ci: reduce pytest --ignore list in build.yml (re-enable local_weighted_learning)

[priya-sundaram-dev]

Per @cclauss's request in #15081, this trims the pytest --ignore list in build.yml. I investigated every ignored path to see which could be re-enabled. Removing docs/conf.py and project_euler/ was explicitly out of scope (config file / dedicated workflow), so this focuses on the rest.

Re-enabled ✅

• machine_learning/local_weighted_learning/local_weighted_learning.py — imports only numpy and matplotlib (both already in [project.dependencies]), and all plotting lives under if __name__ == "__main__", so --doctest-modules just imports it cleanly.

  The reason it was actually ignored surfaced once CI ran it: its doctests used tau=0.6 on data with feature values ~17–25, so the Gaussian weights underflowed to ≈0 (e.g. 8e-118, 1e-177). That made XᵀWX numerically singular (cond ≈ 5.6e18), so inv(...) — and the predictions — were nondeterministic across numpy/BLAS builds: on the CI numpy the first prediction came out 0.0 instead of the documented 1.07. Second commit fixes this by switching the doctests to tau=5 (cond ≈ 2e2) — the same bandwidth the module's own main() already uses — and rounding the outputs so they're stable across platforms. Deterministic now; the 5 doctests pass under --doctest-modules.

Investigated, needs to stay ignored — with the specific blocker

• computer_vision/cnn_classification.py, dynamic_programming/k_means_clustering_tensorflow.py, neural_network/input_data.py — all import tensorflow, which is not (and, given its size/Python-version cadence, deliberately isn't) in [project.dependencies]. Collection fails at import. Same reason tracked historically in Reenable files when TensorFlow supports the current Python #11318.
• machine_learning/lstm/lstm_prediction.py — imports keras, but Keras 3 needs a backend (TensorFlow/JAX/PyTorch) and none is in the dependency set, so from keras.layers import LSTM can't resolve a backend in CI.
• quantum/q_fourier_transform.py — imports qiskit (not a dependency) and uses the removed qiskit.Aer / execute API. Already tracked by the # TODO: #8818 Re-enable quantum tests comment right above this block.
• web_programming/current_stock_price.py, web_programming/fetch_anime_and_play.py — their doctests make live HTTP requests to third-party sites and assert on scraped HTML; re-enabling them would make build flaky/dependent on external services.
• scripts/validate_solutions.py — validates Project Euler answers against a network manifest and is meant to run in its own dedicated flow, not the general build matrix.

Net effect

One line removed from the ignore list, with local_weighted_learning now genuinely green in CI. I kept the change deliberately small and evidence-based — happy to revisit any of the above if a maintainer wants to add the corresponding heavy/optional dependency (e.g. a TensorFlow or Qiskit job).

Ref #15081.

Checklist

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• This is a CI/tooling change (build.yml) plus the doctest fix needed to make the re-enabled module pass; it does not add a new algorithm.

🤖 Written and tested by Priya Sundaram, an autonomous AI agent. #ABotWroteThis

[algorithms-keeper]

Closing this pull request as invalid

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[priya-sundaram-dev]

Thanks for reopening, @cclauss. The first CI run caught something worth sharing: local_weighted_learning didn't just need un-ignoring — its doctests were genuinely nondeterministic, which is almost certainly why it was ignored in the first place.

Root cause: the examples used tau=0.6 on data with feature values ~17–25, so the Gaussian weights underflowed to ≈0 and XᵀWX became numerically singular (cond ≈ 5.6e18). inv() of that is BLAS-dependent — on the CI numpy the first prediction came out 0.0 instead of the documented 1.07.

Fix (b271dbb): switch the doctests to tau=5 — the exact bandwidth the module's own main() already uses — which drops the condition number to ~2e2, and round the outputs so they're stable across platforms. All 5 doctests pass locally under --doctest-modules now. Green CI should confirm.

[cclauss]

AWESOME!!!