deselected_tests.yaml

#===============================================================================
# Copyright 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#===============================================================================

# This file lists node ids (following pytest format) of scikit-learn tests
# that are to be deselected during the test discovery step.
#
# Deselection can be predicated on the version of scikit-learn used.
# Use - node_id cond, or - node_id cond1,cond2  where cond is OPver.
# Supported OPs are >=, <=, ==, !=, >, <
# For example,
#    - tests/test_isotonic.py::test_permutation_invariance >0.18,<=0.19
#  will exclude deselection in versions 0.18.1, and 0.18.2 only.

deselected_tests:
  # 'kulsinski' distance was deprecated in scipy 1.11 but still marked as supported in scikit-learn < 1.3
  - neighbors/tests/test_neighbors.py::test_kneighbors_brute_backend[float64-kulsinski] <1.3
  - neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[kulsinski] <1.3

  # sklearnex PCA always chooses "covariance_eigh" solver instead of "full" when solver="auto"
  # resulting in solver assignment check failure for sklearn version >= 1.5
  - 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]

  # Same as above but for macOS only
  - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-auto] >=1.2 darwin
  - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-ball_tree] >=1.2 darwin
  - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[0.5-True-kd_tree] >=1.2 darwin
  - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-auto] >=1.2 darwin
  - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-ball_tree] >=1.2 darwin
  - neighbors/tests/test_lof.py::test_lof_dtype_equivalence[auto-True-kd_tree] >=1.2 darwin

  # 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)
  - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[0.5-1]
  - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[0.5-2]
  - 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]
  - tests/test_common.py::test_estimators[SVR()-check_methods_subset_invariance]
  - tests/test_common.py::test_estimators[NuSVR()-check_methods_subset_invariance]

  # SVR.fit fails when input is two samples of one class
  - preprocessing/tests/test_data.py::test_cv_pipeline_precomputed

  # KDtree kNN rarely misses 0-distance points when kneighbors is used on same-fitting data
  - manifold/tests/test_spectral_embedding.py::test_precomputed_nearest_neighbors_filtering

  # Cache directory is not accessible on some systems
  - utils/tests/test_validation.py::test_check_memory

  # oneDAL doesn't throw error if resulting coeffs are not finite
  - linear_model/tests/test_coordinate_descent.py::test_enet_nonfinite_params
  - svm/tests/test_svm.py::test_svc_nonfinite_params

  # Different exception types in scikit-learn-intelex and scikit-learn
  - utils/tests/test_validation.py::test_check_array_links_to_imputer_doc_only_for_X[asarray-X]
  - utils/tests/test_validation.py::test_check_array_links_to_imputer_doc_only_for_X[csr_matrix-X]

  # 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
  - svm/tests/test_sparse.py::test_svc
  - svm/tests/test_sparse.py::test_svc_iris
  - svm/tests/test_sparse.py::test_sparse_realdata

  # 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]
  - svm/tests/test_svm.py::test_svc_ovr_tie_breaking[NuSVC]

  # Different models between oneAPI Data Analytics Library (oneDAL) and stock scikit-learn with custom and precompute kernel
  - svm/tests/test_svm.py::test_svc_clone_with_callable_kernel
  - svm/tests/test_svm.py::test_precomputed

  # scikit-learn expects an exception for sparse matrices with 64-bit integer indices,
  # scikit-learn-intelex works correctly with 64-bit integer indices
  - tests/test_common.py::test_estimators[NuSVC()-check_estimator_sparse_data]
  - tests/test_common.py::test_estimators[NuSVC()-check_estimator_sparse_array]
  - tests/test_common.py::test_estimators[NuSVC()-check_estimator_sparse_matrix]
  - utils/tests/test_estimator_checks.py::test_xfail_ignored_in_check_estimator

  # SVC._dual_coef_ is changing after fitting, but the result of prediction is still the same
  - svm/tests/test_svm.py::test_tweak_params

  # 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_relocated_clusters >=0.23,<0.24
  - 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,
  # unweighted does. Since convergence does not occur (comment in the test
  # suggests that) and because coefficients are slightly different,
  # it results in a prediction disagreement in 1 case.
  - ensemble/tests/test_stacking.py::test_stacking_with_sample_weight[StackingClassifier]

  # Insufficient accuracy of "coefs" and "intercept" in Elastic Net for multi-target problems
  # 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

  # 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

  # scikit-learn-intelex RF threads are used internally and are not explicitly specified
  - ensemble/tests/test_forest.py::test_backend_respected

  # scikit-learn-intelex does not support accessing trees through the result variable
  - ensemble/tests/test_forest.py::test_warm_start
  - inspection/tests/test_partial_dependence.py::test_recursion_decision_tree_vs_forest_and_gbdt[0] >=0.23

