refactor: Disambiguate the different (_?)estimator(_?) attributes

[auguste-probabl]

• estimator becomes the original estimator passed at initialization
• estimator_ becomes the fitted estimator (following sklearn convention); has the same type as estimator
• _estimator becomes learner_, the estimator wrapped in a SkrubLearner-compatible interface (used internally)

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[github-actions]

Coverage Report for skore/

File	Stmts	Miss	Branch	BrPart	Cover	Missing
skore/src/skore
__init__.py	39	2	2	0	94%	112–113
_config.py	58	3	12	1	94%	71, 118–119
exceptions.py	4	4	0	0	0%	4, 15, 19, 23
skore/src/skore/_plugins
__init__.py	12	0	0	0	100%
skore/src/skore/_plugins/hub
__init__.py	9	2	0	0	77%	15, 20
exception.py	2	0	0	0	100%
json.py	10	1	2	1	90%	16
skore/src/skore/_plugins/hub/artifact
__init__.py	0	0	0	0	100%
artifact.py	23	0	4	0	100%
serializer.py	27	0	2	0	100%
upload.py	26	0	4	0	100%
skore/src/skore/_plugins/hub/artifact/media
__init__.py	5	0	0	0	100%
data.py	20	0	0	0	100%
inspection.py	72	10	20	8	86%	46–49, 51, 53, 60, 62, 68, 107
media.py	10	0	0	0	100%
model.py	10	0	0	0	100%
performance.py	106	1	8	1	99%	43
skore/src/skore/_plugins/hub/artifact/pickle
__init__.py	2	0	0	0	100%
pickle.py	24	0	2	0	100%
skore/src/skore/_plugins/hub/authentication
__init__.py	0	0	0	0	100%
apikey.py	7	0	0	0	100%
login.py	28	4	4	2	85%	37, 42–43, 52
token.py	80	0	8	0	100%
uri.py	6	0	0	0	100%
skore/src/skore/_plugins/hub/client
__init__.py	0	0	0	0	100%
client.py	88	10	18	3	88%	140, 187–189, 191–192, 194, 196, 198, 230
skore/src/skore/_plugins/hub/metric
__init__.py	10	0	0	0	100%
accuracy.py	35	0	0	0	100%
brier_score.py	35	0	0	0	100%
log_loss.py	35	0	0	0	100%
metric.py	55	4	2	1	92%	38, 77–78, 84
precision.py	57	0	0	0	100%
r2.py	35	0	0	0	100%
recall.py	59	0	0	0	100%
rmse.py	35	0	0	0	100%
roc_auc.py	35	0	0	0	100%
timing.py	76	4	0	0	94%	45–46, 104–105
skore/src/skore/_plugins/hub/project
__init__.py	0	0	0	0	100%
project.py	138	6	26	5	95%	84, 109, 124, 323, 403, 433
skore/src/skore/_plugins/hub/report
__init__.py	3	0	0	0	100%
cross_validation_report.py	121	2	28	3	98%	224, 264
estimator_report.py	10	0	0	0	100%
report.py	60	0	6	0	100%
skore/src/skore/_plugins/local
__init__.py	2	0	0	0	100%
metadata.py	81	3	8	1	96%	29, 141–142
project.py	93	1	30	1	98%	238
storage.py	42	2	6	1	95%	45, 189
skore/src/skore/_plugins/mlflow
__init__.py	5	0	0	0	100%
project.py	209	15	54	5	92%	202, 233–234, 380, 382, 398, 400–405, 407–409
reports.py	155	6	34	4	96%	129, 170, 207–208, 270, 283
skore/src/skore/_project
__init__.py	0	0	0	0	100%
_summary.py	80	1	38	3	98%	121
_widget.py	191	0	44	2	100%
dependencies.py	19	0	6	0	100%
git.py	25	0	4	0	100%
login.py	17	3	6	2	82%	62, 71–72
plugin.py	12	0	2	0	100%
project.py	56	2	16	3	96%	132, 141
types.py	3	0	0	0	100%
skore/src/skore/_sklearn
__init__.py	8	0	0	0	100%
_base.py	54	1	10	0	98%	44
compare.py	5	0	0	0	100%
evaluate.py	43	0	24	0	100%
feature_names.py	26	0	12	0	100%
find_ml_task.py	61	0	46	2	100%
metrics.py	316	0	76	1	100%
types.py	19	1	0	0	94%	31
skore/src/skore/_sklearn/_checks
__init__.py	3	0	0	0	100%
_utils.py	47	3	20	3	93%	47, 147, 155
accessor.py	33	1	14	0	96%	17
base.py	76	5	24	3	93%	128–129, 169, 257–258
model_checks.py	163	4	40	4	97%	320, 328, 364, 368
skore/src/skore/_sklearn/_comparison
__init__.py	9	0	0	0	100%
inspection_accessor.py	27	1	2	0	96%	347
metrics_accessor.py	122	4	18	4	96%	259–260, 329, 1115
report.py	160	5	68	0	96%	577, 583, 641–643
