter.scale { - ParameterScale::Identity | ParameterScale::Log => 1.0, - ParameterScale::Logit { lower, upper } | ParameterScale::Probit { lower, upper } => { - 0.5 * (lower + upper) - } - } -} - -fn information_failure_status(reason: String) -> InformationStatus { - if reason.contains("censored") { - InformationStatus::Unsupported(reason) - } else if reason.contains("non-finite") { - InformationStatus::NonFinite - } else { - InformationStatus::Ineligible(reason) - } -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::estimation::parametric::transforms::{phi_to_psi, psi_to_phi}; - use crate::estimation::parametric::ParametricPrior; - use crate::estimation::{EstimationProblem, Iov, Omega, ParametricErrorModel}; - use crate::model::Parameter; - use crate::results::{ - FitResult, PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, - PopulationUncertaintyStatus, - }; - use pharmsol::prelude::*; - use pharmsol::SubjectBuilderExt; - - #[test] - fn finite_improvement_is_eligible_without_a_convergence_flag() { - assert!(non_iiv_candidate_improves(10.0, 9.0)); - assert!(!non_iiv_candidate_improves(10.0, 10.0)); - assert!(!non_iiv_candidate_improves(10.0, f64::NAN)); - } - - #[test] - fn censored_information_failure_has_explicit_unsupported_status() { - let reason = "analytic information is unsupported for censored observations".to_string(); - assert_eq!( - information_failure_status(reason.clone()), - InformationStatus::Unsupported(reason) - ); - } - - fn one_compartment_metadata() -> pharmsol::equation::ModelMetadata { - equation::metadata::new("one_compartment_saem") - .parameters(["ke", "v"]) - .states(["central"]) - .outputs(["0"]) - .route(equation::Route::bolus("0").to_state("central")) - } - - fn one_compartment() -> pharmsol::ODE { - equation::ODE::new( - |x, p, _t, dx, b, _rateiv, _cov| { - fetch_params!(p, ke); - dx[0] = -ke * x[0] + b[0]; - }, - |_p, _t, _cov| lag! {}, - |_p, _t, _cov| fa! {}, - |_p, _t, _cov, _x| {}, - |x, p, _t, _cov, y| { - fetch_params!(p, v); - y[0] = x[0] / v; - }, - ) - .with_nstates(1) - .with_ndrugs(1) - .with_nout(1) - .with_metadata(one_compartment_metadata()) - .unwrap() - } - - fn sparse_second_output_problem() -> EstimationProblem { - let equation = equation::ODE::new( - |x, p, _t, dx, b, _rateiv, _cov| { - fetch_params!(p, ke); - dx[0] = -ke * x[0] + b[0]; - }, - |_p, _t, _cov| lag! {}, - |_p, _t, _cov| fa! {}, - |_p, _t, _cov, _x| {}, - |x, p, _t, _cov, y| { - fetch_params!(p, v); - y[0] = x[0]; - y[1] = x[0] / v; - }, - ) - .with_nstates(1) - .with_ndrugs(1) - .with_nout(2) - .with_metadata( - equation::metadata::new("sparse_second_output") - .parameters(["ke", "v"]) - .states(["central"]) - .outputs(["unmeasured", "measured"]) - .route(equation::Route::bolus("dose").to_state("central")), - ) - .unwrap(); - let data = Data::new(vec![Subject::builder("sparse") - .bolus(0.0, 100.0, "dose") - .observation(1.0, 8.0, "measured") - .observation(2.0, 6.0, "measured") - .build()]); - - EstimationProblem::parametric(equation, data) - .parameter( - Parameter::log("ke") - .with_initial(0.2) - .fixed() - .without_random_effect(), - ) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .error_model("measured", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn mixed_residual_output_problem() -> EstimationProblem { - let equation = equation::ODE::new( - |x, p, _t, dx, b, _rateiv, _cov| { - fetch_params!(p, ke); - dx[0] = -ke * x[0] + b[0]; - }, - |_p, _t, _cov| lag! {}, - |_p, _t, _cov| fa! {}, - |_p, _t, _cov, _x| {}, - |x, p, _t, _cov, y| { - fetch_params!(p, v); - y[0] = x[0] / v; - y[1] = x[0] / v; - }, - ) - .with_nstates(1) - .with_ndrugs(1) - .with_nout(2) - .with_metadata( - equation::metadata::new("mixed_residual_outputs") - .parameters(["ke", "v"]) - .states(["central"]) - .outputs(["fixed", "mixed"]) - .route(equation::Route::bolus("dose").to_state("central")), - ) - .expect("mixed residual equation metadata should validate"); - let data = Data::new(vec![Subject::builder("mixed") - .bolus(0.0, 100.0, "dose") - .observation(1.0, 8.5, "fixed") - .observation(2.0, 6.5, "fixed") - .observation(1.0, 8.0, "mixed") - .observation(2.0, 6.0, "mixed") - .build()]); - - EstimationProblem::parametric(equation, data) - .parameter( - Parameter::log("ke") - .with_initial(0.2) - .fixed() - .without_random_effect(), - ) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .error_model( - "fixed", - ParametricErrorModel::new(ResidualErrorModel::constant(0.5)).fixed(), - ) - .error_model( - "mixed", - ParametricErrorModel::new(ResidualErrorModel::combined(0.0, 0.1)) - .fixed_combined_additive(), - ) - .build() - .expect("mixed residual output problem should validate") - } - - fn data() -> Data { - Data::new(vec![ - Subject::builder("s1") - .bolus(0.0, 100.0, "0") - .observation(1.0, 12.0, "0") - .observation(4.0, 4.0, "0") - .build(), - Subject::builder("s2") - .bolus(0.0, 80.0, "0") - .observation(0.5, 9.0, "0") - .observation(3.0, 2.5, "0") - .build(), - ]) - } - - fn covariate_problem() -> EstimationProblem { - let subjects = [-1.0, 0.0, 1.0] - .into_iter() - .enumerate() - .map(|(index, wt)| { - Subject::builder(format!("cov{index}")) - .covariate("wt", 0.0, wt) - .covariate("sex", 0.0, if index == 2 { 1.0 } else { 0.0 }) - .bolus(0.0, 100.0, "0") - .observation(1.0, 8.0 + index as f64, "0") - .build() - }) - .collect(); - EstimationProblem::parametric(one_compartment(), Data::new(subjects)) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .covariate_effect( - crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) - .with_initial(0.0), - ) - .covariate_effect( - crate::estimation::parametric::CovariateEffect::categorical("v", "sex", 0.0, 1.0) - .with_initial(0.0), - ) - .error_model( - "0", - ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), - ) - .build() - .unwrap() - } - - fn fixed_covariate_iiv_problem() -> EstimationProblem { - let subjects = [-1.0, 1.0] - .into_iter() - .enumerate() - .map(|(index, wt)| { - Subject::builder(format!("fixed-cov-iiv-{index}")) - .covariate("wt", 0.0, wt) - .bolus(0.0, 100.0, "0") - .observation(1.0, 5.0 + index as f64, "0") - .build() - }) - .collect(); - EstimationProblem::parametric(one_compartment(), Data::new(subjects)) - .parameter(Parameter::log("ke").with_initial(0.2).fixed()) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .omega(Omega::diagonal([("ke", 1.0)])) - .covariate_effect( - crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) - .with_initial(0.0) - .fixed(), - ) - .error_model( - "0", - ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), - ) - .build() - .unwrap() - } - - fn fixed_covariate_without_iiv_problem() -> EstimationProblem { - let subjects = [0.0, 1.0] - .into_iter() - .enumerate() - .map(|(index, wt)| { - Subject::builder(format!("fixed-cov-{index}")) - .covariate("wt", 0.0, wt) - .bolus(0.0, 100.0, "0") - .observation(1.0, 5.0 + index as f64, "0") - .build() - }) - .collect(); - EstimationProblem::parametric(one_compartment(), Data::new(subjects)) - .parameter( - Parameter::log("ke") - .with_initial(0.2) - .fixed() - .without_random_effect(), - ) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .covariate_effect( - crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) - .with_initial(0.2) - .fixed(), - ) - .error_model( - "0", - ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), - ) - .build() - .unwrap() - } - - fn problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .error_model( - "0", - ParametricErrorModel::new(ResidualErrorModel::combined(0.5, 0.1)).fixed(), - ) - .build() - .unwrap() - } - - fn constant_error_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn partial_iiv_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn iov_data() -> Data { - Data::new(vec![Subject::builder("s1") - .bolus(0.0, 100.0, "0") - .observation(1.0, 12.0, "0") - .reset() - .bolus(0.0, 100.0, "0") - .observation(1.0, 10.0, "0") - .build()]) - } - - fn iov_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), iov_data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .iov(Iov::diagonal([("ke", 0.1)])) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn markov_iov_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), iov_data()) - .parameter(Parameter::log("ke").with_initial(0.2).fixed()) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .omega(Omega::new().fixed_variance("ke", 0.1)) - .iov(Iov::new().fixed_variance("ke", 0.1)) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn uneven_iov_problem() -> EstimationProblem { - let data = Data::new(vec![ - Subject::builder("one") - .bolus(0.0, 100.0, "0") - .observation(1.0, 12.0, "0") - .build(), - Subject::builder("two") - .bolus(0.0, 100.0, "0") - .observation(1.0, 12.0, "0") - .reset() - .bolus(0.0, 100.0, "0") - .observation(1.0, 10.0, "0") - .build(), - Subject::builder("three") - .bolus(0.0, 100.0, "0") - .observation(1.0, 12.0, "0") - .reset() - .bolus(0.0, 100.0, "0") - .observation(1.0, 10.0, "0") - .reset() - .bolus(0.0, 100.0, "0") - .observation(1.0, 11.0, "0") - .build(), - ]); - EstimationProblem::parametric(one_compartment(), data) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .iov(Iov::diagonal([("ke", 0.1)])) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn configured_iov_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), iov_data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .iov( - Iov::diagonal([("ke", 0.10)]) - .fixed_variance("v", 0.20) - .fixed_covariance("ke", "v", 0.05), - ) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn ordered_metadata_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), iov_data()) - .parameter(Parameter::real("ke").with_initial(0.2)) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .iov(Iov::diagonal([("v", 0.20)])) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn configured_omega_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .omega(Omega::diagonal([("ke", 0.25)]).fixed_variance("v", 0.5)) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn correlated_omega_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .omega(Omega::diagonal([("ke", 0.25), ("v", 0.25)]).covariance("ke", "v", 0.20)) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn fixed_population_iiv_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2).fixed()) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - fn fixed_no_iiv_problem() -> EstimationProblem { - EstimationProblem::parametric(one_compartment(), data()) - .parameter( - Parameter::log("ke") - .with_initial(0.2) - .fixed() - .without_random_effect(), - ) - .parameter( - Parameter::log("v") - .with_initial(10.0) - .fixed() - .without_random_effect(), - ) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap() - } - - #[test] - fn initialization_builds_initial_objective() { - let initialization = - SaemInitialization::create(&problem(), &SaemConfig::default()).unwrap(); - - assert_eq!( - initialization.initial_population_parameters, - vec![0.2, 10.0] - ); - assert_eq!(initialization.initial_subject_log_likelihoods.len(), 2); - assert!(initialization.initial_negative_log_likelihood.is_finite()); - } - - #[test] - fn initialization_rejects_estimated_iiv_variance_below_floor() { - let mut config = SaemConfig::new(); - config.omega_min_variance = 0.3; - - let error = SaemInitialization::create(&configured_omega_problem(), &config) - .unwrap_err() - .to_string(); - - assert!(error.contains( - "initial Omega variance for estimated effect 'ke' (0.25) is below configured omega_min_variance (0.3)" - )); - } - - #[test] - fn initialization_rejects_estimated_iov_variance_below_floor() { - let config = SaemConfig::new().omega_iov_min_variance(0.11); - - let error = SaemInitialization::create(&configured_iov_problem(), &config) - .unwrap_err() - .to_string(); - - assert!(error.contains( - "initial Omega_IOV variance for estimated effect 'ke' (0.1) is below configured omega_iov_min_variance (0.11)" - )); - } - - #[test] - fn initialization_floor_exempts_fixed_covariance_diagonals() { - let problem = EstimationProblem::parametric(one_compartment(), data()) - .parameter(Parameter::log("ke").with_initial(0.2)) - .parameter(Parameter::log("v").with_initial(10.0)) - .omega(Omega::diagonal([("ke", 0.25)]).fixed_variance("v", 0.01)) - .error_model("0", ResidualErrorModel::constant(1.0)) - .build() - .unwrap(); - let mut config = SaemConfig::new(); - config.omega_min_variance = 0.1; - - let initialization = SaemInitialization::create(&problem, &config).unwrap(); - - assert_eq!(initialization.omega.initial()[[0, 0]], 0.25); - assert_eq!(initialization.omega.initial()[[1, 1]], 0.01); - assert!(!initialization.omega.estimated_mask()[[1, 1]]); - } - - #[test] - fn schedule_counts_real_internal_phases() { - let config = SaemConfig::new() - .burn_in(100) - .k1_iterations(400) - .k2_iterations(700); - let schedule = SaemSchedule::from_config(&config); - let counts = (1..=schedule.total_iterations).fold([0_usize; 3], |mut counts, cycle| { - match schedule.phase(cycle) { - SaemPhase::BurnIn => counts[0] += 1, - SaemPhase::Exploration => counts[1] += 1, - SaemPhase::Smoothing => counts[2] += 1, - } - counts - }); - - assert_eq!(counts, [100, 300, 700]); - assert_eq!(schedule.total_iterations, 1100); - } - - #[test] - fn covariate_omega_cap_applies_only_during_exploration() { - assert_eq!( - covariate_omega_update_maximum_fraction(true, SaemPhase::BurnIn, 0.1), - 1.0 - ); - assert_eq!( - covariate_omega_update_maximum_fraction(true, SaemPhase::Exploration, 0.1), - 0.1 - ); - assert_eq!( - covariate_omega_update_maximum_fraction(true, SaemPhase::Smoothing, 0.1), - 1.0 - ); - assert_eq!( - covariate_omega_update_maximum_fraction(false, SaemPhase::Exploration, 0.1), - 1.0 - ); - } - - #[derive(Debug)] - struct CommonMomentCycle { - expected_phi: Vec>, - global_second_moment: Array2, - beta: Vec, - subject_means: Vec>, - covariance_target: Array2, - omega: Array2, - } - - fn common_moment_cycle( - statistics: &mut CovariateSufficientStatistics, - observed: &CovariateSufficientStatistics, - gain: f64, - designs: &[Array2], - current_omega: &Array2, - omega_specification: &ResolvedOmega, - ) -> Result { - statistics.stochastic_update(observed, gain)?; - let offsets = vec![vec![0.0]; designs.len()]; - let beta = solve_covariate_gls(CovariateGlsProblem { - design: designs, - expected_phi: &statistics.expected_phi, - offset: &offsets, - omega: current_omega, - })?; - let subject_means = designs - .iter() - .map(|design| vec![design[[0, 0]] * beta[0] + design[[0, 1]] * beta[1]]) - .collect::>(); - let covariance_target = subject_centered_omega( - &statistics.global_second_moment, - &statistics.expected_phi, - &subject_means, - )?; - let omega = omega_specification - .update_with_status(current_omega, &covariance_target, 1e-6)? - .matrix; - Ok(CommonMomentCycle { - expected_phi: statistics.expected_phi.clone(), - global_second_moment: statistics.global_second_moment.clone(), - beta, - subject_means, - covariance_target, - omega, - }) - } - - fn assert_nested_close(actual: &[Vec], expected: &[Vec]) { - assert_eq!(actual.len(), expected.len()); - for (actual_row, expected_row) in actual.iter().zip(expected) { - assert_eq!(actual_row.len(), expected_row.len()); - for (actual_value, expected_value) in actual_row.iter().zip(expected_row) { - assert!((actual_value - expected_value).abs() <= 1e-12); - } - } - } - - #[test] - fn common_gain_raw_moments_are_coherent_cycle_by_cycle() { - let designs = [-1.0, 0.0, 1.0] - .into_iter() - .map(|covariate| ndarray::array![[1.0, covariate]]) - .collect::>(); - let parameters = [Parameter::log("x")].into_iter().collect(); - let prior = - ParametricPrior::new(parameters, Some(Omega::diagonal([("x", 1.0)])), None).unwrap(); - let mut current_omega = prior.omega().clone(); - let mut statistics = CovariateSufficientStatistics { - expected_phi: vec![vec![0.0]; 3], - global_second_moment: ndarray::array![[1.0]], - }; - let exploration_observed = CovariateSufficientStatistics::from_subject_chains(&[ - vec![vec![-1.4], vec![-0.6]], - vec![vec![-0.4], vec![0.4]], - vec![vec![0.6], vec![1.4]], - ]) - .unwrap(); - let first_smoothing_observed = CovariateSufficientStatistics::from_subject_chains(&[ - vec![vec![-1.5], vec![-0.5]], - vec![vec![0.5], vec![1.5]], - vec![vec![2.5], vec![3.5]], - ]) - .unwrap(); - let second_smoothing_observed = CovariateSufficientStatistics::from_subject_chains(&[ - vec![vec![-3.0], vec![-1.0]], - vec![vec![-1.0], vec![1.0]], - vec![vec![1.0], vec![3.0]], - ]) - .unwrap(); - - let burn = common_moment_cycle( - &mut statistics, - &exploration_observed, - 0.0, - &designs, - ¤t_omega, - prior.resolved_omega(), - ) - .unwrap(); - assert_eq!(burn.expected_phi, vec![vec![0.0]; 3]); - assert_eq!(burn.global_second_moment, ndarray::array![[1.0]]); - assert_eq!(burn.beta, vec![0.0, 0.0]); - assert_eq!(burn.subject_means, vec![vec![0.0]; 3]); - assert_eq!(burn.covariance_target, ndarray::array![[1.0]]); - assert_eq!(burn.omega, ndarray::array![[1.0]]); - - let exploration = common_moment_cycle( - &mut statistics, - &exploration_observed, - 1.0, - &designs, - ¤t_omega, - prior.resolved_omega(), - ) - .unwrap(); - assert_nested_close( - &exploration.expected_phi, - &[vec![-1.0], vec![0.0], vec![1.0]], - ); - assert!((exploration.global_second_moment[[0, 0]] - 62.0 / 75.0).abs() <= 1e-12); - assert!((exploration.beta[0] - 0.0).abs() <= 1e-12); - assert!((exploration.beta[1] - 1.0).abs() <= 1e-12); - assert_nested_close(&exploration.subject_means, &exploration.expected_phi); - assert!((exploration.covariance_target[[0, 0]] - 0.16).abs() <= 1e-12); - assert!((exploration.omega[[0, 0]] - 0.16).abs() <= 1e-12); - current_omega = exploration.omega.clone(); - - let first_smoothing = common_moment_cycle( - &mut statistics, - &first_smoothing_observed, - 1.0, - &designs, - ¤t_omega, - prior.resolved_omega(), - ) - .unwrap(); - assert_nested_close( - &first_smoothing.expected_phi, - &[vec![-1.0], vec![1.0], vec![3.0]], - ); - assert!((first_smoothing.global_second_moment[[0, 0]] - 47.0 / 12.0).abs() <= 1e-12); - assert!((first_smoothing.beta[0] - 1.0).abs() <= 1e-12); - assert!((first_smoothing.beta[1] - 2.0).abs() <= 1e-12); - assert_nested_close( - &first_smoothing.subject_means, - &first_smoothing.expected_phi, - ); - assert!((first_smoothing.covariance_target[[0, 0]] - 0.25).abs() <= 1e-12); - assert!((first_smoothing.omega[[0, 0]] - 0.25).abs() <= 1e-12); - current_omega = first_smoothing.omega.clone(); - - let second_smoothing = common_moment_cycle( - &mut statistics, - &second_smoothing_observed, - 0.5, - &designs, - ¤t_omega, - prior.resolved_omega(), - ) - .unwrap(); - assert_nested_close( - &second_smoothing.expected_phi, - &[vec![-1.5], vec![0.5], vec![2.5]], - ); - assert!((second_smoothing.global_second_moment[[0, 0]] - 91.0 / 24.0).abs() <= 1e-12); - assert!((second_smoothing.beta[0] - 0.5).abs() <= 1e-12); - assert!((second_smoothing.beta[1] - 2.0).abs() <= 1e-12); - assert_nested_close( - &second_smoothing.subject_means, - &second_smoothing.expected_phi, - ); - assert!((second_smoothing.covariance_target[[0, 0]] - 0.875).abs() <= 1e-12); - assert!((second_smoothing.omega[[0, 0]] - 0.875).abs() <= 1e-12); - - for cycle in [burn, exploration, first_smoothing, second_smoothing] { - let mean_square = cycle - .expected_phi - .iter() - .map(|row| row[0] * row[0]) - .sum::() - / cycle.expected_phi.len() as f64; - assert!(cycle.global_second_moment[[0, 0]] + 1e-12 >= mean_square); - assert!(cycle.covariance_target[[0, 0]] >= -1e-12); - } - } - - #[test] - fn coherent_covariance_target_precedes_structured_gem_constraints() { - let coherent_target = ndarray::array![[0.002, 0.0], [0.0, 0.04]]; - assert!(cholesky_lower(&coherent_target).is_ok()); - let parameters = [Parameter::log("ke"), Parameter::log("v")] - .into_iter() - .collect(); - let prior = ParametricPrior::new( - parameters, - Some( - Omega::new() - .variance("ke", 0.02) - .fixed_variance("v", 0.04) - .fixed_covariance("ke", "v", 0.012), - ), - None, - ) - .unwrap(); - - let constrained = prior - .resolved_omega() - .update_with_status(prior.omega(), &coherent_target, 0.0) - .unwrap(); - - assert_eq!(coherent_target[[0, 0]], 0.002); - assert!((constrained.matrix[[0, 0]] - 0.0092).abs() <= 1e-10); - assert_eq!(constrained.matrix[[0, 1]], 0.012); - assert_eq!(constrained.matrix[[1, 1]], 0.04); - assert_ne!(constrained.matrix, coherent_target); - } - - #[test] - fn covariate_update_uses_common_moments_and_no_second_smoothing_gain() { - let mut statistics = - CovariateSufficientStatistics::from_subject_chains(&[vec![vec![0.0], vec![2.0]]]) - .unwrap(); - let exploration_observed = - CovariateSufficientStatistics::from_subject_chains(&[vec![vec![2.0], vec![4.0]]]) - .unwrap(); - statistics - .stochastic_update(&exploration_observed, 1.0) - .unwrap(); - assert_eq!(statistics.expected_phi, vec![vec![3.0]]); - assert_eq!(statistics.global_second_moment, ndarray::array![[10.0]]); - let exploration_variance = statistics.global_second_moment[[0, 0]] - - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; - let exploration_candidate = ndarray::array![[exploration_variance]]; - assert_eq!(exploration_candidate, ndarray::array![[1.0]]); - - let parameters = [Parameter::log("x")].into_iter().collect(); - let prior = - ParametricPrior::new(parameters, Some(Omega::diagonal([("x", 0.25)])), None).unwrap(); - let exploration = prior - .resolved_omega() - .update_with_status_and_max_fraction( - prior.omega(), - &exploration_candidate, - 0.0, - covariate_omega_update_maximum_fraction(true, SaemPhase::Exploration, 0.1), - ) - .unwrap(); - assert!((exploration.matrix[[0, 0]] - 0.325).abs() <= 1e-12); - - let smoothing_observed = - CovariateSufficientStatistics::from_subject_chains(&[vec![vec![4.0], vec![6.0]]]) - .unwrap(); - statistics - .stochastic_update(&smoothing_observed, 0.5) - .unwrap(); - assert_eq!(statistics.expected_phi, vec![vec![4.0]]); - assert_eq!(statistics.global_second_moment, ndarray::array![[18.0]]); - let smoothing_variance = statistics.global_second_moment[[0, 0]] - - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; - let smoothing_candidate = ndarray::array![[smoothing_variance]]; - assert_eq!(smoothing_candidate, ndarray::array![[2.0]]); - - let smoothing = prior - .resolved_omega() - .update_with_status(&exploration.matrix, &smoothing_candidate, 0.0) - .unwrap(); - assert_eq!(smoothing.matrix, smoothing_candidate); - } - - #[test] - fn covariate_state_m_step_caps_exploration_and_does_not_resmooth_omega() { - let config = SaemConfig::new() - .n_chains(2) - .mcmc_iterations(1) - .burn_in(1) - .k1_iterations(2) - .k2_iterations(2) - .omega_sa_max_step(0.1) - .compute_map(false); - let mut state = SaemState::from_problem(fixed_covariate_iiv_problem(), &config).unwrap(); - - for subject_chains in &mut state.etas { - subject_chains[0][0] = 2.0; - subject_chains[1][0] = -2.0; - } - state.cycle = 2; - assert_eq!( - state.initialization.schedule.phase(state.cycle), - SaemPhase::Exploration - ); - assert_eq!( - state - .initialization - .schedule - .stochastic_approximation_step(state.cycle), - 1.0 - ); - state.m_step().unwrap(); - - assert!((state.iiv_second_moment[[0, 0]] - 4.0).abs() <= 1e-12); - assert!((state.omega[[0, 0]] - 1.3).abs() <= 1e-12); - - for subject_chains in &mut state.etas { - subject_chains[0][0] = 4.0; - subject_chains[1][0] = -4.0; - } - state.cycle = 4; - assert_eq!( - state.initialization.schedule.phase(state.cycle), - SaemPhase::Smoothing - ); - assert_eq!( - state - .initialization - .schedule - .stochastic_approximation_step(state.cycle), - 0.5 - ); - state.m_step().unwrap(); - - // The common raw history moves from variance 4 toward 16 with gain 0.5, - // giving 10. Omega installs that coherent target directly. Applying the - // smoothing gain a second time would instead leave Omega below 10. - assert!((state.iiv_second_moment[[0, 0]] - 10.0).abs() <= 1e-12); - assert!((state.omega[[0, 0]] - 10.0).abs() <= 1e-12); - } - - #[test] - fn schedule_splits_burn_in_exploration_and_smoothing() { - let config = SaemConfig::new() - .k1_iterations(300) - .k2_iterations(100) - .burn_in(5); - let schedule = SaemSchedule::from_config(&config); - - assert_eq!(schedule.pure_burn_in, 5); - assert_eq!(schedule.exploration_iterations, 295); - assert_eq!(schedule.smoothing_iterations, 100); - assert_eq!(schedule.total_iterations, 400); - assert_eq!(schedule.variance_floor_iterations, 150); - assert_eq!(schedule.minimum_residual_sigma, 1e-6); - assert_eq!(schedule.stochastic_approximation_step(1), 0.0); - assert_eq!(schedule.stochastic_approximation_step(6), 1.0); - assert_eq!(schedule.stochastic_approximation_step(301), 1.0); - assert_eq!(schedule.stochastic_approximation_step(302), 0.5); - assert_eq!(schedule.covariance_step(1), 0.1); - assert_eq!(schedule.covariance_step(6), 0.1); - assert_eq!(schedule.covariance_step(300), 0.1); - assert_eq!(schedule.covariance_step(301), 1.0); - assert_eq!(schedule.covariance_step(302), 0.5); - assert!(!schedule.covariance_update_active(5)); - assert!(schedule.covariance_update_active(6)); - assert_eq!(schedule.guarded_residual_sigma(1, 1.0, 0.1), 0.97); - assert_eq!(schedule.guarded_residual_sigma(151, 1.0, 0.1), 0.1); - assert_eq!(schedule.guarded_residual_sigma(151, 1.0, 0.0), 1e-6); - } - - #[test] - fn averaged_schedule_uses_alpha_only_during_smoothing() { - let schedule = SaemSchedule::from_config( - &SaemConfig::new() - .k1_iterations(3) - .burn_in(1) - .k2_iterations(4) - .averaged_iterates(0.75), - ); - assert_eq!(schedule.stochastic_approximation_step(1), 0.0); - assert_eq!(schedule.stochastic_approximation_step(2), 1.0); - assert_eq!(schedule.stochastic_approximation_step(3), 1.0); - assert_eq!(schedule.stochastic_approximation_step(4), 1.0); - assert_eq!( - schedule.stochastic_approximation_step(5), - 2.0_f64.powf(-0.75) - ); - assert_eq!( - schedule.stochastic_approximation_step(7), - 4.0_f64.powf(-0.75) - ); - } - - #[test] - fn averaged_result_uses_only_completed_smoothing_iterates() { - let config = SaemConfig::new() - .k1_iterations(2) - .burn_in(1) - .k2_iterations(3) - .averaged_iterates(0.75) - .compute_map(false) - .seed(9981); - let result = problem().fit_with(config).unwrap(); - let metadata = result.estimator_metadata(); - assert!(metadata.average_applied); - assert_eq!(metadata.averaging_start_cycle, Some(3)); - assert_eq!(metadata.averaged_iterations, 3); - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - - let smoothing = &result.cycle_diagnostics()[2..]; - for parameter_index in 0..result.population_parameters().len() { - if !result.estimated_parameters()[parameter_index] { - continue; - } - let expected = smoothing - .iter() - .map(|cycle| { - population_phi(&cycle.population_parameters, result.parameter_scales()).unwrap() - [parameter_index] - }) - .sum::() - / smoothing.len() as f64; - let installed = - population_phi(result.population_parameters(), result.parameter_scales()).unwrap() - [parameter_index]; - assert!((installed - expected).abs() < 1e-12); - } - for row in 0..result.omega().nrows() { - for col in 0..result.omega().ncols() { - let expected = smoothing - .iter() - .map(|cycle| cycle.omega[[row, col]]) - .sum::() - / smoothing.len() as f64; - assert!((result.omega()[[row, col]] - expected).abs() < 1e-12); - } - } - cholesky_lower(result.omega()).unwrap(); - } - - #[test] - fn averaged_iov_installation_is_canonical_and_preserves_latent_coordinates() { - let config = SaemConfig::new() - .n_chains(2) - .mcmc_iterations(2) - .k1_iterations(1) - .k2_iterations(3) - .burn_in(0) - .averaged_iterates(0.75) - .compute_map(false) - .seed(71_004); - let mut state = SaemState::from_problem(configured_iov_problem(), &config) - .expect("averaged IOV state should initialize"); - while matches!(state.status, Status::Continue) { - state.step().expect("averaged IOV cycle should complete"); - } - let cycle_records = state.cycle_diagnostics.clone(); - let smoothing = &cycle_records[1..]; - let terminal_phi = population_phi( - &state.population_parameters, - &state.initialization.parameter_scales, - ) - .expect("terminal population phi should be valid"); - let terminal_absolute_phi = state - .etas - .iter() - .map(|chains| { - chains - .iter() - .map(|eta| { - state - .initialization - .random_effect_indices - .iter() - .enumerate() - .map(|(eta_index, parameter_index)| { - terminal_phi[*parameter_index] + eta[eta_index] - }) - .collect::>() - }) - .collect::>() - }) - .collect::>(); - let terminal_kappas = state.kappas.clone(); - let average = state - .iterate_average - .clone() - .expect("completed smoothing average"); - - let metadata = state - .install_iterate_average() - .expect("averaged IOV state should install"); - assert!(metadata.average_applied); - assert_eq!(metadata.averaging_start_cycle, Some(2)); - assert_eq!(metadata.averaged_iterations, 3); - assert_eq!(state.cycle_diagnostics, cycle_records); - assert_eq!(state.kappas, terminal_kappas); - - let installed_phi = population_phi( - &state.population_parameters, - &state.initialization.parameter_scales, - ) - .expect("installed population phi should be valid"); - assert_eq!(installed_phi, average.population_phi); - for (subject_index, chains) in state.etas.iter().enumerate() { - for (chain_index, eta) in chains.iter().enumerate() { - for (eta_index, parameter_index) in state - .initialization - .random_effect_indices - .iter() - .copied() - .enumerate() - { - assert!( - (installed_phi[parameter_index] + eta[eta_index] - - terminal_absolute_phi[subject_index][chain_index][eta_index]) - .abs() - < 1e-14 - ); - } - } - } - - let omega_iov = state.omega_iov.as_ref().expect("installed Omega_IOV"); - let iov_specification = state - .initialization - .omega_iov - .as_ref() - .expect("IOV specification"); - assert_eq!(omega_iov, &average.omega_iov.expect("averaged Omega_IOV")); - for row in 0..omega_iov.nrows() { - for col in 0..omega_iov.ncols() { - let expected = if iov_specification.estimated_mask()[[row, col]] { - smoothing - .iter() - .map(|cycle| { - cycle - .omega_iov - .as_ref() - .expect("smoothing cycle should retain Omega_IOV")[[row, col]] - }) - .sum::() - / smoothing.len() as f64 - } else { - iov_specification.initial()[[row, col]] - }; - assert!((omega_iov[[row, col]] - expected).abs() < 1e-12); - } - } - - let n_chains = state.initialization.n_chains as f64; - let mut direct_likelihoods = vec![0.0; state.initialization.subject_ids.len()]; - let mut direct_eta_priors = vec![0.0; state.initialization.subject_ids.len()]; - let mut direct_kappa_priors = vec![0.0; state.initialization.subject_ids.len()]; - for subject_index in 0..state.initialization.subject_ids.len() { - for chain_index in 0..state.initialization.n_chains { - let score = state - .score_subject_latents( - subject_index, - &state.etas[subject_index][chain_index], - &state.kappas[subject_index][chain_index], - ) - .expect("installed latent score should be directly calculable"); - direct_likelihoods[subject_index] += score.log_likelihood / n_chains; - direct_eta_priors[subject_index] += score.eta_log_prior / n_chains; - direct_kappa_priors[subject_index] += score.kappa_log_prior / n_chains; - } - } - assert_eq!(state.subject_log_likelihoods, direct_likelihoods); - assert_eq!(state.subject_log_priors, direct_eta_priors); - assert_eq!(state.subject_kappa_log_priors, direct_kappa_priors); - assert_eq!( - state.negative_log_likelihood, - negative_log_likelihood(&direct_likelihoods) - ); - } - - #[test] - fn frozen_markov_diagnostic_is_repeatable_and_canonical_result_is_unchanged() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - - let base = SaemConfig::new() - .k1_iterations(100) - .k2_iterations(50) - .burn_in(1) - .n_chains(2) - .eta_block_iterations(1) - .compute_map(true) - .seed(91) - .averaged_iterates(0.75); - let diagnostic_config = MarkovSimulationVarianceConfig::new( - 700, - 2, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 64 * 1024, - ); - let disabled = markov_iov_problem().fit_with(base.clone()).unwrap(); - let enabled = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(diagnostic_config)) - .unwrap(); - let repeated = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(diagnostic_config)) - .unwrap(); - let changed_seed = markov_iov_problem() - .fit_with( - base.clone() - .markov_simulation_variance(MarkovSimulationVarianceConfig::new( - 701, - 2, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 64 * 1024, - )), - ) - .unwrap(); - - assert_eq!( - enabled.markov_simulation_variance(), - repeated.markov_simulation_variance() - ); - assert_ne!( - enabled.markov_simulation_variance(), - changed_seed.markov_simulation_variance() - ); - assert_ne!( - enabled.markov_simulation_variance().status, - MarkovSimulationVarianceStatus::Disabled - ); - assert!(!enabled.markov_simulation_variance().chains.is_empty()); - // One subject, one eta block, one component eta, and two occasion-kappa - // blocks are attempted in that exact compound-kernel order per retained - // transition. Warmup attempts are absent from the exported count. - assert!(enabled - .markov_simulation_variance() - .chains - .iter() - .all(|chain| chain.proposals == 12 * (1 + 1 + 2))); - assert_eq!( - enabled.population_parameters(), - disabled.population_parameters() - ); - assert_eq!(enabled.omega(), disabled.omega()); - assert_eq!(enabled.omega_iov(), disabled.omega_iov()); - assert_eq!( - enabled.residual_error_estimates(), - disabled.residual_error_estimates() - ); - assert_eq!(enabled.eta_chain_means(), disabled.eta_chain_means()); - assert_eq!(enabled.kappa_chain_means(), disabled.kappa_chain_means()); - assert!(!enabled.conditional_modes().is_empty()); - assert_eq!(enabled.conditional_modes(), disabled.conditional_modes()); - assert_eq!( - enabled.information_diagnostics(), - disabled.information_diagnostics() - ); - assert_eq!(enabled.cycle_diagnostics(), disabled.cycle_diagnostics()); - assert_eq!(enabled.warnings(), disabled.warnings()); - assert_eq!(enabled.conditional_n2ll(), disabled.conditional_n2ll()); - assert_eq!(enabled.termination_reason(), disabled.termination_reason()); - assert_eq!( - enabled.population_parameters(), - changed_seed.population_parameters() - ); - assert_eq!(enabled.omega(), changed_seed.omega()); - assert_eq!( - enabled.residual_error_estimates(), - changed_seed.residual_error_estimates() - ); - assert_eq!(enabled.eta_chain_means(), changed_seed.eta_chain_means()); - assert_eq!( - enabled.cycle_diagnostics(), - changed_seed.cycle_diagnostics() - ); - assert_eq!(enabled.warnings(), changed_seed.warnings()); - assert_eq!(enabled.conditional_n2ll(), changed_seed.conditional_n2ll()); - let enabled_predictions = enabled.population_predictions(0.0, 0.0).unwrap(); - let disabled_predictions = disabled.population_predictions(0.0, 0.0).unwrap(); - assert_eq!(enabled_predictions.len(), disabled_predictions.len()); - for (actual, expected) in enabled_predictions.iter().zip(&disabled_predictions) { - assert_prediction_points_equal(actual, expected); - } - let enabled_conditional = enabled.conditional_predictions(0.0, 0.0).unwrap(); - let disabled_conditional = disabled.conditional_predictions(0.0, 0.0).unwrap(); - assert_eq!(enabled_conditional.len(), disabled_conditional.len()); - for (actual, expected) in enabled_conditional.iter().zip(&disabled_conditional) { - assert_prediction_points_equal(actual, expected); - } - } - - #[test] - fn rank_diagnostics_computed_for_multiple_chains_and_iov() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; - - let base = SaemConfig::new() - .k1_iterations(30) - .k2_iterations(20) - .burn_in(1) - .n_chains(2) - .eta_block_iterations(1) - .compute_map(false) - .seed(91) - .averaged_iterates(0.75); - let diag = MarkovSimulationVarianceConfig::new( - 700, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 1024 * 1024, - ); - let result = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(diag)) - .unwrap(); - let rank = &result.markov_simulation_variance().rank_diagnostics; - assert_eq!(rank.diagnostic_chains, 2); - assert_eq!(rank.draws_per_chain, 12); - assert_eq!(rank.original_chains, 2); - assert_eq!(rank.status, RankDiagnosticStatus::Available); - assert!(!rank.traces.is_empty()); - // First trace is a score coordinate. - assert!(matches!( - rank.traces[0].trace, - DiagnosticTraceCoordinate::Score { .. } - )); - let score_count = result.information_diagnostics().coordinates.len(); - let eta_count = result - .eta_chain_means() - .iter() - .map(|estimate| estimate.values.len()) - .sum::(); - let kappa_count = result - .kappa_chain_means() - .iter() - .map(|estimate| estimate.values.len()) - .sum::(); - assert_eq!(rank.traces.len(), score_count + eta_count + kappa_count); - for (trace, coordinate) in rank - .traces - .iter() - .take(score_count) - .zip(&result.information_diagnostics().coordinates) - { - assert!(matches!( - &trace.trace, - DiagnosticTraceCoordinate::Score { index, .. } if *index == coordinate.index - )); - } - assert!(rank - .traces - .iter() - .skip(score_count) - .take(eta_count) - .all(|trace| matches!(trace.trace, DiagnosticTraceCoordinate::Eta { .. }))); - assert!(rank - .traces - .iter() - .skip(score_count + eta_count) - .all(|trace| matches!(trace.trace, DiagnosticTraceCoordinate::Kappa { .. }))); - assert!(rank.diagnostic_mean_lrv.is_some()); - assert!(rank.operational_lrv.is_some()); - - // Repeatability: same seed produces identical rank diagnostics. - let repeated = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(diag)) - .unwrap(); - assert_eq!( - result.markov_simulation_variance().rank_diagnostics, - repeated.markov_simulation_variance().rank_diagnostics - ); - - // Canonical result is unchanged by rank diagnostic presence. - let disabled = markov_iov_problem().fit_with(base).unwrap(); - assert_eq!( - result.population_parameters(), - disabled.population_parameters() - ); - assert_eq!(result.omega(), disabled.omega()); - assert_eq!(result.conditional_n2ll(), disabled.conditional_n2ll()); - assert_eq!(result.termination_reason(), disabled.termination_reason()); - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - } - - #[test] - fn score_failure_does_not_discard_valid_eta_rank_diagnostics() { - use crate::results::{ - DiagnosticTraceCoordinate, InformationCoordinateKind, RankDiagnosticStatus, - }; - - let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); - let traces = vec![ - vec![vec![f64::NAN; 8], vec![f64::NAN; 8]], - vec![ - vec![1.0, 4.0, 2.0, 3.0, 2.0, 4.0, 1.0, 3.0], - vec![2.1, 3.1, 1.1, 4.1, 3.1, 1.1, 4.1, 2.1], - ], - ]; - let coordinates = vec![ - DiagnosticTraceCoordinate::Score { - index: 0, - name: "score".into(), - kind: InformationCoordinateKind::Population { parameter_index: 0 }, - }, - DiagnosticTraceCoordinate::Eta { - subject: "1".into(), - effect_index: 0, - effect_name: "CL".into(), - }, - ]; - let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); - assert_eq!( - diagnostics[0].rank_rhat_status, - RankDiagnosticStatus::ScoreUnavailable - ); - assert!(diagnostics[0].rank_rhat.is_none()); - assert_eq!( - diagnostics[1].rank_rhat_status, - RankDiagnosticStatus::Available - ); - assert!(diagnostics[1].rank_rhat.is_some()); - } - - #[test] - fn multimodal_latent_trace_is_detected_while_mixed_score_trace_passes() { - use crate::results::{ - DiagnosticTraceCoordinate, InformationCoordinateKind, RankDiagnosticStatus, - }; - - let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); - let traces = vec![ - vec![ - vec![1.0, 4.0, 2.0, 3.0, 2.0, 4.0, 1.0, 3.0], - vec![2.1, 3.1, 1.1, 4.1, 3.1, 1.1, 4.1, 2.1], - ], - vec![ - vec![-10.0, -9.0, -11.0, -8.0, -9.5, -8.5, -10.5, -7.5], - vec![8.0, 11.0, 9.0, 10.0, 8.5, 10.5, 7.5, 9.5], - ], - ]; - let coordinates = vec![ - DiagnosticTraceCoordinate::Score { - index: 0, - name: "score".into(), - kind: InformationCoordinateKind::Population { parameter_index: 0 }, - }, - DiagnosticTraceCoordinate::Eta { - subject: "1".into(), - effect_index: 0, - effect_name: "CL".into(), - }, - ]; - let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); - assert_eq!( - diagnostics[0].rank_rhat_status, - RankDiagnosticStatus::Available - ); - assert!(diagnostics[0].rank_rhat.is_some_and(|rhat| rhat < 1.1)); - assert_eq!( - diagnostics[1].rank_rhat_status, - RankDiagnosticStatus::Available - ); - assert!(diagnostics[1].rank_rhat.is_some_and(|rhat| rhat > 1.1)); - } - - #[test] - fn rank_coordinate_retains_valid_rhats_when_bulk_ess_is_unavailable() { - use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; - - let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); - let traces = vec![vec![vec![1.0, 2.0, 4.0, 3.0], vec![1.5, 2.5, 4.5, 3.5]]]; - let coordinates = vec![DiagnosticTraceCoordinate::Eta { - subject: "1".into(), - effect_index: 0, - effect_name: "CL".into(), - }]; - let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); - let diagnostic = &diagnostics[0]; - assert!(diagnostic.rank_rhat.is_some()); - assert_eq!(diagnostic.rank_rhat_status, RankDiagnosticStatus::Available); - assert!(diagnostic.folded_rhat.is_some()); - assert_eq!( - diagnostic.folded_rhat_status, - RankDiagnosticStatus::Available - ); - assert!(diagnostic.bulk_ess.is_none()); - assert!(diagnostic.tau.is_none()); - assert_eq!( - diagnostic.bulk_ess_status, - RankDiagnosticStatus::TooFewDraws - ); - assert_eq!(diagnostic.status, RankDiagnosticStatus::PartialAvailability); - } - - #[test] - fn derived_max_rhat_requires_both_rank_and_folded_components() { - use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; - - let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); - let traces = vec![vec![ - vec![1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0], - vec![2.0, -2.0, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0], - ]]; - let coordinates = vec![DiagnosticTraceCoordinate::Eta { - subject: "1".into(), - effect_index: 0, - effect_name: "CL".into(), - }]; - - let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); - let diagnostic = &diagnostics[0]; - assert!(diagnostic.rank_rhat.is_some()); - assert_eq!(diagnostic.rank_rhat_status, RankDiagnosticStatus::Available); - assert!(diagnostic.folded_rhat.is_none()); - assert_eq!( - diagnostic.folded_rhat_status, - RankDiagnosticStatus::ConstantDraws - ); - assert!(diagnostic.max_rhat.is_none()); - assert_eq!( - diagnostic.max_rhat_status, - RankDiagnosticStatus::ConstantDraws - ); - assert_eq!(worst_valid_max_rhat(&diagnostics), None); - } - - #[test] - fn rank_diagnostics_available_when_markov_config_enabled() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - use crate::results::RankDiagnosticStatus; - - let base = SaemConfig::new() - .k1_iterations(100) - .k2_iterations(50) - .burn_in(1) - .n_chains(2) - .eta_block_iterations(1) - .compute_map(false) - .seed(77) - .averaged_iterates(0.75); - let diag = MarkovSimulationVarianceConfig::new( - 42, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 1024 * 1024, - ); - let result = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(diag)) - .unwrap(); - let rank = &result.markov_simulation_variance().rank_diagnostics; - // Rank diagnostics object is always present when markov config enabled; - // status reflects whether data supported valid computation. - assert_eq!(rank.diagnostic_chains, 2); - assert_eq!(rank.original_chains, 2); - assert!(!matches!(rank.status, RankDiagnosticStatus::Disabled)); - } - - #[test] - fn one_diagnostic_chain_retains_markov_lrv_but_rank_is_unavailable() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - use crate::results::RankDiagnosticStatus; - - let config = SaemConfig::new() - .k1_iterations(30) - .k2_iterations(20) - .burn_in(1) - .n_chains(2) - .eta_block_iterations(1) - .compute_map(false) - .seed(93) - .averaged_iterates(0.75) - .markov_simulation_variance(MarkovSimulationVarianceConfig::new( - 702, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 1, - 1024 * 1024, - )); - let result = markov_iov_problem().fit_with(config).unwrap(); - let markov = result.markov_simulation_variance(); - assert_eq!( - markov.rank_diagnostics.status, - RankDiagnosticStatus::TooFewChains - ); - assert_eq!(markov.chains.len(), 1); - assert!(!markov.lambda.is_empty()); - assert!(markov.rank_diagnostics.operational_lrv.is_some()); - assert!(markov.rank_diagnostics.traces.iter().all(|trace| { - trace.status == RankDiagnosticStatus::TooFewChains - && trace.rank_rhat.is_none() - && trace.bulk_ess.is_none() - })); - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - assert!(!result.converged()); - } - - #[test] - fn rank_diagnostics_trace_byte_cap_exceeded_is_reported() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - use crate::results::RankDiagnosticStatus; - - let base = SaemConfig::new() - .k1_iterations(100) - .k2_iterations(50) - .burn_in(1) - .n_chains(2) - .eta_block_iterations(1) - .compute_map(false) - .seed(91) - .averaged_iterates(0.75); - let tiny_cap = MarkovSimulationVarianceConfig::new( - 700, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 1, // 1 byte cap — guaranteed to be exceeded - ); - let result = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(tiny_cap)) - .unwrap(); - let rank = &result.markov_simulation_variance().rank_diagnostics; - assert_eq!(rank.status, RankDiagnosticStatus::TraceByteCapExceeded); - assert!(rank.traces.is_empty()); - assert!(rank.diagnostic_mean_lrv.is_none()); - assert!(rank.operational_lrv.is_none()); - assert_eq!(rank.max_trace_bytes, 1); - assert!(rank.accounted_peak_trace_bytes_required > rank.max_trace_bytes); - assert_eq!(rank.accounted_peak_trace_bytes_used, 0); - let markov = result.markov_simulation_variance(); - assert!(matches!( - markov.status, - MarkovSimulationVarianceStatus::InvalidConfiguration(_) - )); - assert_eq!(markov.lambda_status, markov.status); - assert_eq!(markov.xi_status, markov.status); - assert_eq!(markov.simulation_covariance_status, markov.status); - assert!(markov.chains.is_empty()); - // Canonical result is unchanged. - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - - let generous = MarkovSimulationVarianceConfig::new( - 700, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 1024 * 1024, - ); - let measured = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(generous)) - .unwrap(); - let measured_rank = &measured.markov_simulation_variance().rank_diagnostics; - let trace_count = measured_rank.traces.len(); - let score_width = measured.markov_simulation_variance().coordinates.len(); - let vec_header = std::mem::size_of::>(); - let persistent_bytes = 2 * 12 * trace_count * std::mem::size_of::() - + trace_count * 2 * vec_header - + trace_count * vec_header; - let score_transient_bytes = score_width * 12 * std::mem::size_of::() + 12 * vec_header; - let rank_transient_bytes = 2 * 12 * 8 * std::mem::size_of::() + 2 * 16 * vec_header; - let expected_bytes = persistent_bytes + score_transient_bytes.max(rank_transient_bytes); - assert_eq!( - measured_rank.accounted_peak_trace_bytes_required, - expected_bytes - ); - assert_eq!( - measured_rank.accounted_peak_trace_bytes_used, - expected_bytes - ); - - let exact_cap = MarkovSimulationVarianceConfig::new( - 700, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - expected_bytes, - ); - let exact = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(exact_cap)) - .unwrap(); - assert_eq!( - exact - .markov_simulation_variance() - .rank_diagnostics - .accounted_peak_trace_bytes_used, - expected_bytes - ); - assert!(!exact - .markov_simulation_variance() - .rank_diagnostics - .traces - .is_empty()); - - let under_cap = MarkovSimulationVarianceConfig::new( - 700, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - expected_bytes - 1, - ); - let rejected = markov_iov_problem() - .fit_with(base.clone().markov_simulation_variance(under_cap)) - .unwrap(); - assert_eq!( - rejected - .markov_simulation_variance() - .rank_diagnostics - .status, - RankDiagnosticStatus::TraceByteCapExceeded - ); - assert_eq!( - rejected - .markov_simulation_variance() - .rank_diagnostics - .accounted_peak_trace_bytes_used, - 0 - ); - - let overflow = MarkovSimulationVarianceConfig::new( - 700, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - usize::MAX / 2 + 1, - usize::MAX, - ); - let overflowed = markov_iov_problem() - .fit_with(base.markov_simulation_variance(overflow)) - .unwrap(); - let overflowed = overflowed.markov_simulation_variance(); - assert_eq!( - overflowed.rank_diagnostics.status, - RankDiagnosticStatus::TraceMemoryAccountingOverflow - ); - assert_eq!( - overflowed.status, - MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow - ); - assert_eq!( - overflowed - .rank_diagnostics - .accounted_peak_trace_bytes_required, - 0 - ); - assert_eq!( - overflowed.rank_diagnostics.accounted_peak_trace_bytes_used, - 0 - ); - assert!(overflowed.chains.is_empty()); - } - - #[test] - fn operational_and_frozen_iov_transitions_preserve_compound_kernel_order() { - let seed = 0x5eed; - let mut operational = SaemState::from_problem( - markov_iov_problem(), - &SaemConfig::new() - .n_chains(2) - .mcmc_iterations(1) - .eta_block_iterations(1) - .adapt_interval(50) - .seed(seed), - ) - .unwrap(); - let initial_eta_scales = operational.proposal_step_sizes.clone(); - let initial_eta_block_scales = operational.eta_block_step_sizes.clone(); - let initial_kappa_scales = operational.kappa_proposal_step_sizes.clone(); - let mut frozen = FrozenDiagnosticState { - etas: operational.etas.clone(), - kappas: operational.kappas.clone(), - }; - let mut frozen_rng = StdRng::seed_from_u64(seed); - let mut frozen_counts = vec![(0, 0, 0); operational.initialization.n_chains]; - - // This single compound transition is order-sensitive: eta blocks consume - // the stream first, followed by component etas and then occasion kappas. - operational - .frozen_diagnostic_transition(&mut frozen, &mut frozen_rng, &mut frozen_counts, None) - .unwrap(); - operational.e_step().unwrap(); - - assert_eq!(operational.etas, frozen.etas); - assert_eq!(operational.kappas, frozen.kappas); - assert_eq!(operational.proposal_step_sizes, initial_eta_scales); - assert_eq!(operational.eta_block_step_sizes, initial_eta_block_scales); - assert_eq!(operational.kappa_proposal_step_sizes, initial_kappa_scales); - - let diagnostics = operational.cycle_diagnostics.last().unwrap(); - let frozen_proposals = frozen_counts.iter().map(|count| count.0).sum::(); - let frozen_accepts = frozen_counts.iter().map(|count| count.1).sum::(); - let frozen_changes = frozen_counts.iter().map(|count| count.2).sum::(); - assert_eq!(diagnostics.eta_block_proposals, 2); - assert_eq!(diagnostics.eta_proposals, 4); - assert_eq!(diagnostics.kappa_proposals, 4); - assert_eq!(frozen_proposals, 8); - assert_eq!( - frozen_accepts, - diagnostics.eta_accepted + diagnostics.kappa_accepted - ); - assert_eq!(frozen_changes, frozen_accepts); - assert_eq!( - diagnostics.eta_rejected + diagnostics.kappa_rejected, - frozen_proposals - frozen_accepts - ); - assert_eq!(diagnostics.eta_non_finite, 0); - assert_eq!(diagnostics.kappa_non_finite, 0); - - let operational_continuation = operational.rng.random::(); - let frozen_continuation = frozen_rng.random::(); - assert_eq!(operational_continuation, frozen_continuation); - } - - #[test] - fn warmup_movement_cannot_satisfy_retained_movement_accounting() { - let mut counts = [(12, 7, 4), (8, 1, 1)]; - begin_retained_transition_accounting(&mut counts); - // Retained proposals that are accepted without an actual state change - // still leave the chain eligible for the exact stuck guard. - counts[0].0 += 3; - counts[0].1 += 3; - assert_eq!(counts, [(3, 3, 0), (0, 0, 0)]); - let stuck: Vec<_> = counts - .iter() - .enumerate() - .filter_map(|(chain, count)| (count.2 == 0).then_some(chain)) - .collect(); - assert_eq!(stuck, [0, 1]); - } - - #[test] - fn no_latent_state_reports_exact_zero_markov_variance() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - - let result = fixed_no_iiv_problem() - .fit_with( - SaemConfig::new() - .k1_iterations(30) - .k2_iterations(20) - .burn_in(1) - .compute_map(false) - .averaged_iterates(0.75) - .markov_simulation_variance(MarkovSimulationVarianceConfig::new( - 4, - 100, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 2, - 1024, - )), - ) - .unwrap(); - let diagnostic = result.markov_simulation_variance(); - assert_eq!( - diagnostic.status, - MarkovSimulationVarianceStatus::ExactZeroNoLatentState - ); - assert!(diagnostic.chains.is_empty()); - assert_eq!( - diagnostic.rank_diagnostics.status, - RankDiagnosticStatus::NoLatent - ); - assert!(diagnostic.rank_diagnostics.traces.is_empty()); - assert!(diagnostic - .lambda - .iter() - .flatten() - .all(|value| *value == 0.0)); - assert!(diagnostic.xi.iter().flatten().all(|value| *value == 0.0)); - assert!(diagnostic - .simulation_covariance - .iter() - .flatten() - .all(|value| *value == 0.0)); - } - - #[test] - fn explicit_terminal_policy_preserves_default_trajectory() { - let base = SaemConfig::new() - .k1_iterations(2) - .burn_in(1) - .k2_iterations(2) - .compute_map(false) - .seed(7788); - let default = problem().fit_with(base.clone()).unwrap(); - let explicit = problem() - .fit_with(base.estimator_policy(SaemEstimatorPolicy::TerminalIterate)) - .unwrap(); - assert_eq!(default.cycle_diagnostics(), explicit.cycle_diagnostics()); - assert_eq!( - default.population_parameters(), - explicit.population_parameters() - ); - assert_eq!(default.omega(), explicit.omega()); - assert_eq!(default.conditional_n2ll(), explicit.conditional_n2ll()); - assert_eq!(default.termination_reason(), Some(&StopReason::MaxCycles)); - assert_eq!(explicit.termination_reason(), Some(&StopReason::MaxCycles)); - } - - fn residual_phase_schedule() -> SaemSchedule { - let mut schedule = SaemSchedule::from_config( - &SaemConfig::new() - .burn_in(0) - .k1_iterations(4) - .k2_iterations(3), - ); - schedule.variance_floor_iterations = 1; - schedule - } - - #[test] - fn combined_residual_component_anneals_during_configured_period() { - let schedule = residual_phase_schedule(); - let applied = applied_combined_residual_component(&schedule, 1, 1.0, 0.1, true); - assert_eq!(applied, schedule.annealing_alpha); - } - - #[test] - fn combined_residual_component_replaces_directly_in_remaining_exploration() { - let schedule = residual_phase_schedule(); - assert_eq!( - applied_combined_residual_component(&schedule, 2, 1.0, 0.1, true), - 0.1 - ); - } - - #[test] - fn combined_residual_component_smooths_in_k2() { - let schedule = residual_phase_schedule(); - assert_eq!( - applied_combined_residual_component(&schedule, 6, 1.0, 0.2, true), - 0.6 - ); - } - - #[test] - fn combined_residual_component_preserves_fixed_value() { - let schedule = residual_phase_schedule(); - assert_eq!( - applied_combined_residual_component(&schedule, 1, 1.0, 0.1, false), - 1.0 - ); - assert_eq!( - applied_combined_residual_component(&schedule, 6, 1.0, 0.1, false), - 1.0 - ); - } - - #[test] - fn burn_in_warms_covariance_statistics_without_updating_parameters() { - let config = SaemConfig::new() - .n_chains(1) - .burn_in(2) - .k1_iterations(4) - .omega_sa_max_step(0.1); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - for subject_chains in &mut state.etas { - subject_chains[0].fill(2.0); - } - let initial_population = state.population_parameters.clone(); - let initial_omega = state.omega.clone(); - let initial_iiv_second_moment = state.iiv_second_moment.clone(); - let initial_phi_second_moment = state.sufficient_statistics.second_moment.clone(); - - state.step().unwrap(); - - assert_eq!(state.cycle, 1); - assert_eq!(state.cycle_diagnostics[0].phase, SaemPhase::BurnIn); - assert_eq!(state.population_parameters, initial_population); - assert_eq!(state.omega, initial_omega); - assert_ne!(state.iiv_second_moment, initial_iiv_second_moment); - assert_ne!( - state.sufficient_statistics.second_moment, - initial_phi_second_moment - ); - } - - #[test] - fn chain_count_auto_scales_for_small_datasets() { - assert_eq!(n_chains(&SaemConfig::default(), 2), 25); - assert_eq!(n_chains(&SaemConfig::new().n_chains(3), 2), 3); - assert_eq!(n_chains(&SaemConfig::default(), 100), 1); - } - - #[test] - fn result_retains_requested_config_and_separate_effective_chain_count() { - let config = SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1) - .compute_map(false) - .seed(9876); - let serialized_config = serde_json::to_value(&config).unwrap(); - let state = SaemState::from_problem(problem(), &config).unwrap(); - - let result = Box::new(state).into_result().unwrap(); - - assert_eq!(result.config().n_chains, 1); - assert_eq!(result.effective_n_chains(), 25); - assert_eq!( - serde_json::to_value(result.config()).unwrap(), - serialized_config - ); - } - - #[test] - fn result_parameter_metadata_preserves_declaration_order() { - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1) - .compute_map(false); - let state = SaemState::from_problem(ordered_metadata_problem(), &config).unwrap(); - - let result = Box::new(state).into_result().unwrap(); - - assert_eq!(result.parameter_names(), ["ke", "v"]); - assert_eq!( - result.parameter_scales(), - [ParameterScale::Identity, ParameterScale::Log] - ); - assert_eq!(result.estimated_parameters(), [true, false]); - assert_eq!(result.random_effect_indices(), [0]); - assert_eq!(result.random_effect_names(), ["ke"]); - assert_eq!(result.iov_effect_indices(), [1]); - assert_eq!(result.iov_effect_names(), ["v"]); - } - - #[test] - fn result_retains_exact_symmetric_iiv_covariance_masks() { - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1) - .compute_map(false); - let configured = - Box::new(SaemState::from_problem(configured_omega_problem(), &config).unwrap()) - .into_result() - .unwrap(); - let correlated = - Box::new(SaemState::from_problem(correlated_omega_problem(), &config).unwrap()) - .into_result() - .unwrap(); - - assert_eq!(configured.random_effect_names(), ["ke", "v"]); - assert_eq!( - configured.omega_structural_mask(), - &ndarray::array![[true, false], [false, true]] - ); - assert_eq!( - configured.omega_estimated_mask(), - &ndarray::array![[true, false], [false, false]] - ); - assert_eq!( - correlated.omega_structural_mask(), - &ndarray::array![[true, true], [true, true]] - ); - assert_eq!( - correlated.omega_estimated_mask(), - &ndarray::array![[true, true], [true, true]] - ); - } - - #[test] - fn result_retains_ordered_iov_masks_and_none_without_iov() { - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1) - .compute_map(false); - let iov = Box::new(SaemState::from_problem(configured_iov_problem(), &config).unwrap()) - .into_result() - .unwrap(); - let no_iov = Box::new(SaemState::from_problem(problem(), &config).unwrap()) - .into_result() - .unwrap(); - - assert_eq!(iov.iov_effect_indices(), [0, 1]); - assert_eq!(iov.iov_effect_names(), ["ke", "v"]); - assert_eq!( - iov.omega_iov_structural_mask(), - Some(&ndarray::array![[true, true], [true, true]]) - ); - assert_eq!( - iov.omega_iov_estimated_mask(), - Some(&ndarray::array![[true, false], [false, false]]) - ); - assert_eq!(no_iov.omega_iov_structural_mask(), None); - assert_eq!(no_iov.omega_iov_estimated_mask(), None); - } - - #[test] - fn state_initializes_zero_eta_chains() { - let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); - - assert_eq!(state.etas.len(), 2); - assert_eq!(state.etas[0].len(), 25); - assert_eq!(state.etas[0][0], vec![0.0, 0.0]); - assert_eq!(state.etas[1][24], vec![0.0, 0.0]); - assert_eq!(state.omega_diagonal(), Some(vec![1.0, 1.0])); - } - - #[test] - fn covariate_state_joint_gls_rebases_eta_and_builds_subject_omega() { - let mut state = SaemState::from_problem( - covariate_problem(), - &SaemConfig::new().n_chains(2).compute_map(false), - ) - .unwrap(); - let intercept = [0.2_f64.ln(), 10.0_f64.ln()]; - let beta = 0.35; - let expected_phi = [-1.0, 0.0, 1.0] - .into_iter() - .map(|design| vec![intercept[0] + beta * design, intercept[1]]) - .collect::>(); - let desired_omega = ndarray::array![[0.4, 0.1], [0.1, 0.3]]; - let mut second = desired_omega.clone(); - for mean in &expected_phi { - for row in 0..2 { - for column in 0..2 { - second[[row, column]] += mean[row] * mean[column] / 3.0; - } - } - } - let old_means = state.subject_mu_phi.clone().unwrap(); - for chains in &mut state.etas { - for eta in chains { - eta[0] = 0.1; - eta[1] = -0.2; - } - } - let absolute_before = old_means - .iter() - .map(|mean| vec![mean[0] + 0.1, mean[1] - 0.2]) - .collect::>(); - state.covariate_statistics = Some(CovariateSufficientStatistics { - expected_phi, - global_second_moment: second, - }); - - let candidate = state - .update_covariate_population_and_recenter_etas() - .unwrap(); - let model = state.covariate_model.as_ref().unwrap(); - assert!((model.estimates()[0].estimate() - beta).abs() < 1e-10); - assert!((candidate[[0, 0]] - desired_omega[[0, 0]]).abs() < 1e-10); - assert!((candidate[[0, 1]] - desired_omega[[0, 1]]).abs() < 1e-10); - for (subject, mean) in state.subject_mu_phi.as_ref().unwrap().iter().enumerate() { - for coordinate in 0..2 { - assert!( - (mean[coordinate] + state.etas[subject][0][coordinate] - - absolute_before[subject][coordinate]) - .abs() - < 1e-10 - ); - } - } - } - - #[test] - fn covariate_fit_executes_and_retains_subject_population_parameters() { - let result = covariate_problem() - .fit_with( - SaemConfig::new() - .n_chains(2) - .mcmc_iterations(1) - .burn_in(1) - .k1_iterations(2) - .k2_iterations(2) - .averaged_iterates(0.75) - .compute_map(false), - ) - .unwrap(); - assert!(result.estimator_metadata().average_applied); - assert_eq!(result.covariate_estimates().unwrap().len(), 2); - assert!(result.covariate_estimates().unwrap()[0].estimate() < 0.0); - assert_eq!( - result - .covariate_subject_population_parameters() - .unwrap() - .unwrap() - .len(), - 3 - ); - assert!(result.cycle_diagnostics().iter().all(|cycle| cycle - .covariate_betas - .as_ref() - .is_some_and(|values| values.len() == 2))); - let tables = result.tables(1.0, 0.0).unwrap(); - assert_eq!(tables.covariate_effects.len(), 2); - assert_eq!(tables.subject_covariates.len(), 6); - assert_eq!(tables.subject_population_parameters.len(), 6); - - let directory = - std::env::temp_dir().join(format!("pmcore-schema7-covariate-{}", std::process::id())); - result.write_outputs(&directory, 1.0, 0.0).unwrap(); - let record = - crate::results::ParametricResultRecord::read_json(directory.join("result.json")) - .unwrap(); - assert_eq!(record.schema_version, 9); - assert_eq!(record.source_metadata.covariate_effects.len(), 2); - let warm = record - .warm_start_problem(one_compartment(), result.data().clone()) - .unwrap(); - let warm_estimates = warm - .covariates() - .unwrap() - .estimates() - .iter() - .map(|estimate| estimate.estimate()) - .collect::>(); - let result_estimates = result - .covariate_estimates() - .unwrap() - .iter() - .map(|estimate| estimate.estimate()) - .collect::>(); - assert!(warm_estimates - .iter() - .zip(result_estimates) - .all(|(warm, result)| (warm - result).abs() <= 2.0 * f64::EPSILON)); - std::fs::remove_dir_all(directory).unwrap(); - } - - #[test] - fn fixed_covariate_without_iiv_executes_subject_specific_predictions() { - let result = fixed_covariate_without_iiv_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .burn_in(0) - .k1_iterations(1) - .k2_iterations(0) - .compute_map(false), - ) - .unwrap(); - assert!(result.random_effect_names().is_empty()); - let means = result - .covariate_subject_population_parameters() - .unwrap() - .unwrap(); - assert!((means[0].psi()[0] - 0.2).abs() < 1e-12); - assert!((means[1].psi()[0] - 0.2 * 0.2_f64.exp()).abs() < 1e-12); - let predictions = result.population_predictions(0.0, 0.0).unwrap(); - assert_ne!( - predictions[0].predictions()[0].prediction(), - predictions[1].predictions()[0].prediction() - ); - } - - #[test] - fn explicit_iiv_mask_controls_eta_and_omega_dimensions() { - let mut state = - SaemState::from_problem(partial_iiv_problem(), &SaemConfig::new().n_chains(2)).unwrap(); - - assert_eq!(state.initialization.random_effect_indices, vec![0]); - assert_eq!(state.initialization.random_effect_names, vec!["ke"]); - assert!(state - .etas - .iter() - .flat_map(|subject_chains| subject_chains.iter()) - .all(|eta| eta.len() == 1)); - assert_eq!(state.omega.dim(), (1, 1)); - assert_eq!(state.proposal_step_sizes.len(), 1); - - state.etas[0][0][0] = 2.0_f64.ln(); - let individual = state.individual_parameters(0, 0); - assert!((individual[0] - 0.4).abs() < 1e-12); - assert!((individual[1] - 10.0).abs() < 1e-12); - } - - #[test] - fn all_fixed_parameters_support_zero_dimensional_iiv() { - let config = SaemConfig::new() - .n_chains(1) - .burn_in(1) - .k1_iterations(1) - .k2_iterations(1); - let state = SaemState::from_problem(fixed_no_iiv_problem(), &config).expect( - "fixed population plus estimated residual error should support zero-dimensional IIV", - ); - assert!(state.initialization.random_effect_names.is_empty()); - assert!(state.omega.is_empty()); - assert!(state.iiv_second_moment.is_empty()); - assert!(state - .etas - .iter() - .all(|chains| chains.iter().all(Vec::is_empty))); - - let result = fixed_no_iiv_problem().fit_with(config).unwrap(); - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - assert_eq!(result.iterations(), 2); - assert!(result.objf().is_finite()); - assert!(result.conditional_modes().is_empty()); - assert_eq!(result.omega_structural_mask().dim(), (0, 0)); - assert_eq!(result.omega_estimated_mask().dim(), (0, 0)); - assert!(result.omega_structural_mask().is_empty()); - assert!(result.omega_estimated_mask().is_empty()); - assert_eq!(result.omega_iov_structural_mask(), None); - assert_eq!(result.omega_iov_estimated_mask(), None); - assert!(result - .eta_chain_means() - .iter() - .all(|estimate| estimate.values.is_empty())); - assert!(result.kappa_chain_means().is_empty()); - } - - #[test] - fn iov_state_tracks_one_kappa_per_subject_occasion_and_chain() { - let state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); - - assert_eq!(state.initialization.iov_effect_names, vec!["ke"]); - assert_eq!(state.omega_iov, Some(ndarray::array![[0.1]])); - assert_eq!(state.kappas.len(), 1); - assert_eq!(state.kappas[0].len(), 2); - assert_eq!(state.kappas[0][0], vec![vec![0.0], vec![0.0]]); - } - - #[test] - fn uneven_occasion_counts_preserve_kappa_shapes_order_and_named_lookup() { - let result = uneven_iov_problem() - .fit_with( - SaemConfig::new() - .n_chains(2) - .mcmc_iterations(1) - .burn_in(0) - .k1_iterations(2) - .k2_iterations(0) - .compute_map(false), - ) - .unwrap(); - - assert_eq!(result.kappa_chain_means().len(), 6); - assert!(result.kappa_chain_mean("one", 0).is_some()); - assert!(result.kappa_chain_mean("one", 1).is_none()); - assert!(result.kappa_chain_mean("two", 0).is_some()); - assert!(result.kappa_chain_mean("two", 1).is_some()); - assert!(result.kappa_chain_mean("three", 0).is_some()); - assert!(result.kappa_chain_mean("three", 1).is_some()); - assert!(result.kappa_chain_mean("three", 2).is_some()); - assert!(result.eta_chain_mean("two").is_some()); - assert!(result.eta_chain_mean("missing").is_none()); - assert!(result.conditional_mode("two").is_none()); - assert!(result - .cycle_diagnostics() - .iter() - .all(|cycle| cycle.kappa_proposals == 12)); - } - - #[test] - fn iov_scores_per_occasion_kappa_prior_and_conditional_proposal() { - let state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); - let score = state - .score_subject_latents(0, &state.etas[0][0], &state.kappas[0][0]) - .unwrap(); - - assert!((score.log_likelihood - state.subject_log_likelihoods[0]).abs() < 1e-12); - assert!((score.kappa_log_prior - state.subject_kappa_log_priors[0]).abs() < 1e-12); - assert!(score.kappa_log_prior.is_finite()); - assert_eq!( - state - .kappa_proposal_log_acceptance_ratio(0, 0, 0, &[0.0]) - .unwrap(), - 0.0 - ); - } - - #[test] - fn iov_controller_exposes_kappa_covariance_and_runs_conditional_mcmc() { - let mut controller = iov_problem() - .fit_controller( - SaemConfig::new() - .n_chains(2) - .k1_iterations(2) - .k2_iterations(0) - .burn_in(2), - ) - .unwrap(); - - assert_eq!( - controller.iov_effect_names(), - Some(["ke".to_string()].as_slice()) - ); - assert_eq!(controller.omega_iov(), Some(&ndarray::array![[0.1]])); - assert!(controller.kappa_log_prior().is_finite()); - assert_eq!( - controller.log_posterior(), - controller.likelihood() + controller.eta_log_prior() + controller.kappa_log_prior() - ); - - controller.step().unwrap(); - assert!(controller.likelihood().is_finite()); - assert!(controller.kappa_log_prior().is_finite()); - assert!(controller.acceptance_rate().is_some()); - assert!(controller - .kappa_acceptance_rate() - .is_some_and(|rate| (0.0..=1.0).contains(&rate))); - } - - #[test] - fn correlated_random_walk_reuses_one_standard_normal_vector() { - let proposed = - correlated_random_walk(&[1.0, 2.0], &[vec![2.0], vec![1.0, 3.0]], &[0.5, -1.0], 0.2) - .unwrap(); - - assert!((proposed[0] - 1.2).abs() < 1e-12); - assert!((proposed[1] - 1.5).abs() < 1e-12); - assert!(correlated_random_walk(&[0.0], &[vec![1.0]], &[0.0, 1.0], 1.0).is_err()); - } - - #[test] - fn eta_block_proposal_uses_covariance_scale_and_adaptation() { - let lower = vec![vec![1.0], vec![0.8, 0.6]]; - let normals = [[0.5, -1.0], [-0.25, 0.75], [1.2, 0.1], [-0.8, -0.4]]; - let uniforms = [0.2_f64, 0.9, 0.4, 0.7]; - let expected_trace = [ - [0.65, -0.3], - [0.525, -0.175], - [0.525, -0.175], - [0.525, -0.175], - ]; - let expected_ratios = [ - -0.9451955782312924, - 0.6944515306122447, - -2.211747363945578, - -0.4124850340136057, - ]; - let expected_accepts = [true, true, false, false]; - let expected_scales = [0.55, 0.495]; - let expected_checkpoint_counts = [(2, 2), (0, 2)]; - let log_likelihood = |eta: &[f64]| { - -0.5 * ((eta[0] - 0.3) / 0.5).powi(2) - 0.5 * ((eta[1] + 0.1) / 0.7).powi(2) - }; - let log_prior = |eta: &[f64]| { - -0.5 / (1.0 - 0.8_f64.powi(2)) - * (eta[0].powi(2) - 1.6 * eta[0] * eta[1] + eta[1].powi(2)) - }; - - let mut eta = vec![0.4, -0.2]; - let mut scale = 0.5; - let mut accepted = 0; - let mut proposed = 0; - let mut scale_index = 0; - for (step, (z, uniform)) in normals.iter().zip(uniforms).enumerate() { - let proposal = correlated_random_walk(&eta, &lower, z, scale).unwrap(); - let reference = [ - eta[0] + scale * lower[0][0] * z[0], - eta[1] + scale * (lower[1][0] * z[0] + lower[1][1] * z[1]), - ]; - assert!((proposal[0] - reference[0]).abs() < 1e-15); - assert!((proposal[1] - reference[1]).abs() < 1e-15); - - let current_score = SubjectPosteriorScore { - log_likelihood: log_likelihood(&eta), - eta_log_prior: log_prior(&eta), - kappa_log_prior: 0.0, - }; - let proposed_score = SubjectPosteriorScore { - log_likelihood: log_likelihood(&proposal), - eta_log_prior: log_prior(&proposal), - kappa_log_prior: 0.0, - }; - let ratio = current_score.log_acceptance_ratio(proposed_score); - let reference_ratio = proposed_score.log_posterior() - current_score.log_posterior(); - assert!((ratio - reference_ratio).abs() < 1e-15); - assert!((ratio - expected_ratios[step]).abs() < 1e-12); - - let accept = ratio >= 0.0 || uniform.ln() < ratio; - assert_eq!(accept, expected_accepts[step]); - proposed += 1; - if accept { - eta = proposal; - accepted += 1; - } - assert!((eta[0] - expected_trace[step][0]).abs() < 1e-12); - assert!((eta[1] - expected_trace[step][1]).abs() < 1e-12); - - if (step + 1) % 2 == 0 { - assert_eq!( - (accepted, proposed), - expected_checkpoint_counts[scale_index] - ); - scale = adapt_block_step_size( - scale, - accepted as f64 / proposed as f64, - ETA_BLOCK_TARGET_ACCEPTANCE, - ); - assert!((scale - expected_scales[scale_index]).abs() < 1e-12); - scale_index += 1; - accepted = 0; - proposed = 0; - } - } - - let eta_unchanged = [0.7, -0.3]; - let kappa_0_unchanged = [0.1, 0.2]; - let kappa_1 = correlated_random_walk( - &[-0.2, 0.4], - &[vec![0.5], vec![0.1, 0.4]], - &[-0.5, 0.25], - 0.3, - ) - .unwrap(); - assert_eq!