Skip to content

Improve tests by using global_random_seed fixture to make them less seed-sensitive #22827

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Open
ogrisel opened this issue Mar 14, 2022 · 35 comments
Open

Improve tests by using global_random_seed fixture to make them less seed-sensitive #22827

ogrisel opened this issue Mar 14, 2022 · 35 comments
Labels
Hard Hard level of difficulty help wanted Meta-issue General issue associated to an identified list of tasks module:test-suite everything related to our tests

Comments

@ogrisel
Copy link
Member

ogrisel commented Mar 14, 2022 •
edited by glemaitre
Loading

Context: the new global_random_seed fixture

#22749 introduces a new global_random_seed fixture to make it possible to run the same test with any seed between 0 and 99 included. By default, when SKLEARN_TESTS_GLOBAL_RANDOM_SEED is not set, this fixture is deterministically returning 42 to keep test runs deterministic by default and avoid any unnecessary disruption. However different CI builds set this seed to other arbitrary values (still deterministic) and nightly schedule builds on Azure now use SKLEARN_TESTS_GLOBAL_RANDOM_SEED="any" to progressively explore any seed on the 0-99 range.

Motivation

The aim of this new fixture is to make sure that we avoid writing tests that artificially depend on a specific value of the random seed and therefore hiding a real mathematical problem in our code unknowingly (see e.g. #21701 (comment)). At the same time we still want to keep the test deterministic and independent of the execution order by default to avoid introducing unnecessary maintenance overhead.

In addition to making the tests insensitive, randomizing those tests with different seeds has the side benefit of making the assertions of those tests robust to small numerical variations that could otherwise stem from other sources such as platform-specific / dependency-specific numerical rounding variations that we do not cover in our existing CI infrastructure.

More details about the fixture in the online dev doc for the SKLEARN_TESTS_GLOBAL_RANDOM_SEED env variable:

https://scikit-learn.org/dev/computing/parallelism.html#environment-variables

Guidelines to convert existing tests

  • We probably do not need to convert all scikit-learn tests to use this fixture. We should instead focus our efforts on tests that actually check for important mathematical properties of our estimators or model evaluation tools. For instance, there is no need to check for the seed-insensitivity of tests that checks for the exception messages raised when passing invalid inputs.

  • To avoid having to review huge PRs that impact many files at once and can lead to conflicts, let's open PRs that edit at most one test file at a time. For instance use a title such as:

TST use global_random_seed in sklearn/_loss/tests/test_glm_distribution.py

  • Please reference #22827 in the description of the PR and put the full filename of the test file you edit in the title of the PR.

  • To convert an existing test with a fixed seed, the general pattern is to rewrite a function such as:

def test_some_function():
    rng = np.random.RandomState(0)
    ...

to:

def test_some_function(global_random_seed):
    rng = np.random.RandomState(global_random_seed)
    ...

and then check that the test function is actually seed-insensitive by running with all seeds between 0 and 99 locally (can be slow! only run for one specific test at a time!):

SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all" pytest sklearn/some_module/test/test_some_module.py -k test_some_function

If this is not the case, the test will probably need to be reworked to find a more stable to way to check the interesting mathematical properties.

  • if the failing assertions are related to the generalization performance of a model, maybe the training set size should be slightly bigger (while keeping the test runtime as fast as possible), or with fewer noisy features or the training should be done with stronger regularization. Or more simply we can relax the tolerance threshold while ensuring it does not become trivial (e.g. by comparing to a trivial baseline);

  • if the failing assertions depend on some regularities of a synthetically generated dataset, making decreasing the noise level of the datasets;

  • some tests might also fail when encountering data that trigger edge cases such as (near-)tied distances between datapoints that make the outcome of computation unstable. Changing the data generation code to significantly decrease the likelihood of those edge case (e.g. by adding more noise to the input features) can help in those cases.

  • Note: in most cases, tweaking the tolerances of the assertions is not the appropriate way to make the tests pass. The first thing to do is try to understand what the test is checking, if the test is correct, if the expectations of the test are realistic. Then if the test seems correct and should pass for all random seed but doesn't, investigate if the estimator or function is bugged. As a last resort, tolerances can be loosened if the test is considered valid but aims to check a statistical property that is highly sensitive to the random seed.