  # scikit-learn-intelex implementation builds different trees compared to scikit-learn
  # Comparison of tree forest will fail
  - ensemble/tests/test_forest.py::test_class_weights
  - ensemble/tests/test_forest.py::test_poisson_vs_mse
  - inspection/tests/test_permutation_importance.py::test_robustness_to_high_cardinality_noisy_feature >=0.23
  - tests/test_common.py::test_estimators[SVC()-check_sample_weights_invariance(kind=zeros)] <1.0
  - tests/test_common.py::test_estimators[SVR()-check_sample_weights_invariance(kind=zeros)] <1.0
  - tests/test_common.py::test_estimators[NuSVC()-check_sample_weights_invariance(kind=zeros)] <1.0
  - tests/test_common.py::test_estimators[NuSVR()-check_sample_weights_invariance(kind=zeros)] <1.0
  - tests/test_common.py::test_estimators[NuSVC()-check_class_weight_classifiers] <1.0
  - 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
  - linear_model/tests/test_coordinate_descent.py::test_assure_warning_when_normalize[True-1-LassoCV] >=1.1,<1.2
  - linear_model/tests/test_coordinate_descent.py::test_assure_warning_when_normalize[True-1-ElasticNetCV] >=1.1,<1.2
  - linear_model/tests/test_coordinate_descent.py::test_assure_warning_when_normalize[False-1-LassoCV] >=1.1,<1.2
  - linear_model/tests/test_coordinate_descent.py::test_assure_warning_when_normalize[False-1-ElasticNetCV] >=1.1,<1.2
  - 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

  # Patched and unpatched kmeans set same values to different clusters. Need to investigate.
  - preprocessing/tests/test_discretization.py::test_nonuniform_strategies[kmeans-expected_2bins1-expected_3bins1-expected_5bins1] >=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

  # few-percent numerical differences in ExtraTreesRegressor, but 6 digits are checked
  - 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.
  - decomposition/tests/test_incremental_pca.py::test_whitening

  # The test fails because of changing of 'auto' strategy in PCA to improve performance.
  # 'randomized' PCA expected, but 'full' is given.
  - decomposition/tests/test_pca.py::test_pca_svd_solver_auto[data3-10-randomized]

  # Scikit-learn logistic regression predict depends from decision_function while d4p is not.
  # Assertion error in check_estimator (PoorScoreLogisticRegression())
  - utils/tests/test_estimator_checks.py::test_check_estimator >=0.24

  # 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

  # Failure due to non-uniformity in the MT2203 engine causing
  # bad Random Forest fits for small datasets with large n_estimators
  # Had been solved by using MT19937, but oneDAL forces use of MT2203
  - tests/test_multioutput.py::test_classifier_chain_tuple_order

  # oneDAL decision forest trains individual trees differently than
  # sklearn. Attempts to compare individual sklearn trees to oneDAL
  # trees will fail, especially since two different RNGs are used.
  - ensemble/tests/test_forest.py::test_estimators_samples

  # Tests migrated from gpu deselection set starting from sklearn 1.4 for unknowm reason(s)
  - ensemble/tests/test_bagging.py::test_estimators_samples >=1.4
  - ensemble/tests/test_voting.py::test_sample_weight >=1.4
  - svm/tests/test_svm.py::test_auto_weight >=1.4
  - tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence >=1.4
  - tests/test_calibration.py::test_calibrated_classifier_cv_zeros_sample_weights_equivalence >=1.4
  - tests/test_common.py::test_estimators[LogisticRegression()-check_sample_weights_invariance(kind=ones)] >=1.4
  - tests/test_common.py::test_estimators[LogisticRegression()-check_sample_weights_invariance(kind=zeros)] >=1.4
  - tests/test_multioutput.py::test_classifier_chain_fit_and_predict_with_sparse_data >=1.4

  # New failing sklearn1.4.1 tests for kmeans associated with incorrect n_iter_ values in daal4py
  - cluster/tests/test_k_means.py::test_relocating_with_duplicates[lloyd-dense] >=1.4
  - cluster/tests/test_k_means.py::test_relocating_with_duplicates[lloyd-sparse_matrix] >=1.4
  - cluster/tests/test_k_means.py::test_relocating_with_duplicates[lloyd-sparse_array] >=1.4
  - cluster/tests/test_k_means.py::test_relocating_with_duplicates[elkan-dense] >=1.4
  - cluster/tests/test_k_means.py::test_relocating_with_duplicates[elkan-sparse_matrix] >=1.4
  - cluster/tests/test_k_means.py::test_relocating_with_duplicates[elkan-sparse_array] >=1.4

