diff --git a/deselected_tests.yaml b/deselected_tests.yaml index df29e14f06..6b31909972 100755 --- a/deselected_tests.yaml +++ b/deselected_tests.yaml @@ -54,20 +54,8 @@ deselected_tests: - decomposition/tests/test_pca.py::test_pca_svd_solver_auto[1000-500-400-full] >=1.5 - decomposition/tests/test_pca.py::test_pca_svd_solver_auto[1000-500-0.5-full] >=1.5 - # test for KMeans FutureWarning is not removed from sklearn tests suit yet - - cluster/tests/test_k_means.py::test_change_n_init_future_warning[KMeans-10] ==1.4.dev0 - - # Non-critical, but there are significant numerical differences in doctest results - - pipeline.py::sklearn.pipeline.FeatureUnion - - ensemble/_forest.py::sklearn.ensemble._forest.RandomForestRegressor - - ensemble/_voting.py::sklearn.ensemble._voting.VotingRegressor - # Non-critical, but there are significant differences due to different implementations - - ensemble/tests/test_forest.py::test_importances[RandomForestClassifier-gini-float64] - - ensemble/tests/test_voting.py::test_set_estimator_drop - linear_model/tests/test_common.py::test_balance_property[42-True-LinearRegression] - - linear_model/tests/test_logistic.py::test_logistic_regression_multinomial - - cluster/tests/test_k_means.py::test_k_means_fit_predict >=0.23,<0.24 - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-brute] - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-brute] @@ -79,12 +67,6 @@ deselected_tests: - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-ball_tree] >=1.2 win32 - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-kd_tree] >=1.2 win32 - # TODO: fix for Linear Regression failed to converge on macOS only - - inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est0] >=0.23 darwin - - inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est1] >=0.23 darwin - - inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est2] >=0.23 darwin - - inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est3] >=0.23 darwin - # Sklearnex RandomForestClassifier RNG is different from scikit-learn and daal4py # resulting in different feature importances for small number of trees (10). # Issue dissappears with bigger number of trees (>=20) @@ -93,12 +75,6 @@ deselected_tests: - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[1.0-1] - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[1.0-2] - # Random forest classifier selects a different most-important feature - # Feature importances: - # scikit-learn-intelex [0. 0.00553064 0.71323666 0.2812327 ] - # scikit-learn [1.59232288e-04 2.65131818e-02 2.63581110e-01 7.09746476e-01] - - feature_selection/tests/test_from_model.py::test_prefit_get_feature_names_out - # TODO: add support of subset invariance to SVM - tests/test_common.py::test_estimators[SVC()-check_methods_subset_invariance] - tests/test_common.py::test_estimators[NuSVC()-check_methods_subset_invariance] @@ -125,32 +101,16 @@ deselected_tests: # TODO: investigate copy failure of read-only buffer - linear_model/tests/test_coordinate_descent.py::test_read_only_buffer - # Warm starting issue on 1 iteration of LogReg - - linear_model/tests/test_logistic.py::test_warm_start[multinomial-True-True-lbfgs] - - linear_model/tests/test_logistic.py::test_warm_start[multinomial-True-True-newton-cg] - - linear_model/tests/test_logistic.py::test_warm_start[multinomial-False-True-newton-cg] - - linear_model/tests/test_logistic.py::test_warm_start[multinomial-False-True-lbfgs] - - # Difference of assigned dtype: int64 (scikit-learn) vs. int32 (oneDAL/scikit-learn-intelex) - - cluster/_dbscan.py::sklearn.cluster._dbscan.DBSCAN - - neighbors/_base.py::sklearn.neighbors._base.KNeighborsMixin.kneighbors - # Difference between scikit-learn and scikit-learn-intelex methods of kNN - neighbors/tests/test_neighbors.py::test_unsupervised_kneighbors[float64-euclidean-True-1000-5-100-1] - neighbors/tests/test_neighbors.py::test_unsupervised_kneighbors[float64-minkowski-True-1000-5-100-1] - neighbors/tests/test_neighbors.py::test_unsupervised_kneighbors[float64-l2-True-1000-5-100-1] - - neighbors/tests/test_neighbors.py::test_unsupervised_kneighbors[float64-l1-True-1000-5-100-1] - - neighbors/tests/test_neighbors.py::test_unsupervised_kneighbors[float64-cityblock-True-1000-5-100-1] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-euclidean-1000-5-100] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-minkowski-1000-5-100] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-euclidean-1000-5-100] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-minkowski-1000-5-100] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-l2-1000-5-100] - - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-l1-1000-5-100] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-l2-1000-5-100] - - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-l1-1000-5-100] - - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-1-100-cityblock-1000-5-100] - - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-cityblock-1000-5-100] - neighbors/tests/test_neighbors.py::test_KNeighborsClassifier_multioutput # Models with sparse data are different between oneAPI Data Analytics Library (oneDAL) and stock scikit-learn @@ -160,7 +120,6 @@ deselected_tests: # Decision function is different, 1.83697605e-06 - ensemble/tests/test_bagging.py::test_sparse_classification - - ensemble/tests/test_bagging.py::test_sparse_regression <0.23 # Same results as in scikit-learn, but in a different order - svm/tests/test_svm.py::test_svc_ovr_tie_breaking[SVC] @@ -183,38 +142,17 @@ deselected_tests: # Bitwise comparison of SVR score using a print (diff = 2.220446049250313e-16) - svm/tests/test_svm.py::test_custom_kernel_not_array_input[SVR] - # This test needs a warning from scikit-learn. scikit-learn-intelex raises the same warning - - tests/test_common.py::test_estimators[SVC()-check_supervised_y_2d] - - tests/test_common.py::test_estimators[SVR()-check_supervised_y_2d] - - tests/test_common.py::test_estimators[NuSVC()-check_supervised_y_2d] - - tests/test_common.py::test_estimators[NuSVR()-check_supervised_y_2d] - - # Bitwise comparison of probabilities using a print. - - metrics/tests/test_classification.py >=0.22,<0.24 - - # Max absolute difference: 0.04 for rocauc, and 0.01 for precision_recall - - metrics/tests/test_ranking.py::test_roc_curve_hard >=0.23,<0.24 - # test_non_uniform_strategies fails due to differences in handling of vacuous clusters after update # See https://github.com/IntelPython/daal4py/issues/69 - cluster/tests/test_k_means.py::test_kmeans_relocated_clusters >=0.24 - # In scikit-learn, these algorithms are not included in this test. However, scikit-learn-intelex - # does and throws an error. This is due to the different structure of the transformer.__module__.split("."). - - tests/test_common.py::test_transformers_get_feature_names_out[KMeans()] >=1.0 - # oneAPI Data Analytics Library (oneDAL) does not check convergence for tol == 0.0 for ease of benchmarking - cluster/tests/test_k_means.py::test_kmeans_convergence >=0.23 - cluster/tests/test_k_means.py::test_kmeans_verbose >=0.23 - # The Newton-CG solver solution computed in float32 disagrees with that of float64 by a small - # margin above the test threshold, see https://github.com/scikit-learn/scikit-learn/pull/13645 - - linear_model/tests/test_logistic.py::test_dtype_match - # Logistic Regression coeffs change due to fix for loss scaling # (https://github.com/scikit-learn/scikit-learn/pull/26721) - feature_selection/tests/test_from_model.py::test_importance_getter[estimator0-named_steps.logisticregression.coef_] - - inspection/_plot/tests/test_boundary_decision_display.py::test_class_of_interest_binary[predict_proba] - linear_model/tests/test_sag.py::test_sag_pobj_matches_logistic_regression # This fails on certain platforms. While weighted data does not go through DAAL, @@ -227,10 +165,6 @@ deselected_tests: # https://github.com/oneapi-src/oneDAL/issues/494 - linear_model/tests/test_coordinate_descent.py::test_enet_multitarget - # Insufficient accuracy of objective function in Elastic Net in case of warm_start - # https://github.com/oneapi-src/oneDAL/issues/495 - - linear_model/tests/test_coordinate_descent.py::test_warm_start_convergence_with_regularizer_decrement <0.24 - # oneAPI Data Analytics Library (oneDAL) doesn't support sample_weight (back to scikit-learn), # sufficient accuracy (similar to previous cases) - linear_model/tests/test_coordinate_descent.py::test_enet_sample_weight_consistency >=0.23 @@ -238,7 +172,6 @@ deselected_tests: # Different interpretation of trees compared to scikit-learn # Looks like we need to align tree traversal. This problem will be fixed - ensemble/tests/test_forest.py::test_min_samples_leaf - # Different random number generation engine in oneDAL and scikit-learn # The result is depend on random state, for random_state=777 in RandomForestClassifier the test is passed - ensemble/tests/test_voting.py::test_majority_label_iris @@ -263,7 +196,6 @@ deselected_tests: - tests/test_multioutput.py::test_multi_output_classification # Linear Regression - minor mismatches in error/warning messages - - model_selection/tests/test_search.py::test_grid_search_pipeline_steps - linear_model/tests/test_base.py::test_linear_regression_pd_sparse_dataframe_warning # L1 Linear models with sklearn 1.1 + numpy > 1.25 - extra warnings from numpy lead to test fail @@ -274,23 +206,13 @@ deselected_tests: - linear_model/tests/test_coordinate_descent.py::test_assure_warning_when_normalize[deprecated-0-LassoCV] >=1.1,<1.2 - linear_model/tests/test_coordinate_descent.py::test_assure_warning_when_normalize[deprecated-0-ElasticNetCV] >=1.1,<1.2 - # Different results scikit-learn-intelex and scikit-learn linear regression with weights. Need to investigate. - - inspection/tests/test_permutation_importance.py::test_permutation_importance_sample_weight >=0.24 - # OOB scores in scikit-learn and oneDAL are different because of different random number generators - - ensemble/tests/test_forest.py::test_forest_classifier_oob[X1-y1-0.65-array-ExtraTreesClassifier] - - ensemble/tests/test_forest.py::test_forest_classifier_oob[True-X1-y1-0.65-array-ExtraTreesClassifier] >=1.3 - ensemble/tests/test_forest.py::test_forest_regressor_oob[True-X0-y0-0.7-array-ExtraTreesRegressor] >=1.3 - - ensemble/tests/test_forest.py::test_forest_regressor_oob[X0-y0-0.7-array-RandomForestRegressor] >=1.2 darwin - - ensemble/tests/test_forest.py::test_forest_regressor_oob[True-X0-y0-0.7-array-RandomForestRegressor] >=1.3 darwin - ensemble/tests/test_forest.py::test_importances[ExtraTreesRegressor-squared_error-float64] >=0.23 darwin - ensemble/tests/test_forest.py::test_forest_regressor_oob[X0-y0-0.7-array-ExtraTreesRegressor] - ensemble/tests/test_forest.py::test_warm_start_oob - ensemble/tests/test_forest.py::test_distribution - # Need a warning from scikit-learn if some samples do not have OOB scores that can not be raised from oneDAL - - ensemble/tests/test_forest.py::test_forest_oob_warning - # Different behavior when 1 class enters the input - feature_selection/tests/test_rfe.py::test_rfe_cv_groups @@ -298,24 +220,13 @@ deselected_tests: - ensemble/tests/test_forest.py::test_memory_layout[float64-ExtraTreesRegressor] - ensemble/tests/test_forest.py::test_memory_layout[float32-ExtraTreesRegressor] - # The bugs are fixed in 2021.2 release - - ensemble/tests/test_stacking.py::test_stacking_cv_influence - # module name should starts with 'sklearn.' but we have 'daal4py.sklearn.' - - tests/test_common.py::test_check_n_features_in_after_fitting[LogisticRegression()] >=0.24,<1.0 - - tests/test_common.py::test_check_n_features_in_after_fitting[SVC()] >=0.24,<1.0 - metrics/tests/test_score_objects.py::test_scoring_is_not_metric - utils/tests/test_estimator_checks.py::test_check_dataframe_column_names_consistency >=1.0 # Stability issue with max absolute difference: 4.33846826e-08/1.17613697e-11. Remove in next release - ensemble/tests/test_bagging.py::test_estimators_samples_deterministic - # We use similar statements, but with different words - - ensemble/tests/test_gradient_boosting.py::test_gradient_boosting_with_init_pipeline >=1.0 - - # Data for the tests is generated by using SVC. It is not equal to stock scikit-learn - - metrics/tests/test_ranking.py::test_precision_recall_curve >=0.22,<0.24 - # Some values in PCA.components_ (in the last component) aren't equal (0.6 on average # for absolute error in this test) because of different implementations of PCA. # The results are also not stable. @@ -332,29 +243,16 @@ deselected_tests: # RandomForestRegressor sum(y_pred)!