In some cases, it might be very hard to write a seed-insensitive test that tolerate all seeds between 0 and 99 while still running in less than 1s. In those (hopefully rare) cases, I think it's fine to reduce the range of admissible seeds with the following pattern:

def test_some_function(global_random_seed):
    # Making this test seed-insensitive for the 0-99 range would
    # be too costly. Restricting to the 0-9 range is necessary to
    # use small enough datasets that avoid increasing the run time
    # too much.
    rng = np.random.RandomState(global_random_seed % 10)
    ...
  • Run the CI for tests that take a global_random_seed by pushing a commit message with the following structure:
<title> [all random seeds]
<test_name_1>
<test_name_2>
...

Note, running git commit --allow-empty allows you to have a commit message without any changes.

See the following issue for more details on why testing on the CI is necessary:

List of test modules to upgrade

find sklearn -name "test_*.py"
  • sklearn/_loss/tests/test_glm_distribution.py
  • sklearn/_loss/tests/test_link.py
  • sklearn/_loss/tests/test_loss.py TST ensure that sklearn/_loss/tests/test_loss.py is seed insensitive #22847
  • sklearn/cluster/tests/test_affinity_propagation.py
  • sklearn/cluster/tests/test_bicluster.py
  • sklearn/cluster/tests/test_birch.py
  • sklearn/cluster/tests/test_dbscan.py
  • sklearn/cluster/tests/test_feature_agglomeration.py TST use global_random_seed in sklearn/cluster/tests/test_feature_agglomeration.py #23700
  • sklearn/cluster/tests/test_hierarchical.py
  • sklearn/cluster/tests/test_k_means.py
  • sklearn/cluster/tests/test_mean_shift.py TST Use global_random_seed in sklearn/cluster/tests/test_mean_shift.py #30517
  • sklearn/cluster/tests/test_optics.py TST Incorporate global_random_seed in test_optics.py #30844
  • sklearn/cluster/tests/test_spectral.py TST use global_random_seed in sklearn/cluster/tests/test_spectral.py #24802
  • sklearn/compose/tests/test_column_transformer.py
  • sklearn/compose/tests/test_target.py
  • sklearn/covariance/tests/test_covariance.py
  • sklearn/covariance/tests/test_elliptic_envelope.py
  • sklearn/covariance/tests/test_graphical_lasso.py
  • sklearn/covariance/tests/test_robust_covariance.py
  • sklearn/cross_decomposition/tests/test_pls.py
  • sklearn/datasets/tests/test_20news.py
  • sklearn/datasets/tests/test_base.py
  • sklearn/datasets/tests/test_california_housing.py
  • sklearn/datasets/tests/test_common.py
  • sklearn/datasets/tests/test_covtype.py
  • sklearn/datasets/tests/test_kddcup99.py
  • sklearn/datasets/tests/test_lfw.py
  • sklearn/datasets/tests/test_olivetti_faces.py
  • sklearn/datasets/tests/test_openml.py
  • sklearn/datasets/tests/test_rcv1.py
  • sklearn/datasets/tests/test_samples_generator.py
  • sklearn/datasets/tests/test_svmlight_format.py
  • sklearn/decomposition/tests/test_dict_learning.py
  • sklearn/decomposition/tests/test_factor_analysis.py
  • sklearn/decomposition/tests/test_fastica.py
  • sklearn/decomposition/tests/test_incremental_pca.py
  • sklearn/decomposition/tests/test_kernel_pca.py TST use global_random_seed in sklearn/decomposition/tests/test_kernel_pca.py #30518
  • sklearn/decomposition/tests/test_nmf.py
  • sklearn/decomposition/tests/test_online_lda.py
  • sklearn/decomposition/tests/test_pca.py TST use global_random_seed in sklearn/decomposition/tests/test_pca.py #26403
  • sklearn/decomposition/tests/test_sparse_pca.py
  • sklearn/decomposition/tests/test_truncated_svd.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_bitset.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_predictor.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_splitting.py
  • sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py
  • sklearn/ensemble/tests/test_bagging.py
  • sklearn/ensemble/tests/test_base.py
  • sklearn/ensemble/tests/test_common.py
  • sklearn/ensemble/tests/test_forest.py
  • sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py
  • sklearn/ensemble/tests/test_gradient_boosting.py
  • sklearn/ensemble/tests/test_iforest.py TST use global_random_seed in sklearn/ensemble/tests/test_iforest.py #22901
