[Qa-jenkins-scm] Build failed in Jenkins: reproducible_builder_i386_14 #23414
jenkins at jenkins.debian.net
jenkins at jenkins.debian.net
Wed Nov 16 19:33:29 UTC 2016
https://jenkins.debian.net/job/reproducible_builder_i386_14/23414/------------------------------------------
[...truncated 14589 lines...]
sklearn.decomposition.tests.test_pca.test_pca_score3 ... ok
sklearn.decomposition.tests.test_pca.test_svd_solver_auto ... ok
sklearn.decomposition.tests.test_pca.test_deprecation_randomized_pca ... ok
sklearn.decomposition.tests.test_sparse_pca.test_correct_shapes ... ok
sklearn.decomposition.tests.test_sparse_pca.test_fit_transform ... ok
sklearn.decomposition.tests.test_sparse_pca.test_transform_nan ... ok
sklearn.decomposition.tests.test_sparse_pca.test_fit_transform_tall ... ok
sklearn.decomposition.tests.test_sparse_pca.test_initialization ... ok
sklearn.decomposition.tests.test_sparse_pca.test_mini_batch_correct_shapes ... ok
sklearn.decomposition.tests.test_sparse_pca.test_mini_batch_fit_transform ... SKIP: skipping mini_batch_fit_transform.
sklearn.decomposition.tests.test_sparse_pca.test_fit_transform_parallel ... ok
sklearn.decomposition.tests.test_truncated_svd.test_algorithms ... ok
sklearn.decomposition.tests.test_truncated_svd.test_attributes ... ok
sklearn.decomposition.tests.test_truncated_svd.test_too_many_components ... ok
sklearn.decomposition.tests.test_truncated_svd.test_sparse_formats ... ok
sklearn.decomposition.tests.test_truncated_svd.test_inverse_transform ... ok
sklearn.decomposition.tests.test_truncated_svd.test_integers ... ok
sklearn.decomposition.tests.test_truncated_svd.test_explained_variance ... ok
sklearn.ensemble.tests.test_bagging.test_classification ... ok
sklearn.ensemble.tests.test_bagging.test_sparse_classification ... /build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
/build/scikit-learn-0.18/debian/tmp/usr/lib/python3/dist-packages/sklearn/ensemble/bagging.py:747: RuntimeWarning: divide by zero encountered in log
return np.log(self.predict_proba(X))
ok
sklearn.ensemble.tests.test_bagging.test_regression ... ok
sklearn.ensemble.tests.test_bagging.test_sparse_regression ... ok
sklearn.ensemble.tests.test_bagging.test_bootstrap_samples ... ok
sklearn.ensemble.tests.test_bagging.test_bootstrap_features ... ok
sklearn.ensemble.tests.test_bagging.test_probability ... ok
sklearn.ensemble.tests.test_bagging.test_oob_score_classification ... ok
sklearn.ensemble.tests.test_bagging.test_oob_score_regression ... ok
sklearn.ensemble.tests.test_bagging.test_single_estimator ... ok
sklearn.ensemble.tests.test_bagging.test_error ... ok
sklearn.ensemble.tests.test_bagging.test_parallel_classification ... ok
sklearn.ensemble.tests.test_bagging.test_parallel_regression ... ok
sklearn.ensemble.tests.test_bagging.test_gridsearch ... ok
sklearn.ensemble.tests.test_bagging.test_base_estimator ... ok
sklearn.ensemble.tests.test_bagging.test_bagging_with_pipeline ... ok
sklearn.ensemble.tests.test_bagging.test_bagging_sample_weight_unsupported_but_passed ... ok
sklearn.ensemble.tests.test_bagging.test_warm_start ... ok
sklearn.ensemble.tests.test_bagging.test_warm_start_smaller_n_estimators ... ok
sklearn.ensemble.tests.test_bagging.test_warm_start_equal_n_estimators ... ok
sklearn.ensemble.tests.test_bagging.test_warm_start_equivalence ... ok
sklearn.ensemble.tests.test_bagging.test_warm_start_with_oob_score_fails ... ok
sklearn.ensemble.tests.test_bagging.test_oob_score_removed_on_warm_start ... ok
sklearn.ensemble.tests.test_bagging.test_oob_score_consistency ... ok
sklearn.ensemble.tests.test_bagging.test_estimators_samples ... ok
sklearn.ensemble.tests.test_bagging.test_max_samples_consistency ... ok
sklearn.ensemble.tests.test_base.test_base ... ok
sklearn.ensemble.tests.test_base.test_base_zero_n_estimators ... ok
sklearn.ensemble.tests.test_base.test_set_random_states ... ok
sklearn.ensemble.tests.test_forest.test_classification_toy('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_classification_toy('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_iris('RandomForestClassifier', 'gini') ... ok
sklearn.ensemble.tests.test_forest.test_iris('RandomForestClassifier', 'entropy') ... ok
sklearn.ensemble.tests.test_forest.test_iris('ExtraTreesClassifier', 'gini') ... ok
sklearn.ensemble.tests.test_forest.test_iris('ExtraTreesClassifier', 'entropy') ... ok
sklearn.ensemble.tests.test_forest.test_boston('RandomForestRegressor', 'mse') ... ok
sklearn.ensemble.tests.test_forest.test_boston('RandomForestRegressor', 'mae') ... ok
sklearn.ensemble.tests.test_forest.test_boston('RandomForestRegressor', 'friedman_mse') ... ok
sklearn.ensemble.tests.test_forest.test_boston('ExtraTreesRegressor', 'mse') ... ok
sklearn.ensemble.tests.test_forest.test_boston('ExtraTreesRegressor', 'mae') ... ok
