Re-enable five disabled algorithms and the perceptron - #15208
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cclauss merged 1 commit intoSep 6, 2026
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Re-enable the four scikit-learn machine-learning examples and the
neural-network perceptron that had been disabled (renamed to
.broken.txt / .DISABLED), and modernize them so they import and run
cleanly on current scikit-learn and pass the doctest CI:
machine_learning/gaussian_naive_bayes.py
machine_learning/random_forest_classifier.py
- Replace the removed sklearn.metrics.plot_confusion_matrix with
ConfusionMatrixDisplay.from_estimator (removed in scikit-learn 1.2).
- Drop the artificial time.sleep() calls.
machine_learning/gradient_boosting_regressor.py
machine_learning/random_forest_regressor.py
- Replace the removed load_boston dataset (removed in scikit-learn
1.2 for ethical reasons) with the bundled load_diabetes dataset so
the examples run offline.
- Avoid an unused-variable lint (RUF059).
neural_network/perceptron.py
- Use a dedicated seeded random.Random instance instead of the global
random state, so training is reproducible and thread-safe under the
parallel test runner.
- Cap training at epoch_number epochs so it always terminates even on
non-linearly-separable data (previously an unbounded while True).
- Have training() and sort() return their results instead of printing,
per the contribution guidelines, and update the doctests accordingly.
Requested by @cclauss in TheAlgorithms#8029; perceptron follow-up to TheAlgorithms#15206.
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Re-enables the four disabled scikit-learn ML examples and the neural-network perceptron, as requested in #8029, and modernizes them so they import and run cleanly on current scikit-learn and pass the doctest CI. (Perceptron is the follow-up discussed in #15206.)
Why they were failing
These files had drifted against scikit-learn's API and could no longer be imported by the
--doctest-modulestest run:sklearn.metrics.plot_confusion_matrixwas removed in scikit-learn 1.2 (replaced byConfusionMatrixDisplay).sklearn.datasets.load_bostonwas removed in scikit-learn 1.2 (for ethical reasons).Changes
machine_learning/gaussian_naive_bayes.py,machine_learning/random_forest_classifier.pyplot_confusion_matrix(...)→ConfusionMatrixDisplay.from_estimator(...).time.sleep()calls.machine_learning/gradient_boosting_regressor.py,machine_learning/random_forest_regressor.pyload_diabetesdataset, so the examples run offline with no download.neural_network/perceptron.pyrandom.Randominstance instead of the globalrandomstate → training is reproducible and thread-safe under the parallel test runner.epoch_numberso it always terminates, even on non-linearly-separable data (was an unboundedwhile True).training()/sort()now return their results instead of printing, per the contribution guidelines; doctests updated accordingly.Checklist
python3 -m doctest neural_network/perceptron.pypasses.ruff checkandruff format --checkpass on all five files.if __name__ == "__main__":, so the doctest CI only imports the modules (no model training during collection).The ML examples are thin scikit-learn demos; I kept their structure intact and limited changes to what's needed to make them import/run again. Happy to iterate if you'd prefer a different dataset or a deeper rewrite of any of them.