(eta_unchanged, [0.7, -0.3]); - assert_eq!(kappa_0_unchanged, [0.1, 0.2]); - assert!((kappa_1[0] + 0.275).abs() < 1e-12); - assert!((kappa_1[1] - 0.415).abs() < 1e-12); - } - - #[test] - fn eta_block_kernel_runs_before_component_sweep_and_records_diagnostics() { - let mut state = SaemState::from_problem( - problem(), - &SaemConfig::new() - .n_chains(2) - .mcmc_iterations(1) - .eta_block_iterations(2) - .adapt_interval(50) - .seed(2024), - ) - .unwrap(); - - state.e_step().unwrap(); - - let diagnostics = state.cycle_diagnostics.last().unwrap(); - assert_eq!(diagnostics.eta_block_proposals, 2 * 2 * 2); - assert_eq!( - diagnostics.eta_block_accepted + diagnostics.eta_block_rejected, - diagnostics.eta_block_proposals - ); - assert_eq!(diagnostics.eta_proposals, 2 * 2 * 2 + 2 * 2 * 2); - assert_eq!(diagnostics.eta_block_subject_acceptance_rates.len(), 2); - assert_eq!( - diagnostics.eta_block_step_sizes_before_adaptation, - vec![0.5, 0.5] - ); - assert_eq!( - diagnostics.eta_block_step_sizes_after_adaptation, - vec![0.5, 0.5] - ); - } - - #[test] - fn controller_exposes_opt_in_eta_block_acceptance_and_scales() { - let mut controller = problem() - .fit_controller( - SaemConfig::new() - .n_chains(2) - .eta_block_iterations(1) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1), - ) - .unwrap(); - - assert_eq!( - controller.eta_block_step_sizes(), - Some([0.5, 0.5].as_slice()) - ); - assert_eq!(controller.eta_block_acceptance_rate(), None); - controller.step().unwrap(); - assert!(controller - .eta_block_acceptance_rate() - .is_some_and(|rate| (0.0..=1.0).contains(&rate))); - } - - #[test] - fn eta_block_scale_adapts_per_subject_toward_acceptance_target() { - let mut state = SaemState::from_problem( - problem(), - &SaemConfig::new() - .n_chains(1) - .eta_block_iterations(1) - .adapt_interval(1), - ) - .unwrap(); - assert_eq!(state.eta_block_step_sizes, vec![0.5, 0.5]); - - state.eta_block_adaptation_accept_counts = vec![1, 0]; - state.eta_block_adaptation_proposal_counts = vec![1, 1]; - state.steps_since_adapt = 1; - state.adapt_proposal_step_sizes(); - assert_eq!(state.eta_block_step_sizes, vec![0.55, 0.45]); - assert_eq!(state.eta_block_adaptation_accept_counts, vec![0, 0]); - assert_eq!(state.eta_block_adaptation_proposal_counts, vec![0, 0]); - } - - #[test] - fn kappa_block_scale_adapts_per_subject_toward_acceptance_target() { - let mut state = SaemState::from_problem( - iov_problem(), - &SaemConfig::new().n_chains(2).adapt_interval(1), - ) - .unwrap(); - assert_eq!(state.kappa_proposal_step_sizes, vec![0.5]); - - state.kappa_adaptation_accept_counts[0] = 1; - state.kappa_adaptation_proposal_counts[0] = 1; - state.steps_since_adapt = 1; - state.adapt_proposal_step_sizes(); - assert!((state.kappa_proposal_step_sizes[0] - 0.55).abs() < 1e-12); - - state.kappa_adaptation_accept_counts[0] = 0; - state.kappa_adaptation_proposal_counts[0] = 1; - state.steps_since_adapt = 1; - state.adapt_proposal_step_sizes(); - assert!((state.kappa_proposal_step_sizes[0] - 0.495).abs() < 1e-12); - } - - #[test] - fn iov_second_moment_weights_each_occasion_chain_sample_equally() { - let kappas = vec![ - vec![vec![vec![1.0, 2.0]]], - vec![vec![vec![3.0, 4.0], vec![5.0, 6.0]]], - ]; - - let covariance = covariance_from_kappas(&kappas).unwrap(); - - assert!((covariance[[0, 0]] - 35.0 / 3.0).abs() < 1e-12); - assert!((covariance[[0, 1]] - 44.0 / 3.0).abs() < 1e-12); - assert!((covariance[[1, 0]] - 44.0 / 3.0).abs() < 1e-12); - assert!((covariance[[1, 1]] - 56.0 / 3.0).abs() < 1e-12); - } - - #[test] - fn iov_m_step_updates_omega_from_all_occasions() { - let mut state = SaemState::from_problem( - iov_problem(), - &SaemConfig::new() - .n_chains(2) - .burn_in(0) - .omega_sa_max_step(1.0), - ) - .unwrap(); - state.cycle = 1; - state.e_step().unwrap(); - for kappas in &mut state.kappas[0] { - kappas[0][0] = 0.2; - kappas[1][0] = -0.1; - } - - state.m_step().unwrap(); - - assert!((state.omega_iov.as_ref().unwrap()[[0, 0]] - 0.025).abs() < 1e-12); - assert!( - !state - .cycle_diagnostics - .last() - .unwrap() - .omega_iov_update_rejected - ); - } - - #[test] - fn covariance_update_status_drives_iiv_and_iov_cycle_rejection_diagnostics() { - let config = SaemConfig::new() - .n_chains(2) - .burn_in(0) - .omega_sa_max_step(1.0); - let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); - state.cycle = 1; - state.e_step().unwrap(); - state.iiv_second_moment.fill(f64::NAN); - state.iov_second_moment.as_mut().unwrap().fill(f64::NAN); - - state.m_step().unwrap(); - - let diagnostics = state.cycle_diagnostics.last().unwrap(); - assert!(diagnostics.omega_update_rejected); - assert!(diagnostics.omega_iov_update_rejected); - } - - #[test] - fn iov_second_moment_uses_saem_smoothing_step() { - let config = SaemConfig::new() - .n_chains(2) - .burn_in(0) - .omega_sa_max_step(1.0) - .k1_iterations(1) - .k2_iterations(2); - let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); - for kappas in &mut state.kappas[0] { - kappas[0][0] = 0.2; - kappas[1][0] = -0.1; - } - state.cycle = 1; - state.m_step().unwrap(); - - for kappas in &mut state.kappas[0] { - kappas[0][0] = 0.2; - kappas[1][0] = 0.2; - } - state.cycle = 3; // first smoothing iteration after K1: γ = 1/2 - state.m_step().unwrap(); - - assert!((state.omega_iov.as_ref().unwrap()[[0, 0]] - 0.0325).abs() < 1e-12); - } - - #[test] - fn iov_m_step_preserves_fixed_entries_and_positive_definiteness_jointly() { - let config = SaemConfig::new() - .n_chains(2) - .burn_in(0) - .omega_sa_max_step(1.0); - let mut state = SaemState::from_problem(configured_iov_problem(), &config).unwrap(); - state.cycle = 1; - for chain in &mut state.kappas[0] { - for kappa in chain { - kappa[0] = 0.3; - kappa[1] = 1.0; - } - } - - state.m_step().unwrap(); - - let omega_iov = state.omega_iov.as_ref().unwrap(); - // With fixed b=.20 and c=.05, the exact constrained profile optimum is - // S11 - 2(c/b)S12 + c²/b + (c²/b²)S22 = .015. - assert!((omega_iov[[0, 0]] - 0.015).abs() < 1e-12); - assert_eq!(omega_iov[[0, 1]], 0.05); - assert_eq!(omega_iov[[1, 0]], 0.05); - assert_eq!(omega_iov[[1, 1]], 0.20); - assert!(omega_iov[[0, 0]] * omega_iov[[1, 1]] - omega_iov[[0, 1]].powi(2) > 0.0); - } - - #[test] - fn state_uses_declared_initial_omega() { - let state = - SaemState::from_problem(configured_omega_problem(), &SaemConfig::new().n_chains(2)) - .unwrap(); - - assert_eq!(state.omega, ndarray::array![[0.25, 0.0], [0.0, 0.5]]); - assert_eq!(state.proposal_step_sizes, vec![0.25, 0.25 * 2.0_f64.sqrt()]); - } - - #[test] - fn individual_parameters_add_eta_in_phi_space() { - let mut state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); - - let initial = state.individual_parameters(0, 0); - assert!((initial[0] - 0.2).abs() < 1e-12); - assert!((initial[1] - 10.0).abs() < 1e-12); - - state.etas[0][0][0] = 2.0_f64.ln(); - state.etas[0][0][1] = 0.5_f64.ln(); - let individual = state.individual_parameters(0, 0); - - assert!((individual[0] - 0.4).abs() < 1e-12); - assert!((individual[1] - 5.0).abs() < 1e-12); - } - - #[test] - fn bounded_transforms_round_trip() { - let logit = ParameterScale::Logit { - lower: 0.0, - upper: 1.0, - }; - let probit = ParameterScale::Probit { - lower: 0.0, - upper: 1.0, - }; - - assert!((phi_to_psi(psi_to_phi(0.25, logit), logit) - 0.25).abs() < 1e-12); - assert!((phi_to_psi(psi_to_phi(0.25, probit), probit) - 0.25).abs() < 1e-12); - } - - #[test] - fn parametric_fit_controller_steps_like_nonparametric_controller() { - let config = SaemConfig::new() - .k1_iterations(2) - .k2_iterations(1) - .burn_in(1); - let mut controller = problem().fit_controller(config).unwrap(); - - assert_eq!(controller.cycle(), 0); - assert!(controller.status().is_continue()); - assert!(controller.likelihood().is_finite()); - assert_eq!(controller.population_parameters(), &[0.2, 10.0]); - assert_eq!(controller.random_effect_names(), &["ke", "v"]); - assert_eq!(controller.iov_effect_names(), None); - assert_eq!(controller.omega_iov(), None); - assert_eq!(controller.residual_sigmas(), &[0.5]); - assert_eq!(controller.acceptance_rate(), None); - assert_eq!(controller.kappa_acceptance_rate(), None); - assert_eq!(controller.rejected_proposals(), None); - assert_eq!(controller.non_finite_proposals(), None); - assert_eq!(controller.parameter_acceptance_rates(), None); - assert_eq!( - controller.proposal_step_sizes(), - Some([0.5, 0.5].as_slice()) - ); - assert!(controller.eta_log_prior().is_finite()); - assert_eq!( - controller.log_posterior(), - controller.likelihood() + controller.eta_log_prior() - ); - assert!(controller.negative_log_likelihood().is_finite()); - assert_eq!( - controller.negative_log_likelihood(), - -controller.likelihood() - ); - assert!(controller.n2ll().is_finite()); - assert_eq!(controller.n_chains(), Some(25)); - assert_eq!( - controller.omega(), - Some(&ndarray::array![[1.0, 0.0], [0.0, 1.0]]) - ); - assert_eq!(controller.omega_diagonal(), Some(vec![1.0, 1.0])); - assert_eq!( - controller.log_acceptance_ratios(), - Some([0.0, 0.0].as_slice()) - ); - assert_eq!(controller.total_iterations(), 3); - assert_eq!(controller.step_size(), 0.0); - - assert!(controller.step().unwrap().is_continue()); - assert_eq!(controller.cycle(), 1); - assert_eq!(controller.step_size(), 0.0); - assert_eq!(controller.population_parameters(), &[0.2, 10.0]); - assert_eq!( - controller.omega(), - Some(&ndarray::array![[1.0, 0.0], [0.0, 1.0]]) - ); - assert!(controller.acceptance_rate().is_some()); - assert_eq!(controller.kappa_acceptance_rate(), None); - assert!(controller.rejected_proposals().is_some()); - assert_eq!(controller.non_finite_proposals(), Some(0)); - let parameter_acceptance_rates = controller.parameter_acceptance_rates().unwrap(); - assert_eq!(parameter_acceptance_rates.len(), 2); - assert!(parameter_acceptance_rates - .iter() - .all(|rate| (0.0..=1.0).contains(rate))); - assert!(controller.step().unwrap().is_continue()); - assert_eq!(controller.cycle(), 2); - assert_eq!(controller.step_size(), 1.0); - assert!(controller.step().unwrap().is_stop()); - assert_eq!(controller.cycle(), 3); - } - - #[test] - fn aborted_controller_preserves_typed_termination_reason() { - let mut controller = problem() - .fit_controller(SaemConfig::new().compute_map(false)) - .unwrap(); - controller.step().unwrap(); - controller.request_stop(); - - let result = controller.into_result().unwrap(); - - assert!(!result.converged()); - assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); - assert_ne!(result.termination_reason(), Some(&StopReason::MaxCycles)); - assert_ne!( - result.termination_reason(), - Some(&StopReason::NumericalFailure) - ); - assert_eq!(result.iterations(), 1); - } - - #[test] - fn expectation_numerical_failure_stops_and_blocks_result() { - let mut state = SaemState::from_problem( - problem(), - &SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .compute_map(false), - ) - .unwrap(); - state.omega[[0, 0]] = f64::NAN; - - let error = state.step().unwrap_err(); - let failure = error - .downcast_ref::() - .expect("step error should retain its numerical failure type") - .clone(); - - assert_eq!(failure.attempted_cycle(), 1); - assert_eq!(failure.phase(), NumericalFailurePhase::Expectation); - assert!(!failure.source_message().is_empty()); - assert_eq!(state.status, Status::Stop(StopReason::NumericalFailure)); - assert_eq!( - state.step().unwrap(), - Status::Stop(StopReason::NumericalFailure) - ); - - let result_error = Box::new(state).into_result().unwrap_err(); - assert_eq!( - result_error.downcast_ref::(), - Some(&failure) - ); - } - - #[test] - fn maximization_numerical_failure_stops_fit() { - let mut state = SaemState::from_problem( - problem(), - &SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .compute_map(false), - ) - .unwrap(); - state.sufficient_statistics.mean_phi.pop(); - - let error = state.step().unwrap_err(); - let failure = error - .downcast_ref::() - .expect("step error should retain its numerical failure type"); - - assert_eq!(failure.attempted_cycle(), 1); - assert_eq!(failure.phase(), NumericalFailurePhase::Maximization); - assert!(!failure.source_message().is_empty()); - assert_eq!(state.status, Status::Stop(StopReason::NumericalFailure)); - } - - #[test] - fn result_assembly_numerical_failure_returns_no_result() { - let mut state = - SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1).compute_map(false)) - .unwrap(); - state.etas[0].clear(); - - let error = Box::new(state).into_result().unwrap_err(); - let failure = error - .downcast_ref::() - .expect("result error should retain its numerical failure type"); - - assert_eq!(failure.attempted_cycle(), 0); - assert_eq!(failure.phase(), NumericalFailurePhase::ResultAssembly); - assert!(!failure.source_message().is_empty()); - } - - #[test] - fn proposal_score_uses_pmcore_likelihood_and_eta_prior() { - let state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); - let current_eta = state.etas[0][0].clone(); - let score = state - .score_subject_latents(0, ¤t_eta, &state.kappas[0][0]) - .unwrap(); - - assert_eq!(score.log_likelihood, state.subject_log_likelihoods[0]); - assert_eq!(score.eta_log_prior, state.subject_log_priors[0]); - assert_eq!( - state - .proposal_log_acceptance_ratio(0, 0, ¤t_eta) - .unwrap(), - 0.0 - ); - } - - #[test] - fn component_random_walk_changes_only_selected_eta() { - let mut state = - SaemState::from_problem(problem(), &SaemConfig::new().n_chains(2).seed(2024)).unwrap(); - let current = vec![1.0, 2.0]; - - let proposed = state.component_random_walk_eta(¤t, 1); - - assert_eq!(proposed[0], current[0]); - assert_ne!(proposed[1], current[1]); - } - - #[test] - fn component_scale_adaptation_uses_acceptance_bands_and_clamps() { - assert!((adapt_component_step_size(1.0, 0.45) - 1.1).abs() < 1e-12); - assert!((adapt_component_step_size(1.0, 0.44) - 0.9).abs() < 1e-12); - assert_eq!(adapt_component_step_size(5.0, 1.0), 5.0); - assert_eq!(adapt_component_step_size(1e-6, 0.0), 1e-6); - } - - #[test] - fn component_scale_adaptation_waits_for_interval_and_resets_counts() { - let mut state = - SaemState::from_problem(problem(), &SaemConfig::new().n_chains(2).adapt_interval(2)) - .unwrap(); - state.adaptation_accept_counts = vec![9, 1]; - state.adaptation_proposal_counts = vec![10, 10]; - state.steps_since_adapt = 1; - - state.adapt_proposal_step_sizes(); - assert_eq!(state.proposal_step_sizes, vec![0.5, 0.5]); - - state.steps_since_adapt = 2; - state.adapt_proposal_step_sizes(); - assert_eq!(state.proposal_step_sizes, vec![0.55, 0.45]); - assert_eq!(state.adaptation_accept_counts, vec![0, 0]); - assert_eq!(state.adaptation_proposal_counts, vec![0, 0]); - assert_eq!(state.steps_since_adapt, 0); - } - - #[test] - fn e_step_runs_seeded_random_walk_for_all_chains_and_records_acceptance_rate() { - let config = SaemConfig::new().n_chains(3).mcmc_iterations(2).seed(2024); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - let initial_etas = state.etas.clone(); - - state.e_step().unwrap(); - - let acceptance_rate = state.acceptance_rate().unwrap(); - assert!((0.0..=1.0).contains(&acceptance_rate)); - assert_eq!(state.last_log_acceptance_ratios.len(), 2); - assert_eq!(state.last_parameter_acceptance_rates.len(), 2); - assert!(state - .last_parameter_acceptance_rates - .iter() - .all(|rate| (0.0..=1.0).contains(rate))); - assert!(state - .last_log_acceptance_ratios - .iter() - .all(|value| value.is_finite())); - assert_ne!(state.etas, initial_etas); - assert!(state - .etas - .iter() - .flat_map(|subject_chains| subject_chains.iter()) - .all(|eta| eta.len() == 2)); - } - - #[test] - fn cycle_diagnostics_separate_eta_kappa_counts_and_schedule_phases() { - let config = SaemConfig::new() - .n_chains(2) - .mcmc_iterations(1) - .burn_in(1) - .k1_iterations(2) - .k2_iterations(1); - let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); - - state.step().unwrap(); - state.step().unwrap(); - state.step().unwrap(); - - assert_eq!(state.cycle_diagnostics.len(), 3); - assert_eq!(state.cycle_diagnostics[0].phase, SaemPhase::BurnIn); - assert_eq!(state.cycle_diagnostics[1].phase, SaemPhase::Exploration); - assert_eq!(state.cycle_diagnostics[2].phase, SaemPhase::Smoothing); - for diagnostics in &state.cycle_diagnostics { - assert_eq!(diagnostics.eta_proposals, 4); - assert_eq!( - diagnostics.eta_accepted + diagnostics.eta_rejected, - diagnostics.eta_proposals - ); - assert_eq!(diagnostics.kappa_proposals, 4); - assert_eq!( - diagnostics.kappa_accepted + diagnostics.kappa_rejected, - diagnostics.kappa_proposals - ); - assert_eq!(diagnostics.eta_parameter_acceptance_rates.len(), 2); - assert_eq!( - diagnostics.eta_proposal_step_sizes_before_adaptation.len(), - 2 - ); - assert_eq!( - diagnostics.eta_proposal_step_sizes_after_adaptation.len(), - 2 - ); - assert_eq!(diagnostics.kappa_subject_acceptance_rates.len(), 1); - assert_eq!( - diagnostics - .kappa_proposal_step_sizes_before_adaptation - .len(), - 1 - ); - assert_eq!( - diagnostics.kappa_proposal_step_sizes_after_adaptation.len(), - 1 - ); - } - assert_eq!( - state.cycle_diagnostics[0].stochastic_approximation_step, - 0.0 - ); - assert_eq!(state.cycle_diagnostics[0].covariance_step, 0.1); - } - - #[test] - fn warning_aggregation_preserves_kind_output_first_cycle_and_counts() { - let config = SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .burn_in(0) - .k1_iterations(1) - .k2_iterations(0); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - state.step().unwrap(); - let cycle = &mut state.cycle_diagnostics[0]; - cycle.omega_update_rejected = true; - cycle.eta_non_finite = 2; - cycle.eta_block_non_finite = 7; - let residual = &mut cycle.residual_diagnostics[0]; - residual.update_rejected = true; - residual.proportional_floor_count = 3; - residual.non_finite_prediction_count = 4; - residual.exponential_domain_violation_count = 5; - residual.combined_additive_collapse_warning = true; - residual.optimizer_converged = Some(false); - - let warnings = parametric_warnings(&state.cycle_diagnostics, None); - - assert!(warnings.contains(&ParametricWarning::OmegaUpdateRejected { - first_iteration: 1, - cycles: 1, - })); - assert!( - warnings.contains(&ParametricWarning::EtaNonFiniteProposals { - first_iteration: 1, - count: 2, - }) - ); - assert!( - warnings.contains(&ParametricWarning::EtaBlockNonFiniteProposals { - first_iteration: 1, - count: 7, - }) - ); - assert!( - warnings.contains(&ParametricWarning::ResidualUpdateRejected { - output: "0".to_owned(), - first_iteration: 1, - cycles: 1, - }) - ); - assert!( - warnings.contains(&ParametricWarning::ProportionalPredictionFloor { - output: "0".to_owned(), - first_iteration: 1, - count: 3, - }) - ); - assert!( - warnings.contains(&ParametricWarning::NonFiniteResidualPrediction { - output: "0".to_owned(), - first_iteration: 1, - count: 4, - }) - ); - assert!( - warnings.contains(&ParametricWarning::ExponentialDomainViolation { - output: "0".to_owned(), - first_iteration: 1, - count: 5, - }) - ); - assert!( - warnings.contains(&ParametricWarning::CombinedAdditiveCollapse { - output: "0".to_owned(), - first_iteration: 1, - cycles: 1, - }) - ); - assert!( - warnings.contains(&ParametricWarning::ResidualOptimizerNotConverged { - output: "0".to_owned(), - first_iteration: 1, - cycles: 1, - }) - ); - } - - #[test] - fn covariance_stability_records_fixed_iiv_and_iov_margins_and_output_rows() { - let result = markov_iov_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .burn_in(0) - .k1_iterations(1) - .k2_iterations(0) - .covariance_stability(CovarianceStabilityConfig::new(0.01, 1)), - ) - .unwrap(); - let cycle = &result.cycle_diagnostics()[0]; - assert!((cycle.omega_relative_spd_margin.unwrap() - 1.0).abs() < 1e-12); - assert!((cycle.omega_iov_relative_spd_margin.unwrap() - 1.0).abs() < 1e-12); - - let tables = result.tables(0.0, 0.0).unwrap(); - let stability_rows = tables - .statistics - .iter() - .filter(|row| row.kind == "covariance_stability") - .collect::>(); - assert_eq!(stability_rows.len(), 2); - assert!(stability_rows.iter().any(|row| { - row.name == "omega_relative_spd_margin" - && row.value.is_some_and(|value| (value - 1.0).abs() < 1e-12) - })); - assert!(stability_rows.iter().any(|row| { - row.name == "omega_iov_relative_spd_margin" - && row.value.is_some_and(|value| (value - 1.0).abs() < 1e-12) - })); - } - - #[test] - fn covariance_boundary_rejection_requires_a_complete_consecutive_window() { - let config = SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .burn_in(0) - .k1_iterations(1) - .k2_iterations(0); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - state.step().unwrap(); - let base = state.cycle_diagnostics[0].clone(); - let policy = CovarianceStabilityConfig::new(0.01, 3); - let pattern = [ - (1, 0.005, true), - (2, 0.004, true), - (3, 0.02, true), - (4, 0.003, true), - (5, 0.002, true), - (6, 0.001, true), - ]; - let cycles = pattern - .into_iter() - .map(|(iteration, margin, rejected)| { - let mut cycle = base.clone(); - cycle.iteration = iteration; - cycle.omega_relative_spd_margin = Some(margin); - cycle.omega_update_rejected = rejected; - cycle - }) - .collect::>(); - - assert_eq!( - covariance_boundary_rejection_summary(&cycles[..2], policy, false), - CovarianceBoundaryRejectionSummary { - first_iteration: None, - longest_run: 2, - } - ); - assert_eq!( - covariance_boundary_rejection_summary(&cycles, policy, false), - CovarianceBoundaryRejectionSummary { - first_iteration: Some(4), - longest_run: 3, - } - ); - let warnings = parametric_warnings(&cycles, Some(policy)); - assert!( - warnings.contains(&ParametricWarning::OmegaBoundaryRejection { - first_iteration: 4, - longest_run: 3, - }) - ); - - let mut mismatched_iov = base.clone(); - mismatched_iov.omega_iov_relative_spd_margin = Some(0.005); - mismatched_iov.omega_update_rejected = true; - mismatched_iov.omega_iov_update_rejected = false; - assert_eq!( - covariance_boundary_rejection_summary(&[mismatched_iov.clone()], policy, true), - CovarianceBoundaryRejectionSummary::default() - ); - mismatched_iov.omega_iov_update_rejected = true; - assert_eq!( - covariance_boundary_rejection_summary(&[mismatched_iov], policy, true).longest_run, - 1 - ); - - let iov_cycles = (1..=3) - .map(|iteration| { - let mut cycle = base.clone(); - cycle.iteration = iteration; - cycle.omega_iov_relative_spd_margin = Some(policy.minimum_relative_spd_margin); - cycle.omega_iov_update_rejected = true; - cycle - }) - .collect::>(); - assert_eq!( - covariance_boundary_rejection_summary(&iov_cycles, policy, true), - CovarianceBoundaryRejectionSummary { - first_iteration: Some(1), - longest_run: 3, - } - ); - assert!(parametric_warnings(&iov_cycles, Some(policy)).contains( - &ParametricWarning::OmegaIovBoundaryRejection { - first_iteration: 1, - longest_run: 3, - } - )); - - let criterion = evaluate_criterion( - "omega_boundary_rejection_run", - Some(3.0), - policy.rejection_window as f64, - |observed| observed < policy.rejection_window as f64, - ); - assert_eq!( - criterion.status, - OperationalConvergenceCriterionStatus::NotSatisfied - ); - } - - #[test] - fn m_step_recenters_etas_before_updating_iiv_second_moment() { - let mut state = SaemState::from_problem( - problem(), - &SaemConfig::new() - .n_chains(1) - .burn_in(0) - .omega_sa_max_step(0.1), - ) - .unwrap(); - state.cycle = 1; - for subject_chains in &mut state.etas { - for eta in subject_chains { - eta[0] = 2.0_f64.ln(); - } - } - let individual_before = state.individual_parameters(0, 0); - - state.m_step().unwrap(); - - let individual_after = state.individual_parameters(0, 0); - assert!((individual_before[0] - individual_after[0]).abs() < 1e-12); - assert!(state - .etas - .iter() - .flat_map(|subject_chains| subject_chains.iter()) - .all(|eta| eta[0].abs() < 1e-12)); - assert!((state.population_parameters[0] - 0.4).abs() < 1e-12); - assert!((state.population_parameters[1] - 10.0).abs() < 1e-12); - let information = state.information.diagnostics(); - let ke_coordinate = information - .coordinates - .iter() - .position(|coordinate| coordinate.name == "phi:ke") - .unwrap(); - // Two pre-M-step absolute phi values each differ from the old - // population by ln(2). Post-update or un-recentered evaluation would - // give a different score (zero or double-counted population shift). - assert!((information.delta[ke_coordinate] - 2.0 * 2.0_f64.ln()).abs() < 1e-12); - let expected_omega = ndarray::array![[0.9, 0.0], [0.0, 0.9]]; - assert!(state - .iiv_second_moment - .iter() - .zip(expected_omega.iter()) - .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); - assert!(state - .omega - .iter() - .zip(expected_omega.iter()) - .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); - } - - #[test] - fn exploration_covariance_cap_prevents_one_draw_rank_one_collapse() { - fn correlation(omega: &Array2) -> f64 { - omega[[0, 1]] / (omega[[0, 0]] * omega[[1, 1]]).sqrt() - } - - let make_state = |omega_sa_max_step| { - SaemState::from_problem( - correlated_omega_problem(), - &SaemConfig::new() - .n_chains(1) - .burn_in(0) - .omega_sa_max_step(omega_sa_max_step), - ) - .unwrap() - }; - let mut guarded = make_state(0.1); - let mut uncapped = make_state(1.0); - for state in [&mut guarded, &mut uncapped] { - state.cycle = 1; - state.etas[0][0] = vec![2.0, 2.0]; - state.etas[1][0] = vec![-2.0, -2.0]; - state.m_step().unwrap(); - assert!(state.omega[[0, 0]] >= state.initialization.schedule.minimum_variance); - assert!(state.omega[[1, 1]] >= state.initialization.schedule.minimum_variance); - assert!( - state.omega[[0, 0]] * state.omega[[1, 1]] - state.omega[[0, 1]].powi(2) > 0.0, - "omega: {:?}", - state.omega - ); - } - - let guarded_correlation = correlation(&guarded.omega); - let uncapped_correlation = correlation(&uncapped.omega); - assert!(guarded_correlation < 0.85); - assert!(uncapped_correlation > 0.85); - assert!(uncapped_correlation - guarded_correlation > 0.05); - } - - #[test] - fn m_step_preserves_fixed_omega_and_structural_zeros() { - let mut state = SaemState::from_problem( - configured_omega_problem(), - &SaemConfig::new() - .n_chains(2) - .burn_in(0) - .omega_sa_max_step(1.0), - ) - .unwrap(); - state.cycle = 1; - for (subject_index, subject_chains) in state.etas.iter_mut().enumerate() { - let sign = if subject_index == 0 { 1.0 } else { -1.0 }; - for eta in subject_chains { - eta[0] = sign; - eta[1] = 2.0 * sign; - } - } - - state.m_step().unwrap(); - - assert!((state.omega[[0, 0]] - 1.0).abs() < 1e-12); - assert!((state.omega[[1, 1]] - 0.5).abs() < 1e-12); - assert_eq!(state.omega[[0, 1]], 0.0); - assert_eq!(state.omega[[1, 0]], 0.0); - } - - #[test] - fn fixed_population_effect_is_not_updated_and_omega_uses_fixed_center() { - let mut state = SaemState::from_problem( - fixed_population_iiv_problem(), - &SaemConfig::new() - .n_chains(2) - .burn_in(0) - .omega_sa_max_step(1.0), - ) - .unwrap(); - state.cycle = 1; - for subject_chains in &mut state.etas { - for eta in subject_chains { - eta[0] = 2.0_f64.ln(); - } - } - - state.m_step().unwrap(); - - assert!((state.population_parameters[0] - 0.2).abs() < 1e-12); - assert!(state - .etas - .iter() - .flat_map(|subject_chains| subject_chains.iter()) - .all(|eta| (eta[0] - 2.0_f64.ln()).abs() < 1e-12)); - assert!((state.omega[[0, 0]] - 2.0_f64.ln().powi(2)).abs() < 1e-12); - let individual = state.individual_parameters(0, 0); - assert!((individual[0] - 0.4).abs() < 1e-12); - } - - #[test] - fn m_step_updates_simple_residual_sigma_from_statrese() { - let mut state = SaemState::from_problem( - constant_error_problem(), - &SaemConfig::new().n_chains(1).burn_in(0), - ) - .unwrap(); - state.cycle = 1; - let candidate_sigma = state - .current_residual_statistics() - .unwrap() - .output(0) - .and_then(|statistic| statistic.sigma()) - .unwrap(); - let expected_sigma = state.initialization.schedule.guarded_residual_sigma( - state.cycle, - state.residual_sigmas[0], - candidate_sigma, - ); - - state.m_step().unwrap(); - - assert!((state.residual_sigmas[0] - expected_sigma).abs() < 1e-12); - assert_eq!( - state.error_models.get(0), - Some(&ResidualErrorModel::constant(expected_sigma)) - ); - } - - #[test] - fn sparse_second_output_reports_only_declared_residual_model() { - let result = sparse_second_output_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(0) - .compute_map(false), - ) - .unwrap(); - - assert_eq!(result.residual_sigmas().len(), 1); - assert_eq!(result.residual_error_estimates().len(), 1); - assert_eq!(result.residual_error_estimates()[0].output, "measured"); - assert_eq!(result.residual_error_estimates()[0].output_index, 1); - assert_eq!(result.cycle_diagnostics().len(), 1); - assert_eq!(result.cycle_diagnostics()[0].residual_diagnostics.len(), 1); - assert_eq!( - result.cycle_diagnostics()[0].residual_diagnostics[0].output, - "measured" - ); - assert_eq!( - result.cycle_diagnostics()[0].residual_diagnostics[0].output_index, - 1 - ); - } - - #[test] - fn averaged_sparse_second_output_preserves_index_name_and_arithmetic_mean() { - let result = sparse_second_output_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(3) - .burn_in(0) - .averaged_iterates(0.75) - .compute_map(false) - .seed(71_002), - ) - .expect("averaged sparse-output fit should complete"); - - let metadata = result.estimator_metadata(); - assert!(metadata.average_applied); - assert_eq!(metadata.averaging_start_cycle, Some(2)); - assert_eq!(metadata.averaged_iterations, 3); - let estimate = result - .residual_error_estimates() - .first() - .expect("sparse residual estimate"); - assert_eq!( - (estimate.output_index, estimate.output.as_str()), - (1, "measured") - ); - let smoothing = &result.cycle_diagnostics()[1..]; - let expected = smoothing - .iter() - .map(|cycle| { - let residual = cycle - .residual_error_estimates - .first() - .expect("sparse cycle residual"); - assert_eq!( - (residual.output_index, residual.output.as_str()), - (1, "measured") - ); - primary_sigma_parameter(&residual.model) - }) - .sum::() - / smoothing.len() as f64; - assert!((primary_sigma_parameter(&estimate.model) - expected).abs() < 1e-12); - } - - #[test] - fn averaged_multi_output_residuals_preserve_fixed_and_fixed_zero_components() { - let result = mixed_residual_output_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(3) - .burn_in(0) - .averaged_iterates(0.75) - .compute_map(false) - .seed(71_003), - ) - .expect("averaged mixed-output fit should complete"); - let estimates = result.residual_error_estimates(); - assert_eq!(estimates.len(), 2); - assert_eq!( - (estimates[0].output_index, estimates[0].output.as_str()), - (0, "fixed") - ); - assert_eq!(estimates[0].model, ResidualErrorModel::constant(0.5)); - assert!(!estimates[0].estimated); - assert_eq!( - (estimates[1].output_index, estimates[1].output.as_str()), - (1, "mixed") - ); - assert_eq!(estimates[1].combined_additive_estimated, Some(false)); - assert_eq!(estimates[1].combined_proportional_estimated, Some(true)); - let ResidualErrorModel::Combined { a, b } = estimates[1].model else { - panic!("expected combined residual model"); - }; - assert_eq!(a, 0.0); - let smoothing = &result.cycle_diagnostics()[1..]; - let expected_b = smoothing - .iter() - .map(|cycle| match cycle.residual_error_estimates[1].model { - ResidualErrorModel::Combined { a, b } => { - assert_eq!(a, 0.0); - b - } - _ => panic!("expected combined cycle residual model"), - }) - .sum::() - / smoothing.len() as f64; - assert!((b - expected_b).abs() < 1e-12); - assert!(result.cycle_diagnostics().iter().all(|cycle| { - cycle.residual_error_estimates[0].model == ResidualErrorModel::constant(0.5) - })); - } - - #[test] - fn correlated_residual_averaging_preserves_fixed_components_and_rejects_family_changes() { - let averaged = average_residual_model( - ResidualErrorModel::correlated_combined(0.3, 0.1, 0.2), - ResidualErrorModel::correlated_combined(0.5, 0.2, -0.4), - true, - [true, true], - [false, true, true], - 2, - ) - .unwrap(); - let ResidualErrorModel::CorrelatedCombined { a, b, rho } = averaged else { - panic!("expected correlated-combined average") - }; - assert_eq!(a, 0.3); - assert!((b - 0.15).abs() < 1e-15); - assert!((rho + 0.1).abs() < 1e-15); - assert!(average_residual_model( - averaged, - ResidualErrorModel::combined(0.3, 0.15), - true, - [true, true], - [true, true, true], - 3, - ) - .is_err()); - } - - fn assert_prediction_points_equal( - actual: &pharmsol::simulator::prediction::SubjectPredictions, - expected: &pharmsol::simulator::prediction::SubjectPredictions, - ) { - assert_eq!(actual.predictions().len(), expected.predictions().len()); - for (actual, expected) in actual.predictions().iter().zip(expected.predictions()) { - assert_eq!(actual.time(), expected.time()); - assert_eq!(actual.observation(), expected.observation()); - assert_eq!(actual.prediction(), expected.prediction()); - assert_eq!(actual.outeq(), expected.outeq()); - assert_eq!(actual.errorpoly(), expected.errorpoly()); - assert_eq!(actual.state(), expected.state()); - assert_eq!(actual.occasion(), expected.occasion()); - assert_eq!(actual.censoring(), expected.censoring()); - } - } - - #[test] - fn population_predictions_match_direct_execution_and_metadata() { - let result = problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1), - ) - .unwrap(); - let predictions = result.population_predictions(0.25, 0.0).unwrap(); - let expanded = result.data().clone().expand(0.25, 0.0); - - assert_eq!(predictions.len(), expanded.subjects().len()); - assert_eq!(expanded.subjects()[0].id(), "s1"); - assert_eq!(expanded.subjects()[1].id(), "s2"); - for (subject, actual) in expanded.subjects().iter().zip(&predictions) { - let expected = result - .equation() - .estimate_predictions_dense(subject, result.population_parameters()) - .unwrap(); - assert_prediction_points_equal(actual, &expected); - } - } - - #[test] - fn fixed_zero_latent_conditional_predictions_equal_population_predictions() { - let result = fixed_no_iiv_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1), - ) - .unwrap(); - - assert!(result.conditional_modes().is_empty()); - let population = result.population_predictions(0.25, 0.0).unwrap(); - let conditional = result.conditional_predictions(0.25, 0.0).unwrap(); - assert_eq!(conditional.len(), population.len()); - for (conditional, population) in conditional.iter().zip(&population) { - assert_prediction_points_equal(conditional, population); - } - } - - #[test] - fn iov_conditional_predictions_use_each_occasion_kappa_in_order() { - let mut result = iov_problem() - .fit_with( - SaemConfig::new() - .n_chains(1) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1), - ) - .unwrap(); - result.conditional_modes[0].eta.fill(0.0); - result.conditional_modes[0].kappas[0].values[0] = -0.2; - result.conditional_modes[0].kappas[1].values[0] = 0.3; - - let actual = result.conditional_predictions(0.25, 0.0).unwrap(); - assert_eq!(actual.len(), 1); - let expanded = result.data().clone().expand(0.25, 0.0); - let subject = &expanded.subjects()[0]; - let mode = &result.conditional_modes()[0]; - let mut expected_points = Vec::new(); - for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { - let parameters = occasion_psi( - result.population_parameters(), - &result.parameter_scales, - &result.random_effect_indices, - &mode.eta, - &result.iov_effect_indices, - &kappa.values, - ) - .unwrap(); - let occasion_subject = - Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); - for mut prediction in result - .equation() - .estimate_predictions_dense(&occasion_subject, ¶meters) - .unwrap() - .predictions() - .iter() - .cloned() - { - *prediction.mut_occasion() = occasion.index(); - expected_points.push(prediction); - } - } - let expected = pharmsol::simulator::prediction::SubjectPredictions::from(expected_points); - assert_prediction_points_equal(&actual[0], &expected); - assert!(actual[0] - .predictions() - .windows(2) - .any(|pair| pair[0].occasion() != pair[1].occasion())); - let occasion_predictions = subject - .occasions() - .iter() - .map(|occasion| { - actual[0] - .predictions() - .iter() - .find(|prediction| { - prediction.occasion() == occasion.index() - && prediction.observation().is_some() - }) - .unwrap() - .prediction() - }) - .collect::>(); - assert_ne!(occasion_predictions[0], occasion_predictions[1]); - } - - #[test] - fn e_step_rescores_chain_zero_parameters() { - let mut state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); - let initial = state.log_likelihood(); - - state.etas[0][0][0] = 2.0_f64.ln(); - state.e_step().unwrap(); - - assert!(state.log_likelihood().is_finite()); - assert_ne!(state.log_likelihood(), initial); - assert_eq!(state.negative_log_likelihood(), -state.log_likelihood()); - } - - #[test] - fn iov_result_retains_named_omega_iov() { - let result = iov_problem() - .fit_with( - SaemConfig::new() - .n_chains(2) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1), - ) - .unwrap(); - - assert_eq!(result.iov_effect_names(), &["ke"]); - assert_eq!(result.omega_iov(), Some(&ndarray::array![[0.1]])); - assert_eq!(result.conditional_modes().len(), 1); - assert_eq!(result.conditional_modes()[0].kappas.len(), 2); - assert!(result.conditional_modes()[0].objective.is_finite()); - } - - #[test] - fn result_reports_final_chain_means_for_eta_and_kappa() { - let mut state = - SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); - state.etas[0][0][0] = 0.2; - state.etas[0][1][0] = 0.4; - state.kappas[0][0][0][0] = -0.2; - state.kappas[0][1][0][0] = 0.4; - state.kappas[0][0][1][0] = 0.1; - state.kappas[0][1][1][0] = 0.3; - - let result = Box::new(state).into_result().unwrap(); - - assert_eq!(result.eta_chain_means().len(), 1); - assert!((result.eta_chain_means()[0].values[0] - 0.3).abs() < 1e-12); - assert_eq!(result.kappa_chain_means().len(), 2); - assert_eq!(result.kappa_chain_means()[0].occasion_index, 0); - assert!((result.kappa_chain_means()[0].values[0] - 0.1).abs() < 1e-12); - assert_eq!(result.kappa_chain_means()[1].occasion_index, 1); - assert!((result.kappa_chain_means()[1].values[0] - 0.2).abs() < 1e-12); - } - - #[test] - fn result_retains_immutable_cycle_diagnostics() { - let config = SaemConfig::new() - .n_chains(1) - .mcmc_iterations(1) - .burn_in(1) - .k1_iterations(1) - .k2_iterations(1) - .compute_map(false); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - state.step().unwrap(); - state.step().unwrap(); - - let result = Box::new(state).into_result().unwrap(); - - assert_eq!(result.parameter_names(), ["ke", "v"]); - assert_eq!(result.data().subjects().len(), 2); - assert_eq!( - result - .equation() - .metadata() - .expect("retained equation metadata") - .outputs()[0] - .name(), - "0" - ); - assert_eq!(result.cycle_diagnostics().len(), 2); - assert_eq!(result.cycle_diagnostics()[0].iteration, 1); - assert_eq!(result.cycle_diagnostics()[0].phase, SaemPhase::BurnIn); - assert_eq!(result.cycle_diagnostics()[1].iteration, 2); - assert_eq!(result.cycle_diagnostics()[1].phase, SaemPhase::Smoothing); - assert_eq!( - result.cycle_diagnostics()[0].population_parameters, - vec![0.2, 10.0] - ); - let final_cycle = &result.cycle_diagnostics()[1]; - assert_eq!( - final_cycle.population_parameters, - result.population_parameters() - ); - assert_eq!(&final_cycle.omega, result.omega()); - assert_eq!(final_cycle.omega_iov.as_ref(), result.omega_iov()); - assert_eq!( - final_cycle.residual_error_estimates, - result.residual_error_estimates() - ); - assert!(final_cycle.conditional_negative_log_likelihood.is_finite()); - assert!(final_cycle.eta_log_prior.is_finite()); - assert!(final_cycle.kappa_log_prior.is_finite()); - } - - #[test] - fn conditional_modes_can_be_disabled_without_relabeling_chain_means() { - let result = problem() - .fit_with( - SaemConfig::new() - .n_chains(2) - .k1_iterations(1) - .k2_iterations(0) - .burn_in(1) - .compute_map(false), - ) - .unwrap(); - - assert!(result.conditional_modes().is_empty()); - assert_eq!(result.eta_chain_means().len(), 2); - let error = result.conditional_predictions(0.25, 0.0).unwrap_err(); - assert_eq!( - error.to_string(), - "conditional predictions require conditional modes; rerun with compute_map(true)" - ); - } - - #[test] - fn population_uncertainty_wires_analytical_fit_summary_without_changing_estimates() { - let equation = analytical! { - name: "population_uncertainty_summary_fixture", - params: [ke, v], - states: [central], - outputs: [cp], - routes: [infusion(iv) -> central], - structure: one_compartment, - out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, - }; - let data = Data::new(vec![ - Subject::builder("uncertainty-1") - .infusion(0.0, 100.0, "iv", 0.5) - .observation(1.0, 4.8, "cp") - .observation(3.0, 3.0, "cp") - .build(), - Subject::builder("uncertainty-2") - .infusion(0.0, 120.0, "iv", 0.5) - .observation(1.0, 5.4, "cp") - .observation(3.0, 3.2, "cp") - .build(), - ]); - let problem = EstimationProblem::parametric(equation, data) - .parameter(Parameter::log("ke").with_initial(0.25)) - .parameter( - Parameter::log("v") - .with_initial(20.0) - .fixed() - .without_random_effect(), - ) - .omega(Omega::new().fixed_variance("ke", 0.09)) - .error_model( - "cp", - ParametricErrorModel::new(ResidualErrorModel::constant(0.4)).fixed(), - ) - .build() - .expect("population uncertainty analytical fixture"); - let mut result = problem - .fit_with( - SaemConfig::new() - .seed(0x6a_2026) - .n_chains(2) - .mcmc_iterations(1) - .burn_in(1) - .k1_iterations(1) - .k2_iterations(0) - .compute_map(false), - ) - .expect("population uncertainty analytical fit"); - let estimates_before = result.population_parameters().to_vec(); - let objective_before = result.objf(); - assert_eq!(estimates_before, vec![0.25, 20.0]); - assert_eq!(result.estimated_parameters(), &[true, false]); - assert_eq!( - result.population_uncertainty(), - &derive_population_uncertainty(result.information_diagnostics()) - ); - - let coordinates = result.information_diagnostics().coordinates.clone(); - assert_eq!(coordinates.len(), 1); - assert_eq!( - coordinates[0].kind, - InformationCoordinateKind::Population { parameter_index: 0 } - ); - result.population_uncertainty = PopulationUncertaintyDiagnostics { - coordinates, - free_covariance: Some(vec![vec![0.04]]), - free_standard_errors: Some(vec![0.2]), - spectral_condition_number: Some(1.0), - status: PopulationUncertaintyStatus::Available, - regularization: PopulationUncertaintyRegularization::None, - }; - - let summary = result.population_summary(); - assert_eq!(result.population_parameters(), estimates_before); - assert_eq!(result.objf().to_bits(), objective_before.to_bits()); - assert_eq!( - summary - .parameters - .iter() - .map(|parameter| parameter.estimate) - .collect::>(), - estimates_before - ); - assert!( - (summary.parameters[0] - .sd - .expect("free log-scale parameter SE") - - 0.2 * estimates_before[0]) - .abs() - < 1e-12 - ); - assert!( - (summary.parameters[0] - .cv_percent - .expect("free log-scale parameter CV") - - 20.0) - .abs() - < 1e-12 - ); - assert_eq!(summary.parameters[1].sd, None); - assert_eq!(summary.parameters[1].cv_percent, None); - } - - #[test] - fn initialization_result_is_non_converged_snapshot() { - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(1) - .burn_in(1); - let result = problem().fit_with(config).unwrap(); - let summary = result.summary(); - - assert!(!result.converged()); - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - assert_ne!(result.termination_reason(), Some(&StopReason::Aborted)); - assert_ne!( - result.termination_reason(), - Some(&StopReason::NumericalFailure) - ); - assert_eq!(result.iterations(), 2); - assert_eq!(summary.subject_count, 2); - assert_eq!(summary.observation_count, 4); - assert_eq!(summary.parameter_count, 2); - assert!(result.objf().is_finite()); - assert_eq!(result.population_parameters().len(), 2); - assert_eq!(result.random_effect_names(), &["ke", "v"]); - assert_eq!(result.omega().dim(), (2, 2)); - assert_eq!(result.residual_sigmas().len(), 1); - assert_eq!(result.eta_chain_means().len(), 2); - assert!(result.kappa_chain_means().is_empty()); - assert_eq!(result.conditional_modes().len(), 2); - assert!(result - .conditional_modes() - .iter() - .all(|mode| mode.objective.is_finite())); - assert_eq!(result.population_summary().parameters.len(), 2); - assert_eq!(result.individual_summaries().len(), 2); - } - - // ─── Operational convergence tests ─────────────────────────────────── - - #[test] - fn operational_convergence_disabled_when_config_is_none() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let config = SaemConfig::new() - .k1_iterations(2) - .k2_iterations(2) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .compute_map(false) - .seed(42); - let result = problem().fit_with(config).unwrap(); - let ops = result.operational_diagnostics(); - assert!(ops.checks.is_empty()); - assert!(!ops.used_for_termination); - assert!(!ops.final_check_reused); - assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); - } - - #[test] - fn operational_convergence_records_checkpoints_when_configured() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(3) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) - .operational_convergence(oc) - .compute_map(false) - .seed(43); - let result = problem().fit_with(config).unwrap(); - let ops = result.operational_diagnostics(); - // Should have at least one checkpoint (smoothing phase produces checkpoints) - assert!(!ops.checks.is_empty(), "expected at least one checkpoint"); - // Each checkpoint should have all fields populated - for check in &ops.checks { - assert!(check.checkpoint_seed.is_some()); - assert!(check.z_quantile.is_some()); - assert!(check.implied_minimum_ess.is_some()); - assert!(!check.criteria.is_empty()); - assert!(check.markov.is_some()); - assert_eq!( - check.averaged_iterations, - check.markov.as_ref().unwrap().n_avg - ); - } - } - - #[test] - fn operational_convergence_has_exact_criterion_names() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(3) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) - .operational_convergence(oc) - .compute_map(false) - .seed(44); - let result = problem().fit_with(config).unwrap(); - let ops = result.operational_diagnostics(); - assert!(!ops.checks.is_empty()); - let first_check = &ops.checks[0]; - let names: Vec<&str> = first_check - .criteria - .iter() - .map(|c| c.name.as_str()) - .collect(); - assert!(names.contains(&"max_rhat")); - assert!(names.contains(&"min_bulk_ess")); - assert!(names.contains(&"min_average_bulk_ess_per_split_chain")); - assert!(names.contains(&"relative_fixed_width")); - assert!(names.contains(&"newton_displacement")); - assert!(names.contains(&"newton_displacement_mc_sd")); - assert!(names.contains(&"omega_boundary_rejection_run")); - assert!(names.contains(&"omega_iov_boundary_rejection_run")); - } - - #[test] - fn covariance_boundary_rejection_blocks_converged_stop_reason() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 100.0, 100.0); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(2) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.99, 1)) - .operational_convergence(oc) - .compute_map(false) - .seed(47); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - state.step().unwrap(); - state.cycle_diagnostics[0].omega_relative_spd_margin = Some(0.5); - state.cycle_diagnostics[0].omega_update_rejected = true; - - state.step().unwrap(); - - let check = state - .operational_diagnostics - .checks - .last() - .expect("operational checkpoint"); - let boundary = check - .criteria - .iter() - .find(|criterion| criterion.name == "omega_boundary_rejection_run") - .expect("Omega boundary criterion"); - assert_eq!( - boundary.status, - OperationalConvergenceCriterionStatus::NotSatisfied - ); - assert_ne!(state.status, Status::Stop(StopReason::Converged)); - } - - #[test] - fn iov_boundary_rejection_blocks_converged_stop_reason() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(2) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.99, 1)) - .operational_convergence(OperationalConvergenceConfig::literature_guided( - 1, 1, 1.0, 0.95, 100.0, 100.0, - )) - .compute_map(false) - .seed(48); - let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); - state.step().unwrap(); - state.cycle_diagnostics[0].omega_iov_relative_spd_margin = Some(0.5); - state.cycle_diagnostics[0].omega_iov_update_rejected = true; - state.step().unwrap(); - - let check = state - .operational_diagnostics - .checks - .last() - .expect("operational checkpoint"); - let boundary = check - .criteria - .iter() - .find(|criterion| criterion.name == "omega_iov_boundary_rejection_run") - .expect("Omega_IOV boundary criterion"); - assert_eq!( - boundary.status, - OperationalConvergenceCriterionStatus::NotSatisfied - ); - assert_ne!(state.status, Status::Stop(StopReason::Converged)); - } - - #[test] - fn operational_convergence_waits_for_complete_covariance_window() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(5) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.01, 5)) - .operational_convergence(OperationalConvergenceConfig::literature_guided( - 1, 1, 1.0, 0.95, 100.0, 100.0, - )) - .compute_map(false) - .seed(49); - let mut state = SaemState::from_problem(problem(), &config).unwrap(); - state.step().unwrap(); - state.step().unwrap(); - - let first = state - .operational_diagnostics - .checks - .last() - .expect("first operational checkpoint"); - let first_boundary = first - .criteria - .iter() - .find(|criterion| criterion.name == "omega_boundary_rejection_run") - .expect("Omega boundary criterion"); - assert!(matches!( - first_boundary.status, - OperationalConvergenceCriterionStatus::Unavailable(_) - )); - assert!(matches!( - first.outcome, - OperationalConvergenceOutcome::Ineligible { .. } - )); - assert_ne!(state.status, Status::Stop(StopReason::Converged)); - - while state.cycle < 5 && !state.status.is_stop() { - state.step().unwrap(); - } - let eligible = state - .operational_diagnostics - .checks - .last() - .expect("fifth-cycle operational checkpoint"); - assert_eq!(eligible.iteration, 5); - let eligible_boundary = eligible - .criteria - .iter() - .find(|criterion| criterion.name == "omega_boundary_rejection_run") - .expect("Omega boundary criterion"); - assert_eq!( - eligible_boundary.status, - OperationalConvergenceCriterionStatus::Satisfied - ); - } - - #[test] - fn operational_convergence_final_checkpoint_runs_once_with_truthful_flags() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - // check_interval=1 means every smoothing iteration is a checkpoint, - // so the last scheduled checkpoint and the mandatory final will overlap. - let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(2) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) - .operational_convergence(oc) - .compute_map(false) - .seed(45); - let result = problem().fit_with(config).unwrap(); - let ops = result.operational_diagnostics(); - assert!(!ops.final_check_reused); - let final_check = ops.checks.last().expect("final checkpoint"); - assert!(final_check.scheduled); - assert!(final_check.mandatory_final); - assert_eq!( - ops.checks - .iter() - .filter(|check| check.iteration == final_check.iteration) - .count(), - 1 - ); - } - - #[test] - fn operational_convergence_checkpoint_seed_is_deterministic_and_global_seed_is_unchanged() { - use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; - let markov = MarkovSimulationVarianceConfig::new( - 7, - 0, - 12, - 6, - LugsailConfig::over_lugsail_bartlett(), - 4, - 1024 * 1024, - ); - let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); - let config = SaemConfig::new() - .k1_iterations(1) - .k2_iterations(3) - .burn_in(0) - .averaged_iterates(0.75) - .markov_simulation_variance(markov) - .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) - .operational_convergence(oc) - .compute_map(false) - .seed(46); - let result1 = problem().fit_with(config.clone()).unwrap(); - let result2 = problem().fit_with(config).unwrap(); - - let ops1 = result1.operational_diagnostics(); - let ops2 = result2.operational_diagnostics(); - assert_eq!(ops1.checks.len(), ops2.checks.len()); - for (c1, c2) in ops1.checks.iter().zip(ops2.checks.iter()) { - assert_eq!(c1.checkpoint_seed, c2.checkpoint_seed); - assert_eq!(c1.z_quantile, c2.z_quantile); - assert_eq!(c1.outcome, c2.outcome); - } - // Canonical fit result must be unchanged by operational convergence - assert_eq!( - result1.population_parameters(), - result2.population_parameters() - ); - assert_eq!(result1.omega(), result2.omega()); - assert_eq!(result1.conditional_n2ll(), result2.conditional_n2ll()); - } - - #[test] - fn normal_two_sided_z_covers_common_confidence_levels() { - use statrs::distribution::{ContinuousCDF, Normal}; - let norm = Normal::new(0.0, 1.0).unwrap(); - for p in [0.90, 0.95, 0.99] { - let expected = norm.inverse_cdf(p + (1.0 - p) / 2.0); - let actual = normal_two_sided_z(p); - assert!((actual - expected).abs() < 1e-10); - } - } - - #[test] - fn gong_flegal_fixed_width_and_implied_ess_are_exact() { - let z = normal_two_sided_z(0.95); - let epsilon = 0.05; - let implied = 4.0 * z * z / (epsilon * epsilon); - assert!((implied - 6146.34).abs() < 0.1); - let boundary_fraction = epsilon / (2.0 * z); - assert!(2.0 * z * boundary_fraction <= epsilon); - assert!(2.0 * z * (boundary_fraction + 1e-12) > epsilon); - } - - #[test] - fn evaluate_criterion_detects_satisfied_not_satisfied_and_unavailable() { - let satisfied = evaluate_criterion("test", Some(0.5), 1.0, |v| v <= 1.0); - assert_eq!( - satisfied.status, - OperationalConvergenceCriterionStatus::Satisfied - ); - assert_eq!(satisfied.observed, Some(0.5)); - - let not_satisfied = evaluate_criterion("test", Some(2.0), 1.0, |v| v <= 1.0); - assert_eq!( - not_satisfied.status, - OperationalConvergenceCriterionStatus::NotSatisfied - ); - assert_eq!(not_satisfied.observed, Some(2.0)); - - let unavailable_none = evaluate_criterion("test", None, 1.0, |v| v <= 1.0); - assert!(matches!( - unavailable_none.status, - OperationalConvergenceCriterionStatus::Unavailable(_) - )); - assert_eq!(unavailable_none.observed, None); - - let unavailable_nan = evaluate_criterion("test", Some(f64::NAN), 1.0, |v| v <= 1.0); - assert!(matches!( - unavailable_nan.status, - OperationalConvergenceCriterionStatus::Unavailable(_) - )); - } - - #[test] - fn newton_displacement_requires_matching_dimensions() { - let empty_info = InformationDiagnostics { - coordinates: vec![], - recursion_cycles: 0, - delta: vec![], - g: vec![], - expected_complete_hessian: vec![], - observed_hessian: vec![], - observed_information: vec![], - status: InformationStatus::Available, - }; - let empty_markov = MarkovSimulationVarianceDiagnostics::disabled(); - assert_eq!(newton_displacement(&empty_info, &empty_markov), None); - assert_eq!(newton_displacement_mc_sd(&empty_info, &empty_markov), None); - } -} diff --git a/src/algorithms/parametric/saem/mod.rs b/src/algorithms/parametric/saem/mod.rs new file mode 100644 index 000000000..3db2e6dd0 --- /dev/null +++ b/src/algorithms/parametric/saem/mod.rs @@ -0,0 +1,652 @@ +use std::collections::BTreeMap; + +use anyhow::{anyhow, Result}; +use argmin::{ + core::{CostFunction, Error as ArgminError, Executor}, + solver::neldermead::NelderMead, +}; +use ndarray::Array2; +use pharmsol::{Data, Equation, Event, Subject}; +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; + +use crate::algorithms::{Status, StopReason}; +use crate::estimation::likelihood::batch::{ + parametric_occasion_log_likelihood, parametric_subject_log_likelihood, +}; +use crate::estimation::likelihood::objective::parametric_subject_log_likelihoods; +use crate::estimation::parametric::conditional_uncertainty::{ + conditional_mode_curvature, ConditionalModeMetadata, JointLatentCoordinate, + JointLatentCoordinateKind, +}; +use crate::estimation::parametric::covariance::{ + cholesky_lower, relative_spd_margin, worst_contrast, +}; +use crate::estimation::parametric::covariates::{ + rebase_eta, solve_covariate_gls, subject_centered_omega, CovariateGlsProblem, CovariateModel, +}; +use crate::estimation::parametric::individual::{ + individual_phi, individual_phi_from_subject_mean, individual_psi, + individual_psi_from_subject_mean, occasion_psi, occasion_psi_from_subject_mean, population_phi, + population_psi, +}; +use crate::estimation::parametric::information::{ + CompleteDerivative, InformationLayout, InformationRecursion, +}; +use crate::estimation::parametric::marginal_likelihood::{ + calculate_population_marginal_likelihood, unavailable_population_marginal_likelihood, + MarginalLikelihoodDiagnostics, MarginalLikelihoodFailureReason, MarginalSubject, +}; +use crate::estimation::parametric::markov_variance::{ + classify_psd, lugsail_batch_means, rows, scale_lrv_sum, transform_simulation_variance, + MatrixClassification, +}; +use crate::estimation::parametric::posterior::{ + eta_log_prior_from_omega, eta_log_priors, SubjectPosteriorScore, +}; +use crate::estimation::parametric::posthoc::optimize_conditional_mode; +use crate::estimation::parametric::prior::CovarianceUpdateResult; +use crate::estimation::parametric::rank_diagnostics::{ + bulk_ess, folded_split_rhat, rank_normalized_split_rhat, RankDiagnosticError, +}; +use crate::estimation::parametric::residual::{ + combined_additive_sigma_collapsed, optimize_combined_residual, + optimize_correlated_combined_residual, primary_sigma_parameter, primary_sigma_parameters, + residual_statistics_for_subject, update_estimated_combined_residual_model, + update_estimated_correlated_combined_residual_model, + update_estimated_simple_residual_model_with_sigma, ResidualSufficientStatistics, +}; +use crate::estimation::parametric::sufficient::{ + CovariateSufficientStatistics, PhiSufficientStatistics, +}; +use crate::estimation::parametric::{CovarianceUpdateStatus, ResolvedOmega}; +use crate::estimation::{EstimationProblem, Parametric, ParametricErrorModels}; +use crate::model::{ParameterScale, UnboundedParameter}; +use crate::ResidualErrorModel; + +use crate::results::{ + CovarianceCycleUpdateDiagnostics, CovarianceCycleUpdateOutcome, + CovarianceUpdateNotAttemptedReason, DiagnosticTraceCoordinate, InformationCoordinateKind, + InformationDiagnostics, InformationStatus, MarkovSimulationVarianceChainDiagnostics, + MarkovSimulationVarianceDiagnostics, MarkovSimulationVarianceStatus, OccasionKappaEstimate, + OperationalConvergenceCheck, OperationalConvergenceCriterion, + OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, + OperationalConvergenceOutcome, ParametricWarning, RankDiagnosticStatus, RankMixingDiagnostic, + RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, SaemCycleDiagnostics, + SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, +}; + +use super::{ + CovarianceStabilityConfig, NumericalFailure, OperationalConvergenceConfig, SaemConfig, + SaemEstimatorPolicy, +}; + +fn pending_covariance_update_diagnostics( + phase: SaemPhase, + configured: bool, + has_estimated_entries: bool, +) -> CovarianceCycleUpdateDiagnostics { + let reason = if !configured { + CovarianceUpdateNotAttemptedReason::NotConfigured + } else if !has_estimated_entries { + CovarianceUpdateNotAttemptedReason::NoEstimatedEntries + } else if phase == SaemPhase::BurnIn { + CovarianceUpdateNotAttemptedReason::BurnIn + } else { + CovarianceUpdateNotAttemptedReason::UpdateInactive + }; + CovarianceCycleUpdateDiagnostics::not_attempted(reason) +} + +fn completed_covariance_update_diagnostics( + proposal: &Array2, + update: &CovarianceUpdateResult, +) -> Result { + let outcome = match update.status { + CovarianceUpdateStatus::Accepted => CovarianceCycleUpdateOutcome::Accepted, + CovarianceUpdateStatus::NoOp => CovarianceCycleUpdateOutcome::NoOp, + CovarianceUpdateStatus::Rejected => CovarianceCycleUpdateOutcome::Rejected { + reason: update.rejection_reason.ok_or_else(|| { + anyhow!("rejected covariance update lacks a typed diagnostic reason") + })?, + }, + }; + Ok(CovarianceCycleUpdateDiagnostics { + proposal: Some(proposal.clone()), + solved_target: update.solved_target.clone(), + outcome, + accepted_fraction: update.accepted_fraction, + attempted_fractions: update.attempted_fractions.clone(), + trial_rejections: update.trial_rejections.clone(), + }) +} + +const COMPONENT_TARGET_ACCEPTANCE: f64 = 0.44; +const ETA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; +const KAPPA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; +const PROPOSAL_SCALE_INCREASE: f64 = 1.1; +const MARKOV_VARIANCE_ASSUMPTIONS: &str = concat!( + "diagnostic only: prior draws at frozen averaged Omega/Omega_IOV; ", + "per-chain seed = config.seed.wrapping_add(i).wrapping_mul(0x9E3779B97F4A7C15); ", + "frozen-kernel stationarity, adequate mixing, the Poisson equation, and the ", + "controlled-Markov averaged-SA CLT are unverified; lugsail batch means alone is not a ", + "mixing diagnostic; failure detection (non-finite, ", + "constant, stuck, byte overflow, non-positive tau) is not a convergence claim; ", + "literature recommendations for R̂ and ESS are referenced but no threshold " +); + +#[derive(Clone)] +struct FrozenDiagnosticState { + etas: Vec>>, + kappas: Vec>>>, +} + +struct DiagnosticCandidate { + population_parameters: Vec, + covariate_model: Option, + omega: Array2, + omega_iov: Option>, + error_models: ParametricErrorModels, +} + +#[derive(Debug, Clone)] +struct NonIivCoordinateLayout { + population_indices: Vec, + covariate_indices: Vec, +} + +impl NonIivCoordinateLayout { + fn len(&self) -> usize { + self.population_indices.len() + self.covariate_indices.len() + } + + fn is_empty(&self) -> bool { + self.len() == 0 + } +} + +type NonIivCandidateComponents = (Vec, Option, Option>>); + +fn parameters_are_strictly_in_domain(values: &[f64], scales: &[ParameterScale]) -> bool { + values.len() == scales.len() + && values.iter().zip(scales).all(|(value, scale)| { + value.is_finite() + && match scale { + ParameterScale::Identity => true, + ParameterScale::Log => *value > 0.0, + ParameterScale::Logit { lower, upper } + | ParameterScale::Probit { lower, upper } => *value > *lower && *value < *upper, + } + }) +} + +fn non_iiv_candidate_improves(current: f64, candidate: f64) -> bool { + candidate.is_finite() && candidate < current +} + +struct NonIivPopulationCost<'a, E: Equation> { + state: &'a SaemState, + layout: &'a NonIivCoordinateLayout, +} + +impl CostFunction for NonIivPopulationCost<'_, E> { + type Param = Vec; + type Output = f64; + + fn cost(&self, coordinates: &Self::Param) -> std::result::Result { + Ok(self + .state + .non_iiv_observation_nll(self.layout, coordinates) + .unwrap_or(NON_IIV_OPTIMIZER_PENALTY)) + } +} + +const NON_IIV_OPTIMIZER_MAX_ITERATIONS: u64 = 100; +const NON_IIV_OPTIMIZER_PENALTY: f64 = 1e100; +const NON_IIV_OPTIMIZER_SD_TOLERANCE: f64 = 1e-8; +const PROPOSAL_SCALE_DECREASE: f64 = 0.9; +const MIN_PROPOSAL_SCALE: f64 = 1e-6; +const MAX_PROPOSAL_SCALE: f64 = 5.0; + +/// SAEM iteration schedule derived from [`SaemConfig`]. +/// +/// This uses the established high-level split: a pure burn-in +/// region, an exploration region with full stochastic approximation updates, +/// then a smoothing region with decreasing step size. +#[derive(Debug, Clone, PartialEq)] +pub(crate) struct SaemSchedule { + pub(crate) pure_burn_in: usize, + pub(crate) exploration_iterations: usize, + pub(crate) smoothing_iterations: usize, + pub(crate) total_iterations: usize, + pub(crate) variance_floor_iterations: usize, + pub(crate) annealing_alpha: f64, + pub(crate) omega_sa_max_step: f64, + pub(crate) minimum_variance: f64, + pub(crate) minimum_iov_variance: f64, + pub(crate) minimum_residual_sigma: f64, + pub(crate) averaging_alpha: Option, +} + +impl SaemSchedule { + pub(crate) fn from_config(config: &SaemConfig) -> Self { + let pure_burn_in = config.burn_in; + let exploration_iterations = config.k1_iterations.saturating_sub(pure_burn_in); + let smoothing_iterations = config.k2_iterations; + let total_iterations = config.k1_iterations + config.k2_iterations; + let variance_floor_iterations = if config.sa_iterations > 0 { + config.sa_iterations + } else { + config.k1_iterations / 2 + }; + + Self { + pure_burn_in, + exploration_iterations, + smoothing_iterations, + total_iterations, + variance_floor_iterations, + annealing_alpha: config.sa_cooling_factor, + omega_sa_max_step: config.omega_sa_max_step, + minimum_variance: config.omega_min_variance, + minimum_iov_variance: config.omega_iov_min_variance, + minimum_residual_sigma: config.residual_min_sigma, + averaging_alpha: match config.estimator_policy { + SaemEstimatorPolicy::TerminalIterate => None, + SaemEstimatorPolicy::AveragedIterates { alpha } => Some(alpha), + }, + } + } + + pub(crate) fn stochastic_approximation_step(&self, iteration: usize) -> f64 { + if iteration <= self.pure_burn_in { + 0.0 + } else if iteration <= self.pure_burn_in + self.exploration_iterations { + 1.0 + } else { + let smoothing_iteration = iteration + .saturating_sub(self.pure_burn_in + self.exploration_iterations) + .max(1); + match self.averaging_alpha { + Some(alpha) => (smoothing_iteration as f64).powf(-alpha), + None => 1.0 / smoothing_iteration as f64, + } + } + } + + /// Stochastic-approximation step for Ω/Ω_IOV sufficient statistics. + /// + /// Covariance learning is damped during both pure chain + /// warm-up and exploration so one un-equilibrated draw cannot overwrite a + /// correlated covariance. The cap is lifted in smoothing. + pub(crate) fn covariance_step(&self, iteration: usize) -> f64 { + if iteration <= self.pure_burn_in + self.exploration_iterations { + self.omega_sa_max_step.min(1.0) + } else { + self.stochastic_approximation_step(iteration) + } + } + + pub(crate) fn covariance_update_active(&self, iteration: usize) -> bool { + iteration > self.pure_burn_in + } + + pub(crate) fn phase(&self, iteration: usize) -> SaemPhase { + if iteration <= self.pure_burn_in { + SaemPhase::BurnIn + } else if iteration <= self.pure_burn_in + self.exploration_iterations { + SaemPhase::Exploration + } else { + SaemPhase::Smoothing + } + } + + /// Guard an estimated residual SD against early collapse. + /// + /// During simulated annealing, PMcore cools the previous residual SD by + /// `alpha.sa` and takes the larger of that value and the M-step candidate. + /// The configured residual floor always applies. Fixed residual models are + /// left untouched. + pub(crate) fn guarded_residual_sigma( + &self, + iteration: usize, + previous: f64, + candidate: f64, + ) -> f64 { + let mut guarded = candidate.max(self.minimum_residual_sigma); + if iteration <= self.variance_floor_iterations { + guarded = guarded.max(previous * self.annealing_alpha); + } + guarded + } +} + +fn covariate_omega_update_maximum_fraction( + has_covariates: bool, + phase: SaemPhase, + covariance_step: f64, +) -> f64 { + if has_covariates && phase == SaemPhase::Exploration { + covariance_step + } else { + 1.0 + } +} + +fn applied_combined_residual_component( + schedule: &SaemSchedule, + iteration: usize, + previous: f64, + candidate: f64, + estimated: bool, +) -> f64 { + if !estimated { + return previous; + } + let guarded_candidate = candidate.max(schedule.minimum_residual_sigma); + if iteration <= schedule.variance_floor_iterations { + return guarded_candidate.max(previous * schedule.annealing_alpha); + } + if schedule.phase(iteration) != SaemPhase::Smoothing { + return guarded_candidate; + } + let gamma = schedule.stochastic_approximation_step(iteration); + previous + gamma * (guarded_candidate - previous) +} + +/// Immutable SAEM setup computed once before the iterations begin. +/// +/// Parameter metadata, random/IOV effect indices, the resolved omega +/// specification, and initial subject-conditioned log-likelihoods are all +/// resolved here so the runner state only carries mutable estimation state. +#[derive(Debug, Clone)] +pub(crate) struct SaemInitialization { + pub(crate) schedule: SaemSchedule, + pub(crate) n_chains: usize, + pub(crate) parameter_names: Vec, + pub(crate) parameter_scales: Vec, + pub(crate) estimated_parameters: Vec, + pub(crate) random_effect_indices: Vec, + pub(crate) random_effect_names: Vec, + pub(crate) omega: ResolvedOmega, + pub(crate) iov_effect_indices: Vec, + pub(crate) iov_effect_names: Vec, + pub(crate) omega_iov: Option, + pub(crate) occasion_counts: Vec, + pub(crate) subject_ids: Vec, + pub(crate) observation_count: usize, + pub(crate) initial_population_parameters: Vec, + pub(crate) initial_subject_log_likelihoods: Vec, + pub(crate) initial_negative_log_likelihood: f64, + pub(crate) covariate_model: Option, + pub(crate) initial_subject_mu_phi: Option>>, + pub(crate) initial_residual_values: Vec>, + pub(crate) initial_residual_estimated: Vec>, +} + +fn applied_correlated_residual_correlation( + schedule: &SaemSchedule, + iteration: usize, + previous: f64, + candidate: f64, + estimated: bool, +) -> f64 { + if !estimated { + return previous; + } + if schedule.phase(iteration) != SaemPhase::Smoothing { + return candidate; + } + let gamma = schedule.stochastic_approximation_step(iteration); + previous + gamma * (candidate - previous) +} + +fn validate_initial_estimated_variance_floor( + covariance_name: &str, + floor_name: &str, + omega: &ResolvedOmega, + minimum_variance: f64, +) -> Result<()> { + for (index, effect_name) in omega.names().iter().enumerate() { + let initial_variance = omega.initial()[[index, index]]; + if omega.estimated_mask()[[index, index]] && initial_variance < minimum_variance { + anyhow::bail!( + "SAEM initial {covariance_name} variance for estimated effect '{effect_name}' ({initial_variance}) is below configured {floor_name} ({minimum_variance})" + ); + } + } + Ok(()) +} + +impl SaemInitialization { + pub(crate) fn create( + problem: &EstimationProblem, + config: &SaemConfig, + ) -> Result + where + E: Equation, + { + config.validate()?; + let omega = problem.prior.resolved_omega().clone(); + let n_subjects = problem.data.subjects().len(); + let initial_row = initial_parameter_row(problem.parameters().iter()); + let random_effect_indices = problem + .parameters() + .iter() + .enumerate() + .filter_map(|(index, parameter)| parameter.random_effect.then_some(index)) + .collect::>(); + let random_effect_names = random_effect_indices + .iter() + .map(|index| problem.parameters().items[*index].name.clone()) + .collect(); + let (iov_effect_indices, iov_effect_names, omega_iov) = problem + .prior + .resolved_iov() + .map(|iov| { + ( + iov.parameter_indices().to_vec(), + iov.omega().names().to_vec(), + Some(iov.omega().clone()), + ) + }) + .unwrap_or_else(|| (Vec::new(), Vec::new(), None)); + validate_initial_estimated_variance_floor( + "Omega", + "omega_min_variance", + &omega, + config.omega_min_variance, + )?; + if let Some(omega_iov) = omega_iov.as_ref() { + validate_initial_estimated_variance_floor( + "Omega_IOV", + "omega_iov_min_variance", + omega_iov, + config.omega_iov_min_variance, + )?; + } + if config.marginal_likelihood.is_some() + && (!random_effect_indices.is_empty() || !iov_effect_indices.is_empty()) + && !config.compute_map + { + anyhow::bail!