=sum(y_true) - ensemble/tests/test_forest.py::test_balance_property_random_forest[squared_error] >=1.0 - # This test fails on mac mini 8.1 with stock scikit-learn - - semi_supervised/tests/test_label_propagation.py - # This test fails because with patch config_context with new options, but the # test checks only the exact number of options that are used - tests/test_config.py::test_config_context - # Some scikit-learn-intelex docstrings differ from scikit-learn. - - tests/test_docstrings.py >=1.0.2 # Accuracy of scikit-learn-intelex and scikit-learn may differ due to different approaches - - manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately_with_precomputed_distances - manifold/tests/test_t_sne.py::test_bh_match_exact - manifold/tests/test_t_sne.py::test_uniform_grid[barnes_hut] - - manifold/tests/test_t_sne.py::test_sparse_precomputed_distance - - manifold/tests/test_t_sne.py::test_tsne_different_square_distances >=0.24 - - # Temporary deselected up to 2021.6 release. Need to fix - - ensemble/tests/test_bagging.py::test_classification # Failure related to incompatibility of older sklearn versions with updated dependencies - - ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py::test_same_predictions_multiclass_classification >=0.24,<1.0 - - ensemble/tests/test_gradient_boosting.py::test_gradient_boosting_with_init_pipeline >=0.24,<1.0 - utils/tests/test_validation.py::test_check_array_pandas_dtype_casting >=1.0,<1.2 - utils/tests/test_validation.py::test_check_sparse_pandas_sp_format <1.2 @@ -474,9 +372,6 @@ reduced_tests: public: - tests/test_common.py::test_estimators - # Failed in stock scikit-learn - - metrics/tests/test_common.py::test_not_symmetric_metric[precision_recall_curve] - - metrics/tests/test_common.py::test_binary_sample_weight_invariance[precision_recall_curve] # Fails from numpy 2.0 and sklearn 1.4+ - neighbors/tests/test_neighbors.py::test_KNeighborsClassifier_raise_on_all_zero_weights @@ -488,93 +383,15 @@ gpu: # Fails - cluster/tests/test_dbscan.py::test_weighted_dbscan - cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-1e-100-sparse-normal] - - cluster/tests/test_k_means.py::test_kmeans_elkan_results[42-1e-100-sparse-blobs] - model_selection/tests/test_search.py::test_unsupervised_grid_search - - - ensemble/tests/test_bagging.py::test_gridsearch - ensemble/tests/test_bagging.py::test_estimators_samples - - ensemble/tests/test_common.py::test_ensemble_heterogeneous_estimators_behavior - - ensemble/tests/test_voting.py::test_parallel_fit - ensemble/tests/test_voting.py::test_sample_weight - - - feature_selection/tests/test_rfe.py::test_number_of_subsets_of_features - - - manifold/tests/test_t_sne.py::test_preserve_trustworthiness_approximately - - manifold/tests/test_t_sne.py::test_uniform_grid - - manifold/tests/test_t_sne.py::test_tsne_different_square_distances - - - metrics/tests/test_classification.py::test_precision_recall_f1_score_binary - - metrics/tests/test_classification.py::test_precision_recall_fscore_support_errors - - metrics/tests/test_classification.py::test_confusion_matrix_binary - - metrics/tests/test_classification.py::test_multilabel_confusion_matrix_binary - - metrics/tests/test_ranking.py::test_roc_curve - - metrics/tests/test_ranking.py::test_roc_returns_consistency - - metrics/tests/test_ranking.py::test_roc_curve_confidence - - metrics/tests/test_ranking.py::test_precision_recall_curve - - metrics/tests/test_ranking.py::test_score_scale_invariance - - metrics/tests/test_ranking.py::test_partial_roc_auc_score - metrics/tests/test_score_objects.py::test_average_precision_pos_label - - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_weights_prior_initialisation - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_mean_prior_initialisation - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_precisions_prior_initialisation - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_weights - - mixture/tests/test_bayesian_mixture.py::test_monotonic_likelihood - - mixture/tests/test_bayesian_mixture.py::test_compare_covar_type - - mixture/tests/test_bayesian_mixture.py::test_check_covariance_precision - - mixture/tests/test_bayesian_mixture.py::test_invariant_translation - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_fit_predict - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_fit_predict_n_init - - mixture/tests/test_bayesian_mixture.py::test_bayesian_mixture_predict_predict_proba - - mixture/tests/test_gaussian_mixture.py::test_check_weights - - mixture/tests/test_gaussian_mixture.py::test_check_means - - mixture/tests/test_gaussian_mixture.py::test_check_precisions - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_estimate_log_prob_resp - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_predict_predict_proba - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_fit_predict - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_fit_predict_n_init - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_fit - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_fit_best_params - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_fit_convergence_warning - - mixture/tests/test_gaussian_mixture.py::test_multiple_init - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_n_parameters - - mixture/tests/test_gaussian_mixture.py::test_bic_1d_1component - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_aic_bic - - mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_verbose - - mixture/tests/test_gaussian_mixture.py::test_warm_start - - mixture/tests/test_gaussian_mixture.py::test_convergence_detected_with_warm_start - - mixture/tests/test_gaussian_mixture.py::test_score - - mixture/tests/test_gaussian_mixture.py::test_score_samples - - mixture/tests/test_gaussian_mixture.py::test_monotonic_likelihood - - mixture/tests/test_gaussian_mixture.py::test_regularisation - - mixture/tests/test_gaussian_mixture.py::test_property - - mixture/tests/test_gaussian_mixture.py::test_sample - - mixture/tests/test_gaussian_mixture.py::test_init - - mixture/tests/test_mixture.py::test_gaussian_mixture_n_iter - - - model_selection/tests/test_search.py::test_grid_search_one_grid_point - model_selection/tests/test_search.py::test_search_default_iid - - model_selection/tests/test_search.py::test_random_search_cv_results_multimetric - - model_selection/tests/test_search.py::test_predict_proba_disabled - - - model_selection/tests/test_validation.py::test_cross_val_predict_sparse_prediction - - model_selection/tests/test_validation.py::test_fit_and_score_verbosity - - neighbors/tests/test_neighbors.py::test_unsupervised_kneighbors - - - neighbors/tests/test_neighbors.py::test_kneighbors_classifier - - neighbors/tests/test_neighbors.py::test_KNeighborsClassifier_multioutput - neighbors/tests/test_neighbors.py::test_neighbors_metrics - - - neighbors/tests/test_neighbors.py::test_neighbors_iris - - - semi_supervised/tests/test_self_training.py::test_early_stopping - - svm/tests/test_sparse.py::test_svc - - svm/tests/test_sparse.py::test_svc_with_custom_kernel - svm/tests/test_sparse.py::test_svc_iris - - svm/tests/test_sparse.py::test_error - - svm/tests/test_sparse.py::test_sample_weights - svm/tests/test_sparse.py::test_sparse_realdata - svm/tests/test_svm.py::test_precomputed - svm/tests/test_svm.py::test_tweak_params @@ -583,119 +400,20 @@ gpu: - svm/tests/test_svm.py::test_negative_weights_svc_leave_two_labels[partial-mask-label-1-SVC] - svm/tests/test_svm.py::test_negative_weights_svc_leave_two_labels[partial-mask-label-2-SVC] - svm/tests/test_svm.py::test_svc_clone_with_callable_kernel - - svm/tests/test_svm.py::test_custom_kernel_not_array_input[SVR] - - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_dtypes] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_fit_score_takes_y] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_fit_returns_self] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_fit_returns_self(readonly_memmap=True)] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_dtype_object] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_pipeline_consistency] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_nan_inf] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_overwrite_params] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_pickle] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_estimators_pickle(readonly_memmap=True)] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_methods_sample_order_invariance] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_methods_subset_invariance] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_fit2d_1feature] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_dict_unchanged] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_dont_overwrite_parameters] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_fit_idempotent] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_n_features_in] - - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_fit2d_predict1d] # sparse