  • sklearn/ensemble/tests/test_stacking.py
  • sklearn/ensemble/tests/test_voting.py
  • sklearn/ensemble/tests/test_weight_boosting.py
  • sklearn/experimental/tests/test_enable_hist_gradient_boosting.py
  • sklearn/experimental/tests/test_enable_iterative_imputer.py
  • sklearn/experimental/tests/test_enable_successive_halving.py
  • sklearn/feature_extraction/tests/test_dict_vectorizer.py
  • sklearn/feature_extraction/tests/test_feature_hasher.py
  • sklearn/feature_extraction/tests/test_image.py
  • sklearn/feature_extraction/tests/test_text.py
  • sklearn/feature_selection/tests/test_base.py
  • sklearn/feature_selection/tests/test_chi2.py
  • sklearn/feature_selection/tests/test_feature_select.py
  • sklearn/feature_selection/tests/test_from_model.py
  • sklearn/feature_selection/tests/test_mutual_info.py
  • sklearn/feature_selection/tests/test_rfe.py
  • sklearn/feature_selection/tests/test_sequential.py
  • sklearn/feature_selection/tests/test_variance_threshold.py
  • sklearn/gaussian_process/tests/test_gpc.py
  • sklearn/gaussian_process/tests/test_gpr.py
  • sklearn/gaussian_process/tests/test_kernels.py
  • sklearn/impute/tests/test_base.py
  • sklearn/impute/tests/test_common.py
  • sklearn/impute/tests/test_impute.py TST test_impute use global random seed #25894
  • sklearn/impute/tests/test_knn.py
  • sklearn/inspection/_plot/tests/test_plot_partial_dependence.py
  • sklearn/inspection/tests/test_partial_dependence.py
  • sklearn/inspection/tests/test_permutation_importance.py
  • sklearn/linear_model/_glm/tests/test_glm.py
  • sklearn/linear_model/_glm/tests/test_link.py
  • sklearn/linear_model/tests/test_base.py
  • sklearn/linear_model/tests/test_bayes.py
  • sklearn/linear_model/tests/test_common.py
  • sklearn/linear_model/tests/test_coordinate_descent.py
  • sklearn/linear_model/tests/test_huber.py TST Use global_random_seed in test_huber.py #30912
  • sklearn/linear_model/tests/test_least_angle.py
  • sklearn/linear_model/tests/test_linear_loss.py
  • sklearn/linear_model/tests/test_logistic.py
  • sklearn/linear_model/tests/test_omp.py
  • sklearn/linear_model/tests/test_passive_aggressive.py
  • sklearn/linear_model/tests/test_perceptron.py
  • sklearn/linear_model/tests/test_quantile.py
  • sklearn/linear_model/tests/test_ransac.py
  • sklearn/linear_model/tests/test_ridge.py
  • sklearn/linear_model/tests/test_sag.py
  • sklearn/linear_model/tests/test_sgd.py
  • sklearn/linear_model/tests/test_sparse_coordinate_descent.py
  • sklearn/linear_model/tests/test_theil_sen.py
  • sklearn/manifold/tests/test_isomap.py
  • sklearn/manifold/tests/test_locally_linear.py
  • sklearn/manifold/tests/test_mds.py
  • sklearn/manifold/tests/test_spectral_embedding.py
  • sklearn/manifold/tests/test_t_sne.py
  • sklearn/metrics/_plot/tests/test_base.py
  • sklearn/metrics/_plot/tests/test_common_curve_display.py
  • sklearn/metrics/_plot/tests/test_confusion_matrix_display.py
  • sklearn/metrics/_plot/tests/test_det_curve_display.py
  • sklearn/metrics/_plot/tests/test_plot_confusion_matrix.py
  • sklearn/metrics/_plot/tests/test_plot_curve_common.py
  • sklearn/metrics/_plot/tests/test_plot_det_curve.py
  • sklearn/metrics/_plot/tests/test_plot_precision_recall.py
  • sklearn/metrics/_plot/tests/test_plot_roc_curve.py
  • sklearn/metrics/_plot/tests/test_precision_recall_display.py
  • sklearn/metrics/_plot/tests/test_roc_curve_display.py
  • sklearn/metrics/cluster/tests/test_bicluster.py
  • sklearn/metrics/cluster/tests/test_common.py
  • sklearn/metrics/cluster/tests/test_supervised.py
  • sklearn/metrics/cluster/tests/test_unsupervised.py
  • sklearn/metrics/tests/test_classification.py
  • sklearn/metrics/tests/test_common.py
  • sklearn/metrics/tests/test_dist_metrics.py
  • sklearn/metrics/tests/test_pairwise_distances_reduction.py TST Ensure that sklearn/metrics/tests/test_pairwise_distances_reduction.py is seed insensitive #22862
  • sklearn/metrics/tests/test_pairwise.py
  • sklearn/metrics/tests/test_ranking.py
  • sklearn/metrics/tests/test_regression.py TST use global_random_seed in sklearn/metrics/tests/test_regression.py #30865