( + "N2 with latent dimensions requires compute_map=true; conditional modes are not enabled" + ); + } + let covariate_model = problem.covariates().cloned(); + let initial_population_phi = population_phi( + &initial_row, + &problem + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect::>(), + )?; + let initial_subject_population = covariate_model + .as_ref() + .map(|model| { + model.subject_population_parameters( + &initial_population_phi, + &problem + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect::>(), + ) + }) + .transpose()?; + let initial_subject_mu_phi = initial_subject_population.as_ref().map(|rows| { + rows.iter() + .map(|row| row.phi().to_vec()) + .collect::>() + }); + let initial_individual_parameters = match initial_subject_population.as_ref() { + Some(rows) => { + Array2::from_shape_fn((n_subjects, initial_row.len()), |(i, j)| rows[i].psi()[j]) + } + None => Array2::from_shape_fn((n_subjects, initial_row.len()), |(_, j)| initial_row[j]), + }; + let initial_subject_log_likelihoods = + parametric_subject_log_likelihoods(problem, &initial_individual_parameters)?; + if let Some((subject_index, _)) = initial_subject_log_likelihoods + .iter() + .enumerate() + .find(|(_, value)| !value.is_finite()) + { + let subject = problem.data.subjects()[subject_index]; + if let Ok(statistics) = residual_statistics_for_subject( + &problem.model.equation, + subject, + &initial_row, + &problem.error_models, + ) { + for (output_index, _) in problem.error_models.models().iter() { + let Some(statistic) = statistics.output(output_index) else { + continue; + }; + if statistic.exponential_domain_violation_count > 0 { + let output = problem + .error_models + .output_name(output_index) + .map(str::to_owned) + .unwrap_or_else(|| format!("output_{output_index}")); + anyhow::bail!( + "initial conditional likelihood is non-finite for subject '{}' because exponential residual model output '{}' has {} non-positive or non-finite observation/prediction pair(s); exponential errors require positive finite observations and predictions", + subject.id(), + output, + statistic.exponential_domain_violation_count + ); + } + } + } + anyhow::bail!( + "initial conditional likelihood is non-finite for subject '{}'; verify parameter values, predictions, observations, and residual-model domain", + subject.id() + ); + } + let initial_negative_log_likelihood = + negative_log_likelihood(&initial_subject_log_likelihoods); + Ok(Self { + schedule: SaemSchedule::from_config(config), + n_chains: n_chains(config, n_subjects), + parameter_names: problem.parameters().names(), + parameter_scales: problem + .parameters() + .iter() + .map(|parameter| parameter.scale) + .collect(), + estimated_parameters: problem + .parameters() + .iter() + .map(|parameter| parameter.estimate) + .collect(), + random_effect_indices, + random_effect_names, + omega, + iov_effect_indices, + iov_effect_names, + omega_iov, + occasion_counts: problem + .data + .subjects() + .iter() + .map(|subject| subject.occasions().len()) + .collect(), + subject_ids: problem + .data + .subjects() + .iter() + .map(|subject| subject.id().clone()) + .collect(), + observation_count: count_observations(&problem.data), + initial_population_parameters: initial_row, + initial_subject_log_likelihoods, + initial_negative_log_likelihood, + covariate_model, + initial_subject_mu_phi, + initial_residual_values: Vec::new(), + initial_residual_estimated: Vec::new(), + }) + } +} + +fn negative_log_likelihood(subject_log_likelihoods: &[f64]) -> f64 { + if subject_log_likelihoods.iter().any(|ll| !ll.is_finite()) { + f64::INFINITY + } else { + -subject_log_likelihoods.iter().sum::() + } +} + +fn count_observations(data: &Data) -> usize { + data.subjects() + .iter() + .flat_map(|subject| subject.occasions()) + .flat_map(|occasion| occasion.events()) + .filter(|event| matches!(event, Event::Observation(_))) + .count() +} + +fn n_chains(config: &SaemConfig, n_subjects: usize) -> usize { + if n_subjects > 0 && n_subjects < 50 && config.n_chains == 1 { + ((50.0 / n_subjects as f64).ceil() as usize).max(1) + } else { + config.n_chains + } +} + +fn initial_parameter_row<'a>( + parameters: impl IntoIterator, +) -> Vec { + parameters + .into_iter() + .map(initial_parameter_value) + .collect() +} + +fn initial_parameter_value(parameter: &UnboundedParameter) -> f64 { + if let Some(initial) = parameter.initial { + return initial; + } + + match parameter.scale { + ParameterScale::Identity | ParameterScale::Log => 1.0, + ParameterScale::Logit { lower, upper } | ParameterScale::Probit { lower, upper } => { + 0.5 * (lower + upper) + } + } +} + +fn information_failure_status(reason: String) -> InformationStatus { + if reason.contains("censored") { + InformationStatus::Unsupported(reason) + } else if reason.contains("non-finite") { + InformationStatus::NonFinite + } else { + InformationStatus::Ineligible(reason) + } +} + +mod state; + +pub(crate) use state::SaemState; diff --git a/src/algorithms/parametric/saem/state/diagnostics.rs b/src/algorithms/parametric/saem/state/diagnostics.rs new file mode 100644 index 000000000..b9d628f82 --- /dev/null +++ b/src/algorithms/parametric/saem/state/diagnostics.rs @@ -0,0 +1,1641 @@ +use super::*; + +impl SaemState { + pub(super) fn markov_variance_diagnostics( + &self, + estimator: &SaemEstimatorMetadata, + information: &InformationDiagnostics, + ) -> MarkovSimulationVarianceDiagnostics { + self.markov_variance_diagnostics_with_seed(estimator, information, None, None) + } + + /// Frozen-kernel diagnostic with an optional deterministic seed override. + /// + /// The override gives each operational checkpoint its own deterministic + /// stream; `None` preserves the exact + /// post-fit path seeded by the diagnostic configuration. + pub(super) fn markov_variance_diagnostics_with_seed( + &self, + estimator: &SaemEstimatorMetadata, + information: &InformationDiagnostics, + seed_override: Option, + candidate: Option<&DiagnosticCandidate>, + ) -> MarkovSimulationVarianceDiagnostics { + let Some(config) = self.config.markov_simulation_variance else { + return MarkovSimulationVarianceDiagnostics::disabled(); + }; + let diagnostic_seed = seed_override.unwrap_or(config.seed); + let cd = config.diagnostic_chains; + let cf = self.initialization.n_chains; + let mut diagnostic = MarkovSimulationVarianceDiagnostics { + config: Some(config), + coordinates: information.coordinates.clone(), + chain_count: cd, + n_avg: estimator.averaged_iterations, + chains: Vec::new(), + grand_score_mean: Vec::new(), + lambda: Vec::new(), + lambda_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + xi: Vec::new(), + xi_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + simulation_covariance: Vec::new(), + simulation_covariance_status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + status: MarkovSimulationVarianceStatus::AssumptionsUnverified, + assumptions: MARKOV_VARIANCE_ASSUMPTIONS.into(), + rank_diagnostics: RankMixingDiagnostics { + diagnostic_chains: cd, + draws_per_chain: config.draws_per_chain, + original_chains: cf, + traces: Vec::new(), + lrv_per_chain: Vec::new(), + lrv_chain_statuses: Vec::new(), + diagnostic_mean_lrv: None, + operational_lrv: None, + max_trace_bytes: 0, + accounted_peak_trace_bytes_required: 0, + accounted_peak_trace_bytes_used: 0, + worst_rhat: None, + min_bulk_ess: None, + min_avg_ess_per_split_chain: None, + assumptions: MARKOV_VARIANCE_ASSUMPTIONS.into(), + status: RankDiagnosticStatus::Disabled, + }, + }; + let width = information.coordinates.len(); + let information_eligible = estimator.average_applied + && matches!(information.status, InformationStatus::Available) + && width > 0; + let observed_information = if information_eligible { + match matrix_from_rows(&information.observed_information, width) { + Ok(matrix) => Some(matrix), + Err(_) => { + diagnostic.xi_status = MarkovSimulationVarianceStatus::CoordinateMismatch; + None + } + } + } else { + None + }; + if self.initialization.random_effect_indices.is_empty() + && self.initialization.iov_effect_indices.is_empty() + { + let zero = Array2::zeros((width, width)); + diagnostic.lambda = rows(&zero); + diagnostic.lambda_status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.xi = rows(&zero); + diagnostic.xi_status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.simulation_covariance = rows(&zero); + diagnostic.simulation_covariance_status = + MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.status = MarkovSimulationVarianceStatus::ExactZeroNoLatentState; + diagnostic.rank_diagnostics.status = RankDiagnosticStatus::NoLatent; + diagnostic + .rank_diagnostics + .lrv_chain_statuses + .fill(RankDiagnosticStatus::NoLatent); + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + return diagnostic; + } + + // ── Pre-execution byte-cap check (checked) ─────────────────────── + let trace_shape = self + .initialization + .random_effect_indices + .len() + .checked_mul(self.initialization.subject_ids.len()) + .and_then(|n_eta| { + self.initialization + .occasion_counts + .iter() + .try_fold(0usize, |total, count| total.checked_add(*count)) + .and_then(|occasions| { + occasions + .checked_mul(self.initialization.iov_effect_indices.len()) + .and_then(|n_kappa| width.checked_add(n_eta)?.checked_add(n_kappa)) + }) + }); + let Some(n_traces) = trace_shape else { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::TraceMemoryAccountingOverflow, + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, + ); + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + return diagnostic; + }; + // Deterministic requested-capacity accounting. `traces` is nested + // coordinate-major storage, so its heap-resident Vec headers count in + // addition to every f64 leaf payload. The peak adds the larger of: + // (a) one nested draw-major score view, or (b) a conservative upper + // bound for the live rank/folding/ESS workspaces. The latter is eight + // payload-widths per retained draw (including the 24-byte ranked tuple) + // plus sixteen Vec headers per chain. This upper-bounds all capacities + // explicitly requested by the current rank helpers; allocator metadata + // and allocator size-class rounding are intentionally not claimed. + let vec_header = std::mem::size_of::>(); + let f64_bytes = std::mem::size_of::(); + let accounted = cd + .checked_mul(config.draws_per_chain) + .and_then(|samples_per_coordinate| { + samples_per_coordinate + .checked_mul(n_traces) + .and_then(|values| values.checked_mul(f64_bytes)) + .and_then(|leaf_payload| { + n_traces + .checked_mul(cd) + .and_then(|headers| headers.checked_mul(vec_header)) + .and_then(|leaf_headers| leaf_payload.checked_add(leaf_headers)) + }) + .and_then(|bytes| { + n_traces + .checked_mul(vec_header) + .and_then(|middle_headers| bytes.checked_add(middle_headers)) + }) + .and_then(|persistent_bytes| { + config + .draws_per_chain + .checked_mul(width) + .and_then(|values| values.checked_mul(f64_bytes)) + .and_then(|payload| { + config + .draws_per_chain + .checked_mul(vec_header) + .and_then(|headers| payload.checked_add(headers)) + }) + .and_then(|score_transient_bytes| { + samples_per_coordinate + .checked_mul(8 * f64_bytes) + .and_then(|payload| { + cd.checked_mul(16) + .and_then(|headers| headers.checked_mul(vec_header)) + .and_then(|headers| payload.checked_add(headers)) + }) + .and_then(|rank_transient_bytes| { + persistent_bytes + .checked_add( + score_transient_bytes.max(rank_transient_bytes), + ) + .map(|required_bytes| { + ( + persistent_bytes, + score_transient_bytes, + required_bytes, + ) + }) + }) + }) + }) + }); + let Some((persistent_bytes, score_transient_bytes, required_bytes)) = accounted else { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::TraceMemoryAccountingOverflow, + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow, + ); + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + return diagnostic; + }; + diagnostic + .rank_diagnostics + .accounted_peak_trace_bytes_required = required_bytes; + diagnostic.rank_diagnostics.max_trace_bytes = config.max_trace_bytes; + if required_bytes > config.max_trace_bytes { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::TraceByteCapExceeded, + MarkovSimulationVarianceStatus::InvalidConfiguration(format!( + "diagnostic trace accounted peak requires {required_bytes} bytes, exceeding cap {}", + config.max_trace_bytes + )), + ); + return diagnostic; + } + diagnostic.rank_diagnostics.lrv_per_chain = vec![None; cd]; + diagnostic.rank_diagnostics.lrv_chain_statuses = + vec![RankDiagnosticStatus::Unavailable; cd]; + + // ── Trace coordinate metadata ───────────────────────────────────── + let mut trace_coords: Vec = Vec::with_capacity(n_traces); + for coord in &information.coordinates { + trace_coords.push(DiagnosticTraceCoordinate::Score { + index: coord.index, + name: coord.name.clone(), + kind: coord.kind.clone(), + }); + } + for subject_id in &self.initialization.subject_ids { + for (eff_idx, name) in self.initialization.random_effect_names.iter().enumerate() { + trace_coords.push(DiagnosticTraceCoordinate::Eta { + subject: subject_id.clone(), + effect_index: eff_idx, + effect_name: name.clone(), + }); + } + } + if !self.initialization.iov_effect_indices.is_empty() { + for (subject_idx, subject_id) in self.initialization.subject_ids.iter().enumerate() { + for occasion in self.data.subjects()[subject_idx].occasions() { + for (eff_idx, name) in self.initialization.iov_effect_names.iter().enumerate() { + trace_coords.push(DiagnosticTraceCoordinate::Kappa { + subject: subject_id.clone(), + occasion_index: occasion.index(), + effect_index: eff_idx, + effect_name: name.clone(), + }); + } + } + } + } + + // ── Cd < 2 → still execute frozen chains, LRV, and Xi ──────────── + // Only per-trace rank diagnostics are unavailable (TooFewChains). + let rank_possible = cd >= 2; + + // ── Fresh prior-drawn chains ────────────────────────────────────── + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + let omega_lower = match cholesky_lower(omega) { + Ok(lower) => lower, + Err(_) => { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::InvalidVariance, + MarkovSimulationVarianceStatus::Indefinite, + ); + return diagnostic; + } + }; + let iov_lower = if self.initialization.iov_effect_indices.is_empty() { + None + } else { + match omega_iov.map(cholesky_lower) { + Some(Ok(lower)) => Some(lower), + Some(Err(_)) | None => { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::InvalidVariance, + MarkovSimulationVarianceStatus::Indefinite, + ); + return diagnostic; + } + } + }; + + // Canonical storage: [score_0..score_{w-1}, eta_0.., kappa_0..]. + // A draw-major score view is created one chain at a time for LRV and + // released before the next chain. + let mut traces: Vec>> = (0..n_traces) + .map(|_| vec![Vec::with_capacity(config.draws_per_chain); cd]) + .collect(); + diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = persistent_bytes; + let score_eligible = + width > 0 && matches!(information.status, InformationStatus::Available); + + // Initialize Cd independent diagnostic chains with domain-separated seeds. + // Seed derivation: per-chain seed = base.wrapping_add(i).wrapping_mul(GOLDEN_RATIO) + // where GOLDEN_RATIO = 0x9E3779B97F4A7C15 (2^64 / φ) and base is the + // configured diagnostic seed or the deterministic checkpoint override. + let mut chain_states: Vec = (0..cd) + .map(|chain| { + let chain_seed = diagnostic_seed + .wrapping_add(chain as u64) + .wrapping_mul(0x9E3779B97F4A7C15); + let mut chain_rng = StdRng::seed_from_u64(chain_seed); + FrozenDiagnosticState { + etas: self.draw_prior_etas(&omega_lower, &mut chain_rng), + kappas: self.draw_prior_kappas(iov_lower.as_deref(), &mut chain_rng), + } + }) + .collect(); + // Independent RNG streams for transitions (offset by +1 to separate + // from prior-initialization streams). + let mut chain_rngs: Vec = (0..cd) + .map(|chain| { + let chain_seed = diagnostic_seed + .wrapping_add(chain as u64) + .wrapping_mul(0x9E3779B97F4A7C15) + .wrapping_add(1); + StdRng::seed_from_u64(chain_seed) + }) + .collect(); + let mut chain_counts = vec![(0usize, 0usize, 0usize); cd]; + + // ── Warmup ──────────────────────────────────────────────────────── + for _ in 0..config.warmup_transitions { + for chain in 0..cd { + let mut single = [chain_counts[chain]]; + if self + .frozen_diagnostic_transition( + &mut chain_states[chain], + &mut chain_rngs[chain], + &mut single, + candidate, + ) + .is_err() + { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::Unavailable, + MarkovSimulationVarianceStatus::UnsupportedScore( + "frozen diagnostic warmup transition failed".into(), + ), + ); + return diagnostic; + } + chain_counts[chain] = single[0]; + } + } + begin_retained_transition_accounting(&mut chain_counts); + + // ── Single retained-draw pass: transition → collect traces ────── + for _ in 0..config.draws_per_chain { + for chain in 0..cd { + let mut single = [chain_counts[chain]]; + if self + .frozen_diagnostic_transition( + &mut chain_states[chain], + &mut chain_rngs[chain], + &mut single, + candidate, + ) + .is_err() + { + mark_diagnostic_failure( + &mut diagnostic, + RankDiagnosticStatus::Unavailable, + MarkovSimulationVarianceStatus::UnsupportedScore( + "frozen retained diagnostic transition failed".into(), + ), + ); + return diagnostic; + } + chain_counts[chain] = single[0]; + + // Score failure never discards independently valid latent draws. + let score = if score_eligible { + match self.frozen_complete_score(&chain_states[chain], 0, candidate) { + Ok(values) if values.len() == width => Some(values), + Ok(_) | Err(_) => None, + } + } else { + None + }; + for coord_idx in 0..width { + traces[coord_idx][chain] + .push(score.as_ref().map_or(f64::NAN, |values| values[coord_idx])); + } + + // Collect eta coordinates: subject-major, coordinate-major. + let mut trace_idx = width; + for subject_etas in &chain_states[chain].etas { + for eta_coord in &subject_etas[0] { + traces[trace_idx][chain].push(*eta_coord); + trace_idx += 1; + } + } + + // Collect kappa coordinates. + for subject_kappas in &chain_states[chain].kappas { + for kappa_vec in &subject_kappas[0] { + for kappa_coord in kappa_vec { + traces[trace_idx][chain].push(*kappa_coord); + trace_idx += 1; + } + } + } + } + } + + // Preserve the raw grand complete-score mean used by the invariant + // stationarity diagnostic. Any non-finite score leaves it unavailable. + if score_eligible { + let denominator = (cd * config.draws_per_chain) as f64; + let means = (0..width) + .map(|coordinate| { + traces[coordinate].iter().flatten().copied().sum::() / denominator + }) + .collect::>(); + if means.iter().all(|value| value.is_finite()) { + diagnostic.grand_score_mean = means; + } + } + + // ── Per-chain score LRV from transient draw-major views ───────── + let mut lrv_matrices: Vec>> = Vec::with_capacity(cd); + for chain in 0..cd { + let (proposals, accepts, state_changes) = chain_counts[chain]; + let score_view = (0..config.draws_per_chain) + .map(|draw| (0..width).map(|coord| traces[coord][chain][draw]).collect()) + .collect::>>(); + diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = persistent_bytes + .checked_add(score_transient_bytes) + .unwrap_or(required_bytes); + let lrv_result = if score_eligible { + match lugsail_batch_means(&score_view, config.batch_size, config.lugsail) { + Ok(value) => Some(value), + Err(_) => { + diagnostic.xi_status = MarkovSimulationVarianceStatus::UnsupportedScore( + "per-chain score LRV failed".into(), + ); + None + } + } + } else { + None + }; + if let Some((coarse, fine, lrv)) = lrv_result { + let classification = classify_psd(&lrv); + let lrv_status = markov_matrix_status(classification); + diagnostic + .chains + .push(MarkovSimulationVarianceChainDiagnostics { + chain, + bm_batch: rows(&coarse), + bm_batch_over_r: rows(&fine), + lugsail_lrv: rows(&lrv), + status: lrv_status, + proposals, + accepts, + state_changes, + }); + diagnostic.rank_diagnostics.lrv_per_chain[chain] = Some(rows(&lrv)); + diagnostic.rank_diagnostics.lrv_chain_statuses[chain] = match classification { + MatrixClassification::EligiblePsd => RankDiagnosticStatus::Available, + MatrixClassification::NonFinite => RankDiagnosticStatus::NonFiniteDraws, + MatrixClassification::NonSymmetric | MatrixClassification::Indefinite => { + RankDiagnosticStatus::InvalidVariance + } + }; + lrv_matrices.push(Some(lrv)); + } else { + diagnostic + .chains + .push(MarkovSimulationVarianceChainDiagnostics { + chain, + bm_batch: Vec::new(), + bm_batch_over_r: Vec::new(), + lugsail_lrv: Vec::new(), + status: MarkovSimulationVarianceStatus::UnsupportedScore( + "complete-score trace or information unavailable".into(), + ), + proposals, + accepts, + state_changes, + }); + diagnostic.rank_diagnostics.lrv_per_chain[chain] = None; + diagnostic.rank_diagnostics.lrv_chain_statuses[chain] = + RankDiagnosticStatus::ScoreUnavailable; + lrv_matrices.push(None); + } + } + + let stuck_chain = chain_counts + .iter() + .enumerate() + .find(|(_, count)| count.2 == 0) + .map(|(chain, _)| chain); + + // Aggregate only when every chain has an eligible score LRV. + let all_lrvs_available = lrv_matrices.len() == cd + && lrv_matrices.iter().all(Option::is_some) + && diagnostic + .rank_diagnostics + .lrv_chain_statuses + .iter() + .all(|status| matches!(status, RankDiagnosticStatus::Available)); + if all_lrvs_available { + let mut lrv_sum = Array2::zeros((width, width)); + for lrv in &lrv_matrices { + lrv_sum += lrv + .as_ref() + .expect("all per-chain LRV matrices were checked available"); + } + let (diag_mean, operational) = scale_lrv_sum(&lrv_sum, cd, cf); + diagnostic.rank_diagnostics.diagnostic_mean_lrv = Some(rows(&diag_mean)); + diagnostic.lambda = rows(&diag_mean); + diagnostic.lambda_status = markov_matrix_status(classify_psd(&diag_mean)); + + // Cd != Cf is intentional: operational scale is Σ/(Cd*Cf). + diagnostic.rank_diagnostics.operational_lrv = Some(rows(&operational)); + if let Some(observed_information) = observed_information.as_ref() { + match transform_simulation_variance( + observed_information, + &operational, + estimator.averaged_iterations, + ) { + Ok((xi, covariance)) => { + diagnostic.xi = rows(&xi); + diagnostic.xi_status = markov_matrix_status(classify_psd(&xi)); + diagnostic.simulation_covariance = rows(&covariance); + diagnostic.simulation_covariance_status = + markov_matrix_status(classify_psd(&covariance)); + } + Err(_) => { + diagnostic.xi_status = MarkovSimulationVarianceStatus::NonFinite; + diagnostic.simulation_covariance_status = + MarkovSimulationVarianceStatus::NonFinite; + } + } + } else { + diagnostic.xi_status = MarkovSimulationVarianceStatus::InformationUnavailable( + format!("{:?}", information.status), + ); + diagnostic.simulation_covariance_status = diagnostic.xi_status.clone(); + } + } else { + let failure = if !score_eligible { + MarkovSimulationVarianceStatus::InformationUnavailable(format!( + "{:?}", + information.status + )) + } else { + diagnostic + .chains + .iter() + .map(|chain| &chain.status) + .find(|status| { + !matches!( + status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + }) + .cloned() + .unwrap_or_else(|| { + MarkovSimulationVarianceStatus::UnsupportedScore( + "one or more configured diagnostic-chain score LRVs failed".into(), + ) + }) + }; + diagnostic.lambda_status = failure.clone(); + diagnostic.xi_status = failure.clone(); + diagnostic.simulation_covariance_status = failure; + } + + // ── Rank/mixing diagnostics from traces ───────────────────────── + // The prechecked rank workspace is the accounted peak whenever rank + // diagnostics execute; no allocator-specific byte claim is made. + if rank_possible { + diagnostic.rank_diagnostics.accounted_peak_trace_bytes_used = required_bytes; + diagnostic.rank_diagnostics.traces = + self.rank_diagnostics_from_traces(cd, &traces, &trace_coords); + } else { + diagnostic.rank_diagnostics.traces = trace_coords + .iter() + .map(|coord| RankMixingDiagnostic { + trace: coord.clone(), + rank_rhat: None, + rank_rhat_status: RankDiagnosticStatus::TooFewChains, + folded_rhat: None, + folded_rhat_status: RankDiagnosticStatus::TooFewChains, + max_rhat: None, + max_rhat_status: RankDiagnosticStatus::TooFewChains, + bulk_ess: None, + bulk_ess_status: RankDiagnosticStatus::TooFewChains, + avg_ess_per_split_chain: None, + tau: None, + status: RankDiagnosticStatus::TooFewChains, + }) + .collect(); + } + + // ── Aggregate per-coordinate worst/min across traces ──────────── + diagnostic.rank_diagnostics.worst_rhat = + worst_valid_max_rhat(&diagnostic.rank_diagnostics.traces); + diagnostic.rank_diagnostics.min_bulk_ess = diagnostic + .rank_diagnostics + .traces + .iter() + .filter_map(|t| t.bulk_ess) + .min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + diagnostic.rank_diagnostics.min_avg_ess_per_split_chain = diagnostic + .rank_diagnostics + .traces + .iter() + .filter_map(|t| t.avg_ess_per_split_chain) + .min_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + + // Aggregate status: ineligible if any coordinate or LRV is non-available. + let any_coord_non_available = diagnostic + .rank_diagnostics + .traces + .iter() + .any(|t| !matches!(t.status, RankDiagnosticStatus::Available)); + let any_lrv_non_available = diagnostic + .rank_diagnostics + .lrv_chain_statuses + .iter() + .any(|s| !matches!(s, RankDiagnosticStatus::Available)); + if rank_possible + && (any_coord_non_available || any_lrv_non_available || stuck_chain.is_some()) + { + diagnostic.rank_diagnostics.status = if diagnostic + .rank_diagnostics + .traces + .iter() + .any(|trace| matches!(trace.status, RankDiagnosticStatus::Available)) + { + RankDiagnosticStatus::PartialAvailability + } else { + RankDiagnosticStatus::Unavailable + }; + } else if !rank_possible { + diagnostic.rank_diagnostics.status = RankDiagnosticStatus::TooFewChains; + } else { + diagnostic.rank_diagnostics.status = RankDiagnosticStatus::Available; + } + + // ── Final aggregate markov status ─────────────────────────────── + diagnostic.status = if let Some(chain) = stuck_chain { + MarkovSimulationVarianceStatus::StuckChain { chain } + } else if !estimator.average_applied { + MarkovSimulationVarianceStatus::AverageNotApplied + } else if !matches!(information.status, InformationStatus::Available) { + MarkovSimulationVarianceStatus::InformationUnavailable(format!( + "{:?}", + information.status + )) + } else { + diagnostic + .chains + .iter() + .map(|chain| &chain.status) + .chain([ + &diagnostic.lambda_status, + &diagnostic.xi_status, + &diagnostic.simulation_covariance_status, + ]) + .find(|status| { + !matches!( + status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + }) + .cloned() + .unwrap_or(MarkovSimulationVarianceStatus::AssumptionsUnverified) + }; + diagnostic + } + + /// Build per-coordinate rank/mixing diagnostics from collected trace chains. + pub(super) fn rank_diagnostics_from_traces( + &self, + cd: usize, + traces: &[Vec>], + trace_coords: &[DiagnosticTraceCoordinate], + ) -> Vec { + trace_coords + .iter() + .enumerate() + .map(|(idx, coord)| { + let chains = &traces[idx]; + let rank_result = rank_normalized_split_rhat(chains); + let folded_result = folded_split_rhat(chains); + let ess_result = bulk_ess(chains); + + let rank_rhat = match rank_result.as_ref() { + Ok(value) => Some(*value), + Err(_) => None, + }; + let folded_rhat = match folded_result.as_ref() { + Ok(value) => Some(*value), + Err(_) => None, + }; + let (bulk_ess, tau) = match ess_result.as_ref() { + Ok((ess, tau)) => (Some(*ess), Some(*tau)), + Err(_) => (None, None), + }; + let avg_ess_per_split_chain = bulk_ess.map(|ess| ess / (2.0 * cd as f64)); + + let score_unavailable = matches!(coord, DiagnosticTraceCoordinate::Score { .. }) + && chains.iter().flatten().any(|draw| !draw.is_finite()); + let statistic_status = |result: Result<(), &RankDiagnosticError>| { + if score_unavailable { + RankDiagnosticStatus::ScoreUnavailable + } else { + result + .map(|()| RankDiagnosticStatus::Available) + .unwrap_or_else(rank_diagnostic_error_status) + } + }; + let rank_rhat_status = statistic_status(rank_result.as_ref().map(|_| ())); + let folded_rhat_status = statistic_status(folded_result.as_ref().map(|_| ())); + let max_rhat = match (rank_rhat, folded_rhat) { + (Some(rank), Some(folded)) => Some(rank.max(folded)), + _ => None, + }; + let max_rhat_status = if matches!(rank_rhat_status, RankDiagnosticStatus::Available) + && matches!(folded_rhat_status, RankDiagnosticStatus::Available) + { + RankDiagnosticStatus::Available + } else if !matches!(rank_rhat_status, RankDiagnosticStatus::Available) { + rank_rhat_status.clone() + } else { + folded_rhat_status.clone() + }; + let bulk_ess_status = statistic_status(ess_result.as_ref().map(|_| ())); + let statuses = [&rank_rhat_status, &folded_rhat_status, &bulk_ess_status]; + let available = statuses + .iter() + .filter(|status| matches!(status, RankDiagnosticStatus::Available)) + .count(); + let status = if available == statuses.len() { + RankDiagnosticStatus::Available + } else if available > 0 { + RankDiagnosticStatus::PartialAvailability + } else if statuses.iter().all(|status| *status == statuses[0]) { + statuses[0].clone() + } else { + RankDiagnosticStatus::Unavailable + }; + + RankMixingDiagnostic { + trace: coord.clone(), + rank_rhat, + rank_rhat_status, + folded_rhat, + folded_rhat_status, + max_rhat, + max_rhat_status, + bulk_ess, + bulk_ess_status, + avg_ess_per_split_chain, + tau, + status, + } + }) + .collect() + } + + /// Draw initial η vectors from N(0, Omega) for fresh diagnostic chains. + pub(super) fn draw_prior_etas( + &self, + omega_lower: &[Vec], + rng: &mut StdRng, + ) -> Vec>> { + let n_eta = self.initialization.random_effect_indices.len(); + if n_eta == 0 { + return vec![vec![Vec::new(); 1]; self.initialization.subject_ids.len()]; + } + self.initialization + .subject_ids + .iter() + .map(|_| { + let normals: Vec = (0..n_eta) + .map(|_| diagnostic_standard_normal(rng)) + .collect(); + let eta = (0..n_eta) + .map(|row| { + (0..=row) + .map(|col| omega_lower[row][col] * normals[col]) + .sum() + }) + .collect::>(); + vec![eta] + }) + .collect() + } + + /// Draw initial κ vectors from N(0, Omega_IOV) for fresh diagnostic chains. + pub(super) fn draw_prior_kappas( + &self, + iov_lower: Option<&[Vec]>, + rng: &mut StdRng, + ) -> Vec>>> { + let Some(iov_lower) = iov_lower else { + return vec![vec![Vec::new(); 1]; self.initialization.subject_ids.len()]; + }; + let n_kappa = self.initialization.iov_effect_indices.len(); + self.initialization + .occasion_counts + .iter() + .map(|&n_occasions| { + let kappas: Vec> = (0..n_occasions) + .map(|_| { + let normals: Vec = (0..n_kappa) + .map(|_| diagnostic_standard_normal(rng)) + .collect(); + (0..n_kappa) + .map(|row| { + (0..=row) + .map(|col| iov_lower[row][col] * normals[col]) + .sum() + }) + .collect() + }) + .collect(); + vec![kappas] + }) + .collect() + } + + pub(super) fn frozen_diagnostic_transition( + &self, + state: &mut FrozenDiagnosticState, + rng: &mut StdRng, + counts: &mut [(usize, usize, usize)], + candidate: Option<&DiagnosticCandidate>, + ) -> std::result::Result<(), String> { + for _ in 0..self.eta_block_iterations { + for subject in 0..self.initialization.subject_ids.len() { + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let lower = cholesky_lower(omega).map_err(|error| error.to_string())?; + for (chain, count) in counts.iter_mut().enumerate() { + let current = state.etas[subject][chain].clone(); + let normals = (0..current.len()) + .map(|_| diagnostic_standard_normal(rng)) + .collect::>(); + let proposed = correlated_random_walk( + ¤t, + &lower, + &normals, + self.eta_block_step_sizes[subject], + ) + .map_err(|error| error.to_string())?; + let current_score = self + .score_subject_latents_at( + subject, + ¤t, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + let proposed_score = self + .score_subject_latents_at( + subject, + &proposed, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + count.0 += 1; + if diagnostic_accept(rng, current_score.log_acceptance_ratio(proposed_score)) { + count.1 += 1; + if proposed != current { + count.2 += 1; + } + state.etas[subject][chain] = proposed; + } + } + } + } + for _ in 0..self.mcmc_iterations { + for subject in 0..self.initialization.subject_ids.len() { + for (chain, count) in counts.iter_mut().enumerate() { + for parameter in 0..self.initialization.random_effect_indices.len() { + let current = state.etas[subject][chain].clone(); + let mut proposed = current.clone(); + proposed[parameter] += + self.proposal_step_sizes[parameter] * diagnostic_standard_normal(rng); + let current_score = self + .score_subject_latents_at( + subject, + ¤t, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + let proposed_score = self + .score_subject_latents_at( + subject, + &proposed, + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + count.0 += 1; + if diagnostic_accept( + rng, + current_score.log_acceptance_ratio(proposed_score), + ) { + count.1 += 1; + if proposed != current { + count.2 += 1; + } + state.etas[subject][chain] = proposed; + } + } + let omega_iov = + candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + if let Some(omega_iov) = omega_iov { + let lower = cholesky_lower(omega_iov).map_err(|error| error.to_string())?; + for occasion in 0..state.kappas[subject][chain].len() { + let current = state.kappas[subject][chain][occasion].clone(); + let normals = (0..current.len()) + .map(|_| diagnostic_standard_normal(rng)) + .collect::>(); + let proposed = correlated_random_walk( + ¤t, + &lower, + &normals, + self.kappa_proposal_step_sizes[subject], + ) + .map_err(|error| error.to_string())?; + let current_score = self + .score_subject_latents_at( + subject, + &state.etas[subject][chain], + &state.kappas[subject][chain], + candidate, + ) + .map_err(|error| error.to_string())?; + let mut proposed_kappas = state.kappas[subject][chain].clone(); + proposed_kappas[occasion] = proposed.clone(); + let proposed_score = self + .score_subject_latents_at( + subject, + &state.etas[subject][chain], + &proposed_kappas, + candidate, + ) + .map_err(|error| error.to_string())?; + count.0 += 1; + if diagnostic_accept( + rng, + current_score.log_acceptance_ratio(proposed_score), + ) { + count.1 += 1; + if proposed != current { + count.2 += 1; + } + state.kappas[subject][chain][occasion] = proposed; + } + } + } + } + } + } + Ok(()) + } + + // ─── Operational convergence ───────────────────────────────────────── + + /// Evaluate an operational convergence checkpoint if one is due. + pub(super) fn evaluate_operational_convergence( + &mut self, + iteration: usize, + scheduled: bool, + mandatory_final: bool, + ) -> Result<()> { + let Some(settings) = self.operational_settings else { + return Ok(()); + }; + // Only check during smoothing, unless this is a mandatory final check. + if !mandatory_final && self.initialization.schedule.phase(iteration) != SaemPhase::Smoothing + { + return Ok(()); + } + let Some(ref average) = self.iterate_average else { + return Ok(()); + }; + let n_averaged = average.count; + if n_averaged < settings.first_eligible_averaged_iteration { + return Ok(()); + } + + // Cadence: periodic checkpoints are evaluated every check_interval + // iterations starting from first_eligible_averaged_iteration. + if scheduled && !mandatory_final { + let smoothing_start = self.initialization.schedule.pure_burn_in + + self.initialization.schedule.exploration_iterations + + 1; + let smoothing_offset = iteration.saturating_sub(smoothing_start) + 1; + if smoothing_offset < settings.first_eligible_averaged_iteration + || !(smoothing_offset - settings.first_eligible_averaged_iteration) + .is_multiple_of(settings.check_interval) + { + return Ok(()); + } + } + + // Defensive caching: if this is a mandatory final check and we already + // evaluated at this iteration, reuse instead of rerunning. + if mandatory_final { + if let Some(last) = self.operational_diagnostics.checks.last() { + if last.iteration == iteration { + self.operational_diagnostics.final_check_reused = true; + return Ok(()); + } + } + } + + // Build the deterministic per-checkpoint seed. + let checkpoint_seed = self + .config + .markov_simulation_variance + .expect("operational policy validation requires Markov diagnostics") + .seed + .wrapping_add(OPERATIONAL_CHECKPOINT_SEED_DOMAIN) + .wrapping_add(iteration as u64); + + // Two-sided standard normal quantile. + let z_quantile = normal_two_sided_z(settings.confidence_level); + + let implied_averaged_iterations = + Some(4.0 * z_quantile * z_quantile / settings.relative_fixed_width_epsilon.powi(2)); + + let info = self.information.diagnostics(); + let avg_psi = match population_psi( + &average.population_phi, + &self.initialization.parameter_scales, + ) { + Ok(psi) => psi, + Err(_) => { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + Vec::new(), + "averaged population psi conversion failed".to_string(), + ); + return Ok(()); + } + }; + let mut candidate_error_models = self.error_models.clone(); + for (output_index, model) in &average.residual_models { + match *model { + ResidualErrorModel::Combined { a, b } => update_estimated_combined_residual_model( + &mut candidate_error_models, + *output_index, + a, + b, + ), + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + update_estimated_correlated_combined_residual_model( + &mut candidate_error_models, + *output_index, + a, + b, + rho, + ) + } + ResidualErrorModel::Constant { .. } + | ResidualErrorModel::Proportional { .. } + | ResidualErrorModel::Exponential { .. } => { + update_estimated_simple_residual_model_with_sigma( + &mut candidate_error_models, + *output_index, + primary_sigma_parameter(model), + ) + } + } + } + let candidate_covariate_model = match ( + self.covariate_model.as_ref(), + average.covariate_betas.as_ref(), + ) { + (Some(model), Some(values)) => Some(model.with_estimates(values)?), + (None, None) => None, + _ => anyhow::bail!("averaged covariate metadata dimension mismatch"), + }; + let candidate = DiagnosticCandidate { + population_parameters: avg_psi, + covariate_model: candidate_covariate_model, + omega: average.omega.clone(), + omega_iov: average.omega_iov.clone(), + error_models: candidate_error_models, + }; + let candidate_free_coordinates = match operational_free_coordinates(&info, average) { + Ok(values) if !values.is_empty() => values, + Ok(_) => { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + Vec::new(), + "no free coordinates".to_string(), + ); + return Ok(()); + } + Err(error) => { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + Vec::new(), + error.to_string(), + ); + return Ok(()); + } + }; + if self.initialization.random_effect_indices.is_empty() + && self.initialization.iov_effect_indices.is_empty() + { + self.record_ineligible_checkpoint( + iteration, + n_averaged, + scheduled, + mandatory_final, + checkpoint_seed, + z_quantile, + implied_averaged_iterations, + candidate_free_coordinates, + "no latent coordinates".to_string(), + ); + return Ok(()); + } + + let diagnostic_metadata = SaemEstimatorMetadata { + policy: self.config.estimator_policy, + average_applied: true, + averaging_start_cycle: Some(average.start_cycle), + averaged_iterations: n_averaged, + }; + + let markov = self.markov_variance_diagnostics_with_seed( + &diagnostic_metadata, + &info, + Some(checkpoint_seed), + Some(&candidate), + ); + + let rank = &markov.rank_diagnostics; + let simulation_sd_fraction = operational_simulation_sd_fraction(&info, &markov); + let fixed_width = simulation_sd_fraction.map(|fraction| 2.0 * z_quantile * fraction); + let fixed_width_ratio = + fixed_width.map(|width| width / settings.relative_fixed_width_epsilon); + let newton_value = newton_displacement(&info, &markov).filter(|value| value.is_finite()); + let newton_mc_sd = + newton_displacement_mc_sd(&info, &markov).filter(|value| value.is_finite()); + let matrix_valid = matches!(info.status, InformationStatus::Available) + && matches!( + markov.lambda_status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + && matches!( + markov.xi_status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ) + && matches!( + markov.simulation_covariance_status, + MarkovSimulationVarianceStatus::AssumptionsUnverified + ); + let every_chain_moved = + !markov.chains.is_empty() && markov.chains.iter().all(|chain| chain.state_changes > 0); + let every_trace_valid = !rank.traces.is_empty() + && rank.traces.iter().all(|trace| { + trace.rank_rhat.is_some() + && trace.folded_rhat.is_some() + && trace.max_rhat.is_some() + && trace.bulk_ess.is_some() + && matches!(trace.rank_rhat_status, RankDiagnosticStatus::Available) + && matches!(trace.folded_rhat_status, RankDiagnosticStatus::Available) + && matches!(trace.max_rhat_status, RankDiagnosticStatus::Available) + && matches!(trace.bulk_ess_status, RankDiagnosticStatus::Available) + }); + let covariance_policy = self + .config + .covariance_stability + .expect("operational policy validation requires covariance stability"); + let omega_boundary = covariance_boundary_rejection_summary( + &self.cycle_diagnostics, + covariance_policy, + false, + ); + let omega_iov_boundary = + covariance_boundary_rejection_summary(&self.cycle_diagnostics, covariance_policy, true); + let covariance_active_cycles = + iteration.saturating_sub(self.initialization.schedule.pure_burn_in); + let covariance_window_available = + covariance_active_cycles >= covariance_policy.rejection_window; + let boundary_criterion = |name: &str, longest_run: usize| { + if covariance_window_available { + evaluate_criterion( + name, + Some(longest_run as f64), + covariance_policy.rejection_window as f64, + |observed| observed < covariance_policy.rejection_window as f64, + ) + } else { + OperationalConvergenceCriterion { + name: name.to_string(), + observed: Some(longest_run as f64), + threshold: covariance_policy.rejection_window as f64, + status: OperationalConvergenceCriterionStatus::Unavailable(format!( + "covariance-stability window requires {} active cycles; {covariance_active_cycles} completed", + covariance_policy.rejection_window + )), + } + } + }; + let criteria: Vec = vec![ + evaluate_criterion( + "valid_information_and_matrices", + Some(matrix_valid as u8 as f64), + 1.0, + |value| value == 1.0, + ), + evaluate_criterion( + "every_diagnostic_chain_moved", + Some(every_chain_moved as u8 as f64), + 1.0, + |value| value == 1.0, + ), + evaluate_criterion( + "every_rank_diagnostic_valid", + Some(every_trace_valid as u8 as f64), + 1.0, + |value| value == 1.0, + ), + evaluate_criterion("max_rhat", rank.worst_rhat, settings.max_rhat, |observed| { + observed < settings.max_rhat + }), + evaluate_criterion( + "min_bulk_ess", + rank.min_bulk_ess, + settings.min_bulk_ess, + |observed| observed > settings.min_bulk_ess, + ), + evaluate_criterion( + "min_average_bulk_ess_per_split_chain", + rank.min_avg_ess_per_split_chain, + settings.min_average_bulk_ess_per_split_chain, + |observed| observed >= settings.min_average_bulk_ess_per_split_chain, + ), + evaluate_criterion( + "worst_simulation_sd_fraction", + simulation_sd_fraction, + settings.relative_fixed_width_epsilon / (2.0 * z_quantile), + |observed| 2.0 * z_quantile * observed <= settings.relative_fixed_width_epsilon, + ), + evaluate_criterion( + "relative_fixed_width", + fixed_width, + settings.relative_fixed_width_epsilon, + |observed| observed <= settings.relative_fixed_width_epsilon, + ), + evaluate_criterion( + "newton_displacement", + newton_value, + settings.max_newton_displacement, + |observed| observed <= settings.max_newton_displacement, + ), + evaluate_criterion( + "newton_displacement_mc_sd", + newton_mc_sd, + settings.max_newton_displacement_mc_sd, + |observed| observed <= settings.max_newton_displacement_mc_sd, + ), + boundary_criterion("omega_boundary_rejection_run", omega_boundary.longest_run), + boundary_criterion( + "omega_iov_boundary_rejection_run", + omega_iov_boundary.longest_run, + ), + ]; + + let mut ineligible_reasons = criteria + .iter() + .filter_map(|criterion| match &criterion.status { + OperationalConvergenceCriterionStatus::Unavailable(reason) => { + Some(format!("{}: {reason}", criterion.name)) + } + _ => None, + }) + .collect::>(); + if !matrix_valid { + ineligible_reasons.push("information or matrix validation failed".to_string()); + } + if !every_trace_valid { + ineligible_reasons.push("one or more rank diagnostics unavailable".to_string()); + } + if !every_chain_moved { + ineligible_reasons + .push("one or more retained diagnostic chains did not move".to_string()); + } + let failed_criteria = criteria + .iter() + .filter(|criterion| { + matches!( + criterion.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ) + }) + .map(|criterion| criterion.name.clone()) + .collect::>(); + let outcome = if !ineligible_reasons.is_empty() { + OperationalConvergenceOutcome::Ineligible { + reasons: ineligible_reasons, + } + } else if !failed_criteria.is_empty() { + OperationalConvergenceOutcome::Failed { + criteria: failed_criteria, + } + } else { + OperationalConvergenceOutcome::Passed + }; + + let passed = matches!(outcome, OperationalConvergenceOutcome::Passed); + self.operational_diagnostics.final_status = Some(outcome.clone()); + self.operational_diagnostics.worst_rhat = rank.worst_rhat; + self.operational_diagnostics.min_bulk_ess = rank.min_bulk_ess; + self.operational_diagnostics.fixed_width_ratio = fixed_width_ratio; + self.operational_diagnostics.fixed_width_epsilon = + Some(settings.relative_fixed_width_epsilon); + self.operational_diagnostics.implied_minimum_ess = implied_averaged_iterations; + self.operational_diagnostics.newton_displacement = newton_value; + self.operational_diagnostics.newton_displacement_mc_sd = newton_mc_sd; + let checkpoint = OperationalConvergenceCheck { + iteration, + averaged_iterations: n_averaged, + scheduled, + mandatory_final, + checkpoint_seed: Some(checkpoint_seed), + z_quantile: Some(z_quantile), + implied_minimum_ess: implied_averaged_iterations, + candidate_free_coordinates, + information: Some(info), + criteria, + outcome, + markov: Some(markov), + }; + + self.operational_diagnostics.checks.push(checkpoint); + + // Terminate early if converged and this was a scheduled check. + if passed { + self.operational_diagnostics.used_for_termination = true; + self.status = Status::Stop(StopReason::Converged); + } + + Ok(()) + } + + /// Record an ineligible checkpoint (candidate unavailable). + #[allow(clippy::too_many_arguments)] + pub(super) fn record_ineligible_checkpoint( + &mut self, + iteration: usize, + averaged_iterations: usize, + scheduled: bool, + mandatory_final: bool, + checkpoint_seed: u64, + z_quantile: f64, + implied_averaged_iterations: Option, + candidate_free_coordinates: Vec, + reason: String, + ) { + let settings = self + .operational_settings + .expect("ineligible operational checkpoint requires configured settings"); + let unavailable = |name: &str, threshold: f64| OperationalConvergenceCriterion { + name: name.to_string(), + observed: None, + threshold, + status: OperationalConvergenceCriterionStatus::Unavailable(reason.clone()), + }; + let criteria = vec![ + unavailable("candidate_available", 1.0), + unavailable("valid_information_and_matrices", 1.0), + unavailable("every_diagnostic_chain_moved", 1.0), + unavailable("every_rank_diagnostic_valid", 1.0), + unavailable("max_rhat", settings.max_rhat), + unavailable("min_bulk_ess", settings.min_bulk_ess), + unavailable( + "min_average_bulk_ess_per_split_chain", + settings.min_average_bulk_ess_per_split_chain, + ), + unavailable( + "worst_simulation_sd_fraction", + settings.relative_fixed_width_epsilon / (2.0 * z_quantile), + ), + unavailable( + "relative_fixed_width", + settings.relative_fixed_width_epsilon, + ), + unavailable("newton_displacement", settings.max_newton_displacement), + unavailable( + "newton_displacement_mc_sd", + settings.max_newton_displacement_mc_sd, + ), + ]; + let outcome = OperationalConvergenceOutcome::Ineligible { + reasons: vec![reason], + }; + self.operational_diagnostics.final_status = Some(outcome.clone()); + self.operational_diagnostics + .checks + .push(OperationalConvergenceCheck { + iteration, + averaged_iterations, + scheduled, + mandatory_final, + checkpoint_seed: Some(checkpoint_seed), + z_quantile: Some(z_quantile), + implied_minimum_ess: implied_averaged_iterations, + candidate_free_coordinates, + information: None, + criteria, + outcome, + markov: None, + }); + } + + pub(super) fn frozen_complete_score( + &self, + state: &FrozenDiagnosticState, + chain: usize, + candidate: Option<&DiagnosticCandidate>, + ) -> std::result::Result, String> { + let layout = self.information.layout(); + let population_parameters = candidate + .map_or(self.population_parameters.as_slice(), |value| { + value.population_parameters.as_slice() + }); + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + let error_models = candidate.map_or(&self.error_models, |value| &value.error_models); + let mut derivative = CompleteDerivative::zero(layout.len()); + for subject_index in 0..self.initialization.subject_ids.len() { + let covariate_model = candidate + .and_then(|value| value.covariate_model.as_ref()) + .or(self.covariate_model.as_ref()); + match covariate_model { + Some(model) => derivative.add_covariate_population_prior( + &state.etas[subject_index][chain], + omega, + &self.initialization.random_effect_indices, + model.parameter_indices(), + model.subject_design()[subject_index].values(), + layout, + ), + None => derivative.add_population_prior( + &state.etas[subject_index][chain], + omega, + &self.initialization.random_effect_indices, + layout, + ), + } + .map_err(|error| error.to_string())?; + let calculated_mu = if candidate.is_some() { + covariate_model + .map(|model| { + let phi = population_phi( + population_parameters, + &self.initialization.parameter_scales, + )?; + Ok::<_, anyhow::Error>( + model.subject_population_parameters( + &phi, + &self.initialization.parameter_scales, + )?[subject_index] + .phi() + .to_vec(), + ) + }) + .transpose() + .map_err(|error| error.to_string())? + } else { + None + }; + let subject_mu = calculated_mu.as_deref().or_else(|| { + self.subject_mu_phi + .as_ref() + .map(|means| means[subject_index].as_slice()) + }); + let subject = self.data.subjects()[subject_index]; + if let Some(omega_iov) = omega_iov { + let occasions = subject.occasions(); + let kappas = &state.kappas[subject_index][chain]; + if occasions.len() != kappas.len() { + return Err(format!( + "subject {} has {} occasions but {} diagnostic kappa states", + subject.id(), + occasions.len(), + kappas.len() + )); + } + for (occasion, kappa) in occasions.iter().zip(kappas) { + derivative + .add_iov_prior(kappa, omega_iov, layout) + .map_err(|error| error.to_string())?; + let parameters = match subject_mu { + Some(mean) => occasion_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + &self.initialization.iov_effect_indices, + kappa, + ), + } + .map_err(|error| error.to_string())?; + let occasion_subject = + Subject::from_occasions(subject.id().to_owned(), vec![occasion.clone()]); + let predictions = self + .equation + .estimate_predictions_dense(&occasion_subject, ¶meters) + .map_err(|error| error.to_string())?; + derivative + .add_predictions_strict(&predictions, error_models, layout) + .map_err(|error| error.to_string())?; + } + } else { + let parameters = match subject_mu { + Some(mean) => individual_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + ), + None => individual_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &state.etas[subject_index][chain], + ), + } + .map_err(|error| error.to_string())?; + let predictions = self + .equation + .estimate_predictions_dense(subject, ¶meters) + .map_err(|error| error.to_string())?; + derivative + .add_predictions_strict(&predictions, error_models, layout) + .map_err(|error| error.to_string())?; + } + } + Ok(derivative.score) + } +} + +fn diagnostic_standard_normal(rng: &mut StdRng) -> f64 { + let u1 = rng.random::().max(f64::MIN_POSITIVE); + let u2 = rng.random::(); + (-2.0 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() +} + +fn diagnostic_accept(rng: &mut StdRng, ratio: f64) -> bool { + ratio.is_finite() && (ratio >= 0.0 || rng.random::().max(f64::MIN_POSITIVE).ln() < ratio) +} + +pub(super) fn begin_retained_transition_accounting(counts: &mut [(usize, usize, usize)]) { + counts.fill((0, 0, 0)); +} + +fn mark_diagnostic_failure( + diagnostic: &mut MarkovSimulationVarianceDiagnostics, + rank_status: RankDiagnosticStatus, + markov_status: MarkovSimulationVarianceStatus, +) { + diagnostic.rank_diagnostics.status = rank_status.clone(); + diagnostic + .rank_diagnostics + .lrv_chain_statuses + .fill(rank_status); + diagnostic.lambda_status = markov_status.clone(); + diagnostic.xi_status = markov_status.clone(); + diagnostic.simulation_covariance_status = markov_status.clone(); + diagnostic.status = markov_status; +} + +fn markov_matrix_status(classification: MatrixClassification) -> MarkovSimulationVarianceStatus { + match classification { + MatrixClassification::EligiblePsd => MarkovSimulationVarianceStatus::AssumptionsUnverified, + MatrixClassification::NonFinite => MarkovSimulationVarianceStatus::NonFinite, + MatrixClassification::NonSymmetric => MarkovSimulationVarianceStatus::NonSymmetric, + MatrixClassification::Indefinite => MarkovSimulationVarianceStatus::Indefinite, + } +} + +pub(super) fn worst_valid_max_rhat(traces: &[RankMixingDiagnostic]) -> Option { + traces + .iter() + .filter(|trace| matches!(trace.max_rhat_status, RankDiagnosticStatus::Available)) + .filter_map(|trace| trace.max_rhat) + .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) +} + +fn rank_diagnostic_error_status(error: &RankDiagnosticError) -> RankDiagnosticStatus { + match error { + RankDiagnosticError::NoChains => RankDiagnosticStatus::NoChains, + RankDiagnosticError::TooFewChains { .. } => RankDiagnosticStatus::TooFewChains, + RankDiagnosticError::UnequalChainLengths { .. } => { + RankDiagnosticStatus::UnequalChainLengths + } + RankDiagnosticError::OddChainLength { .. } => RankDiagnosticStatus::OddDraws, + RankDiagnosticError::NonFiniteDraw => RankDiagnosticStatus::NonFiniteDraws, + RankDiagnosticError::TooFewDraws { .. } => RankDiagnosticStatus::TooFewDraws, + RankDiagnosticError::ConstantDraws => RankDiagnosticStatus::ConstantDraws, + RankDiagnosticError::InvalidVariance => RankDiagnosticStatus::InvalidVariance, + RankDiagnosticError::NonPositiveTau { .. } => RankDiagnosticStatus::NonPositiveTau, + } +} diff --git a/src/algorithms/parametric/saem/state/mod.rs b/src/algorithms/parametric/saem/state/mod.rs new file mode 100644 index 000000000..2b284a24b --- /dev/null +++ b/src/algorithms/parametric/saem/state/mod.rs @@ -0,0 +1,2301 @@ +use super::*; + +#[derive(Debug, Clone)] +struct SaemIterateAverage { + population_phi: Vec, + covariate_betas: Option>, + omega: Array2, + omega_iov: Option>, + residual_model_width: usize, + residual_models: Vec<(usize, ResidualErrorModel)>, + start_cycle: usize, + count: usize, +} + +// ─── Operational convergence lifecycle ──────────────────────────────────── +// +// Result types live in `crate::results::fit_result`. +// `OperationalConvergenceConfig` is the source of truth for settings. + +/// Domain-separation constant for deterministic per-checkpoint seeds. +/// +/// Its fixed bytes are combined with the SAEM seed via wrapping addition. +const OPERATIONAL_CHECKPOINT_SEED_DOMAIN: u64 = 0x4E31_4F50_4352_4954; + +/// Per-cycle SAEM estimation state. +/// +/// MCMC chains, stochastic-approximation sufficient statistics, and the +/// current population / omega / sigma estimates are updated in-place. +#[derive(Debug)] +pub(crate) struct SaemState { + equation: E, + data: Data, + error_models: ParametricErrorModels, + config: SaemConfig, + pub(crate) initialization: SaemInitialization, + cycle: usize, + status: Status, + numerical_failure: Option, + etas: Vec>>, + kappas: Vec>>>, + population_parameters: Vec, + omega: Array2, + omega_iov: Option>, + iiv_second_moment: Array2, + iov_second_moment: Option>, + sufficient_statistics: PhiSufficientStatistics, + covariate_statistics: Option, + subject_mu_phi: Option>>, + covariate_model: Option, + residual_statistics: ResidualSufficientStatistics, + residual_sigmas: Vec, + information: InformationRecursion, + proposal_step_sizes: Vec, + eta_block_step_sizes: Vec, + kappa_proposal_step_sizes: Vec, + mcmc_iterations: usize, + eta_block_iterations: usize, + adapt_interval: usize, + residual_optimizer_max_iterations: usize, + compute_map: bool, + map_max_iterations: usize, + map_sd_tolerance: f64, + map_initial_step: f64, + steps_since_adapt: usize, + adaptation_accept_counts: Vec, + adaptation_proposal_counts: Vec, + eta_block_adaptation_accept_counts: Vec, + eta_block_adaptation_proposal_counts: Vec, + kappa_adaptation_accept_counts: Vec, + kappa_adaptation_proposal_counts: Vec, + rng: StdRng, + subject_log_likelihoods: Vec, + subject_log_priors: Vec, + subject_kappa_log_priors: Vec, + last_log_acceptance_ratios: Vec, + last_acceptance_rate: Option, + last_eta_block_acceptance_rate: Option, + last_kappa_acceptance_rate: Option, + last_rejected_proposals: Option, + last_non_finite_proposals: Option, + last_parameter_acceptance_rates: Vec, + cycle_diagnostics: Vec, + negative_log_likelihood: f64, + iterate_average: Option, + operational_settings: Option, + operational_diagnostics: OperationalConvergenceDiagnostics, +} + +impl SaemState { + pub(crate) fn from_problem( + problem: EstimationProblem, + config: &SaemConfig, + ) -> Result { + let mut initialization = SaemInitialization::create(&problem, config)?; + let EstimationProblem { + model, + data, + error_models, + .. + } = problem; + // Capture immutable initial residual values and estimated masks before + // any SAEM cycle modifies them. + let mut initial_residual_values = Vec::new(); + let mut initial_residual_estimated = Vec::new(); + for (outeq, model) in error_models.models().iter() { + let estimate = error_models.is_estimated(outeq); + let combined = error_models.combined_component_estimated(outeq); + let correlated = error_models.correlated_combined_component_estimated(outeq); + let (additive, proportional, correlation) = + if matches!(model, ResidualErrorModel::CorrelatedCombined { .. }) { + (correlated[0], correlated[1], Some(correlated[2])) + } else { + (combined[0], combined[1], None) + }; + let components = crate::results::parametric_output::residual_components( + *model, + estimate, + Some(additive), + Some(proportional), + correlation, + ); + initial_residual_values.push(components.iter().map(|c| c.1).collect()); + initial_residual_estimated.push(components.iter().map(|c| c.2).collect()); + } + initialization.initial_residual_values = initial_residual_values; + initialization.initial_residual_estimated = initial_residual_estimated; + Ok(Self::new( + model.equation, + data, + error_models, + initialization, + config, + )) + } + + pub(crate) fn new( + equation: E, + data: Data, + error_models: ParametricErrorModels, + initialization: SaemInitialization, + config: &SaemConfig, + ) -> Self { + let n_random_effects = initialization.random_effect_indices.len(); + let etas = zero_etas( + initialization.subject_ids.len(), + initialization.n_chains, + n_random_effects, + ); + let kappas = zero_kappas( + &initialization.occasion_counts, + initialization.n_chains, + initialization.iov_effect_indices.len(), + ); + let population_parameters = initialization.initial_population_parameters.clone(); + let omega = initialization.omega.initial().clone(); + let iiv_second_moment = omega.clone(); + let omega_iov = initialization + .omega_iov + .as_ref() + .map(|omega| omega.initial().clone()); + let iov_second_moment = omega_iov.clone(); + let initial_subject_phi = zero_eta_subject_phi(&population_parameters, &initialization) + .expect("initial population parameters should produce valid phi statistics"); + let mut sufficient_statistics = + PhiSufficientStatistics::from_subject_phi(&initial_subject_phi) + .expect("initial phi statistics should be valid"); + for (eta_row, parameter_row) in initialization.random_effect_indices.iter().enumerate() { + for (eta_col, parameter_col) in initialization.random_effect_indices.iter().enumerate() + { + sufficient_statistics.second_moment[[*parameter_row, *parameter_col]] += + omega[[eta_row, eta_col]]; + } + } + let subject_mu_phi = initialization.initial_subject_mu_phi.clone(); + let covariate_model = initialization.covariate_model.clone(); + let covariate_statistics = subject_mu_phi.as_ref().map(|means| { + let expected_phi = means + .iter() + .map(|mean| { + initialization + .random_effect_indices + .iter() + .map(|index| mean[*index]) + .collect::>() + }) + .collect::>(); + let mut global_second_moment = Array2::zeros((n_random_effects, n_random_effects)); + for mean in &expected_phi { + for row in 0..n_random_effects { + for column in 0..n_random_effects { + global_second_moment[[row, column]] += + mean[row] * mean[column] / expected_phi.len() as f64; + } + } + } + global_second_moment += ω + CovariateSufficientStatistics { + expected_phi, + global_second_moment, + } + }); + let subject_log_priors = eta_log_priors(&etas, &omega, 0) + .expect("validated initial omega should produce finite eta priors"); + let subject_kappa_log_priors = omega_iov + .as_ref() + .map(|omega| { + kappas + .iter() + .map(|subject_chains| { + subject_chains[0] + .iter() + .map(|kappa| eta_log_prior_from_omega(kappa, omega)) + .collect::>>() + .map(|priors| priors.into_iter().sum()) + }) + .collect::>>() + .expect("validated initial omega_iov should produce finite kappa priors") + }) + .unwrap_or_else(|| vec![0.0; initialization.subject_ids.len()]); + let residual_statistics = ResidualSufficientStatistics::zero(error_models.models().len()); + let residual_sigmas = primary_sigma_parameters(error_models.models()); + let proposal_step_sizes = initial_proposal_step_sizes(&omega, config.rw_init); + let eta_block_step_sizes = if config.eta_block_iterations > 0 { + vec![config.rw_init; initialization.subject_ids.len()] + } else { + Vec::new() + }; + let kappa_proposal_step_sizes = omega_iov + .as_ref() + .map(|_| vec![config.rw_init; initialization.subject_ids.len()]) + .unwrap_or_default(); + let mcmc_iterations = config.mcmc_iterations; + let eta_block_iterations = config.eta_block_iterations; + let adapt_interval = config.adapt_interval; + let steps_since_adapt = 0; + let adaptation_accept_counts = vec![0; n_random_effects]; + let adaptation_proposal_counts = vec![0; n_random_effects]; + let eta_block_adaptation_accept_counts = vec![0; eta_block_step_sizes.len()]; + let eta_block_adaptation_proposal_counts = vec![0; eta_block_step_sizes.len()]; + let kappa_adaptation_accept_counts = vec![0; initialization.subject_ids.len()]; + let kappa_adaptation_proposal_counts = vec![0; initialization.subject_ids.len()]; + let rng = StdRng::seed_from_u64(config.seed); + let last_log_acceptance_ratios = vec![0.0; initialization.subject_ids.len()]; + let last_acceptance_rate = None; + let last_parameter_acceptance_rates = vec![0.0; n_random_effects]; + let covariate_effect_names = covariate_model + .as_ref() + .map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.name().to_string()) + .collect::>() + }) + .unwrap_or_default(); + let covariate_estimated = covariate_model + .as_ref() + .map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimated()) + .collect::>() + }) + .unwrap_or_default(); + let information_layout = InformationLayout::new( + &initialization.parameter_names, + &initialization.estimated_parameters, + &covariate_effect_names, + &covariate_estimated, + &initialization.random_effect_names, + initialization.omega.structural_mask(), + initialization.omega.estimated_mask(), + &initialization.iov_effect_names, + initialization + .omega_iov + .as_ref() + .map(|omega| omega.structural_mask()), + initialization + .omega_iov + .as_ref() + .map(|omega| omega.estimated_mask()), + &error_models, + ) + .expect("validated SAEM metadata must produce an information layout"); + let mut information = InformationRecursion::new(information_layout); + let has_non_iiv_population = + initialization + .estimated_parameters + .iter() + .enumerate() + .any(|(index, estimated)| { + *estimated && !initialization.random_effect_indices.contains(&index) + }); + let has_non_iiv_covariate = covariate_model.as_ref().is_some_and(|model| { + model + .estimates() + .iter() + .enumerate() + .any(|(index, estimate)| { + estimate.estimated() + && !initialization + .random_effect_indices + .contains(&model.parameter_indices()[index]) + }) + }); + if has_non_iiv_population || has_non_iiv_covariate { + information.mark_unavailable(InformationStatus::Unsupported( + "structural observation sensitivities are unavailable for estimated non-IIV population or covariate coordinates" + .to_string(), + )); + } + + Self { + equation, + data, + error_models, + config: config.clone(), + etas, + kappas, + population_parameters, + omega, + omega_iov, + iiv_second_moment, + iov_second_moment, + sufficient_statistics, + covariate_statistics, + subject_mu_phi, + covariate_model, + residual_statistics, + residual_sigmas, + information, + proposal_step_sizes, + eta_block_step_sizes, + kappa_proposal_step_sizes, + mcmc_iterations, + eta_block_iterations, + adapt_interval, + residual_optimizer_max_iterations: config.residual_optimizer_max_iterations, + compute_map: config.compute_map, + map_max_iterations: config.map_max_iterations, + map_sd_tolerance: config.map_sd_tolerance, + map_initial_step: config.map_initial_step, + steps_since_adapt, + adaptation_accept_counts, + adaptation_proposal_counts, + eta_block_adaptation_accept_counts, + eta_block_adaptation_proposal_counts, + kappa_adaptation_accept_counts, + kappa_adaptation_proposal_counts, + rng, + subject_log_likelihoods: initialization.initial_subject_log_likelihoods.clone(), + subject_log_priors, + subject_kappa_log_priors, + last_log_acceptance_ratios, + last_acceptance_rate, + last_eta_block_acceptance_rate: None, + last_kappa_acceptance_rate: None, + last_rejected_proposals: None, + last_non_finite_proposals: None, + last_parameter_acceptance_rates, + cycle_diagnostics: Vec::with_capacity(initialization.schedule.total_iterations), + negative_log_likelihood: initialization.initial_negative_log_likelihood, + iterate_average: None, + operational_settings: config.operational_convergence, + operational_diagnostics: OperationalConvergenceDiagnostics { + config: config.operational_convergence, + ..OperationalConvergenceDiagnostics::default() + }, + initialization, + cycle: 0, + status: Status::Continue, + numerical_failure: None, + } + } + + fn e_step(&mut self) -> Result<()> { + let mut eta_accepted = 0usize; + let mut eta_rejected = 0usize; + let mut eta_non_finite = 0usize; + let mut eta_proposed = 0usize; + let mut eta_block_accepted = 0usize; + let mut eta_block_rejected = 0usize; + let mut eta_block_non_finite = 0usize; + let mut eta_block_proposed = 0usize; + let mut kappa_accepted = 0usize; + let mut kappa_rejected = 0usize; + let mut kappa_non_finite = 0usize; + let mut kappa_proposed = 0usize; + let eta_step_sizes_before = self.proposal_step_sizes.clone(); + let eta_block_step_sizes_before = self.eta_block_step_sizes.clone(); + let kappa_step_sizes_before = self.kappa_proposal_step_sizes.clone(); + let kappa_subject_count = if self.omega_iov.is_some() { + self.initialization.subject_ids.len() + } else { + 0 + }; + let mut kappa_subject_accept_counts = vec![0usize; kappa_subject_count]; + let mut kappa_subject_proposal_counts = vec![0usize; kappa_subject_count]; + let eta_block_subject_count = if self.eta_block_iterations > 0 { + self.initialization.subject_ids.len() + } else { + 0 + }; + let mut eta_block_subject_accept_counts = vec![0usize; eta_block_subject_count]; + let mut eta_block_subject_proposal_counts = vec![0usize; eta_block_subject_count]; + let n_parameters = self.initialization.random_effect_indices.len(); + let mut subject_log_acceptance_sums = vec![0.0; self.initialization.subject_ids.len()]; + let mut subject_proposal_counts = vec![0usize; self.initialization.subject_ids.len()]; + let mut parameter_accept_counts = vec![0usize; n_parameters]; + let mut parameter_proposal_counts = vec![0usize; n_parameters]; + + // Compound-kernel order: Omega-scaled eta blocks first, followed by + // component eta walks and occasion-level kappa blocks. Eta blocks are + // opt-in. + for _ in 0..self.eta_block_iterations { + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + let current_eta = self.etas[subject_index][chain_index].clone(); + let proposed_eta = self.block_random_walk_eta(¤t_eta, subject_index)?; + let log_acceptance_ratio = self.proposal_log_acceptance_ratio( + subject_index, + chain_index, + &proposed_eta, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + eta_block_subject_proposal_counts[subject_index] += 1; + self.eta_block_adaptation_proposal_counts[subject_index] += 1; + eta_block_proposed += 1; + eta_proposed += 1; + if !log_acceptance_ratio.is_finite() { + eta_block_non_finite += 1; + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + eta_block_subject_accept_counts[subject_index] += 1; + self.eta_block_adaptation_accept_counts[subject_index] += 1; + eta_block_accepted += 1; + eta_accepted += 1; + } else { + eta_block_rejected += 1; + eta_rejected += 1; + } + } + } + } + + for _ in 0..self.mcmc_iterations { + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + for parameter_index in 0..n_parameters { + let current_eta = self.etas[subject_index][chain_index].clone(); + let proposed_eta = + self.component_random_walk_eta(¤t_eta, parameter_index); + let log_acceptance_ratio = self.proposal_log_acceptance_ratio( + subject_index, + chain_index, + &proposed_eta, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + parameter_proposal_counts[parameter_index] += 1; + eta_proposed += 1; + if !log_acceptance_ratio.is_finite() { + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + parameter_accept_counts[parameter_index] += 1; + eta_accepted += 1; + } else { + eta_rejected += 1; + } + } + + // Gibbs sweep over occasion-specific κ blocks. Every + // proposal is evaluated against the full subject posterior, + // keeping η and all other occasions fixed. + if self.omega_iov.is_some() { + for occasion_index in 0..self.kappas[subject_index][chain_index].len() { + let current_kappa = + self.kappas[subject_index][chain_index][occasion_index].clone(); + let proposed_kappa = + self.block_random_walk_kappa(¤t_kappa, subject_index)?; + let log_acceptance_ratio = self.kappa_proposal_log_acceptance_ratio( + subject_index, + chain_index, + occasion_index, + &proposed_kappa, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + kappa_proposed += 1; + kappa_subject_proposal_counts[subject_index] += 1; + self.kappa_adaptation_proposal_counts[subject_index] += 1; + if !log_acceptance_ratio.is_finite() { + kappa_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.kappas[subject_index][chain_index][occasion_index] = + proposed_kappa; + kappa_accepted += 1; + kappa_subject_accept_counts[subject_index] += 1; + self.kappa_adaptation_accept_counts[subject_index] += 1; + } else { + kappa_rejected += 1; + } + } + } + } + } + } + + self.refresh_subject_scores_from_chains()?; + self.last_log_acceptance_ratios = subject_log_acceptance_sums + .into_iter() + .zip(subject_proposal_counts) + .map(|(sum, count)| if count > 0 { sum / count as f64 } else { 0.0 }) + .collect(); + let proposed = eta_proposed + kappa_proposed; + let accepted = eta_accepted + kappa_accepted; + self.last_acceptance_rate = if proposed > 0 { + Some(accepted as f64 / proposed as f64) + } else { + None + }; + self.last_eta_block_acceptance_rate = if self.eta_block_iterations > 0 { + Some(eta_block_accepted as f64 / eta_block_proposed.max(1) as f64) + } else { + None + }; + self.last_kappa_acceptance_rate = if self.omega_iov.is_some() { + Some(kappa_accepted as f64 / kappa_proposed.max(1) as f64) + } else { + None + }; + self.last_rejected_proposals = Some(eta_rejected + kappa_rejected); + self.last_non_finite_proposals = Some(eta_non_finite + kappa_non_finite); + self.last_parameter_acceptance_rates = parameter_accept_counts + .iter() + .zip(parameter_proposal_counts.iter()) + .map(|(accepted, proposed)| { + if *proposed > 0 { + *accepted as f64 / *proposed as f64 + } else { + 0.0 + } + }) + .collect(); + for parameter_index in 0..n_parameters { + self.adaptation_accept_counts[parameter_index] += + parameter_accept_counts[parameter_index]; + self.adaptation_proposal_counts[parameter_index] += + parameter_proposal_counts[parameter_index]; + } + self.steps_since_adapt += 1; + self.adapt_proposal_step_sizes(); + let phase = self.initialization.schedule.phase(self.cycle); + let omega_update = pending_covariance_update_diagnostics( + phase, + true, + self.initialization.omega.has_estimated_entries(), + ); + let omega_iov_update = pending_covariance_update_diagnostics( + phase, + self.initialization.omega_iov.is_some(), + self.initialization + .omega_iov + .as_ref() + .is_some_and(ResolvedOmega::has_estimated_entries), + ); + self.cycle_diagnostics.push(SaemCycleDiagnostics { + iteration: self.cycle, + phase, + stochastic_approximation_step: self + .initialization + .schedule + .stochastic_approximation_step(self.cycle), + covariance_step: self.initialization.schedule.covariance_step(self.cycle), + eta_proposals: eta_proposed, + eta_accepted, + eta_rejected, + eta_non_finite, + eta_parameter_acceptance_rates: self.last_parameter_acceptance_rates.clone(), + eta_proposal_step_sizes_before_adaptation: eta_step_sizes_before, + eta_proposal_step_sizes_after_adaptation: self.proposal_step_sizes.clone(), + eta_block_proposals: eta_block_proposed, + eta_block_accepted, + eta_block_rejected, + eta_block_non_finite, + eta_block_subject_acceptance_rates: eta_block_subject_accept_counts + .iter() + .zip(eta_block_subject_proposal_counts.iter()) + .map(|(accepted, proposed)| { + if *proposed > 0 { + *accepted as f64 / *proposed as f64 + } else { + 0.0 + } + }) + .collect(), + eta_block_step_sizes_before_adaptation: eta_block_step_sizes_before, + eta_block_step_sizes_after_adaptation: self.eta_block_step_sizes.clone(), + kappa_proposals: kappa_proposed, + kappa_accepted, + kappa_rejected, + kappa_non_finite, + kappa_subject_acceptance_rates: kappa_subject_accept_counts + .iter() + .zip(kappa_subject_proposal_counts.iter()) + .map(|(accepted, proposed)| { + if *proposed > 0 { + *accepted as f64 / *proposed as f64 + } else { + 0.0 + } + }) + .collect(), + kappa_proposal_step_sizes_before_adaptation: kappa_step_sizes_before, + kappa_proposal_step_sizes_after_adaptation: self.kappa_proposal_step_sizes.clone(), + simulated_annealing_active: self.cycle + <= self.initialization.schedule.variance_floor_iterations, + population_parameters: self.population_parameters.clone(), + omega: self.omega.clone(), + omega_iov: self.omega_iov.clone(), + residual_error_estimates: self.residual_error_estimates(), + residual_diagnostics: Vec::new(), + conditional_negative_log_likelihood: self.negative_log_likelihood, + eta_log_prior: self.subject_log_priors.iter().sum(), + kappa_log_prior: self.subject_kappa_log_priors.iter().sum(), + omega_update_rejected: false, + omega_iov_update_rejected: false, + omega_update, + omega_iov_update, + omega_relative_spd_margin: None, + omega_iov_relative_spd_margin: None, + covariate_betas: self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }), + covariate_beta_estimated: self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimated()) + .collect() + }), + }); + self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); + Ok(()) + } + + fn m_step(&mut self) -> Result<()> { + let parameter_step = self + .initialization + .schedule + .stochastic_approximation_step(self.cycle); + let covariance_step = self.initialization.schedule.covariance_step(self.cycle); + if self.covariate_model.is_some() { + let observed = self.current_covariate_statistics()?; + self.covariate_statistics + .as_mut() + .expect("covariate model has initialized statistics") + .stochastic_update(&observed, parameter_step)?; + } else { + let observed_statistics = self.current_phi_statistics()?; + self.sufficient_statistics.stochastic_update_with_steps( + &observed_statistics, + parameter_step, + covariance_step, + )?; + } + + if let Some(second_moment) = self.iov_second_moment.as_mut() { + let observed_second_moment = covariance_from_kappas(&self.kappas)?; + *second_moment = + &*second_moment + &((&observed_second_moment - &*second_moment) * covariance_step); + } + + // Pure burn-in warms the latent chains and their centered covariance + // statistics while theta, Omega, Omega_IOV, and sigma remain fixed. Raw + // covariate phi moments remain unchanged, matching their zero SA gain. + if parameter_step == 0.0 { + let observed_second_moment = second_moment_from_etas(&self.etas)?; + self.iiv_second_moment = &self.iiv_second_moment + + &((&observed_second_moment - &self.iiv_second_moment) * covariance_step); + self.finalize_cycle_diagnostics()?; + return Ok(()); + } + + let pre_update_residual_evidence = self.current_residual_statistics_and_information()?; + if self.covariate_model.is_some() { + // The raw first and second phi moments already share the SAEM gain. + // Keep their centered covariance candidate coherent; exploration + // robustness is applied later to the accepted Omega iterate rather + // than introducing a second sufficient-statistic recursion. + self.iiv_second_moment = self.update_covariate_population_and_recenter_etas()?; + } else { + self.update_population_and_recenter_etas()?; + let observed_second_moment = second_moment_from_etas(&self.etas)?; + self.iiv_second_moment = &self.iiv_second_moment + + &((&observed_second_moment - &self.iiv_second_moment) * covariance_step); + } + + self.update_non_iiv_population(parameter_step)?; + let (observed_residual_statistics, information_replicates) = pre_update_residual_evidence; + match information_replicates { + Ok(replicates) => self.information.update(&replicates, parameter_step), + Err(reason) => self + .information + .mark_unavailable(information_failure_status(reason)), + } + let mut residual_diagnostics = self + .error_models + .models() + .iter() + .map(|(output_index, _)| { + let statistic = observed_residual_statistics + .output(output_index) + .unwrap_or_default(); + ResidualCycleDiagnostics { + output: self + .error_models + .output_name(output_index) + .map(str::to_owned) + .unwrap_or_else(|| format!