sklearn.ensemble.tests.test_forest.test_boston('ExtraTreesRegressor', 'friedman_mse') ... ok
sklearn.ensemble.tests.test_forest.test_regressor_attributes('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_regressor_attributes('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_probability('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_probability('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_importances_asymptotic ... ok
sklearn.ensemble.tests.test_forest.test_1d_input ... ok
sklearn.ensemble.tests.test_forest.test_random_hasher ... ok
sklearn.ensemble.tests.test_forest.test_unfitted_feature_importances('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_unfitted_feature_importances('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_unfitted_feature_importances('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_unfitted_feature_importances('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_unfitted_feature_importances('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('RandomForestClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('RandomForestClassifier', <150x4 sparse matrix of type '<class 'numpy.float64'>' ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('RandomForestClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('ExtraTreesClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('ExtraTreesClassifier', <150x4 sparse matrix of type '<class 'numpy.float64'>' ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('ExtraTreesClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('RandomForestRegressor', array([[ 3.16360000e+00, 0.00000000e+00, 1.81000000e+01, ..., ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('RandomForestRegressor', <506x13 sparse matrix of type '<class 'numpy.float64'>' ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('ExtraTreesRegressor', array([[ 3.16360000e+00, 0.00000000e+00, 1.81000000e+01, ..., ... ok
sklearn.ensemble.tests.test_forest.test_oob_score('ExtraTreesRegressor', <506x13 sparse matrix of type '<class 'numpy.float64'>' ... ok
sklearn.ensemble.tests.test_forest.test_oob_score_raise_error('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_oob_score_raise_error('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_oob_score_raise_error('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_oob_score_raise_error('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_oob_score_raise_error('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_gridsearch('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_gridsearch('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_parallel('RandomForestClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_parallel('ExtraTreesClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_parallel('RandomForestRegressor', array([[ 3.16360000e+00, 0.00000000e+00, 1.81000000e+01, ..., ... ok
sklearn.ensemble.tests.test_forest.test_parallel('ExtraTreesRegressor', array([[ 3.16360000e+00, 0.00000000e+00, 1.81000000e+01, ..., ... ok
sklearn.ensemble.tests.test_forest.test_pickle('RandomForestClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_pickle('ExtraTreesClassifier', array([[ 5.8, 2.8, 5.1, 2.4], ... ok
sklearn.ensemble.tests.test_forest.test_pickle('RandomForestRegressor', array([[ 3.16360000e+00, 0.00000000e+00, 1.81000000e+01, ..., ... ok
sklearn.ensemble.tests.test_forest.test_pickle('ExtraTreesRegressor', array([[ 3.16360000e+00, 0.00000000e+00, 1.81000000e+01, ..., ... ok
sklearn.ensemble.tests.test_forest.test_multioutput('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_multioutput('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_multioutput('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_multioutput('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_classes_shape('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_classes_shape('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_random_trees_dense_type ... ok
sklearn.ensemble.tests.test_forest.test_random_trees_dense_equal ... ok
sklearn.ensemble.tests.test_forest.test_random_hasher_sparse_data ... ok
sklearn.ensemble.tests.test_forest.test_parallel_train ... ok
sklearn.ensemble.tests.test_forest.test_distribution ... ok
sklearn.ensemble.tests.test_forest.test_max_leaf_nodes_max_depth('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_max_leaf_nodes_max_depth('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_max_leaf_nodes_max_depth('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_max_leaf_nodes_max_depth('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_max_leaf_nodes_max_depth('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_importances ... SKIP: Test skipped on 32bit platforms.
