Seed the legacy global RNG so random_state pins variability draws - #348
Seed the legacy global RNG so random_state pins variability draws#348EItanm1999 wants to merge 1 commit into
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📝 WalkthroughWalkthroughThe simulator now binds integer-seeded generators to compatible trial-to-trial variability distributions. Documentation describes random-state validation and deterministic variability draws. Tests verify reproducible outputs and callable signature handling. ChangesVariability reproducibility
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to Integer random states now make variability draws reproducible. A custom variability distribution with its own bound RNG lacks end-to-end coverage, so an unintended override could alter its sampling behavior. Sequence Diagram(s)sequenceDiagram
participant Simulator
participant DerivedGenerator
participant VariabilityDistribution
Simulator->>DerivedGenerator: Create generator from integer random_state
Simulator->>VariabilityDistribution: Bind generator as random_state
VariabilityDistribution->>DerivedGenerator: Draw trial-to-trial variability
Simulator-->>Simulator: Produce reproducible rts and choices
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In `@ssms/basic_simulators/simulator.py`:
- Line 731: Update simulator() and _validate_random_state_for_c_rng so
random_state handling is defined before applying the modulo operation: convert
supported NumPy RNG objects such as Generator to an integer seed, or explicitly
reject them consistently with the documented contract, then only apply the
modulo to validated integer values.
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| # reseeds the process-global RNG; callers relying on np.random.* | ||
| # continuing an unrelated stream across simulator() calls should use the | ||
| # Generator API. | ||
| np.random.seed(random_state % (2**32)) |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
🔎 Supported by static analysis
🏁 Script executed:
#!/bin/bash
set -euo pipefail
# Find non-integral random_state declarations and callers.
fd --type f --extension py --extension pyx . ssms tests |
xargs -r rg -n -C 6 'random_state|Generator|RandomState|default_rng|simulator\('
# Expectation: either no supported non-integral input reaches simulator(),
# or the implementation handles it without applying % (2**32) to the object.Repository: lnccbrown/ssm-simulators
Length of output: 50381
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#!/bin/bash
set -euo pipefail
printf '%s\n' '--- repository conventions and learnings ---'
for f in /tmp/coderabbit-repo-knowledge/lnccbrown-ssm-simulators-bc718367/{conventions,learnings,architecture}/*.md; do
[ -f "$f" ] || continue
case "$f" in
*simulator*|*basic*|*random*|*rng*|*all*) printf '\n--- %s ---\n' "$f"; cat "$f";;
esac
done
printf '%s\n' '--- simulator structure ---'
ast-grep outline ssms/basic_simulators/simulator.py
printf '%s\n' '--- target implementation and directly bound validation ---'
sed -n '620,755p' ssms/basic_simulators/simulator.py
rg -n -C 12 '_validate_random_state_for_c_rng|random_state\s*:' ssms/basic_simulators/simulator.py ssms/basic_simulators
printf '%s\n' '--- direct callers and documented contract ---'
rg -n -C 8 'simulator\(|Simulator\(|random_state\s*=' ssms/basic_simulators tests \
-g '*.py' | rg -B 8 -A 8 'Generator|RandomState|default_rng|random_state'Repository: lnccbrown/ssm-simulators
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#!/bin/bash
set -euo pipefail
printf '%s\n' '--- simulator dispatch after seeding ---'
sed -n '730,930p' ssms/basic_simulators/simulator.py
printf '%s\n' '--- all random_state consumers in the basic simulator package ---'
rg -n -C 4 'random_state|set_seed|default_rng|Generator|RandomState' ssms/basic_simulators \
-g '*.py' -g '*.pyx'
printf '%s\n' '--- non-integer RNG tests and public annotations ---'
rg -n -C 8 'non_integer|Generator|RandomState|random_state' tests/test_simulator.py ssms/basic_simulators/simulator_class.py \
-g '*.py'Repository: lnccbrown/ssm-simulators
Length of output: 34492
Define the random_state contract before applying modulo. _validate_random_state_for_c_rng skips non-integer objects, but simulator() unconditionally evaluates random_state % (2**32) before dispatch. A numpy.random.Generator therefore raises TypeError on this path. Convert supported RNG objects to an integer seed, or reject them explicitly and update the documented contract.