  • sklearn/metrics/tests/test_score_objects.py
  • sklearn/mixture/tests/test_bayesian_mixture.py
  • sklearn/mixture/tests/test_gaussian_mixture.py
  • sklearn/mixture/tests/test_mixture.py
  • sklearn/model_selection/tests/test_search.py
  • sklearn/model_selection/tests/test_split.py
  • sklearn/model_selection/tests/test_successive_halving.py
  • sklearn/model_selection/tests/test_validation.py
  • sklearn/neighbors/tests/test_ball_tree.py
  • sklearn/neighbors/tests/test_graph.py
  • sklearn/neighbors/tests/test_kd_tree.py
  • sklearn/neighbors/tests/test_kde.py
  • sklearn/neighbors/tests/test_lof.py
  • sklearn/neighbors/tests/test_nca.py
  • sklearn/neighbors/tests/test_nearest_centroid.py
  • sklearn/neighbors/tests/test_neighbors_pipeline.py
  • sklearn/neighbors/tests/test_neighbors_tree.py
  • sklearn/neighbors/tests/test_neighbors.py
  • sklearn/neighbors/tests/test_quad_tree.py
  • sklearn/neural_network/tests/test_base.py
  • sklearn/neural_network/tests/test_mlp.py
  • sklearn/neural_network/tests/test_rbm.py
  • sklearn/neural_network/tests/test_stochastic_optimizers.py
  • sklearn/preprocessing/tests/test_common.py
  • sklearn/preprocessing/tests/test_data.py
  • sklearn/preprocessing/tests/test_discretization.py
  • sklearn/preprocessing/tests/test_encoders.py
  • sklearn/preprocessing/tests/test_function_transformer.py
  • sklearn/preprocessing/tests/test_label.py
  • sklearn/preprocessing/tests/test_polynomial.py
  • sklearn/semi_supervised/tests/test_label_propagation.py
  • sklearn/semi_supervised/tests/test_self_training.py
  • sklearn/svm/tests/test_bounds.py
  • sklearn/svm/tests/test_sparse.py
  • sklearn/svm/tests/test_svm.py TST random seed global /svm/tests/test_svm.py #25891
  • sklearn/tests/test_base.py
  • sklearn/tests/test_build.py
  • sklearn/tests/test_calibration.py
  • sklearn/tests/test_check_build.py
  • sklearn/tests/test_common.py
  • sklearn/tests/test_config.py
  • sklearn/tests/test_discriminant_analysis.py
  • sklearn/tests/test_docstring_parameters.py
  • sklearn/tests/test_docstrings.py
  • sklearn/tests/test_dummy.py
  • sklearn/tests/test_init.py
  • sklearn/tests/test_isotonic.py
  • sklearn/tests/test_kernel_approximation.py
  • sklearn/tests/test_kernel_ridge.py
  • sklearn/tests/test_metaestimators.py
  • sklearn/tests/test_min_dependencies_readme.py
  • sklearn/tests/test_multiclass.py
  • sklearn/tests/test_multioutput.py
  • sklearn/tests/test_naive_bayes.py
  • sklearn/tests/test_pipeline.py
  • sklearn/tests/test_random_projection.py
  • sklearn/tree/tests/test_export.py
  • sklearn/tree/tests/test_reingold_tilford.py
  • sklearn/tree/tests/test_tree.py
  • sklearn/utils/tests/test_arpack.py
  • sklearn/utils/tests/test_arrayfuncs.py
  • sklearn/utils/tests/test_class_weight.py
  • sklearn/utils/tests/test_cython_blas.py
  • sklearn/utils/tests/test_cython_templating.py
  • sklearn/utils/tests/test_deprecation.py
  • sklearn/utils/tests/test_encode.py
  • sklearn/utils/tests/test_estimator_checks.py
  • sklearn/utils/tests/test_estimator_html_repr.py
  • sklearn/utils/tests/test_extmath.py
  • sklearn/utils/tests/test_fast_dict.py
  • sklearn/utils/tests/test_fixes.py
  • sklearn/utils/tests/test_graph.py
  • sklearn/utils/tests/test_metaestimators.py
  • sklearn/utils/tests/test_mocking.py
  • sklearn/utils/tests/test_multiclass.py
  • sklearn/utils/tests/test_murmurhash.py
  • sklearn/utils/tests/test_optimize.py TST use global_random_seed in sklearn/utils/tests/test_optimize.py #30112
  • sklearn/utils/tests/test_parallel.py
  • sklearn/utils/tests/test_pprint.py
  • sklearn/utils/tests/test_random.py
  • sklearn/utils/tests/test_readonly_wrapper.py
  • sklearn/utils/tests/test_seq_dataset.py
  • sklearn/utils/tests/test_shortest_path.py
  • sklearn/utils/tests/test_show_versions.py
  • sklearn/utils/tests/test_sparsefuncs.py
  • sklearn/utils/tests/test_stats.py TST use global_random_seed in sklearn/utils/tests/test_stats.py #30857
  • sklearn/utils/tests/test_tags.py
  • sklearn/utils/tests/test_testing.py
  • sklearn/utils/tests/test_utils.py
  • sklearn/utils/tests/test_validation.py
  • sklearn/utils/tests/test_weight_vector.py