skore/src/skore/_sklearn/_cross_validation
__init__.py	11	0	0	0	100%
data_accessor.py	36	2	12	2	94%	48, 74
inspection_accessor.py	27	1	2	0	96%	319
metrics_accessor.py	120	3	18	3	97%	214–215, 1077
report.py	202	10	48	6	95%	73, 78, 83, 324, 598, 642, 648, 734–736
skore/src/skore/_sklearn/_estimator
__init__.py	11	0	0	0	100%
data_accessor.py	48	2	20	1	95%	61, 178
inspection_accessor.py	37	1	8	2	97%	278
metrics_accessor.py	125	0	22	0	100%
report.py	329	14	92	10	95%	60, 74, 295, 368, 453, 710, 799, 818, 820, 826–827, 914–916
skore/src/skore/_sklearn/_plot
__init__.py	3	0	0	0	100%
base.py	61	2	14	1	96%	61–62
utils.py	145	3	66	3	97%	254–255, 428
skore/src/skore/_sklearn/_plot/data
__init__.py	2	0	0	0	100%
table_report.py	177	1	60	1	99%	670
skore/src/skore/_sklearn/_plot/inspection
__init__.py	0	0	0	0	100%
coefficients.py	181	0	88	1	100%
impurity_decrease.py	103	2	34	3	98%	423, 467
permutation_importance.py	198	1	90	1	99%	585
utils.py	32	0	10	0	100%
skore/src/skore/_sklearn/_plot/metrics
__init__.py	6	0	0	0	100%
confusion_matrix.py	198	0	66	2	100%
metrics_summary_display.py	152	1	74	2	99%	290
precision_recall_curve.py	113	0	32	1	100%
prediction_error.py	166	0	54	2	100%
roc_curve.py	119	0	34	2	100%
skore/src/skore/_sklearn/train_test_split
__init__.py	2	0	0	0	100%
train_test_split.py	71	0	34	2	100%
skore/src/skore/_sklearn/train_test_split/warning
__init__.py	8	0	0	0	100%
high_class_imbalance_too_few_examples_warning.py	19	1	6	1	94%	83
high_class_imbalance_warning.py	20	0	6	0	100%
random_state_unset_warning.py	10	0	2	0	100%
shuffle_true_warning.py	9	0	2	0	100%
stratify_is_set_warning.py	10	0	2	0	100%
time_based_column_warning.py	21	0	4	0	100%
train_test_split_warning.py	3	0	0	0	100%
skore/src/skore/_utils
__init__.py	6	2	0	0	66%	8, 13
_accessor.py	106	28	30	13	73%	12–13, 15, 36, 61–65, 68, 70–71, 76, 81, 83, 85, 92–94, 120–121, 123–124, 126, 132, 164, 218, 238
_cache.py	37	0	2	1	100%
_cache_key.py	35	5	22	5	85%	22, 24, 51, 59, 68
_callable_name.py	9	0	4	0	100%
_dataframe.py	43	4	18	4	90%	27, 46, 48, 63
_environment.py	33	2	10	3	93%	46, 49
_fixes.py	8	0	2	0	100%
_index.py	5	0	2	0	100%
_jupyter.py	8	2	0	0	75%	13–14
_measure_time.py	10	0	0	0	100%
_parallel.py	17	0	0	0	100%
_patch.py	21	12	8	8	42%	30, 35–39, 42–43, 46–47, 58, 60
_progress_bar.py	42	4	4	0	90%	53–54, 64–65
_show_versions.py	38	0	12	0	100%
_skrub.py	37	0	4	0	100%
_testing.py	128	14	12	2	89%	24, 33, 71–72, 94, 193, 202, 213–218, 220
skore/src/skore/_utils/repr
__init__.py	2	0	0	0	100%
base.py	54	0	4	0	100%
data.py	128	0	30	1	100%
html_repr.py	40	0	0	0	100%
rich_repr.py	80	0	22	3	100%
utils.py	11	0	2	0	100%
TOTAL	7302	228	1838	150	96%

Tests	Skipped	Failures	Errors	Time
2564	6 💤	0 ❌	0 🔥	8m 50s ⏱️

[github-actions]

Documentation preview @ 12a7bef

[jeromedockes]

thanks @auguste-probabl ! I think your proposed organization is good:

• original_estimator is whatever the user gave, without transformations
• learner_ is the fitted object, with a skrublearner interface (predict({...}) ). if original_estimator had the scikit-learn interface (predict(X, y)) it is wrapped in an adapter. learner_ is what is used almost everywhere internally
• estimator_ is the fitted object without wrapping, ie with the same interface as original_estimator -- the interface expected by the user

@glemaitre WDYT

[jeromedockes]

thank you @auguste-probabl , LGTM!

[glemaitre]

LGTM

[glemaitre]

Thanks @auguste-probabl

[jeromedockes]

great! 🎉