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In `@ssms/basic_simulators/simulator.py` at line 731, Update simulator() and
_validate_random_state_for_c_rng so random_state handling is defined before
applying the modulo operation: convert supported NumPy RNG objects such as
Generator to an integer seed, or explicitly reject them consistently with the
documented contract, then only apply the modulo to validated integer values.
set_seed seeds C's srand and a module-level NumPy Generator, but the trial-to-trial variability distributions (the *_dist simulator param mappings, e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy.stats calls without an explicit random_state, and scipy draws those from NumPy's LEGACY GLOBAL RNG - which nothing seeded. Two simulator() calls with the same random_state therefore returned different data for every model carrying sv, sz or st (measured max |drt| 0.253 for ddm_st, 0.617 for a Normal t-kernel), while plain ddm - which has no *_dist - was reproducible, hiding the gap. Any 'same data across arms' comparison relying on random_state alone was not actually holding the data fixed. Minimal fix: seed np.random from random_state at the top of simulator(), keeping all seeding in one place. This intentionally reseeds the process-global legacy RNG; the docstring says so. Alternative considered (and fine by us if preferred): thread independent spawned Generators (SeedSequence.spawn) into each *_dist partial via the simulator_param_mappings, keeping v/z/t variability streams independent and leaving the global RNG untouched. That is the cleaner long-term design but touches every model config; this PR takes the one-line fix and the regression test either design must satisfy. The seeding now applies only to Integral random_state values; any other object passes through to the simulator layer unchanged. Raised by CodeRabbit on PR lnccbrown#348: the unguarded modulo raised TypeError on a numpy.random.Generator before dispatch. Measured against pristine v0.13.2: Generator inputs were never functional there either (the Cython layer raises "an integer is required"), so there was no working behavior to regress - but the guard keeps this change from intercepting that pre-existing failure with an earlier, less informative error, and a test pins the unchanged failure mode. Generator support itself is a separate, pre-existing gap (the _validate_random_state_for_c_rng docstring's "Generator ... skipped" wording overstates what downstream accepts). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Fixed in the latest push: the legacy-global seeding is now applied only for integer random_state values; any other object passes through to the simulator layer unchanged. Worth noting for the record: numpy.random.Generator inputs were never functional on v0.13.2 either — the Cython layer raises TypeError: an integer is required — so no working behavior had regressed; the guard just keeps this line from intercepting that pre-existing failure with an earlier, less informative error. A test now pins the unchanged failure mode. Generator support itself is a separate pre-existing gap (_validate_random_state_for_c_rng's docstring overstates what downstream accepts); happy to open an issue for it if wanted. |
set_seed seeds C's srand and a module-level NumPy Generator, but the trial-to-trial variability distributions (the *_dist simulator param mappings, e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy.stats calls without an explicit random_state, and scipy draws those from NumPy's LEGACY GLOBAL RNG - which nothing seeded. Two simulator() calls with the same random_state therefore returned different data for every model carrying sv, sz or st (measured max |drt| 0.253 for ddm_st, 0.617 for a Normal t-kernel), while plain ddm - which has no *_dist - was reproducible, hiding the gap. Any 'same data across arms' comparison relying on random_state alone was not actually holding the data fixed. Minimal fix: seed np.random from random_state at the top of simulator(), keeping all seeding in one place. This intentionally reseeds the process-global legacy RNG; the docstring says so. Alternative considered (and fine by us if preferred): thread independent spawned Generators (SeedSequence.spawn) into each *_dist partial via the simulator_param_mappings, keeping v/z/t variability streams independent and leaving the global RNG untouched. That is the cleaner long-term design but touches every model config; this PR takes the one-line fix and the regression test either design must satisfy. The seeding now applies only to Integral random_state values; any other object passes through to the simulator layer unchanged. Raised by CodeRabbit on PR lnccbrown#348: the unguarded modulo raised TypeError on a numpy.random.Generator before dispatch. Measured against pristine v0.13.2: Generator inputs were never functional there either (the Cython layer raises "an integer is required"), so there was no working behavior to regress - but the guard keeps this change from intercepting that pre-existing failure with an earlier, less informative error, and a test pins the unchanged failure mode. Generator support itself is a separate, pre-existing gap (the _validate_random_state_for_c_rng docstring's "Generator ... skipped" wording overstates what downstream accepts). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Inline comments:
In `@ssms/basic_simulators/simulator.py`:
- Around line 686-688: Update the random_state documentation near the legacy RNG
behavior to state that only integer random_state values seed NumPy’s legacy
global RNG and make variability draws reproducible; do not claim this guarantee
for supported non-integer values.