("output_{output_index}")), + output_index, + prediction_evaluation_count: statistic.observation_count, + proportional_floor_count: statistic.proportional_floor_count, + non_finite_prediction_count: statistic.non_finite_prediction_count, + exponential_domain_violation_count: statistic + .exponential_domain_violation_count, + update_rejected: false, + optimizer_objective: None, + optimizer_converged: None, + optimizer_iterations: None, + optimizer_termination: None, + combined_additive_collapse_warning: false, + } + }) + .collect::>(); + let residual_observations = (0..self.error_models.len()) + .map(|output_index| { + observed_residual_statistics + .observations(output_index) + .unwrap_or_default() + .to_vec() + }) + .collect::>(); + self.residual_statistics = self + .residual_statistics + .stochastic_update(observed_residual_statistics, parameter_step); + + if self + .initialization + .schedule + .covariance_update_active(self.cycle) + { + if self.initialization.omega.has_estimated_entries() { + let phase = self.initialization.schedule.phase(self.cycle); + let update = if self.covariate_model.is_some() && phase == SaemPhase::Exploration { + self.initialization + .omega + .update_with_status_and_max_fraction( + &self.omega, + &self.iiv_second_moment, + self.initialization.schedule.minimum_variance, + covariate_omega_update_maximum_fraction(true, phase, covariance_step), + )? + } else { + // Preserve the established floor-after-interpolation path + // for non-covariate IIV and for uncapped covariate smoothing. + self.initialization.omega.update_with_status( + &self.omega, + &self.iiv_second_moment, + self.initialization.schedule.minimum_variance, + )? + }; + let status = update.status; + let update_diagnostics = + completed_covariance_update_diagnostics(&self.iiv_second_moment, &update)?; + self.omega = update.matrix; + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.omega_update_rejected = status == CovarianceUpdateStatus::Rejected; + diagnostics.omega_update = update_diagnostics; + } + } + if let (Some(specification), Some(omega_iov), Some(second_moment)) = ( + self.initialization.omega_iov.as_ref(), + self.omega_iov.as_mut(), + self.iov_second_moment.as_ref(), + ) { + if specification.has_estimated_entries() { + let update = specification.update_with_status( + omega_iov, + second_moment, + self.initialization.schedule.minimum_iov_variance, + )?; + let status = update.status; + let update_diagnostics = + completed_covariance_update_diagnostics(second_moment, &update)?; + *omega_iov = update.matrix; + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.omega_iov_update_rejected = + status == CovarianceUpdateStatus::Rejected; + diagnostics.omega_iov_update = update_diagnostics; + } + } + } + } + for residual_diagnostic in &mut residual_diagnostics { + let outeq = residual_diagnostic.output_index; + if !self.error_models.is_estimated(outeq) { + continue; + } + let Some(model) = self.error_models.models().get(outeq).copied() else { + residual_diagnostic.update_rejected = true; + continue; + }; + if let ResidualErrorModel::Combined { a, b } = model { + match optimize_combined_residual( + &residual_observations[outeq], + a, + b, + self.error_models.combined_component_estimated(outeq), + self.initialization.schedule.minimum_residual_sigma, + self.residual_optimizer_max_iterations as u64, + ) { + Ok(solution) => { + let component_estimated = + self.error_models.combined_component_estimated(outeq); + let additive_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + a, + solution.additive_sd, + component_estimated[0], + ); + let proportional_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + b, + solution.proportional_sd, + component_estimated[1], + ); + residual_diagnostic.combined_additive_collapse_warning = + combined_additive_sigma_collapsed(additive_sd, component_estimated[0]); + update_estimated_combined_residual_model( + &mut self.error_models, + outeq, + additive_sd, + proportional_sd, + ); + residual_diagnostic.optimizer_objective = Some(solution.objective); + residual_diagnostic.optimizer_converged = Some(solution.converged); + residual_diagnostic.optimizer_iterations = Some(solution.iterations); + residual_diagnostic.optimizer_termination = Some(solution.termination); + } + Err(error) => { + residual_diagnostic.update_rejected = true; + residual_diagnostic.optimizer_termination = Some(error.to_string()); + } + } + continue; + } + if let ResidualErrorModel::CorrelatedCombined { a, b, rho } = model { + match optimize_correlated_combined_residual( + &residual_observations[outeq], + a, + b, + rho, + self.error_models + .correlated_combined_component_estimated(outeq), + self.initialization.schedule.minimum_residual_sigma, + self.residual_optimizer_max_iterations as u64, + ) { + Ok(solution) => { + let component_estimated = self + .error_models + .correlated_combined_component_estimated(outeq); + let additive_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + a, + solution.additive_sd, + component_estimated[0], + ); + let proportional_sd = applied_combined_residual_component( + &self.initialization.schedule, + self.cycle, + b, + solution.proportional_sd, + component_estimated[1], + ); + let correlation = applied_correlated_residual_correlation( + &self.initialization.schedule, + self.cycle, + rho, + solution.correlation, + component_estimated[2], + ); + if !correlation.is_finite() || correlation <= -1.0 || correlation >= 1.0 { + residual_diagnostic.update_rejected = true; + residual_diagnostic.optimizer_termination = Some( + "correlated-combined residual update left (-1, 1)".to_string(), + ); + continue; + } + residual_diagnostic.combined_additive_collapse_warning = + combined_additive_sigma_collapsed(additive_sd, component_estimated[0]); + update_estimated_correlated_combined_residual_model( + &mut self.error_models, + outeq, + additive_sd, + proportional_sd, + correlation, + ); + residual_diagnostic.optimizer_objective = Some(solution.objective); + residual_diagnostic.optimizer_converged = Some(solution.converged); + residual_diagnostic.optimizer_iterations = Some(solution.iterations); + residual_diagnostic.optimizer_termination = Some(solution.termination); + } + Err(error) => { + residual_diagnostic.update_rejected = true; + residual_diagnostic.optimizer_termination = Some(error.to_string()); + } + } + continue; + } + let Some(candidate_sigma) = self + .residual_statistics + .output(outeq) + .and_then(|statistic| statistic.sigma()) + else { + residual_diagnostic.update_rejected = true; + continue; + }; + let previous_sigma = primary_sigma_parameter(&model); + let sigma = self.initialization.schedule.guarded_residual_sigma( + self.cycle, + previous_sigma, + candidate_sigma, + ); + update_estimated_simple_residual_model_with_sigma(&mut self.error_models, outeq, sigma); + } + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.residual_diagnostics = residual_diagnostics; + } + self.residual_sigmas = primary_sigma_parameters(self.error_models.models()); + self.refresh_subject_scores_from_chains()?; + self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); + self.update_iterate_average()?; + self.finalize_cycle_diagnostics()?; + Ok(()) + } + + fn update_iterate_average(&mut self) -> Result<()> { + if self.initialization.schedule.phase(self.cycle) != SaemPhase::Smoothing + || !matches!( + self.config.estimator_policy, + SaemEstimatorPolicy::AveragedIterates { .. } + ) + { + return Ok(()); + } + let population_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let residual_models = self + .error_models + .models() + .iter() + .map(|(output_index, model)| (output_index, *model)) + .collect::>(); + let residual_model_width = self.error_models.models().len(); + let Some(average) = self.iterate_average.as_mut() else { + self.iterate_average = Some(SaemIterateAverage { + population_phi, + covariate_betas: self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }), + omega: self.omega.clone(), + omega_iov: self.omega_iov.clone(), + residual_model_width, + residual_models, + start_cycle: self.cycle, + count: 1, + }); + return Ok(()); + }; + let next_count = average.count + 1; + for (index, value) in population_phi.iter().copied().enumerate() { + if self.initialization.estimated_parameters[index] { + average.population_phi[index] = + incremental_average(average.population_phi[index], value, next_count); + } + } + if let (Some(average_betas), Some(model)) = ( + average.covariate_betas.as_mut(), + self.covariate_model.as_ref(), + ) { + for (index, estimate) in model.estimates().iter().enumerate() { + if estimate.estimated() { + average_betas[index] = + incremental_average(average_betas[index], estimate.estimate(), next_count); + } + } + } + average_covariance( + &mut average.omega, + &self.omega, + self.initialization.omega.estimated_mask(), + next_count, + ); + if let (Some(average_iov), Some(current_iov), Some(specification)) = ( + average.omega_iov.as_mut(), + self.omega_iov.as_ref(), + self.initialization.omega_iov.as_ref(), + ) { + average_covariance( + average_iov, + current_iov, + specification.estimated_mask(), + next_count, + ); + } + if residual_model_width != average.residual_model_width + || residual_models.len() != average.residual_models.len() + { + anyhow::bail!("residual output declarations changed while accumulating SAEM averages"); + } + for ((average_output_index, previous), (output_index, current)) in + average.residual_models.iter_mut().zip(residual_models) + { + if *average_output_index != output_index { + anyhow::bail!( + "residual output declarations changed while accumulating SAEM averages" + ); + } + let estimated = self.error_models.is_estimated(output_index); + let components = self.error_models.combined_component_estimated(output_index); + let correlated_components = self + .error_models + .correlated_combined_component_estimated(output_index); + *previous = average_residual_model( + *previous, + current, + estimated, + components, + correlated_components, + next_count, + )?; + } + average.count = next_count; + Ok(()) + } + + fn install_iterate_average(&mut self) -> Result { + let policy = self.config.estimator_policy; + let Some(average) = self.iterate_average.clone() else { + tracing::info!("averaged SAEM estimate was not available; retaining terminal iterate"); + return Ok(SaemEstimatorMetadata { + policy, + ..SaemEstimatorMetadata::default() + }); + }; + let terminal_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + validate_average_population(&average.population_phi, &self.initialization)?; + validate_average_covariance(&average.omega, &self.initialization.omega, "Omega")?; + if let (Some(matrix), Some(specification)) = ( + average.omega_iov.as_ref(), + self.initialization.omega_iov.as_ref(), + ) { + validate_average_covariance(matrix, specification, "Omega_IOV")?; + } + validate_average_residuals( + average.residual_model_width, + &average.residual_models, + &self.error_models, + )?; + + self.population_parameters = population_psi( + &average.population_phi, + &self.initialization.parameter_scales, + )?; + if let (Some(model), Some(beta_values), Some(old_means)) = ( + self.covariate_model.as_ref(), + average.covariate_betas.as_ref(), + self.subject_mu_phi.as_ref(), + ) { + let averaged_model = model.with_estimates(beta_values)?; + let new_rows = averaged_model.subject_population_parameters( + &average.population_phi, + &self.initialization.parameter_scales, + )?; + let new_means = new_rows + .iter() + .map(|row| row.phi().to_vec()) + .collect::>(); + for (subject_index, chains) in self.etas.iter_mut().enumerate() { + let old_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| old_means[subject_index][*index]) + .collect::>(); + let new_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| new_means[subject_index][*index]) + .collect::>(); + for eta in chains { + rebase_eta(eta, &old_random, &new_random)?; + } + } + self.covariate_model = Some(averaged_model); + self.subject_mu_phi = Some(new_means); + } else { + for (eta_index, parameter_index) in self + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + let shift = terminal_phi[parameter_index] - average.population_phi[parameter_index]; + for subject_chains in &mut self.etas { + for eta in subject_chains { + eta[eta_index] += shift; + } + } + } + } + self.omega = average.omega; + self.omega_iov = average.omega_iov; + for (output_index, model) in average.residual_models { + match model { + ResidualErrorModel::Combined { a, b } => update_estimated_combined_residual_model( + &mut self.error_models, + output_index, + a, + b, + ), + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + update_estimated_correlated_combined_residual_model( + &mut self.error_models, + output_index, + a, + b, + rho, + ) + } + ResidualErrorModel::Constant { .. } + | ResidualErrorModel::Proportional { .. } + | ResidualErrorModel::Exponential { .. } => { + update_estimated_simple_residual_model_with_sigma( + &mut self.error_models, + output_index, + primary_sigma_parameter(&model), + ) + } + } + } + self.residual_sigmas = primary_sigma_parameters(self.error_models.models()); + self.refresh_subject_scores_from_chains()?; + self.negative_log_likelihood = negative_log_likelihood(&self.subject_log_likelihoods); + tracing::info!( + start_cycle = average.start_cycle, + averaged_iterations = average.count, + "installed averaged SAEM estimate" + ); + Ok(SaemEstimatorMetadata { + policy, + average_applied: true, + averaging_start_cycle: Some(average.start_cycle), + averaged_iterations: average.count, + }) + } + + fn residual_error_estimates(&self) -> Vec { + self.error_models + .models() + .iter() + .map(|(output_index, model)| { + let model = *model; + let combined_components = + self.error_models.combined_component_estimated(output_index); + let correlated_components = self + .error_models + .correlated_combined_component_estimated(output_index); + let is_combined = matches!(model, ResidualErrorModel::Combined { .. }); + let is_correlated = matches!(model, ResidualErrorModel::CorrelatedCombined { .. }); + ResidualErrorEstimate { + output: self + .error_models + .output_name(output_index) + .map(str::to_owned) + .expect("declared residual models have output names"), + output_index, + model, + estimated: self.error_models.is_estimated(output_index), + combined_additive_estimated: if is_combined { + Some(combined_components[0]) + } else { + is_correlated.then_some(correlated_components[0]) + }, + combined_proportional_estimated: if is_combined { + Some(combined_components[1]) + } else { + is_correlated.then_some(correlated_components[1]) + }, + correlation_estimated: is_correlated.then_some(correlated_components[2]), + } + }) + .collect() + } + + fn finalize_cycle_diagnostics(&mut self) -> Result<()> { + let population_parameters = self.population_parameters.clone(); + let omega = self.omega.clone(); + let omega_iov = self.omega_iov.clone(); + let residual_error_estimates = self.residual_error_estimates(); + let conditional_negative_log_likelihood = self.negative_log_likelihood; + let eta_log_prior = self.subject_log_priors.iter().sum(); + let kappa_log_prior = self.subject_kappa_log_priors.iter().sum(); + let (omega_relative_spd_margin, omega_iov_relative_spd_margin) = + if self.config.covariance_stability.is_some() { + let initial_omega = self.initialization.omega.initial(); + let omega_margin = (initial_omega.nrows() > 0) + .then(|| relative_spd_margin(&omega, initial_omega)) + .transpose()?; + let omega_iov_margin = + match (self.initialization.omega_iov.as_ref(), omega_iov.as_ref()) { + (Some(specification), Some(matrix)) + if specification.initial().nrows() > 0 => + { + Some(relative_spd_margin(matrix, specification.initial())?) + } + _ => None, + }; + (omega_margin, omega_iov_margin) + } else { + (None, None) + }; + let covariate_betas = self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }); + let covariate_beta_estimated = self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimated()) + .collect() + }); + if let Some(diagnostics) = self.cycle_diagnostics.last_mut() { + diagnostics.population_parameters = population_parameters; + diagnostics.omega = omega; + diagnostics.omega_iov = omega_iov; + diagnostics.omega_relative_spd_margin = omega_relative_spd_margin; + diagnostics.omega_iov_relative_spd_margin = omega_iov_relative_spd_margin; + diagnostics.residual_error_estimates = residual_error_estimates; + diagnostics.conditional_negative_log_likelihood = conditional_negative_log_likelihood; + diagnostics.eta_log_prior = eta_log_prior; + diagnostics.kappa_log_prior = kappa_log_prior; + diagnostics.covariate_betas = covariate_betas; + diagnostics.covariate_beta_estimated = covariate_beta_estimated; + } + Ok(()) + } + + fn update_population_and_recenter_etas(&mut self) -> Result> { + let old_population_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let mut new_population_phi = old_population_phi.clone(); + for (parameter_index, parameter_phi) in new_population_phi.iter_mut().enumerate() { + if self.initialization.estimated_parameters[parameter_index] + && self + .initialization + .random_effect_indices + .contains(¶meter_index) + { + *parameter_phi = self.sufficient_statistics.mean_phi[parameter_index]; + } + } + + for (eta_index, parameter_index) in self + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + let realized_shift = + new_population_phi[parameter_index] - old_population_phi[parameter_index]; + for subject_chains in &mut self.etas { + for eta in subject_chains { + eta[eta_index] -= realized_shift; + } + } + } + self.population_parameters = + population_psi(&new_population_phi, &self.initialization.parameter_scales)?; + Ok(new_population_phi) + } + + fn update_covariate_population_and_recenter_etas(&mut self) -> Result> { + let model = self + .covariate_model + .as_ref() + .expect("covariate update requires a resolved model") + .clone(); + let statistics = self + .covariate_statistics + .as_ref() + .expect("covariate update requires sufficient statistics") + .clone(); + let q = self.initialization.random_effect_indices.len(); + let old_population_phi = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let old_subject_mu = self + .subject_mu_phi + .as_ref() + .expect("covariate update requires subject means") + .clone(); + + let free_intercepts = self + .initialization + .random_effect_indices + .iter() + .copied() + .filter(|index| self.initialization.estimated_parameters[*index]) + .collect::>(); + let free_effects = model + .estimates() + .iter() + .enumerate() + .filter_map(|(index, estimate)| { + (estimate.estimated() + && self + .initialization + .random_effect_indices + .contains(&model.parameter_indices()[index])) + .then_some(index) + }) + .collect::>(); + let width = free_intercepts.len() + free_effects.len(); + let random_row = self + .initialization + .random_effect_indices + .iter() + .enumerate() + .map(|(row, parameter)| (*parameter, row)) + .collect::>(); + let mut designs = Vec::with_capacity(model.subject_design().len()); + let mut offsets = Vec::with_capacity(model.subject_design().len()); + for subject in model.subject_design() { + let mut design = Array2::zeros((q, width)); + let mut offset = vec![0.0; q]; + for (row, parameter_index) in self + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + if let Some(column) = free_intercepts + .iter() + .position(|index| *index == parameter_index) + { + design[[row, column]] = 1.0; + } else { + offset[row] = old_population_phi[parameter_index]; + } + } + for (effect_index, value) in subject.values().iter().copied().enumerate() { + let parameter_index = model.parameter_indices()[effect_index]; + let Some(&row) = random_row.get(¶meter_index) else { + continue; + }; + if let Some(effect_column) = + free_effects.iter().position(|index| *index == effect_index) + { + design[[row, free_intercepts.len() + effect_column]] = value; + } else { + offset[row] += value * model.estimates()[effect_index].estimate(); + } + } + designs.push(design); + offsets.push(offset); + } + + let solution = if width == 0 { + Vec::new() + } else { + solve_covariate_gls(CovariateGlsProblem { + design: &designs, + expected_phi: &statistics.expected_phi, + offset: &offsets, + omega: &self.omega, + })? + }; + let mut new_population_phi = old_population_phi; + for (column, parameter_index) in free_intercepts.iter().copied().enumerate() { + new_population_phi[parameter_index] = solution[column]; + } + let mut beta_values = model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + for (column, effect_index) in free_effects.iter().copied().enumerate() { + beta_values[effect_index] = solution[free_intercepts.len() + column]; + } + let updated_model = model.with_estimates(&beta_values)?; + let subject_population = updated_model.subject_population_parameters( + &new_population_phi, + &self.initialization.parameter_scales, + )?; + let new_subject_mu = subject_population + .iter() + .map(|row| row.phi().to_vec()) + .collect::>(); + for (subject_index, subject_chains) in self.etas.iter_mut().enumerate() { + let old_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| old_subject_mu[subject_index][*index]) + .collect::>(); + let new_random = self + .initialization + .random_effect_indices + .iter() + .map(|index| new_subject_mu[subject_index][*index]) + .collect::>(); + for eta in subject_chains { + rebase_eta(eta, &old_random, &new_random)?; + } + } + let subject_mu_random = new_subject_mu + .iter() + .map(|mean| { + self.initialization + .random_effect_indices + .iter() + .map(|index| mean[*index]) + .collect::>() + }) + .collect::>(); + let candidate = if q == 0 { + Array2::zeros((0, 0)) + } else { + subject_centered_omega( + &statistics.global_second_moment, + &statistics.expected_phi, + &subject_mu_random, + )? + }; + self.population_parameters = + population_psi(&new_population_phi, &self.initialization.parameter_scales)?; + self.subject_mu_phi = Some(new_subject_mu); + self.covariate_model = Some(updated_model); + Ok(candidate) + } + + fn adapt_proposal_step_sizes(&mut self) { + if self.steps_since_adapt < self.adapt_interval { + return; + } + + for parameter_index in 0..self.proposal_step_sizes.len() { + let proposed = self.adaptation_proposal_counts[parameter_index].max(1); + let acceptance_rate = + self.adaptation_accept_counts[parameter_index] as f64 / proposed as f64; + self.proposal_step_sizes[parameter_index] = adapt_component_step_size( + self.proposal_step_sizes[parameter_index], + acceptance_rate, + ); + self.adaptation_accept_counts[parameter_index] = 0; + self.adaptation_proposal_counts[parameter_index] = 0; + } + for subject_index in 0..self.eta_block_step_sizes.len() { + let proposed = self.eta_block_adaptation_proposal_counts[subject_index].max(1); + let acceptance_rate = + self.eta_block_adaptation_accept_counts[subject_index] as f64 / proposed as f64; + self.eta_block_step_sizes[subject_index] = adapt_block_step_size( + self.eta_block_step_sizes[subject_index], + acceptance_rate, + ETA_BLOCK_TARGET_ACCEPTANCE, + ); + self.eta_block_adaptation_accept_counts[subject_index] = 0; + self.eta_block_adaptation_proposal_counts[subject_index] = 0; + } + for subject_index in 0..self.kappa_proposal_step_sizes.len() { + let proposed = self.kappa_adaptation_proposal_counts[subject_index].max(1); + let acceptance_rate = + self.kappa_adaptation_accept_counts[subject_index] as f64 / proposed as f64; + self.kappa_proposal_step_sizes[subject_index] = adapt_block_step_size( + self.kappa_proposal_step_sizes[subject_index], + acceptance_rate, + KAPPA_BLOCK_TARGET_ACCEPTANCE, + ); + self.kappa_adaptation_accept_counts[subject_index] = 0; + self.kappa_adaptation_proposal_counts[subject_index] = 0; + } + self.steps_since_adapt = 0; + } + + fn component_random_walk_eta( + &mut self, + current_eta: &[f64], + parameter_index: usize, + ) -> Vec { + let mut proposed_eta = current_eta.to_vec(); + proposed_eta[parameter_index] += + self.proposal_step_sizes[parameter_index] * self.standard_normal(); + proposed_eta + } + + fn block_random_walk_eta( + &mut self, + current_eta: &[f64], + subject_index: usize, + ) -> Result> { + let lower = cholesky_lower(&self.omega)?; + let standard_normals = (0..current_eta.len()) + .map(|_| self.standard_normal()) + .collect::>(); + correlated_random_walk( + current_eta, + &lower, + &standard_normals, + self.eta_block_step_sizes[subject_index], + ) + } + + fn block_random_walk_kappa( + &mut self, + current_kappa: &[f64], + subject_index: usize, + ) -> Result> { + let omega_iov = self + .omega_iov + .as_ref() + .ok_or_else(|| anyhow::anyhow!("kappa proposal requires configured omega_iov"))?; + let lower = cholesky_lower(omega_iov)?; + let standard_normals = (0..current_kappa.len()) + .map(|_| self.standard_normal()) + .collect::>(); + correlated_random_walk( + current_kappa, + &lower, + &standard_normals, + self.kappa_proposal_step_sizes[subject_index], + ) + } + + fn standard_normal(&mut self) -> f64 { + let u1 = self.rng.random::().max(f64::MIN_POSITIVE); + let u2 = self.rng.random::(); + (-2.0_f64 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos() + } + + fn accept_proposal(&mut self, log_acceptance_ratio: f64) -> bool { + if !log_acceptance_ratio.is_finite() { + return false; + } + if log_acceptance_ratio >= 0.0 { + return true; + } + self.rng.random::().max(f64::MIN_POSITIVE).ln() < log_acceptance_ratio + } + + fn individual_parameters(&self, subject_index: usize, chain_index: usize) -> Vec { + self.individual_parameters_from_eta(subject_index, &self.etas[subject_index][chain_index]) + .expect("stored eta should match parameter dimensions") + } + + fn individual_parameters_from_eta( + &self, + subject_index: usize, + eta: &[f64], + ) -> Result> { + match self.subject_mu_phi.as_ref() { + Some(means) => individual_psi_from_subject_mean( + &means[subject_index], + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + None => individual_psi( + &self.population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + } + } + + fn individual_phi(&self, subject_index: usize, chain_index: usize) -> Result> { + match self.subject_mu_phi.as_ref() { + Some(means) => individual_phi_from_subject_mean( + &means[subject_index], + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + ), + None => individual_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + ), + } + } + + fn current_phi_statistics(&self) -> Result { + let mut subject_phi = Vec::with_capacity( + self.initialization.subject_ids.len() * self.initialization.n_chains, + ); + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + subject_phi.push(self.individual_phi(subject_index, chain_index)?); + } + } + PhiSufficientStatistics::from_subject_phi(&subject_phi) + } + + fn current_covariate_statistics(&self) -> Result { + let mut subjects = Vec::with_capacity(self.initialization.subject_ids.len()); + for subject_index in 0..self.initialization.subject_ids.len() { + let mut chains = Vec::with_capacity(self.initialization.n_chains); + for chain_index in 0..self.initialization.n_chains { + let phi = self.individual_phi(subject_index, chain_index)?; + chains.push( + self.initialization + .random_effect_indices + .iter() + .map(|index| phi[*index]) + .collect(), + ); + } + subjects.push(chains); + } + CovariateSufficientStatistics::from_subject_chains(&subjects) + } + + fn current_residual_statistics_and_information( + &self, + ) -> Result<( + ResidualSufficientStatistics, + std::result::Result, String>, + )> { + let mut total = ResidualSufficientStatistics::zero(self.error_models.len()); + let layout = self.information.layout(); + let mut replicates = (0..self.initialization.n_chains) + .map(|_| CompleteDerivative::zero(layout.len())) + .collect::>(); + let mut information_error = None; + // Preserve the established subject-major/chain-minor prediction and + // accumulation order so this diagnostic cannot alter fit trajectories. + for subject_index in 0..self.initialization.subject_ids.len() { + let subject = self.data.subjects()[subject_index]; + for (chain_index, derivative) in replicates.iter_mut().enumerate() { + if information_error.is_none() { + let derivative_result = match self.covariate_model.as_ref() { + Some(model) => derivative.add_covariate_population_prior( + &self.etas[subject_index][chain_index], + &self.omega, + &self.initialization.random_effect_indices, + model.parameter_indices(), + model.subject_design()[subject_index].values(), + layout, + ), + None => derivative.add_population_prior( + &self.etas[subject_index][chain_index], + &self.omega, + &self.initialization.random_effect_indices, + layout, + ), + }; + if let Err(error) = derivative_result { + information_error = Some(error.to_string()); + } + } + if self.omega_iov.is_none() { + let parameters = self.individual_parameters(subject_index, chain_index); + let predictions = self + .equation + .estimate_predictions_dense(subject, ¶meters)?; + total.add_assign(&ResidualSufficientStatistics::from_predictions( + &predictions, + &self.error_models, + )); + if information_error.is_none() { + if let Err(error) = + derivative.add_predictions(&predictions, &self.error_models, layout) + { + information_error = Some(error.to_string()); + } + } + continue; + } + for (occasion, kappa) in subject + .occasions() + .iter() + .zip(&self.kappas[subject_index][chain_index]) + { + if information_error.is_none() { + if let Some(omega_iov) = self.omega_iov.as_ref() { + if let Err(error) = derivative.add_iov_prior(kappa, omega_iov, layout) { + information_error = Some(error.to_string()); + } + } + } + let parameters = match self.subject_mu_phi.as_ref() { + Some(means) => occasion_psi_from_subject_mean( + &means[subject_index], + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + &self.population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + &self.etas[subject_index][chain_index], + &self.initialization.iov_effect_indices, + kappa, + ), + }?; + let occasion_subject = + Subject::from_occasions(subject.id().to_owned(), vec![occasion.clone()]); + let predictions = self + .equation + .estimate_predictions_dense(&occasion_subject, ¶meters)?; + total.add_assign(&ResidualSufficientStatistics::from_predictions( + &predictions, + &self.error_models, + )); + if information_error.is_none() { + if let Err(error) = + derivative.add_predictions(&predictions, &self.error_models, layout) + { + information_error = Some(error.to_string()); + } + } + } + } + } + Ok(( + total, + match information_error { + Some(error) => Err(error), + None => Ok(replicates), + }, + )) + } + + #[cfg(test)] + fn current_residual_statistics(&self) -> Result { + self.current_residual_statistics_and_information() + .map(|(statistics, _)| statistics) + } + + fn refresh_subject_scores_from_chains(&mut self) -> Result<()> { + let n_chains = self.initialization.n_chains as f64; + let mut subject_log_likelihoods = vec![0.0; self.initialization.subject_ids.len()]; + let mut subject_log_priors = vec![0.0; self.initialization.subject_ids.len()]; + let mut subject_kappa_log_priors = vec![0.0; self.initialization.subject_ids.len()]; + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + let score = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + &self.kappas[subject_index][chain_index], + )?; + subject_log_likelihoods[subject_index] += score.log_likelihood / n_chains; + subject_log_priors[subject_index] += score.eta_log_prior / n_chains; + subject_kappa_log_priors[subject_index] += score.kappa_log_prior / n_chains; + } + } + self.subject_log_likelihoods = subject_log_likelihoods; + self.subject_log_priors = subject_log_priors; + self.subject_kappa_log_priors = subject_kappa_log_priors; + Ok(()) + } + + fn score_subject_latents( + &self, + subject_index: usize, + eta: &[f64], + kappas: &[Vec], + ) -> Result { + self.score_subject_latents_at(subject_index, eta, kappas, None) + } + + fn non_iiv_coordinate_layout(&self) -> NonIivCoordinateLayout { + let population_indices = self + .initialization + .estimated_parameters + .iter() + .enumerate() + .filter_map(|(index, estimated)| { + (*estimated && !self.initialization.random_effect_indices.contains(&index)) + .then_some(index) + }) + .collect(); + let covariate_indices = self + .covariate_model + .as_ref() + .map(|model| { + model + .estimates() + .iter() + .enumerate() + .filter_map(|(index, estimate)| { + (estimate.estimated() + && !self + .initialization + .random_effect_indices + .contains(&model.parameter_indices()[index])) + .then_some(index) + }) + .collect() + }) + .unwrap_or_default(); + NonIivCoordinateLayout { + population_indices, + covariate_indices, + } + } + + fn non_iiv_population_update_active(&self, parameter_step: f64) -> bool { + let Some(post_burn_start) = self.initialization.schedule.pure_burn_in.checked_add(1) else { + return false; + }; + let first_active_cycle = self + .initialization + .schedule + .variance_floor_iterations + .max(post_burn_start); + parameter_step.is_finite() && parameter_step > 0.0 && self.cycle >= first_active_cycle + } + + fn pack_non_iiv_coordinates(&self, layout: &NonIivCoordinateLayout) -> Result> { + let population = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + let mut coordinates = layout + .population_indices + .iter() + .map(|index| population[*index]) + .collect::>(); + if let Some(model) = self.covariate_model.as_ref() { + coordinates.extend( + layout + .covariate_indices + .iter() + .map(|index| model.estimates()[*index].estimate()), + ); + } + Ok(coordinates) + } + + fn non_iiv_candidate_components( + &self, + layout: &NonIivCoordinateLayout, + coordinates: &[f64], + ) -> Result { + if coordinates.len() != layout.len() || coordinates.iter().any(|value| !value.is_finite()) { + anyhow::bail!("non-IIV population coordinate width or value is invalid"); + } + let mut population = population_phi( + &self.population_parameters, + &self.initialization.parameter_scales, + )?; + for (coordinate, parameter_index) in coordinates + .iter() + .copied() + .zip(layout.population_indices.iter().copied()) + { + population[parameter_index] = coordinate; + } + let population_parameters = + population_psi(&population, &self.initialization.parameter_scales)?; + if !parameters_are_strictly_in_domain( + &population_parameters, + &self.initialization.parameter_scales, + ) { + anyhow::bail!("non-IIV population candidate violates its declared parameter domain"); + } + + let covariate_model = match self.covariate_model.as_ref() { + Some(model) => { + let mut values = model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + for (coordinate, effect_index) in coordinates[layout.population_indices.len()..] + .iter() + .copied() + .zip(layout.covariate_indices.iter().copied()) + { + values[effect_index] = coordinate; + } + Some(model.with_estimates(&values)?) + } + None if layout.covariate_indices.is_empty() => None, + None => anyhow::bail!("non-IIV covariate coordinates lack a covariate model"), + }; + let subject_rows = covariate_model + .as_ref() + .map(|model| { + model.subject_population_parameters( + &population, + &self.initialization.parameter_scales, + ) + }) + .transpose()?; + if subject_rows.as_ref().is_some_and(|rows| { + rows.iter().any(|row| { + !parameters_are_strictly_in_domain(row.psi(), &self.initialization.parameter_scales) + }) + }) { + anyhow::bail!("non-IIV covariate candidate violates a declared parameter domain"); + } + let subject_means = subject_rows.map(|rows| { + rows.into_iter() + .map(|row| row.phi().to_vec()) + .collect::>() + }); + Ok((population_parameters, covariate_model, subject_means)) + } + + pub(super) fn non_iiv_observation_nll( + &self, + layout: &NonIivCoordinateLayout, + coordinates: &[f64], + ) -> Result { + let (population_parameters, _covariate_model, subject_means) = + self.non_iiv_candidate_components(layout, coordinates)?; + let chain_count = self.initialization.n_chains; + if chain_count == 0 { + anyhow::bail!("non-IIV observation objective requires at least one chain"); + } + let mut objective = 0.0; + for subject_index in 0..self.initialization.subject_ids.len() { + let subject = self.data.subjects()[subject_index]; + let subject_mean = subject_means + .as_ref() + .map(|means| means[subject_index].as_slice()); + for chain_index in 0..chain_count { + let eta = &self.etas[subject_index][chain_index]; + let log_likelihood = if self.omega_iov.is_none() { + let parameters = match subject_mean { + Some(mean) => individual_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + None => individual_psi( + &population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + }?; + parametric_subject_log_likelihood( + &self.equation, + subject, + ¶meters, + &self.error_models, + ) + } else { + let kappas = &self.kappas[subject_index][chain_index]; + if kappas.len() != subject.occasions().len() { + anyhow::bail!("non-IIV objective kappa/occasion dimension mismatch"); + } + let mut value = 0.0; + for (occasion, kappa) in subject.occasions().iter().zip(kappas) { + let parameters = match subject_mean { + Some(mean) => occasion_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + &population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + }?; + let occasion_value = parametric_occasion_log_likelihood( + &self.equation, + subject.id(), + occasion, + ¶meters, + &self.error_models, + ); + if !occasion_value.is_finite() { + anyhow::bail!("non-IIV observation objective is non-finite"); + } + value += occasion_value; + } + value + }; + if !log_likelihood.is_finite() { + anyhow::bail!("non-IIV observation objective is non-finite"); + } + objective -= log_likelihood / chain_count as f64; + } + } + if !objective.is_finite() { + anyhow::bail!("non-IIV observation objective is non-finite"); + } + Ok(objective) + } + + fn update_non_iiv_population(&mut self, parameter_step: f64) -> Result { + let layout = self.non_iiv_coordinate_layout(); + if layout.is_empty() || !self.non_iiv_population_update_active(parameter_step) { + return Ok(false); + } + + let initial = self.pack_non_iiv_coordinates(&layout)?; + let initial_objective = self.non_iiv_observation_nll(&layout, &initial)?; + if !initial_objective.is_finite() { + anyhow::bail!("current non-IIV observation objective is non-finite"); + } + + let mut simplex = Vec::with_capacity(initial.len() + 1); + simplex.push(initial.clone()); + for coordinate in 0..initial.len() { + let mut point = initial.clone(); + point[coordinate] += 0.1 * initial[coordinate].abs().max(1.0); + simplex.push(point); + } + let solver = NelderMead::new(simplex).with_sd_tolerance(NON_IIV_OPTIMIZER_SD_TOLERANCE)?; + let execution = Executor::new( + NonIivPopulationCost { + state: self, + layout: &layout, + }, + solver, + ) + .configure(|state| state.max_iters(NON_IIV_OPTIMIZER_MAX_ITERATIONS)) + .run(); + let result = match execution { + Ok(result) => result, + Err(error) => { + tracing::warn!( + error = %error, + "Non-IIV population optimizer failed; retaining current state" + ); + return Ok(false); + } + }; + let Some(candidate) = result.state.best_param.as_ref() else { + return Ok(false); + }; + let candidate_objective = match self.non_iiv_observation_nll(&layout, candidate) { + Ok(value) if value.is_finite() => value, + _ => return Ok(false), + }; + if !non_iiv_candidate_improves(initial_objective, candidate_objective) { + return Ok(false); + } + + let applied = initial + .iter() + .zip(candidate) + .map(|(current, target)| current + parameter_step * (target - current)) + .collect::>(); + match self.non_iiv_observation_nll(&layout, &applied) { + Ok(value) if value.is_finite() => {} + _ => return Ok(false), + } + + let (population_parameters, covariate_model, subject_means) = + self.non_iiv_candidate_components(&layout, &applied)?; + self.population_parameters = population_parameters; + self.covariate_model = covariate_model; + self.subject_mu_phi = subject_means; + Ok(true) + } + + fn score_subject_latents_at( + &self, + subject_index: usize, + eta: &[f64], + kappas: &[Vec], + candidate: Option<&DiagnosticCandidate>, + ) -> Result { + if eta.len() != self.initialization.random_effect_indices.len() { + anyhow::bail!( + "eta has {} values but there are {} random effects", + eta.len(), + self.initialization.random_effect_indices.len() + ); + } + + let subject = self.data.subjects()[subject_index]; + let population_parameters = candidate + .map_or(self.population_parameters.as_slice(), |value| { + value.population_parameters.as_slice() + }); + let omega = candidate.map_or(&self.omega, |value| &value.omega); + let omega_iov = candidate.map_or(self.omega_iov.as_ref(), |value| value.omega_iov.as_ref()); + let error_models = candidate.map_or(&self.error_models, |value| &value.error_models); + let candidate_covariates = candidate + .and_then(|value| value.covariate_model.as_ref()) + .or(self.covariate_model.as_ref()); + let calculated_subject_mu = if candidate.is_some() { + candidate_covariates + .map(|model| { + let phi = population_phi( + population_parameters, + &self.initialization.parameter_scales, + )?; + Ok::<_, anyhow::Error>( + model.subject_population_parameters( + &phi, + &self.initialization.parameter_scales, + )?[subject_index] + .phi() + .to_vec(), + ) + }) + .transpose()? + } else { + None + }; + let subject_mu = calculated_subject_mu.as_deref().or_else(|| { + self.subject_mu_phi + .as_ref() + .map(|means| means[subject_index].as_slice()) + }); + let eta_log_prior = eta_log_prior_from_omega(eta, omega)?; + if omega_iov.is_none() { + let parameters = match subject_mu { + Some(mean) => individual_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + None => individual_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + ), + }?; + return Ok(SubjectPosteriorScore { + log_likelihood: parametric_subject_log_likelihood( + &self.equation, + subject, + ¶meters, + error_models, + ), + eta_log_prior, + kappa_log_prior: 0.0, + }); + } + + if kappas.len() != subject.occasions().len() { + anyhow::bail!( + "subject '{}' has {} occasions but {} kappa states", + subject.id(), + subject.occasions().len(), + kappas.len() + ); + } + let omega_iov = omega_iov.expect("checked above"); + let mut log_likelihood = 0.0; + let mut kappa_log_prior = 0.0; + for (occasion, kappa) in subject.occasions().iter().zip(kappas) { + let parameters = match subject_mu { + Some(mean) => occasion_psi_from_subject_mean( + mean, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + None => occasion_psi( + population_parameters, + &self.initialization.parameter_scales, + &self.initialization.random_effect_indices, + eta, + &self.initialization.iov_effect_indices, + kappa, + ), + }?; + let occasion_log_likelihood = parametric_occasion_log_likelihood( + &self.equation, + subject.id(), + occasion, + ¶meters, + error_models, + ); + if !occasion_log_likelihood.is_finite() { + log_likelihood = f64::NEG_INFINITY; + } else if log_likelihood.is_finite() { + log_likelihood += occasion_log_likelihood; + } + kappa_log_prior += eta_log_prior_from_omega(kappa, omega_iov)?; + } + + Ok(SubjectPosteriorScore { + log_likelihood, + eta_log_prior, + kappa_log_prior, + }) + } + + fn proposal_log_acceptance_ratio( + &self, + subject_index: usize, + chain_index: usize, + proposed_eta: &[f64], + ) -> Result { + let current = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + &self.kappas[subject_index][chain_index], + )?; + let proposed = self.score_subject_latents( + subject_index, + proposed_eta, + &self.kappas[subject_index][chain_index], + )?; + Ok(current.log_acceptance_ratio(proposed)) + } + + fn kappa_proposal_log_acceptance_ratio( + &self, + subject_index: usize, + chain_index: usize, + occasion_index: usize, + proposed_kappa: &[f64], + ) -> Result { + let current_kappas = &self.kappas[subject_index][chain_index]; + let current = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + current_kappas, + )?; + let mut proposed_kappas = current_kappas.clone(); + proposed_kappas[occasion_index] = proposed_kappa.to_vec(); + let proposed = self.score_subject_latents( + subject_index, + &self.etas[subject_index][chain_index], + &proposed_kappas, + )?; + Ok(current.log_acceptance_ratio(proposed)) + } +} + +mod diagnostics; +mod runner; +mod support; + +use support::*; + +#[cfg(test)] +mod tests; diff --git a/src/algorithms/parametric/saem/state/runner.rs b/src/algorithms/parametric/saem/state/runner.rs new file mode 100644 index 000000000..c53b61d49 --- /dev/null +++ b/src/algorithms/parametric/saem/state/runner.rs @@ -0,0 +1,465 @@ +use super::*; +use crate::algorithms::parametric::{NumericalFailurePhase, ParametricRunner}; +use crate::estimation::parametric::information::derive_population_uncertainty; +use crate::estimation::parametric::marginal_likelihood::MarginalLikelihoodStatus; +use crate::estimation::parametric::shrinkage::{ + derive_eta_map_shrinkage, derive_eta_posterior_mean_shrinkage, derive_kappa_map_shrinkage, + derive_kappa_posterior_mean_shrinkage, ShrinkageDiagnostics, +}; +use crate::results::{derive_information_criteria, ParametricResult, SubjectEtaEstimate}; + +impl ParametricRunner for SaemState { + fn step(&mut self) -> Result { + if self.status.is_stop() { + return Ok(self.status.clone()); + } + + if self.cycle >= self.initialization.schedule.total_iterations { + self.status = Status::Stop(StopReason::MaxCycles); + return Ok(self.status.clone()); + } + + self.cycle += 1; + if let Err(error) = self.e_step() { + let failure = NumericalFailure::new( + self.cycle, + NumericalFailurePhase::Expectation, + format!("{error:#}"), + ); + self.status = Status::Stop(StopReason::NumericalFailure); + self.numerical_failure = Some(failure.clone()); + return Err(failure.into()); + } + // m_step also accumulates damped covariance sufficient statistics + // during pure burn-in while leaving theta, Omega, Omega_IOV, and sigma + // unchanged, so it must run in every schedule phase. + if let Err(error) = self.m_step() { + let failure = NumericalFailure::new( + self.cycle, + NumericalFailurePhase::Maximization, + format!("{error:#}"), + ); + self.status = Status::Stop(StopReason::NumericalFailure); + self.numerical_failure = Some(failure.clone()); + return Err(failure.into()); + } + + if self.cycle >= self.initialization.schedule.total_iterations { + self.status = Status::Stop(StopReason::MaxCycles); + let scheduled = self + .operational_settings + .zip(self.iterate_average.as_ref()) + .is_some_and(|(policy, average)| { + average.count >= policy.first_eligible_averaged_iteration + && (average.count - policy.first_eligible_averaged_iteration) + .is_multiple_of(policy.check_interval) + }); + self.evaluate_operational_convergence(self.cycle, scheduled, true)?; + } else { + self.evaluate_operational_convergence(self.cycle, true, false)?; + } + + Ok(self.status.clone()) + } + + fn request_stop(&mut self, reason: StopReason) { + if self.status.is_continue() && self.numerical_failure.is_none() { + self.status = Status::Stop(reason); + } + } + + fn cycle(&self) -> usize { + self.cycle + } + + fn status(&self) -> &Status { + &self.status + } + + fn cycle_diagnostics(&self) -> &[SaemCycleDiagnostics] { + &self.cycle_diagnostics + } + + fn log_likelihood(&self) -> f64 { + self.subject_log_likelihoods.iter().sum() + } + + fn population_parameters(&self) -> &[f64] { + &self.population_parameters + } + + fn covariate_betas(&self) -> Option> { + self.covariate_model.as_ref().map(|model| { + model + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect() + }) + } + + fn random_effect_names(&self) -> &[String] { + &self.initialization.random_effect_names + } + + fn iov_effect_names(&self) -> Option<&[String]> { + (!self.initialization.iov_effect_names.is_empty()) + .then_some(&self.initialization.iov_effect_names) + } + + fn eta_log_prior(&self) -> f64 { + self.subject_log_priors.iter().sum() + } + + fn kappa_log_prior(&self) -> f64 { + self.subject_kappa_log_priors.iter().sum() + } + + fn acceptance_rate(&self) -> Option { + self.last_acceptance_rate + } + + fn eta_block_acceptance_rate(&self) -> Option { + self.last_eta_block_acceptance_rate + } + + fn kappa_acceptance_rate(&self) -> Option { + self.last_kappa_acceptance_rate + } + + fn rejected_proposals(&self) -> Option { + self.last_rejected_proposals + } + + fn non_finite_proposals(&self) -> Option { + self.last_non_finite_proposals + } + + fn parameter_acceptance_rates(&self) -> Option<&[f64]> { + self.last_acceptance_rate + .map(|_| self.last_parameter_acceptance_rates.as_slice()) + } + + fn proposal_step_sizes(&self) -> Option<&[f64]> { + Some(&self.proposal_step_sizes) + } + + fn eta_block_step_sizes(&self) -> Option<&[f64]> { + (self.eta_block_iterations > 0).then_some(self.eta_block_step_sizes.as_slice()) + } + + fn log_acceptance_ratios(&self) -> Option<&[f64]> { + Some(&self.last_log_acceptance_ratios) + } + + fn negative_log_likelihood(&self) -> f64 { + self.negative_log_likelihood + } + + fn n_chains(&self) -> Option { + self.etas + .first() + .map(|subject_chains| subject_chains.len()) + .or(Some(self.initialization.n_chains)) + } + + fn omega(&self) -> Option<&Array2> { + Some(&self.omega) + } + + fn omega_iov(&self) -> Option<&Array2> { + self.omega_iov.as_ref() + } + + fn residual_sigmas(&self) -> &[f64] { + &self.residual_sigmas + } + + fn step_size(&self) -> f64 { + self.initialization + .schedule + .stochastic_approximation_step(self.cycle) + } + + fn total_iterations(&self) -> usize { + self.initialization.schedule.total_iterations + } + + fn into_result(mut self: Box) -> Result> { + if let Some(failure) = self.numerical_failure.as_ref() { + return Err(failure.clone().into()); + } + + let result_cycle = self.cycle; + let estimator_metadata = match self.config.estimator_policy { + SaemEstimatorPolicy::TerminalIterate => SaemEstimatorMetadata::default(), + SaemEstimatorPolicy::AveragedIterates { .. } => { + self.install_iterate_average().map_err(|error| { + NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + })? + } + }; + let information_diagnostics = self.information.diagnostics(); + let population_uncertainty = derive_population_uncertainty(&information_diagnostics); + let markov_simulation_variance = if self.operational_settings.is_some() { + self.operational_diagnostics + .checks + .last() + .and_then(|check| check.markov.clone()) + .unwrap_or_else(MarkovSimulationVarianceDiagnostics::disabled) + } else { + self.markov_variance_diagnostics(&estimator_metadata, &information_diagnostics) + }; + let (conditional_modes, conditional_mode_error) = match conditional_modes(&self) { + Ok(modes) => (modes, None), + Err(error) if self.config.marginal_likelihood.is_some() => { + (Vec::new(), Some(format!("{error:#}"))) + } + Err(error) => { + return Err(NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + .into()) + } + }; + let marginal_likelihood = calculate_result_marginal_likelihood( + &self, + &conditional_modes, + conditional_mode_error.as_deref(), + ); + let information_criteria = derive_information_criteria( + marginal_likelihood.as_ref(), + &information_diagnostics.coordinates, + self.initialization.subject_ids.len(), + ); + let eta_chain_means = self + .initialization + .subject_ids + .iter() + .enumerate() + .map(|(subject_index, subject_id)| { + Ok(SubjectEtaEstimate { + subject_id: subject_id.clone(), + values: mean_vectors( + self.etas[subject_index].iter().map(|eta| eta.as_slice()), + )?, + }) + }) + .collect::>>() + .map_err(|error| { + NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + })?; + let mut kappa_chain_means = Vec::new(); + if self.omega_iov.is_some() { + for (subject_index, subject_id) in self.initialization.subject_ids.iter().enumerate() { + for (occasion_position, occasion) in self.data.subjects()[subject_index] + .occasions() + .iter() + .enumerate() + { + kappa_chain_means.push(OccasionKappaEstimate { + subject_id: subject_id.clone(), + occasion_index: occasion.index(), + values: mean_vectors( + self.kappas[subject_index] + .iter() + .map(|chain| chain[occasion_position].as_slice()), + ) + .map_err(|error| { + NumericalFailure::new( + result_cycle, + NumericalFailurePhase::ResultAssembly, + format!("{error:#}"), + ) + })?, + }); + } + } + } + let eta_variances = (0..self.omega.nrows()) + .map(|index| self.omega[[index, index]]) + .collect::>(); + let eta_posterior_rows = eta_chain_means + .iter() + .map(|estimate| estimate.values.clone()) + .collect::>(); + let eta_map_rows = (!conditional_modes.is_empty()).then(|| { + conditional_modes + .iter() + .map(|mode| mode.eta.clone()) + .collect::>() + }); + let kappa_variances = self + .omega_iov + .as_ref() + .map(|omega| { + (0..omega.nrows()) + .map(|index| omega[[index, index]]) + .collect::>() + }) + .unwrap_or_default(); + let kappa_posterior_rows = kappa_chain_means + .iter() + .map(|estimate| estimate.values.clone()) + .collect::>(); + let kappa_map_rows = (!conditional_modes.is_empty()).then(|| { + conditional_modes + .iter() + .flat_map(|mode| mode.kappas.iter().map(|kappa| kappa.values.clone())) + .collect::>() + }); + let shrinkage = ShrinkageDiagnostics { + eta_posterior_mean: derive_eta_posterior_mean_shrinkage( + &self.initialization.random_effect_names, + &eta_variances, + &eta_posterior_rows, + ), + eta_map: derive_eta_map_shrinkage( + &self.initialization.random_effect_names, + &eta_variances, + eta_map_rows.as_deref(), + ), + kappa_posterior_mean: derive_kappa_posterior_mean_shrinkage( + &self.initialization.iov_effect_names, + &kappa_variances, + &kappa_posterior_rows, + ), + kappa_map: derive_kappa_map_shrinkage( + &self.initialization.iov_effect_names, + &kappa_variances, + kappa_map_rows.as_deref(), + ), + }; + let residual_error_estimates = self.residual_error_estimates(); + let mut warnings = + parametric_warnings(&self.cycle_diagnostics, self.config.covariance_stability); + if let Some(diagnostics) = marginal_likelihood.as_ref() { + match &diagnostics.status { + MarginalLikelihoodStatus::Unavailable { failures } => { + warnings.push(ParametricWarning::MarginalLikelihoodUnavailable { + subjects: failures + .iter() + .map(|failure| failure.subject_id.clone()) + .collect(), + }); + } + MarginalLikelihoodStatus::AvailableWithNonconvergedModes { subjects } => { + warnings.push(ParametricWarning::MarginalLikelihoodNonconvergedModes { + subjects: subjects.clone(), + }); + } + MarginalLikelihoodStatus::Available => {} + } + } + let omega_structural_mask = self.initialization.omega.structural_mask().clone(); + let omega_estimated_mask = self.initialization.omega.estimated_mask().clone(); + let omega_iov_structural_mask = self + .initialization + .omega_iov + .as_ref() + .map(|omega| omega.structural_mask().clone()); + let omega_iov_estimated_mask = self + .initialization + .omega_iov + .as_ref() + .map(|omega| omega.estimated_mask().clone()); + let individual_estimates = if conditional_modes.is_empty() { + self.initialization + .subject_ids + .iter() + .enumerate() + .map(|(subject_index, subject_id)| { + ( + subject_id.clone(), + self.individual_parameters(subject_index, 0), + ) + }) + .collect() + } else { + conditional_modes + .iter() + .map(|mode| (mode.subject_id.clone(), mode.parameters.clone())) + .collect() + }; + + let SaemState { + equation, + data, + config, + negative_log_likelihood: final_negative_log_likelihood, + initialization, + cycle, + status, + population_parameters, + omega, + omega_iov, + residual_sigmas, + cycle_diagnostics, + operational_diagnostics, + covariate_model, + .. + } = *self; + + Ok(ParametricResult { + equation, + data, + config, + effective_n_chains: initialization.n_chains, + objective_function: 2.0 * final_negative_log_likelihood, + converged: status.converged(), + termination_reason: status.stop_reason().cloned(), + iterations: cycle, + subject_count: initialization.subject_ids.len(), + observation_count: initialization.observation_count, + parameter_names: initialization.parameter_names, + parameter_scales: initialization.parameter_scales, + estimated_parameters: initialization.estimated_parameters, + population_initial: initialization.initial_population_parameters.clone(), + population_estimates: population_parameters, + random_effect_indices: initialization.random_effect_indices, + random_effect_names: initialization.random_effect_names, + omega, + omega_structural_mask, + omega_estimated_mask, + omega_initial: initialization.omega.initial().clone(), + iov_effect_indices: initialization.iov_effect_indices, + iov_effect_names: initialization.iov_effect_names, + omega_iov, + omega_iov_structural_mask, + omega_iov_estimated_mask, + omega_iov_initial: initialization + .omega_iov + .as_ref() + .map(|omega| omega.initial().clone()), + residual_sigmas, + residual_error_estimates, + residual_initial_values: initialization.initial_residual_values.clone(), + residual_initial_estimated: initialization.initial_residual_estimated.clone(), + eta_chain_means, + kappa_chain_means, + conditional_modes, + shrinkage, + cycle_diagnostics, + warnings, + information_diagnostics, + population_uncertainty, + markov_simulation_variance, + operational_diagnostics, + marginal_likelihood, + information_criteria, + estimator_metadata, + individual_estimates, + covariate_model, + }) + } +} diff --git a/src/algorithms/parametric/saem/state/support.rs b/src/algorithms/parametric/saem/state/support.rs new file mode 100644 index 000000000..d0c411428 --- /dev/null +++ b/src/algorithms/parametric/saem/state/support.rs @@ -0,0 +1,1118 @@ +use super::*; + +pub(super) fn matrix_from_rows(values: &[Vec], width: usize) -> Result> { + if values.len() != width || values.iter().any(|row| row.len() != width) { + anyhow::bail!("matrix coordinate width mismatch"); + } + Ok(Array2::from_shape_vec( + (width, width), + values.iter().flatten().copied().collect(), + )?) +} + +pub(super) fn normal_two_sided_z(p: f64) -> f64 { + use statrs::distribution::{ContinuousCDF, Normal}; + let norm = Normal::new(0.0, 1.0).expect("standard normal parameters are valid"); + let one_sided = p + (1.0 - p) / 2.0; + norm.inverse_cdf(one_sided) +} + +/// Evaluate one operational convergence criterion. +pub(super) fn evaluate_criterion( + name: &str, + observed: Option, + threshold: f64, + predicate: impl FnOnce(f64) -> bool, +) -> OperationalConvergenceCriterion { + let status = match observed { + Some(value) if value.is_finite() && predicate(value) => { + OperationalConvergenceCriterionStatus::Satisfied + } + Some(value) if value.is_finite() => OperationalConvergenceCriterionStatus::NotSatisfied, + Some(_) => OperationalConvergenceCriterionStatus::Unavailable( + "observed value is non-finite".to_string(), + ), + None => OperationalConvergenceCriterionStatus::Unavailable( + "criterion could not be evaluated".to_string(), + ), + }; + OperationalConvergenceCriterion { + name: name.to_string(), + observed, + threshold, + status, + } +} + +pub(super) fn operational_free_coordinates( + information: &InformationDiagnostics, + average: &SaemIterateAverage, +) -> Result> { + information + .coordinates + .iter() + .map(|coordinate| match &coordinate.kind { + InformationCoordinateKind::Population { parameter_index } => average + .population_phi + .get(*parameter_index) + .copied() + .ok_or_else(|| anyhow::anyhow!("population coordinate out of range")), + InformationCoordinateKind::CovariateEffect { effect_index } => average + .covariate_betas + .as_ref() + .and_then(|values| values.get(*effect_index)) + .copied() + .ok_or_else(|| anyhow::anyhow!("covariate coordinate out of range")), + InformationCoordinateKind::Omega { row, column } => average + .omega + .get((*row, *column)) + .copied() + .ok_or_else(|| anyhow::anyhow!("Omega coordinate out of range")), + InformationCoordinateKind::OmegaIov { row, column } => average + .omega_iov + .as_ref() + .and_then(|matrix| matrix.get((*row, *column))) + .copied() + .ok_or_else(|| anyhow::anyhow!("Omega_IOV coordinate out of range")), + InformationCoordinateKind::Residual { + output_index, + component, + } => { + let model = average + .residual_models + .iter() + .find(|(index, _)| index == output_index) + .map(|(_, model)| model) + .ok_or_else(|| anyhow::anyhow!("residual coordinate output unavailable"))?; + match (model, component.as_str()) { + (ResidualErrorModel::Constant { a }, "sigma") => Ok(*a), + (ResidualErrorModel::Exponential { sigma }, "sigma") => Ok(*sigma), + (ResidualErrorModel::Proportional { b }, "proportional") => Ok(*b), + (ResidualErrorModel::Combined { a, .. }, "additive") + | (ResidualErrorModel::CorrelatedCombined { a, .. }, "additive") => Ok(*a), + (ResidualErrorModel::Combined { b, .. }, "proportional") + | (ResidualErrorModel::CorrelatedCombined { b, .. }, "proportional") => Ok(*b), + (ResidualErrorModel::CorrelatedCombined { rho, .. }, "correlation") => Ok(*rho), + _ => anyhow::bail!("residual coordinate component mismatch"), + } + } + }) + .collect() +} + +pub(super) fn operational_simulation_sd_fraction( + information: &InformationDiagnostics, + markov: &MarkovSimulationVarianceDiagnostics, +) -> Option { + let width = information.coordinates.len(); + let observed = matrix_from_rows(&information.observed_information, width).ok()?; + let covariance = matrix_from_rows(&markov.simulation_covariance, width).ok()?; + worst_contrast(&observed, &covariance).ok() +} + +pub(super) fn solve_spd(matrix: &Array2, rhs: &[f64]) -> Option> { + if matrix.nrows() != matrix.ncols() || matrix.nrows() != rhs.len() { + return None; + } + let lower = cholesky_lower(matrix).ok()?; + let n = rhs.len(); + let mut y = vec![0.0; n]; + for row in 0..n { + let subtotal = (0..row) + .map(|column| lower[row][column] * y[column]) + .sum::(); + y[row] = (rhs[row] - subtotal) / lower[row][row]; + } + let mut result = vec![0.0; n]; + for row in (0..n).rev() { + let subtotal = ((row + 1)..n) + .map(|column| lower[column][row] * result[column]) + .sum::(); + result[row] = (y[row] - subtotal) / lower[row][row]; + } + result + .iter() + .all(|value| value.is_finite()) + .then_some(result) +} + +/// Invariant Newton displacement `sqrt(g^T Iobs^-1 g)`. +pub(super) fn newton_displacement( + info: &InformationDiagnostics, + markov: &MarkovSimulationVarianceDiagnostics, +) -> Option { + let width = info.coordinates.len(); + if width == 0 || markov.grand_score_mean.len() != width { + return None; + } + let observed = matrix_from_rows(&info.observed_information, width).ok()?; + let displacement = solve_spd(&observed, &markov.grand_score_mean)?; + let squared = markov + .grand_score_mean + .iter() + .zip(&displacement) + .map(|(score, step)| score * step) + .sum::(); + (squared.is_finite() && squared >= 0.0).then(|| squared.sqrt()) +} + +/// Worst-direction Newton-step MC SD from diagnostic-mean LRV/draws. +pub(super) fn newton_displacement_mc_sd( + info: &InformationDiagnostics, + markov: &MarkovSimulationVarianceDiagnostics, +) -> Option { + let width = info.coordinates.len(); + let draws = markov.config?.draws_per_chain; + if width == 0 || draws == 0 { + return None; + } + let observed = matrix_from_rows(&info.observed_information, width).ok()?; + let mut score_covariance = + matrix_from_rows(markov.rank_diagnostics.diagnostic_mean_lrv.as_ref()?, width).ok()?; + score_covariance /= draws as f64; + let mut inverse = Array2::zeros((width, width)); + for column in 0..width { + let mut unit = vec![0.0; width]; + unit[column] = 1.0; + let solved = solve_spd(&observed, &unit)?; + for row in 0..width { + inverse[[row, column]] = solved[row]; + } + } + let mut mapped = Array2::zeros((width, width)); + for row in 0..width { + for column in 0..=row { + let mut value = 0.0; + for left in 0..width { + for right in 0..width { + value += inverse[[row, left]] + * score_covariance[[left, right]] + * inverse[[column, right]]; + } + } + mapped[[row, column]] = value; + mapped[[column, row]] = value; + } + } + worst_contrast(&observed, &mapped).ok() +} + +pub(super) fn incremental_average(previous: f64, current: f64, count: usize) -> f64 { + previous + (current - previous) / count as f64 +} + +pub(super) fn average_covariance( + average: &mut Array2, + current: &Array2, + estimated_mask: &Array2, + count: usize, +) { + for row in 0..average.nrows() { + for col in 0..=row { + if estimated_mask[[row, col]] { + let value = incremental_average(average[[row, col]], current[[row, col]], count); + average[[row, col]] = value; + average[[col, row]] = value; + } + } + } +} + +pub(super) fn average_residual_model( + previous: ResidualErrorModel, + current: ResidualErrorModel, + estimated: bool, + components: [bool; 2], + correlated_components: [bool; 3], + count: usize, +) -> Result { + let averaged = match (previous, current) { + (ResidualErrorModel::Constant { a }, ResidualErrorModel::Constant { a: current }) => { + ResidualErrorModel::Constant { + a: if estimated { + incremental_average(a, current, count) + } else { + a + }, + } + } + ( + ResidualErrorModel::Proportional { b }, + ResidualErrorModel::Proportional { b: current }, + ) => ResidualErrorModel::Proportional { + b: if estimated { + incremental_average(b, current, count) + } else { + b + }, + }, + ( + ResidualErrorModel::Exponential { sigma }, + ResidualErrorModel::Exponential { sigma: current }, + ) => ResidualErrorModel::Exponential { + sigma: if estimated { + incremental_average(sigma, current, count) + } else { + sigma + }, + }, + ( + ResidualErrorModel::Combined { a, b }, + ResidualErrorModel::Combined { + a: current_a, + b: current_b, + }, + ) => ResidualErrorModel::Combined { + a: if components[0] { + incremental_average(a, current_a, count) + } else { + a + }, + b: if components[1] { + incremental_average(b, current_b, count) + } else { + b + }, + }, + ( + ResidualErrorModel::CorrelatedCombined { a, b, rho }, + ResidualErrorModel::CorrelatedCombined { + a: current_a, + b: current_b, + rho: current_rho, + }, + ) => ResidualErrorModel::CorrelatedCombined { + a: if correlated_components[0] { + incremental_average(a, current_a, count) + } else { + a + }, + b: if correlated_components[1] { + incremental_average(b, current_b, count) + } else { + b + }, + rho: if correlated_components[2] { + incremental_average(rho, current_rho, count) + } else { + rho + }, + }, + _ => anyhow::bail!