Source: numpy, scikit-learn Control: found -1 numpy/1:1.19.0-1 Control: found -1 scikit-learn/0.22.2.post1+dfsg-7 Severity: serious Tags: sid bullseye X-Debbugs-CC: debian-ci@lists.debian.org User: debian-ci@lists.debian.org Usertags: breaks needs-update Dear maintainer(s), With a recent upload of numpy the autopkgtest of scikit-learn fails in testing when that autopkgtest is run with the binary packages of numpy from unstable. It passes when run with only packages from testing. In tabular form: pass fail numpy from testing 1:1.19.0-1 scikit-learn from testing 0.22.2.post1+dfsg-7 all others from testing from testing I copied some of the output at the bottom of this report. Currently this regression is blocking the migration of numpy to testing [1]. Due to the nature of this issue, I filed this bug report against both packages. Can you please investigate the situation and reassign the bug to the right package? More information about this bug and the reason for filing it can be found on https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation Paul [1] https://qa.debian.org/excuses.php?package=numpy https://ci.debian.net/data/autopkgtest/testing/amd64/s/scikit-learn/6058472/log.gz =================================== FAILURES =================================== ________________________ test_set_estimator_none[drop] _________________________ drop = 'drop' @pytest.mark.parametrize("drop", [None, 'drop']) def test_set_estimator_none(drop): """VotingClassifier set_params should be able to set estimators as None or drop""" # Test predict clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(n_estimators=10, random_state=123) clf3 = GaussianNB() eclf1 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('nb', clf3)], voting='hard', weights=[1, 0, 0.5]).fit(X, y) eclf2 = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('nb', clf3)], voting='hard', weights=[1, 1, 0.5]) with pytest.warns(None) as record: eclf2.set_params(rf=drop).fit(X, y) > assert record if drop is None else not record E assert False /usr/lib/python3/dist-packages/sklearn/ensemble/tests/test_voting.py:378: AssertionError ________________ test_logistic_regression_path_convergence_fail ________________ def test_logistic_regression_path_convergence_fail(): rng = np.random.RandomState(0) X = np.concatenate((rng.randn(100, 2) + [1, 1], rng.randn(100, 2))) y = [1] * 100 + [-1] * 100 Cs = [1e3] # Check that the convergence message points to both a model agnostic # advice (scaling the data) and to the logistic regression specific # documentation that includes hints on the solver configuration. with pytest.warns(ConvergenceWarning) as record: _logistic_regression_path( X, y, Cs=Cs, tol=0., max_iter=1, random_state=0, verbose=0) > assert len(record) == 1 E assert 6 == 1 E -6 E +1 /usr/lib/python3/dist-packages/sklearn/linear_model/tests/test_logistic.py:401: AssertionError
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