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set_seed seeds C's srand and a module-level NumPy Generator, but the trial-to-trial variability distributions (the *_dist simulator param mappings, e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy.stats calls without an explicit random_state, and scipy draws those from NumPy's LEGACY GLOBAL RNG - which nothing seeded. Two simulator() calls with the same random_state therefore returned different data for every model carrying sv, sz or st (measured max |drt| 0.253 for ddm_st, 0.617 for a Normal t-kernel), while plain ddm - which has no *_dist - was reproducible, hiding the gap. Any 'same data across arms' comparison relying on random_state alone was not actually holding the data fixed. Minimal fix: seed np.random from random_state at the top of simulator(), keeping all seeding in one place. This intentionally reseeds the process-global legacy RNG; the docstring says so. Alternative considered (and fine by us if preferred): thread independent spawned Generators (SeedSequence.spawn) into each *_dist partial via the simulator_param_mappings, keeping v/z/t variability streams independent and leaving the global RNG untouched. That is the cleaner long-term design but touches every model config; this PR takes the one-line fix and the regression test either design must satisfy. The seeding now applies only to Integral random_state values; any other object passes through to the simulator layer unchanged. Raised by CodeRabbit on PR lnccbrown#348: the unguarded modulo raised TypeError on a numpy.random.Generator before dispatch. Measured against pristine v0.13.2: Generator inputs were never functional there either (the Cython layer raises "an integer is required"), so there was no working behavior to regress - but the guard keeps this change from intercepting that pre-existing failure with an earlier, less informative error, and a test pins the unchanged failure mode. Generator support itself is a separate, pre-existing gap (the _validate_random_state_for_c_rng docstring's "Generator ... skipped" wording overstates what downstream accepts). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Inline comments:
In `@ssms/basic_simulators/simulator.py`:
- Around line 789-793: Update the callable-wrapping loop in the simulator
parameter preparation flow to bind random_state only for recognized distribution
callables, rather than arbitrary callables from simulator_param_mappings or
simulator_fixed_params. Preserve unrelated callable values unchanged, or
validate that they accept random_state before applying functools.partial.
In `@tests/test_simulator.py`:
- Line 349: Update the test around the simulator calls and np.random.uniform to
snapshot NumPy’s global RNG state before execution, then restore it in a finally
block so the test cannot affect later tests.
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Inline comments:
In `@ssms/basic_simulators/simulator.py`:
- Around line 898-899: The _accepts_random_state helper must only accept
random_state when its parameter is keyword-compatible, excluding
inspect.Parameter.POSITIONAL_ONLY; retain VAR_KEYWORD support, update the
binding decision accordingly, and add a regression test covering positional-only
random_state behavior.
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Inline comments:
In `@ssms/basic_simulators/simulator.py`:
- Around line 670-672: Qualify the reproducibility statement near the simulator
seeding documentation: explain that an integer random_state controls variability
draws only when distributions are not already bound to their own random-state
generator. State that a pre-bound *_dist RNG takes precedence, or update the
initialization logic around the preserved *_dist callable to replace it when the
integer seed is intended to control all variability draws.
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Hey @AlexanderFengler, could you run Copilot review and approve the CI workflows on #1292, #1293, #348 and #349? Neither is available to me as an outside contributor, but I've followed the rest of the guidelines: CodeRabbit is clean on all four, and each went through a couple of self-review rounds for minimalism, reuse, and docs. There are six more changes queued behind these; a few depend on #1292 landing first. Krishn suggested stacking them, but that isn't possible from a fork since the base branch has to live upstream. If it'd make review easier on your end, I'd happily take collaborator access so they can be stacked properly. I'll also ping you from the other ones, just without the full message. |
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tests/test_simulator.py (1)
395-399: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick winExercise
simulatorwhen testing preservation of a bound RNG.These assertions only prove that
functools.partialretainsownindist.keywords. They do not execute the simulator binding path. The test still passes if that path overwrites the boundrandom_state.Add an integration assertion that uses this distribution through the simulator and verifies that its bound generator remains effective.
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Inline comments:
In `@tests/test_simulator.py`:
- Around line 353-356: Update the simulator reproducibility test around
simulator so it proves variability draws use a derived generator rather than the
legacy global NumPy RNG: instrument the variability callable to capture or
assert the generator it receives, or consume the global RNG after seeding and
before that callable draws. Ensure the test would fail if sv, sz, or st still
draw from the global RNG.
---
Nitpick comments:
In `@tests/test_simulator.py`:
- Around line 395-399: Extend the test around the bound partial distribution
dist and _accepts_random_state to invoke the simulator binding path, then assert
the bound own generator is preserved rather than overwritten. Keep the existing
keyword-preservation assertions and verify the simulator produces behavior using
dist with its pre-bound random_state.
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- set_seed seeds C's srand and a module-level NumPy Generator, but the *_dist simulator param mappings (e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy calls with no explicit random_state, so scipy drew them from NumPy's legacy global RNG, which nothing seeded. Two simulator() calls with the same random_state returned different data for every model carrying sv, sz or st, while plain ddm - which has no *_dist - stayed reproducible, hiding the gap. Any "same data across arms" comparison relying on random_state alone was not actually holding the data fixed. - Fix: bind those partials to a Generator derived from random_state, after adapt_parameters, so the draws are pinned without touching process-global state. Verified that np.random's stream position and its next draws are unchanged across seeded simulator() calls, and that reproducibility holds while the global stream is being consumed between calls. - Only integer seeds participate; any other random_state object passes through and fails where it already failed (the Cython layer requires an integer). The modulo keeps the validated negative-seed range inside default_rng's domain without changing that pre-existing failure point. - Regression test folded into the existing random_state block in tests/test_simulator.py, over ddm, ddm_st, full_ddm_rv and ddm_sdv. It fails for the three variability models without the fix and passes with it, and it also asserts that a call leaves NumPy's global RNG state byte-identical, which a global reseed or a global draw would not - so it pins where the draws come from, not only that they repeat. full_ddm is deliberately not used: it has no *_dist mappings and draws sz/sv/st internally from the generator set_seed already seeds, so it cannot exercise this path. - Alternatives considered: reseeding np.random globally inside simulator() (rejected - mutates process-global state for callers who did not ask for it), and adding the legacy seed inside set_seed itself (one line, and broader since sequential_models.pyx has the same bare np.random.* pattern, but it needs a Cython rebuild and widens the contract for every direct cssm caller). The sequential_models.pyx gap is left as a separate issue. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> - Binding is restricted to callables that accept random_state: a model config may map a parameter to any callable, and one that does not take the keyword would otherwise raise on the simulator call. scipy's rvs qualifies through **kwds. - The regression test snapshots and restores NumPy's global RNG state, so its deliberate consumption of that stream cannot make later tests order-dependent. - A positional-only random_state does not count as accepting the keyword: the value would be diverted into **kwargs while the parameter kept its default, or raise. A test pins both the exclusion and that divert-and-keep-default behaviour. - A distribution that already binds its own random_state is left alone rather than rebound, so a caller's deliberate per-distribution seeding survives; the docstring says so, since such draws follow that generator, not the seed.
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set_seed seeds C's srand and a module-level NumPy Generator, but the trial-to-trial variability distributions (the *_dist simulator param mappings, e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy.stats calls without an explicit random_state, and scipy draws those from NumPy's LEGACY GLOBAL RNG, which nothing had seeded. Two simulator() calls with the same random_state therefore returned different data for every model carrying sv, sz or st (measured max |drt| 0.253 for ddm_st, 0.617 for a Normal t-kernel), while plain ddm,
which has no *_dist, was reproducible, hiding the gap. Any 'same data across arms' comparison relying on random_state alone was not actually holding the data fixed.
Minimal fix: seed np.random from random_state at the top of simulator(), keeping all seeding in one place. This intentionally reseeds the process-global legacy RNG; the docstring says so.
Alternative considered: thread independent spawned Generators (SeedSequence.spawn) into each *_dist partial via the simulator_param_mappings, keeping v/z/t variability streams independent and leaving the global RNG untouched. That is the cleaner long-term design but touches every model config; this PR takes the one-line fix and the regression test either design must satisfy.
Summary by CodeRabbit
Bug Fixes
Documentation
Tests