("residual family changed while accumulating SAEM averages"), + }; + Ok(averaged) +} + +pub(super) fn validate_average_population( + values: &[f64], + initialization: &SaemInitialization, +) -> Result<()> { + let initial = population_phi( + &initialization.initial_population_parameters, + &initialization.parameter_scales, + )?; + if values.len() != initial.len() || values.iter().any(|value| !value.is_finite()) { + anyhow::bail!("averaged population phi values must be finite and retain their width"); + } + for index in 0..values.len() { + if !initialization.estimated_parameters[index] && values[index] != initial[index] { + anyhow::bail!("averaged population phi changed fixed coordinate {index}"); + } + } + Ok(()) +} + +pub(super) fn validate_average_covariance( + matrix: &Array2, + specification: &ResolvedOmega, + label: &str, +) -> Result<()> { + if matrix.raw_dim() != specification.initial().raw_dim() { + anyhow::bail!("averaged {label} has an invalid shape"); + } + for row in 0..matrix.nrows() { + for col in 0..matrix.ncols() { + let value = matrix[[row, col]]; + if !value.is_finite() || value != matrix[[col, row]] { + anyhow::bail!("averaged {label} must be finite and symmetric"); + } + if !specification.structural_mask()[[row, col]] && value != 0.0 { + anyhow::bail!("averaged {label} changed a structural zero"); + } + if !specification.estimated_mask()[[row, col]] + && value != specification.initial()[[row, col]] + { + anyhow::bail!("averaged {label} changed a fixed entry"); + } + } + } + cholesky_lower(matrix) + .map(|_| ()) + .map_err(|error| anyhow::anyhow!("averaged {label} is not positive definite: {error}")) +} + +pub(super) fn validate_average_residuals( + original_width: usize, + models: &[(usize, ResidualErrorModel)], + declarations: &ParametricErrorModels, +) -> Result<()> { + if original_width != declarations.models().len() + || models.len() != declarations.models().iter().count() + { + anyhow::bail!("averaged residual output collection changed"); + } + for ((output, model), (declared_output, terminal)) in models.iter().copied().zip( + declarations + .models() + .iter() + .map(|(index, model)| (index, *model)), + ) { + if output != declared_output || output >= original_width { + anyhow::bail!("averaged residual output indices changed"); + } + let output_name = declarations + .output_name(output) + .ok_or_else(|| anyhow::anyhow!("averaged residual output {output} has no name"))?; + let components = declarations.combined_component_estimated(output); + if !declarations.is_estimated(output) && model != terminal { + anyhow::bail!( + "averaged residual model changed fixed output '{output_name}' at index {output}" + ); + } + if let ( + ResidualErrorModel::Combined { a, b }, + ResidualErrorModel::Combined { + a: terminal_a, + b: terminal_b, + }, + ) = (model, terminal) + { + if (!components[0] && a != terminal_a) || (!components[1] && b != terminal_b) { + anyhow::bail!( + "averaged residual model changed a fixed component for output '{output_name}' at index {output}" + ); + } + } + let correlated_components = declarations.correlated_combined_component_estimated(output); + if let ( + ResidualErrorModel::CorrelatedCombined { a, b, rho }, + ResidualErrorModel::CorrelatedCombined { + a: terminal_a, + b: terminal_b, + rho: terminal_rho, + }, + ) = (model, terminal) + { + if (!correlated_components[0] && a != terminal_a) + || (!correlated_components[1] && b != terminal_b) + || (!correlated_components[2] && rho != terminal_rho) + { + anyhow::bail!( + "averaged correlated-combined model changed a fixed component for output '{output_name}' at index {output}" + ); + } + } + let valid = match model { + ResidualErrorModel::Constant { a } => a.is_finite() && a > 0.0, + ResidualErrorModel::Proportional { b } => b.is_finite() && b > 0.0, + ResidualErrorModel::Exponential { sigma } => sigma.is_finite() && sigma > 0.0, + ResidualErrorModel::Combined { a, b } => { + a.is_finite() + && b.is_finite() + && a >= 0.0 + && b >= 0.0 + && (!components[0] || a > 0.0) + && (!components[1] || b > 0.0) + } + ResidualErrorModel::CorrelatedCombined { a, b, rho } => { + a.is_finite() + && a > 0.0 + && b.is_finite() + && b > 0.0 + && rho.is_finite() + && rho > -1.0 + && rho < 1.0 + } + }; + if !valid { + anyhow::bail!( + "averaged residual model for output '{output_name}' at index {output} is outside its domain" + ); + } + } + Ok(()) +} + +#[derive(Debug, Default)] +struct WarningCount { + first_iteration: Option, + cycles: usize, + count: usize, +} + +impl WarningCount { + fn record_cycle(&mut self, iteration: usize) { + self.first_iteration.get_or_insert(iteration); + self.cycles += 1; + } + + fn record_count(&mut self, iteration: usize, count: usize) { + if count == 0 { + return; + } + self.first_iteration.get_or_insert(iteration); + self.count += count; + } +} + +#[derive(Debug, Default, PartialEq, Eq)] +pub(super) struct CovarianceBoundaryRejectionSummary { + pub(super) first_iteration: Option, + pub(super) longest_run: usize, +} + +pub(super) fn covariance_boundary_rejection_summary( + cycles: &[SaemCycleDiagnostics], + policy: CovarianceStabilityConfig, + iov: bool, +) -> CovarianceBoundaryRejectionSummary { + let mut summary = CovarianceBoundaryRejectionSummary::default(); + let mut current_run = 0usize; + let mut current_start = None; + for cycle in cycles { + let (rejected, margin) = if iov { + ( + cycle.omega_iov_update_rejected, + cycle.omega_iov_relative_spd_margin, + ) + } else { + (cycle.omega_update_rejected, cycle.omega_relative_spd_margin) + }; + if rejected && margin.is_some_and(|value| value <= policy.minimum_relative_spd_margin) { + if current_run == 0 { + current_start = Some(cycle.iteration); + } + current_run += 1; + summary.longest_run = summary.longest_run.max(current_run); + if current_run >= policy.rejection_window && summary.first_iteration.is_none() { + summary.first_iteration = current_start; + } + } else { + current_run = 0; + current_start = None; + } + } + summary +} + +pub(super) fn parametric_warnings( + cycles: &[SaemCycleDiagnostics], + covariance_stability: Option, +) -> Vec { + let mut omega = WarningCount::default(); + let mut omega_iov = WarningCount::default(); + let mut eta_non_finite = WarningCount::default(); + let mut eta_block_non_finite = WarningCount::default(); + let mut kappa_non_finite = WarningCount::default(); + let mut residual_rejected = BTreeMap::::new(); + let mut proportional_floor = BTreeMap::::new(); + let mut residual_non_finite = BTreeMap::::new(); + let mut exponential_domain = BTreeMap::::new(); + let mut additive_collapse = BTreeMap::::new(); + let mut optimizer_not_converged = BTreeMap::::new(); + + for cycle in cycles { + if cycle.omega_update_rejected { + omega.record_cycle(cycle.iteration); + } + if cycle.omega_iov_update_rejected { + omega_iov.record_cycle(cycle.iteration); + } + eta_non_finite.record_count(cycle.iteration, cycle.eta_non_finite); + eta_block_non_finite.record_count(cycle.iteration, cycle.eta_block_non_finite); + kappa_non_finite.record_count(cycle.iteration, cycle.kappa_non_finite); + for residual in &cycle.residual_diagnostics { + if residual.update_rejected { + residual_rejected + .entry(residual.output.clone()) + .or_default() + .record_cycle(cycle.iteration); + } + proportional_floor + .entry(residual.output.clone()) + .or_default() + .record_count(cycle.iteration, residual.proportional_floor_count); + residual_non_finite + .entry(residual.output.clone()) + .or_default() + .record_count(cycle.iteration, residual.non_finite_prediction_count); + exponential_domain + .entry(residual.output.clone()) + .or_default() + .record_count(cycle.iteration, residual.exponential_domain_violation_count); + if residual.combined_additive_collapse_warning { + additive_collapse + .entry(residual.output.clone()) + .or_default() + .record_cycle(cycle.iteration); + } + if residual.optimizer_converged == Some(false) { + optimizer_not_converged + .entry(residual.output.clone()) + .or_default() + .record_cycle(cycle.iteration); + } + } + } + + let mut warnings = Vec::new(); + if let Some(first_iteration) = omega.first_iteration { + warnings.push(ParametricWarning::OmegaUpdateRejected { + first_iteration, + cycles: omega.cycles, + }); + } + if let Some(first_iteration) = omega_iov.first_iteration { + warnings.push(ParametricWarning::OmegaIovUpdateRejected { + first_iteration, + cycles: omega_iov.cycles, + }); + } + if let Some(policy) = covariance_stability { + let omega_boundary = covariance_boundary_rejection_summary(cycles, policy, false); + if let Some(first_iteration) = omega_boundary.first_iteration { + warnings.push(ParametricWarning::OmegaBoundaryRejection { + first_iteration, + longest_run: omega_boundary.longest_run, + }); + } + let omega_iov_boundary = covariance_boundary_rejection_summary(cycles, policy, true); + if let Some(first_iteration) = omega_iov_boundary.first_iteration { + warnings.push(ParametricWarning::OmegaIovBoundaryRejection { + first_iteration, + longest_run: omega_iov_boundary.longest_run, + }); + } + } + if let Some(first_iteration) = eta_non_finite.first_iteration { + warnings.push(ParametricWarning::EtaNonFiniteProposals { + first_iteration, + count: eta_non_finite.count, + }); + } + if let Some(first_iteration) = eta_block_non_finite.first_iteration { + warnings.push(ParametricWarning::EtaBlockNonFiniteProposals { + first_iteration, + count: eta_block_non_finite.count, + }); + } + if let Some(first_iteration) = kappa_non_finite.first_iteration { + warnings.push(ParametricWarning::KappaNonFiniteProposals { + first_iteration, + count: kappa_non_finite.count, + }); + } + for (output, warning) in residual_rejected { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ResidualUpdateRejected { + output, + first_iteration, + cycles: warning.cycles, + }); + } + } + for (output, warning) in proportional_floor { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ProportionalPredictionFloor { + output, + first_iteration, + count: warning.count, + }); + } + } + for (output, warning) in residual_non_finite { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::NonFiniteResidualPrediction { + output, + first_iteration, + count: warning.count, + }); + } + } + for (output, warning) in exponential_domain { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ExponentialDomainViolation { + output, + first_iteration, + count: warning.count, + }); + } + } + for (output, warning) in additive_collapse { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::CombinedAdditiveCollapse { + output, + first_iteration, + cycles: warning.cycles, + }); + } + } + for (output, warning) in optimizer_not_converged { + if let Some(first_iteration) = warning.first_iteration { + warnings.push(ParametricWarning::ResidualOptimizerNotConverged { + output, + first_iteration, + cycles: warning.cycles, + }); + } + } + warnings +} + +pub(super) fn calculate_result_marginal_likelihood( + state: &SaemState, + conditional_modes: &[SubjectConditionalMode], + conditional_mode_error: Option<&str>, +) -> Option { + let config = state.config.marginal_likelihood?; + let n_eta = state.initialization.random_effect_indices.len(); + let n_kappa = state.initialization.iov_effect_indices.len(); + let latent = n_eta > 0 || n_kappa > 0; + let occasion_indices = state + .data + .subjects() + .iter() + .map(|subject| { + if n_kappa == 0 { + Vec::new() + } else { + subject + .occasions() + .iter() + .map(|occasion| occasion.index()) + .collect() + } + }) + .collect::>>(); + let mut flattened_modes = Vec::with_capacity(state.initialization.subject_ids.len()); + let mut converged = Vec::with_capacity(state.initialization.subject_ids.len()); + let mut validation_failures = Vec::with_capacity(state.initialization.subject_ids.len()); + + for (subject_index, subject_id) in state.initialization.subject_ids.iter().enumerate() { + if !latent { + flattened_modes.push(Vec::new()); + converged.push(None); + validation_failures.push(None); + continue; + } + let Some(mode) = conditional_modes.get(subject_index) else { + flattened_modes.push(Vec::new()); + converged.push(None); + validation_failures.push(Some( + MarginalLikelihoodFailureReason::MissingConditionalMode, + )); + continue; + }; + let mut validation_failure = None; + if mode.subject_id != *subject_id { + validation_failure.get_or_insert(MarginalLikelihoodFailureReason::SubjectIdMismatch { + expected: subject_id.clone(), + actual: mode.subject_id.clone(), + }); + } + if mode.eta.len() != n_eta { + validation_failure.get_or_insert(MarginalLikelihoodFailureReason::EtaWidthMismatch { + expected: n_eta, + actual: mode.eta.len(), + }); + } + if mode.kappas.len() != occasion_indices[subject_index].len() { + validation_failure.get_or_insert(MarginalLikelihoodFailureReason::KappaCountMismatch { + expected: occasion_indices[subject_index].len(), + actual: mode.kappas.len(), + }); + } + for (position, kappa) in mode.kappas.iter().enumerate() { + if let Some(expected) = occasion_indices[subject_index].get(position) { + if kappa.occasion_index != *expected { + validation_failure.get_or_insert( + MarginalLikelihoodFailureReason::KappaOccasionMismatch { + position, + expected: *expected, + actual: kappa.occasion_index, + }, + ); + } + } + if kappa.values.len() != n_kappa { + validation_failure.get_or_insert( + MarginalLikelihoodFailureReason::KappaWidthMismatch { + position, + expected: n_kappa, + actual: kappa.values.len(), + }, + ); + } + } + let mut flattened = mode.eta.clone(); + for kappa in &mode.kappas { + flattened.extend_from_slice(&kappa.values); + } + if flattened.iter().any(|value| !value.is_finite()) { + validation_failure + .get_or_insert(MarginalLikelihoodFailureReason::NonFiniteModeCoordinate); + } + flattened_modes.push(flattened); + converged.push(Some(mode.converged)); + validation_failures.push(validation_failure); + } + + let curvature_covariances = conditional_modes + .iter() + .map(|mode| { + mode.uncertainty + .latent_covariance + .as_ref() + .and_then(|rows| matrix_from_rows(rows, rows.len()).ok()) + }) + .collect::>(); + let subjects = state + .initialization + .subject_ids + .iter() + .enumerate() + .map(|(index, subject_id)| MarginalSubject { + subject_id, + occasion_indices: &occasion_indices[index], + mode: &flattened_modes[index], + mode_converged: converged[index], + eta_dimension: n_eta, + kappa_dimension: n_kappa, + validation_failure: validation_failures[index].clone(), + curvature_availability: conditional_modes + .get(index) + .map(|mode| &mode.uncertainty.status), + curvature_covariance: curvature_covariances.get(index).and_then(Option::as_ref), + }) + .collect::>(); + if let Some(error) = conditional_mode_error { + return Some(unavailable_population_marginal_likelihood( + config, + &subjects, + MarginalLikelihoodFailureReason::ConditionalModeCalculationFailed(format!( + "global conditional mode calculation failed: {error}" + )), + )); + } + Some(calculate_population_marginal_likelihood( + config, + &subjects, + &state.omega, + state.omega_iov.as_ref(), + |subject_index, eta, kappas| { + state + .score_subject_latents(subject_index, eta, kappas) + .map(SubjectPosteriorScore::log_posterior) + }, + )) +} + +pub(super) fn conditional_modes( + state: &SaemState, +) -> Result> { + if !state.compute_map { + return Ok(Vec::new()); + } + + let n_eta = state.initialization.random_effect_indices.len(); + let n_kappa = state.initialization.iov_effect_indices.len(); + if n_eta == 0 && n_kappa == 0 { + return Ok(Vec::new()); + } + let mut modes = Vec::with_capacity(state.initialization.subject_ids.len()); + for (subject_index, subject_id) in state.initialization.subject_ids.iter().enumerate() { + let eta_start = mean_vectors(state.etas[subject_index].iter().map(|eta| eta.as_slice()))?; + let occasion_count = if state.omega_iov.is_some() { + state.data.subjects()[subject_index].occasions().len() + } else { + 0 + }; + let mut kappa_start = Vec::with_capacity(occasion_count); + for occasion_position in 0..occasion_count { + kappa_start.push(mean_vectors( + state.kappas[subject_index] + .iter() + .map(|chain| chain[occasion_position].as_slice()), + )?); + } + let mut initial = eta_start; + for kappa in &kappa_start { + initial.extend_from_slice(kappa); + } + + let step_fraction = state.map_initial_step; + let mut scales = (0..n_eta) + .map(|index| state.omega[[index, index]].sqrt() * step_fraction) + .collect::>(); + if let Some(omega_iov) = state.omega_iov.as_ref() { + for _ in 0..occasion_count { + scales.extend( + (0..n_kappa).map(|index| omega_iov[[index, index]].sqrt() * step_fraction), + ); + } + } + for scale in &mut scales { + *scale = scale.max(1e-6); + } + + let solution = optimize_conditional_mode( + initial, + &scales, + state.map_max_iterations as u64, + state.map_sd_tolerance, + |coordinates| { + let (eta, kappas) = unflatten_latents(coordinates, n_eta, occasion_count, n_kappa); + match state.score_subject_latents(subject_index, eta, &kappas) { + Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), + _ => f64::INFINITY, + } + }, + )?; + let mut coordinates = (0..n_eta) + .map(|index| JointLatentCoordinate { + index, + name: format!("eta:{}", state.initialization.random_effect_names[index]), + kind: JointLatentCoordinateKind::Eta { + parameter_index: state.initialization.random_effect_indices[index], + }, + prior_sd: state.omega[[index, index]].sqrt(), + }) + .collect::>(); + if let Some(omega_iov) = state.omega_iov.as_ref() { + for occasion_position in 0..occasion_count { + let occasion_index = + state.data.subjects()[subject_index].occasions()[occasion_position].index(); + for effect_index in 0..n_kappa { + coordinates.push(JointLatentCoordinate { + index: n_eta + occasion_position * n_kappa + effect_index, + name: format!( + "kappa:{occasion_index}:{}", + state.initialization.iov_effect_names[effect_index] + ), + kind: JointLatentCoordinateKind::Kappa { + occasion_index, + effect_index, + parameter_index: state.initialization.iov_effect_indices[effect_index], + }, + prior_sd: omega_iov[[effect_index, effect_index]].sqrt(), + }); + } + } + } + let prior_sds = coordinates + .iter() + .map(|coordinate| coordinate.prior_sd) + .collect::>(); + let mode_metadata = ConditionalModeMetadata { + converged: solution.converged, + iterations: solution.iterations, + objective_value: solution.objective, + termination_message: solution.termination.clone(), + }; + let uncertainty = conditional_mode_curvature( + &solution.coordinates, + &prior_sds, + &coordinates, + &mode_metadata, + |coordinates| { + let (eta, kappas) = unflatten_latents(coordinates, n_eta, occasion_count, n_kappa); + match state.score_subject_latents(subject_index, eta, &kappas) { + Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), + _ => f64::INFINITY, + } + }, + ); + let (eta, kappas) = + unflatten_latents(&solution.coordinates, n_eta, occasion_count, n_kappa); + let parameters = state.individual_parameters_from_eta(subject_index, eta)?; + let kappa_estimates = kappas + .into_iter() + .enumerate() + .map(|(occasion_position, values)| OccasionKappaEstimate { + subject_id: subject_id.clone(), + occasion_index: state.data.subjects()[subject_index].occasions()[occasion_position] + .index(), + values, + }) + .collect(); + modes.push(SubjectConditionalMode { + subject_id: subject_id.clone(), + eta: eta.to_vec(), + kappas: kappa_estimates, + parameters, + objective: solution.objective, + converged: solution.converged, + iterations: solution.iterations, + termination: solution.termination, + uncertainty, + }); + } + Ok(modes) +} + +pub(super) fn unflatten_latents( + coordinates: &[f64], + n_eta: usize, + occasion_count: usize, + n_kappa: usize, +) -> (&[f64], Vec>) { + let eta = &coordinates[..n_eta]; + let kappas = (0..occasion_count) + .map(|occasion| { + let start = n_eta + occasion * n_kappa; + coordinates[start..start + n_kappa].to_vec() + }) + .collect(); + (eta, kappas) +} + +pub(super) fn mean_vectors<'a>(vectors: impl IntoIterator) -> Result> { + let mut vectors = vectors.into_iter(); + let Some(first) = vectors.next() else { + anyhow::bail!("cannot summarize random effects without chains"); + }; + let mut mean = first.to_vec(); + let mut count = 1usize; + for vector in vectors { + if vector.len() != mean.len() { + anyhow::bail!("random-effect chains have inconsistent dimensions"); + } + for (sum, value) in mean.iter_mut().zip(vector) { + *sum += value; + } + count += 1; + } + for value in &mut mean { + *value /= count as f64; + } + Ok(mean) +} + +pub(super) fn zero_etas( + n_subjects: usize, + n_chains: usize, + n_parameters: usize, +) -> Vec>> { + vec![vec![vec![0.0; n_parameters]; n_chains]; n_subjects] +} + +pub(super) fn zero_kappas( + occasion_counts: &[usize], + n_chains: usize, + n_kappa: usize, +) -> Vec>>> { + occasion_counts + .iter() + .map(|&n_occasions| vec![vec![vec![0.0; n_kappa]; n_occasions]; n_chains]) + .collect() +} + +pub(super) fn second_moment_from_etas(etas: &[Vec>]) -> Result> { + let mut samples = etas.iter().flat_map(|subject_chains| subject_chains.iter()); + let Some(first) = samples.next() else { + anyhow::bail!("cannot update omega without subject-chain samples"); + }; + let dimension = first.len(); + let mut second_moment = Array2::zeros((dimension, dimension)); + let mut count = 0usize; + for eta in std::iter::once(first).chain(samples) { + if eta.len() != dimension { + anyhow::bail!("eta samples have inconsistent dimensions"); + } + for row in 0..dimension { + for col in 0..dimension { + second_moment[[row, col]] += eta[row] * eta[col]; + } + } + count += 1; + } + second_moment.mapv_inplace(|value| value / count as f64); + Ok(second_moment) +} + +pub(super) fn covariance_from_kappas(kappas: &[Vec>>]) -> Result> { + let mut samples = kappas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .flat_map(|chains| chains.iter()); + let Some(first) = samples.next() else { + anyhow::bail!("cannot update omega_iov without occasion samples"); + }; + let dimension = first.len(); + let mut covariance = Array2::zeros((dimension, dimension)); + let mut count = 0usize; + for kappa in std::iter::once(first).chain(samples) { + if kappa.len() != dimension { + anyhow::bail!("kappa samples have inconsistent dimensions"); + } + for row in 0..dimension { + for col in 0..dimension { + covariance[[row, col]] += kappa[row] * kappa[col]; + } + } + count += 1; + } + covariance.mapv_inplace(|value| value / count as f64); + Ok(covariance) +} + +pub(super) fn correlated_random_walk( + current: &[f64], + lower: &[Vec], + standard_normals: &[f64], + scale: f64, +) -> Result> { + anyhow::ensure!( + lower.len() == current.len() + && standard_normals.len() == current.len() + && lower + .iter() + .enumerate() + .all(|(row, values)| values.len() > row), + "correlated random-walk dimensions do not match" + ); + Ok((0..current.len()) + .map(|row| { + let perturbation = (0..=row) + .map(|column| lower[row][column] * standard_normals[column]) + .sum::(); + current[row] + scale * perturbation + }) + .collect()) +} + +pub(super) fn initial_proposal_step_sizes(omega: &Array2, rw_init: f64) -> Vec { + (0..omega.nrows()) + .map(|index| omega[[index, index]].max(f64::EPSILON).sqrt() * rw_init) + .collect() +} + +pub(super) fn adapt_component_step_size(current: f64, acceptance_rate: f64) -> f64 { + adapt_block_step_size(current, acceptance_rate, COMPONENT_TARGET_ACCEPTANCE) +} + +pub(super) fn adapt_block_step_size(current: f64, acceptance_rate: f64, target: f64) -> f64 { + if acceptance_rate > target { + (current * PROPOSAL_SCALE_INCREASE).min(MAX_PROPOSAL_SCALE) + } else { + (current * PROPOSAL_SCALE_DECREASE).max(MIN_PROPOSAL_SCALE) + } +} + +pub(super) fn zero_eta_subject_phi( + population_parameters: &[f64], + initialization: &SaemInitialization, +) -> Result>> { + let phi = population_phi(population_parameters, &initialization.parameter_scales)?; + Ok(vec![phi; initialization.subject_ids.len()]) +} diff --git a/src/algorithms/parametric/saem/state/tests/controller.rs b/src/algorithms/parametric/saem/state/tests/controller.rs new file mode 100644 index 000000000..9079d029c --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/controller.rs @@ -0,0 +1,582 @@ +use super::*; +#[test] +fn parametric_fit_controller_steps_like_nonparametric_controller() { + let config = SaemConfig::new() + .k1_iterations(2) + .k2_iterations(1) + .burn_in(1); + let mut controller = problem().fit_controller(config).unwrap(); + + assert_eq!(controller.cycle(), 0); + assert!(controller.status().is_continue()); + assert!(controller.likelihood().is_finite()); + assert_eq!(controller.population_parameters(), &[0.2, 10.0]); + assert_eq!(controller.random_effect_names(), &["ke", "v"]); + assert_eq!(controller.iov_effect_names(), None); + assert_eq!(controller.omega_iov(), None); + assert_eq!(controller.residual_sigmas(), &[0.5]); + assert_eq!(controller.acceptance_rate(), None); + assert_eq!(controller.kappa_acceptance_rate(), None); + assert_eq!(controller.rejected_proposals(), None); + assert_eq!(controller.non_finite_proposals(), None); + assert_eq!(controller.parameter_acceptance_rates(), None); + assert_eq!( + controller.proposal_step_sizes(), + Some([0.5, 0.5].as_slice()) + ); + assert!(controller.eta_log_prior().is_finite()); + assert_eq!( + controller.log_posterior(), + controller.likelihood() + controller.eta_log_prior() + ); + assert!(controller.negative_log_likelihood().is_finite()); + assert_eq!( + controller.negative_log_likelihood(), + -controller.likelihood() + ); + assert!(controller.n2ll().is_finite()); + assert_eq!(controller.n_chains(), Some(25)); + assert_eq!( + controller.omega(), + Some(&ndarray::array![[1.0, 0.0], [0.0, 1.0]]) + ); + assert_eq!(controller.omega_diagonal(), Some(vec![1.0, 1.0])); + assert_eq!( + controller.log_acceptance_ratios(), + Some([0.0, 0.0].as_slice()) + ); + assert_eq!(controller.total_iterations(), 3); + assert_eq!(controller.step_size(), 0.0); + + assert!(controller.step().unwrap().is_continue()); + assert_eq!(controller.cycle(), 1); + assert_eq!(controller.step_size(), 0.0); + assert_eq!(controller.population_parameters(), &[0.2, 10.0]); + assert_eq!( + controller.omega(), + Some(&ndarray::array![[1.0, 0.0], [0.0, 1.0]]) + ); + assert!(controller.acceptance_rate().is_some()); + assert_eq!(controller.kappa_acceptance_rate(), None); + assert!(controller.rejected_proposals().is_some()); + assert_eq!(controller.non_finite_proposals(), Some(0)); + let parameter_acceptance_rates = controller.parameter_acceptance_rates().unwrap(); + assert_eq!(parameter_acceptance_rates.len(), 2); + assert!(parameter_acceptance_rates + .iter() + .all(|rate| (0.0..=1.0).contains(rate))); + assert!(controller.step().unwrap().is_continue()); + assert_eq!(controller.cycle(), 2); + assert_eq!(controller.step_size(), 1.0); + assert!(controller.step().unwrap().is_stop()); + assert_eq!(controller.cycle(), 3); +} + +#[test] +fn aborted_controller_preserves_typed_termination_reason() { + let mut controller = problem() + .fit_controller(SaemConfig::new().compute_map(false)) + .unwrap(); + controller.step().unwrap(); + controller.request_stop(); + + let result = controller.into_result().unwrap(); + + assert!(!result.converged()); + assert_eq!(result.termination_reason(), Some(&StopReason::Aborted)); + assert_ne!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert_ne!( + result.termination_reason(), + Some(&StopReason::NumericalFailure) + ); + assert_eq!(result.iterations(), 1); +} + +#[test] +fn expectation_numerical_failure_stops_and_blocks_result() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .compute_map(false), + ) + .unwrap(); + state.omega[[0, 0]] = f64::NAN; + + let error = state.step().unwrap_err(); + let failure = error + .downcast_ref::() + .expect("step error should retain its numerical failure type") + .clone(); + + assert_eq!(failure.attempted_cycle(), 1); + assert_eq!(failure.phase(), NumericalFailurePhase::Expectation); + assert!(!failure.source_message().is_empty()); + assert_eq!(state.status, Status::Stop(StopReason::NumericalFailure)); + assert_eq!( + state.step().unwrap(), + Status::Stop(StopReason::NumericalFailure) + ); + + let result_error = Box::new(state).into_result().unwrap_err(); + assert_eq!( + result_error.downcast_ref::(), + Some(&failure) + ); +} + +#[test] +fn maximization_numerical_failure_stops_fit() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .compute_map(false), + ) + .unwrap(); + state.sufficient_statistics.mean_phi.pop(); + + let error = state.step().unwrap_err(); + let failure = error + .downcast_ref::() + .expect("step error should retain its numerical failure type"); + + assert_eq!(failure.attempted_cycle(), 1); + assert_eq!(failure.phase(), NumericalFailurePhase::Maximization); + assert!(!failure.source_message().is_empty()); + assert_eq!(state.status, Status::Stop(StopReason::NumericalFailure)); +} + +#[test] +fn result_assembly_numerical_failure_returns_no_result() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1).compute_map(false)) + .unwrap(); + state.etas[0].clear(); + + let error = Box::new(state).into_result().unwrap_err(); + let failure = error + .downcast_ref::() + .expect("result error should retain its numerical failure type"); + + assert_eq!(failure.attempted_cycle(), 0); + assert_eq!(failure.phase(), NumericalFailurePhase::ResultAssembly); + assert!(!failure.source_message().is_empty()); +} + +#[test] +fn proposal_score_uses_pmcore_likelihood_and_eta_prior() { + let state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); + let current_eta = state.etas[0][0].clone(); + let score = state + .score_subject_latents(0, ¤t_eta, &state.kappas[0][0]) + .unwrap(); + + assert_eq!(score.log_likelihood, state.subject_log_likelihoods[0]); + assert_eq!(score.eta_log_prior, state.subject_log_priors[0]); + assert_eq!( + state + .proposal_log_acceptance_ratio(0, 0, ¤t_eta) + .unwrap(), + 0.0 + ); +} + +#[test] +fn component_random_walk_changes_only_selected_eta() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(2).seed(2024)).unwrap(); + let current = vec![1.0, 2.0]; + + let proposed = state.component_random_walk_eta(¤t, 1); + + assert_eq!(proposed[0], current[0]); + assert_ne!(proposed[1], current[1]); +} + +#[test] +fn component_scale_adaptation_uses_acceptance_bands_and_clamps() { + assert!((adapt_component_step_size(1.0, 0.45) - 1.1).abs() < 1e-12); + assert!((adapt_component_step_size(1.0, 0.44) - 0.9).abs() < 1e-12); + assert_eq!(adapt_component_step_size(5.0, 1.0), 5.0); + assert_eq!(adapt_component_step_size(1e-6, 0.0), 1e-6); +} + +#[test] +fn component_scale_adaptation_waits_for_interval_and_resets_counts() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(2).adapt_interval(2)) + .unwrap(); + state.adaptation_accept_counts = vec![9, 1]; + state.adaptation_proposal_counts = vec![10, 10]; + state.steps_since_adapt = 1; + + state.adapt_proposal_step_sizes(); + assert_eq!(state.proposal_step_sizes, vec![0.5, 0.5]); + + state.steps_since_adapt = 2; + state.adapt_proposal_step_sizes(); + assert_eq!(state.proposal_step_sizes, vec![0.55, 0.45]); + assert_eq!(state.adaptation_accept_counts, vec![0, 0]); + assert_eq!(state.adaptation_proposal_counts, vec![0, 0]); + assert_eq!(state.steps_since_adapt, 0); +} + +#[test] +fn e_step_runs_seeded_random_walk_for_all_chains_and_records_acceptance_rate() { + let config = SaemConfig::new().n_chains(3).mcmc_iterations(2).seed(2024); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + let initial_etas = state.etas.clone(); + + state.e_step().unwrap(); + + let acceptance_rate = state.acceptance_rate().unwrap(); + assert!((0.0..=1.0).contains(&acceptance_rate)); + assert_eq!(state.last_log_acceptance_ratios.len(), 2); + assert_eq!(state.last_parameter_acceptance_rates.len(), 2); + assert!(state + .last_parameter_acceptance_rates + .iter() + .all(|rate| (0.0..=1.0).contains(rate))); + assert!(state + .last_log_acceptance_ratios + .iter() + .all(|value| value.is_finite())); + assert_ne!(state.etas, initial_etas); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| eta.len() == 2)); +} + +#[test] +fn cycle_diagnostics_separate_eta_kappa_counts_and_schedule_phases() { + let config = SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(1); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + + state.step().unwrap(); + state.step().unwrap(); + state.step().unwrap(); + + assert_eq!(state.cycle_diagnostics.len(), 3); + assert_eq!(state.cycle_diagnostics[0].phase, SaemPhase::BurnIn); + assert_eq!(state.cycle_diagnostics[1].phase, SaemPhase::Exploration); + assert_eq!(state.cycle_diagnostics[2].phase, SaemPhase::Smoothing); + for diagnostics in &state.cycle_diagnostics { + assert_eq!(diagnostics.eta_proposals, 4); + assert_eq!( + diagnostics.eta_accepted + diagnostics.eta_rejected, + diagnostics.eta_proposals + ); + assert_eq!(diagnostics.kappa_proposals, 4); + assert_eq!( + diagnostics.kappa_accepted + diagnostics.kappa_rejected, + diagnostics.kappa_proposals + ); + assert_eq!(diagnostics.eta_parameter_acceptance_rates.len(), 2); + assert_eq!( + diagnostics.eta_proposal_step_sizes_before_adaptation.len(), + 2 + ); + assert_eq!( + diagnostics.eta_proposal_step_sizes_after_adaptation.len(), + 2 + ); + assert_eq!(diagnostics.kappa_subject_acceptance_rates.len(), 1); + assert_eq!( + diagnostics + .kappa_proposal_step_sizes_before_adaptation + .len(), + 1 + ); + assert_eq!( + diagnostics.kappa_proposal_step_sizes_after_adaptation.len(), + 1 + ); + } + assert_eq!( + state.cycle_diagnostics[0].stochastic_approximation_step, + 0.0 + ); + assert_eq!(state.cycle_diagnostics[0].covariance_step, 0.1); +} + +#[test] +fn warning_aggregation_preserves_kind_output_first_cycle_and_counts() { + let config = SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + let cycle = &mut state.cycle_diagnostics[0]; + cycle.omega_update_rejected = true; + cycle.eta_non_finite = 2; + cycle.eta_block_non_finite = 7; + let residual = &mut cycle.residual_diagnostics[0]; + residual.update_rejected = true; + residual.proportional_floor_count = 3; + residual.non_finite_prediction_count = 4; + residual.exponential_domain_violation_count = 5; + residual.combined_additive_collapse_warning = true; + residual.optimizer_converged = Some(false); + + let warnings = parametric_warnings(&state.cycle_diagnostics, None); + + assert!(warnings.contains(&ParametricWarning::OmegaUpdateRejected { + first_iteration: 1, + cycles: 1, + })); + assert!( + warnings.contains(&ParametricWarning::EtaNonFiniteProposals { + first_iteration: 1, + count: 2, + }) + ); + assert!( + warnings.contains(&ParametricWarning::EtaBlockNonFiniteProposals { + first_iteration: 1, + count: 7, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ResidualUpdateRejected { + output: "0".to_owned(), + first_iteration: 1, + cycles: 1, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ProportionalPredictionFloor { + output: "0".to_owned(), + first_iteration: 1, + count: 3, + }) + ); + assert!( + warnings.contains(&ParametricWarning::NonFiniteResidualPrediction { + output: "0".to_owned(), + first_iteration: 1, + count: 4, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ExponentialDomainViolation { + output: "0".to_owned(), + first_iteration: 1, + count: 5, + }) + ); + assert!( + warnings.contains(&ParametricWarning::CombinedAdditiveCollapse { + output: "0".to_owned(), + first_iteration: 1, + cycles: 1, + }) + ); + assert!( + warnings.contains(&ParametricWarning::ResidualOptimizerNotConverged { + output: "0".to_owned(), + first_iteration: 1, + cycles: 1, + }) + ); +} + +#[test] +fn covariance_stability_records_fixed_iiv_and_iov_margins_and_output_rows() { + let result = markov_iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 1)), + ) + .unwrap(); + let cycle = &result.cycle_diagnostics()[0]; + assert!((cycle.omega_relative_spd_margin.unwrap() - 1.0).abs() < 1e-12); + assert!((cycle.omega_iov_relative_spd_margin.unwrap() - 1.0).abs() < 1e-12); + + let tables = result.tables(0.0, 0.0).unwrap(); + let stability_rows = tables + .statistics + .iter() + .filter(|row| row.kind == "covariance_stability") + .collect::>(); + assert_eq!(stability_rows.len(), 2); + assert!(stability_rows.iter().any(|row| { + row.name == "omega_relative_spd_margin" + && row.value.is_some_and(|value| (value - 1.0).abs() < 1e-12) + })); + assert!(stability_rows.iter().any(|row| { + row.name == "omega_iov_relative_spd_margin" + && row.value.is_some_and(|value| (value - 1.0).abs() < 1e-12) + })); +} + +#[test] +fn covariance_boundary_rejection_requires_a_complete_consecutive_window() { + let config = SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + let base = state.cycle_diagnostics[0].clone(); + let policy = CovarianceStabilityConfig::new(0.01, 3); + let pattern = [ + (1, 0.005, true), + (2, 0.004, true), + (3, 0.02, true), + (4, 0.003, true), + (5, 0.002, true), + (6, 0.001, true), + ]; + let cycles = pattern + .into_iter() + .map(|(iteration, margin, rejected)| { + let mut cycle = base.clone(); + cycle.iteration = iteration; + cycle.omega_relative_spd_margin = Some(margin); + cycle.omega_update_rejected = rejected; + cycle + }) + .collect::>(); + + assert_eq!( + covariance_boundary_rejection_summary(&cycles[..2], policy, false), + CovarianceBoundaryRejectionSummary { + first_iteration: None, + longest_run: 2, + } + ); + assert_eq!( + covariance_boundary_rejection_summary(&cycles, policy, false), + CovarianceBoundaryRejectionSummary { + first_iteration: Some(4), + longest_run: 3, + } + ); + let warnings = parametric_warnings(&cycles, Some(policy)); + assert!( + warnings.contains(&ParametricWarning::OmegaBoundaryRejection { + first_iteration: 4, + longest_run: 3, + }) + ); + + let mut mismatched_iov = base.clone(); + mismatched_iov.omega_iov_relative_spd_margin = Some(0.005); + mismatched_iov.omega_update_rejected = true; + mismatched_iov.omega_iov_update_rejected = false; + assert_eq!( + covariance_boundary_rejection_summary(&[mismatched_iov.clone()], policy, true), + CovarianceBoundaryRejectionSummary::default() + ); + mismatched_iov.omega_iov_update_rejected = true; + assert_eq!( + covariance_boundary_rejection_summary(&[mismatched_iov], policy, true).longest_run, + 1 + ); + + let iov_cycles = (1..=3) + .map(|iteration| { + let mut cycle = base.clone(); + cycle.iteration = iteration; + cycle.omega_iov_relative_spd_margin = Some(policy.minimum_relative_spd_margin); + cycle.omega_iov_update_rejected = true; + cycle + }) + .collect::>(); + assert_eq!( + covariance_boundary_rejection_summary(&iov_cycles, policy, true), + CovarianceBoundaryRejectionSummary { + first_iteration: Some(1), + longest_run: 3, + } + ); + assert!(parametric_warnings(&iov_cycles, Some(policy)).contains( + &ParametricWarning::OmegaIovBoundaryRejection { + first_iteration: 1, + longest_run: 3, + } + )); + + let criterion = evaluate_criterion( + "omega_boundary_rejection_run", + Some(3.0), + policy.rejection_window as f64, + |observed| observed < policy.rejection_window as f64, + ); + assert_eq!( + criterion.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); +} + +#[test] +fn m_step_recenters_etas_before_updating_iiv_second_moment() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .burn_in(0) + .omega_sa_max_step(0.1), + ) + .unwrap(); + state.cycle = 1; + for subject_chains in &mut state.etas { + for eta in subject_chains { + eta[0] = 2.0_f64.ln(); + } + } + let individual_before = state.individual_parameters(0, 0); + + state.m_step().unwrap(); + + let individual_after = state.individual_parameters(0, 0); + assert!((individual_before[0] - individual_after[0]).abs() < 1e-12); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| eta[0].abs() < 1e-12)); + assert!((state.population_parameters[0] - 0.4).abs() < 1e-12); + assert!((state.population_parameters[1] - 10.0).abs() < 1e-12); + let information = state.information.diagnostics(); + let ke_coordinate = information + .coordinates + .iter() + .position(|coordinate| coordinate.name == "phi:ke") + .unwrap(); + // Two pre-M-step absolute phi values each differ from the old + // population by ln(2). Post-update or un-recentered evaluation would + // give a different score (zero or double-counted population shift). + assert!((information.delta[ke_coordinate] - 2.0 * 2.0_f64.ln()).abs() < 1e-12); + let expected_omega = ndarray::array![[0.9, 0.0], [0.0, 0.9]]; + assert!(state + .iiv_second_moment + .iter() + .zip(expected_omega.iter()) + .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); + assert!(state + .omega + .iter() + .zip(expected_omega.iter()) + .all(|(actual, expected)| (actual - expected).abs() < 1e-12)); +} diff --git a/src/algorithms/parametric/saem/state/tests/diagnostics.rs b/src/algorithms/parametric/saem/state/tests/diagnostics.rs new file mode 100644 index 000000000..f306f0934 --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/diagnostics.rs @@ -0,0 +1,717 @@ +use super::*; +#[test] +fn frozen_markov_diagnostic_is_repeatable_and_canonical_result_is_unchanged() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + + let base = SaemConfig::new() + .k1_iterations(100) + .k2_iterations(50) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(true) + .seed(91) + .averaged_iterates(0.75); + let diagnostic_config = MarkovSimulationVarianceConfig::new( + 700, + 2, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 64 * 1024, + ); + let disabled = markov_iov_problem().fit_with(base.clone()).unwrap(); + let enabled = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diagnostic_config)) + .unwrap(); + let repeated = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diagnostic_config)) + .unwrap(); + let changed_seed = markov_iov_problem() + .fit_with( + base.clone() + .markov_simulation_variance(MarkovSimulationVarianceConfig::new( + 701, + 2, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 64 * 1024, + )), + ) + .unwrap(); + + assert_eq!( + enabled.markov_simulation_variance(), + repeated.markov_simulation_variance() + ); + assert_ne!( + enabled.markov_simulation_variance(), + changed_seed.markov_simulation_variance() + ); + assert_ne!( + enabled.markov_simulation_variance().status, + MarkovSimulationVarianceStatus::Disabled + ); + assert!(!enabled.markov_simulation_variance().chains.is_empty()); + // One subject, one eta block, one component eta, and two occasion-kappa + // blocks are attempted in that exact compound-kernel order per retained + // transition. Warmup attempts are absent from the exported count. + assert!(enabled + .markov_simulation_variance() + .chains + .iter() + .all(|chain| chain.proposals == 12 * (1 + 1 + 2))); + assert_eq!( + enabled.population_parameters(), + disabled.population_parameters() + ); + assert_eq!(enabled.omega(), disabled.omega()); + assert_eq!(enabled.omega_iov(), disabled.omega_iov()); + assert_eq!( + enabled.residual_error_estimates(), + disabled.residual_error_estimates() + ); + assert_eq!(enabled.eta_chain_means(), disabled.eta_chain_means()); + assert_eq!(enabled.kappa_chain_means(), disabled.kappa_chain_means()); + assert!(!enabled.conditional_modes().is_empty()); + assert_eq!(enabled.conditional_modes(), disabled.conditional_modes()); + assert_eq!( + enabled.information_diagnostics(), + disabled.information_diagnostics() + ); + assert_eq!(enabled.cycle_diagnostics(), disabled.cycle_diagnostics()); + assert_eq!(enabled.warnings(), disabled.warnings()); + assert_eq!(enabled.conditional_n2ll(), disabled.conditional_n2ll()); + assert_eq!(enabled.termination_reason(), disabled.termination_reason()); + assert_eq!( + enabled.population_parameters(), + changed_seed.population_parameters() + ); + assert_eq!(enabled.omega(), changed_seed.omega()); + assert_eq!( + enabled.residual_error_estimates(), + changed_seed.residual_error_estimates() + ); + assert_eq!(enabled.eta_chain_means(), changed_seed.eta_chain_means()); + assert_eq!( + enabled.cycle_diagnostics(), + changed_seed.cycle_diagnostics() + ); + assert_eq!(enabled.warnings(), changed_seed.warnings()); + assert_eq!(enabled.conditional_n2ll(), changed_seed.conditional_n2ll()); + let enabled_predictions = enabled.population_predictions(0.0, 0.0).unwrap(); + let disabled_predictions = disabled.population_predictions(0.0, 0.0).unwrap(); + assert_eq!(enabled_predictions.len(), disabled_predictions.len()); + for (actual, expected) in enabled_predictions.iter().zip(&disabled_predictions) { + assert_prediction_points_equal(actual, expected); + } + let enabled_conditional = enabled.conditional_predictions(0.0, 0.0).unwrap(); + let disabled_conditional = disabled.conditional_predictions(0.0, 0.0).unwrap(); + assert_eq!(enabled_conditional.len(), disabled_conditional.len()); + for (actual, expected) in enabled_conditional.iter().zip(&disabled_conditional) { + assert_prediction_points_equal(actual, expected); + } +} + +#[test] +fn rank_diagnostics_computed_for_multiple_chains_and_iov() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; + + let base = SaemConfig::new() + .k1_iterations(30) + .k2_iterations(20) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(91) + .averaged_iterates(0.75); + let diag = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let result = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diag)) + .unwrap(); + let rank = &result.markov_simulation_variance().rank_diagnostics; + assert_eq!(rank.diagnostic_chains, 2); + assert_eq!(rank.draws_per_chain, 12); + assert_eq!(rank.original_chains, 2); + assert_eq!(rank.status, RankDiagnosticStatus::Available); + assert!(!rank.traces.is_empty()); + // First trace is a score coordinate. + assert!(matches!( + rank.traces[0].trace, + DiagnosticTraceCoordinate::Score { .. } + )); + let score_count = result.information_diagnostics().coordinates.len(); + let eta_count = result + .eta_chain_means() + .iter() + .map(|estimate| estimate.values.len()) + .sum::(); + let kappa_count = result + .kappa_chain_means() + .iter() + .map(|estimate| estimate.values.len()) + .sum::(); + assert_eq!(rank.traces.len(), score_count + eta_count + kappa_count); + for (trace, coordinate) in rank + .traces + .iter() + .take(score_count) + .zip(&result.information_diagnostics().coordinates) + { + assert!(matches!( + &trace.trace, + DiagnosticTraceCoordinate::Score { index, .. } if *index == coordinate.index + )); + } + assert!(rank + .traces + .iter() + .skip(score_count) + .take(eta_count) + .all(|trace| matches!(trace.trace, DiagnosticTraceCoordinate::Eta { .. }))); + assert!(rank + .traces + .iter() + .skip(score_count + eta_count) + .all(|trace| matches!(trace.trace, DiagnosticTraceCoordinate::Kappa { .. }))); + assert!(rank.diagnostic_mean_lrv.is_some()); + assert!(rank.operational_lrv.is_some()); + + // Repeatability: same seed produces identical rank diagnostics. + let repeated = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diag)) + .unwrap(); + assert_eq!( + result.markov_simulation_variance().rank_diagnostics, + repeated.markov_simulation_variance().rank_diagnostics + ); + + // Canonical result is unchanged by rank diagnostic presence. + let disabled = markov_iov_problem().fit_with(base).unwrap(); + assert_eq!( + result.population_parameters(), + disabled.population_parameters() + ); + assert_eq!(result.omega(), disabled.omega()); + assert_eq!(result.conditional_n2ll(), disabled.conditional_n2ll()); + assert_eq!(result.termination_reason(), disabled.termination_reason()); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); +} + +#[test] +fn score_failure_does_not_discard_valid_eta_rank_diagnostics() { + use crate::results::{ + DiagnosticTraceCoordinate, InformationCoordinateKind, RankDiagnosticStatus, + }; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![ + vec![vec![f64::NAN; 8], vec![f64::NAN; 8]], + vec![ + vec![1.0, 4.0, 2.0, 3.0, 2.0, 4.0, 1.0, 3.0], + vec![2.1, 3.1, 1.1, 4.1, 3.1, 1.1, 4.1, 2.1], + ], + ]; + let coordinates = vec![ + DiagnosticTraceCoordinate::Score { + index: 0, + name: "score".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }, + DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }, + ]; + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + assert_eq!( + diagnostics[0].rank_rhat_status, + RankDiagnosticStatus::ScoreUnavailable + ); + assert!(diagnostics[0].rank_rhat.is_none()); + assert_eq!( + diagnostics[1].rank_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostics[1].rank_rhat.is_some()); +} + +#[test] +fn multimodal_latent_trace_is_detected_while_mixed_score_trace_passes() { + use crate::results::{ + DiagnosticTraceCoordinate, InformationCoordinateKind, RankDiagnosticStatus, + }; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![ + vec![ + vec![1.0, 4.0, 2.0, 3.0, 2.0, 4.0, 1.0, 3.0], + vec![2.1, 3.1, 1.1, 4.1, 3.1, 1.1, 4.1, 2.1], + ], + vec![ + vec![-10.0, -9.0, -11.0, -8.0, -9.5, -8.5, -10.5, -7.5], + vec![8.0, 11.0, 9.0, 10.0, 8.5, 10.5, 7.5, 9.5], + ], + ]; + let coordinates = vec![ + DiagnosticTraceCoordinate::Score { + index: 0, + name: "score".into(), + kind: InformationCoordinateKind::Population { parameter_index: 0 }, + }, + DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }, + ]; + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + assert_eq!( + diagnostics[0].rank_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostics[0].rank_rhat.is_some_and(|rhat| rhat < 1.1)); + assert_eq!( + diagnostics[1].rank_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostics[1].rank_rhat.is_some_and(|rhat| rhat > 1.1)); +} + +#[test] +fn rank_coordinate_retains_valid_rhats_when_bulk_ess_is_unavailable() { + use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![vec![vec![1.0, 2.0, 4.0, 3.0], vec![1.5, 2.5, 4.5, 3.5]]]; + let coordinates = vec![DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }]; + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + let diagnostic = &diagnostics[0]; + assert!(diagnostic.rank_rhat.is_some()); + assert_eq!(diagnostic.rank_rhat_status, RankDiagnosticStatus::Available); + assert!(diagnostic.folded_rhat.is_some()); + assert_eq!( + diagnostic.folded_rhat_status, + RankDiagnosticStatus::Available + ); + assert!(diagnostic.bulk_ess.is_none()); + assert!(diagnostic.tau.is_none()); + assert_eq!( + diagnostic.bulk_ess_status, + RankDiagnosticStatus::TooFewDraws + ); + assert_eq!(diagnostic.status, RankDiagnosticStatus::PartialAvailability); +} + +#[test] +fn derived_max_rhat_requires_both_rank_and_folded_components() { + use crate::results::{DiagnosticTraceCoordinate, RankDiagnosticStatus}; + + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + let traces = vec![vec![ + vec![1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0], + vec![2.0, -2.0, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0], + ]]; + let coordinates = vec![DiagnosticTraceCoordinate::Eta { + subject: "1".into(), + effect_index: 0, + effect_name: "CL".into(), + }]; + + let diagnostics = state.rank_diagnostics_from_traces(2, &traces, &coordinates); + let diagnostic = &diagnostics[0]; + assert!(diagnostic.rank_rhat.is_some()); + assert_eq!(diagnostic.rank_rhat_status, RankDiagnosticStatus::Available); + assert!(diagnostic.folded_rhat.is_none()); + assert_eq!( + diagnostic.folded_rhat_status, + RankDiagnosticStatus::ConstantDraws + ); + assert!(diagnostic.max_rhat.is_none()); + assert_eq!( + diagnostic.max_rhat_status, + RankDiagnosticStatus::ConstantDraws + ); + assert_eq!(worst_valid_max_rhat(&diagnostics), None); +} + +#[test] +fn rank_diagnostics_available_when_markov_config_enabled() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::RankDiagnosticStatus; + + let base = SaemConfig::new() + .k1_iterations(100) + .k2_iterations(50) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(77) + .averaged_iterates(0.75); + let diag = MarkovSimulationVarianceConfig::new( + 42, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let result = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(diag)) + .unwrap(); + let rank = &result.markov_simulation_variance().rank_diagnostics; + // Rank diagnostics object is always present when markov config enabled; + // status reflects whether data supported valid computation. + assert_eq!(rank.diagnostic_chains, 2); + assert_eq!(rank.original_chains, 2); + assert!(!matches!(rank.status, RankDiagnosticStatus::Disabled)); +} + +#[test] +fn one_diagnostic_chain_retains_markov_lrv_but_rank_is_unavailable() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::RankDiagnosticStatus; + + let config = SaemConfig::new() + .k1_iterations(30) + .k2_iterations(20) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(93) + .averaged_iterates(0.75) + .markov_simulation_variance(MarkovSimulationVarianceConfig::new( + 702, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 1, + 1024 * 1024, + )); + let result = markov_iov_problem().fit_with(config).unwrap(); + let markov = result.markov_simulation_variance(); + assert_eq!( + markov.rank_diagnostics.status, + RankDiagnosticStatus::TooFewChains + ); + assert_eq!(markov.chains.len(), 1); + assert!(!markov.lambda.is_empty()); + assert!(markov.rank_diagnostics.operational_lrv.is_some()); + assert!(markov.rank_diagnostics.traces.iter().all(|trace| { + trace.status == RankDiagnosticStatus::TooFewChains + && trace.rank_rhat.is_none() + && trace.bulk_ess.is_none() + })); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert!(!result.converged()); +} + +#[test] +fn rank_diagnostics_trace_byte_cap_exceeded_is_reported() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + use crate::results::RankDiagnosticStatus; + + let base = SaemConfig::new() + .k1_iterations(100) + .k2_iterations(50) + .burn_in(1) + .n_chains(2) + .eta_block_iterations(1) + .compute_map(false) + .seed(91) + .averaged_iterates(0.75); + let tiny_cap = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1, // 1 byte cap — guaranteed to be exceeded + ); + let result = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(tiny_cap)) + .unwrap(); + let rank = &result.markov_simulation_variance().rank_diagnostics; + assert_eq!(rank.status, RankDiagnosticStatus::TraceByteCapExceeded); + assert!(rank.traces.is_empty()); + assert!(rank.diagnostic_mean_lrv.is_none()); + assert!(rank.operational_lrv.is_none()); + assert_eq!(rank.max_trace_bytes, 1); + assert!(rank.accounted_peak_trace_bytes_required > rank.max_trace_bytes); + assert_eq!(rank.accounted_peak_trace_bytes_used, 0); + let markov = result.markov_simulation_variance(); + assert!(matches!( + markov.status, + MarkovSimulationVarianceStatus::InvalidConfiguration(_) + )); + assert_eq!(markov.lambda_status, markov.status); + assert_eq!(markov.xi_status, markov.status); + assert_eq!(markov.simulation_covariance_status, markov.status); + assert!(markov.chains.is_empty()); + // Canonical result is unchanged. + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + + let generous = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024 * 1024, + ); + let measured = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(generous)) + .unwrap(); + let measured_rank = &measured.markov_simulation_variance().rank_diagnostics; + let trace_count = measured_rank.traces.len(); + let score_width = measured.markov_simulation_variance().coordinates.len(); + let vec_header = std::mem::size_of::>(); + let persistent_bytes = 2 * 12 * trace_count * std::mem::size_of::() + + trace_count * 2 * vec_header + + trace_count * vec_header; + let score_transient_bytes = score_width * 12 * std::mem::size_of::() + 12 * vec_header; + let rank_transient_bytes = 2 * 12 * 8 * std::mem::size_of::() + 2 * 16 * vec_header; + let expected_bytes = persistent_bytes + score_transient_bytes.max(rank_transient_bytes); + assert_eq!( + measured_rank.accounted_peak_trace_bytes_required, + expected_bytes + ); + assert_eq!( + measured_rank.accounted_peak_trace_bytes_used, + expected_bytes + ); + + let exact_cap = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + expected_bytes, + ); + let exact = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(exact_cap)) + .unwrap(); + assert_eq!( + exact + .markov_simulation_variance() + .rank_diagnostics + .accounted_peak_trace_bytes_used, + expected_bytes + ); + assert!(!exact + .markov_simulation_variance() + .rank_diagnostics + .traces + .is_empty()); + + let under_cap = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + expected_bytes - 1, + ); + let rejected = markov_iov_problem() + .fit_with(base.clone().markov_simulation_variance(under_cap)) + .unwrap(); + assert_eq!( + rejected + .markov_simulation_variance() + .rank_diagnostics + .status, + RankDiagnosticStatus::TraceByteCapExceeded + ); + assert_eq!( + rejected + .markov_simulation_variance() + .rank_diagnostics + .accounted_peak_trace_bytes_used, + 0 + ); + + let overflow = MarkovSimulationVarianceConfig::new( + 700, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + usize::MAX / 2 + 1, + usize::MAX, + ); + let overflowed = markov_iov_problem() + .fit_with(base.markov_simulation_variance(overflow)) + .unwrap(); + let overflowed = overflowed.markov_simulation_variance(); + assert_eq!( + overflowed.rank_diagnostics.status, + RankDiagnosticStatus::TraceMemoryAccountingOverflow + ); + assert_eq!( + overflowed.status, + MarkovSimulationVarianceStatus::TraceMemoryAccountingOverflow + ); + assert_eq!( + overflowed + .rank_diagnostics + .accounted_peak_trace_bytes_required, + 0 + ); + assert_eq!( + overflowed.rank_diagnostics.accounted_peak_trace_bytes_used, + 0 + ); + assert!(overflowed.chains.is_empty()); +} + +#[test] +fn operational_and_frozen_iov_transitions_preserve_compound_kernel_order() { + let seed = 0x5eed; + let mut operational = SaemState::from_problem( + markov_iov_problem(), + &SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .eta_block_iterations(1) + .adapt_interval(50) + .seed(seed), + ) + .unwrap(); + let initial_eta_scales = operational.proposal_step_sizes.clone(); + let initial_eta_block_scales = operational.eta_block_step_sizes.clone(); + let initial_kappa_scales = operational.kappa_proposal_step_sizes.clone(); + let mut frozen = FrozenDiagnosticState { + etas: operational.etas.clone(), + kappas: operational.kappas.clone(), + }; + let mut frozen_rng = StdRng::seed_from_u64(seed); + let mut frozen_counts = vec![(0, 0, 0); operational.initialization.n_chains]; + + // This single compound transition is order-sensitive: eta blocks consume + // the stream first, followed by component etas and then occasion kappas. + operational + .frozen_diagnostic_transition(&mut frozen, &mut frozen_rng, &mut frozen_counts, None) + .unwrap(); + operational.e_step().unwrap(); + + assert_eq!(operational.etas, frozen.etas); + assert_eq!(operational.kappas, frozen.kappas); + assert_eq!(operational.proposal_step_sizes, initial_eta_scales); + assert_eq!(operational.eta_block_step_sizes, initial_eta_block_scales); + assert_eq!(operational.kappa_proposal_step_sizes, initial_kappa_scales); + + let diagnostics = operational.cycle_diagnostics.last().unwrap(); + let frozen_proposals = frozen_counts.iter().map(|count| count.0).sum::(); + let frozen_accepts = frozen_counts.iter().map(|count| count.1).sum::(); + let frozen_changes = frozen_counts.iter().map(|count| count.2).sum::(); + assert_eq!(diagnostics.eta_block_proposals, 2); + assert_eq!(diagnostics.eta_proposals, 4); + assert_eq!(diagnostics.kappa_proposals, 4); + assert_eq!(frozen_proposals, 8); + assert_eq!( + frozen_accepts, + diagnostics.eta_accepted + diagnostics.kappa_accepted + ); + assert_eq!(frozen_changes, frozen_accepts); + assert_eq!( + diagnostics.eta_rejected + diagnostics.kappa_rejected, + frozen_proposals - frozen_accepts + ); + assert_eq!(diagnostics.eta_non_finite, 0); + assert_eq!(diagnostics.kappa_non_finite, 0); + + let operational_continuation = operational.rng.random::(); + let frozen_continuation = frozen_rng.random::(); + assert_eq!(operational_continuation, frozen_continuation); +} + +#[test] +fn warmup_movement_cannot_satisfy_retained_movement_accounting() { + let mut counts = [(12, 7, 4), (8, 1, 1)]; + begin_retained_transition_accounting(&mut counts); + // Retained proposals that are accepted without an actual state change + // still leave the chain eligible for the exact stuck guard. + counts[0].0 += 3; + counts[0].1 += 3; + assert_eq!(counts, [(3, 3, 0), (0, 0, 0)]); + let stuck: Vec<_> = counts + .iter() + .enumerate() + .filter_map(|(chain, count)| (count.2 == 0).then_some(chain)) + .collect(); + assert_eq!(stuck, [0, 1]); +} + +#[test] +fn no_latent_state_reports_exact_zero_markov_variance() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + + let result = fixed_no_iiv_problem() + .fit_with( + SaemConfig::new() + .k1_iterations(30) + .k2_iterations(20) + .burn_in(1) + .compute_map(false) + .averaged_iterates(0.75) + .markov_simulation_variance(MarkovSimulationVarianceConfig::new( + 4, + 100, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 2, + 1024, + )), + ) + .unwrap(); + let diagnostic = result.markov_simulation_variance(); + assert_eq!( + diagnostic.status, + MarkovSimulationVarianceStatus::ExactZeroNoLatentState + ); + assert!(diagnostic.chains.is_empty()); + assert_eq!( + diagnostic.rank_diagnostics.status, + RankDiagnosticStatus::NoLatent + ); + assert!(diagnostic.rank_diagnostics.traces.is_empty()); + assert!(diagnostic + .lambda + .iter() + .flatten() + .all(|value| *value == 0.0)); + assert!(diagnostic.xi.iter().flatten().all(|value| *value == 0.0)); + assert!(diagnostic + .simulation_covariance + .iter() + .flatten() + .all(|value| *value == 0.0)); +} diff --git a/src/algorithms/parametric/saem/state/tests/estimation.rs b/src/algorithms/parametric/saem/state/tests/estimation.rs new file mode 100644 index 000000000..1129f6ffd --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/estimation.rs @@ -0,0 +1,421 @@ +use super::*; +#[test] +fn exploration_covariance_cap_prevents_one_draw_rank_one_collapse() { + fn correlation(omega: &Array2) -> f64 { + omega[[0, 1]] / (omega[[0, 0]] * omega[[1, 1]]).sqrt() + } + + let make_state = |omega_sa_max_step| { + SaemState::from_problem( + correlated_omega_problem(), + &SaemConfig::new() + .n_chains(1) + .burn_in(0) + .omega_sa_max_step(omega_sa_max_step), + ) + .unwrap() + }; + let mut guarded = make_state(0.1); + let mut uncapped = make_state(1.0); + for state in [&mut guarded, &mut uncapped] { + state.cycle = 1; + state.etas[0][0] = vec![2.0, 2.0]; + state.etas[1][0] = vec![-2.0, -2.0]; + state.m_step().unwrap(); + assert!(state.omega[[0, 0]] >= state.initialization.schedule.minimum_variance); + assert!(state.omega[[1, 1]] >= state.initialization.schedule.minimum_variance); + assert!( + state.omega[[0, 0]] * state.omega[[1, 1]] - state.omega[[0, 1]].powi(2) > 0.0, + "omega: {:?}", + state.omega + ); + } + + let guarded_correlation = correlation(&guarded.omega); + let uncapped_correlation = correlation(&uncapped.omega); + assert!(guarded_correlation < 0.85); + assert!(uncapped_correlation > 0.85); + assert!(uncapped_correlation - guarded_correlation > 0.05); +} + +#[test] +fn m_step_preserves_fixed_omega_and_structural_zeros() { + let mut state = SaemState::from_problem( + configured_omega_problem(), + &SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0), + ) + .unwrap(); + state.cycle = 1; + for (subject_index, subject_chains) in state.etas.iter_mut().enumerate() { + let sign = if subject_index == 0 { 1.0 } else { -1.0 }; + for eta in subject_chains { + eta[0] = sign; + eta[1] = 2.0 * sign; + } + } + + state.m_step().unwrap(); + + assert!((state.omega[[0, 0]] - 1.0).abs() < 1e-12); + assert!((state.omega[[1, 1]] - 0.5).abs() < 1e-12); + assert_eq!(state.omega[[0, 1]], 0.0); + assert_eq!(state.omega[[1, 0]], 0.0); +} + +#[test] +fn fixed_population_effect_is_not_updated_and_omega_uses_fixed_center() { + let mut state = SaemState::from_problem( + fixed_population_iiv_problem(), + &SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0), + ) + .unwrap(); + state.cycle = 1; + for subject_chains in &mut state.etas { + for eta in subject_chains { + eta[0] = 2.0_f64.ln(); + } + } + + state.m_step().unwrap(); + + assert!((state.population_parameters[0] - 0.2).abs() < 1e-12); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| (eta[0] - 2.0_f64.ln()).abs() < 1e-12)); + assert!((state.omega[[0, 0]] - 2.0_f64.ln().powi(2)).abs() < 1e-12); + let individual = state.individual_parameters(0, 0); + assert!((individual[0] - 0.4).abs() < 1e-12); +} + +#[test] +fn m_step_updates_simple_residual_sigma_from_statrese() { + let mut state = SaemState::from_problem( + constant_error_problem(), + &SaemConfig::new().n_chains(1).burn_in(0), + ) + .unwrap(); + state.cycle = 1; + let candidate_sigma = state + .current_residual_statistics() + .unwrap() + .output(0) + .and_then(|statistic| statistic.sigma()) + .unwrap(); + let expected_sigma = state.initialization.schedule.guarded_residual_sigma( + state.cycle, + state.residual_sigmas[0], + candidate_sigma, + ); + + state.m_step().unwrap(); + + assert!((state.residual_sigmas[0] - expected_sigma).abs() < 1e-12); + assert_eq!( + state.error_models.get(0), + Some(&ResidualErrorModel::constant(expected_sigma)) + ); +} + +#[test] +fn sparse_second_output_reports_only_declared_residual_model() { + let result = sparse_second_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(0) + .compute_map(false), + ) + .unwrap(); + + assert_eq!(result.residual_sigmas().len(), 1); + assert_eq!(result.residual_error_estimates().len(), 1); + assert_eq!(result.residual_error_estimates()[0].output, "measured"); + assert_eq!(result.residual_error_estimates()[0].output_index, 1); + assert_eq!(result.cycle_diagnostics().len(), 1); + assert_eq!(result.cycle_diagnostics()[0].residual_diagnostics.len(), 1); + assert_eq!( + result.cycle_diagnostics()[0].residual_diagnostics[0].output, + "measured" + ); + assert_eq!( + result.cycle_diagnostics()[0].residual_diagnostics[0].output_index, + 1 + ); +} + +#[test] +fn averaged_sparse_second_output_preserves_index_name_and_arithmetic_mean() { + let result = sparse_second_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .compute_map(false) + .seed(71_002), + ) + .expect("averaged sparse-output fit should complete"); + + let metadata = result.estimator_metadata(); + assert!(metadata.average_applied); + assert_eq!(metadata.averaging_start_cycle, Some(2)); + assert_eq!(metadata.averaged_iterations, 3); + let estimate = result + .residual_error_estimates() + .first() + .expect("sparse residual estimate"); + assert_eq!( + (estimate.output_index, estimate.output.as_str()), + (1, "measured") + ); + let smoothing = &result.cycle_diagnostics()[1..]; + let expected = smoothing + .iter() + .map(|cycle| { + let residual = cycle + .residual_error_estimates + .first() + .expect("sparse cycle residual"); + assert_eq!( + (residual.output_index, residual.output.as_str()), + (1, "measured") + ); + primary_sigma_parameter(&residual.model) + }) + .sum::() + / smoothing.len() as f64; + assert!((primary_sigma_parameter(&estimate.model) - expected).abs() < 1e-12); +} + +#[test] +fn averaged_multi_output_residuals_preserve_fixed_and_fixed_zero_components() { + let result = mixed_residual_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .compute_map(false) + .seed(71_003), + ) + .expect("averaged mixed-output fit should complete"); + let estimates = result.residual_error_estimates(); + assert_eq!(estimates.len(), 2); + assert_eq!( + (estimates[0].output_index, estimates[0].output.as_str()), + (0, "fixed") + ); + assert_eq!(estimates[0].model, ResidualErrorModel::constant(0.5)); + assert!(!estimates[0].estimated); + assert_eq!( + (estimates[1].output_index, estimates[1].output.as_str()), + (1, "mixed") + ); + assert_eq!(estimates[1].combined_additive_estimated, Some(false)); + assert_eq!(estimates[1].combined_proportional_estimated, Some(true)); + let ResidualErrorModel::Combined { a, b } = estimates[1].model else { + panic!("expected combined residual model"); + }; + assert_eq!(a, 0.0); + let smoothing = &result.cycle_diagnostics()[1..]; + let expected_b = smoothing + .iter() + .map(|cycle| match cycle.residual_error_estimates[1].model { + ResidualErrorModel::Combined { a, b } => { + assert_eq!(a, 0.0); + b + } + _ => panic!("expected combined cycle residual model"), + }) + .sum::() + / smoothing.len() as f64; + assert!((b - expected_b).abs() < 1e-12); + assert!(result.cycle_diagnostics().iter().all(|cycle| { + cycle.residual_error_estimates[0].model == ResidualErrorModel::constant(0.5) + })); +} + +#[test] +fn correlated_residual_averaging_preserves_fixed_components_and_rejects_family_changes() { + let averaged = average_residual_model( + ResidualErrorModel::correlated_combined(0.3, 0.1, 0.2), + ResidualErrorModel::correlated_combined(0.5, 0.2, -0.4), + true, + [true, true], + [false, true, true], + 2, + ) + .unwrap(); + let ResidualErrorModel::CorrelatedCombined { a, b, rho } = averaged else { + panic!("expected correlated-combined average") + }; + assert_eq!(a, 0.3); + assert!((b - 0.15).abs() < 1e-15); + assert!((rho + 0.1).abs() < 1e-15); + assert!(average_residual_model( + averaged, + ResidualErrorModel::combined(0.3, 0.15), + true, + [true, true], + [true, true, true], + 3, + ) + .is_err()); +} + +#[test] +fn population_predictions_match_direct_execution_and_metadata() { + let result = problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + let predictions = result.population_predictions(0.25, 0.0).unwrap(); + let expanded = result.data().clone().expand(0.25, 0.0, &[]); + + assert_eq!(predictions.len(), expanded.subjects().len()); + assert_eq!(expanded.subjects()[0].id(), "s1"); + assert_eq!(expanded.subjects()[1].id(), "s2"); + for (subject, actual) in expanded.subjects().iter().zip(&predictions) { + let expected = result + .equation() + .estimate_predictions_dense(subject, result.population_parameters()) + .unwrap(); + assert_prediction_points_equal(actual, &expected); + } +} + +#[test] +fn sparse_output_prediction_expansion_preserves_observed_outputs_only() { + let result = sparse_second_output_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + let population = result.population_predictions(0.25, 0.0).unwrap(); + let conditional = result.conditional_predictions(0.25, 0.0).unwrap(); + for predictions in population.iter().chain(&conditional) { + assert!(predictions + .predictions() + .iter() + .all(|prediction| prediction.output().as_str() == "measured")); + } + let tables = result.tables(0.25, 0.0).unwrap(); + assert!(tables + .predictions + .iter() + .all(|prediction| prediction.output_index == 1)); +} + +#[test] +fn fixed_zero_latent_conditional_predictions_equal_population_predictions() { + let result = fixed_no_iiv_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + assert!(result.conditional_modes().is_empty()); + let population = result.population_predictions(0.25, 0.0).unwrap(); + let conditional = result.conditional_predictions(0.25, 0.0).unwrap(); + assert_eq!(conditional.len(), population.len()); + for (conditional, population) in conditional.iter().zip(&population) { + assert_prediction_points_equal(conditional, population); + } +} + +#[test] +fn iov_conditional_predictions_use_each_occasion_kappa_in_order() { + let mut result = iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + result.conditional_modes[0].eta.fill(0.0); + result.conditional_modes[0].kappas[0].values[0] = -0.2; + result.conditional_modes[0].kappas[1].values[0] = 0.3; + + let actual = result.conditional_predictions(0.25, 0.0).unwrap(); + assert_eq!