feat: Add cache_predictions method to ComparisonReport

Closes #1346

skore/src/skore/sklearn/_comparison/report.py

@@ -10,6 +10,7 @@
 from skore.externals._pandas_accessors import DirNamesMixin
 from skore.sklearn._base import _BaseReport
 from skore.sklearn._estimator.report import EstimatorReport
+from skore.utils._progress_bar import progress_decorator
 
 
 class ComparisonReport(_BaseReport, DirNamesMixin):
@@ -144,6 +145,9 @@ def __init__(
 
         self.estimator_reports_ = reports
 
+        # used to know if a parent launches a progress bar manager
+        self._parent_progress = None
+
         # NEEDED FOR METRICS ACCESSOR
         self.n_jobs = n_jobs
         self._rng = np.random.default_rng(time.time_ns())
@@ -153,6 +157,103 @@ def __init__(
         self._cache = {}
         self._ml_task = self.estimator_reports_[0]._ml_task
 
+    def clear_cache(self):
+        """Clear the cache.
+
+        Examples
+        --------
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.model_selection import train_test_split
+        >>> from skore import ComparisonReport
+        >>> X, y = make_classification(random_state=42)
+        >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+        >>> estimator_1 = LogisticRegression()
+        >>> estimator_report_1 = EstimatorReport(
+        ...     estimator_1,
+        ...     X_train=X_train,
+        ...     y_train=y_train,
+        ...     X_test=X_test,
+        ...     y_test=y_test
+        ... )
+        >>> estimator_2 = LogisticRegression(C=2)  # Different regularization
+        >>> estimator_report_2 = EstimatorReport(
+        ...     estimator_2,
+        ...     X_train=X_train,
+        ...     y_train=y_train,
+        ...     X_test=X_test,
+        ...     y_test=y_test
+        ... )
+        >>> report = ComparisonReport([estimator_report_1, estimator_report_2])
+        >>> report.cache_predictions()
+        >>> report.clear_cache()
+        >>> report._cache
+        {}
+        """
+        for report in self.estimator_reports_:
+            report.clear_cache()
+        self._cache = {}
+
+    @progress_decorator(description="Estimator predictions")
+    def cache_predictions(self, response_methods="auto", n_jobs=None):
+        """Cache the predictions for sub-estimators reports.
+
+        Parameters
+        ----------
+        response_methods : {"auto", "predict", "predict_proba", "decision_function"},\
+                default="auto
+            The methods to use to compute the predictions.
+
+        n_jobs : int, default=None
+            The number of jobs to run in parallel. If `None`, we use the `n_jobs`
+            parameter when initializing the report.
+
+        Examples
+        --------
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.model_selection import train_test_split
+        >>> from skore import ComparisonReport
+        >>> X, y = make_classification(random_state=42)
+        >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+        >>> estimator_1 = LogisticRegression()
+        >>> estimator_report_1 = EstimatorReport(
+        ...     estimator_1,
+        ...     X_train=X_train,
+        ...     y_train=y_train,
+        ...     X_test=X_test,
+        ...     y_test=y_test
+        ... )
+        >>> estimator_2 = LogisticRegression(C=2)  # Different regularization
+        >>> estimator_report_2 = EstimatorReport(
+        ...     estimator_2,
+        ...     X_train=X_train,
+        ...     y_train=y_train,
+        ...     X_test=X_test,
+        ...     y_test=y_test
+        ... )
+        >>> report = ComparisonReport([estimator_report_1, estimator_report_2])
+        >>> report.cache_predictions()
+        >>> report._cache
+        {...}
+        """
+        if n_jobs is None:
+            n_jobs = self.n_jobs
+
+        progress = self._progress_info["current_progress"]
+        main_task = self._progress_info["current_task"]
+
+        total_estimators = len(self.estimator_reports_)
+        progress.update(main_task, total=total_estimators)
+
+        for estimator_report in self.estimator_reports_:
+            # Pass the progress manager to child tasks
+            estimator_report._parent_progress = progress
+            estimator_report.cache_predictions(
+                response_methods=response_methods, n_jobs=n_jobs
+            )
+            progress.update(main_task, advance=1, refresh=True)
+
     ####################################################################################
     # Methods related to the help and repr
     ####################################################################################