10000 check_decision_proba_consistency fails with LinearDiscriminantAnalysis · Issue #19224 · scikit-learn/scikit-learn · GitHub
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ogrisel opened this issue Jan 21, 2021 · 0 comments
Open

check_decision_proba_consistency fails with LinearDiscriminantAnalysis #19224

ogrisel opened this issue Jan 21, 2021 · 0 comments
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Bug module:test-suite everything related to our tests

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@ogrisel
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ogrisel commented Jan 21, 2021 •
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The following common check recently started to fail on a Windows CI job:

name = 'LinearDiscriminantAnalysis'
estimator_orig = LinearDiscriminantAnalysis()

    @ignore_warnings(category=FutureWarning)
    def check_decision_proba_consistency(name, estimator_orig):
        # Check whether an estimator having both decision_function and
        # predict_proba methods has outputs with perfect rank correlation.
    
        centers = [(2, 2), (4, 4)]
        X, y = make_blobs(n_samples=100, random_state=0, n_features=4,
                          centers=centers, cluster_std=1.0, shuffle=True)
        X_test = np.random.randn(20, 2) + 4
        estimator = clone(estimator_orig)
    
        if (hasattr(estimator, "decision_function") and
                hasattr(estimator, "predict_proba")):
    
            estimator.fit(X, y)
            # Since the link function from decision_function() to predict_proba()
            # is sometimes not precise enough (typically expit), we round to the
            # 10th decimal to avoid numerical issues.
            a = estimator.predict_proba(X_test)[:, 1].round(decimals=10)
            b = estimator.decision_function(X_test).round(decimals=10)
>           assert_array_equal(rankdata(a), rankdata(b))
E           AssertionError: 
E           Arrays are not equal
E           
E           Mismatched elements: 2 / 20 (10%)
E           Max absolute difference: 0.5
E           Max relative difference: 0.02631579
E            x: array([ 7. ,  8. , 11. ,  9. , 17. , 10. ,  5. , 14. ,  6. ,  1. , 19.5,
E                   4. ,  2. , 16. , 12. , 13. ,  3. , 15. , 19.5, 18. ])
E            y: array([ 7.,  8., 11.,  9., 17., 10.,  5., 14.,  6.,  1., 20.,  4.,  2.,
E                  16., 12., 13.,  3., 15., 19., 18.])

This happened on this PR which should not have any impact on the behavior LinearDiscriminantAnalysis.predict_proba: #17743.

https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=25474&view=logs&j=d32b16b6-cb9d-571b-e765-de83708fb8dd&t=b93f76c1-c2c9-579e-c2ec-c4f438af1261

I suspect the test to be too brittle. Maybe using a test set more related to the original distribution (blobs) would avoid ties or caused by arbitrary rounding?
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