(actual.len(), 1); + let expanded = result.data().clone().expand(0.25, 0.0, &[]); + let subject = &expanded.subjects()[0]; + let mode = &result.conditional_modes()[0]; + let mut expected = pharmsol::simulator::prediction::SubjectPredictions::default(); + expected.set_id(subject.id().clone()); + for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { + let parameters = occasion_psi( + result.population_parameters(), + &result.parameter_scales, + &result.random_effect_indices, + &mode.eta, + &result.iov_effect_indices, + &kappa.values, + ) + .unwrap(); + let occasion_subject = + Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); + for prediction in result + .equation() + .estimate_predictions_dense(&occasion_subject, ¶meters) + .unwrap() + .predictions() + .iter() + .cloned() + { + expected.add_prediction(prediction, occasion.index()); + } + } + assert_prediction_points_equal(&actual[0], &expected); + assert!(actual[0] + .occasions() + .windows(2) + .any(|pair| pair[0] != pair[1])); + let occasion_predictions = subject + .occasions() + .iter() + .map(|occasion| { + actual[0] + .predictions() + .iter() + .zip(actual[0].occasions()) + .find(|(prediction, prediction_occasion)| { + **prediction_occasion == occasion.index() && prediction.observation().is_some() + }) + .unwrap() + .0 + .prediction() + }) + .collect::>(); + assert_ne!(occasion_predictions[0], occasion_predictions[1]); +} diff --git a/src/algorithms/parametric/saem/state/tests/mod.rs b/src/algorithms/parametric/saem/state/tests/mod.rs new file mode 100644 index 000000000..00ea13e2a --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/mod.rs @@ -0,0 +1,497 @@ +use super::diagnostics::{begin_retained_transition_accounting, worst_valid_max_rhat}; +use super::*; +use crate::algorithms::parametric::{NumericalFailurePhase, ParametricRunner}; +use crate::estimation::parametric::information::derive_population_uncertainty; +use crate::estimation::parametric::transforms::{phi_to_psi, psi_to_phi}; +use crate::estimation::parametric::ParametricPrior; +use crate::estimation::{EstimationProblem, Iov, Omega, ParametricErrorModel}; +use crate::model::Parameter; +use crate::results::{ + FitResult, PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, + PopulationUncertaintyStatus, +}; +use pharmsol::prelude::*; +use pharmsol::SubjectBuilderExt; + +#[test] +fn finite_improvement_is_eligible_without_a_convergence_flag() { + assert!(non_iiv_candidate_improves(10.0, 9.0)); + assert!(!non_iiv_candidate_improves(10.0, 10.0)); + assert!(!non_iiv_candidate_improves(10.0, f64::NAN)); +} + +#[test] +fn censored_information_failure_has_explicit_unsupported_status() { + let reason = "analytic information is unsupported for censored observations".to_string(); + assert_eq!( + information_failure_status(reason.clone()), + InformationStatus::Unsupported(reason) + ); +} + +fn one_compartment_metadata() -> pharmsol::equation::ModelMetadata { + equation::metadata::new("one_compartment_saem") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["0"]) + .route(equation::Route::bolus("0").to_state("central")) +} + +fn one_compartment() -> pharmsol::equation::ODE { + equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata(one_compartment_metadata()) + .unwrap() +} + +fn sparse_second_output_problem() -> EstimationProblem { + let equation = equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0]; + y[1] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + equation::metadata::new("sparse_second_output") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["unmeasured", "measured"]) + .route(equation::Route::bolus("dose").to_state("central")), + ) + .unwrap(); + let data = Data::new(vec![Subject::builder("sparse") + .bolus(0.0, 100.0, "dose") + .observation(1.0, 8.0, "measured") + .observation(2.0, 6.0, "measured") + .build()]); + + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("measured", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn mixed_residual_output_problem() -> EstimationProblem { + let equation = equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + fetch_params!(p, ke); + dx[0] = -ke * x[0] + b[0]; + }, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + fetch_params!(p, v); + y[0] = x[0] / v; + y[1] = x[0] / v; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + equation::metadata::new("mixed_residual_outputs") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["fixed", "mixed"]) + .route(equation::Route::bolus("dose").to_state("central")), + ) + .expect("mixed residual equation metadata should validate"); + let data = Data::new(vec![Subject::builder("mixed") + .bolus(0.0, 100.0, "dose") + .observation(1.0, 8.5, "fixed") + .observation(2.0, 6.5, "fixed") + .observation(1.0, 8.0, "mixed") + .observation(2.0, 6.0, "mixed") + .build()]); + + EstimationProblem::parametric(equation, data) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model( + "fixed", + ParametricErrorModel::new(ResidualErrorModel::constant(0.5)).fixed(), + ) + .error_model( + "mixed", + ParametricErrorModel::new(ResidualErrorModel::combined(0.0, 0.1)) + .fixed_combined_additive(), + ) + .build() + .expect("mixed residual output problem should validate") +} + +fn data() -> Data { + Data::new(vec![ + Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .observation(4.0, 4.0, "0") + .build(), + Subject::builder("s2") + .bolus(0.0, 80.0, "0") + .observation(0.5, 9.0, "0") + .observation(3.0, 2.5, "0") + .build(), + ]) +} + +fn covariate_problem() -> EstimationProblem { + let subjects = [-1.0, 0.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + Subject::builder(format!("cov{index}")) + .covariate("wt", 0.0, wt) + .covariate("sex", 0.0, if index == 2 { 1.0 } else { 0.0 }) + .bolus(0.0, 100.0, "0") + .observation(1.0, 8.0 + index as f64, "0") + .build() + }) + .collect(); + EstimationProblem::parametric(one_compartment(), Data::new(subjects)) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.0), + ) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::categorical("v", "sex", 0.0, 1.0) + .with_initial(0.0), + ) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .unwrap() +} + +fn fixed_covariate_iiv_problem() -> EstimationProblem { + let subjects = [-1.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + Subject::builder(format!("fixed-cov-iiv-{index}")) + .covariate("wt", 0.0, wt) + .bolus(0.0, 100.0, "0") + .observation(1.0, 5.0 + index as f64, "0") + .build() + }) + .collect(); + EstimationProblem::parametric(one_compartment(), Data::new(subjects)) + .parameter(Parameter::log("ke").with_initial(0.2).fixed()) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::diagonal([("ke", 1.0)])) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.0) + .fixed(), + ) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .unwrap() +} + +fn fixed_covariate_without_iiv_problem() -> EstimationProblem { + let subjects = [0.0, 1.0] + .into_iter() + .enumerate() + .map(|(index, wt)| { + Subject::builder(format!("fixed-cov-{index}")) + .covariate("wt", 0.0, wt) + .bolus(0.0, 100.0, "0") + .observation(1.0, 5.0 + index as f64, "0") + .build() + }) + .collect(); + EstimationProblem::parametric(one_compartment(), Data::new(subjects)) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .covariate_effect( + crate::estimation::parametric::CovariateEffect::continuous("ke", "wt", 0.0) + .with_initial(0.2) + .fixed(), + ) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::constant(1.0)).fixed(), + ) + .build() + .unwrap() +} + +fn problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .error_model( + "0", + ParametricErrorModel::new(ResidualErrorModel::combined(0.5, 0.1)).fixed(), + ) + .build() + .unwrap() +} + +fn constant_error_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn partial_iiv_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn iov_data() -> Data { + Data::new(vec![Subject::builder("s1") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 10.0, "0") + .build()]) +} + +fn iov_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .iov(Iov::diagonal([("ke", 0.1)])) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn markov_iov_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::log("ke").with_initial(0.2).fixed()) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.1)) + .iov(Iov::new().fixed_variance("ke", 0.1)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn uneven_iov_problem() -> EstimationProblem { + let data = Data::new(vec![ + Subject::builder("one") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .build(), + Subject::builder("two") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 10.0, "0") + .build(), + Subject::builder("three") + .bolus(0.0, 100.0, "0") + .observation(1.0, 12.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 10.0, "0") + .reset() + .bolus(0.0, 100.0, "0") + .observation(1.0, 11.0, "0") + .build(), + ]); + EstimationProblem::parametric(one_compartment(), data) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .iov(Iov::diagonal([("ke", 0.1)])) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn configured_iov_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .iov( + Iov::diagonal([("ke", 0.10)]) + .fixed_variance("v", 0.20) + .fixed_covariance("ke", "v", 0.05), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn ordered_metadata_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), iov_data()) + .parameter(Parameter::real("ke").with_initial(0.2)) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .iov(Iov::diagonal([("v", 0.20)])) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn configured_omega_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25)]).fixed_variance("v", 0.5)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn correlated_omega_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25), ("v", 0.25)]).covariance("ke", "v", 0.20)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn fixed_population_iiv_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2).fixed()) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn fixed_no_iiv_problem() -> EstimationProblem { + EstimationProblem::parametric(one_compartment(), data()) + .parameter( + Parameter::log("ke") + .with_initial(0.2) + .fixed() + .without_random_effect(), + ) + .parameter( + Parameter::log("v") + .with_initial(10.0) + .fixed() + .without_random_effect(), + ) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap() +} + +fn assert_prediction_points_equal( + actual: &pharmsol::simulator::prediction::SubjectPredictions, + expected: &pharmsol::simulator::prediction::SubjectPredictions, +) { + assert_eq!(actual.predictions().len(), expected.predictions().len()); + assert_eq!(actual.occasions(), expected.occasions()); + for (actual, expected) in actual.predictions().iter().zip(expected.predictions()) { + assert_eq!(actual.time(), expected.time()); + assert_eq!(actual.observation(), expected.observation()); + assert_eq!(actual.prediction(), expected.prediction()); + assert_eq!(actual.output(), expected.output()); + assert_eq!(actual.errorpoly(), expected.errorpoly()); + assert_eq!(actual.censoring(), expected.censoring()); + } +} + +mod controller; +mod diagnostics; +mod estimation; +mod results; +mod schedule; +mod state_and_iov; diff --git a/src/algorithms/parametric/saem/state/tests/results.rs b/src/algorithms/parametric/saem/state/tests/results.rs new file mode 100644 index 000000000..52c7161e2 --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/results.rs @@ -0,0 +1,694 @@ +use super::*; +#[test] +fn e_step_rescores_chain_zero_parameters() { + let mut state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); + let initial = state.log_likelihood(); + + state.etas[0][0][0] = 2.0_f64.ln(); + state.e_step().unwrap(); + + assert!(state.log_likelihood().is_finite()); + assert_ne!(state.log_likelihood(), initial); + assert_eq!(state.negative_log_likelihood(), -state.log_likelihood()); +} + +#[test] +fn iov_result_retains_named_omega_iov() { + let result = iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + assert_eq!(result.iov_effect_names(), &["ke"]); + assert_eq!(result.omega_iov(), Some(&ndarray::array![[0.1]])); + assert_eq!(result.conditional_modes().len(), 1); + assert_eq!(result.conditional_modes()[0].kappas.len(), 2); + assert!(result.conditional_modes()[0].objective.is_finite()); +} + +#[test] +fn result_reports_final_chain_means_for_eta_and_kappa() { + let mut state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + state.etas[0][0][0] = 0.2; + state.etas[0][1][0] = 0.4; + state.kappas[0][0][0][0] = -0.2; + state.kappas[0][1][0][0] = 0.4; + state.kappas[0][0][1][0] = 0.1; + state.kappas[0][1][1][0] = 0.3; + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.eta_chain_means().len(), 1); + assert!((result.eta_chain_means()[0].values[0] - 0.3).abs() < 1e-12); + assert_eq!(result.kappa_chain_means().len(), 2); + assert_eq!(result.kappa_chain_means()[0].occasion_index, 0); + assert!((result.kappa_chain_means()[0].values[0] - 0.1).abs() < 1e-12); + assert_eq!(result.kappa_chain_means()[1].occasion_index, 1); + assert!((result.kappa_chain_means()[1].values[0] - 0.2).abs() < 1e-12); +} + +#[test] +fn result_retains_immutable_cycle_diagnostics() { + let config = SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(1) + .compute_map(false); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + state.step().unwrap(); + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.parameter_names(), ["ke", "v"]); + assert_eq!(result.data().subjects().len(), 2); + assert_eq!( + result + .equation() + .metadata() + .expect("retained equation metadata") + .outputs()[0] + .name(), + "0" + ); + assert_eq!(result.cycle_diagnostics().len(), 2); + assert_eq!(result.cycle_diagnostics()[0].iteration, 1); + assert_eq!(result.cycle_diagnostics()[0].phase, SaemPhase::BurnIn); + assert_eq!(result.cycle_diagnostics()[1].iteration, 2); + assert_eq!(result.cycle_diagnostics()[1].phase, SaemPhase::Smoothing); + assert_eq!( + result.cycle_diagnostics()[0].population_parameters, + vec![0.2, 10.0] + ); + let final_cycle = &result.cycle_diagnostics()[1]; + assert_eq!( + final_cycle.population_parameters, + result.population_parameters() + ); + assert_eq!(&final_cycle.omega, result.omega()); + assert_eq!(final_cycle.omega_iov.as_ref(), result.omega_iov()); + assert_eq!( + final_cycle.residual_error_estimates, + result.residual_error_estimates() + ); + assert!(final_cycle.conditional_negative_log_likelihood.is_finite()); + assert!(final_cycle.eta_log_prior.is_finite()); + assert!(final_cycle.kappa_log_prior.is_finite()); +} + +#[test] +fn conditional_modes_can_be_disabled_without_relabeling_chain_means() { + let result = problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false), + ) + .unwrap(); + + assert!(result.conditional_modes().is_empty()); + assert_eq!(result.eta_chain_means().len(), 2); + let error = result.conditional_predictions(0.25, 0.0).unwrap_err(); + assert_eq!( + error.to_string(), + "conditional predictions require conditional modes; rerun with compute_map(true)" + ); +} + +#[test] +fn population_uncertainty_wires_analytical_fit_summary_without_changing_estimates() { + let equation = analytical! { + name: "population_uncertainty_summary_fixture", + params: [ke, v], + states: [central], + outputs: [cp], + routes: [infusion(iv) -> central], + structure: one_compartment, + out: |x, _p, _t, _cov, y| { y[cp] = x[central] / v; }, + }; + let data = Data::new(vec![ + Subject::builder("uncertainty-1") + .infusion(0.0, 100.0, "iv", 0.5) + .observation(1.0, 4.8, "cp") + .observation(3.0, 3.0, "cp") + .build(), + Subject::builder("uncertainty-2") + .infusion(0.0, 120.0, "iv", 0.5) + .observation(1.0, 5.4, "cp") + .observation(3.0, 3.2, "cp") + .build(), + ]); + let problem = EstimationProblem::parametric(equation, data) + .parameter(Parameter::log("ke").with_initial(0.25)) + .parameter( + Parameter::log("v") + .with_initial(20.0) + .fixed() + .without_random_effect(), + ) + .omega(Omega::new().fixed_variance("ke", 0.09)) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.4)).fixed(), + ) + .build() + .expect("population uncertainty analytical fixture"); + let mut result = problem + .fit_with( + SaemConfig::new() + .seed(0x6a_2026) + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .expect("population uncertainty analytical fit"); + let estimates_before = result.population_parameters().to_vec(); + let objective_before = result.objf(); + assert_eq!(estimates_before, vec![0.25, 20.0]); + assert_eq!(result.estimated_parameters(), &[true, false]); + assert_eq!( + result.population_uncertainty(), + &derive_population_uncertainty(result.information_diagnostics()) + ); + + let coordinates = result.information_diagnostics().coordinates.clone(); + assert_eq!(coordinates.len(), 1); + assert_eq!( + coordinates[0].kind, + InformationCoordinateKind::Population { parameter_index: 0 } + ); + result.population_uncertainty = PopulationUncertaintyDiagnostics { + coordinates, + free_covariance: Some(vec![vec![0.04]]), + free_standard_errors: Some(vec![0.2]), + spectral_condition_number: Some(1.0), + status: PopulationUncertaintyStatus::Available, + regularization: PopulationUncertaintyRegularization::None, + }; + + let summary = result.population_summary(); + assert_eq!(result.population_parameters(), estimates_before); + assert_eq!(result.objf().to_bits(), objective_before.to_bits()); + assert_eq!( + summary + .parameters + .iter() + .map(|parameter| parameter.estimate) + .collect::>(), + estimates_before + ); + assert!( + (summary.parameters[0] + .sd + .expect("free log-scale parameter SE") + - 0.2 * estimates_before[0]) + .abs() + < 1e-12 + ); + assert!( + (summary.parameters[0] + .cv_percent + .expect("free log-scale parameter CV") + - 20.0) + .abs() + < 1e-12 + ); + assert_eq!(summary.parameters[1].sd, None); + assert_eq!(summary.parameters[1].cv_percent, None); +} + +#[test] +fn initialization_result_is_non_converged_snapshot() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(1) + .burn_in(1); + let result = problem().fit_with(config).unwrap(); + let summary = result.summary(); + + assert!(!result.converged()); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert_ne!(result.termination_reason(), Some(&StopReason::Aborted)); + assert_ne!( + result.termination_reason(), + Some(&StopReason::NumericalFailure) + ); + assert_eq!(result.iterations(), 2); + assert_eq!(summary.subject_count, 2); + assert_eq!(summary.observation_count, 4); + assert_eq!(summary.parameter_count, 2); + assert!(result.objf().is_finite()); + assert_eq!(result.population_parameters().len(), 2); + assert_eq!(result.random_effect_names(), &["ke", "v"]); + assert_eq!(result.omega().dim(), (2, 2)); + assert_eq!(result.residual_sigmas().len(), 1); + assert_eq!(result.eta_chain_means().len(), 2); + assert!(result.kappa_chain_means().is_empty()); + assert_eq!(result.conditional_modes().len(), 2); + assert!(result + .conditional_modes() + .iter() + .all(|mode| mode.objective.is_finite())); + assert_eq!(result.population_summary().parameters.len(), 2); + assert_eq!(result.individual_summaries().len(), 2); +} + +// ─── Operational convergence tests ─────────────────────────────────── + +#[test] +fn operational_convergence_disabled_when_config_is_none() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let config = SaemConfig::new() + .k1_iterations(2) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .compute_map(false) + .seed(42); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + assert!(ops.checks.is_empty()); + assert!(!ops.used_for_termination); + assert!(!ops.final_check_reused); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); +} + +#[test] +fn operational_convergence_records_checkpoints_when_configured() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(43); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + // Should have at least one checkpoint (smoothing phase produces checkpoints) + assert!(!ops.checks.is_empty(), "expected at least one checkpoint"); + // Each checkpoint should have all fields populated + for check in &ops.checks { + assert!(check.checkpoint_seed.is_some()); + assert!(check.z_quantile.is_some()); + assert!(check.implied_minimum_ess.is_some()); + assert!(!check.criteria.is_empty()); + assert!(check.markov.is_some()); + assert_eq!( + check.averaged_iterations, + check.markov.as_ref().unwrap().n_avg + ); + } +} + +#[test] +fn operational_convergence_has_exact_criterion_names() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(44); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + assert!(!ops.checks.is_empty()); + let first_check = &ops.checks[0]; + let names: Vec<&str> = first_check + .criteria + .iter() + .map(|c| c.name.as_str()) + .collect(); + assert!(names.contains(&"max_rhat")); + assert!(names.contains(&"min_bulk_ess")); + assert!(names.contains(&"min_average_bulk_ess_per_split_chain")); + assert!(names.contains(&"relative_fixed_width")); + assert!(names.contains(&"newton_displacement")); + assert!(names.contains(&"newton_displacement_mc_sd")); + assert!(names.contains(&"omega_boundary_rejection_run")); + assert!(names.contains(&"omega_iov_boundary_rejection_run")); +} + +#[test] +fn covariance_boundary_rejection_blocks_converged_stop_reason() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 100.0, 100.0); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.99, 1)) + .operational_convergence(oc) + .compute_map(false) + .seed(47); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + state.cycle_diagnostics[0].omega_relative_spd_margin = Some(0.5); + state.cycle_diagnostics[0].omega_update_rejected = true; + + state.step().unwrap(); + + let check = state + .operational_diagnostics + .checks + .last() + .expect("operational checkpoint"); + let boundary = check + .criteria + .iter() + .find(|criterion| criterion.name == "omega_boundary_rejection_run") + .expect("Omega boundary criterion"); + assert_eq!( + boundary.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + assert_ne!(state.status, Status::Stop(StopReason::Converged)); +} + +#[test] +fn iov_boundary_rejection_blocks_converged_stop_reason() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.99, 1)) + .operational_convergence(OperationalConvergenceConfig::literature_guided( + 1, 1, 1.0, 0.95, 100.0, 100.0, + )) + .compute_map(false) + .seed(48); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + state.step().unwrap(); + state.cycle_diagnostics[0].omega_iov_relative_spd_margin = Some(0.5); + state.cycle_diagnostics[0].omega_iov_update_rejected = true; + state.step().unwrap(); + + let check = state + .operational_diagnostics + .checks + .last() + .expect("operational checkpoint"); + let boundary = check + .criteria + .iter() + .find(|criterion| criterion.name == "omega_iov_boundary_rejection_run") + .expect("Omega_IOV boundary criterion"); + assert_eq!( + boundary.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + assert_ne!(state.status, Status::Stop(StopReason::Converged)); +} + +#[test] +fn operational_convergence_waits_for_complete_covariance_window() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(5) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 5)) + .operational_convergence(OperationalConvergenceConfig::literature_guided( + 1, 1, 1.0, 0.95, 100.0, 100.0, + )) + .compute_map(false) + .seed(49); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + state.step().unwrap(); + state.step().unwrap(); + + let first = state + .operational_diagnostics + .checks + .last() + .expect("first operational checkpoint"); + let first_boundary = first + .criteria + .iter() + .find(|criterion| criterion.name == "omega_boundary_rejection_run") + .expect("Omega boundary criterion"); + assert!(matches!( + first_boundary.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + )); + assert!(matches!( + first.outcome, + OperationalConvergenceOutcome::Ineligible { .. } + )); + assert_ne!(state.status, Status::Stop(StopReason::Converged)); + + while state.cycle < 5 && !state.status.is_stop() { + state.step().unwrap(); + } + let eligible = state + .operational_diagnostics + .checks + .last() + .expect("fifth-cycle operational checkpoint"); + assert_eq!(eligible.iteration, 5); + let eligible_boundary = eligible + .criteria + .iter() + .find(|criterion| criterion.name == "omega_boundary_rejection_run") + .expect("Omega boundary criterion"); + assert_eq!( + eligible_boundary.status, + OperationalConvergenceCriterionStatus::Satisfied + ); +} + +#[test] +fn operational_convergence_final_checkpoint_runs_once_with_truthful_flags() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + // check_interval=1 means every smoothing iteration is a checkpoint, + // so the last scheduled checkpoint and the mandatory final will overlap. + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(2) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(45); + let result = problem().fit_with(config).unwrap(); + let ops = result.operational_diagnostics(); + assert!(!ops.final_check_reused); + let final_check = ops.checks.last().expect("final checkpoint"); + assert!(final_check.scheduled); + assert!(final_check.mandatory_final); + assert_eq!( + ops.checks + .iter() + .filter(|check| check.iteration == final_check.iteration) + .count(), + 1 + ); +} + +#[test] +fn operational_convergence_checkpoint_seed_is_deterministic_and_global_seed_is_unchanged() { + use crate::algorithms::parametric::{LugsailConfig, MarkovSimulationVarianceConfig}; + let markov = MarkovSimulationVarianceConfig::new( + 7, + 0, + 12, + 6, + LugsailConfig::over_lugsail_bartlett(), + 4, + 1024 * 1024, + ); + let oc = OperationalConvergenceConfig::literature_guided(1, 1, 1.0, 0.95, 0.1, 0.02); + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .markov_simulation_variance(markov) + .covariance_stability(CovarianceStabilityConfig::new(0.01, 2)) + .operational_convergence(oc) + .compute_map(false) + .seed(46); + let result1 = problem().fit_with(config.clone()).unwrap(); + let result2 = problem().fit_with(config).unwrap(); + + let ops1 = result1.operational_diagnostics(); + let ops2 = result2.operational_diagnostics(); + assert_eq!(ops1.checks.len(), ops2.checks.len()); + for (c1, c2) in ops1.checks.iter().zip(ops2.checks.iter()) { + assert_eq!(c1.checkpoint_seed, c2.checkpoint_seed); + assert_eq!(c1.z_quantile, c2.z_quantile); + assert_eq!(c1.outcome, c2.outcome); + } + // Canonical fit result must be unchanged by operational convergence + assert_eq!( + result1.population_parameters(), + result2.population_parameters() + ); + assert_eq!(result1.omega(), result2.omega()); + assert_eq!(result1.conditional_n2ll(), result2.conditional_n2ll()); +} + +#[test] +fn normal_two_sided_z_covers_common_confidence_levels() { + use statrs::distribution::{ContinuousCDF, Normal}; + let norm = Normal::new(0.0, 1.0).unwrap(); + for p in [0.90, 0.95, 0.99] { + let expected = norm.inverse_cdf(p + (1.0 - p) / 2.0); + let actual = normal_two_sided_z(p); + assert!((actual - expected).abs() < 1e-10); + } +} + +#[test] +fn gong_flegal_fixed_width_and_implied_ess_are_exact() { + let z = normal_two_sided_z(0.95); + let epsilon = 0.05; + let implied = 4.0 * z * z / (epsilon * epsilon); + assert!((implied - 6146.34).abs() < 0.1); + let boundary_fraction = epsilon / (2.0 * z); + assert!(2.0 * z * boundary_fraction <= epsilon); + assert!(2.0 * z * (boundary_fraction + 1e-12) > epsilon); +} + +#[test] +fn evaluate_criterion_detects_satisfied_not_satisfied_and_unavailable() { + let satisfied = evaluate_criterion("test", Some(0.5), 1.0, |v| v <= 1.0); + assert_eq!( + satisfied.status, + OperationalConvergenceCriterionStatus::Satisfied + ); + assert_eq!(satisfied.observed, Some(0.5)); + + let not_satisfied = evaluate_criterion("test", Some(2.0), 1.0, |v| v <= 1.0); + assert_eq!( + not_satisfied.status, + OperationalConvergenceCriterionStatus::NotSatisfied + ); + assert_eq!(not_satisfied.observed, Some(2.0)); + + let unavailable_none = evaluate_criterion("test", None, 1.0, |v| v <= 1.0); + assert!(matches!( + unavailable_none.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + )); + assert_eq!(unavailable_none.observed, None); + + let unavailable_nan = evaluate_criterion("test", Some(f64::NAN), 1.0, |v| v <= 1.0); + assert!(matches!( + unavailable_nan.status, + OperationalConvergenceCriterionStatus::Unavailable(_) + )); +} + +#[test] +fn newton_displacement_requires_matching_dimensions() { + let empty_info = InformationDiagnostics { + coordinates: vec![], + recursion_cycles: 0, + delta: vec![], + g: vec![], + expected_complete_hessian: vec![], + observed_hessian: vec![], + observed_information: vec![], + status: InformationStatus::Available, + }; + let empty_markov = MarkovSimulationVarianceDiagnostics::disabled(); + assert_eq!(newton_displacement(&empty_info, &empty_markov), None); + assert_eq!(newton_displacement_mc_sd(&empty_info, &empty_markov), None); +} diff --git a/src/algorithms/parametric/saem/state/tests/schedule.rs b/src/algorithms/parametric/saem/state/tests/schedule.rs new file mode 100644 index 000000000..6935ac064 --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/schedule.rs @@ -0,0 +1,659 @@ +use super::*; +#[test] +fn initialization_builds_initial_objective() { + let initialization = SaemInitialization::create(&problem(), &SaemConfig::default()).unwrap(); + + assert_eq!( + initialization.initial_population_parameters, + vec![0.2, 10.0] + ); + assert_eq!(initialization.initial_subject_log_likelihoods.len(), 2); + assert!(initialization.initial_negative_log_likelihood.is_finite()); +} + +#[test] +fn initialization_rejects_estimated_iiv_variance_below_floor() { + let mut config = SaemConfig::new(); + config.omega_min_variance = 0.3; + + let error = SaemInitialization::create(&configured_omega_problem(), &config) + .unwrap_err() + .to_string(); + + assert!(error.contains( + "initial Omega variance for estimated effect 'ke' (0.25) is below configured omega_min_variance (0.3)" + )); +} + +#[test] +fn initialization_rejects_estimated_iov_variance_below_floor() { + let config = SaemConfig::new().omega_iov_min_variance(0.11); + + let error = SaemInitialization::create(&configured_iov_problem(), &config) + .unwrap_err() + .to_string(); + + assert!(error.contains( + "initial Omega_IOV variance for estimated effect 'ke' (0.1) is below configured omega_iov_min_variance (0.11)" + )); +} + +#[test] +fn initialization_floor_exempts_fixed_covariance_diagonals() { + let problem = EstimationProblem::parametric(one_compartment(), data()) + .parameter(Parameter::log("ke").with_initial(0.2)) + .parameter(Parameter::log("v").with_initial(10.0)) + .omega(Omega::diagonal([("ke", 0.25)]).fixed_variance("v", 0.01)) + .error_model("0", ResidualErrorModel::constant(1.0)) + .build() + .unwrap(); + let mut config = SaemConfig::new(); + config.omega_min_variance = 0.1; + + let initialization = SaemInitialization::create(&problem, &config).unwrap(); + + assert_eq!(initialization.omega.initial()[[0, 0]], 0.25); + assert_eq!(initialization.omega.initial()[[1, 1]], 0.01); + assert!(!initialization.omega.estimated_mask()[[1, 1]]); +} + +#[test] +fn schedule_counts_real_internal_phases() { + let config = SaemConfig::new() + .burn_in(100) + .k1_iterations(400) + .k2_iterations(700); + let schedule = SaemSchedule::from_config(&config); + let counts = (1..=schedule.total_iterations).fold([0_usize; 3], |mut counts, cycle| { + match schedule.phase(cycle) { + SaemPhase::BurnIn => counts[0] += 1, + SaemPhase::Exploration => counts[1] += 1, + SaemPhase::Smoothing => counts[2] += 1, + } + counts + }); + + assert_eq!(counts, [100, 300, 700]); + assert_eq!(schedule.total_iterations, 1100); +} + +#[test] +fn covariate_omega_cap_applies_only_during_exploration() { + assert_eq!( + covariate_omega_update_maximum_fraction(true, SaemPhase::BurnIn, 0.1), + 1.0 + ); + assert_eq!( + covariate_omega_update_maximum_fraction(true, SaemPhase::Exploration, 0.1), + 0.1 + ); + assert_eq!( + covariate_omega_update_maximum_fraction(true, SaemPhase::Smoothing, 0.1), + 1.0 + ); + assert_eq!( + covariate_omega_update_maximum_fraction(false, SaemPhase::Exploration, 0.1), + 1.0 + ); +} + +#[derive(Debug)] +struct CommonMomentCycle { + expected_phi: Vec>, + global_second_moment: Array2, + beta: Vec, + subject_means: Vec>, + covariance_target: Array2, + omega: Array2, +} + +fn common_moment_cycle( + statistics: &mut CovariateSufficientStatistics, + observed: &CovariateSufficientStatistics, + gain: f64, + designs: &[Array2], + current_omega: &Array2, + omega_specification: &ResolvedOmega, +) -> Result { + statistics.stochastic_update(observed, gain)?; + let offsets = vec![vec![0.0]; designs.len()]; + let beta = solve_covariate_gls(CovariateGlsProblem { + design: designs, + expected_phi: &statistics.expected_phi, + offset: &offsets, + omega: current_omega, + })?; + let subject_means = designs + .iter() + .map(|design| vec![design[[0, 0]] * beta[0] + design[[0, 1]] * beta[1]]) + .collect::>(); + let covariance_target = subject_centered_omega( + &statistics.global_second_moment, + &statistics.expected_phi, + &subject_means, + )?; + let omega = omega_specification + .update_with_status(current_omega, &covariance_target, 1e-6)? + .matrix; + Ok(CommonMomentCycle { + expected_phi: statistics.expected_phi.clone(), + global_second_moment: statistics.global_second_moment.clone(), + beta, + subject_means, + covariance_target, + omega, + }) +} + +fn assert_nested_close(actual: &[Vec], expected: &[Vec]) { + assert_eq!(actual.len(), expected.len()); + for (actual_row, expected_row) in actual.iter().zip(expected) { + assert_eq!(actual_row.len(), expected_row.len()); + for (actual_value, expected_value) in actual_row.iter().zip(expected_row) { + assert!((actual_value - expected_value).abs() <= 1e-12); + } + } +} + +#[test] +fn common_gain_raw_moments_are_coherent_cycle_by_cycle() { + let designs = [-1.0, 0.0, 1.0] + .into_iter() + .map(|covariate| ndarray::array![[1.0, covariate]]) + .collect::>(); + let parameters = [Parameter::log("x")].into_iter().collect(); + let prior = + ParametricPrior::new(parameters, Some(Omega::diagonal([("x", 1.0)])), None).unwrap(); + let mut current_omega = prior.omega().clone(); + let mut statistics = CovariateSufficientStatistics { + expected_phi: vec![vec![0.0]; 3], + global_second_moment: ndarray::array![[1.0]], + }; + let exploration_observed = CovariateSufficientStatistics::from_subject_chains(&[ + vec![vec![-1.4], vec![-0.6]], + vec![vec![-0.4], vec![0.4]], + vec![vec![0.6], vec![1.4]], + ]) + .unwrap(); + let first_smoothing_observed = CovariateSufficientStatistics::from_subject_chains(&[ + vec![vec![-1.5], vec![-0.5]], + vec![vec![0.5], vec![1.5]], + vec![vec![2.5], vec![3.5]], + ]) + .unwrap(); + let second_smoothing_observed = CovariateSufficientStatistics::from_subject_chains(&[ + vec![vec![-3.0], vec![-1.0]], + vec![vec![-1.0], vec![1.0]], + vec![vec![1.0], vec![3.0]], + ]) + .unwrap(); + + let burn = common_moment_cycle( + &mut statistics, + &exploration_observed, + 0.0, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_eq!(burn.expected_phi, vec![vec![0.0]; 3]); + assert_eq!(burn.global_second_moment, ndarray::array![[1.0]]); + assert_eq!(burn.beta, vec![0.0, 0.0]); + assert_eq!(burn.subject_means, vec![vec![0.0]; 3]); + assert_eq!(burn.covariance_target, ndarray::array![[1.0]]); + assert_eq!(burn.omega, ndarray::array![[1.0]]); + + let exploration = common_moment_cycle( + &mut statistics, + &exploration_observed, + 1.0, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_nested_close( + &exploration.expected_phi, + &[vec![-1.0], vec![0.0], vec![1.0]], + ); + assert!((exploration.global_second_moment[[0, 0]] - 62.0 / 75.0).abs() <= 1e-12); + assert!((exploration.beta[0] - 0.0).abs() <= 1e-12); + assert!((exploration.beta[1] - 1.0).abs() <= 1e-12); + assert_nested_close(&exploration.subject_means, &exploration.expected_phi); + assert!((exploration.covariance_target[[0, 0]] - 0.16).abs() <= 1e-12); + assert!((exploration.omega[[0, 0]] - 0.16).abs() <= 1e-12); + current_omega = exploration.omega.clone(); + + let first_smoothing = common_moment_cycle( + &mut statistics, + &first_smoothing_observed, + 1.0, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_nested_close( + &first_smoothing.expected_phi, + &[vec![-1.0], vec![1.0], vec![3.0]], + ); + assert!((first_smoothing.global_second_moment[[0, 0]] - 47.0 / 12.0).abs() <= 1e-12); + assert!((first_smoothing.beta[0] - 1.0).abs() <= 1e-12); + assert!((first_smoothing.beta[1] - 2.0).abs() <= 1e-12); + assert_nested_close( + &first_smoothing.subject_means, + &first_smoothing.expected_phi, + ); + assert!((first_smoothing.covariance_target[[0, 0]] - 0.25).abs() <= 1e-12); + assert!((first_smoothing.omega[[0, 0]] - 0.25).abs() <= 1e-12); + current_omega = first_smoothing.omega.clone(); + + let second_smoothing = common_moment_cycle( + &mut statistics, + &second_smoothing_observed, + 0.5, + &designs, + ¤t_omega, + prior.resolved_omega(), + ) + .unwrap(); + assert_nested_close( + &second_smoothing.expected_phi, + &[vec![-1.5], vec![0.5], vec![2.5]], + ); + assert!((second_smoothing.global_second_moment[[0, 0]] - 91.0 / 24.0).abs() <= 1e-12); + assert!((second_smoothing.beta[0] - 0.5).abs() <= 1e-12); + assert!((second_smoothing.beta[1] - 2.0).abs() <= 1e-12); + assert_nested_close( + &second_smoothing.subject_means, + &second_smoothing.expected_phi, + ); + assert!((second_smoothing.covariance_target[[0, 0]] - 0.875).abs() <= 1e-12); + assert!((second_smoothing.omega[[0, 0]] - 0.875).abs() <= 1e-12); + + for cycle in [burn, exploration, first_smoothing, second_smoothing] { + let mean_square = cycle + .expected_phi + .iter() + .map(|row| row[0] * row[0]) + .sum::() + / cycle.expected_phi.len() as f64; + assert!(cycle.global_second_moment[[0, 0]] + 1e-12 >= mean_square); + assert!(cycle.covariance_target[[0, 0]] >= -1e-12); + } +} + +#[test] +fn coherent_covariance_target_precedes_structured_gem_constraints() { + let coherent_target = ndarray::array![[0.002, 0.0], [0.0, 0.04]]; + assert!(cholesky_lower(&coherent_target).is_ok()); + let parameters = [Parameter::log("ke"), Parameter::log("v")] + .into_iter() + .collect(); + let prior = ParametricPrior::new( + parameters, + Some( + Omega::new() + .variance("ke", 0.02) + .fixed_variance("v", 0.04) + .fixed_covariance("ke", "v", 0.012), + ), + None, + ) + .unwrap(); + + let constrained = prior + .resolved_omega() + .update_with_status(prior.omega(), &coherent_target, 0.0) + .unwrap(); + + assert_eq!(coherent_target[[0, 0]], 0.002); + assert!((constrained.matrix[[0, 0]] - 0.0092).abs() <= 1e-10); + assert_eq!(constrained.matrix[[0, 1]], 0.012); + assert_eq!(constrained.matrix[[1, 1]], 0.04); + assert_ne!(constrained.matrix, coherent_target); +} + +#[test] +fn covariate_update_uses_common_moments_and_no_second_smoothing_gain() { + let mut statistics = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![0.0], vec![2.0]]]).unwrap(); + let exploration_observed = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![2.0], vec![4.0]]]).unwrap(); + statistics + .stochastic_update(&exploration_observed, 1.0) + .unwrap(); + assert_eq!(statistics.expected_phi, vec![vec![3.0]]); + assert_eq!(statistics.global_second_moment, ndarray::array![[10.0]]); + let exploration_variance = statistics.global_second_moment[[0, 0]] + - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; + let exploration_candidate = ndarray::array![[exploration_variance]]; + assert_eq!(exploration_candidate, ndarray::array![[1.0]]); + + let parameters = [Parameter::log("x")].into_iter().collect(); + let prior = + ParametricPrior::new(parameters, Some(Omega::diagonal([("x", 0.25)])), None).unwrap(); + let exploration = prior + .resolved_omega() + .update_with_status_and_max_fraction( + prior.omega(), + &exploration_candidate, + 0.0, + covariate_omega_update_maximum_fraction(true, SaemPhase::Exploration, 0.1), + ) + .unwrap(); + assert!((exploration.matrix[[0, 0]] - 0.325).abs() <= 1e-12); + + let smoothing_observed = + CovariateSufficientStatistics::from_subject_chains(&[vec![vec![4.0], vec![6.0]]]).unwrap(); + statistics + .stochastic_update(&smoothing_observed, 0.5) + .unwrap(); + assert_eq!(statistics.expected_phi, vec![vec![4.0]]); + assert_eq!(statistics.global_second_moment, ndarray::array![[18.0]]); + let smoothing_variance = statistics.global_second_moment[[0, 0]] + - statistics.expected_phi[0][0] * statistics.expected_phi[0][0]; + let smoothing_candidate = ndarray::array![[smoothing_variance]]; + assert_eq!(smoothing_candidate, ndarray::array![[2.0]]); + + let smoothing = prior + .resolved_omega() + .update_with_status(&exploration.matrix, &smoothing_candidate, 0.0) + .unwrap(); + assert_eq!(smoothing.matrix, smoothing_candidate); +} + +#[test] +fn covariate_state_m_step_caps_exploration_and_does_not_resmooth_omega() { + let config = SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(2) + .omega_sa_max_step(0.1) + .compute_map(false); + let mut state = SaemState::from_problem(fixed_covariate_iiv_problem(), &config).unwrap(); + + for subject_chains in &mut state.etas { + subject_chains[0][0] = 2.0; + subject_chains[1][0] = -2.0; + } + state.cycle = 2; + assert_eq!( + state.initialization.schedule.phase(state.cycle), + SaemPhase::Exploration + ); + assert_eq!( + state + .initialization + .schedule + .stochastic_approximation_step(state.cycle), + 1.0 + ); + state.m_step().unwrap(); + + assert!((state.iiv_second_moment[[0, 0]] - 4.0).abs() <= 1e-12); + assert!((state.omega[[0, 0]] - 1.3).abs() <= 1e-12); + + for subject_chains in &mut state.etas { + subject_chains[0][0] = 4.0; + subject_chains[1][0] = -4.0; + } + state.cycle = 4; + assert_eq!( + state.initialization.schedule.phase(state.cycle), + SaemPhase::Smoothing + ); + assert_eq!( + state + .initialization + .schedule + .stochastic_approximation_step(state.cycle), + 0.5 + ); + state.m_step().unwrap(); + + // The common raw history moves from variance 4 toward 16 with gain 0.5, + // giving 10. Omega installs that coherent target directly. Applying the + // smoothing gain a second time would instead leave Omega below 10. + assert!((state.iiv_second_moment[[0, 0]] - 10.0).abs() <= 1e-12); + assert!((state.omega[[0, 0]] - 10.0).abs() <= 1e-12); +} + +#[test] +fn schedule_splits_burn_in_exploration_and_smoothing() { + let config = SaemConfig::new() + .k1_iterations(300) + .k2_iterations(100) + .burn_in(5); + let schedule = SaemSchedule::from_config(&config); + + assert_eq!(schedule.pure_burn_in, 5); + assert_eq!(schedule.exploration_iterations, 295); + assert_eq!(schedule.smoothing_iterations, 100); + assert_eq!(schedule.total_iterations, 400); + assert_eq!(schedule.variance_floor_iterations, 150); + assert_eq!(schedule.minimum_residual_sigma, 1e-6); + assert_eq!(schedule.stochastic_approximation_step(1), 0.0); + assert_eq!(schedule.stochastic_approximation_step(6), 1.0); + assert_eq!(schedule.stochastic_approximation_step(301), 1.0); + assert_eq!(schedule.stochastic_approximation_step(302), 0.5); + assert_eq!(schedule.covariance_step(1), 0.1); + assert_eq!(schedule.covariance_step(6), 0.1); + assert_eq!(schedule.covariance_step(300), 0.1); + assert_eq!(schedule.covariance_step(301), 1.0); + assert_eq!(schedule.covariance_step(302), 0.5); + assert!(!schedule.covariance_update_active(5)); + assert!(schedule.covariance_update_active(6)); + assert_eq!(schedule.guarded_residual_sigma(1, 1.0, 0.1), 0.97); + assert_eq!(schedule.guarded_residual_sigma(151, 1.0, 0.1), 0.1); + assert_eq!(schedule.guarded_residual_sigma(151, 1.0, 0.0), 1e-6); +} + +#[test] +fn averaged_schedule_uses_alpha_only_during_smoothing() { + let schedule = SaemSchedule::from_config( + &SaemConfig::new() + .k1_iterations(3) + .burn_in(1) + .k2_iterations(4) + .averaged_iterates(0.75), + ); + assert_eq!(schedule.stochastic_approximation_step(1), 0.0); + assert_eq!(schedule.stochastic_approximation_step(2), 1.0); + assert_eq!(schedule.stochastic_approximation_step(3), 1.0); + assert_eq!(schedule.stochastic_approximation_step(4), 1.0); + assert_eq!( + schedule.stochastic_approximation_step(5), + 2.0_f64.powf(-0.75) + ); + assert_eq!( + schedule.stochastic_approximation_step(7), + 4.0_f64.powf(-0.75) + ); +} + +#[test] +fn averaged_result_uses_only_completed_smoothing_iterates() { + let config = SaemConfig::new() + .k1_iterations(2) + .burn_in(1) + .k2_iterations(3) + .averaged_iterates(0.75) + .compute_map(false) + .seed(9981); + let result = problem().fit_with(config).unwrap(); + let metadata = result.estimator_metadata(); + assert!(metadata.average_applied); + assert_eq!(metadata.averaging_start_cycle, Some(3)); + assert_eq!(metadata.averaged_iterations, 3); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + + let smoothing = &result.cycle_diagnostics()[2..]; + for parameter_index in 0..result.population_parameters().len() { + if !result.estimated_parameters()[parameter_index] { + continue; + } + let expected = smoothing + .iter() + .map(|cycle| { + population_phi(&cycle.population_parameters, result.parameter_scales()).unwrap() + [parameter_index] + }) + .sum::() + / smoothing.len() as f64; + let installed = population_phi(result.population_parameters(), result.parameter_scales()) + .unwrap()[parameter_index]; + assert!((installed - expected).abs() < 1e-12); + } + for row in 0..result.omega().nrows() { + for col in 0..result.omega().ncols() { + let expected = smoothing + .iter() + .map(|cycle| cycle.omega[[row, col]]) + .sum::() + / smoothing.len() as f64; + assert!((result.omega()[[row, col]] - expected).abs() < 1e-12); + } + } + cholesky_lower(result.omega()).unwrap(); +} + +#[test] +fn averaged_iov_installation_is_canonical_and_preserves_latent_coordinates() { + let config = SaemConfig::new() + .n_chains(2) + .mcmc_iterations(2) + .k1_iterations(1) + .k2_iterations(3) + .burn_in(0) + .averaged_iterates(0.75) + .compute_map(false) + .seed(71_004); + let mut state = SaemState::from_problem(configured_iov_problem(), &config) + .expect("averaged IOV state should initialize"); + while matches!(state.status, Status::Continue) { + state.step().expect("averaged IOV cycle should complete"); + } + let cycle_records = state.cycle_diagnostics.clone(); + let smoothing = &cycle_records[1..]; + let terminal_phi = population_phi( + &state.population_parameters, + &state.initialization.parameter_scales, + ) + .expect("terminal population phi should be valid"); + let terminal_absolute_phi = state + .etas + .iter() + .map(|chains| { + chains + .iter() + .map(|eta| { + state + .initialization + .random_effect_indices + .iter() + .enumerate() + .map(|(eta_index, parameter_index)| { + terminal_phi[*parameter_index] + eta[eta_index] + }) + .collect::>() + }) + .collect::>() + }) + .collect::>(); + let terminal_kappas = state.kappas.clone(); + let average = state + .iterate_average + .clone() + .expect("completed smoothing average"); + + let metadata = state + .install_iterate_average() + .expect("averaged IOV state should install"); + assert!(metadata.average_applied); + assert_eq!(metadata.averaging_start_cycle, Some(2)); + assert_eq!(metadata.averaged_iterations, 3); + assert_eq!(state.cycle_diagnostics, cycle_records); + assert_eq!(state.kappas, terminal_kappas); + + let installed_phi = population_phi( + &state.population_parameters, + &state.initialization.parameter_scales, + ) + .expect("installed population phi should be valid"); + assert_eq!(installed_phi, average.population_phi); + for (subject_index, chains) in state.etas.iter().enumerate() { + for (chain_index, eta) in chains.iter().enumerate() { + for (eta_index, parameter_index) in state + .initialization + .random_effect_indices + .iter() + .copied() + .enumerate() + { + assert!( + (installed_phi[parameter_index] + eta[eta_index] + - terminal_absolute_phi[subject_index][chain_index][eta_index]) + .abs() + < 1e-14 + ); + } + } + } + + let omega_iov = state.omega_iov.as_ref().expect("installed Omega_IOV"); + let iov_specification = state + .initialization + .omega_iov + .as_ref() + .expect("IOV specification"); + assert_eq!(omega_iov, &average.omega_iov.expect("averaged Omega_IOV")); + for row in 0..omega_iov.nrows() { + for col in 0..omega_iov.ncols() { + let expected = if iov_specification.estimated_mask()[[row, col]] { + smoothing + .iter() + .map(|cycle| { + cycle + .omega_iov + .as_ref() + .expect("smoothing cycle should retain Omega_IOV")[[row, col]] + }) + .sum::() + / smoothing.len() as f64 + } else { + iov_specification.initial()[[row, col]] + }; + assert!((omega_iov[[row, col]] - expected).abs() < 1e-12); + } + } + + let n_chains = state.initialization.n_chains as f64; + let mut direct_likelihoods = vec![0.0; state.initialization.subject_ids.len()]; + let mut direct_eta_priors = vec![0.0; state.initialization.subject_ids.len()]; + let mut direct_kappa_priors = vec![0.0; state.initialization.subject_ids.len()]; + for subject_index in 0..state.initialization.subject_ids.len() { + for chain_index in 0..state.initialization.n_chains { + let score = state + .score_subject_latents( + subject_index, + &state.etas[subject_index][chain_index], + &state.kappas[subject_index][chain_index], + ) + .expect("installed latent score should be directly calculable"); + direct_likelihoods[subject_index] += score.log_likelihood / n_chains; + direct_eta_priors[subject_index] += score.eta_log_prior / n_chains; + direct_kappa_priors[subject_index] += score.kappa_log_prior / n_chains; + } + } + assert_eq!(state.subject_log_likelihoods, direct_likelihoods); + assert_eq!(state.subject_log_priors, direct_eta_priors); + assert_eq!(state.subject_kappa_log_priors, direct_kappa_priors); + assert_eq!( + state.negative_log_likelihood, + negative_log_likelihood(&direct_likelihoods) + ); +} diff --git a/src/algorithms/parametric/saem/state/tests/state_and_iov.rs b/src/algorithms/parametric/saem/state/tests/state_and_iov.rs new file mode 100644 index 000000000..6f5c47707 --- /dev/null +++ b/src/algorithms/parametric/saem/state/tests/state_and_iov.rs @@ -0,0 +1,888 @@ +use super::*; +#[test] +fn explicit_terminal_policy_preserves_default_trajectory() { + let base = SaemConfig::new() + .k1_iterations(2) + .burn_in(1) + .k2_iterations(2) + .compute_map(false) + .seed(7788); + let default = problem().fit_with(base.clone()).unwrap(); + let explicit = problem() + .fit_with(base.estimator_policy(SaemEstimatorPolicy::TerminalIterate)) + .unwrap(); + assert_eq!(default.cycle_diagnostics(), explicit.cycle_diagnostics()); + assert_eq!( + default.population_parameters(), + explicit.population_parameters() + ); + assert_eq!(default.omega(), explicit.omega()); + assert_eq!(default.conditional_n2ll(), explicit.conditional_n2ll()); + assert_eq!(default.termination_reason(), Some(&StopReason::MaxCycles)); + assert_eq!(explicit.termination_reason(), Some(&StopReason::MaxCycles)); +} + +fn residual_phase_schedule() -> SaemSchedule { + let mut schedule = SaemSchedule::from_config( + &SaemConfig::new() + .burn_in(0) + .k1_iterations(4) + .k2_iterations(3), + ); + schedule.variance_floor_iterations = 1; + schedule +} + +#[test] +fn combined_residual_component_anneals_during_configured_period() { + let schedule = residual_phase_schedule(); + let applied = applied_combined_residual_component(&schedule, 1, 1.0, 0.1, true); + assert_eq!(applied, schedule.annealing_alpha); +} + +#[test] +fn combined_residual_component_replaces_directly_in_remaining_exploration() { + let schedule = residual_phase_schedule(); + assert_eq!( + applied_combined_residual_component(&schedule, 2, 1.0, 0.1, true), + 0.1 + ); +} + +#[test] +fn combined_residual_component_smooths_in_k2() { + let schedule = residual_phase_schedule(); + assert_eq!( + applied_combined_residual_component(&schedule, 6, 1.0, 0.2, true), + 0.6 + ); +} + +#[test] +fn combined_residual_component_preserves_fixed_value() { + let schedule = residual_phase_schedule(); + assert_eq!( + applied_combined_residual_component(&schedule, 1, 1.0, 0.1, false), + 1.0 + ); + assert_eq!( + applied_combined_residual_component(&schedule, 6, 1.0, 0.1, false), + 1.0 + ); +} + +#[test] +fn burn_in_warms_covariance_statistics_without_updating_parameters() { + let config = SaemConfig::new() + .n_chains(1) + .burn_in(2) + .k1_iterations(4) + .omega_sa_max_step(0.1); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + for subject_chains in &mut state.etas { + subject_chains[0].fill(2.0); + } + let initial_population = state.population_parameters.clone(); + let initial_omega = state.omega.clone(); + let initial_iiv_second_moment = state.iiv_second_moment.clone(); + let initial_phi_second_moment = state.sufficient_statistics.second_moment.clone(); + + state.step().unwrap(); + + assert_eq!(state.cycle, 1); + assert_eq!(state.cycle_diagnostics[0].phase, SaemPhase::BurnIn); + assert_eq!(state.population_parameters, initial_population); + assert_eq!(state.omega, initial_omega); + assert_ne!(state.iiv_second_moment, initial_iiv_second_moment); + assert_ne!( + state.sufficient_statistics.second_moment, + initial_phi_second_moment + ); +} + +#[test] +fn chain_count_auto_scales_for_small_datasets() { + assert_eq!(n_chains(&SaemConfig::default(), 2), 25); + assert_eq!(n_chains(&SaemConfig::new().n_chains(3), 2), 3); + assert_eq!(n_chains(&SaemConfig::default(), 100), 1); +} + +#[test] +fn result_retains_requested_config_and_separate_effective_chain_count() { + let config = SaemConfig::new() + .n_chains(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false) + .seed(9876); + let serialized_config = serde_json::to_value(&config).unwrap(); + let state = SaemState::from_problem(problem(), &config).unwrap(); + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.config().n_chains, 1); + assert_eq!(result.effective_n_chains(), 25); + assert_eq!( + serde_json::to_value(result.config()).unwrap(), + serialized_config + ); +} + +#[test] +fn result_parameter_metadata_preserves_declaration_order() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false); + let state = SaemState::from_problem(ordered_metadata_problem(), &config).unwrap(); + + let result = Box::new(state).into_result().unwrap(); + + assert_eq!(result.parameter_names(), ["ke", "v"]); + assert_eq!( + result.parameter_scales(), + [ParameterScale::Identity, ParameterScale::Log] + ); + assert_eq!(result.estimated_parameters(), [true, false]); + assert_eq!(result.random_effect_indices(), [0]); + assert_eq!(result.random_effect_names(), ["ke"]); + assert_eq!(result.iov_effect_indices(), [1]); + assert_eq!(result.iov_effect_names(), ["v"]); +} + +#[test] +fn result_retains_exact_symmetric_iiv_covariance_masks() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false); + let configured = + Box::new(SaemState::from_problem(configured_omega_problem(), &config).unwrap()) + .into_result() + .unwrap(); + let correlated = + Box::new(SaemState::from_problem(correlated_omega_problem(), &config).unwrap()) + .into_result() + .unwrap(); + + assert_eq!(configured.random_effect_names(), ["ke", "v"]); + assert_eq!( + configured.omega_structural_mask(), + &ndarray::array![[true, false], [false, true]] + ); + assert_eq!( + configured.omega_estimated_mask(), + &ndarray::array![[true, false], [false, false]] + ); + assert_eq!( + correlated.omega_structural_mask(), + &ndarray::array![[true, true], [true, true]] + ); + assert_eq!( + correlated.omega_estimated_mask(), + &ndarray::array![[true, true], [true, true]] + ); +} + +#[test] +fn result_retains_ordered_iov_masks_and_none_without_iov() { + let config = SaemConfig::new() + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1) + .compute_map(false); + let iov = Box::new(SaemState::from_problem(configured_iov_problem(), &config).unwrap()) + .into_result() + .unwrap(); + let no_iov = Box::new(SaemState::from_problem(problem(), &config).unwrap()) + .into_result() + .unwrap(); + + assert_eq!(iov.iov_effect_indices(), [0, 1]); + assert_eq!(iov.iov_effect_names(), ["ke", "v"]); + assert_eq!( + iov.omega_iov_structural_mask(), + Some(&ndarray::array![[true, true], [true, true]]) + ); + assert_eq!( + iov.omega_iov_estimated_mask(), + Some(&ndarray::array![[true, false], [false, false]]) + ); + assert_eq!(no_iov.omega_iov_structural_mask(), None); + assert_eq!(no_iov.omega_iov_estimated_mask(), None); +} + +#[test] +fn state_initializes_zero_eta_chains() { + let state = SaemState::from_problem(problem(), &SaemConfig::default()).unwrap(); + + assert_eq!(state.etas.len(), 2); + assert_eq!(state.etas[0].len(), 25); + assert_eq!(state.etas[0][0], vec![0.0, 0.0]); + assert_eq!(state.etas[1][24], vec![0.0, 0.0]); + assert_eq!(state.omega_diagonal(), Some(vec![1.0, 1.0])); +} + +#[test] +fn covariate_state_joint_gls_rebases_eta_and_builds_subject_omega() { + let mut state = SaemState::from_problem( + covariate_problem(), + &SaemConfig::new().n_chains(2).compute_map(false), + ) + .unwrap(); + let intercept = [0.2_f64.ln(), 10.0_f64.ln()]; + let beta = 0.35; + let expected_phi = [-1.0, 0.0, 1.0] + .into_iter() + .map(|design| vec![intercept[0] + beta * design, intercept[1]]) + .collect::>(); + let desired_omega = ndarray::array![[0.4, 0.1], [0.1, 0.3]]; + let mut second = desired_omega.clone(); + for mean in &expected_phi { + for row in 0..2 { + for column in 0..2 { + second[[row, column]] += mean[row] * mean[column] / 3.0; + } + } + } + let old_means = state.subject_mu_phi.clone().unwrap(); + for chains in &mut state.etas { + for eta in chains { + eta[0] = 0.1; + eta[1] = -0.2; + } + } + let absolute_before = old_means + .iter() + .map(|mean| vec![mean[0] + 0.1, mean[1] - 0.2]) + .collect::>(); + state.covariate_statistics = Some(CovariateSufficientStatistics { + expected_phi, + global_second_moment: second, + }); + + let candidate = state + .update_covariate_population_and_recenter_etas() + .unwrap(); + let model = state.covariate_model.as_ref().unwrap(); + assert!((model.estimates()[0].estimate() - beta).abs() < 1e-10); + assert!((candidate[[0, 0]] - desired_omega[[0, 0]]).abs() < 1e-10); + assert!((candidate[[0, 1]] - desired_omega[[0, 1]]).abs() < 1e-10); + for (subject, mean) in state.subject_mu_phi.as_ref().unwrap().iter().enumerate() { + for coordinate in 0..2 { + assert!( + (mean[coordinate] + state.etas[subject][0][coordinate] + - absolute_before[subject][coordinate]) + .abs() + < 1e-10 + ); + } + } +} + +#[test] +fn covariate_fit_executes_and_retains_subject_population_parameters() { + let result = covariate_problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(1) + .k1_iterations(2) + .k2_iterations(2) + .averaged_iterates(0.75) + .compute_map(false), + ) + .unwrap(); + assert!(result.estimator_metadata().average_applied); + assert_eq!(result.covariate_estimates().unwrap().len(), 2); + assert!(result.covariate_estimates().unwrap()[0].estimate() < 0.0); + assert_eq!( + result + .covariate_subject_population_parameters() + .unwrap() + .unwrap() + .len(), + 3 + ); + assert!(result.cycle_diagnostics().iter().all(|cycle| cycle + .covariate_betas + .as_ref() + .is_some_and(|values| values.len() == 2))); + let tables = result.tables(1.0, 0.0).unwrap(); + assert_eq!(tables.covariate_effects.len(), 2); + assert_eq!(tables.subject_covariates.len(), 6); + assert_eq!(tables.subject_population_parameters.len(), 6); + + let directory = + std::env::temp_dir().join(format!("pmcore-schema7-covariate-{}", std::process::id())); + result.write_outputs(&directory, 1.0, 0.0).unwrap(); + let record = + crate::results::ParametricResultRecord::read_json(directory.join("result.json")).unwrap(); + assert_eq!(record.schema_version, 9); + assert_eq!(record.source_metadata.covariate_effects.len(), 2); + let warm = record + .warm_start_problem(one_compartment(), result.data().clone()) + .unwrap(); + let warm_estimates = warm + .covariates() + .unwrap() + .estimates() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + let result_estimates = result + .covariate_estimates() + .unwrap() + .iter() + .map(|estimate| estimate.estimate()) + .collect::>(); + assert!(warm_estimates + .iter() + .zip(result_estimates) + .all(|(warm, result)| (warm - result).abs() <= 2.0 * f64::EPSILON)); + std::fs::remove_dir_all(directory).unwrap(); +} + +#[test] +fn fixed_covariate_without_iiv_executes_subject_specific_predictions() { + let result = fixed_covariate_without_iiv_problem() + .fit_with( + SaemConfig::new() + .n_chains(1) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(1) + .k2_iterations(0) + .compute_map(false), + ) + .unwrap(); + assert!(result.random_effect_names().is_empty()); + let means = result + .covariate_subject_population_parameters() + .unwrap() + .unwrap(); + assert!((means[0].psi()[0] - 0.2).abs() < 1e-12); + assert!((means[1].psi()[0] - 0.2 * 0.2_f64.exp()).abs() < 1e-12); + let predictions = result.population_predictions(0.0, 0.0).unwrap(); + assert_ne!( + predictions[0].predictions()[0].prediction(), + predictions[1].predictions()[0].prediction() + ); +} + +#[test] +fn explicit_iiv_mask_controls_eta_and_omega_dimensions() { + let mut state = + SaemState::from_problem(partial_iiv_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + + assert_eq!(state.initialization.random_effect_indices, vec![0]); + assert_eq!(state.initialization.random_effect_names, vec!["ke"]); + assert!(state + .etas + .iter() + .flat_map(|subject_chains| subject_chains.iter()) + .all(|eta| eta.len() == 1)); + assert_eq!(state.omega.dim(), (1, 1)); + assert_eq!(state.proposal_step_sizes.len(), 1); + + state.etas[0][0][0] = 2.0_f64.ln(); + let individual = state.individual_parameters(0, 0); + assert!((individual[0] - 0.4).abs() < 1e-12); + assert!((individual[1] - 10.0).abs() < 1e-12); +} + +#[test] +fn all_fixed_parameters_support_zero_dimensional_iiv() { + let config = SaemConfig::new() + .n_chains(1) + .burn_in(1) + .k1_iterations(1) + .k2_iterations(1); + let state = SaemState::from_problem(fixed_no_iiv_problem(), &config).expect( + "fixed population plus estimated residual error should support zero-dimensional IIV", + ); + assert!(state.initialization.random_effect_names.is_empty()); + assert!(state.omega.is_empty()); + assert!(state.iiv_second_moment.is_empty()); + assert!(state + .etas + .iter() + .all(|chains| chains.iter().all(Vec::is_empty))); + + let result = fixed_no_iiv_problem().fit_with(config).unwrap(); + assert_eq!(result.termination_reason(), Some(&StopReason::MaxCycles)); + assert_eq!(result.iterations(), 2); + assert!(result.objf().is_finite()); + assert!(result.conditional_modes().is_empty()); + assert_eq!(result.omega_structural_mask().dim(), (0, 0)); + assert_eq!(result.omega_estimated_mask().dim(), (0, 0)); + assert!(result.omega_structural_mask().is_empty()); + assert!(result.omega_estimated_mask().is_empty()); + assert_eq!(result.omega_iov_structural_mask(), None); + assert_eq!(result.omega_iov_estimated_mask(), None); + assert!(result + .eta_chain_means() + .iter() + .all(|estimate| estimate.values.is_empty())); + assert!(result.kappa_chain_means().is_empty()); +} + +#[test] +fn iov_state_tracks_one_kappa_per_subject_occasion_and_chain() { + let state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + + assert_eq!(state.initialization.iov_effect_names, vec!["ke"]); + assert_eq!(state.omega_iov, Some(ndarray::array![[0.1]])); + assert_eq!(state.kappas.len(), 1); + assert_eq!(state.kappas[0].len(), 2); + assert_eq!(state.kappas[0][0], vec![vec![0.0], vec![0.0]]); +} + +#[test] +fn uneven_occasion_counts_preserve_kappa_shapes_order_and_named_lookup() { + let result = uneven_iov_problem() + .fit_with( + SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .burn_in(0) + .k1_iterations(2) + .k2_iterations(0) + .compute_map(false), + ) + .unwrap(); + + assert_eq!(result.kappa_chain_means().len(), 6); + assert!(result.kappa_chain_mean("one", 0).is_some()); + assert!(result.kappa_chain_mean("one", 1).is_none()); + assert!(result.kappa_chain_mean("two", 0).is_some()); + assert!(result.kappa_chain_mean("two", 1).is_some()); + assert!(result.kappa_chain_mean("three", 0).is_some()); + assert!(result.kappa_chain_mean("three", 1).is_some()); + assert!(result.kappa_chain_mean("three", 2).is_some()); + assert!(result.eta_chain_mean("two").is_some()); + assert!(result.eta_chain_mean("missing").is_none()); + assert!(result.conditional_mode("two").is_none()); + assert!(result + .cycle_diagnostics() + .iter() + .all(|cycle| cycle.kappa_proposals == 12)); +} + +#[test] +fn iov_scores_per_occasion_kappa_prior_and_conditional_proposal() { + let state = SaemState::from_problem(iov_problem(), &SaemConfig::new().n_chains(2)).unwrap(); + let score = state + .score_subject_latents(0, &state.etas[0][0], &state.kappas[0][0]) + .unwrap(); + + assert!((score.log_likelihood - state.subject_log_likelihoods[0]).abs() < 1e-12); + assert!((score.kappa_log_prior - state.subject_kappa_log_priors[0]).abs() < 1e-12); + assert!(score.kappa_log_prior.is_finite()); + assert_eq!( + state + .kappa_proposal_log_acceptance_ratio(0, 0, 0, &[0.0]) + .unwrap(), + 0.0 + ); +} + +#[test] +fn iov_controller_exposes_kappa_covariance_and_runs_conditional_mcmc() { + let mut controller = iov_problem() + .fit_controller( + SaemConfig::new() + .n_chains(2) + .k1_iterations(2) + .k2_iterations(0) + .burn_in(2), + ) + .unwrap(); + + assert_eq!( + controller.iov_effect_names(), + Some(["ke".to_string()].as_slice()) + ); + assert_eq!(controller.omega_iov(), Some(&ndarray::array![[0.1]])); + assert!(controller.kappa_log_prior().is_finite()); + assert_eq!( + controller.log_posterior(), + controller.likelihood() + controller.eta_log_prior() + controller.kappa_log_prior() + ); + + controller.step().unwrap(); + assert!(controller.likelihood().is_finite()); + assert!(controller.kappa_log_prior().is_finite()); + assert!(controller.acceptance_rate().is_some()); + assert!(controller + .kappa_acceptance_rate() + .is_some_and(|rate| (0.0..=1.0).contains(&rate))); +} + +#[test] +fn correlated_random_walk_reuses_one_standard_normal_vector() { + let proposed = + correlated_random_walk(&[1.0, 2.0], &[vec![2.0], vec![1.0, 3.0]], &[0.5, -1.0], 0.2) + .unwrap(); + + assert!((proposed[0] - 1.2).abs() < 1e-12); + assert!((proposed[1] - 1.5).abs() < 1e-12); + assert!(correlated_random_walk(&[0.0], &[vec![1.0]], &[0.0, 1.0], 1.0).is_err()); +} + +#[test] +fn eta_block_proposal_uses_covariance_scale_and_adaptation() { + let lower = vec![vec![1.0], vec![0.8, 0.6]]; + let normals = [[0.5, -1.0], [-0.25, 0.75], [1.2, 0.1], [-0.8, -0.4]]; + let uniforms = [0.2_f64, 0.9, 0.4, 0.7]; + let expected_trace = [ + [0.65, -0.3], + [0.525, -0.175], + [0.525, -0.175], + [0.525, -0.175], + ]; + let expected_ratios = [ + -0.9451955782312924, + 0.6944515306122447, + -2.211747363945578, + -0.4124850340136057, + ]; + let expected_accepts = [true, true, false, false]; + let expected_scales = [0.55, 0.495]; + let expected_checkpoint_counts = [(2, 2), (0, 2)]; + let log_likelihood = + |eta: &[f64]| -0.5 * ((eta[0] - 0.3) / 0.5).powi(2) - 0.5 * ((eta[1] + 0.1) / 0.7).powi(2); + let log_prior = |eta: &[f64]| { + -0.5 / (1.0 - 0.8_f64.powi(2)) * (eta[0].powi(2) - 1.6 * eta[0] * eta[1] + eta[1].powi(2)) + }; + + let mut eta = vec![0.4, -0.2]; + let mut scale = 0.5; + let mut accepted = 0; + let mut proposed = 0; + let mut scale_index = 0; + for (step, (z, uniform)) in normals.iter().zip(uniforms).enumerate() { + let proposal = correlated_random_walk(&eta, &lower, z, scale).unwrap(); + let reference = [ + eta[0] + scale * lower[0][0] * z[0], + eta[1] + scale * (lower[1][0] * z[0] + lower[1][1] * z[1]), + ]; + assert!((proposal[0] - reference[0]).abs() < 1e-15); + assert!((proposal[1] - reference[1]).abs() < 1e-15); + + let current_score = SubjectPosteriorScore { + log_likelihood: log_likelihood(&eta), + eta_log_prior: log_prior(&eta), + kappa_log_prior: 0.0, + }; + let proposed_score = SubjectPosteriorScore { + log_likelihood: log_likelihood(&proposal), + eta_log_prior: log_prior(&proposal), + kappa_log_prior: 0.0, + }; + let ratio = current_score.log_acceptance_ratio(proposed_score); + let reference_ratio = proposed_score.log_posterior() - current_score.log_posterior(); + assert!((ratio - reference_ratio).abs() < 1e-15); + assert!((ratio - expected_ratios[step]).abs() < 1e-12); + + let accept = ratio >= 0.0 || uniform.ln() < ratio; + assert_eq!(accept, expected_accepts[step]); + proposed += 1; + if accept { + eta = proposal; + accepted += 1; + } + assert!((eta[0] - expected_trace[step][0]).abs() < 1e-12); + assert!((eta[1] - expected_trace[step][1]).abs() < 1e-12); + + if (step + 1) % 2 == 0 { + assert_eq!( + (accepted, proposed), + expected_checkpoint_counts[scale_index] + ); + scale = adapt_block_step_size( + scale, + accepted as f64 / proposed as f64, + ETA_BLOCK_TARGET_ACCEPTANCE, + ); + assert!((scale - expected_scales[scale_index]).abs() < 1e-12); + scale_index += 1; + accepted = 0; + proposed = 0; + } + } + + let eta_unchanged = [0.7, -0.3]; + let kappa_0_unchanged = [0.1, 0.2]; + let kappa_1 = correlated_random_walk( + &[-0.2, 0.4], + &[vec![0.5], vec![0.1, 0.4]], + &[-0.5, 0.25], + 0.3, + ) + .unwrap(); + assert_eq!(eta_unchanged, [0.7, -0.3]); + assert_eq!(kappa_0_unchanged, [0.1, 0.2]); + assert!((kappa_1[0] + 0.275).abs() < 1e-12); + assert!((kappa_1[1] - 0.415).abs() < 1e-12); +} + +#[test] +fn eta_block_kernel_runs_before_component_sweep_and_records_diagnostics() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(2) + .mcmc_iterations(1) + .eta_block_iterations(2) + .adapt_interval(50) + .seed(2024), + ) + .unwrap(); + + state.e_step().unwrap(); + + let diagnostics = state.cycle_diagnostics.last().unwrap(); + assert_eq!(diagnostics.eta_block_proposals, 2 * 2 * 2); + assert_eq!( + diagnostics.eta_block_accepted + diagnostics.eta_block_rejected, + diagnostics.eta_block_proposals + ); + assert_eq!(diagnostics.eta_proposals, 2 * 2 * 2 + 2 * 2 * 2); + assert_eq!(diagnostics.eta_block_subject_acceptance_rates.len(), 2); + assert_eq!( + diagnostics.eta_block_step_sizes_before_adaptation, + vec![0.5, 0.5] + ); + assert_eq!( + diagnostics.eta_block_step_sizes_after_adaptation, + vec![0.5, 0.5] + ); +} + +#[test] +fn controller_exposes_opt_in_eta_block_acceptance_and_scales() { + let mut controller = problem() + .fit_controller( + SaemConfig::new() + .n_chains(2) + .eta_block_iterations(1) + .k1_iterations(1) + .k2_iterations(0) + .burn_in(1), + ) + .unwrap(); + + assert_eq!( + controller.eta_block_step_sizes(), + Some([0.5, 0.5].as_slice()) + ); + assert_eq!(controller.eta_block_acceptance_rate(), None); + controller.step().unwrap(); + assert!(controller + .eta_block_acceptance_rate() + .is_some_and(|rate| (0.0..=1.0).contains(&rate))); +} + +#[test] +fn eta_block_scale_adapts_per_subject_toward_acceptance_target() { + let mut state = SaemState::from_problem( + problem(), + &SaemConfig::new() + .n_chains(1) + .eta_block_iterations(1) + .adapt_interval(1), + ) + .unwrap(); + assert_eq!(state.eta_block_step_sizes, vec![0.5, 0.5]); + + state.eta_block_adaptation_accept_counts = vec![1, 0]; + state.eta_block_adaptation_proposal_counts = vec![1, 1]; + state.steps_since_adapt = 1; + state.adapt_proposal_step_sizes(); + assert_eq!(state.eta_block_step_sizes, vec![0.55, 0.45]); + assert_eq!(state.eta_block_adaptation_accept_counts, vec![0, 0]); + assert_eq!(state.eta_block_adaptation_proposal_counts, vec![0, 0]); +} + +#[test] +fn kappa_block_scale_adapts_per_subject_toward_acceptance_target() { + let mut state = SaemState::from_problem( + iov_problem(), + &SaemConfig::new().n_chains(2).adapt_interval(1), + ) + .unwrap(); + assert_eq!(state.kappa_proposal_step_sizes, vec![0.5]); + + state.kappa_adaptation_accept_counts[0] = 1; + state.kappa_adaptation_proposal_counts[0] = 1; + state.steps_since_adapt = 1; + state.adapt_proposal_step_sizes(); + assert!((state.kappa_proposal_step_sizes[0] - 0.55).abs() < 1e-12); + + state.kappa_adaptation_accept_counts[0] = 0; + state.kappa_adaptation_proposal_counts[0] = 1; + state.steps_since_adapt = 1; + state.adapt_proposal_step_sizes(); + assert!