input is not implemented for DBSCAN. - - tests/test_common.py::test_estimators[DBSCAN()-check_estimator_sparse_data] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_dtypes] - - tests/test_common.py::test_estimators[GaussianMixture()-check_fit_score_takes_y] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_fit_returns_self] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_fit_returns_self(readonly_memmap=True)] - - tests/test_common.py::test_estimators[GaussianMixture()-check_dtype_object] - - tests/test_common.py::test_estimators[GaussianMixture()-check_pipeline_consistency] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_nan_inf] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_overwrite_params] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_pickle] - - tests/test_common.py::test_estimators[GaussianMixture()-check_estimators_pickle(readonly_memmap=True)] - - tests/test_common.py::test_estimators[GaussianMixture()-check_methods_sample_order_invariance] - - tests/test_common.py::test_estimators[GaussianMixture()-check_methods_subset_invariance] - - tests/test_common.py::test_estimators[GaussianMixture()-check_fit2d_1feature] - - tests/test_common.py::test_estimators[GaussianMixture()-check_dict_unchanged] - - tests/test_common.py::test_estimators[GaussianMixture()-check_dont_overwrite_parameters] - - tests/test_common.py::test_estimators[GaussianMixture()-check_fit_idempotent] - - tests/test_common.py::test_estimators[GaussianMixture()-check_n_features_in] - - tests/test_common.py::test_estimators[GaussianMixture()-check_fit2d_predict1d] - tests/test_common.py::test_estimators[RandomForestClassifier()-check_class_weight_classifiers] - - tests/test_common.py::test_estimators[SVC()-check_sample_weights_pandas_series] - tests/test_common.py::test_estimators[SVC()-check_sample_weights_not_an_array] - - tests/test_common.py::test_estimators[SVC()-check_sample_weights_shape] - - tests/test_common.py::test_estimators[SVC()-check_pipeline_consistency] - - tests/test_common.py::test_estimators[SVC()-check_estimators_nan_inf] - - tests/test_common.py::test_estimators[SVC()-check_estimators_pickle] - tests/test_common.py::test_estimators[SVC()-check_classifier_data_not_an_array] - - tests/test_common.py::test_estimators[SVC()-check_classifiers_classes] - - tests/test_common.py::test_estimators[SVC()-check_classifiers_train] - - tests/test_common.py::test_estimators[SVC()-check_class_weight_classifiers] - - tests/test_common.py::test_estimators[SVC()-check_fit2d_1feature] - - tests/test_common.py::test_estimators[SVC()-check_dict_unchanged] - - tests/test_common.py::test_estimators[SVC()-check_fit_idempotent] - - tests/test_common.py::test_estimators[SVC()-check_n_features_in] - - tests/test_common.py::test_estimators[SelfTrainingClassifier(base_estimator=LogisticRegression(C=1))-check_classifiers_classes] - - tests/test_common.py::test_estimators[SelfTrainingClassifier(base_estimator=LogisticRegression(C=1))-check_decision_proba_consistency] - - tests/test_common.py::test_estimators[StackingClassifier(estimators=[('est1',LogisticRegression(C=0.1)),('est2',LogisticRegression(C=1))])-check_sample_weights_invariance(kind=ones)] - - tests/test_common.py::test_estimators[StackingClassifier(estimators=[('est1',LogisticRegression(C=0.1)),('est2',LogisticRegression(C=1))])-check_sample_weights_invariance(kind=zeros)] - - tests/test_common.py::test_estimators[TSNE()-check_fit_idempotent] - - tests/test_common.py::test_estimators[TSNE()-check_n_features_in] - tests/test_common.py::test_search_cv[RandomizedSearchCV(estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]})-check_classifiers_classes] - tests/test_common.py::test_search_cv[RandomizedSearchCV(estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]})-check_decision_proba_consistency] - - tests/test_common.py::test_check_n_features_in_after_fitting[TSNE()] - - - tests/test_multiclass.py::test_ovr_fit_predict_sparse - - tests/test_multiclass.py::test_ovr_binary - - tests/test_multiclass.py::test_ovr_fit_predict_svc - - tests/test_multiclass.py::test_ovr_multilabel_predict_proba - - tests/test_multiclass.py::test_ovr_multilabel_decision_function - - tests/test_multiclass.py::test_ovr_single_label_decision_function - - tests/test_multiclass.py::test_ovr_coef_ - - tests/test_multiclass.py::test_ovr_deprecated_coef_intercept - - tests/test_multiclass.py::test_pairwise_cross_val_score - - tests/test_multioutput.py::test_multiclass_multioutput_estimator_predict_proba - tests/test_multioutput.py::test_classifier_chain_fit_and_predict_with_sparse_data - # Very slow execution due to SVC - - model_selection/tests/test_validation.py::test_validation_curve_cv_splits_consistency - - model_selection/tests/test_search.py::test_grid_search_cv_results - - model_selection/tests/test_search.py::test_random_search_cv_results - # Segmentation faults on GPU - tests/test_common.py::test_search_cv - - manifold/tests/test_t_sne.py::test_n_iter_without_progress # Other device issues - - tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[StackingClassifier] - - tests/test_multiclass.py::test_ovr_always_present - - tests/test_multiclass.py::test_support_missing_values[OneVsRestClassifier] - - tests/test_multiclass.py::test_support_missing_values[OneVsOneClassifier] - - tests/test_multioutput.py::test_multi_output_delegate_predict_proba - - tests/test_multioutput.py::test_classifier_chain_vs_independent_models - - tests/test_multioutput.py::test_base_chain_fit_and_predict - - tests/test_multioutput.py::test_base_chain_crossval_fit_and_predict - - tests/test_multioutput.py::test_multi_output_classes_[estimator1] - - tests/test_multioutput.py::test_multi_output_classes_[estimator2] - - tests/test_multioutput.py::test_support_missing_values[MultiOutputClassifier-LogisticRegression] - tests/test_multioutput.py::test_classifier_chain_tuple_order[list] - - tests/test_multioutput.py::test_classifier_chain_tuple_order[array] - tests/test_multioutput.py::test_classifier_chain_tuple_order[tuple] - - tests/test_pipeline.py::test_pipeline_methods_anova - - tests/test_pipeline.py::test_score_samples_on_pipeline_without_score_samples - - tests/test_pipeline.py::test_pipeline_methods_preprocessing_svm - - tests/test_pipeline.py::test_pipeline_transform - - tests/test_pipeline.py::test_feature_union_weights - - tests/test_pipeline.py::test_classes_property - - tests/test_pipeline.py::test_set_feature_union_passthrough - - tests/test_pipeline.py::test_pipeline_missing_values_leniency - - tests/test_pipeline.py::test_pipeline_set_output_integration - - tests/test_pipeline.py::test_feature_union_set_output - - tests/test_parallel.py::test_dispatch_config_parallel[1] - - tests/test_parallel.py::test_dispatch_config_parallel[2] # KD Tree (not implemented for GPU) - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l2-1000-5-100] - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l2-1000-5-100] @@ -723,13 +441,6 @@ gpu: - ensemble/tests/test_forest.py::test_max_samples_boundary_regressors # numerical issues in GPU Forest algorithms which require further investigation - - ensemble/tests/test_forest.py::test_forest_classifier_oob[X0-y0-0.9-array-ExtraTreesClassifier] - - ensemble/tests/test_forest.py::test_forest_classifier_oob[X0-y0-0.9-array-RandomForestClassifier] - - ensemble/tests/test_forest.py::test_forest_classifier_oob[X1-y1-0.65-array-RandomForestClassifier] - - ensemble/tests/test_forest.py::test_forest_classifier_oob[X2-y2-0.65-array-ExtraTreesClassifier] - - ensemble/tests/test_forest.py::test_forest_classifier_oob[X2-y2-0.65-array-RandomForestClassifier] - - ensemble/tests/test_forest.py::test_forest_regressor_oob[X0-y0-0.7-array-RandomForestRegressor] - - ensemble/tests/test_stacking.py::test_stacking_regressor_drop_estimator - ensemble/tests/test_voting.py::test_predict_on_toy_problem[42] - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_class_weight_classifiers] - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_sample_weights_invariance(kind=zeros)]