fix(skore): Don't rely on _repr_html_ to display the summary widget

[FrancoisPgm]

Fixes #2039

The colab button to convert a dataframe into an interactive table appears to call the dataframe's _repr_html_, so when it is used on a summary (which inherits from the dataframe class), the widget would be displayed a second time as _repr_html_ is called again.

The proposed solution is to display the widget in _repr_mimebundle_ and override _repr_html_ to return an empty string.

[github-actions]

Caution

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Please sign all your commits; you can find more information here.
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[github-actions]

Documentation preview @ 9427af5

[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%
types.py	27	1	96%	28
skore/src/skore/_sklearn/_comparison
__init__.py	7	0	100%
feature_importance_accessor.py	39	2	94%	90, 109
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%	128, 131, 134
feature_importance_accessor.py	24	0	100%
metrics_accessor.py	182	1	99%	244
report.py	135	1	99%	487
skore/src/skore/_sklearn/_estimator
__init__.py	9	0	100%
data_accessor.py	58	0	100%
feature_importance_accessor.py	144	2	98%	223–224
metrics_accessor.py	356	8	97%	200, 202, 209, 300, 369, 373, 388, 423
report.py	167	2	98%	448–449
skore/src/skore/_sklearn/_plot
__init__.py	3	0	100%
base.py	102	7	93%	61–62, 200, 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	280	5	98%	455, 555, 559, 619, 751
prediction_error.py	227	5	97%	179, 186, 422, 505, 705
roc_curve.py	292	8	97%	385, 508, 513, 614, 619, 623, 692, 832
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	0	100%
_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	13	5	61%	21, 23–24, 35, 37
_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	165	6	96%	436, 439–441, 525–526
TOTAL	3997	117	97%

Tests	Skipped	Failures	Errors	Time
1062	5 💤	0 ❌	0 🔥	4m 19s ⏱️

[auguste-probabl]

Nice work!

[auguste-probabl]

I see that your commits are not signed btw

[FrancoisPgm]

The two first ones are not but the third one should be, I created a GPG key after seeing the gh action warning.

[auguste-probabl]

You can sign commits after making them, if you can force-push.
Once the commits are signed (you can use an SSH key to sign, not only GPG), you need to give GitHub your public key for it to verify the signature; that's why your 3rd commit is marked "Unverified".
More info here: https://docs.github.com/en/authentication/managing-commit-signature-verification/signing-commits

[thomass-dev]

This PR changes the representation of the Summary object in IPython:

In [3]: project.summarize()
Out[3]: <IPython.core.display.HTML object>
VBox(children=(VBox(children=(HBox(children=(Dropdown(description='Report Type:', layout=Layout(flex='1'), opt…
FigureWidget({
    'data': [{'dimensions': [{'label': 'Learner',
                              'ticktext': array(['LinearRegression'], dtype=object),
                              'tickvals': array([0]),
                              'values': array([0, 0], dtype=int8)},
                             {'label': 'RMSE', 'values': array([66.40851226, 66.40851226])}],
              'labelangle': -30,
              'line': {'color': array([66.40851226, 66.40851226]),
                       'colorbar': {'title': {'text': 'RMSE'}},
                       'colorscale': [[0.0, '#fde725'], [0.1111111111111111,
                                      '#b5de2b'], [0.2222222222222222, '#6ece58'],
                                      [0.3333333333333333, '#35b779'],
                                      [0.4444444444444444, '#1f9e89'],
                                      [0.5555555555555556, '#26828e'],
                                      [0.6666666666666666, '#31688e'],
                                      [0.7777777777777778, '#3e4989'],
                                      [0.8888888888888888, '#482878'], [1.0,
                                      '#440154']],
                       'showscale': True},
              'type': 'parcoords',
              'uid': '7a1497a3-1a3d-4fd0-b3fb-ab09b9fefaba'}],
    'layout': {'font': {'size': 18}, 'height': 500, 'margin': {'b': 30, 'l': 250, 'r': 0, 't': 120}, 'template': '...'}
})

                                                                          key                              date           learner     ml_task       report_type  ...   rmse_mean  log_loss_mean roc_auc_mean fit_time_mean  predict_time_mean
  id                                                                                                                                                             ...                                                                         
0 skore:report:estimator:1                 estimator-report: LinearRegression  2025-10-14T10:23:53.132733+00:00  LinearRegression  regression         estimator  ...         NaN           None         None           NaN                NaN
1 skore:report:cross-validation:2   cross-validation-report: LinearRegression  2025-10-14T10:24:57.862750+00:00  LinearRegression  regression  cross-validation  ...  108.639532           None         None      0.003301           0.000277
2 skore:report:estimator:28                estimator-report: LinearRegression  2025-10-17T08:41:33.197927+00:00  LinearRegression  regression         estimator  ...         NaN           None         None           NaN                NaN
3 skore:report:cross-validation:29  cross-validation-report: LinearRegression  2025-10-17T08:42:19.266918+00:00  LinearRegression  regression  cross-validation  ...  108.639532           None         None      0.000754           0.000282

[4 rows x 16 columns]

Could you find a way to disable the representation of the widget in IPython and let Pandas make its representation?