((state.kappa_proposal_step_sizes[0] - 0.495).abs() < 1e-12); +} + +#[test] +fn iov_second_moment_weights_each_occasion_chain_sample_equally() { + let kappas = vec![ + vec![vec![vec![1.0, 2.0]]], + vec![vec![vec![3.0, 4.0], vec![5.0, 6.0]]], + ]; + + let covariance = covariance_from_kappas(&kappas).unwrap(); + + assert!((covariance[[0, 0]] - 35.0 / 3.0).abs() < 1e-12); + assert!((covariance[[0, 1]] - 44.0 / 3.0).abs() < 1e-12); + assert!((covariance[[1, 0]] - 44.0 / 3.0).abs() < 1e-12); + assert!((covariance[[1, 1]] - 56.0 / 3.0).abs() < 1e-12); +} + +#[test] +fn iov_m_step_updates_omega_from_all_occasions() { + let mut state = SaemState::from_problem( + iov_problem(), + &SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0), + ) + .unwrap(); + state.cycle = 1; + state.e_step().unwrap(); + for kappas in &mut state.kappas[0] { + kappas[0][0] = 0.2; + kappas[1][0] = -0.1; + } + + state.m_step().unwrap(); + + assert!((state.omega_iov.as_ref().unwrap()[[0, 0]] - 0.025).abs() < 1e-12); + assert!( + !state + .cycle_diagnostics + .last() + .unwrap() + .omega_iov_update_rejected + ); +} + +#[test] +fn covariance_update_status_drives_iiv_and_iov_cycle_rejection_diagnostics() { + let config = SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + state.cycle = 1; + state.e_step().unwrap(); + state.iiv_second_moment.fill(f64::NAN); + state.iov_second_moment.as_mut().unwrap().fill(f64::NAN); + + state.m_step().unwrap(); + + let diagnostics = state.cycle_diagnostics.last().unwrap(); + assert!(diagnostics.omega_update_rejected); + assert!(diagnostics.omega_iov_update_rejected); +} + +#[test] +fn iov_second_moment_uses_saem_smoothing_step() { + let config = SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0) + .k1_iterations(1) + .k2_iterations(2); + let mut state = SaemState::from_problem(iov_problem(), &config).unwrap(); + for kappas in &mut state.kappas[0] { + kappas[0][0] = 0.2; + kappas[1][0] = -0.1; + } + state.cycle = 1; + state.m_step().unwrap(); + + for kappas in &mut state.kappas[0] { + kappas[0][0] = 0.2; + kappas[1][0] = 0.2; + } + state.cycle = 3; // first smoothing iteration after K1: γ = 1/2 + state.m_step().unwrap(); + + assert!((state.omega_iov.as_ref().unwrap()[[0, 0]] - 0.0325).abs() < 1e-12); +} + +#[test] +fn iov_m_step_preserves_fixed_entries_and_positive_definiteness_jointly() { + let config = SaemConfig::new() + .n_chains(2) + .burn_in(0) + .omega_sa_max_step(1.0); + let mut state = SaemState::from_problem(configured_iov_problem(), &config).unwrap(); + state.cycle = 1; + for chain in &mut state.kappas[0] { + for kappa in chain { + kappa[0] = 0.3; + kappa[1] = 1.0; + } + } + + state.m_step().unwrap(); + + let omega_iov = state.omega_iov.as_ref().unwrap(); + // With fixed b=.20 and c=.05, the exact constrained profile optimum is + // S11 - 2(c/b)S12 + c²/b + (c²/b²)S22 = .015. + assert!((omega_iov[[0, 0]] - 0.015).abs() < 1e-12); + assert_eq!(omega_iov[[0, 1]], 0.05); + assert_eq!(omega_iov[[1, 0]], 0.05); + assert_eq!(omega_iov[[1, 1]], 0.20); + assert!(omega_iov[[0, 0]] * omega_iov[[1, 1]] - omega_iov[[0, 1]].powi(2) > 0.0); +} + +#[test] +fn state_uses_declared_initial_omega() { + let state = SaemState::from_problem(configured_omega_problem(), &SaemConfig::new().n_chains(2)) + .unwrap(); + + assert_eq!(state.omega, ndarray::array![[0.25, 0.0], [0.0, 0.5]]); + assert_eq!(state.proposal_step_sizes, vec![0.25, 0.25 * 2.0_f64.sqrt()]); +} + +#[test] +fn individual_parameters_add_eta_in_phi_space() { + let mut state = SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1)).unwrap(); + + let initial = state.individual_parameters(0, 0); + assert!((initial[0] - 0.2).abs() < 1e-12); + assert!((initial[1] - 10.0).abs() < 1e-12); + + state.etas[0][0][0] = 2.0_f64.ln(); + state.etas[0][0][1] = 0.5_f64.ln(); + let individual = state.individual_parameters(0, 0); + + assert!((individual[0] - 0.4).abs() < 1e-12); + assert!((individual[1] - 5.0).abs() < 1e-12); +} + +#[test] +fn bounded_transforms_round_trip() { + let logit = ParameterScale::Logit { + lower: 0.0, + upper: 1.0, + }; + let probit = ParameterScale::Probit { + lower: 0.0, + upper: 1.0, + }; + + assert!((phi_to_psi(psi_to_phi(0.25, logit), logit) - 0.25).abs() < 1e-12); + assert!((phi_to_psi(psi_to_phi(0.25, probit), probit) - 0.25).abs() < 1e-12); +} diff --git a/src/bestdose/cost.rs b/src/bestdose/cost.rs index a5da34b77..36f954f3f 100644 --- a/src/bestdose/cost.rs +++ b/src/bestdose/cost.rs @@ -77,6 +77,23 @@ pub(crate) struct Evaluation { pub achievements: Vec, } +fn numeric_output_index(output: &pharmsol::OutputLabel) -> Result { + let label = output.as_str(); + label + .parse::() + .ok() + .or_else(|| { + label + .strip_prefix("outeq_") + .and_then(|index| index.parse::().ok()) + }) + .ok_or_else(|| { + anyhow::anyhow!( + "BestDose AUC calculations require numeric observation output labels; got `{output}`" + ) + }) +} + /// Calculate cost function for a candidate dose regimen /// /// This is the core objective function minimized by the Nelder-Mead optimizer. @@ -243,10 +260,10 @@ pub(crate) fn evaluate( .iter() .flat_map(|occ| occ.events()) .filter_map(|event| match event { - Event::Observation(obs) => Some(obs.outeq_index().unwrap_or(0)), + Event::Observation(obs) => Some(numeric_output_index(obs.outeq())), _ => None, }) - .collect(); + .collect::>>()?; let n_obs = obs_vec.len(); @@ -315,16 +332,9 @@ pub(crate) fn evaluate( .iter() .flat_map(|occ| occ.events()) .filter_map(|event| match event { - Event::Observation(obs) => Some( - obs.outeq_index() - .map(|outeq| (obs.time(), outeq)) - .ok_or_else(|| { - anyhow::anyhow!( - "BestDose AUC calculations require numeric observation output labels; got `{}`", - obs.outeq() - ) - }), - ), + Event::Observation(obs) => { + Some(numeric_output_index(obs.outeq()).map(|outeq| (obs.time(), outeq))) + } _ => None, }) .collect::>>()?; @@ -355,7 +365,7 @@ pub(crate) fn evaluate( for prediction in dense_predictions_with_outeq { outeq_predictions - .entry(prediction.outeq()) + .entry(numeric_output_index(prediction.output())?) .or_default() .push(prediction.prediction()); } @@ -444,16 +454,9 @@ pub(crate) fn evaluate( .iter() .flat_map(|occ| occ.events()) .filter_map(|event| match event { - Event::Observation(obs) => Some( - obs.outeq_index() - .map(|outeq| (obs.time(), outeq)) - .ok_or_else(|| { - anyhow::anyhow!( - "BestDose AUC calculations require numeric observation output labels; got `{}`", - obs.outeq() - ) - }), - ), + Event::Observation(obs) => { + Some(numeric_output_index(obs.outeq()).map(|outeq| (obs.time(), outeq))) + } _ => None, }) .collect::>>()?; @@ -484,7 +487,7 @@ pub(crate) fn evaluate( for prediction in dense_predictions_with_outeq { outeq_predictions - .entry(prediction.outeq()) + .entry(numeric_output_index(prediction.output())?) .or_default() .push(prediction.prediction()); } @@ -598,7 +601,7 @@ mod tests { use crate::model::{BoundedParameter, ParameterSpace}; use pharmsol::prelude::*; - fn one_compartment() -> pharmsol::ODE { + fn one_compartment() -> pharmsol::equation::ODE { equation::ODE::new( |x, p, _t, dx, b, _rateiv, _cov| { fetch_params!(p, ke, _v); @@ -612,6 +615,17 @@ mod tests { y[0] = x[0] / v; }, ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata( + equation::metadata::new("bestdose_cost_test") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["0"]) + .route(equation::Route::bolus("0").to_state("central")), + ) + .expect("BestDose test metadata should validate") } fn single_point_theta() -> Theta { @@ -622,7 +636,7 @@ mod tests { Theta::from_parts(mat, params).unwrap() } - fn problem_with(target: Subject) -> BestDoseObjective { + fn problem_with(target: Subject) -> BestDoseObjective { BestDoseObjective { target, target_type: Target::Concentration, diff --git a/src/estimation/assay_error.rs b/src/estimation/assay_error.rs index 2f0f173f9..ea61e7823 100644 --- a/src/estimation/assay_error.rs +++ b/src/estimation/assay_error.rs @@ -72,9 +72,12 @@ impl Factor { impl From> for AssayErrorModels { fn from(models: Vec) -> Self { + let output_lookup = (0..models.len()) + .map(|index| (OutputLabel::new(index.to_string()), index)) + .collect(); Self { models, - output_lookup: BTreeMap::new(), + output_lookup, named_models: BTreeMap::new(), } } @@ -177,6 +180,7 @@ impl AssayErrorModels { Self::empty() } + #[cfg(test)] pub(crate) fn assert_compatible_output_names( &self, outputs: I, @@ -224,9 +228,31 @@ impl AssayErrorModels { .collect::>(); if !self.output_lookup.is_empty() { - self.assert_compatible_output_names(outputs.iter().map(String::as_str))?; - return Ok(BoundAssayErrorModels { - storage: BoundAssayErrorModelsStorage::Borrowed(self), + let expected = self.bound_output_names(); + if expected == outputs { + return Ok(BoundAssayErrorModels { + storage: BoundAssayErrorModelsStorage::Borrowed(self), + }); + } + + let dense_context = expected + .iter() + .all(|output| output.parse::().is_ok()); + if dense_context { + let mut bound = self.clone(); + bound.output_lookup = outputs + .iter() + .enumerate() + .map(|(index, output)| (OutputLabel::new(output), index)) + .collect(); + return Ok(BoundAssayErrorModels { + storage: BoundAssayErrorModelsStorage::Owned(bound), + }); + } + + return Err(ErrorModelError::IncompatibleOutputContext { + expected, + found: outputs, }); } @@ -298,10 +324,21 @@ impl AssayErrorModels { self.output_lookup .get(&label) .copied() - .or_else(|| label.index()) .ok_or_else(|| ErrorModelError::UnknownOutputLabel(label.to_string())) } + fn resolve_prediction_output(&self, output: &OutputLabel) -> Result { + match self.resolve_output_binding(output) { + Ok(index) => Ok(index), + Err(error) => match output.as_str().parse::() { + Ok(index) if index >= self.models.len() => { + Err(ErrorModelError::InvalidOutputEquation(index)) + } + _ => Err(error), + }, + } + } + fn insert_model_at( &mut self, outeq: usize, @@ -348,12 +385,21 @@ impl AssayErrorModels { let label = OutputLabel::new(outeq); if !self.output_lookup.is_empty() { - let outeq = self.resolve_output_binding(label.clone())?; - self.insert_model_at(outeq, model)?; - return Ok(self); + if let Some(outeq) = self.output_lookup.get(&label).copied() { + self.insert_model_at(outeq, model)?; + return Ok(self); + } + let dense_context = self + .bound_output_names() + .iter() + .all(|output| output.parse::().is_ok()); + if !dense_context { + return Err(ErrorModelError::UnknownOutputLabel(label.to_string())); + } } - if let Some(outeq) = label.index() { + if let Ok(outeq) = label.as_str().parse::() { + self.output_lookup.insert(label, outeq); self.insert_model_at(outeq, model)?; return Ok(self); } @@ -696,14 +742,14 @@ impl AssayErrorModels { /// /// A [`Result`] containing the computed sigma value or an [`ErrorModelError`] if the calculation fails. pub fn sigma(&self, prediction: &Prediction) -> Result { - let outeq = prediction.outeq(); + let outeq = self.resolve_prediction_output(prediction.output())?; if outeq >= self.models.len() { return Err(ErrorModelError::InvalidOutputEquation(outeq)); } if self.models[outeq] == AssayErrorModel::None { return Err(ErrorModelError::NoneErrorModel(outeq)); } - self.models[prediction.outeq()].sigma(prediction) + self.models[outeq].sigma(prediction) } /// Computes the variance for the specified output equation and prediction. @@ -717,14 +763,14 @@ impl AssayErrorModels { /// /// A [`Result`] containing the computed variance or an [`ErrorModelError`] if the calculation fails. pub fn variance(&self, prediction: &Prediction) -> Result { - let outeq = prediction.outeq(); + let outeq = self.resolve_prediction_output(prediction.output())?; if outeq >= self.models.len() { return Err(ErrorModelError::InvalidOutputEquation(outeq)); } if self.models[outeq] == AssayErrorModel::None { return Err(ErrorModelError::NoneErrorModel(outeq)); } - self.models[prediction.outeq()].variance(prediction) + self.models[outeq].variance(prediction) } /// Computes the standard deviation (sigma) for the specified output equation and value. @@ -1199,11 +1245,11 @@ pub enum ErrorModelError { #[cfg(test)] mod tests { use super::*; - use pharmsol::{Event, Observation, SubjectBuilderExt}; + use pharmsol::{Equation, Event, Observation, SubjectBuilderExt}; - fn test_observation(value: f64, outeq: usize) -> Observation { + fn test_observation(value: f64, output: impl ToString) -> Observation { let subject = pharmsol::Subject::builder("test") - .observation(0.0, value, outeq) + .observation(0.0, value, output) .build(); match &subject.occasions()[0].events()[0] { Event::Observation(observation) => observation.clone(), @@ -1211,10 +1257,50 @@ mod tests { } } + fn test_prediction(observation: &Observation) -> Prediction { + let output_names = match observation.outeq().as_str() { + "cp" | "effect" => ["cp", "effect"], + _ => ["0", "1"], + }; + let equation = pharmsol::equation::ODE::new( + |_x, _p, _t, dx, _bolus, _rateiv, _cov| dx[0] = 0.0, + |_p, _t, _cov| std::collections::HashMap::new(), + |_p, _t, _cov| std::collections::HashMap::new(), + |_p, _t, _cov, _x| {}, + |_x, p, _t, _cov, y| { + y[0] = p[0]; + y[1] = p[0]; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + pharmsol::equation::metadata::new("assay_error_test_fixture") + .parameters(["prediction_value"]) + .states(["central"]) + .outputs(output_names) + .route(pharmsol::equation::Route::bolus("input").to_state("central")), + ) + .expect("test fixture metadata is valid"); + let subject = pharmsol::Subject::builder("test") + .observation( + observation.time(), + observation.value().expect("test observation has a value"), + observation.outeq().clone(), + ) + .build(); + equation + .estimate_predictions_dense(&subject, &[10.0]) + .expect("test fixture simulation succeeds") + .predictions()[0] + .clone() + } + #[test] fn test_additive_error_model() { let observation = test_observation(20.0, 0); - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); let model = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); assert_eq!(model.sigma(&prediction).unwrap(), (26.0_f64).sqrt()); } @@ -1222,7 +1308,7 @@ mod tests { #[test] fn test_proportional_error_model() { let observation = test_observation(20.0, 0); - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); let model = AssayErrorModel::proportional(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 2.0); assert_eq!(model.sigma(&prediction).unwrap(), 2.0); } @@ -1382,8 +1468,8 @@ mod tests { .add("cp", model) .unwrap(); - let observation = test_observation(20.0, 0); - let prediction = observation.to_prediction(10.0, vec![]); + let observation = test_observation(20.0, "cp"); + let prediction = test_prediction(&observation); assert_eq!(models.sigma(&prediction).unwrap(), (26.0_f64).sqrt()); } @@ -1509,7 +1595,7 @@ mod tests { let models = AssayErrorModels::empty().add(0, model).unwrap(); let observation = test_observation(20.0, 0); - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); // Non-parametric: sigma from observation let sigma = models.sigma(&prediction).unwrap(); @@ -1522,7 +1608,7 @@ mod tests { let models = AssayErrorModels::empty().add(0, model).unwrap(); let observation = test_observation(20.0, 1); // outeq=1 not in models - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); let result = models.sigma(&prediction); assert!(result.is_err()); @@ -1538,7 +1624,7 @@ mod tests { let models = AssayErrorModels::empty().add(0, model).unwrap(); let observation = test_observation(20.0, 0); - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); let variance = models.variance(&prediction).unwrap(); let expected_sigma = (26.0_f64).sqrt(); @@ -1551,7 +1637,7 @@ mod tests { let models = AssayErrorModels::empty().add(0, model).unwrap(); let observation = test_observation(20.0, 1); // outeq=1 not in models - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); let result = models.variance(&prediction); assert!(result.is_err()); @@ -1692,13 +1778,13 @@ mod tests { // Test with outeq=0 (additive model) let obs1 = test_observation(20.0, 0); - let pred1 = obs1.to_prediction(10.0, vec![]); + let pred1 = test_prediction(&obs1); let sigma1 = models.sigma(&pred1).unwrap(); assert_eq!(sigma1, (26.0_f64).sqrt()); // additive: sqrt(alpha^2 + lambda^2) = sqrt(1^2 + 5^2) = sqrt(26) // Test with outeq=1 (proportional model) let obs2 = test_observation(20.0, 1); - let pred2 = obs2.to_prediction(10.0, vec![]); + let pred2 = test_prediction(&obs2); let sigma2 = models.sigma(&pred2).unwrap(); assert_eq!(sigma2, 2.0); // proportional: gamma * alpha = 2 * 1 = 2 } @@ -1840,7 +1926,7 @@ mod tests { fn test_fixed_parameters_in_calculations() { // Test that fixed and variable parameters produce the same calculation results let observation = test_observation(20.0, 0); - let prediction = observation.to_prediction(10.0, vec![]); + let prediction = test_prediction(&observation); let model_variable = AssayErrorModel::additive(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); let model_fixed = AssayErrorModel::additive_fixed(ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 5.0); diff --git a/src/estimation/error_models.rs b/src/estimation/error_models.rs index 21731312d..4773cfb8f 100644 --- a/src/estimation/error_models.rs +++ b/src/estimation/error_models.rs @@ -1,3 +1,4 @@ +use pharmsol::OutputLabel; use serde::{Deserialize, Serialize}; use super::{AssayErrorModels, ResidualErrorModel, ResidualErrorModels}; @@ -235,4 +236,10 @@ impl ParametricErrorModels { pub fn output_name(&self, outeq: usize) -> Option<&str> { self.output_names.get(outeq).and_then(Option::as_deref) } + + pub(crate) fn output_index(&self, output: &OutputLabel) -> Option { + self.output_names + .iter() + .position(|name| name.as_deref() == Some(output.as_str())) + } } diff --git a/src/estimation/likelihood/batch.rs b/src/estimation/likelihood/batch.rs index 1cf1c608b..d327f55f7 100644 --- a/src/estimation/likelihood/batch.rs +++ b/src/estimation/likelihood/batch.rs @@ -4,7 +4,7 @@ use anyhow::{bail, Result}; use ndarray::{Array2, Axis}; use pharmsol::{Data, Equation, Occasion, Subject}; -use crate::ResidualErrorModels; +use crate::estimation::ParametricErrorModels; use rayon::prelude::*; use super::residual::residual_error_model_log_likelihoods; @@ -16,14 +16,14 @@ pub(crate) fn parametric_subject_log_likelihood( equation: &impl Equation, subject: &Subject, parameter_row: &[f64], - residual_error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) -> f64 { let predictions = match equation.estimate_predictions_dense(subject, parameter_row) { Ok(predictions) => predictions, Err(_) => return f64::NEG_INFINITY, }; - residual_error_model_log_likelihoods(&predictions, residual_error_models) + residual_error_model_log_likelihoods(&predictions, error_models) } /// Score one occasion under its own κ-adjusted parameter vector. @@ -36,15 +36,10 @@ pub(crate) fn parametric_occasion_log_likelihood( subject_id: &str, occasion: &Occasion, parameter_row: &[f64], - residual_error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) -> f64 { let occasion_subject = Subject::from_occasions(subject_id.to_owned(), vec![occasion.clone()]); - parametric_subject_log_likelihood( - equation, - &occasion_subject, - parameter_row, - residual_error_models, - ) + parametric_subject_log_likelihood(equation, &occasion_subject, parameter_row, error_models) } /// Compute parametric subject log-likelihoods in PMcore. @@ -55,7 +50,7 @@ pub(crate) fn parametric_log_likelihood_batch( equation: &impl Equation, subjects: &Data, parameters: &Array2, - residual_error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) -> Result> { let subject_refs = subjects.subjects(); if parameters.nrows() != subject_refs.len() { @@ -77,7 +72,7 @@ pub(crate) fn parametric_log_likelihood_batch( equation, subject, &flat_parameters[start..start + width], - residual_error_models, + error_models, ) }) .collect()) @@ -95,7 +90,7 @@ pub(crate) fn parametric_log_likelihood_batch( equation, subject, ¶meter_rows[i], - residual_error_models, + error_models, ) }) .collect()) @@ -105,6 +100,7 @@ pub(crate) fn parametric_log_likelihood_batch( #[cfg(test)] mod tests { use super::*; + use crate::estimation::ParametricErrorModel; use crate::ResidualErrorModel; use pharmsol::prelude::*; use pharmsol::SubjectBuilderExt; @@ -117,7 +113,7 @@ mod tests { .route(equation::Route::bolus("0").to_state("central")) } - fn one_compartment() -> pharmsol::ODE { + fn one_compartment() -> pharmsol::equation::ODE { equation::ODE::new( |x, p, _t, dx, b, _rateiv, _cov| { fetch_params!(p, ke); @@ -159,8 +155,11 @@ mod tests { let equation = one_compartment(); let data = data(); let parameters = ndarray::array![[0.15, 8.0], [0.30, 12.0]]; - let error_models = - ResidualErrorModels::new().add(0, ResidualErrorModel::combined(0.5, 0.1)); + let error_models = ParametricErrorModels::new().add( + 0, + "0", + ParametricErrorModel::from(ResidualErrorModel::combined(0.5, 0.1)), + ); let scores = parametric_log_likelihood_batch(&equation, &data, ¶meters, &error_models) .expect("pmcore batch"); diff --git a/src/estimation/likelihood/matrix.rs b/src/estimation/likelihood/matrix.rs index 559e046b9..773e5b624 100644 --- a/src/estimation/likelihood/matrix.rs +++ b/src/estimation/likelihood/matrix.rs @@ -78,7 +78,7 @@ mod tests { .route(equation::Route::bolus("0").to_state("central")) } - fn one_compartment() -> pharmsol::ODE { + fn one_compartment() -> pharmsol::equation::ODE { equation::ODE::new( |x, p, _t, dx, b, _rateiv, _cov| { fetch_params!(p, ke); @@ -99,7 +99,7 @@ mod tests { .unwrap() } - fn direct_output() -> pharmsol::ODE { + fn direct_output() -> pharmsol::equation::ODE { equation::ODE::new( |_x, _p, _t, dx, _b, _rateiv, _cov| dx[0] = 0.0, |_p, _t, _cov| lag! {}, diff --git a/src/estimation/likelihood/objective.rs b/src/estimation/likelihood/objective.rs index 885cc0ef8..fe7b94cc8 100644 --- a/src/estimation/likelihood/objective.rs +++ b/src/estimation/likelihood/objective.rs @@ -23,7 +23,7 @@ where &problem.model.equation, &problem.data, individual_parameters, - problem.error_models.models(), + &problem.error_models, ) } @@ -64,7 +64,7 @@ mod tests { .route(equation::Route::bolus("0").to_state("central")) } - fn one_compartment() -> pharmsol::ODE { + fn one_compartment() -> pharmsol::equation::ODE { equation::ODE::new( |x, p, _t, dx, b, _rateiv, _cov| { fetch_params!(p, ke); @@ -85,7 +85,7 @@ mod tests { .unwrap() } - fn problem() -> EstimationProblem { + fn problem() -> EstimationProblem { let data = Data::new(vec![ Subject::builder("s1") .bolus(0.0, 100.0, "0") @@ -120,7 +120,7 @@ mod tests { &problem.model.equation, &problem.data, ¶meters, - problem.error_models.models(), + &problem.error_models, ) .unwrap(); let actual = parametric_subject_log_likelihoods(&problem, ¶meters).unwrap(); diff --git a/src/estimation/likelihood/residual.rs b/src/estimation/likelihood/residual.rs index f8d0a6aef..6d87f5bdc 100644 --- a/src/estimation/likelihood/residual.rs +++ b/src/estimation/likelihood/residual.rs @@ -1,7 +1,8 @@ use pharmsol::prelude::simulator::Prediction; use pharmsol::Predictions; -use crate::{ResidualErrorModel, ResidualErrorModels}; +use crate::estimation::ParametricErrorModels; +use crate::ResidualErrorModel; use super::distributions::log_normal_pdf; @@ -49,13 +50,16 @@ fn residual_log_likelihood_values( #[inline] pub(crate) fn residual_error_model_log_likelihood( prediction: &Prediction, - error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) -> f64 { let Some(obs) = prediction.observation() else { return 0.0; }; - let Some(model) = error_models.get(prediction.outeq()) else { + let Some(output) = error_models.output_index(prediction.output()) else { + return f64::NEG_INFINITY; + }; + let Some(model) = error_models.get(output) else { return f64::NEG_INFINITY; }; @@ -64,7 +68,7 @@ pub(crate) fn residual_error_model_log_likelihood( pub(crate) fn residual_error_model_log_likelihoods

( predictions: &P, - error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) -> f64 where P: Predictions, @@ -200,7 +204,7 @@ mod tests { assert_eq!( residual_error_model_log_likelihood( &Prediction::default(), - &ResidualErrorModels::new(), + &ParametricErrorModels::new(), ), 0.0 ); diff --git a/src/estimation/nonparametric/predictions.rs b/src/estimation/nonparametric/predictions.rs index 5bb6ef0e4..7dcff762d 100644 --- a/src/estimation/nonparametric/predictions.rs +++ b/src/estimation/nonparametric/predictions.rs @@ -1,12 +1,16 @@ use std::path::Path; -use anyhow::{bail, Result}; -use pharmsol::{prelude::simulator::Prediction, Censor, Data, Predictions as PredTrait}; +use anyhow::{bail, Context, Result}; +use pharmsol::{ + prelude::simulator::{Prediction, SubjectPredictions}, + Censor, Data, Predictions as PredTrait, +}; use serde::{Deserialize, Serialize}; use crate::{ estimation::nonparametric::{theta::Theta, weights::Weights}, estimation::nonparametric::{weighted_median, Posterior}, + model::EquationMetadataSource, }; #[derive(Debug, Clone, Serialize, Deserialize)] @@ -115,7 +119,8 @@ impl NPPredictions { } pub fn calculate( - equation: &impl pharmsol::prelude::simulator::Equation, + equation: &(impl pharmsol::equation::EquationTypes

+ + EquationMetadataSource), data: &Data, theta: &Theta, w: &Weights, @@ -125,7 +130,7 @@ impl NPPredictions { ) -> Result { let mut container = NPPredictions::new(); - let data = data.clone().expand(idelta, tad); + let data = data.clone().expand(idelta, tad, &[]); let subjects = data.subjects(); if subjects.len() != posterior.matrix().nrows() { @@ -134,13 +139,19 @@ impl NPPredictions { for (subject_index, subject) in subjects.iter().enumerate() { let mut predictions: Vec> = Vec::new(); + let mut prediction_occasions = None; for spp in theta.matrix().row_iter() { let spp_values = spp.iter().cloned().collect::>(); - let pred = equation - .estimate_predictions_dense(subject, &spp_values)? - .get_predictions(); - predictions.push(pred); + let pred = equation.estimate_predictions_dense(subject, &spp_values)?; + match prediction_occasions.as_ref() { + Some(expected) if expected != pred.occasions() => { + bail!("prediction occasion metadata changed across support points") + } + None => prediction_occasions = Some(pred.occasions().clone()), + _ => {} + } + predictions.push(pred.get_predictions()); } if predictions.is_empty() { @@ -190,12 +201,28 @@ impl NPPredictions { } if let Some(first_spp_preds) = predictions.first() { + let occasions = prediction_occasions + .as_ref() + .context("predictions are present without occasion metadata")?; for (j, p) in first_spp_preds.iter().enumerate() { + let outeq = equation + .equation_metadata() + .and_then(|metadata| metadata.output_for_label(p.output().as_str())) + .with_context(|| { + format!( + "prediction output '{}' is not declared by the model", + p.output() + ) + })?; + let block = occasions + .get(j) + .copied() + .context("prediction is missing parallel occasion metadata")?; let row = NPPredictionRow { id: subject.id().clone(), time: p.time(), - outeq: p.outeq(), - block: p.occasion(), + outeq, + block, obs: p.observation(), cens: p.censoring(), pop_mean: pop_mean[j], diff --git a/src/estimation/nonparametric/result.rs b/src/estimation/nonparametric/result.rs index e5473f19d..4c3f3328b 100644 --- a/src/estimation/nonparametric/result.rs +++ b/src/estimation/nonparametric/result.rs @@ -1,10 +1,13 @@ use std::path::Path; +use pharmsol::equation::EquationTypes; +use pharmsol::prelude::simulator::SubjectPredictions; use pharmsol::Equation; use serde::Serialize; use crate::algorithms::Status; use crate::estimation::nonparametric::{CycleLog, NPPredictions, Posterior, Psi, Theta, Weights}; +use crate::model::EquationMetadataSource; use crate::AssayErrorModels; use pharmsol::Data; @@ -143,7 +146,10 @@ impl NonParametricResult { /// Compute predictions on demand. Nothing is cached on the result; callers /// that need the predictions repeatedly should hold on to the returned /// value themselves. - pub fn predictions(&self, idelta: f64, tad: f64) -> anyhow::Result { + pub fn predictions(&self, idelta: f64, tad: f64) -> anyhow::Result + where + E: EquationMetadataSource + EquationTypes

, + { let posterior = self.posterior()?; self.predictions_with(&posterior, idelta, tad) } @@ -155,7 +161,10 @@ impl NonParametricResult { posterior: &Posterior, idelta: f64, tad: f64, - ) -> anyhow::Result { + ) -> anyhow::Result + where + E: EquationMetadataSource + EquationTypes

, + { NPPredictions::calculate( &self.equation, &self.data, @@ -319,7 +328,10 @@ impl NonParametricResult { /// /// `idelta` is the interval used to densify the prediction grid and `tad` is /// the additional time after the last event to simulate. - pub fn write_predictions(&self, path: &Path, idelta: f64, tad: f64) -> anyhow::Result<()> { + pub fn write_predictions(&self, path: &Path, idelta: f64, tad: f64) -> anyhow::Result<()> + where + E: EquationMetadataSource + EquationTypes

, + { let predictions = self.predictions(idelta, tad)?; predictions.write(path) } @@ -330,7 +342,10 @@ impl NonParametricResult { /// likelihoods, weights, objective function, status, cycle log, posterior /// probabilities, and predictions. `idelta` and `tad` control the density of /// the embedded predictions (see [`predictions`](Self::predictions)). - pub fn write_json(&self, path: &Path, idelta: f64, tad: f64) -> anyhow::Result<()> { + pub fn write_json(&self, path: &Path, idelta: f64, tad: f64) -> anyhow::Result<()> + where + E: EquationMetadataSource + EquationTypes

, + { let posterior = self.posterior()?; let predictions = self.predictions_with(&posterior, idelta, tad)?; self.write_json_with(path, &posterior, &predictions) @@ -385,7 +400,10 @@ impl NonParametricResult { directory: impl AsRef, idelta: f64, tad: f64, - ) -> anyhow::Result<()> { + ) -> anyhow::Result<()> + where + E: EquationMetadataSource + EquationTypes

, + { let dir = directory.as_ref(); std::fs::create_dir_all(dir)?; @@ -434,7 +452,7 @@ mod tests { use pharmsol::equation::metadata; use pharmsol::SubjectBuilderExt; - fn minimal_ode() -> pharmsol::ODE { + fn minimal_ode() -> pharmsol::equation::ODE { pharmsol::equation::ODE::new( |x, p, _t, dx, b, _rateiv, _cov| { let ke = p[0]; @@ -470,6 +488,62 @@ mod tests { pharmsol::Data::new(vec![subject]) } + fn sparse_output_ode() -> pharmsol::equation::ODE { + pharmsol::equation::ODE::new( + |x, p, _t, dx, b, _rateiv, _cov| { + dx[0] = -p[0] * x[0] + b[0]; + }, + |_p, _t, _cov| pharmsol::lag! {}, + |_p, _t, _cov| pharmsol::fa! {}, + |_p, _t, _cov, _x| {}, + |x, p, _t, _cov, y| { + y[0] = x[0]; + y[1] = x[0] / p[1]; + }, + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(2) + .with_metadata( + metadata::new("sparse_nonparametric_predictions") + .parameters(["ke", "v"]) + .states(["central"]) + .outputs(["unmeasured", "measured"]) + .route(pharmsol::equation::Route::bolus("dose").to_state("central")), + ) + .expect("sparse-output metadata should validate") + } + + #[test] + fn sparse_output_prediction_expansion_preserves_observed_outputs_only() { + let data = pharmsol::Data::new(vec![pharmsol::Subject::builder("sparse") + .bolus(0.0, 100.0, "dose") + .observation(1.0, 10.0, "measured") + .observation(2.0, 8.0, "measured") + .build()]); + let params = ParameterSpace::bounded() + .add("ke", 0.001, 3.0) + .add("v", 5.0, 250.0); + let prior = Theta::sobol_with_seed(¶ms, 5, 42).unwrap(); + let error_models = AssayErrorModels::new() + .add( + "measured", + AssayErrorModel::additive(ErrorPoly::new(0.0, 0.5, 0.0, 0.0), 0.0), + ) + .unwrap(); + let result = + EstimationProblem::nonparametric(sparse_output_ode(), data, prior, error_models) + .unwrap() + .fit_with(NpagConfig::new().max_cycles(1)) + .unwrap(); + + let predictions = result.predictions(0.25, 0.0).unwrap(); + assert!(predictions + .predictions() + .iter() + .all(|prediction| prediction.outeq() == 1)); + } + #[test] fn chain_npag_to_npod_preserves_support_points() { let ode = minimal_ode(); diff --git a/src/estimation/parametric/information.rs b/src/estimation/parametric/information.rs index 245458ce9..a786ff108 100644 --- a/src/estimation/parametric/information.rs +++ b/src/estimation/parametric/information.rs @@ -563,11 +563,14 @@ impl CompleteDerivative { if failure.is_some() { return; } - let Some(model) = error_models.get(prediction.outeq()).copied() else { + let Some(output) = error_models.output_index(prediction.output()) else { + return; + }; + let Some(model) = error_models.get(output).copied() else { return; }; if let Err(error) = self.add_residual( - prediction.outeq(), + output, prediction.observation(), prediction.prediction(), prediction.censoring(), @@ -596,7 +599,10 @@ impl CompleteDerivative { if failure.is_some() || prediction.observation().is_none() { return; } - let Some(model) = error_models.get(prediction.outeq()).copied() else { + let Some(output) = error_models.output_index(prediction.output()) else { + return; + }; + let Some(model) = error_models.get(output).copied() else { return; }; let raw_scale = match model { @@ -620,7 +626,7 @@ impl CompleteDerivative { "retained Markov scores are unsupported on an active or equal likelihood-floor branch" )); } else if let Err(error) = self.add_residual( - prediction.outeq(), + output, prediction.observation(), prediction.prediction(), prediction.censoring(), diff --git a/src/estimation/parametric/residual.rs b/src/estimation/parametric/residual.rs index d069f79ad..bb1b07d6f 100644 --- a/src/estimation/parametric/residual.rs +++ b/src/estimation/parametric/residual.rs @@ -42,7 +42,7 @@ impl ResidualSufficientStatistics { } } - pub(crate) fn from_predictions

(predictions: &P, error_models: &ResidualErrorModels) -> Self + pub(crate) fn from_predictions

(predictions: &P, error_models: &ParametricErrorModels) -> Self where P: Predictions, { @@ -56,12 +56,14 @@ impl ResidualSufficientStatistics { fn accumulate_prediction( &mut self, prediction: &Prediction, - error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) { let Some(observation) = prediction.observation() else { return; }; - let outeq = prediction.outeq(); + let Some(outeq) = error_models.output_index(prediction.output()) else { + return; + }; let Some(model) = error_models.get(outeq) else { return; }; @@ -156,7 +158,7 @@ pub(crate) fn residual_statistics_for_subject( equation: &E, subject: &Subject, parameters: &[f64], - error_models: &ResidualErrorModels, + error_models: &ParametricErrorModels, ) -> Result { let predictions = equation.estimate_predictions_dense(subject, parameters)?; Ok(ResidualSufficientStatistics::from_predictions( diff --git a/src/estimation/problem.rs b/src/estimation/problem.rs index c46994ad9..6a23f706d 100644 --- a/src/estimation/problem.rs +++ b/src/estimation/problem.rs @@ -448,8 +448,8 @@ fn validate_nonparametric_error_models( .map_err(|e| anyhow!("invalid assay error model output(s): {e}"))?; // Collect the set of model output indices that are actually observed in the - // data, resolving each observation's output label the same way the simulator - // does (exact name, then the `outeq_` numeric alias). + // data, using pharmsol's metadata resolver: exact names first, then a bare + // numeric label only when the corresponding `outeq_` output is declared. let mut observed_outputs: BTreeSet = BTreeSet::new(); let mut unresolved_labels: BTreeSet = BTreeSet::new(); for subject in data.subjects() { @@ -505,17 +505,16 @@ fn validate_nonparametric_error_models( Ok((*bound).clone()) } -/// Resolves an observation output `label` to a model output index, mirroring the -/// simulator: exact metadata name, then numeric `N` only for a declared `outeq_N`. +/// Resolves an observation output using pharmsol's public-label compatibility +/// rules, without positional fallback. fn resolve_output_index( model: &Model, label: &str, ) -> Option { - model.output_index(label).or_else(|| { - (!label.is_empty() && label.chars().all(|ch| ch.is_ascii_digit())) - .then(|| format!("outeq_{label}")) - .and_then(|alias| model.output_index(&alias)) - }) + model + .equation + .equation_metadata() + .and_then(|metadata| metadata.output_for_label(label)) } fn validate_parametric_data( @@ -734,7 +733,7 @@ mod tests { use pharmsol::prelude::*; use pharmsol::{Censor, Data, Subject, SubjectBuilderExt}; - fn equation_with_outputs(outputs: [&str; 2]) -> pharmsol::ODE { + fn equation_with_outputs(outputs: [&str; 2]) -> pharmsol::equation::ODE { pharmsol::equation::ODE::new( |_x, _p, _t, dx, _b, _rateiv, _cov| dx[0] = 0.0, |_p, _t, _cov| lag! {}, @@ -758,7 +757,7 @@ mod tests { .unwrap() } - fn equation() -> pharmsol::ODE { + fn equation() -> pharmsol::equation::ODE { equation_with_outputs(["cp", "effect"]) } @@ -770,10 +769,10 @@ mod tests { #[test] fn deterministic_model_kind_support_is_fail_closed() { - assert!(reject_sde_estimation::().is_ok()); - assert!(reject_sde_estimation::().is_ok()); + assert!(reject_sde_estimation::().is_ok()); + assert!(reject_sde_estimation::().is_ok()); - let error = reject_sde_estimation::() + let error = reject_sde_estimation::() .expect_err("EstimationProblem must reject SDE models") .to_string(); assert!(error.contains("SDE")); @@ -927,15 +926,17 @@ mod tests { } #[test] - fn numeric_output_aliases_preserve_leading_zeroes() { - assert!(EstimationProblem::parametric( - equation_with_outputs(["outeq_00", "effect"]), - measured_data("00"), - ) - .parameter(Parameter::log("value")) - .error_model("outeq_00", ResidualErrorModel::constant(1.0)) - .build() - .is_ok()); + fn numeric_output_aliases_resolve_only_declared_canonical_labels() { + for (declared, observed) in [("outeq_0", "0"), ("outeq_00", "00")] { + assert!(EstimationProblem::parametric( + equation_with_outputs([declared, "effect"]), + measured_data(observed), + ) + .parameter(Parameter::log("value")) + .error_model(declared, ResidualErrorModel::constant(1.0)) + .build() + .is_ok()); + } let error = EstimationProblem::parametric( equation_with_outputs(["outeq_0", "effect"]), @@ -944,7 +945,7 @@ mod tests { .parameter(Parameter::log("value")) .error_model("outeq_0", ResidualErrorModel::constant(1.0)) .build() - .expect_err("00 must not resolve to outeq_0") + .expect_err("numeric aliases must preserve their exact canonical suffix") .to_string(); assert!(error.contains("unknown model output '00'")); } diff --git a/src/estimation/sde_particle.rs b/src/estimation/sde_particle.rs index dffe85c7b..f55155bc2 100644 --- a/src/estimation/sde_particle.rs +++ b/src/estimation/sde_particle.rs @@ -1,5 +1,5 @@ -use pharmsol::equation::SdeSessionError; -use pharmsol::{Parameters, Subject, SDE}; +use pharmsol::equation::{SdeSessionError, SDE}; +use pharmsol::{Parameters, Subject}; use rand::rngs::StdRng; use rand::{RngExt, SeedableRng}; use thiserror::Error; diff --git a/src/iov/mod.rs b/src/iov/mod.rs index 8a9f00b62..db8ccba4f 100644 --- a/src/iov/mod.rs +++ b/src/iov/mod.rs @@ -35,7 +35,8 @@ mod optimizer; use anyhow::{bail, Context}; -use pharmsol::{Data, SDE}; +use pharmsol::equation::SDE; +use pharmsol::Data; use rayon::prelude::*; use crate::estimation::nonparametric::Theta; diff --git a/src/iov/optimizer.rs b/src/iov/optimizer.rs index 921002b41..d12ef5169 100644 --- a/src/iov/optimizer.rs +++ b/src/iov/optimizer.rs @@ -5,7 +5,8 @@ use std::sync::{ use argmin::core::{CostFunction, Error, Executor}; use argmin::solver::neldermead::NelderMead; -use pharmsol::{Data, Parameters, SDE}; +use pharmsol::equation::SDE; +use pharmsol::{Data, Parameters}; use super::DiffusionConfig; use crate::{AssayErrorModels, SdeParticleConfig, SdeParticleFilter}; diff --git a/src/lib.rs b/src/lib.rs index 1998eb936..dd870709a 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -125,9 +125,8 @@ pub mod prelude { pub use pharmsol::prelude::*; // Items required by downstream code that are not part of `pharmsol::prelude`. - pub use pharmsol::equation::{EquationTypes, Predictions}; + pub use pharmsol::equation::{EquationTypes, Predictions, ODE, SDE}; pub use pharmsol::optimize::effect::get_e2; - pub use pharmsol::{ODE, SDE}; // Organized submodules mirroring pharmsol's grouping. pub mod simulator { diff --git a/src/model/mod.rs b/src/model/mod.rs index 861d16f1c..e7a31d190 100644 --- a/src/model/mod.rs +++ b/src/model/mod.rs @@ -1,6 +1,6 @@ use anyhow::Result; -use pharmsol::equation::Equation; -use pharmsol::{Analytical, ValidatedModelMetadata, ODE, SDE}; +use pharmsol::equation::{Analytical, Equation, ODE, SDE}; +use pharmsol::ValidatedModelMetadata; pub mod metadata; pub mod parameter_space; diff --git a/src/results/fit_result.rs b/src/results/fit_result.rs index b8b600617..a68592f0f 100644 --- a/src/results/fit_result.rs +++ b/src/results/fit_result.rs @@ -865,7 +865,7 @@ impl ParametricResult { E: pharmsol::equation::EquationTypes

, { self.validate_prediction_metadata()?; - let expanded = self.data.clone().expand(idelta, tad); + let expanded = self.data.clone().expand(idelta, tad, &[]); self.validate_expanded_subjects(&expanded)?; let population_phi = self .covariate_model @@ -921,7 +921,7 @@ impl ParametricResult { ); } - let expanded = self.data.clone().expand(idelta, tad); + let expanded = self.data.clone().expand(idelta, tad, &[]); self.validate_expanded_subjects(&expanded)?; if self.conditional_modes.len() != expanded.subjects().len() { bail!( @@ -998,7 +998,8 @@ impl ParametricResult { subject.occasions().len() ); } - let mut combined = Vec::new(); + let mut combined = SubjectPredictions::default(); + combined.set_id(subject.id().clone()); for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { if kappa.subject_id != *subject.id() { bail!( @@ -1068,12 +1069,11 @@ impl ParametricResult { expected_points ); } - for mut prediction in predictions.predictions().iter().cloned() { - *prediction.mut_occasion() = occasion.index(); - combined.push(prediction); + for prediction in predictions.predictions().iter().cloned() { + combined.add_prediction(prediction, occasion.index()); } } - Ok(SubjectPredictions::from(combined)) + Ok(combined) }) .collect() } diff --git a/src/results/parametric_output.rs b/src/results/parametric_output.rs index 125246f9a..72e2a1dd1 100644 --- a/src/results/parametric_output.rs +++ b/src/results/parametric_output.rs @@ -3809,22 +3809,55 @@ impl ParametricResult { .get(subject_index) .context("prediction subject index exceeds retained data")?; let conditional_predictions = conditional.as_ref().map(|value| &value.0[subject_index]); + if predictions.occasions().len() != predictions.predictions().len() { + bail!( + "population prediction occasion count mismatch for subject '{}'", + subject.id() + ); + } if let Some(other) = conditional_predictions { - if other.predictions().len() != predictions.predictions().len() { + if other.predictions().len() != predictions.predictions().len() + || other.occasions().len() != predictions.occasions().len() + { bail!("prediction count mismatch for subject '{}'", subject.id()); } } - for (point_index, point) in predictions.predictions().iter().enumerate() { + for (point_index, (point, block)) in predictions + .predictions() + .iter() + .zip(predictions.occasions()) + .enumerate() + { let conditional_point = conditional_predictions.map(|values| &values.predictions()[point_index]); if let Some(other) = conditional_point { - validate_prediction_pair(subject.id(), point, other)?; + validate_prediction_pair( + subject.id(), + point, + *block, + other, + conditional_predictions + .expect("conditional predictions exist") + .occasions()[point_index], + )?; } + let output_index = self + .residual_error_estimates + .iter() + .find(|estimate| estimate.output == point.output().as_str()) + .map(|estimate| estimate.output_index) + .with_context(|| { + format!( + "prediction output '{}' is not declared for subject '{}'", + point.output(), + subject.id() + ) + })?; rows.push(PredictionRow { subject: subject.id().clone(), time: point.time(), - output_index: point.outeq(), - block: point.occasion(), + output_index, + block: *block, observation: point.observation(), censoring: censor_text(point.censoring()).to_string(), population_prediction: point.prediction(), @@ -6046,11 +6079,13 @@ fn statistic( fn validate_prediction_pair( subject: &str, population: &Prediction, + population_occasion: usize, conditional: &Prediction, + conditional_occasion: usize, ) -> Result<()> { if population.time() != conditional.time() - || population.outeq() != conditional.outeq() - || population.occasion() != conditional.occasion() + || population.output() != conditional.output() + || population_occasion != conditional_occasion || population.observation() != conditional.observation() || population.censoring() != conditional.censoring() { diff --git a/tests/bestdose_tests.rs b/tests/bestdose_tests.rs index e0bb6ee26..80c2c6163 100644 --- a/tests/bestdose_tests.rs +++ b/tests/bestdose_tests.rs @@ -58,7 +58,7 @@ fn parameter_space() -> ParameterSpace { fn error_models() -> AssayErrorModels { AssayErrorModels::new() .add( - 0, + "outeq_0", AssayErrorModel::additive(ErrorPoly::new(0.0, 0.2, 0.0, 0.0), 0.0), ) .unwrap() @@ -335,10 +335,10 @@ fn auc_from_last_dose_optimizes_maintenance_dose() -> Result<()> { fn history_from(point: [f64; 2]) -> Subject { let [ke, v] = point; Subject::builder("history") - .bolus(0.0, 100.0, 0) - .observation(1.0, conc(100.0, ke, v, 1.0), 0) - .observation(3.0, conc(100.0, ke, v, 3.0), 0) - .observation(6.0, conc(100.0, ke, v, 6.0), 0) + .bolus(0.0, 100.0, "input_0") + .observation(1.0, conc(100.0, ke, v, 1.0), "outeq_0") + .observation(3.0, conc(100.0, ke, v, 3.0), "outeq_0") + .observation(6.0, conc(100.0, ke, v, 6.0), "outeq_0") .build() } diff --git a/tests/iov_diffusion_optimizer.rs b/tests/iov_diffusion_optimizer.rs index bccf055d1..e4d2cbd93 100644 --- a/tests/iov_diffusion_optimizer.rs +++ b/tests/iov_diffusion_optimizer.rs @@ -1,6 +1,6 @@ use faer::Mat; -use pharmsol::equation::{metadata, ModelKind, Route}; -use pharmsol::{fa, lag, Data, SubjectBuilderExt, SDE}; +use pharmsol::equation::{metadata, ModelKind, Route, SDE}; +use pharmsol::{fa, lag, Data, SubjectBuilderExt}; use pmcore::iov::{DiffusionConfig, DiffusionOptimize}; use pmcore::prelude::{BoundedParameter, ParameterSpace, Posterior, Theta}; use pmcore::{AssayErrorModel, AssayErrorModels, ErrorPoly}; diff --git a/tests/ode_scoring_parity.rs b/tests/ode_scoring_parity.rs index b4b63d71d..20b495004 100644 --- a/tests/ode_scoring_parity.rs +++ b/tests/ode_scoring_parity.rs @@ -1,6 +1,6 @@ -use pharmsol::equation::{metadata, AnalyticalKernel, ModelKind, Route}; +use pharmsol::equation::{metadata, Analytical, AnalyticalKernel, ModelKind, Route, ODE}; use pharmsol::prelude::models::one_compartment; -use pharmsol::{fa, fetch_params, lag, Analytical, Equation, Parameters, SubjectBuilderExt, ODE}; +use pharmsol::{fa, fetch_params, lag, Equation, Parameters, SubjectBuilderExt}; use pmcore::{ AssayErrorModel, AssayErrorModels, AssayLikelihoodError, ErrorModelError, ErrorPoly, NormalDistributionError, @@ -82,6 +82,9 @@ fn likelihood_calculation_matches_analytical() { ErrorPoly::new(0.0, 0.1, 0.0, 0.0), 0.0, )]); + let bound_error_models = error_models + .bind_outputs(["cp"]) + .expect("bind dense assay model to the declared output"); let analytical_params = Parameters::with_model(&analytical, [("ke", 0.1), ("v", 50.0)]) .expect("analytical parameters should validate"); let ode_params = Parameters::with_model(&ode, [("ke", 0.1), ("v", 50.0)]) @@ -94,7 +97,7 @@ fn likelihood_calculation_matches_analytical() { .estimate_predictions(&subject, &ode_params) .expect("ODE predictions"); - let dense_log_likelihood = error_models + let dense_log_likelihood = bound_error_models .log_likelihood(&analytical_predictions) .expect("PMcore dense analytical likelihood"); let named_error_models = AssayErrorModels::new() @@ -135,7 +138,7 @@ fn likelihood_calculation_matches_analytical() { )); let ll_analytical = dense_log_likelihood.exp(); - let ll_ode = error_models + let ll_ode = bound_error_models .log_likelihood(&ode_predictions) .expect("PMcore ODE likelihood") .exp(); @@ -155,8 +158,11 @@ fn likelihood_calculation_matches_analytical() { ErrorPoly::new(0.0, 0.0, 0.0, 0.0), 0.0, )]); + let bound_zero_sigma_models = zero_sigma_models + .bind_outputs(["cp"]) + .expect("bind zero-sigma assay model to the declared output"); assert!(matches!( - zero_sigma_models.log_likelihood(&analytical_predictions), + bound_zero_sigma_models.log_likelihood(&analytical_predictions), Err(AssayLikelihoodError::Distribution( NormalDistributionError::InvalidSigma(sigma) )) if sigma == 0.0 @@ -172,8 +178,11 @@ fn likelihood_calculation_matches_analytical() { ErrorPoly::new(1.0, 0.0, 0.0, 0.0), 0.0, )]); + let bound_constant_sigma_models = constant_sigma_models + .bind_outputs(["cp"]) + .expect("bind constant-sigma assay model to the declared output"); assert!(matches!( - constant_sigma_models.log_likelihood(&impossible_predictions), + bound_constant_sigma_models.log_likelihood(&impossible_predictions), Err(AssayLikelihoodError::Impossible) )); } diff --git a/tests/ode_solver_profile.rs b/tests/ode_solver_profile.rs index 8d7508830..1afdeae4f 100644 --- a/tests/ode_solver_profile.rs +++ b/tests/ode_solver_profile.rs @@ -103,18 +103,12 @@ fn one_compartment_scale_and_time_panel_meets_d1() { for (index, (release_point, &time)) in release.iter().zip(times).enumerate() { let expected = dose * (-ke * time).exp(); assert_eq!(release_point.time().to_bits(), time.to_bits()); - assert_eq!(release_point.outeq(), 0); - assert_eq!(release_point.state().len(), 1); + assert_eq!(release_point.output().as_str(), "amount"); assert_d1( &format!("S1 case {case_index} point {index} release output"), release_point.prediction(), expected, ); - assert_d1( - &format!("S1 case {case_index} point {index} release state"), - release_point.state()[0], - expected, - ); } } } @@ -161,18 +155,12 @@ fn event_driven_bolus_and_infusion_panel_meets_d1() { + infusion_contribution(80.0, 3.0, 4.0, time, ke) + bolus_contribution(25.0, 9.0, time, ke); assert_eq!(release_point.time().to_bits(), time.to_bits()); - assert_eq!(release_point.outeq(), 0); - assert_eq!(release_point.state().len(), 1); + assert_eq!(release_point.output().as_str(), "amount"); assert_d1( &format!("S2 point {index} release output"), release_point.prediction(), expected, ); - assert_d1( - &format!("S2 point {index} release state"), - release_point.state()[0], - expected, - ); } } @@ -221,20 +209,15 @@ fn two_compartment_stiffness_panel_meets_d1() { let time = times[time_index]; let expected = two_compartment_closed_form(dose, k10, k12, k21, time); assert_eq!(release_point.time().to_bits(), time.to_bits()); - assert_eq!(release_point.outeq(), output_index); - assert_eq!(release_point.state().len(), 2); + assert_eq!( + release_point.output().as_str(), + ["central_amount", "peripheral_amount"][output_index] + ); assert_d1( &format!("S3 case {case_index} point {index} release output"), release_point.prediction(), expected[output_index], ); - for (state_index, expected_state) in expected.iter().copied().enumerate() { - assert_d1( - &format!("S3 case {case_index} point {index} release state {state_index}"), - release_point.state()[state_index], - expected_state, - ); - } } } } diff --git a/tests/particle_filter_scientific.rs b/tests/particle_filter_scientific.rs index 93d2bc285..5054ca499 100644 --- a/tests/particle_filter_scientific.rs +++ b/tests/particle_filter_scientific.rs @@ -1,5 +1,5 @@ -use pharmsol::equation::{metadata, ModelKind, Route}; -use pharmsol::{fa, lag, Parameters, SubjectBuilderExt, SDE}; +use pharmsol::equation::{metadata, ModelKind, Route, SDE}; +use pharmsol::{fa, lag, Parameters, SubjectBuilderExt}; use pmcore::{AssayErrorModel, AssayErrorModels, ErrorPoly, SdeParticleConfig, SdeParticleFilter}; /// Scientific check that the SDE particle-filter likelihood stays finite. diff --git a/tests/saem_outputs.rs b/tests/saem_outputs.rs index a62e9fccb..be0b16ac9 100644 --- a/tests/saem_outputs.rs +++ b/tests/saem_outputs.rs @@ -693,14 +693,21 @@ fn iov_tables_include_lower_triangle_and_ordered_kappas() { population_subject.predictions().len(), conditional_subject.predictions().len() ); - for (population_point, conditional_point) in population_subject - .predictions() - .iter() - .zip(conditional_subject.predictions()) + for ((population_point, population_occasion), (conditional_point, conditional_occasion)) in + population_subject + .predictions() + .iter() + .zip(population_subject.occasions()) + .zip( + conditional_subject + .predictions() + .iter() + .zip(conditional_subject.occasions()), + ) { assert_eq!(population_point.time(), conditional_point.time()); - assert_eq!(population_point.outeq(), conditional_point.outeq()); - assert_eq!(population_point.occasion(), conditional_point.occasion()); + assert_eq!(population_point.output(), conditional_point.output()); + assert_eq!(population_occasion, conditional_occasion); assert_eq!( population_point.observation(), conditional_point.observation() @@ -809,7 +816,7 @@ fn averaged_iov_result_rebuilds_all_deterministic_outputs_from_canonical_state() assert!((row.value - expected).abs() < 1e-12); } - let expanded = averaged.data().clone().expand(0.25, 0.0); + let expanded = averaged.data().clone().expand(0.25, 0.0, &[]); let population = averaged.population_predictions(0.25, 0.0).unwrap(); let mut differs_from_terminal_prediction = false; for (subject, actual) in expanded.subjects().iter().zip(&population) { @@ -822,19 +829,24 @@ fn averaged_iov_result_rebuilds_all_deterministic_outputs_from_canonical_state() .estimate_predictions_dense(subject, &terminal_cycle.population_parameters) .unwrap(); assert_eq!(actual.predictions().len(), expected.predictions().len()); - for ((actual, expected), terminal_expected) in actual + let actual_occasions = actual.occasions(); + let expected_prediction_occasions = expected.occasions(); + for (point_index, ((actual, expected), terminal_expected)) in actual .predictions() .iter() .zip(expected.predictions()) .zip(terminal_expected.predictions()) + .enumerate() { assert_eq!(actual.time(), expected.time()); assert!((actual.prediction() - expected.prediction()).abs() < 1e-12); assert_eq!(actual.observation(), expected.observation()); - assert_eq!(actual.outeq(), expected.outeq()); + assert_eq!(actual.output(), expected.output()); assert_errorpoly_close(actual.errorpoly(), expected.errorpoly()); - assert_float_slice_close(actual.state(), expected.state()); - assert_eq!(actual.occasion(), expected.occasion()); + assert_eq!( + actual_occasions[point_index], + expected_prediction_occasions[point_index] + ); assert_eq!(actual.censoring(), expected.censoring()); differs_from_terminal_prediction |= (actual.prediction() - terminal_expected.prediction()).abs() > 1e-5; @@ -846,6 +858,7 @@ fn averaged_iov_result_rebuilds_all_deterministic_outputs_from_canonical_state() for (subject, actual) in expanded.subjects().iter().zip(&conditional) { let mode = averaged.conditional_mode(subject.id()).unwrap(); let mut expected = Vec::new(); + let mut expected_occasions = Vec::new(); for (occasion, kappa) in subject.occasions().iter().zip(&mode.kappas) { assert_eq!(occasion.index(), kappa.occasion_index); let parameters = [ @@ -854,25 +867,30 @@ fn averaged_iov_result_rebuilds_all_deterministic_outputs_from_canonical_state() ]; let occasion_subject = Subject::from_occasions(subject.id().clone(), vec![occasion.clone()]); - expected.extend( - averaged - .equation() - .estimate_predictions_dense(&occasion_subject, ¶meters) - .unwrap() - .predictions() - .iter() - .cloned(), - ); + let occasion_predictions = averaged + .equation() + .estimate_predictions_dense(&occasion_subject, ¶meters) + .unwrap(); + expected.extend(occasion_predictions.predictions().iter().cloned()); + expected_occasions.extend(std::iter::repeat_n( + occasion.index(), + occasion_predictions.predictions().len(), + )); } assert_eq!(actual.predictions().len(), expected.len()); - for (actual, expected) in actual.predictions().iter().zip(&expected) { + let actual_occasions = actual.occasions(); + for (point_index, (actual, expected)) in + actual.predictions().iter().zip(&expected).enumerate() + { assert_eq!(actual.time(), expected.time()); assert!((actual.prediction() - expected.prediction()).abs() < 1e-12); assert_eq!(actual.observation(), expected.observation()); - assert_eq!(actual.outeq(), expected.outeq()); + assert_eq!(actual.output(), expected.output()); assert_errorpoly_close(actual.errorpoly(), expected.errorpoly()); - assert_float_slice_close(actual.state(), expected.state()); - assert_eq!(actual.occasion(), expected.occasion()); + assert_eq!( + actual_occasions[point_index], + expected_occasions[point_index] + ); assert_eq!(actual.censoring(), expected.censoring()); } } diff --git a/tests/sde_particle_filter.rs b/tests/sde_particle_filter.rs index c97f7fbfb..d47720535 100644 --- a/tests/sde_particle_filter.rs +++ b/tests/sde_particle_filter.rs @@ -1,5 +1,5 @@ -use pharmsol::equation::{metadata, ModelKind, Route, SdeSessionError}; -use pharmsol::{fa, lag, Censor, Parameters, Subject, SubjectBuilderExt, SDE}; +use pharmsol::equation::{metadata, ModelKind, Route, SdeSessionError, SDE}; +use pharmsol::{fa, lag, Censor, Parameters, Subject, SubjectBuilderExt}; use pmcore::{ AssayErrorModel, AssayErrorModels, ErrorPoly, SdeParticleConfig, SdeParticleError, SdeParticleFilter, From 3f62dc28dc6d021467f6368faf4c3f9269db5208 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juli=C3=A1n=20D=2E=20Ot=C3=A1lvaro?= Date: Fri, 31 Jul 2026 01:26:54 +0100 Subject: [PATCH 4/5] Add SAEMix kernels and canonical parameter order --- docs/saem-support.md | 19 +- plans/saem-validation-roadmap.md | 71 ++- src/algorithms/parametric/mod.rs | 2 +- src/algorithms/parametric/saem/mod.rs | 52 ++- src/algorithms/parametric/saem/state/mod.rs | 421 +++++++++++++++++- .../parametric/saem/state/tests/controller.rs | 113 +++++ src/algorithms/parametric/saem_config.rs | 161 ++++++- src/estimation/mod.rs | 1 + src/estimation/problem.rs | 78 +++- src/lib.rs | 9 +- src/results/fit_result.rs | 26 ++ src/results/mod.rs | 4 +- tests/saem_regressions.rs | 45 ++ 13 files changed, 971 insertions(+), 31 deletions(-) diff --git a/docs/saem-support.md b/docs/saem-support.md index b891a12d1..2a633ed77 100644 --- a/docs/saem-support.md +++ b/docs/saem-support.md @@ -38,7 +38,8 @@ accuracy. Eta and kappa are additive in transformed parameter space. Model execution uses natural parameter values. `Parameter::with_initial` is the natural-scale value -at zero eta, kappa, and covariate offsets. +at zero eta, kappa, and covariate offsets. Named parameter declarations are +canonicalized to model metadata order before any numerical vector is built. `Omega::diagonal_variances` and `Iov::diagonal_variances` accept variances. `diagonal_standard_deviations` accepts finite positive SDs and checks overflow @@ -82,10 +83,18 @@ Burn-in performs MCMC without parameter updates. Exploration uses gain one. Smoothing uses a decreasing gain. The default estimator returns the terminal iterate. `AveragedIterates { alpha }` requires `k2 > 0` and `0.5 < alpha < 1`. -MCMC chain counts, iteration counts, adaptation intervals, and proposal scales -must be positive. `eta_block_iterations = 0` disables eta block proposals. Raw -Omega blocks are the default block scale. Conditional-curvature scaling is -opt-in and fails with a typed status when strict curvature is unavailable. +MCMC chain counts, the PMcore component-iteration count, adaptation intervals, +and proposal scales must be positive. `eta_block_iterations = 0` disables eta +block proposals. Raw Omega blocks are the default PMcore block scale. A SAEMix +policy may set individual kernel counts to zero but must enable at least one. + +`SaemConfig::saemix_mcmc([q1, q2, q3, q4])` explicitly selects the SAEMix IIV +kernel order: prior independence, component random walk, rotating subsets, and +early MAP-informed independence. The policy has its own MAP window, optimizer, +and adaptation settings; post-fit `compute_map` remains independent. SAEMix +compatibility rejects IOV rather than approximating its eta/kappa semantics. +Strict q4 curvature failure terminates through the typed expectation-failure +path without regularization or fallback. Covariate raw first and second moments always use the same SA gain. PMcore forms a centered covariance target before applying masks and the constrained local diff --git a/plans/saem-validation-roadmap.md b/plans/saem-validation-roadmap.md index 12686799d..533e6ddc5 100644 --- a/plans/saem-validation-roadmap.md +++ b/plans/saem-validation-roadmap.md @@ -37,10 +37,74 @@ The support matrix and failure semantics are maintained in semantics are maintained in [`docs/saem-convergence.md`](../docs/saem-convergence.md). +## Must Have — completed release blockers + +M7 and M8 are implemented and validated in the current branch. + +### M7 — Canonical parameter ordering — Complete + +PMcore now validates parameter declarations by name and canonicalizes them to +model metadata order before constructing covariates, Omega, IOV, scoring state, +persistence metadata, or results. + +- Canonicalize every parameter-aligned structure to + `model.parameter_names()` before model execution, scoring, diagnostics, + persistence, and result construction. +- Preserve name-based Omega, IOV, covariate, fixed/free, warm-start, and + persistence semantics through the reorder. +- Continue to reject duplicate, unknown, and missing declarations explicitly. +- Add analytical and ODE regressions proving that out-of-order declarations + produce the same predictions, objectives, estimates, and labels as canonical + declarations. +- Do not retain a positional fallback or merely document the unsafe ordering + requirement. + +Completion evidence includes ODE metadata/Omega/IOV resolution and exact +analytical fit, objective, diagnostic, and prediction parity for reordered +declarations. + +### M8 — SAEMix four-kernel compatibility — Complete + +PMcore retains its established component/full-Omega-block policy and now offers +an explicit SAEMix-compatible IIV policy implementing kernels 1 through 4. The +compatibility policy fails closed for IOV; PMcore's established eta/kappa policy +continues to support IOV. + +- Add explicit iteration counts for the prior-independence, componentwise, + rotating-subset, and early MAP-informed kernels without overloading the + existing post-fit MAP controls. +- Implement the kernels in SAEMix order, including the rotating subset-size + schedule, the early-cycle MAP proposal window, Metropolis-Hastings proposal + corrections, and SAEMix-compatible proposal-scale adaptation. +- Retain the current PMcore kernel policy as an explicit supported mode; exact + SAEMix behavior must be selected deliberately rather than introduced as a + silent default change. +- Record proposals, acceptance, non-finite rejection, and adapted scales + separately for each kernel in cycle diagnostics and controller snapshots. +- Add deterministic kernel-level tests and cross-engine tests on equivalent + parameterizations. Cross-engine acceptance must compare estimates and + distributions over multiple seeds, not identical RNG trajectories. +- Keep post-fit `compute_map` behavior distinct from the in-E-step MAP-informed + kernel. + +Completion evidence includes deterministic kernel tests and a bounded +three-seed PMcore/SAEMix theophylline panel with equivalent `ka/V/ke`, diagonal +Omega, residual, schedule, and `c(2,2,2,2)` settings. Mean PMcore-versus-SAEMix +differences were -3.40% for ka, -1.13% for V, +2.68% for ke, +0.31% for sigma, +and -5.70% for the estimable ke variance; run products remain outside the +repository. + +The release validation commands pass after both slices: + +- `cargo fmt --check` +- `cargo check` +- `cargo test saem --lib` +- `cargo test parametric --lib` +- `cargo test likelihood --lib` + ## Deferred post-release work -There is no active implementation slice. The following work is deferred until -after release: +The following work remains deferred post-release: ### Reference-model coverage @@ -71,7 +135,8 @@ after release: These are not release commitments: -- shared-random-stream studies and alternative MCMC kernels; +- shared-random-stream studies and alternative MCMC kernels beyond the required + SAEMix-compatible four-kernel policy; - Hamiltonian Monte Carlo; - automatic differentiation and shared sensitivity infrastructure; - FO, FOCE, and FOCE-I; diff --git a/src/algorithms/parametric/mod.rs b/src/algorithms/parametric/mod.rs index 75d6acb68..6b39b08d6 100644 --- a/src/algorithms/parametric/mod.rs +++ b/src/algorithms/parametric/mod.rs @@ -21,7 +21,7 @@ pub use controller::{CycleFlow, FitController, FitObserver, ParametricFitSnapsho use saem::SaemState; pub use saem_config::{ CovarianceStabilityConfig, LugsailConfig, MarkovSimulationVarianceConfig, - OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, + OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, SaemixMcmcConfig, }; use crate::algorithms::{Algorithm, Status, StopReason}; diff --git a/src/algorithms/parametric/saem/mod.rs b/src/algorithms/parametric/saem/mod.rs index 3db2e6dd0..263158110 100644 --- a/src/algorithms/parametric/saem/mod.rs +++ b/src/algorithms/parametric/saem/mod.rs @@ -16,8 +16,8 @@ use crate::estimation::likelihood::batch::{ }; use crate::estimation::likelihood::objective::parametric_subject_log_likelihoods; use crate::estimation::parametric::conditional_uncertainty::{ - conditional_mode_curvature, ConditionalModeMetadata, JointLatentCoordinate, - JointLatentCoordinateKind, + conditional_mode_curvature, ConditionalCurvatureStatus, ConditionalModeMetadata, + JointLatentCoordinate, JointLatentCoordinateKind, }; use crate::estimation::parametric::covariance::{ cholesky_lower, relative_spd_margin, worst_contrast, @@ -73,12 +73,13 @@ use crate::results::{ OperationalConvergenceCriterionStatus, OperationalConvergenceDiagnostics, OperationalConvergenceOutcome, ParametricWarning, RankDiagnosticStatus, RankMixingDiagnostic, RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, SaemCycleDiagnostics, - SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, + SaemEstimatorMetadata, SaemMcmcKernel, SaemMcmcKernelDiagnostics, SaemPhase, + SubjectConditionalMode, }; use super::{ CovarianceStabilityConfig, NumericalFailure, OperationalConvergenceConfig, SaemConfig, - SaemEstimatorPolicy, + SaemEstimatorPolicy, SaemixMcmcConfig, }; fn pending_covariance_update_diagnostics( @@ -125,6 +126,39 @@ const COMPONENT_TARGET_ACCEPTANCE: f64 = 0.44; const ETA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; const KAPPA_BLOCK_TARGET_ACCEPTANCE: f64 = 0.40; const PROPOSAL_SCALE_INCREASE: f64 = 1.1; + +fn saemix_adapt_step_size( + current: f64, + acceptance_rate: f64, + policy: SaemixMcmcConfig, +) -> Result { + let adapted = + current * (1.0 + policy.adaptation_gain * (acceptance_rate - policy.target_acceptance)); + if !adapted.is_finite() || adapted <= 0.0 { + anyhow::bail!("SAEMix proposal-scale adaptation produced a non-positive value"); + } + Ok(adapted) +} + +fn saemix_prior_independence_log_acceptance( + current: SubjectPosteriorScore, + proposed: SubjectPosteriorScore, +) -> f64 { + proposed.log_likelihood - current.log_likelihood +} + +fn saemix_map_independence_log_acceptance( + current: SubjectPosteriorScore, + proposed: SubjectPosteriorScore, + current_centered: &[f64], + proposed_centered: &[f64], + covariance: &Array2, +) -> Result { + Ok(proposed.log_posterior() - current.log_posterior() + + eta_log_prior_from_omega(current_centered, covariance)? + - eta_log_prior_from_omega(proposed_centered, covariance)?) +} + const MARKOV_VARIANCE_ASSUMPTIONS: &str = concat!( "diagnostic only: prior draws at frozen averaged Omega/Omega_IOV; ", "per-chain seed = config.seed.wrapping_add(i).wrapping_mul(0x9E3779B97F4A7C15); ", @@ -451,6 +485,16 @@ impl SaemInitialization { ) }) .unwrap_or_else(|| (Vec::new(), Vec::new(), None)); + if config.saemix_mcmc.is_some() { + if random_effect_indices.is_empty() { + anyhow::bail!("SAEMix MCMC kernels require at least one IIV random effect"); + } + if omega_iov.is_some() { + anyhow::bail!