8000 MNT Remove ellipsis from doctests by lesteve · Pull Request #31332 · scikit-learn/scikit-learn · GitHub
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MNT Remove ellipsis from doctests #31332

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Merged
merged 4 commits into from
May 7, 2025
Merged

MNT Remove ellipsis from doctests #31332

merged 4 commits into from
May 7, 2025

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lesteve
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@lesteve lesteve commented May 7, 2025 •
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Since we are using scipy-doctest #30496, most ellipsis (aka ...) can be removed from doctests. scipy-doctest uses floating point comparison with a default rtol=1e-3.

A few cases where it is not so convenient to remove the ellipsis:

  • print statements seen in sklearn/linear_model/_huber.py
  • only showing the first few values of a very long array seen in doc/modules/partial_dependence.rst
  • brittle doctest see comment below
8000

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github-actions bot commented May 7, 2025 •
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✔️ Linting Passed

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Generated for commit: c610526. Link to the linter CI: here

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lesteve commented May 7, 2025 •
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It looks like the GradientBoostingRegressor doctest is quite brittle and varies quite a bit on different machines or different package versions (more than the default 1e-3 rtol used by scipy-doctest) so I am going back to an ellipsis. Got 0.445 locally and the two CI builds don't agree, one is 0.434 and the other is 0.440.

From build log

Linux_Runs pylatest_conda_forge_mkl

___________ [doctest] sklearn.ensemble._gb.GradientBoostingRegressor ___________
2046 >>> from sklearn.model_selection import train_test_split
2047 >>> X, y = make_regression(random_state=0)
2048 >>> X_train, X_test, y_train, y_test = train_test_split(
2049 ...     X, y, random_state=0)
2050 >>> reg = GradientBoostingRegressor(random_state=0)
2051 >>> reg.fit(X_train, y_train)
2052 GradientBoostingRegressor(random_state=0)
2053 >>> reg.predict(X_test[1:2])
2054 array([-61.1])
2055 >>> reg.score(X_test, y_test)
Expected:
    0.445
Got:
    0.43437063706910894

Linux pylatest_pip_openblas_pandas

___________ [doctest] sklearn.ensemble._gb.GradientBoostingRegressor ___________
2046 >>> from sklearn.model_selection import train_test_split
2047 >>> X, y = make_regression(random_state=0)
2048 >>> X_train, X_test, y_train, y_test = train_test_split(
2049 ...     X, y, random_state=0)
2050 >>> reg = GradientBoostingRegressor(random_state=0)
2051 >>> reg.fit(X_train, y_train)
2052 GradientBoostingRegressor(random_state=0)
2053 >>> reg.predict(X_test[1:2])
2054 array([-61.1])
2055 >>> reg.score(X_test, y_test)
Expected:
    0.445
Got:
    0.440031029624667

adrinjalali
adrinjalali approved these changes May 7, 2025
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Oh my. Didn't expect you (or anybody 😅 ) to fix them all. Impressive! Thanks.

@adrinjalali adrinjalali merged commit f44350d into scikit-learn:main May 7, 2025
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@adrinjalali adrinjalali mentioned this pull request May 7, 2025
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