Note that some of those files might not have any test to update.
@ogrisel ogrisel added help wanted Moderate Anything that requires some knowledge of conventions and best practices Hard Hard level of difficulty and removed Moderate Anything that requires some knowledge of conventions and best practices labels Mar 14, 2022
@ogrisel
Copy link
Member Author

ogrisel commented Mar 14, 2022 •
edited
Loading

I labeled this issue as hard, as I expect some tests to be hard to upgrade. However I expect the majority of the tests to be easy to convert but we cannot know which will be easy in advance.

@ogrisel ogrisel added the module:test-suite everything related to our tests label Mar 14, 2022
@ogrisel ogrisel mentioned this issue Mar 15, 2022
Merged
@jjerphan jjerphan mentioned this issue Mar 16, 2022
Merged
@jjerphan jjerphan added the Meta-issue General issue associated to an identified list of tasks label Mar 18, 2022
@thomasjpfan thomasjpfan mentioned this issue Mar 18, 2022
Merged
@MaxwellLZH MaxwellLZH mentioned this issue Mar 19, 2022
Merged
@tahmid-haque tahmid-haque mentioned this issue Apr 1, 2022
Open
Tony-beeper added a commit to TheBicPen/scikit-learn that referenced this issue Apr 2, 2022
@Tony-beeper
implemented based on issue scikit-learn#22827, updated global_random_…
d887532 
…seed on test cases
@Tony-beeper Tony-beeper mentioned this issue Apr 2, 2022
Open
@ShoaibKhan ShoaibKhan mentioned this issue Apr 2, 2022
Open
@adrinjalali adrinjalali mentioned this issue Apr 5, 2022
Merged
@ShoaibKhan ShoaibKhan mentioned this issue May 16, 2022
Merged
@matthewmercuri matthewmercuri mentioned this issue May 25, 2022
Closed
This was referenced May 25, 2022
Closed
Merged
This was referenced Jun 6, 2022
Merged
Merged
Closed
@marenwestermann marenwestermann mentioned this issue Jun 20, 2022
Closed
@thomasjpfan
Copy link
Member

thomasjpfan commented Jun 23, 2022 •
edited
Loading

I updated the original message with the feature introduced in #23026 where we can run the CI with all the seeds by pushing a commit with the proper message. @ogrisel Feel free to update the wording if you find it unclear.

