fix(EstimatorReport): Use sklearn's FrozenEstimator

Author: auguste-probabl

skore/src/skore/externals/_sklearn_compat.py

@@ -837,9 +837,170 @@ def parametrize_with_checks(
 
         return parametrize_with_checks(estimators)
 
+    from sklearn.base import BaseEstimator
+    from sklearn.utils.metaestimators import available_if
+    from sklearn.utils.metaestimators import available_if
+    from sklearn.utils.validation import check_is_fitted
+
+    def _estimator_has(attr):
+        """Check that final_estimator has `attr`.
+
+        Used together with `available_if`.
+        """
+
+        def check(self):
+            # raise original `AttributeError` if `attr` does not exist
+            getattr(self.estimator, attr)
+            return True
+
+        return check
+
+    class FrozenEstimator(BaseEstimator):
+        """Estimator that wraps a fitted estimator to prevent re-fitting.
+
+        This meta-estimator takes an estimator and freezes it, in the sense that calling
+        `fit` on it has no effect. `fit_predict` and `fit_transform` are also disabled.
+        All other methods are delegated to the original estimator and original estimator's
+        attributes are accessible as well.
+
+        This is particularly useful when you have a fitted or a pre-trained model as a
+        transformer in a pipeline, and you'd like `pipeline.fit` to have no effect on this
+        step.
+
+        Parameters
+        ----------
+        estimator : estimator
+            The estimator which is to be kept frozen.
+
+        See Also
+        --------
+        None: No similar entry in the scikit-learn documentation.
+
+        Examples
+        --------
+        >>> from sklearn.datasets import make_classification
+        >>> from sklearn.frozen import FrozenEstimator
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> X, y = make_classification(random_state=0)
+        >>> clf = LogisticRegression(random_state=0).fit(X, y)
+        >>> frozen_clf = FrozenEstimator(clf)
+        >>> frozen_clf.fit(X, y)  # No-op
+        FrozenEstimator(estimator=LogisticRegression(random_state=0))
+        >>> frozen_clf.predict(X)  # Predictions from `clf.predict`
+        array(...)
+        """
+
+        def __init__(self, estimator):
+            self.estimator = estimator
+
+        @available_if(_estimator_has("__getitem__"))
+        def __getitem__(self, *args, **kwargs):
+            """__getitem__ is defined in :class:`~sklearn.pipeline.Pipeline` and \
+                :class:`~sklearn.compose.ColumnTransformer`.
+            """
+            return self.estimator.__getitem__(*args, **kwargs)
+
+        def __getattr__(self, name):
+            # `estimator`'s attributes are now accessible except `fit_predict` and
+            # `fit_transform`
+            if name in ["fit_predict", "fit_transform"]:
+                raise AttributeError(f"{name} is not available for frozen estimators.")
+            return getattr(self.estimator, name)
+
+        def __sklearn_clone__(self):
+            return self
+
+        def __sklearn_is_fitted__(self):
+            try:
+                check_is_fitted(self.estimator)
+                return True
+            except NotFittedError:
+                return False
+
+        def fit(self, X, y, *args, **kwargs):
+            """No-op.
+
+            As a frozen estimator, calling `fit` has no effect.
+
+            Parameters
+            ----------
+            X : object
+                Ignored.
+
+            y : object
+                Ignored.
+
+            *args : tuple
+                Additional positional arguments. Ignored, but present for API compatibility
+                with `self.estimator`.
+
+            **kwargs : dict
+                Additional keyword arguments. Ignored, but present for API compatibility
+                with `self.estimator`.
+
+            Returns
+            -------
+            self : object
+                Returns the instance itself.
+            """
+            breakpoint()
+            check_is_fitted(self.estimator)
+            return self
+
+        def set_params(self, **kwargs):
+            """Set the parameters of this estimator.
+
+            The only valid key here is `estimator`. You cannot set the parameters of the
+            inner estimator.
+
+            Parameters
+            ----------
+            **kwargs : dict
+                Estimator parameters.
+
+            Returns
+            -------
+            self : FrozenEstimator
+                This estimator.
+            """
+            estimator = kwargs.pop("estimator", None)
+            if estimator is not None:
+                self.estimator = estimator
+            if kwargs:
+                raise ValueError(
+                    "You cannot set parameters of the inner estimator in a frozen "
+                    "estimator since calling `fit` has no effect. You can use "
+                    "`frozenestimator.estimator.set_params` to set parameters of the inner "
+                    "estimator."
+                )
+
+        def get_params(self, deep=True):
+            """Get parameters for this estimator.
+
+            Returns a `{"estimator": estimator}` dict. The parameters of the inner
+            estimator are not included.
+
+            Parameters
+            ----------
+            deep : bool, default=True
+                Ignored.
+
+            Returns
+            -------
+            params : dict
+                Parameter names mapped to their values.
+            """
+            return {"estimator": self.estimator}
+
+        def __sklearn_tags__(self):
+            tags = deepcopy(get_tags(self.estimator))
+            tags._skip_test = True
+            return tags
+
 else:
     # base
     from sklearn.base import is_clusterer  # noqa: F401
+    from sklearn.frozen import FrozenEstimator  # noqa: F401
 
     # test_common
     # tags infrastructure

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

@@ -13,7 +13,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 from skore.externals._pandas_accessors import DirNamesMixin
-from skore.externals._sklearn_compat import is_clusterer
+from skore.externals._sklearn_compat import FrozenEstimator, is_clusterer
 from skore.sklearn._estimator.base import _BaseAccessor, _HelpMixin
 from skore.sklearn.find_ml_task import _find_ml_task
 
@@ -100,6 +100,8 @@ def __init__(
         else:  # fit is False
             self._estimator = estimator
 
+        self._estimator = FrozenEstimator(self._estimator)
+
         # private storage to be able to invalidate the cache when the user alters
         # those attributes
         self._X_train = X_train