fix(EstimatorReport): Deepcopy estimator

skore/src/skore/sklearn/_estimator/report.py

@@ -1,5 +1,7 @@
+import copy
 import inspect
 import time
+import warnings
 from itertools import product
 
 import joblib
@@ -100,6 +102,18 @@ def __init__(
         else:  # fit is False
             self._estimator = estimator
 
+        try:
+            self._estimator = copy.deepcopy(self._estimator)
+        except Exception as e:
+            warnings.warn(
+                "Deepcopy failed; using estimator as-is. "
+                "Be aware that modifying the estimator outside of "
+                f"{self.__class__.__name__} will modify the internal estimator. "
+                "Consider using a FrozenEstimator from scikit-learn to prevent this. "
+                f"Original error: {e}",
+                stacklevel=1,
+            )
+
         # private storage to be able to invalidate the cache when the user alters
         # those attributes
         self._X_train = X_train

skore/tests/unit/sklearn/test_estimator.py

@@ -187,7 +187,8 @@ def test_estimator_report_from_fitted_estimator(binary_classification_data, fit)
     estimator, X, y = binary_classification_data
     report = EstimatorReport(estimator, fit=fit, X_test=X, y_test=y)
 
-    assert report.estimator is estimator  # we should not clone the estimator
+    check_is_fitted(report.estimator)
+    assert isinstance(report.estimator, RandomForestClassifier)
     assert report.X_train is None
     assert report.y_train is None
     assert report.X_test is X
@@ -209,7 +210,8 @@ def test_estimator_report_from_fitted_pipeline(binary_classification_data_pipeli
     estimator, X, y = binary_classification_data_pipeline
     report = EstimatorReport(estimator, X_test=X, y_test=y)
 
-    assert report.estimator is estimator  # we should not clone the estimator
+    check_is_fitted(report.estimator)
+    assert isinstance(report.estimator, Pipeline)
     assert report.estimator_name == estimator[-1].__class__.__name__
     assert report.X_train is None
     assert report.y_train is None
@@ -950,3 +952,23 @@ def test_estimator_has_side_effects():
     predictions_after = report.estimator.predict_proba(X_test)
 
     np.testing.assert_array_equal(predictions_before, predictions_after)
+
+
+def test_estimator_has_no_deep_copy():
+    X, y = make_classification(n_classes=2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+    estimator = LogisticRegression()
+    # Make it so deepcopy does not work
+    estimator.__reduce_ex__ = None
+    estimator.__reduce__ = None
+
+    with pytest.warns(UserWarning, match="Deepcopy failed"):
+        EstimatorReport(
+            estimator,
+            fit=False,
+            X_train=X_train,
+            X_test=X_test,
+            y_train=y_train,
+            y_test=y_test,
+        )