feat(skore): Add params_search_reports_ as a list of CrossValidationReport

[glemaitre]

closes #2127

This PR is an exploration of what is required to store the results of hyperparmeters search done with a GridSearchCV or RandomizedSearchCV via a CrossValidationReport. Basically, we want to avoid to have to refit anything that already have been fitted with scikit-learn.

In short:

• we need to monkey patch two scikit-learn components to force to store the estimators in cv_results_
• we need two factories to create a ComparisonReport of CrossValidationReport from the cv_results_.

The benefit of building upon ComparisonReport and CrossValidationReport is that we already have the structure to just send those types of reports in the skore-hub.

TODO:

• Need documentation in the user guide
• Need a compelling example

Thoughts:

We should thing further regarding the type of comparison one tries to achieve when comparing results from an hyperparameter search and when comparing different model. Usually, hyperparameters search is related to have a single model and explore the parameter while comparing different models makes that there is nothing in common, potentially.

Therefore, the ComparisonReport could have a couple of functionality dedicated to hyper parameters search that could be activated when passing the results of a cross-validation search.

[github-actions]

Documentation preview @ 5e3cd98

[github-actions]

Coverage Report for skore/

File	Stmts	Miss	Cover	Missing
skore/src/skore
__init__.py	23	0	100%
_config.py	31	0	100%
exceptions.py	4	4	0%	4, 15, 19, 23
skore/src/skore/_sklearn
__init__.py	6	0	100%
_base.py	198	14	92%	45, 58, 127, 130, 183, 186–187, 189–192, 225, 228–229
find_ml_task.py	61	0	100%
search_cv.py	18	0	100%
types.py	27	1	96%	28
skore/src/skore/_sklearn/_comparison
__init__.py	7	0	100%
feature_importance_accessor.py	35	2	94%	88, 107
metrics_accessor.py	178	3	98%	173, 253, 1215
report.py	107	0	100%
utils.py	54	0	100%
skore/src/skore/_sklearn/_cross_validation
__init__.py	9	0	100%
data_accessor.py	45	3	93%	134, 137, 140
feature_importance_accessor.py	24	0	100%
metrics_accessor.py	182	1	99%	244
report.py	154	1	99%	535
skore/src/skore/_sklearn/_estimator
__init__.py	9	0	100%
data_accessor.py	66	1	98%	82
feature_importance_accessor.py	168	2	98%	251–252
metrics_accessor.py	356	8	97%	200, 202, 209, 300, 369, 373, 388, 423
report.py	187	5	97%	246, 254–255, 522–523
skore/src/skore/_sklearn/_plot
__init__.py	3	0	100%
base.py	98	6	93%	61–62, 224–226, 230
utils.py	77	0	100%
skore/src/skore/_sklearn/_plot/data
__init__.py	2	0	100%
table_report.py	185	1	99%	706
skore/src/skore/_sklearn/_plot/metrics
__init__.py	6	0	100%
confusion_matrix.py	70	4	94%	92, 100, 122, 230
feature_importance_display.py	67	21	68%	88, 121–122, 124, 142–146, 148–155, 158–160, 162
metrics_summary_display.py	8	0	100%
precision_recall_curve.py	281	5	98%	455, 555, 559, 619, 751
prediction_error.py	227	5	97%	179, 186, 422, 505, 705
roc_curve.py	294	8	97%	387, 510, 515, 616, 621, 625, 694, 834
skore/src/skore/_sklearn/train_test_split
__init__.py	0	0	100%
train_test_split.py	58	0	100%
skore/src/skore/_sklearn/train_test_split/warning
__init__.py	8	0	100%
high_class_imbalance_too_few_examples_warning.py	19	1	94%	83
high_class_imbalance_warning.py	20	0	100%
random_state_unset_warning.py	10	0	100%
shuffle_true_warning.py	9	0	100%
stratify_is_set_warning.py	10	0	100%
time_based_column_warning.py	21	0	100%
train_test_split_warning.py	3	0	100%
skore/src/skore/_utils
__init__.py	6	2	66%	8, 13
_accessor.py	90	3	96%	34, 146, 190
_environment.py	27	1	96%	40
_fixes.py	8	0	100%
_index.py	5	0	100%
_logger.py	22	4	81%	15–17, 19
_measure_time.py	10	0	100%
_parallel.py	38	3	92%	23, 33, 124
_patch.py	39	6	84%	23, 25–26, 37, 39, 137
_progress_bar.py	46	0	100%
_repr_html.py	8	0	100%
_show_versions.py	38	0	100%
_testing.py	55	0	100%
skore/src/skore/project
__init__.py	2	0	100%
project.py	48	0	100%
summary.py	75	1	98%	120
widget.py	187	0	100%
TOTAL	4129	116	97%

Tests	Skipped	Failures	Errors	Time
1115	5 💤	0 ❌	0 🔥	4m 33s ⏱️