sklearn.ensemble.tests.test_forest.test_min_samples_split('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_split('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_split('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_split('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_split('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_leaf('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_leaf('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_leaf('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_leaf('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_min_samples_leaf('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_min_weight_fraction_leaf('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_min_weight_fraction_leaf('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_min_weight_fraction_leaf('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_min_weight_fraction_leaf('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_min_weight_fraction_leaf('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomForestClassifier', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomForestClassifier', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomForestClassifier', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomForestRegressor', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomForestRegressor', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomForestRegressor', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('ExtraTreesClassifier', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('ExtraTreesClassifier', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('ExtraTreesClassifier', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomTreesEmbedding', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomTreesEmbedding', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('RandomTreesEmbedding', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('ExtraTreesRegressor', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('ExtraTreesRegressor', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_sparse_input('ExtraTreesRegressor', array([[ 3., 1., 4., 0., 3., 0., 7., 2., 1., 0., 5., ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('RandomForestClassifier', <class 'numpy.float64'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('RandomForestClassifier', <class 'numpy.float32'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('ExtraTreesClassifier', <class 'numpy.float64'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('ExtraTreesClassifier', <class 'numpy.float32'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('RandomForestRegressor', <class 'numpy.float64'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('RandomForestRegressor', <class 'numpy.float32'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('ExtraTreesRegressor', <class 'numpy.float64'>) ... ok
sklearn.ensemble.tests.test_forest.test_memory_layout('ExtraTreesRegressor', <class 'numpy.float32'>) ... ok
sklearn.ensemble.tests.test_forest.test_class_weights('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_class_weights('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_class_weight_balanced_and_bootstrap_multi_output('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_class_weight_balanced_and_bootstrap_multi_output('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_class_weight_errors('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_class_weight_errors('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_clear('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_clear('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_clear('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_clear('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_clear('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_smaller_n_estimators('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_smaller_n_estimators('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_smaller_n_estimators('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_smaller_n_estimators('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_smaller_n_estimators('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_equal_n_estimators('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_equal_n_estimators('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_equal_n_estimators('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_equal_n_estimators('RandomTreesEmbedding',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_equal_n_estimators('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_oob('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_oob('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_oob('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_warm_start_oob('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_dtype_convert ... ok
sklearn.ensemble.tests.test_forest.test_decision_path('RandomForestClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_decision_path('ExtraTreesClassifier',) ... ok
sklearn.ensemble.tests.test_forest.test_decision_path('RandomForestRegressor',) ... ok
sklearn.ensemble.tests.test_forest.test_decision_path('ExtraTreesRegressor',) ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_toy('auto', 'deviance') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_toy('auto', 'exponential') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_toy(True, 'deviance') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_toy(True, 'exponential') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_toy(False, 'deviance') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_toy(False, 'exponential') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_parameter_checks ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_loss_function ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_synthetic('auto', 'deviance') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_synthetic('auto', 'exponential') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_synthetic(True, 'deviance') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_synthetic(True, 'exponential') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_synthetic(False, 'deviance') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_classification_synthetic(False, 'exponential') ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_boston('auto', 'ls', 1.0) ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_boston('auto', 'ls', 0.5) ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_boston('auto', 'lad', 1.0) ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_boston('auto', 'lad', 0.5) ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_boston('auto', 'huber', 1.0) ... ok
sklearn.ensemble.tests.test_gradient_boosting.test_boston('auto', 'huber', 0.5) ... /srv/reproducible-results/rbuild-debian-fOjSr0V4/tmp.E8QQXv1pe8: 48.5% -- replaced with /srv/reproducible-results/rbuild-debian-fOjSr0V4/tmp.E8QQXv1pe8.gz
INFO: Starting at 2016-11-16 19:33:27.731359
FATAL: null
java.lang.NullPointerException
at hudson.plugins.build_timeout.BuildTimeoutWrapper$EnvironmentImpl.tearDown(BuildTimeoutWrapper.java:199)
at hudson.model.Build$BuildExecution.doRun(Build.java:173)
at hudson.model.AbstractBuild$AbstractBuildExecution.run(AbstractBuild.java:534)
at hudson.model.Run.execute(Run.java:1720)
at hudson.model.FreeStyleBuild.run(FreeStyleBuild.java:43)
at hudson.model.ResourceController.execute(ResourceController.java:98)
at hudson.model.Executor.run(Executor.java:404)
INFO: Finished at 2016-11-16 19:33:29.189446, took: 0:00:01.458105
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