  • Run the CI for tests that take a global_random_seed by pushing a commit message with the following structure:
<title> [all random seeds]
<test_name_1>
<test_name_2>
...

Note, running git commit --allow-empty allows you to have a commit message without any changes.

This was referenced Jun 23, 2022
Merged
Merged
@svenstehle
Copy link
Contributor

svenstehle commented Jul 3, 2022 •
edited
Loading

working on sklearn/linear_model/tests/test_bayes.py

@svenstehle svenstehle mentioned this issue Jul 3, 2022
Merged
@marenwestermann
Copy link
Member

Working on sklearn/cluster/tests/test_affinity_propagation.py

This was referenced Feb 18, 2025
Merged
Closed
Merged
@sortofamudkip
Copy link
Contributor

Working on sklearn/linear_model/tests/test_linear_loss.py

This was referenced Feb 19, 2025
Merged
Merged
@ArturoSbr ArturoSbr mentioned this issue Feb 27, 2025
Open
@ArturoSbr
Copy link
Contributor

ArturoSbr commented Feb 28, 2025 •
edited
Loading

To anyone interested in contributing, remember to add an empty commit to your PR (in accordance to this issue):

$ git commit --allow-empty -m "empty commit to trigger all CI jobs [all random seeds]
test_parallel"

@Rishab260
Copy link
Contributor

Rishab260 commented Mar 2, 2025 •
edited
Loading

Working on sklearn/decomposition/tests/test_truncated_svd.py
Working on sklearn/ensemble/tests/test_bagging.py

This was referenced Mar 2, 2025
Merged
Merged
@StefanieSenger StefanieSenger mentioned this issue Mar 18, 2025
Open
@ogrisel ogrisel mentioned this issue Mar 25, 2025
Merged
@DeaMariaLeon
Copy link
Contributor

Working on sklearn/compose/tests/test_column_transformer.py

@DeaMariaLeon DeaMariaLeon mentioned this issue Mar 27, 2025
Closed
@DeaMariaLeon
Copy link
Contributor

Working on sklearn/compose/tests/test_target.py

@DeaMariaLeon DeaMariaLeon mentioned this issue Mar 28, 2025
Closed
@DeaMariaLeon
Copy link
Contributor

Commenting here as a friendly reminder @glemaitre

@jeremiedbb
Copy link
Member

Thanks @DeaMariaLeon for the reminder. However, briefly looking at sklearn/datasets/tests/test_samples_generator.py, there are tests to check the properties of the generated datasets, so I think it's legitimate to not cross it off the list.

@DeaMariaLeon
Copy link
Contributor

Sorry for wasting your time. I was looking at the wrong file. 🫣
Now I'm working on sklearn/datasets/tests/test_samples_generator.py

This was referenced Apr 11, 2025
Merged
Merged
@DeaMariaLeon
Copy link
Contributor

Per Guillaume, commenting here that I opened a PR for sklearn/decomposition/tests/test_fastica.py

@DeaMariaLeon
Copy link
Contributor

DeaMariaLeon commented Apr 16, 2025 •
edited
Loading

Working on sklearn/decomposition/tests/test_sparse_pca.py

edit: These tests need to be removed from the list - the PRs have already been merged: sklearn/decomposition/tests/test_truncated_svd.py #30922
sklearn/ensemble/tests/test_bagging.py #30923

@DeaMariaLeon DeaMariaLeon mentioned this issue Apr 16, 2025
Merged
@DeaMariaLeon
Copy link
Contributor

I think that sklearn/ensemble/tests/test_common.py doesn't benefit from the global random seed.
If someone can kindly double check, it would be great:
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/ensemble/tests/test_common.py

@DeaMariaLeon DeaMariaLeon mentioned this issue Apr 25, 2025
Open
@DeaMariaLeon
Copy link
Contributor

I worked on sklearn/decomposition/tests/test_incremental_pca.py

@TheAyos TheAyos mentioned this issue May 5, 2025
Open
@DeaMariaLeon
Copy link
Contributor

I'll work on sklearn/linear_model/tests/test_logistic.py

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
Hard Hard level of difficulty help wanted Meta-issue General issue associated to an identified list of tasks module:test-suite everything related to our tests
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests