TST Relax test_gradient_boosting_early_stopping
#24541
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glemaitre
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Sep 29, 2022
| (gbc, 1e-1, 28), | ||
| (gbr, 1e-1, 13), | ||
| (gbc, 1e-3, 70), | ||
| (gbr, 1e-3, 28), | ||
| ): | ||
| est.set_params(tol=tol) | ||
| est.fit(X_train, y_train) | ||
| assert est.n_estimators_ == early_stop_n_estimators | ||
| assert ( | ||
| expected_early_stop_n_estimators - delta_early_stop_n_estimators |
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I think it would be enough to have an upper bound with a less-than-operator.
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What I don't get here is why Python 3.11 would trigger this issue while other Windows builds are working.
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can we just follow the description of the test and simply check the following ?
gbc(tol=1e-1).fit(X, y).n_estimators_ < gbc(tol=1e-3).fit(X, y).n_estimators_
Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>
ogrisel
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Sep 30, 2022
|
Thanks for this effort! Since this is blocking #24446, what's needed to get it merged? |
|
Just another maintainer's approval. cc @jeremiedbb: does this LGTY? |
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Reference Issues/PRs
Fixes a failure observed in #24446 (comment)
What does this implement/fix? Explain your changes.
Depending on platforms, the number of fitted estimators might slightly vary.
This makes
test_gradient_boosting_early_stoppingfails as it checks it against hard-coded expected values.This PR proposes relaxing
test_gradient_boosting_early_stoppingby checking for an inclusion in an interval centered in the expected number of fitted estimators rather than a strict equality.Any other comments?
Other (better) proposals are welcome!