Modernize PyRCN for scikit-learn 1.6+ (Python 3.10+)#62
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The nn/ PyTorch subpackage was an early draft and is out of scope for the scikit-learn compatibility modernization. Remove it along with the torch, torchvision and torchaudio dependencies so that `import pyrcn` no longer requires PyTorch. - Delete src/pyrcn/nn/ - Drop nn from the top-level package imports and __all__ - Strip torch from util.seed_everything (keep random/numpy seeding) - Remove torch/torchvision/torchaudio from pyproject dependencies Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Prepare for scikit-learn >= 1.6 by removing reliance on private or deprecated symbols. No behavioral change. - base/_activations.py: define the inplace activation table locally instead of importing and mutating the private sklearn.neural_network._base.ACTIVATIONS global. - Drop the deprecated @_deprecate_positional_args decorator throughout; __init__/metric signatures are already keyword-only via `*`. - echo_state_network, extreme_learning_machine: use public sklearn.base.RegressorMixin in type hints instead of the private sklearn.linear_model._base.LinearModel. - node_to_node: import csr_matrix from scipy.sparse, not the removed scipy.sparse.csr submodule. - Replace the typing_extensions / sys.version_info shims with plain typing (Python >= 3.10) and drop now-dead `import sys`. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Custom estimators relied on APIs removed or deprecated in scikit-learn 1.6: - BaseEstimator._validate_data / _check_n_features were removed. Replace them with the public sklearn.utils.validation.validate_data function. - The estimator type is now derived from tags and _estimator_type is deprecated. Per the official developer guide, mixins must precede BaseEstimator in the MRO so __sklearn_tags__ resolves the estimator type correctly; reorder the bases of every estimator accordingly. This restores is_regressor / is_classifier detection. - Coates: replace the getattr(clusterer, "_estimator_type") check with the public sklearn.base.is_clusterer helper. No behavioral change intended. The suite goes from 50 to 21 failing; the remaining failures are all in the sequence-aware metrics wrappers and are addressed separately. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The sequence-aware metric wrappers called scikit-learn's private _check_targets / _check_reg_targets helpers, whose signatures changed in 1.6 (sample_weight became positional), causing unpacking and validation errors. Replace those calls: flatten the per-sequence arrays with the public check_consistent_length + np.concatenate and derive the target type via the public sklearn.utils.multiclass.type_of_target. The public metric performs its own target validation, so the private helpers are no longer needed. Also route mean_squared_error(squared=False) to the public root_mean_squared_error, since the `squared` parameter was removed from sklearn.metrics.mean_squared_error in 1.6. PyRCN's own `squared` argument is preserved. No behavioral change intended. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The repository moved from TUD-STKS/PyRCN to PlasmaControl/PyRCN. Update all GitHub and mybinder links in the README, docs and example notebook. Links to other TUD-STKS repositories (Automatic-Music-Transcription, gci_estimation) are left unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Bump requires-python to >=3.10 (the floor for scikit-learn >= 1.6) and add a scikit-learn>=1.6 dependency floor; list numpy/scipy/joblib/pandas explicitly. - Fix the license classifier (was MIT; the LICENSE file and all source headers are BSD-3-Clause). - Single source of truth for the version: pyproject reads it dynamically from pyrcn._version, and _version.py is set to 0.0.18 (was 0.0.17post1). - Add test and examples optional-dependency extras. - Remove the redundant setup.cfg (pyproject is now canonical). - Fix requirements.txt: the malformed `requests[...]` lines were meant to be matplotlib/seaborn/ipywidgets/ipympl/tqdm; drop obsolete typing-extensions. - mypy: ignore_missing_imports for third-party packages without type stubs. - Read the Docs: build with Python 3.12 (>=3.10 is now required). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- CI matrix: Python 3.10-3.13 (drop 3.9); bump actions/checkout to v4 and actions/setup-python to v5 in both workflows. - Install the package via the `test` extra instead of an ad-hoc plugin list plus requirements.txt; run flake8 (blocking) and mypy (informational, the remaining strict-typing findings are deferred to the code-unification pass). - Remove pytest.ini, whose addopts required cov/mypy plugins just to collect tests; pyproject's [tool.pytest.ini_options] keeps a bare `pytest` working, and coverage is requested explicitly in CI. - Fix a flake8 E231 in tests so linting can cover the test suite too. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Apply modern Python 3.10 idioms (the new requires-python floor) consistently: - Type hints: PEP 604 unions (Union[A, B] -> A | B, Optional[A] -> A | None) and PEP 585 builtin generics (Dict/List/Tuple -> dict/list/tuple); move Callable/Iterable to collections.abc. Applied with pyupgrade --py310-plus. - Replace str.format(...) calls with f-strings. - Sort and group imports consistently (isort) and add `from __future__ import annotations` to every module. - Reflow the multi-line signatures whose alignment shifted. Purely mechanical; no behavioral change. flake8 clean, suite unchanged (85 passed / 2 skipped). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Remove the dead, commented-out NonlinearVectorAutoregression class that had been left as a module-level string literal in blocks/_input_to_node.py. - Replace `raise BaseException(...)` with `raise TypeError(...)` in the ESN and ELM partial_fit guards; BaseException should never be raised directly. - Fix a stray `%s` left in a NodeToNode hidden_layer_size error message. No behavioral change (aside from the more specific exception type on an invalid-regressor error path). flake8 clean, suite unchanged (85 passed). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
_make_sparse takes a random_state argument but drew the retained-weight indices from the global numpy RNG (np.random.choice), so the sparsity mask was not reproducible from a fixed seed. Use the passed random_state. This makes the sparse weight masks deterministic w.r.t. random_state (the exact mask for a given seed changes); the test suite is unaffected. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Coates no longer calls check_random_state in __init__; it stores the raw random_state and resolves it in _extract_random_patches, so get_params / clone round-trip correctly. Numerically equivalent for a single fit. - Replace the mutable default arguments Coates(clusterer=KMeans()) and lorenz(x_0=[1.0, 1.0, 1.0]) with None sentinels, constructing the default inside the method/function to avoid a shared mutable instance. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Resolve the remaining strict-mypy findings so `mypy src/pyrcn` is clean, and drop the informational `continue-on-error` from the CI type-check step. - util.batched: annotate the generator return as Iterator[tuple]. - util.value_to_tuple: correct the signature (value may be a tuple; size is an int) and always return. - BatchIntrinsicPlasticity: initialize self._m/_c as floats (they are assigned float distribution parameters). - Coates: type the pooling axis as `int | None`, and assert self.clusterer is not None at the post-fit use sites (it is resolved to KMeans() in fit). No behavioral change. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Apply the same modernization used for the package to tests/: PEP 604 / PEP 585 type syntax and f-strings (pyupgrade --py310-plus), consistent import sorting with `from __future__ import annotations` (isort), and removal of unused imports (autoflake). No test logic changed; suite unchanged (85 passed). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Summary
Modernize PyRCN to run on the current scientific-Python stack
(scikit-learn 1.9 / NumPy 2.x / SciPy 1.18 / Python 3.10–3.13) and to
clean up the codebase, without changing the public API. The full test suite
passes (85 passed / 2 skipped);
flake8andmypyare clean.The PyTorch
nnsubpackage (an early draft) is removed and torch is droppedas a dependency — a redesigned backend for sequential data is planned
separately.
Phase 1 — scikit-learn 1.6+ compatibility (no behavioral change)
nnsubpackage and the torch/torchvision/torchaudio deps.sklearn/scipyimports with public APIs(own
ACTIVATIONStable,RegressorMixintype hints,scipy.sparse,drop
_deprecate_positional_args, plaintyping).validate_data,mixins-before-
BaseEstimatorMRO (restoresis_regressor/is_classifier),is_clustererin Coates.(drop private
_check_targets/_check_reg_targets; routemean_squared_error(squared=False)toroot_mean_squared_error).requires-python >=3.10,scikit-learn>=1.6, dynamic version(0.0.18), fix the BSD license classifier, drop
setup.cfg/pytest.ini,CI matrix 3.10–3.13, update repo URLs to the PlasmaControl org.
Phase 2 — code unification + bug fixes
f-strings, sorted imports +
from __future__ import annotations,BaseException→TypeError, dead-code removal, strictmypy(now enforcedin CI), and the same modernization applied to
tests/._make_sparsenow uses the passedrandom_stateinstead of the globalNumPy RNG, so sparse weights are reproducible from a seed (the exact mask
for a given seed changes).
random_state(resolved infit) and no longer uses a shared mutableKMeans()default, soclone/get_paramsround-trip correctly. Verifiednumerically identical for a single fit with an explicit clusterer; the
differing cases (clone/
get_params, default clusterer across multipleinstances, repeated
fitwith an int seed) are all where the previousbehavior was a bug.
🤖 Generated with Claude Code