  # Deselected tests for incremental algorithms
  # Need to rework getting policy to correctly obtain it for method without data (finalize_fit)
  # and avoid keeping it in class attribute, also need to investigate how to implement
  # partial result serialization
  - tests/test_common.py::test_estimators[IncrementalEmpiricalCovariance()-check_estimators_pickle]
  - tests/test_common.py::test_estimators[IncrementalEmpiricalCovariance()-check_estimators_pickle(readonly_memmap=True)]
  - tests/test_common.py::test_estimators[IncrementalLinearRegression()-check_estimators_pickle]
  - tests/test_common.py::test_estimators[IncrementalLinearRegression()-check_estimators_pickle(readonly_memmap=True)]
  - tests/test_common.py::test_estimators[IncrementalPCA()-check_estimators_pickle]
  - tests/test_common.py::test_estimators[IncrementalPCA()-check_estimators_pickle(readonly_memmap=True)]
  # There are not enough data to run onedal backend
  - tests/test_common.py::test_estimators[IncrementalLinearRegression()-check_fit2d_1sample]

  # Deselection of LogisticRegression tests over accuracy comparisons with sample_weights
  # and without.  Because scikit-learn-intelex does not support sample_weights, it's doing
  # a fallback to scikit-learn in one case and not in the other, and needs to be investigated.
  - model_selection/tests/test_classification_threshold.py::test_fit_and_score_over_thresholds_sample_weight >=1.5
  - model_selection/tests/test_classification_threshold.py::test_tuned_threshold_classifier_cv_zeros_sample_weights_equivalence >=1.5

  # --------------------------------------------------------
  # No need to test daal4py patching
reduced_tests:
  - cluster/tests/test_affinity_propagation.py
  - cluster/tests/test_bicluster.py
  - cluster/tests/test_birch.py
  - cluster/tests/test_mean_shift.py
  - cluster/tests/test_optics.py

  - compose/tests/test_column_transformer.py

  - decomposition/tests/test_dict_learning.py
  - decomposition/tests/test_factor_analysis.py
  - decomposition/tests/test_nmf.py
  - decomposition/tests/test_online_lda.py

  - ensemble/tests/test_gradient_boosting.py
  - ensemble/tests/test_gradient_boosting_loss_functions.py
  - ensemble/tests/test_iforest.py

  - feature_selection/tests/test_chi2.py
  - feature_selection/tests/test_feature_select.py
  - feature_selection/tests/test_mutual_info.py
  - feature_selection/tests/test_sequential.py
  - feature_selection/tests/test_from_model.py

  - manifold/tests/test_isomap.py
  - manifold/tests/test_locally_linear.py
  - manifold/tests/test_spectral_embedding.py

  - model_selection/tests/test_successive_halving.py

  - neighbors/tests/test_ball_tree.py
  - neighbors/tests/test_kd_tree.py
  - neighbors/tests/test_quad_tree.py

  - tests/test_kernel_approximation.py
  - tests/test_docstring_parameters.py
  - tests/test_dummy.py
  - tests/test_random_projection.py
  - tests/test_naive_bayes.py

  - utils/tests/test_arpack.py
  - utils/tests/test_cython_blas.py
  - utils/tests/test_encode.py
  - utils/tests/test_estimator_html_repr.py
  - utils/tests/test_extmath.py
  - utils/tests/test_fast_dict.py
  - utils/tests/test_mocking.py
  - utils/tests/test_murmurhash.py
  - utils/tests/test_sparsefuncs.py
  - utils/tests/test_utils.py

  - _loss/
  - cross_decomposition/
  - datasets/
  - ensemble/_hist_gradient_boosting/
  - experimental/
  - feature_extraction/
  - gaussian_process/
  - impute/
  - inspection/
  - neural_network/
  - preprocessing/

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

  # --------------------------------------------------------
  # The following tests currently fail with GPU offload
gpu:

  # Segfaults
  - ensemble/tests/test_weight_boosting.py

  # Fails
  - cluster/tests/test_dbscan.py::test_weighted_dbscan
  - cluster/tests/test_k_means.py::test_k_means_fit_predict
  - cluster/tests/test_k_means.py::test_predict

  - 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
  - svm/tests/test_svm.py::test_svm_classifier_sided_sample_weight[estimator0]
  - svm/tests/test_svm.py::test_svm_equivalence_sample_weight_C
  - 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[KMeans()-check_clustering]
  - tests/test_common.py::test_estimators[KMeans()-check_clustering(readonly_memmap=True)]
  - 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

