refactor(skore)!: Rename split_index to split

examples/getting_started/plot_skore_getting_started.py

@@ -122,7 +122,7 @@
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
 # skore has also (re-)implemented a :class:`skore.CrossValidationReport` class that
-# contains several :class:`skore.EstimatorReport`, one for each fold.
+# contains several :class:`skore.EstimatorReport`, one for each split.
 
 # %%
 from skore import CrossValidationReport
@@ -142,21 +142,21 @@
 cv_report.metrics.summarize().frame()
 
 # %%
-# or by individual fold:
+# or by individual split:
 
 # %%
 cv_report.metrics.summarize(aggregate=None).frame()
 
 # %%
-# We display the ROC curves for each fold:
+# We display the ROC curves for each split:
 
 # %%
 roc_plot_cv = cv_report.metrics.roc()
 roc_plot_cv.plot()
 
 # %%
-# We can retrieve the estimator report of a specific fold to investigate further,
-# for example getting the report metrics for the first fold only:
+# We can retrieve the estimator report of a specific split to investigate further,
+# for example getting the report metrics for the first split only:
 
 # %%
 cv_report.estimator_reports_[0].metrics.summarize().frame()

examples/technical_details/plot_cache_mechanism.py

@@ -250,7 +250,7 @@
 # Caching with :class:`~skore.CrossValidationReport`
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
-# :class:`~skore.CrossValidationReport` uses the same caching system for each fold
+# :class:`~skore.CrossValidationReport` uses the same caching system for each split
 # in cross-validation by leveraging the previous :class:`~skore.EstimatorReport`:
 from skore import CrossValidationReport
 
@@ -262,7 +262,7 @@
 # Since a :class:`~skore.CrossValidationReport` uses many
 # :class:`~skore.EstimatorReport`, we will observe the same behaviour as we previously
 # exposed.
-# The first call will be slow because it computes the predictions for each fold.
+# The first call will be slow because it computes the predictions for each split.
 start = time.time()
 result = report.metrics.summarize().frame()
 end = time.time()

examples/use_cases/plot_employee_salaries.py

@@ -319,9 +319,9 @@ def periodic_spline_transformer(period, n_splines=None, degree=3):
 ).frame()
 
 # %%
-# Finally, we can even get a deeper understanding by analyzing each fold in the
+# Finally, we can even get a deeper understanding by analyzing each split in the
 # :class:`~skore.CrossValidationReport`.
-# Here, we plot the actual-vs-predicted values for each fold.
+# Here, we plot the actual-vs-predicted values for each split.
 
 # %%
 linear_model_report.metrics.prediction_error().plot(kind="actual_vs_predicted")

skore/src/skore/_sklearn/_comparison/metrics_accessor.py

@@ -1259,7 +1259,7 @@ def _get_display(
                     y_true.append(
                         YPlotData(
                             estimator_name=report_name,
-                            split_index=None,
+                            split=None,
                             y=report_y,
                         )
                     )
@@ -1280,7 +1280,7 @@ def _get_display(
                             y_pred.append(
                                 YPlotData(
                                     estimator_name=report_name,
-                                    split_index=None,
+                                    split=None,
                                     y=value,
                                 )
                             )
@@ -1301,9 +1301,7 @@ def _get_display(
                 for report, report_name in zip(
                     self._parent.reports_, self._parent.report_names_, strict=False
                 ):
-                    for split_index, estimator_report in enumerate(
-                        report.estimator_reports_
-                    ):
+                    for split, estimator_report in enumerate(report.estimator_reports_):
                         report_X, report_y, _ = (
                             estimator_report.metrics._get_X_y_and_data_source_hash(
                                 data_source=data_source,
@@ -1315,7 +1313,7 @@ def _get_display(
                         y_true.append(
                             YPlotData(
                                 estimator_name=report_name,
-                                split_index=split_index,
+                                split=split,
                                 y=report_y,
                             )
                         )
@@ -1337,7 +1335,7 @@ def _get_display(
                                 y_pred.append(
                                     YPlotData(
                                         estimator_name=report_name,
-                                        split_index=split_index,
+                                        split=split,
                                         y=value,
                                     )
                                 )