( + "SAEMix MCMC compatibility does not support IOV; use PMcore's established eta/kappa kernel policy" + ); + } + } validate_initial_estimated_variance_floor( "Omega", "omega_min_variance", diff --git a/src/algorithms/parametric/saem/state/mod.rs b/src/algorithms/parametric/saem/state/mod.rs index 2b284a24b..21d485445 100644 --- a/src/algorithms/parametric/saem/state/mod.rs +++ b/src/algorithms/parametric/saem/state/mod.rs @@ -12,6 +12,20 @@ struct SaemIterateAverage { count: usize, } +#[derive(Debug, Clone, Copy, Default)] +struct KernelCounters { + proposals: usize, + accepted: usize, + rejected: usize, + non_finite: usize, +} + +struct SaemixMapDistribution { + mode: Vec, + covariance: Array2, + lower: Vec>, +} + // ─── Operational convergence lifecycle ──────────────────────────────────── // // Result types live in `crate::results::fit_result`. @@ -52,9 +66,11 @@ pub(crate) struct SaemState { information: InformationRecursion, proposal_step_sizes: Vec, eta_block_step_sizes: Vec, + saemix_subset_step_sizes: Vec>, kappa_proposal_step_sizes: Vec, mcmc_iterations: usize, eta_block_iterations: usize, + saemix_mcmc: Option, adapt_interval: usize, residual_optimizer_max_iterations: usize, compute_map: bool, @@ -225,6 +241,17 @@ impl SaemState { } else { Vec::new() }; + let saemix_subset_step_sizes = if config + .saemix_mcmc + .is_some_and(|policy| policy.iterations[2] > 0) + { + proposal_step_sizes + .iter() + .map(|step| vec![*step; n_random_effects]) + .collect() + } else { + Vec::new() + }; let kappa_proposal_step_sizes = omega_iov .as_ref() .map(|_| vec![config.rw_init; initialization.subject_ids.len()]) @@ -332,9 +359,11 @@ impl SaemState { information, proposal_step_sizes, eta_block_step_sizes, + saemix_subset_step_sizes, kappa_proposal_step_sizes, mcmc_iterations, eta_block_iterations, + saemix_mcmc: config.saemix_mcmc, adapt_interval, residual_optimizer_max_iterations: config.residual_optimizer_max_iterations, compute_map: config.compute_map, @@ -409,6 +438,49 @@ impl SaemState { let mut subject_proposal_counts = vec![0usize; self.initialization.subject_ids.len()]; let mut parameter_accept_counts = vec![0usize; n_parameters]; let mut parameter_proposal_counts = vec![0usize; n_parameters]; + let mut kernel_counts = [KernelCounters::default(); 4]; + let subset_step_sizes_before = self.saemix_subset_step_sizes.clone(); + let mut saemix_component_step_sizes_after = None; + + if let Some(policy) = self.saemix_mcmc { + let lower = cholesky_lower(&self.omega)?; + for _ in 0..policy.iterations[0] { + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + let current_eta = self.etas[subject_index][chain_index].clone(); + let current_score = self.score_subject_latents( + subject_index, + ¤t_eta, + &self.kappas[subject_index][chain_index], + )?; + let proposed_eta = self.prior_independence_eta(&lower)?; + let proposed_score = self.score_subject_latents( + subject_index, + &proposed_eta, + &self.kappas[subject_index][chain_index], + )?; + let log_acceptance_ratio = + saemix_prior_independence_log_acceptance(current_score, proposed_score); + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + kernel_counts[0].proposals += 1; + eta_proposed += 1; + if !log_acceptance_ratio.is_finite() { + kernel_counts[0].non_finite += 1; + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + kernel_counts[0].accepted += 1; + eta_accepted += 1; + } else { + kernel_counts[0].rejected += 1; + eta_rejected += 1; + } + } + } + } + } // Compound-kernel order: Omega-scaled eta blocks first, followed by // component eta walks and occasion-level kappa blocks. Eta blocks are @@ -447,7 +519,10 @@ impl SaemState { } } - for _ in 0..self.mcmc_iterations { + let component_iterations = self + .saemix_mcmc + .map_or(self.mcmc_iterations, |policy| policy.iterations[1]); + for _ in 0..component_iterations { for subject_index in 0..self.initialization.subject_ids.len() { for chain_index in 0..self.initialization.n_chains { for parameter_index in 0..n_parameters { @@ -463,15 +538,27 @@ impl SaemState { subject_proposal_counts[subject_index] += 1; parameter_proposal_counts[parameter_index] += 1; eta_proposed += 1; + if self.saemix_mcmc.is_some() { + kernel_counts[1].proposals += 1; + } if !log_acceptance_ratio.is_finite() { eta_non_finite += 1; + if self.saemix_mcmc.is_some() { + kernel_counts[1].non_finite += 1; + } } if self.accept_proposal(log_acceptance_ratio) { self.etas[subject_index][chain_index] = proposed_eta; parameter_accept_counts[parameter_index] += 1; eta_accepted += 1; + if self.saemix_mcmc.is_some() { + kernel_counts[1].accepted += 1; + } } else { eta_rejected += 1; + if self.saemix_mcmc.is_some() { + kernel_counts[1].rejected += 1; + } } } @@ -513,6 +600,157 @@ impl SaemState { } } + if let Some(policy) = self.saemix_mcmc { + for parameter_index in 0..n_parameters { + let proposed = parameter_proposal_counts[parameter_index]; + if proposed > 0 { + let acceptance = + parameter_accept_counts[parameter_index] as f64 / proposed as f64; + self.proposal_step_sizes[parameter_index] = saemix_adapt_step_size( + self.proposal_step_sizes[parameter_index], + acceptance, + policy, + )?; + } + } + saemix_component_step_sizes_after = Some(self.proposal_step_sizes.clone()); + + let mut subset_accept_counts = vec![0usize; n_parameters]; + let mut subset_proposal_counts = vec![0usize; n_parameters]; + let mut active_subset_size = None; + for _ in 0..policy.iterations[2] { + let (subset_size, groups) = self.saemix_subset_groups(n_parameters); + active_subset_size = Some(subset_size); + for group in groups { + for subject_index in 0..self.initialization.subject_ids.len() { + for chain_index in 0..self.initialization.n_chains { + let current_eta = self.etas[subject_index][chain_index].clone(); + let proposed_eta = + self.subset_random_walk_eta(¤t_eta, &group, subset_size); + let log_acceptance_ratio = self.proposal_log_acceptance_ratio( + subject_index, + chain_index, + &proposed_eta, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + kernel_counts[2].proposals += 1; + eta_proposed += 1; + for parameter in &group { + subset_proposal_counts[*parameter] += 1; + } + if !log_acceptance_ratio.is_finite() { + kernel_counts[2].non_finite += 1; + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + kernel_counts[2].accepted += 1; + eta_accepted += 1; + for parameter in &group { + subset_accept_counts[*parameter] += 1; + } + } else { + kernel_counts[2].rejected += 1; + eta_rejected += 1; + } + } + } + } + } + if let Some(subset_size) = active_subset_size { + for parameter_index in 0..n_parameters { + let proposed = subset_proposal_counts[parameter_index]; + if proposed > 0 { + let acceptance = + subset_accept_counts[parameter_index] as f64 / proposed as f64; + if n_parameters == 1 { + self.proposal_step_sizes[0] = saemix_adapt_step_size( + self.proposal_step_sizes[0], + acceptance, + policy, + )?; + self.saemix_subset_step_sizes[0][0] = self.proposal_step_sizes[0]; + } else { + let current = + self.saemix_subset_step_sizes[parameter_index][subset_size - 1]; + self.saemix_subset_step_sizes[parameter_index][subset_size - 1] = + saemix_adapt_step_size(current, acceptance, policy)?; + } + } + } + } + + if policy.iterations[3] > 0 && self.cycle < policy.map_cycles { + let mut distributions = Vec::with_capacity(self.initialization.subject_ids.len()); + for subject_index in 0..self.initialization.subject_ids.len() { + let distribution = self.saemix_map_distribution(subject_index, policy)?; + for chain in &mut self.etas[subject_index] { + *chain = distribution.mode.clone(); + } + distributions.push(distribution); + } + for _ in 0..policy.iterations[3] { + for (subject_index, distribution) in distributions.iter().enumerate() { + let mode = &distribution.mode; + let covariance = &distribution.covariance; + let lower = &distribution.lower; + for chain_index in 0..self.initialization.n_chains { + let current_eta = self.etas[subject_index][chain_index].clone(); + let standard_normals = (0..n_parameters) + .map(|_| self.standard_normal()) + .collect::>(); + let proposed_eta = + correlated_random_walk(mode, lower, &standard_normals, 1.0)?; + let current_score = self.score_subject_latents( + subject_index, + ¤t_eta, + &self.kappas[subject_index][chain_index], + )?; + let proposed_score = self.score_subject_latents( + subject_index, + &proposed_eta, + &self.kappas[subject_index][chain_index], + )?; + let current_centered = current_eta + .iter() + .zip(mode) + .map(|(value, center)| value - center) + .collect::>(); + let proposed_centered = proposed_eta + .iter() + .zip(mode) + .map(|(value, center)| value - center) + .collect::>(); + let log_acceptance_ratio = saemix_map_independence_log_acceptance( + current_score, + proposed_score, + ¤t_centered, + &proposed_centered, + covariance, + )?; + subject_log_acceptance_sums[subject_index] += log_acceptance_ratio; + subject_proposal_counts[subject_index] += 1; + kernel_counts[3].proposals += 1; + eta_proposed += 1; + if !log_acceptance_ratio.is_finite() { + kernel_counts[3].non_finite += 1; + eta_non_finite += 1; + } + if self.accept_proposal(log_acceptance_ratio) { + self.etas[subject_index][chain_index] = proposed_eta; + kernel_counts[3].accepted += 1; + eta_accepted += 1; + } else { + kernel_counts[3].rejected += 1; + eta_rejected += 1; + } + } + } + } + } + } + self.refresh_subject_scores_from_chains()?; self.last_log_acceptance_ratios = subject_log_acceptance_sums .into_iter() @@ -549,14 +787,54 @@ impl SaemState { } }) .collect(); - for parameter_index in 0..n_parameters { - self.adaptation_accept_counts[parameter_index] += - parameter_accept_counts[parameter_index]; - self.adaptation_proposal_counts[parameter_index] += - parameter_proposal_counts[parameter_index]; - } - self.steps_since_adapt += 1; - self.adapt_proposal_step_sizes(); + if self.saemix_mcmc.is_none() { + for parameter_index in 0..n_parameters { + self.adaptation_accept_counts[parameter_index] += + parameter_accept_counts[parameter_index]; + self.adaptation_proposal_counts[parameter_index] += + parameter_proposal_counts[parameter_index]; + } + self.steps_since_adapt += 1; + self.adapt_proposal_step_sizes(); + } + let mcmc_kernel_diagnostics = if self.saemix_mcmc.is_some() { + let kernels = [ + SaemMcmcKernel::PriorIndependence, + SaemMcmcKernel::ComponentRandomWalk, + SaemMcmcKernel::RotatingSubset, + SaemMcmcKernel::MapIndependence, + ]; + kernels + .into_iter() + .enumerate() + .map(|(index, kernel)| { + let (before, after) = match kernel { + SaemMcmcKernel::ComponentRandomWalk => ( + vec![eta_step_sizes_before.clone()], + vec![saemix_component_step_sizes_after + .clone() + .unwrap_or_else(|| self.proposal_step_sizes.clone())], + ), + SaemMcmcKernel::RotatingSubset => ( + subset_step_sizes_before.clone(), + self.saemix_subset_step_sizes.clone(), + ), + _ => (Vec::new(), Vec::new()), + }; + SaemMcmcKernelDiagnostics { + kernel, + proposals: kernel_counts[index].proposals, + accepted: kernel_counts[index].accepted, + rejected: kernel_counts[index].rejected, + non_finite: kernel_counts[index].non_finite, + proposal_scales_before: before, + proposal_scales_after: after, + } + }) + .collect() + } else { + Vec::new() + }; let phase = self.initialization.schedule.phase(self.cycle); let omega_update = pending_covariance_update_diagnostics( phase, @@ -579,6 +857,7 @@ impl SaemState { .schedule .stochastic_approximation_step(self.cycle), covariance_step: self.initialization.schedule.covariance_step(self.cycle), + mcmc_kernel_diagnostics, eta_proposals: eta_proposed, eta_accepted, eta_rejected, @@ -1539,6 +1818,130 @@ impl SaemState { proposed_eta } + fn prior_independence_eta(&mut self, lower: &[Vec]) -> Result> { + let standard_normals = (0..lower.len()) + .map(|_| self.standard_normal()) + .collect::>(); + correlated_random_walk(&vec![0.0; lower.len()], lower, &standard_normals, 1.0) + } + + fn saemix_subset_groups(&mut self, n_parameters: usize) -> (usize, Vec>) { + if n_parameters == 1 { + return (1, vec![vec![0]]); + } + let subset_size = self.cycle % (n_parameters - 1) + 2; + if subset_size == n_parameters { + return (subset_size, vec![(0..n_parameters).collect()]); + } + + let mut candidates = (1..n_parameters).collect::>(); + for index in 0..(subset_size - 1) { + let selected = self.rng.random_range(index..candidates.len()); + candidates.swap(index, selected); + } + let mut offsets = vec![0]; + offsets.extend_from_slice(&candidates[..subset_size - 1]); + let groups = (0..n_parameters) + .map(|start| { + offsets + .iter() + .map(|offset| (start + offset) % n_parameters) + .collect() + }) + .collect(); + (subset_size, groups) + } + + fn subset_random_walk_eta( + &mut self, + current_eta: &[f64], + parameters: &[usize], + subset_size: usize, + ) -> Vec { + let mut proposed_eta = current_eta.to_vec(); + for parameter in parameters { + let step = if current_eta.len() == 1 { + self.proposal_step_sizes[*parameter] + } else { + self.saemix_subset_step_sizes[*parameter][subset_size - 1] + }; + proposed_eta[*parameter] += step * self.standard_normal(); + } + proposed_eta + } + + fn saemix_map_distribution( + &self, + subject_index: usize, + policy: SaemixMcmcConfig, + ) -> Result { + let n_eta = self.initialization.random_effect_indices.len(); + let initial = self.etas[subject_index][0].clone(); + let scales = (0..n_eta) + .map(|index| self.omega[[index, index]].sqrt() * policy.map_initial_step) + .collect::>(); + let solution = optimize_conditional_mode( + initial, + &scales, + policy.map_max_iterations as u64, + policy.map_sd_tolerance, + |eta| match self.score_subject_latents(subject_index, eta, &[]) { + Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), + _ => f64::INFINITY, + }, + )?; + let coordinates = (0..n_eta) + .map(|index| JointLatentCoordinate { + index, + name: format!("eta:{}", self.initialization.random_effect_names[index]), + kind: JointLatentCoordinateKind::Eta { + parameter_index: self.initialization.random_effect_indices[index], + }, + prior_sd: self.omega[[index, index]].sqrt(), + }) + .collect::>(); + let prior_sds = coordinates + .iter() + .map(|coordinate| coordinate.prior_sd) + .collect::>(); + let mode_metadata = ConditionalModeMetadata { + converged: solution.converged, + iterations: solution.iterations, + objective_value: solution.objective, + termination_message: solution.termination, + }; + let curvature = conditional_mode_curvature( + &solution.coordinates, + &prior_sds, + &coordinates, + &mode_metadata, + |eta| match self.score_subject_latents(subject_index, eta, &[]) { + Ok(score) if score.log_posterior().is_finite() => -score.log_posterior(), + _ => f64::INFINITY, + }, + ); + if !matches!(curvature.status, ConditionalCurvatureStatus::Available) { + anyhow::bail!( + "SAEMix q4 conditional curvature is unavailable for subject '{}': {:?}", + self.initialization.subject_ids[subject_index], + curvature.status + ); + } + let covariance_rows = curvature.latent_covariance.ok_or_else(|| { + anyhow::anyhow!("available SAEMix q4 curvature lacks latent covariance") + })?; + let covariance = Array2::from_shape_vec( + (n_eta, n_eta), + covariance_rows.into_iter().flatten().collect(), + )?; + let lower = cholesky_lower(&covariance)?; + Ok(SaemixMapDistribution { + mode: solution.coordinates, + covariance, + lower, + }) + } + fn block_random_walk_eta( &mut self, current_eta: &[f64], diff --git a/src/algorithms/parametric/saem/state/tests/controller.rs b/src/algorithms/parametric/saem/state/tests/controller.rs index 9079d029c..9bde3170c 100644 --- a/src/algorithms/parametric/saem/state/tests/controller.rs +++ b/src/algorithms/parametric/saem/state/tests/controller.rs @@ -204,6 +204,119 @@ fn component_scale_adaptation_uses_acceptance_bands_and_clamps() { assert_eq!(adapt_component_step_size(1e-6, 0.0), 1e-6); } +#[test] +fn saemix_acceptance_and_adaptation_formulas_are_explicit() { + let current = SubjectPosteriorScore { + log_likelihood: -8.0, + eta_log_prior: -1.0, + kappa_log_prior: 0.0, + }; + let proposed = SubjectPosteriorScore { + log_likelihood: -6.5, + eta_log_prior: -20.0, + kappa_log_prior: 0.0, + }; + assert_eq!( + saemix_prior_independence_log_acceptance(current, proposed), + 1.5 + ); + + let policy = SaemixMcmcConfig::new([0, 1, 0, 0]); + assert!((saemix_adapt_step_size(0.5, 0.5, policy).unwrap() - 0.52).abs() < 1e-12); + assert!((saemix_adapt_step_size(0.5, 0.3, policy).unwrap() - 0.48).abs() < 1e-12); + + let covariance = ndarray::array![[1.0]]; + let ratio = + saemix_map_independence_log_acceptance(current, proposed, &[0.5], &[1.0], &covariance) + .unwrap(); + let expected = proposed.log_posterior() - current.log_posterior() + + eta_log_prior_from_omega(&[0.5], &covariance).unwrap() + - eta_log_prior_from_omega(&[1.0], &covariance).unwrap(); + assert_eq!(ratio, expected); +} + +#[test] +fn saemix_rotating_subset_schedule_changes_only_selected_coordinates() { + let mut state = + SaemState::from_problem(problem(), &SaemConfig::new().n_chains(1).seed(2024)).unwrap(); + state.cycle = 2; + let (subset_size, groups) = state.saemix_subset_groups(3); + assert_eq!(subset_size, 2); + assert_eq!(groups.len(), 3); + assert!(groups.iter().all(|group| group.len() == 2)); + assert!(groups + .iter() + .all(|group| group[0] != group[1] && group.iter().all(|index| *index < 3))); + + state.saemix_subset_step_sizes = vec![vec![0.5; 3]; 3]; + let current = vec![1.0, 2.0, 3.0]; + let proposed = state.subset_random_walk_eta(¤t, &groups[0], subset_size); + for index in 0..3 { + if groups[0].contains(&index) { + assert_ne!(proposed[index], current[index]); + } else { + assert_eq!(proposed[index], current[index]); + } + } +} + +#[test] +fn saemix_four_kernel_policy_records_order_counts_and_map_window() { + let config = SaemConfig::new() + .n_chains(1) + .k1_iterations(2) + .k2_iterations(0) + .burn_in(2) + .compute_map(false) + .saemix_mcmc_config(SaemixMcmcConfig::new([1, 1, 1, 1]).map_cycles(2)); + let mut state = SaemState::from_problem(problem(), &config).unwrap(); + + state.step().unwrap(); + state.step().unwrap(); + + let expected = [ + SaemMcmcKernel::PriorIndependence, + SaemMcmcKernel::ComponentRandomWalk, + SaemMcmcKernel::RotatingSubset, + SaemMcmcKernel::MapIndependence, + ]; + for cycle in &state.cycle_diagnostics { + assert_eq!( + cycle + .mcmc_kernel_diagnostics + .iter() + .map(|diagnostic| diagnostic.kernel) + .collect::>(), + expected + ); + assert!(cycle + .mcmc_kernel_diagnostics + .iter() + .all(|diagnostic| diagnostic.accepted + diagnostic.rejected == diagnostic.proposals)); + assert!(cycle + .mcmc_kernel_diagnostics + .iter() + .all(|diagnostic| diagnostic.non_finite == 0)); + } + assert!(state.cycle_diagnostics[0].mcmc_kernel_diagnostics[0].proposals > 0); + assert!(state.cycle_diagnostics[0].mcmc_kernel_diagnostics[1].proposals > 0); + assert!(state.cycle_diagnostics[0].mcmc_kernel_diagnostics[2].proposals > 0); + assert!(state.cycle_diagnostics[0].mcmc_kernel_diagnostics[3].proposals > 0); + assert_eq!( + state.cycle_diagnostics[1].mcmc_kernel_diagnostics[3].proposals, + 0 + ); +} + +#[test] +fn saemix_compatibility_fails_closed_for_iov() { + let error = + SaemState::from_problem(iov_problem(), &SaemConfig::new().saemix_mcmc([0, 1, 0, 0])) + .expect_err("SAEMix compatibility must not silently approximate IOV") + .to_string(); + assert!(error.contains("does not support IOV")); +} + #[test] fn component_scale_adaptation_waits_for_interval_and_resets_counts() { let mut state = diff --git a/src/algorithms/parametric/saem_config.rs b/src/algorithms/parametric/saem_config.rs index da0b519e2..a13936b25 100644 --- a/src/algorithms/parametric/saem_config.rs +++ b/src/algorithms/parametric/saem_config.rs @@ -204,6 +204,107 @@ pub enum SaemEstimatorPolicy { AveragedIterates { alpha: f64 }, } +/// Explicit SAEMix-compatible four-kernel E-step policy. +#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)] +#[serde(deny_unknown_fields, default)] +pub struct SaemixMcmcConfig { + /// Iteration counts for q1 prior independence, q2 component random walk, + /// q3 rotating subsets, and q4 MAP-informed independence proposals. + pub iterations: [usize; 4], + /// Early SAEM cycles eligible for q4. The reference runs q4 while + /// `cycle < map_cycles`. + pub map_cycles: usize, + /// Target acceptance probability for q2 and q3 scale adaptation. + pub target_acceptance: f64, + /// Multiplicative adaptation gain for q2 and q3. + pub adaptation_gain: f64, + /// Independent q4 conditional-mode optimization budget. + pub map_max_iterations: usize, + pub map_sd_tolerance: f64, + pub map_initial_step: f64, +} + +impl Default for SaemixMcmcConfig { + fn default() -> Self { + Self { + iterations: [0; 4], + map_cycles: 5, + target_acceptance: 0.4, + adaptation_gain: 0.4, + map_max_iterations: 100, + map_sd_tolerance: 1e-8, + map_initial_step: 0.1, + } + } +} + +impl SaemixMcmcConfig { + pub fn new(iterations: [usize; 4]) -> Self { + Self { + iterations, + ..Self::default() + } + } + + pub fn map_cycles(mut self, cycles: usize) -> Self { + self.map_cycles = cycles; + self + } + + pub fn target_acceptance(mut self, target: f64) -> Self { + self.target_acceptance = target; + self + } + + pub fn adaptation_gain(mut self, gain: f64) -> Self { + self.adaptation_gain = gain; + self + } + + pub fn map_optimizer(mut self, max_iterations: usize, sd_tolerance: f64) -> Self { + self.map_max_iterations = max_iterations; + self.map_sd_tolerance = sd_tolerance; + self + } + + pub fn map_initial_step(mut self, step: f64) -> Self { + self.map_initial_step = step; + self + } + + fn validate(self) -> Result<()> { + if self.iterations.iter().all(|iterations| *iterations == 0) { + anyhow::bail!("SAEMix MCMC requires at least one enabled kernel"); + } + if self.iterations[3] > 0 { + if self.map_cycles == 0 { + anyhow::bail!("SAEMix q4 requires map_cycles greater than zero"); + } + if self.map_max_iterations == 0 { + anyhow::bail!("SAEMix q4 map_max_iterations must be greater than zero"); + } + if !self.map_sd_tolerance.is_finite() || self.map_sd_tolerance <= 0.0 { + anyhow::bail!("SAEMix q4 map tolerance must be finite and positive"); + } + if !self.map_initial_step.is_finite() || self.map_initial_step <= 0.0 { + anyhow::bail!("SAEMix q4 map initial step must be finite and positive"); + } + } + if !self.target_acceptance.is_finite() || !(0.0..1.0).contains(&self.target_acceptance) { + anyhow::bail!("SAEMix MCMC target acceptance must be finite and in (0, 1)"); + } + if !self.adaptation_gain.is_finite() + || self.adaptation_gain <= 0.0 + || self.adaptation_gain * self.target_acceptance >= 1.0 + { + anyhow::bail!( + "SAEMix MCMC adaptation gain must be finite, positive, and keep adaptation multipliers positive" + ); + } + Ok(()) + } +} + #[derive(Debug, Deserialize, Clone, Serialize)] #[serde(deny_unknown_fields, default)] pub struct SaemConfig { @@ -223,6 +324,10 @@ pub struct SaemConfig { pub n_chains: usize, pub mcmc_iterations: usize, pub eta_block_iterations: usize, + /// Explicit SAEMix-compatible q1-q4 policy. `None` preserves PMcore's + /// established component/full-block behavior. + #[serde(default, skip_serializing_if = "Option::is_none")] + pub saemix_mcmc: Option, pub adapt_interval: usize, /// Maximum early covariance stabilization fraction. For covariate IIV, /// this under-relaxes the accepted exploration Ω/GEM displacement. For @@ -271,6 +376,7 @@ impl Default for SaemConfig { mcmc_iterations: 1, // Disabled by default; opt in to the block-mixture kernel. eta_block_iterations: 0, + saemix_mcmc: None, adapt_interval: 50, // Guard against one-draw correlated Ω collapse in exploration. omega_sa_max_step: 0.1, @@ -347,6 +453,18 @@ impl SaemConfig { self } + /// Select SAEMix-compatible q1-q4 E-step behavior. + pub fn saemix_mcmc(mut self, iterations: [usize; 4]) -> Self { + self.saemix_mcmc = Some(SaemixMcmcConfig::new(iterations)); + self + } + + /// Select a fully configured SAEMix-compatible q1-q4 policy. + pub fn saemix_mcmc_config(mut self, config: SaemixMcmcConfig) -> Self { + self.saemix_mcmc = Some(config); + self + } + /// Number of E-steps between proposal-scale adaptations. pub fn adapt_interval(mut self, iterations: usize) -> Self { self.adapt_interval = iterations; @@ -628,6 +746,14 @@ impl SaemConfig { if self.mcmc_iterations == 0 { anyhow::bail!("SAEM mcmc_iterations must be greater than zero"); } + if let Some(config) = self.saemix_mcmc { + config.validate()?; + if self.eta_block_iterations > 0 { + anyhow::bail!( + "SAEMix MCMC compatibility cannot be combined with PMcore eta_block_iterations" + ); + } + } if self.adapt_interval == 0 { anyhow::bail!("SAEM adapt_interval must be greater than zero"); } @@ -685,7 +811,7 @@ impl SaemConfig { mod tests { use super::{ CovarianceStabilityConfig, LugsailConfig, MarkovSimulationVarianceConfig, - OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, + OperationalConvergenceConfig, SaemConfig, SaemEstimatorPolicy, SaemixMcmcConfig, RESIDUAL_OPTIMIZER_MAX_SIGMA, }; use crate::estimation::MarginalLikelihoodConfig; @@ -705,6 +831,39 @@ mod tests { assert_eq!(decoded.eta_block_iterations, 3); } + #[test] + fn saemix_mcmc_is_explicit_validated_and_serde_compatible() { + assert!(SaemConfig::default().saemix_mcmc.is_none()); + let legacy: SaemConfig = serde_json::from_str("{}").unwrap(); + assert!(legacy.saemix_mcmc.is_none()); + + let policy = SaemixMcmcConfig::new([2, 2, 2, 2]); + let config = SaemConfig::new().saemix_mcmc_config(policy); + assert_eq!(config.saemix_mcmc, Some(policy)); + assert!(config.validate().is_ok()); + let decoded: SaemConfig = serde_json::from_value(serde_json::to_value(config).unwrap()) + .expect("SAEMix policy should round-trip"); + assert_eq!(decoded.saemix_mcmc, Some(policy)); + + let partial: SaemConfig = + serde_json::from_str(r#"{"saemix_mcmc":{"iterations":[0,2,0,0]}}"#).unwrap(); + assert_eq!( + partial.saemix_mcmc, + Some(SaemixMcmcConfig::new([0, 2, 0, 0])) + ); + + assert!(SaemConfig::new().saemix_mcmc([0; 4]).validate().is_err()); + assert!(SaemConfig::new() + .saemix_mcmc([0, 2, 0, 0]) + .eta_block_iterations(1) + .validate() + .is_err()); + assert!(SaemConfig::new() + .saemix_mcmc_config(SaemixMcmcConfig::new([0, 0, 0, 1]).map_cycles(0),) + .validate() + .is_err()); + } + #[test] fn invalid_operational_values_fail_closed() { let invalid = [ diff --git a/src/estimation/mod.rs b/src/estimation/mod.rs index 5a50234fe..d5ca95269 100644 --- a/src/estimation/mod.rs +++ b/src/estimation/mod.rs @@ -14,6 +14,7 @@ pub use crate::algorithms::nonparametric::{ pub use crate::algorithms::parametric::{ CovarianceStabilityConfig, LugsailConfig, MarkovSimulationVarianceConfig, OperationalConvergenceConfig, ParametricAlgorithm, SaemConfig, SaemEstimatorPolicy, + SaemixMcmcConfig, }; #[allow(deprecated)] pub use assay_error::{ diff --git a/src/estimation/problem.rs b/src/estimation/problem.rs index 6a23f706d..74fb151fa 100644 --- a/src/estimation/problem.rs +++ b/src/estimation/problem.rs @@ -220,9 +220,10 @@ impl ParametricBuilder { } impl ParametricBuilder { - pub fn build(self) -> Result> { + pub fn build(mut self) -> Result> { reject_sde_estimation::()?; validate_parametric_parameters(&self.model, &self.parameters)?; + canonicalize_parametric_parameters(&self.model, &mut self.parameters)?; validate_parametric_error_models(&self.model, &self.error_models)?; reject_constraints(&self.constraints)?; let covariates = if self.covariate_effects.is_empty() { @@ -384,6 +385,29 @@ fn validate_parametric_parameters( validate_parameter_declarations(model, &names) } +fn canonicalize_parametric_parameters( + model: &ModelBuilder, + parameters: &mut ParameterSpace, +) -> Result<()> { + let canonical_names = model.parameter_names(); + parameters.items.sort_by_key(|parameter| { + canonical_names + .iter() + .position(|name| name == ¶meter.name) + .unwrap_or(usize::MAX) + }); + + let ordered_names = parameters.names(); + if ordered_names != canonical_names { + anyhow::bail!( + "failed to canonicalize parameter declarations: expected {}, found {}", + canonical_names.join(", "), + ordered_names.join(", ") + ); + } + Ok(()) +} + fn validate_parameter_declarations( model: &ModelBuilder, provided_names: &[String], @@ -727,8 +751,9 @@ where #[cfg(test)] mod tests { use super::{reject_sde_estimation, EstimationProblem, RESIDUAL_OPTIMIZER_MAX_SIGMA}; + use crate::estimation::parametric::{Iov, Omega}; use crate::estimation::ParametricErrorModel; - use crate::model::parameter_space::Parameter; + use crate::model::parameter_space::{Parameter, ParameterScale}; use crate::ResidualErrorModel; use pharmsol::prelude::*; use pharmsol::{Censor, Data, Subject, SubjectBuilderExt}; @@ -761,6 +786,27 @@ mod tests { equation_with_outputs(["cp", "effect"]) } + fn ordered_parameter_equation() -> pharmsol::equation::ODE { + pharmsol::equation::ODE::new( + |_x, _p, _t, dx, _b, _rateiv, _cov| dx[0] = 0.0, + |_p, _t, _cov| lag! {}, + |_p, _t, _cov| fa! {}, + |_p, _t, _cov, _x| {}, + |_x, p, _t, _cov, y| y[0] = p[0] + 2.0 * p[1], + ) + .with_nstates(1) + .with_ndrugs(1) + .with_nout(1) + .with_metadata( + equation::metadata::new("ordered_parameters") + .parameters(["first", "second"]) + .states(["state"]) + .outputs(["cp"]) + .route(equation::Route::bolus("dose").to_state("state")), + ) + .unwrap() + } + fn measured_data(output: &str) -> Data { Data::new(vec![Subject::builder("subject-1") .observation(3.5, 1.25, output) @@ -820,6 +866,34 @@ mod tests { } } + #[test] + fn parametric_parameters_are_canonicalized_to_model_metadata_order() { + let problem = + EstimationProblem::parametric(ordered_parameter_equation(), measured_data("cp")) + .parameter(Parameter::log("second").with_initial(20.0).fixed()) + .parameter(Parameter::real("first").with_initial(0.3)) + .omega(Omega::diagonal([("second", 0.2), ("first", 0.1)])) + .iov(Iov::diagonal([("second", 0.3)])) + .error_model("cp", ResidualErrorModel::constant(1.0)) + .build() + .expect("out-of-order declarations should canonicalize by name"); + + let parameters = &problem.parameters().items; + assert_eq!(parameters[0].name, "first"); + assert_eq!(parameters[0].initial, Some(0.3)); + assert_eq!(parameters[0].scale, ParameterScale::Identity); + assert!(parameters[0].estimate); + assert_eq!(parameters[1].name, "second"); + assert_eq!(parameters[1].initial, Some(20.0)); + assert_eq!(parameters[1].scale, ParameterScale::Log); + assert!(!parameters[1].estimate); + assert_eq!(problem.random_effect_names(), ["first", "second"]); + assert_eq!(problem.omega()[[0, 0]], 0.1); + assert_eq!(problem.omega()[[1, 1]], 0.2); + assert_eq!(problem.iov_effect_names().unwrap(), ["second"]); + assert_eq!(problem.omega_iov().unwrap()[[0, 0]], 0.3); + } + #[test] fn combined_estimated_components_respect_optimizer_bound() { let too_large = RESIDUAL_OPTIMIZER_MAX_SIGMA * 2.0; diff --git a/src/lib.rs b/src/lib.rs index dd870709a..7c92d8bd6 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -76,9 +76,9 @@ pub mod prelude { NpmapConfig, NpodConfig, Omega, OperationalConvergenceConfig, ParametricAlgorithm, ParametricConstraint, ParametricErrorModel, ParametricErrorModels, ParametricPrior, ProposalScaleSource, ResidualErrorModel, ResidualErrorModels, SaemConfig, - SaemEstimatorPolicy, SdeParticleConfig, SdeParticleError, SdeParticleFilter, - SdeParticleRecord, SdeParticleResult, ShrinkageDiagnostics, ShrinkageUnavailableReason, - ShrinkageValue, SubjectCovariateDesign, SubjectCovariateValue, + SaemEstimatorPolicy, SaemixMcmcConfig, SdeParticleConfig, SdeParticleError, + SdeParticleFilter, SdeParticleRecord, SdeParticleResult, ShrinkageDiagnostics, + ShrinkageUnavailableReason, ShrinkageValue, SubjectCovariateDesign, SubjectCovariateValue, SubjectMarginalLikelihoodDiagnostics, SubjectPopulationParameters, }; @@ -117,7 +117,8 @@ pub mod prelude { PopulationUncertaintyStatus, PopulationUncertaintyUnavailableReason, PredictionRow, RankDiagnosticStatus, RankMixingDiagnostic, RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, ResidualErrorRow, SaemCycleDiagnostics, - SaemEstimatorMetadata, SaemPhase, StatisticRow, SubjectConditionalMode, SubjectEtaEstimate, + SaemEstimatorMetadata, SaemMcmcKernel, SaemMcmcKernelDiagnostics, SaemPhase, StatisticRow, + SubjectConditionalMode, SubjectEtaEstimate, }; // pharmsol: re-export the crate itself and its curated prelude. diff --git a/src/results/fit_result.rs b/src/results/fit_result.rs index a68592f0f..bf2418247 100644 --- a/src/results/fit_result.rs +++ b/src/results/fit_result.rs @@ -135,6 +135,30 @@ impl CovarianceCycleUpdateDiagnostics { } } +/// One SAEMix-compatible E-step kernel. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize)] +#[serde(rename_all = "snake_case")] +pub enum SaemMcmcKernel { + PriorIndependence, + ComponentRandomWalk, + RotatingSubset, + MapIndependence, +} + +/// Proposal and adaptation diagnostics for one E-step kernel in one cycle. +#[derive(Debug, Clone, PartialEq, Serialize)] +pub struct SaemMcmcKernelDiagnostics { + pub kernel: SaemMcmcKernel, + pub proposals: usize, + pub accepted: usize, + pub rejected: usize, + pub non_finite: usize, + /// Kernel-specific proposal scales. Component kernels use one row; rotating + /// subsets use one row per eta and one column per subset size. + pub proposal_scales_before: Vec>, + pub proposal_scales_after: Vec>, +} + /// MCMC, covariance, and residual diagnostics captured after one complete SAEM cycle. #[derive(Debug, Clone, PartialEq, serde::Serialize)] pub struct SaemCycleDiagnostics { @@ -142,6 +166,8 @@ pub struct SaemCycleDiagnostics { pub phase: SaemPhase, pub stochastic_approximation_step: f64, pub covariance_step: f64, + #[serde(default, skip_serializing_if = "Vec::is_empty")] + pub mcmc_kernel_diagnostics: Vec, pub eta_proposals: usize, pub eta_accepted: usize, pub eta_rejected: usize, diff --git a/src/results/mod.rs b/src/results/mod.rs index 6546600ef..a909c019c 100644 --- a/src/results/mod.rs +++ b/src/results/mod.rs @@ -15,8 +15,8 @@ pub use fit_result::{ PopulationUncertaintyDiagnostics, PopulationUncertaintyRegularization, PopulationUncertaintyStatus, PopulationUncertaintyUnavailableReason, RankDiagnosticStatus, RankMixingDiagnostic, RankMixingDiagnostics, ResidualCycleDiagnostics, ResidualErrorEstimate, - SaemCycleDiagnostics, SaemEstimatorMetadata, SaemPhase, SubjectConditionalMode, - SubjectEtaEstimate, + SaemCycleDiagnostics, SaemEstimatorMetadata, SaemMcmcKernel, SaemMcmcKernelDiagnostics, + SaemPhase, SubjectConditionalMode, SubjectEtaEstimate, }; pub(crate) use information_criteria::derive_information_criteria; diff --git a/tests/saem_regressions.rs b/tests/saem_regressions.rs index 778fb6976..3f3cb8437 100644 --- a/tests/saem_regressions.rs +++ b/tests/saem_regressions.rs @@ -98,6 +98,19 @@ fn validation_problem() -> EstimationProblem EstimationProblem { + EstimationProblem::parametric(analytical_one_compartment(), validation_data()) + .parameter(Parameter::log("v").with_initial(20.0)) + .parameter(Parameter::log("ke").with_initial(0.30)) + .omega(Omega::diagonal([("v", 0.09), ("ke", 0.09)])) + .error_model( + "cp", + ParametricErrorModel::new(ResidualErrorModel::constant(0.25)).fixed(), + ) + .build() + .expect("out-of-order analytical validation problem should build") +} + fn validation_config(seed: u64) -> SaemConfig { SaemConfig::new() .seed(seed) @@ -148,6 +161,38 @@ fn analytical_same_seed_is_exactly_reproducible() { assert!(first.omega().iter().all(|value| value.is_finite())); } +#[test] +fn analytical_fit_is_invariant_to_parameter_declaration_order() { + let canonical = validation_problem() + .fit_with(validation_config(20_260_711)) + .expect("canonical-order fit should complete"); + let reordered = reordered_validation_problem() + .fit_with(validation_config(20_260_711)) + .expect("out-of-order fit should complete"); + + assert_eq!(reordered.parameter_names(), ["ke", "v"]); + assert_eq!(canonical.objf().to_bits(), reordered.objf().to_bits()); + assert_eq!( + canonical.population_parameters(), + reordered.population_parameters() + ); + assert_eq!(canonical.omega(), reordered.omega()); + assert_eq!(canonical.cycle_diagnostics(), reordered.cycle_diagnostics()); + assert_eq!(canonical.eta_chain_means(), reordered.eta_chain_means()); + let canonical_predictions = canonical + .population_predictions(0.1, 24.0) + .expect("canonical predictions should succeed"); + let reordered_predictions = reordered + .population_predictions(0.1, 24.0) + .expect("reordered predictions should succeed"); + assert_eq!(canonical_predictions.len(), reordered_predictions.len()); + for (canonical, reordered) in canonical_predictions.iter().zip(&reordered_predictions) { + assert_eq!(canonical.id(), reordered.id()); + assert_eq!(canonical.flat_times(), reordered.flat_times()); + assert_eq!(canonical.flat_predictions(), reordered.flat_predictions()); + } +} + fn standard_normal(rng: &mut StdRng) -> f64 { let u1 = rng.random::().max(f64::MIN_POSITIVE); let u2 = rng.random::(); From ad02501c9dc83e7b7641a668b63c06d8c28bca00 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Juli=C3=A1n=20D=2E=20Ot=C3=A1lvaro?= Date: Fri, 31 Jul 2026 01:41:54 +0100 Subject: [PATCH 5/5] Remove development artifacts --- .gitignore | 7 +- Cargo.toml | 4 - README.md | 7 +- docs/nonmem-comparison.md | 227 ----------------------- docs/saem-convergence.md | 179 ------------------ docs/saem-support.md | 153 --------------- iiv.md | 195 ------------------- plans/saem-validation-roadmap.md | 149 --------------- src/estimation/parametric/covariance.rs | 5 +- src/estimation/parametric/information.rs | 2 +- src/estimation/parametric/posterior.rs | 2 +- 11 files changed, 8 insertions(+), 922 deletions(-) delete mode 100644 docs/nonmem-comparison.md delete mode 100644 docs/saem-convergence.md delete mode 100644 docs/saem-support.md delete mode 100644 iiv.md delete mode 100644 plans/saem-validation-roadmap.md diff --git a/.gitignore b/.gitignore index d446c9237..ccb8237ee 100644 --- a/.gitignore +++ b/.gitignore @@ -41,10 +41,11 @@ op.csv /r.csv /correlation.csv -# Local research and validation artifacts +# Local planning, documentation, research, and validation artifacts +/plans/ +/docs/ +/iiv.md /validation/ -/docs/roadmap/ -/docs/saem-ode-solver-validation*.md /tests/reference/ /examples/_validation_*.rs /examples/paper_benchmarks/ diff --git a/Cargo.toml b/Cargo.toml index 082636da8..4867eb9f6 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -17,10 +17,6 @@ include = [ "/README.md", "/CHANGELOG.md", "/LICENSE", - "/iiv.md", - "/docs/saem-support.md", - "/docs/saem-convergence.md", - "/docs/nonmem-comparison.md", "/src/**", "/tests/*.rs", "/tests/fixtures/**", diff --git a/README.md b/README.md index 8f5a8eb45..0d31a96ab 100644 --- a/README.md +++ b/README.md @@ -37,8 +37,7 @@ variances and covariances; undeclared covariances are structural zeros. The default finite schedule ends with `MaxCycles`. `Converged` is available only through an explicit operational policy whose information, movement, rank, -precision, stationarity, and covariance-stability checks all pass. See -[SAEM convergence](docs/saem-convergence.md). +precision, stationarity, and covariance-stability checks all pass. `FitResult::objf()` and cycle objectives are conditional N2LL values. An independent opt-in calculation provides population marginal likelihood by @@ -52,10 +51,6 @@ estimated structural effects omit IIV because structural observation sensitivities are not implemented. PMcore reports unavailable diagnostics instead of repairing matrices or fabricating partial uncertainty. -See the [SAEM support matrix](docs/saem-support.md), -[IIV parameterization guide](iiv.md), and -[NONMEM comparison](docs/nonmem-comparison.md) for detailed behavior and syntax. - ### Stochastic differential equations PMcore exposes observation-conditioned particle filtering and bounded diffusion diff --git a/docs/nonmem-comparison.md b/docs/nonmem-comparison.md deleted file mode 100644 index bd3b5d81c..000000000 --- a/docs/nonmem-comparison.md +++ /dev/null @@ -1,227 +0,0 @@ -# NONMEM and PMcore model declarations - -This guide maps common NONMEM declarations to PMcore's parametric API. It is a -syntax and parameterization comparison, not a control-stream converter. - -## Concepts - -| NONMEM | PMcore | -| --- | --- | -| `$THETA` | `Parameter` declarations and `with_initial` | -| `$OMEGA` | `Omega` for IIV and `Iov` for IOV | -| `ETA(n)` | named eta associated with a parameter | -| occasion-specific ETA terms | named kappa generated from `Iov` | -| `$SIGMA` and `$ERROR` | explicit `ParametricErrorModel` per output | -| `$PK` covariate equations | `CovariateEffect` in transformed phi space | -| `$ESTIMATION` | `SaemConfig` and `fit_with` | - -PMcore uses names rather than numeric ETA, kappa, output, and covariance -positions. Model macros supply equation metadata used to validate those names. - -## Population values and IIV - -NONMEM log-normal clearance: - -```text -$THETA -(0, 5) - -$OMEGA -0.09 - -$PK -CL = THETA(1) * EXP(ETA(1)) -``` - -PMcore: - -```rust -.parameter(Parameter::log("cl").with_initial(5.0)) -.omega(Omega::diagonal_variances([("cl", 0.09)])) -``` - -`Parameter::log` means eta is additive on the log scale. The model receives the -natural value `5 * exp(eta)`. `Omega` values are variances, not standard -deviations. - -For additive IIV, use `Parameter::real`. Bounded logit and probit declarations -store their bounds and transformations directly instead of requiring a manual -inverse transformation in the model equation. - -## Correlated IIV - -NONMEM: - -```text -$OMEGA BLOCK(2) -0.09 -0.01 0.04 -``` - -PMcore: - -```rust -.omega( - Omega::diagonal_variances([ - ("cl", 0.09), - ("v", 0.04), - ]) - .covariance("cl", "v", 0.01) -) -``` - -Undeclared PMcore covariances are structural zeros. Use `fixed_variance` and -`fixed_covariance` for fixed entries. PMcore rejects a declaration that is not -finite, symmetric, and strictly positive definite. - -## Inter-occasion variability - -A NONMEM model often selects a distinct ETA by occasion: - -```text -$OMEGA -0.09 ; IIV variance for CL -0.04 ; occasion 1 variance -0.04 ; occasion 2 variance - -$PK -KAPPA = 0 -IF (OCC.EQ.1) KAPPA = ETA(2) -IF (OCC.EQ.2) KAPPA = ETA(3) -CL = THETA(1) * EXP(ETA(1) + KAPPA) -``` - -PMcore declares one kappa distribution and creates one draw for every actual -occasion: - -```rust -.parameter(Parameter::log("cl").with_initial(5.0)) -.omega(Omega::diagonal_variances([("cl", 0.09)])) -.iov(Iov::diagonal_variances([("cl", 0.04)])) -``` - -The individual occasion value is - -```text -CL_i,k = TVCL * exp(eta_i + kappa_i,k). -``` - -In builder-created data, `SubjectBuilderExt::reset()` starts the next occasion: - -```rust -let subject = Subject::builder("1") - .bolus(0.0, 100.0, "iv") - .observation(1.0, 2.1, "cp") - .reset() - .bolus(0.0, 100.0, "iv") - .observation(1.0, 1.9, "cp") - .build(); -``` - -Imported data retains its `Subject -> Occasion -> Event` hierarchy. Kappa is -indexed by subject, actual occasion, and named IOV effect. `Iov` supports the -same fixed/free entries, structural zeros, and variance/SD constructors as -`Omega`. - -## Parameters without IIV - -NONMEM omits ETA from the parameter expression: - -```text -$PK -BASE = THETA(1) -``` - -PMcore makes the choice explicit: - -```rust -.parameter( - Parameter::real("baseline") - .with_initial(1.0) - .without_random_effect() -) -``` - -The population value may still be estimated. PMcore estimates structural no-IIV -population and covariate effects from the observation likelihood. Their -observed-information covariance is currently unavailable. - -## Covariates - -A PMcore continuous effect is linear in transformed phi space. For a log -parameter, - -```rust -.covariate_effect( - CovariateEffect::continuous("cl", "wt", 70.0) - .with_initial(0.01) -) -``` - -defines - -```text -log(CL_i) = log(TVCL) + 0.01 * (WT_i - 70) + eta_i. -``` - -The comparable NONMEM expression is: - -```text -$PK -CL = THETA(1) * EXP(THETA(2) * (WT - 70) + ETA(1)) -``` - -Categorical PMcore effects name the parameter, covariate, reference level, and -active level. Values must be finite, present, and constant within a subject. -PMcore does not infer or rewrite nonlinear covariate equations. - -## Residual error - -NONMEM proportional error with `$SIGMA 0.01`: - -```text -$ERROR -Y = F + F * EPS(1) -``` - -uses an EPS standard deviation of `0.1`. The PMcore declaration receives that -coefficient directly: - -```rust -.error_model("cp", ResidualErrorModel::proportional(0.1)) -``` - -Combined PMcore error uses additive SD `a` and proportional coefficient `b`: - -```rust -.error_model("cp", ResidualErrorModel::combined(0.2, 0.1)) -``` - -Correlated combined error additionally declares `rho` and has - -```text -Var(Y | f) = a^2 + 2 rho a b f + b^2 f^2. -``` - -Each measured output requires its own explicit declaration. PMcore does not use -the data `ErrorPoly` values to select parametric residual scoring. - -## Estimation - -A typical PMcore fit ends with an explicit configuration: - -```rust -let result = problem.fit_with( - SaemConfig::new() - .burn_in(100) - .k1_iterations(300) - .k2_iterations(200) - .n_chains(4) - .seed(42), -)?; -``` - -The default finite schedule reports `MaxCycles`. Operational `Converged` -termination requires an additional explicit policy. Conditional objectives, -population marginal likelihood, information criteria, and uncertainty retain -distinct result fields and availability statuses. diff --git a/docs/saem-convergence.md b/docs/saem-convergence.md deleted file mode 100644 index aee71bece..000000000 --- a/docs/saem-convergence.md +++ /dev/null @@ -1,179 +0,0 @@ -# SAEM convergence and information diagnostics - -PMcore distinguishes finite schedule completion, operational stopping, -information diagnostics, and statistical uncertainty. These are separate -results with separate assumptions. - -## Estimator policies - -`SaemEstimatorPolicy::TerminalIterate` is the default. A finite schedule that -does not satisfy an enabled operational policy returns `MaxCycles`. - -`SaemEstimatorPolicy::AveragedIterates { alpha }` requires `0.5 < alpha < 1`. -During smoothing it uses gain `s^-alpha` and installs the unweighted average of -completed smoothing M-step iterates. Population values are averaged in phi -space, covariance matrices as accepted raw iterates, residual components on -their reported scales, and correlated-combined rho on its raw correlation -scale. Eta is rebased after installation. - -An `OperationalConvergenceConfig` is opt-in. It evaluates immutable averaged -candidates at scheduled checkpoints and may stop with `Converged` only when all -configured checks are eligible and satisfied. A failed or ineligible final -check returns `MaxCycles`. This policy does not prove model correctness or -mathematical convergence. - -## Free-coordinate order - -Information and simulation-variance matrices use this deterministic order: - -1. estimated population parameters in declaration order, in phi space; -2. estimated structural lower-triangle Omega entries; -3. estimated structural lower-triangle Omega_IOV entries; -4. estimated residual components by output index: additive, proportional, then - within-observation correlation. - -Fixed values and structural zeros are excluded. Covariance coordinates are raw -covariances. Residual SDs and rho use their reported raw coordinates. - -## Observed information - -After burn-in, PMcore forms one complete-data replicate per chain by aggregating -all subjects and occasions. Score and Hessian terms are evaluated at the same -pre-M-step parameters and sampled cycle-end latent values used by the SA update. -The current SA gain updates: - -```text -Delta <- E[complete score] -C <- E[complete Hessian] -G <- E[complete Hessian + score score'] -H = G - Delta Delta' -Iobs = -H -``` - -The implementation averages `score score'` across complete chain replicates; it -does not use the outer product of the mean score. Burn-in gain zero leaves the -recursion unchanged. - -Derivatives are analytic for Gaussian eta/kappa priors, free raw -Omega/Omega_IOV entries, and every supported residual family. Missing and -non-observation events contribute nothing. Unsupported censoring, invalid -dimensions, non-finite terms, likelihood-floor boundaries, or covariance -failures make information unavailable. - -Finite symmetry is accepted only within `64 * f64::EPSILON`. Accepted roundoff -is pairwise averaged before strict Cholesky factorization. PMcore does not add -jitter, ridge, clipping, projection, eigenvalue repair, SVD, or a pseudoinverse. -An indefinite information matrix is retained and labeled rather than repaired. - -Population covariance and standard errors are produced only from an unchanged -strict positive-definite observed-information matrix. Identity, log, logit, and -probit coordinates use their exact delta-method transformations. These values -remain unavailable for estimated structural no-IIV effects. - -## Frozen-kernel simulation variance - -`MarkovSimulationVarianceConfig` explicitly sets the diagnostic seed, chain -count, warmup, retained draws, batch size, lugsail parameters, and trace-memory -limit. No budget is inferred. - -After installing the averaged estimate, PMcore starts independent diagnostic -chains from `Normal(0, Omega)` and `Normal(0, Omega_IOV)` draws. Population, -covariance, residual, proposal-scale, and compound-kernel settings remain -fixed. Diagnostic streams do not consume the fit RNG. Each transition runs eta -block attempts, eta component sweeps, and occasion-kappa sweeps without -adaptation or M-steps. - -Before allocation or model execution, checked arithmetic calculates a -conservative upper bound for trace storage and workspaces. Exceeding the limit -returns `TraceByteCapExceeded`; arithmetic overflow returns -`TraceMemoryAccountingOverflow`. Raw traces are temporary and are not persisted. - -For `n = a b`, nonoverlapping multivariate batch means are - -```text -BM_b = b/(a-1) sum_j (mean_j - overall)(mean_j - overall)' -``` - -and the retained lugsail long-run variance is - -```text -Lambda_c = (BM_b - c BM_(b/r)) / (1-c). -``` - -Chains are never concatenated. PMcore retains both the diagnostic-chain mean LRV -and the fit-operational LRV. With strict observed information, - -```text -Xi = Iobs^-1 Lambda_operational Iobs^-T -simulation covariance of the average = Xi / n_avg. -``` - -Every chain and matrix retains its own typed status. Failed or indefinite chains -remain visible and make the aggregate ineligible. No matrix is projected or -repaired. With no latent dimensions, usable information yields exact zero -simulation-variance matrices without additional model execution. - -## Rank and precision checks - -PMcore reports rank-normalized split-R-hat, folded split-R-hat, and bulk ESS for -each retained eta and kappa coordinate. Ties use average ranks and Blom scores. -Bulk ESS uses split-chain autocovariances and the initial positive, monotone pair -sequence. Constant traces, odd draw counts, non-finite values, invalid -variances, and insufficient chains receive typed ineligible statuses. - -Operational stopping requires every configured information, covariance, -movement, score, eta, and kappa check to be eligible. The supplied rank policy -requires at least four diagnostic chains, maximum R-hat below `1.01`, total bulk -ESS above `400`, and average bulk ESS per split chain of at least `50`. - -Relative fixed width is - -```text -2 z_(delta/2) * worst_simulation_sd_fraction <= epsilon. -``` - -Newton displacement is `sqrt(g' Iobs^-1 g)`. Its Monte Carlo SD uses the -diagnostic-mean LRV divided by retained draws. The caller supplies checkpoint -scheduling, confidence, precision, covariance, rejection-window, and -stationarity thresholds. - -## Covariance-boundary guardrail - -Operational convergence requires an explicit `CovarianceStabilityConfig`. For a -current covariance `Omega` and declared initial covariance `Omega0 = L0 L0'`, -PMcore records - -```text -m(Omega; Omega0) = lambda_min(L0^-1 Omega L0^-T). -``` - -The margin is dimensionless and approaches zero near the positive-definite -boundary. A cycle qualifies only when the margin is at or below the caller's -threshold and the matching covariance update was rejected. Once the declared -consecutive window occurs, operational convergence remains blocked. Recording -this diagnostic does not change the fit trajectory or RNG stream. - -## Interpretation - -Passing the operational policy means only that the configured numerical and -sampling checks passed for that fit. Stationarity, adequate mixing, the Markov -Poisson equation, and controlled-Markov stochastic-approximation assumptions -remain unverified. Information and simulation-variance matrices are not by -themselves proof of convergence. For consequential use, compare an independent -fit with larger MCMC and schedule budgets. - -Population marginal likelihood and AIC/BIC are separate post-fit calculations. -They do not change operational stopping and do not turn a conditional objective -into population evidence. - -## References - -- Delyon, B., Lavielle, M., and Moulines, E. (1999). Convergence of a stochastic - approximation version of the EM algorithm. *Annals of Statistics* 27(1), - 94-128. -- Kuhn, E., and Lavielle, M. (2004). Coupling a stochastic approximation version - of EM with an MCMC procedure. *ESAIM: Probability and Statistics* 8, 115-131. -- Vehtari, A. et al. (2021). Rank-normalization, folding, and localization. - *Bayesian Analysis* 16(2), 667-718. -- Vats, D., and Flegal, J. M. (2022). Lugsail lag windows for estimating - time-average covariance matrices. *Biometrika* 109(3), 735-750. diff --git a/docs/saem-support.md b/docs/saem-support.md deleted file mode 100644 index 2a633ed77..000000000 --- a/docs/saem-support.md +++ /dev/null @@ -1,153 +0,0 @@ -# SAEM support - -PMcore validates the model, data, parameter, covariance, residual, and runtime -configuration before fitting. Unsupported combinations fail with an error -instead of selecting a fallback. - -## Models and data - -| Area | Supported behavior | -| --- | --- | -| Equations | Deterministic analytical and ODE equations with complete metadata | -| Subjects | One or more subjects and at least one measured observation | -| Outputs | Explicit metadata names; numeric `N` requires a declared `outeq_N` | -| Missing values | Retained in the event stream and omitted from scoring | -| Censoring | Not supported for parametric estimation | -| Covariates | Finite subject-static continuous and categorical values | -| Assay metadata | `ErrorPoly` C0-C3 values are transported but do not select parametric scoring | - -Every measured output requires an explicit `ParametricErrorModel`. Population -covariate effects are linear in transformed parameter space. Covariate values -must be present, finite, and constant within each subject. - -PMcore executes each ODE with its configured solver and tolerances. Solver -choice is a scientific model input; PMcore does not replace it during fitting. -Stiff models generally require an implicit solver and tolerances selected for -the model's scale. Completion of a finite SAEM schedule does not establish ODE -accuracy. - -## Parameters and variability - -| Area | Supported behavior | -| --- | --- | -| Scales | Identity, Log, Logit, Probit | -| Population values | Independently fixed or estimated, with or without IIV | -| IIV | Named parameter subsets or zero-dimensional | -| IOV | Named parameter subsets, independently of IIV | -| Covariance | Fixed/free entries, structural zeros, strict positive definiteness | - -Eta and kappa are additive in transformed parameter space. Model execution uses -natural parameter values. `Parameter::with_initial` is the natural-scale value -at zero eta, kappa, and covariate offsets. Named parameter declarations are -canonicalized to model metadata order before any numerical vector is built. - -`Omega::diagonal_variances` and `Iov::diagonal_variances` accept variances. -`diagonal_standard_deviations` accepts finite positive SDs and checks overflow -before squaring. Legacy `diagonal` remains variance-based. Undeclared -covariances are structural zeros. - -Covariance updates preserve fixed entries and structural zeros. Invalid, -non-finite, non-symmetric, or non-positive-definite matrices are rejected; no -jitter, ridge, clipping, projection, eigendecomposition repair, or pseudoinverse -is used. - -Estimated no-IIV population and covariate coordinates use the observation -likelihood. Population observed-information covariance and standard errors are -reported as unsupported while those coordinates are estimated. - -## Residual models - -| Family | Parameters | -| --- | --- | -| Constant | fixed or estimated SD | -| Proportional | fixed or estimated coefficient | -| Combined | independently fixed or estimated additive and proportional components | -| Correlated combined | additive SD, proportional coefficient, and within-observation correlation | -| Exponential | fixed or estimated log-scale SD | - -For correlated combined error, - -```text -Var(Y | f) = a^2 + 2 rho a b f + b^2 f^2 -``` - -with finite `a,b > 0` and `-1 < rho < 1`. Correlation is scalar and applies only -to the additive and proportional components of one observation. Serial, -cross-time, cross-output, dense, and general block residual covariance are not -supported. Multiple outputs use independent named residual declarations. - -## Schedule and MCMC - -A valid schedule has `k1 + k2 > 0`, `burn_in <= k1`, and no integer overflow. -Burn-in performs MCMC without parameter updates. Exploration uses gain one. -Smoothing uses a decreasing gain. The default estimator returns the terminal -iterate. `AveragedIterates { alpha }` requires `k2 > 0` and `0.5 < alpha < 1`. - -MCMC chain counts, the PMcore component-iteration count, adaptation intervals, -and proposal scales must be positive. `eta_block_iterations = 0` disables eta -block proposals. Raw Omega blocks are the default PMcore block scale. A SAEMix -policy may set individual kernel counts to zero but must enable at least one. - -`SaemConfig::saemix_mcmc([q1, q2, q3, q4])` explicitly selects the SAEMix IIV -kernel order: prior independence, component random walk, rotating subsets, and -early MAP-informed independence. The policy has its own MAP window, optimizer, -and adaptation settings; post-fit `compute_map` remains independent. SAEMix -compatibility rejects IOV rather than approximating its eta/kappa semantics. -Strict q4 curvature failure terminates through the typed expectation-failure -path without regularization or fallback. - -Covariate raw first and second moments always use the same SA gain. PMcore forms -a centered covariance target before applying masks and the constrained local -GEM update. Exploration may under-relax the accepted displacement; smoothing -applies no second covariance gain. - -## Objectives and uncertainty - -`FitResult::objf()`, cycle records, and compatibility summaries contain -conditional N2LL. They are diagnostics and never select a fit or substitute for -population evidence. - -Population marginal likelihood is an explicit post-fit calculation. It jointly -integrates eta and actual-occasion kappa with normalized Student-t importance -sampling, or evaluates the observation likelihood exactly when there are no -latent dimensions. Results include ESS, zero-weight counts, and delta-method -N2LL Monte Carlo error. AIC and BIC are derived only from available marginal -N2LL and retain that MC error. - -Observed information uses analytic complete-data derivatives in a deterministic -free-coordinate order. Population covariance and standard errors require an -unmodified strict-Cholesky inverse. Conditional eta/kappa uncertainty uses one -joint central-difference curvature in `[eta, kappa_1, ..., kappa_K]` order. -Unavailable curvature or information remains unavailable without fallback. - -Shrinkage is reported separately for posterior-mean and MAP eta/kappa sources. -It uses `100 * (1 - sample_variance / population_variance)` with `N-1` sample -variance and is not clamped. - -## Results and lifecycle - -Results retain the equation, data, ordered parameter metadata, covariance masks, -requested configuration, effective chain count, cycle diagnostics, predictions, -conditional modes, uncertainty statuses, and optional marginal likelihood. -Schema 9 is current; older schemas are rejected. - -The controller supports cycle stepping, post-cycle observers, owned snapshots, -user abort, stop-file termination, and truthful terminal reasons. A stale -current-directory `stop` file is removed before a new run. - -The default finite schedule reports `MaxCycles`. An opt-in operational policy -may report `Converged` only when all configured information, movement, -rank-normalized R-hat, bulk ESS, relative fixed-width, stationarity, and -covariance-stability checks pass. See [SAEM convergence](saem-convergence.md). - -## Unsupported - -- Generic SDE fitting through `EstimationProblem`; use `SdeParticleFilter` or - bounded diffusion optimization. -- Parametric BLOQ or ALOQ censoring. -- Time-varying population covariate effects. -- Arbitrary nonlinear parameter constraints. -- Serial or multivariate residual covariance. -- Observed-information covariance for estimated structural no-IIV effects. -- Automatic theorem-level convergence claims. -- FO, FOCE, and FOCE-I. diff --git a/iiv.md b/iiv.md deleted file mode 100644 index 07f8318b2..000000000 --- a/iiv.md +++ /dev/null @@ -1,195 +0,0 @@ -# Inter-individual variability - -PMcore defines inter-individual variability (IIV) in transformed parameter -space: - -```text -phi(P_i) = phi(TVP_i) + eta_i -eta_i ~ Normal(0, Omega) -``` - -`phi` is selected by the parameter declaration. `TVP_i` includes the population -value and subject-static covariate offsets. Model execution converts the result -back to natural parameter space. - -| PMcore declaration | Individual parameter | -| --- | --- | -| `Parameter::real("p")` | `P_i = TVP_i + eta_i` | -| `Parameter::log("p")` | `P_i = TVP_i * exp(eta_i)` | -| `Parameter::logit("p", lower, upper)` | additive eta on the bounded logit scale | -| `Parameter::probit("p", lower, upper)` | additive eta on the bounded probit scale | - -`Parameter::with_initial` always receives the natural-scale typical value at -zero eta, kappa, and covariate offsets. - -## Additive IIV - -NONMEM: - -```text -$THETA -(0, 10) ; initial TVP - -$OMEGA -4.0 ; variance - -$PK -P = THETA(1) + ETA(1) -``` - -PMcore: - -```rust -use pmcore::prelude::*; - -let parameter = Parameter::real("p").with_initial(10.0); -let omega = Omega::diagonal_variances([("p", 4.0)]); -``` - -Both declarations define `P_i = TVP + eta_i` with `eta_i ~ Normal(0, 4)`. -The initial random-effect standard deviation is `2`. - -## Log-normal IIV - -NONMEM: - -```text -$THETA -(0, 5) ; initial TVCL - -$OMEGA -0.09 ; variance on the log scale - -$PK -CL = THETA(1) * EXP(ETA(1)) -``` - -PMcore: - -```rust -let parameter = Parameter::log("cl").with_initial(5.0); -let omega = Omega::diagonal_variances([("cl", 0.09)]); -``` - -Both define `CL_i = TVCL * exp(eta_CL,i)`. The transformed-space SD is `0.3`. -The corresponding natural-scale coefficient of variation is -`sqrt(exp(0.09) - 1)`, approximately `0.307`. - -## Correlated random effects - -NONMEM: - -```text -$OMEGA BLOCK(2) -0.09 -0.01 0.04 -``` - -PMcore: - -```rust -let omega = Omega::diagonal_variances([ - ("cl", 0.09), - ("v", 0.04), -]) -.covariance("cl", "v", 0.01); -``` - -Both initialize - -```text -Omega = [[0.09, 0.01], - [0.01, 0.04]] -``` - -In PMcore, undeclared covariances are structural zeros. Declare every covariance -that may be estimated. - -`Omega::diagonal_standard_deviations` accepts finite positive SDs and squares -them after checking overflow. Legacy `Omega::diagonal` remains variance-based. - -## Fixed population values and covariance entries - -NONMEM fixes values with `FIX`: - -```text -$THETA -(0, 5 FIX) - -$OMEGA -0.09 FIX -``` - -PMcore fixes the population value and variance independently: - -```rust -let parameter = Parameter::log("cl") - .with_initial(5.0) - .fixed(); - -let omega = Omega::new().fixed_variance("cl", 0.09); -``` - -A fixed population value may retain estimated IIV by using `.fixed()` on the -parameter and an estimated `variance` entry in `Omega`. - -## Parameters without IIV - -A NONMEM parameter has no IIV when its `$PK` expression contains no `ETA` term: - -```text -$THETA -(0, 1) - -$PK -BASE = THETA(1) -``` - -The PMcore equivalent is explicit: - -```rust -let parameter = Parameter::real("baseline") - .with_initial(1.0) - .without_random_effect(); -``` - -The population value may be fixed or estimated. Estimated no-IIV population and -covariate effects use the observation likelihood directly. Their -observed-information covariance and standard errors remain unsupported until -structural observation sensitivities are available. - -## Bounded parameters - -PMcore can put eta on a bounded logit or probit scale directly: - -```rust -let parameter = Parameter::logit("fm", 0.0, 1.0) - .with_initial(0.20); -let omega = Omega::diagonal_variances([("fm", 0.10)]); -``` - -This guarantees `0 < FM_i < 1`. A NONMEM model typically writes the inverse -logit transformation explicitly in `$PK`; PMcore stores the bounds and -transformation in the parameter declaration. - -## Inter-occasion variability - -PMcore `Iov` uses the same variance, covariance, fixedness, and diagonal -constructor semantics as `Omega`. Kappa is additive in transformed parameter -space and indexed by subject and actual occasion. A parameter may have IIV, -IOV, both, or neither. - -See [NONMEM and PMcore model declarations](docs/nonmem-comparison.md) for an IOV -example and a broader syntax comparison. - -## Numerical safeguards - -PMcore requires finite, symmetric, strictly positive-definite covariance -matrices. Updates preserve fixed entries and structural zeros and must not -increase the covariance objective. No jitter, clipping, projection, or matrix -repair is applied. - -Covariate raw first and second moments use the same stochastic-approximation -gain. PMcore forms a coherent centered covariance target before applying masks, -local GEM constraints, and any exploration-only displacement cap. Smoothing -does not apply a second covariance gain. diff --git a/plans/saem-validation-roadmap.md b/plans/saem-validation-roadmap.md deleted file mode 100644 index 533e6ddc5..000000000 --- a/plans/saem-validation-roadmap.md +++ /dev/null @@ -1,149 +0,0 @@ -# SAEM current status and future work - -## Current implementation - -PMcore provides a production SAEM path for deterministic analytical and ODE -models. The implementation includes: - -- transformed-space population parameters with identity, log, logit, and probit - scales; -- IIV and IOV with named fixed/free covariance masks and structural zeros; -- subject-static continuous and categorical covariate effects; -- estimated population and covariate effects with or without IIV; -- additive, proportional, combined, correlated-combined, and exponential - residual models; -- persistent eta and kappa MCMC with component and opt-in block proposals; -- burn-in, exploration, and decreasing-gain smoothing phases; -- terminal-iterate and opt-in averaged estimators; -- strict observed-information and conditional-curvature diagnostics; -- eta and kappa posterior-mean/MAP shrinkage; -- population and conditional predictions; -- post-fit population marginal likelihood, AIC, and BIC; -- cycle-by-cycle controllers, observers, snapshots, and typed termination; -- schema-9 persistence, structured outputs, and warm starts; and -- explicit particle filtering and bounded diffusion optimization for SDE use. - -Covariate raw first and second moments use one common SA gain. PMcore forms the -centered covariance target before applying masks, local GEM constraints, strict -positive-definiteness checks, and any exploration-only displacement cap. -Smoothing does not apply a second covariance gain. - -The default finite SAEM schedule reports `MaxCycles`. `Converged` is available -only through an explicit operational policy. Conditional N2LL remains a -diagnostic; it never substitutes for population marginal likelihood. - -The support matrix and failure semantics are maintained in -[`docs/saem-support.md`](../docs/saem-support.md). Convergence and information -semantics are maintained in -[`docs/saem-convergence.md`](../docs/saem-convergence.md). - -## Must Have — completed release blockers - -M7 and M8 are implemented and validated in the current branch. - -### M7 — Canonical parameter ordering — Complete - -PMcore now validates parameter declarations by name and canonicalizes them to -model metadata order before constructing covariates, Omega, IOV, scoring state, -persistence metadata, or results. - -- Canonicalize every parameter-aligned structure to - `model.parameter_names()` before model execution, scoring, diagnostics, - persistence, and result construction. -- Preserve name-based Omega, IOV, covariate, fixed/free, warm-start, and - persistence semantics through the reorder. -- Continue to reject duplicate, unknown, and missing declarations explicitly. -- Add analytical and ODE regressions proving that out-of-order declarations - produce the same predictions, objectives, estimates, and labels as canonical - declarations. -- Do not retain a positional fallback or merely document the unsafe ordering - requirement. - -Completion evidence includes ODE metadata/Omega/IOV resolution and exact -analytical fit, objective, diagnostic, and prediction parity for reordered -declarations. - -### M8 — SAEMix four-kernel compatibility — Complete - -PMcore retains its established component/full-Omega-block policy and now offers -an explicit SAEMix-compatible IIV policy implementing kernels 1 through 4. The -compatibility policy fails closed for IOV; PMcore's established eta/kappa policy -continues to support IOV. - -- Add explicit iteration counts for the prior-independence, componentwise, - rotating-subset, and early MAP-informed kernels without overloading the - existing post-fit MAP controls. -- Implement the kernels in SAEMix order, including the rotating subset-size - schedule, the early-cycle MAP proposal window, Metropolis-Hastings proposal - corrections, and SAEMix-compatible proposal-scale adaptation. -- Retain the current PMcore kernel policy as an explicit supported mode; exact - SAEMix behavior must be selected deliberately rather than introduced as a - silent default change. -- Record proposals, acceptance, non-finite rejection, and adapted scales - separately for each kernel in cycle diagnostics and controller snapshots. -- Add deterministic kernel-level tests and cross-engine tests on equivalent - parameterizations. Cross-engine acceptance must compare estimates and - distributions over multiple seeds, not identical RNG trajectories. -- Keep post-fit `compute_map` behavior distinct from the in-E-step MAP-informed - kernel. - -Completion evidence includes deterministic kernel tests and a bounded -three-seed PMcore/SAEMix theophylline panel with equivalent `ka/V/ke`, diagonal -Omega, residual, schedule, and `c(2,2,2,2)` settings. Mean PMcore-versus-SAEMix -differences were -3.40% for ka, -1.13% for V, +2.68% for ke, +0.31% for sigma, -and -5.70% for the estimable ke variance; run products remain outside the -repository. - -The release validation commands pass after both slices: - -- `cargo fmt --check` -- `cargo check` -- `cargo test saem --lib` -- `cargo test parametric --lib` -- `cargo test likelihood --lib` - -## Deferred post-release work - -The following work remains deferred post-release: - -### Reference-model coverage - -- Add one maintained large-model regression that exercises the public model, - covariate, residual, persistence, and result APIs without creating a separate - validation framework. -- Expand replicated analytical and ODE coverage only when each fixture protects - a concrete supported behavior. -- Add broader cross-engine comparisons only as bounded development work; keep - external run products outside the product repository. - -### Statistical maturity - -- Evaluate convergence and coverage over larger replicated datasets. -- Improve marginal-likelihood proposal diagnostics and ambiguity handling. -- Extend uncertainty reporting where structural observation sensitivities are - available. -- Add shrinkage and information summaries for new supported coordinate types. - -### Lifecycle maturity - -- Bring nonparametric persistence and lifecycle APIs to the same level as the - parametric controller. -- Review result-schema evolution before adding new persisted diagnostics. -- Keep package examples small, self-contained, and runnable. - -## Optional research - -These are not release commitments: - -- shared-random-stream studies and alternative MCMC kernels beyond the required - SAEMix-compatible four-kernel policy; -- Hamiltonian Monte Carlo; -- automatic differentiation and shared sensitivity infrastructure; -- FO, FOCE, and FOCE-I; -- broader dense residual covariance models; -- generic SDE estimation after the explicit particle-session boundary can - support it without moving likelihood ownership out of PMcore. - -New work should default to post-release unless a focused regression demonstrates -incorrect behavior, silent fallback, or misleading output inside the supported -matrix. diff --git a/src/estimation/parametric/covariance.rs b/src/estimation/parametric/covariance.rs index 829bc261a..f8fb74e53 100644 --- a/src/estimation/parametric/covariance.rs +++ b/src/estimation/parametric/covariance.rs @@ -9,10 +9,7 @@ pub(crate) fn identity_matrix(size: usize) -> Array2 { ) } -/// Lower Cholesky factor of a symmetric positive-definite covariance matrix. -/// -/// Kept small and ndarray-native for now. This is the shared PMcore path for -/// η/Ω prior scoring until a crate-wide linear algebra backend is selected. +/// Lower Cholesky factor used for strict covariance validation and solves. pub(crate) fn cholesky_lower(matrix: &Array2) -> Result>> { if matrix.nrows() != matrix.ncols() { anyhow::bail!("omega must be square"); diff --git a/src/estimation/parametric/information.rs b/src/estimation/parametric/information.rs index a786ff108..3cac2433a 100644 --- a/src/estimation/parametric/information.rs +++ b/src/estimation/parametric/information.rs @@ -538,7 +538,7 @@ impl CompleteDerivative { self.hessian[[*coordinate, omega_coord]] += cross; self.hessian[[omega_coord, *coordinate]] += cross; } - // Omega-Omega (same as original add_gaussian) + // Omega-Omega Hessian block. for other in covariance_coordinates { let other_basis = symmetric_basis(n_random, other.row, other.column); let abs = inverse.dot(&other_basis); diff --git a/src/estimation/parametric/posterior.rs b/src/estimation/parametric/posterior.rs index c35102904..e28c61fb2 100644 --- a/src/estimation/parametric/posterior.rs +++ b/src/estimation/parametric/posterior.rs @@ -5,7 +5,7 @@ use super::covariance::{cholesky_log_determinant, cholesky_lower, solve_lower}; const LOG_2PI: f64 = 1.8378770664093453_f64; -/// Subject-level proposal score used by SAEM MCMC kernels and future FOCE diagnostics. +/// Subject-level posterior score used by SAEM MCMC kernels. #[derive(Debug, Clone, Copy, PartialEq)] pub(crate) struct SubjectPosteriorScore { pub(crate) log_likelihood: f64,