  # KMeans based (unsupported for GPU)
  - cluster/tests/test_k_means.py
  - tests/test_common.py::test_pandas_column_name_consistency[KMeans()]
  - tests/test_common.py::test_pandas_column_name_consistency[GaussianMixture()]
  - tests/test_common.py::test_pandas_column_name_consistency[BayesianGaussianMixture()]
  - tests/test_common.py::test_estimators[KMeans()
  - tests/test_common.py::test_estimators[BayesianGaussianMixture()-check_fit_check_is_fitted]
  - tests/test_common.py::test_estimators[GaussianMixture()-check_fit_check_is_fitted]
  - tests/test_common.py::test_check_n_features_in_after_fitting[BayesianGaussianMixture()]
  - tests/test_common.py::test_check_n_features_in_after_fitting[GaussianMixture()]
  - tests/test_common.py::test_check_n_features_in_after_fitting[KMeans()]
  - tests/test_common.py::test_set_output_transform[KMeans()]
  - tests/test_common.py::test_set_output_transform_pandas[KMeans()]
  - tests/test_common.py::test_global_output_transform_pandas[KMeans()]
  - mixture/tests/test_gaussian_mixture.py
  - model_selection/tests/test_validation.py::test_cross_val_predict
  - metrics/tests/test_score_objects.py::test_supervised_cluster_scorers
  - tests/test_pipeline.py::test_fit_predict_on_pipeline
  - tests/test_discriminant_analysis.py::test_lda_predict
  # 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]
  - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l2-1000-5-100]
  - neighbors/tests/test_neighbors.py::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l2-1000-5-100]
  # failing due to numeric/code error
  - linear_model/tests/test_common.py::test_balance_property[42-False-LogisticRegressionCV]
  - sklearn/manifold/tests/test_t_sne.py::test_n_iter_without_progress
  - model_selection/tests/test_search.py::test_searchcv_raise_warning_with_non_finite_score[RandomizedSearchCV-specialized_params1-False]
  - model_selection/tests/test_search.py::test_searchcv_raise_warning_with_non_finite_score[RandomizedSearchCV-specialized_params1-True]
  - tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence
  - tests/test_calibration.py::test_calibrated_classifier_cv_zeros_sample_weights_equivalence
  - tests/test_common.py::test_estimators[FeatureAgglomeration()-check_parameters_default_constructible]
  - neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_methods_subset_invariance]
  - tests/test_common.py::test_transformers_get_feature_names_out[StackingRegressor(estimators=[('est1',Ridge(alpha=0.1)),('est2',Ridge(alpha=1))])]
  - tests/test_common.py::test_transformers_get_feature_names_out[VotingRegressor(estimators=[('est1',Ridge(alpha=0.1)),('est2',Ridge(alpha=1))])]
  - tests/test_common.py::test_f_contiguous_array_estimator[TSNE]
  - manifold/tests/test_t_sne.py::test_tsne_works_with_pandas_output

  # GPU Forest algorithm implementation does not follow certain Scikit-learn standards
  - ensemble/tests/test_forest.py::test_max_leaf_nodes_max_depth
  - ensemble/tests/test_forest.py::test_min_samples_split[ExtraTreesClassifier]
  - ensemble/tests/test_forest.py::test_min_samples_split[RandomForestClassifier]
  - ensemble/tests/test_forest.py::test_min_samples_split[ExtraTreesRegressor]
  - 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)]
  - tests/test_common.py::test_estimators[RandomForestRegressor()-check_regressor_data_not_an_array]
  - ensemble/tests/test_forest.py::test_max_samples_boundary_classifiers[ExtraTreesClassifier]
  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_classifier_data_not_an_array]
  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_classifiers_train]
  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_classifiers_train(readonly_memmap=True)]
  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_fit_idempotent]
  - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_fit_idempotent]
  - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_regressor_data_not_an_array]

  # GPU implementation of Extra Trees doesn't support sample_weights
  # comparisons to GPU with sample weights will use different algorithms
  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_sample_weights_invariance(kind=ones)]
  - tests/test_common.py::test_estimators[ExtraTreesClassifier()-check_sample_weights_invariance(kind=zeros)]
  - tests/test_common.py::test_estimators[ExtraTreesRegressor()-check_sample_weights_invariance(kind=ones)]

  # RuntimeError: Device support is not implemented, failing as result of fallback to cpu false
  - svm/tests/test_svm.py::test_unfitted
  - tests/test_common.py::test_estimators[SVC()-check_estimators_unfitted]

preview:
  - cluster/tests/test_k_means.py::test_kmeans_elkan_results
  - cluster/tests/test_k_means.py::test_unit_weights_vs_no_weights[KMeans-dense] <1.2
  - cluster/tests/test_k_means.py::test_unit_weights_vs_no_weights[42-KMeans-dense] >=1.2
  - cluster/tests/test_k_means.py::test_predict_sample_weight_deprecation_warning[KMeans] >=1.3