skore/src/skore/_sklearn/_cross_validation/metrics_accessor.py

@@ -1146,7 +1146,7 @@ def _get_display(
                 y_true.append(
                     YPlotData(
                         estimator_name=self._parent.estimator_name_,
-                        split_index=report_idx,
+                        split=report_idx,
                         y=y,
                     )
                 )
@@ -1167,7 +1167,7 @@ def _get_display(
                         y_pred.append(
                             YPlotData(
                                 estimator_name=self._parent.estimator_name_,
-                                split_index=report_idx,
+                                split=report_idx,
                                 y=value,
                             )
                         )

skore/src/skore/_sklearn/_cross_validation/report.py

@@ -92,7 +92,7 @@ class CrossValidationReport(_BaseReport, DirNamesMixin):
         Determines the cross-validation splitting strategy.
         Possible inputs for `splitter` are:
 
-        - int, to specify the number of folds in a `(Stratified)KFold`,
+        - int, to specify the number of splits in a `(Stratified)KFold`,
         - a scikit-learn :term:`CV splitter`,
         - An iterable yielding (train, test) splits as arrays of indices.
 
@@ -339,19 +339,19 @@ def cache_predictions(
         total_estimators = len(self.estimator_reports_)
         progress.update(main_task, total=total_estimators)
 
-        for fold_idx, estimator_report in enumerate(self.estimator_reports_, 1):
+        for split_idx, estimator_report in enumerate(self.estimator_reports_, 1):
             # Share the parent's progress bar with child report
             estimator_report._progress_info = {
                 "current_progress": progress,
-                "fold_info": {"current": fold_idx, "total": total_estimators},
+                "split_info": {"current": split_idx, "total": total_estimators},
             }
 
-            # Update the progress bar description to include the fold number
+            # Update the progress bar description to include the split number
             progress.update(
                 main_task,
                 description=(
-                    "Cross-validation predictions for fold "
-                    f"#{fold_idx}/{total_estimators}"
+                    "Cross-validation predictions for split "
+                    f"#{split_idx}/{total_estimators}"
                 ),
             )
 

skore/src/skore/_sklearn/_estimator/metrics_accessor.py

@@ -1692,14 +1692,14 @@ def _get_display(
                 y_true=[
                     YPlotData(
                         estimator_name=self._parent.estimator_name_,
-                        split_index=None,
+                        split=None,
                         y=y,
                     )
                 ],
                 y_pred=[
                     YPlotData(
                         estimator_name=self._parent.estimator_name_,
-                        split_index=None,
+                        split=None,
                         y=y_pred,
                     )
                 ],

skore/src/skore/_sklearn/_plot/metrics/precision_recall_curve.py

@@ -60,7 +60,7 @@ class PrecisionRecallCurveDisplay(
         The precision-recall curve data to display. The columns are
 
         - `estimator_name`
-        - `split_index` (may be null)
+        - `split` (may be null)
         - `label`
         - `threshold`
         - `precision`
@@ -70,7 +70,7 @@ class PrecisionRecallCurveDisplay(
         The average precision data to display. The columns are
 
         - `estimator_name`
-        - `split_index` (may be null)
+        - `split` (may be null)
         - `label`
         - `average_precision`.
 
@@ -274,8 +274,8 @@ def _plot_cross_validated_estimator(
         line_kwargs: dict[str, Any] = {"drawstyle": "steps-post"}
 
         if self.ml_task == "binary-classification":
-            for split_idx in self.precision_recall["split_index"].cat.categories:
-                query = f"label == {self.pos_label!r} & split_index == {split_idx}"
+            for split_idx in self.precision_recall["split"].cat.categories:
+                query = f"label == {self.pos_label!r} & split == {split_idx}"
                 precision_recall = self.precision_recall.query(query)
                 average_precision = self.average_precision.query(query)[
                     "average_precision"
@@ -285,7 +285,7 @@ def _plot_cross_validated_estimator(
                     line_kwargs, pr_curve_kwargs[split_idx]
                 )
                 line_kwargs_validated["label"] = (
-                    f"Fold #{split_idx + 1} (AP = {average_precision:0.2f})"
+                    f"Split #{split_idx + 1} (AP = {average_precision:0.2f})"
                 )
 
                 (line,) = self.ax_.plot(
@@ -314,8 +314,8 @@ def _plot_cross_validated_estimator(
                 # average_precision_class = self.average_precision[class_]
                 pr_curve_kwargs_class = pr_curve_kwargs[class_idx]
 
-                for split_idx in self.precision_recall["split_index"].cat.categories:
-                    query = f"label == {class_label!r} & split_index == {split_idx}"
+                for split_idx in self.precision_recall["split"].cat.categories:
+                    query = f"label == {class_label!r} & split == {split_idx}"
                     precision_recall = self.precision_recall.query(query)
                     average_precision = self.average_precision.query(query)[
                         "average_precision"
@@ -519,7 +519,7 @@ def _plot_comparison_cross_validation(
                 precision_recall = self.precision_recall.query(query)
 
                 for split_idx, segment in precision_recall.groupby(
-                    "split_index", observed=True
+                    "split", observed=True
                 ):
                     if split_idx == 0:
                         label_kwargs = {
@@ -586,7 +586,7 @@ def _plot_comparison_cross_validation(
                     precision_recall = self.precision_recall.query(query)
 
                     for split_idx, segment in precision_recall.groupby(
-                        "split_index", observed=True
+                        "split", observed=True
                     ):
                         if split_idx == 0:
                             label_kwargs = {
@@ -836,7 +836,7 @@ def _compute_data_for_display(
                     precision_recall_records.append(
                         {
                             "estimator_name": y_true_i.estimator_name,
-                            "split_index": y_true_i.split_index,
+                            "split": y_true_i.split,
                             "label": pos_label_validated,
                             "threshold": threshold,
                             "precision": precision,
@@ -846,7 +846,7 @@ def _compute_data_for_display(
                 average_precision_records.append(
                     {
                         "estimator_name": y_true_i.estimator_name,
-                        "split_index": y_true_i.split_index,
+                        "split": y_true_i.split,
                         "label": pos_label_validated,
                         "average_precision": average_precision_i,
                     }
@@ -879,7 +879,7 @@ def _compute_data_for_display(
                         precision_recall_records.append(
                             {
                                 "estimator_name": y_true_i.estimator_name,
-                                "split_index": y_true_i.split_index,
+                                "split": y_true_i.split,
                                 "label": class_,
                                 "threshold": threshold,
                                 "precision": precision,
@@ -889,15 +889,15 @@ def _compute_data_for_display(
                     average_precision_records.append(
                         {
                             "estimator_name": y_true_i.estimator_name,
-                            "split_index": y_true_i.split_index,
+                            "split": y_true_i.split,
                             "label": class_,
                             "average_precision": average_precision_class_i,
                         }
                     )
 
         dtypes = {
             "estimator_name": "category",
-            "split_index": "category",
+            "split": "category",
             "label": "category",
         }
 
@@ -929,7 +929,7 @@ def frame(self, with_average_precision: bool = False) -> DataFrame:
             depending on the report type:
 
             - `estimator_name`: Name of the estimator (when comparing estimators)
-            - `split_index`: Cross-validation fold ID (when doing cross-validation)
+            - `split`: Cross-validation split ID (when doing cross-validation)
             - `label`: Class label (for multiclass-classification)
             - `threshold`: Decision threshold
             - `precision`: Precision score at threshold
@@ -966,11 +966,11 @@ def frame(self, with_average_precision: bool = False) -> DataFrame:
         if self.report_type == "estimator":
             indexing_columns = []
         elif self.report_type == "cross-validation":
-            indexing_columns = ["split_index"]
+            indexing_columns = ["split"]
         elif self.report_type == "comparison-estimator":
             indexing_columns = ["estimator_name"]
         else:  # self.report_type == "comparison-cross-validation"
-            indexing_columns = ["estimator_name", "split_index"]
+            indexing_columns = ["estimator_name", "split"]
 
         if self.ml_task == "binary-classification":
             columns = indexing_columns + statistical_columns