scikit-learn · glemaitre · Jun 10, 2024 · May 30, 2024 · May 30, 2024 · May 30, 2024
diff --git a/doc/metadata_routing.rst b/doc/metadata_routing.rst
@@ -276,6 +276,7 @@ Meta-estimators and functions supporting metadata routing:
 
 - :class:`sklearn.calibration.CalibratedClassifierCV`
 - :class:`sklearn.compose.ColumnTransformer`
+- :class:`sklearn.compose.TransformedTargetRegressor`
 - :class:`sklearn.covariance.GraphicalLassoCV`
 - :class:`sklearn.ensemble.StackingClassifier`
 - :class:`sklearn.ensemble.StackingRegressor`
@@ -316,7 +317,6 @@ Meta-estimators and functions supporting metadata routing:
 
 Meta-estimators and tools not supporting metadata routing yet:
 
-- :class:`sklearn.compose.TransformedTargetRegressor`
 - :class:`sklearn.ensemble.AdaBoostClassifier`
 - :class:`sklearn.ensemble.AdaBoostRegressor`
 - :class:`sklearn.feature_selection.RFE`

diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
@@ -60,6 +60,11 @@ more details.
   ``**fit_params`` to the underlying estimators via their `fit` methods.
   :pr:`28701` by :user:`Stefanie Senger <StefanieSenger>`.
 
+- |Feature| :class:`compose.TransformedTargetRegressor` now supports metadata
+  routing in its `fit` and `predict` methods and routes the corresponding
+  params to the underlying regressor.
+  :pr:`29136` by :user:`Omar Salman <OmarManzoor>`.
+
 Dropping official support for PyPy
 ----------------------------------
 

diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py
@@ -8,12 +8,18 @@
 
 from ..base import BaseEstimator, RegressorMixin, _fit_context, clone
 from ..exceptions import NotFittedError
+from ..linear_model import LinearRegression
 from ..preprocessing import FunctionTransformer
-from ..utils import _safe_indexing, check_array
+from ..utils import Bunch, _safe_indexing, check_array
+from ..utils._metadata_requests import (
+    MetadataRouter,
+    MethodMapping,
+    _routing_enabled,
+    process_routing,
+)
 from ..utils._param_validation import HasMethods
 from ..utils._tags import _safe_tags
 from ..utils.metadata_routing import (
-    _raise_for_unsupported_routing,
     _RoutingNotSupportedMixin,
 )
 from ..utils.validation import check_is_fitted
@@ -230,15 +236,25 @@ def fit(self, X, y, **fit_params):
             Target values.
 
         **fit_params : dict
-            Parameters passed to the `fit` method of the underlying
-            regressor.
+            - If `enable_metadata_routing=False` (default):
+
+                Parameters directly passed to the `fit` method of the
+                underlying regressor.
+
+            - If `enable_metadata_routing=True`:
+
+                Parameters safely routed to the `fit` method of the
+                underlying regressor.
+
+                .. versionchanged:: 1.6
+                    See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                    more details.
 
         Returns
         -------
         self : object
             Fitted estimator.
         """
-        _raise_for_unsupported_routing(self, "fit", **fit_params)
         if y is None:
             raise ValueError(
                 f"This {self.__class__.__name__} estimator "
@@ -274,14 +290,13 @@ def fit(self, X, y, **fit_params):
         if y_trans.ndim == 2 and y_trans.shape[1] == 1:
             y_trans = y_trans.squeeze(axis=1)
 
-        if self.regressor is None:
-            from ..linear_model import LinearRegression
-
-            self.regressor_ = LinearRegression()
+        self.regressor_ = self._get_regressor(get_clone=True)
+        if _routing_enabled():
+            routed_params = process_routing(self, "fit", **fit_params)
         else:
-            self.regressor_ = clone(self.regressor)
+            routed_params = Bunch(regressor=Bunch(fit=fit_params))
 
-        self.regressor_.fit(X, y_trans, **fit_params)
+        self.regressor_.fit(X, y_trans, **routed_params.regressor.fit)
 
         if hasattr(self.regressor_, "feature_names_in_"):
             self.feature_names_in_ = self.regressor_.feature_names_in_
@@ -300,16 +315,32 @@ def predict(self, X, **predict_params):
             Samples.
 
         **predict_params : dict of str -> object
-            Parameters passed to the `predict` method of the underlying
-            regressor.
+            - If `enable_metadata_routing=False` (default):
+
+                Parameters directly passed to the `predict` method of the
+                underlying regressor.
+
+            - If `enable_metadata_routing=True`:
+
+                Parameters safely routed to the `predict` method of the
+                underlying regressor.
+
+                .. versionchanged:: 1.6
+                    See :ref:`Metadata Routing User Guide <metadata_routing>`
+                    for more details.
 
         Returns
         -------
         y_hat : ndarray of shape (n_samples,)
             Predicted values.
         """
         check_is_fitted(self)
-        pred = self.regressor_.predict(X, **predict_params)
+        if _routing_enabled():
+            routed_params = process_routing(self, "predict", **predict_params)
+        else:
+            routed_params = Bunch(regressor=Bunch(predict=predict_params))
+
+        pred = self.regressor_.predict(X, **routed_params.regressor.predict)
         if pred.ndim == 1:
             pred_trans = self.transformer_.inverse_transform(pred.reshape(-1, 1))
         else:
@@ -324,11 +355,7 @@ def predict(self, X, **predict_params):
         return pred_trans
 
     def _more_tags(self):
-        regressor = self.regressor
-        if regressor is None:
-            from ..linear_model import LinearRegression
-
-            regressor = LinearRegression()
+        regressor = self._get_regressor()
 
         return {
             "poor_score": True,
@@ -350,3 +377,31 @@ def n_features_in_(self):
             ) from nfe
 
         return self.regressor_.n_features_in_
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.6
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            regressor=self._get_regressor(),
+            method_mapping=MethodMapping()
+            .add(caller="fit", callee="fit")
+            .add(caller="predict", callee="predict"),
+        )
+        return router
+
+    def _get_regressor(self, get_clone=False):
+        if self.regressor is None:
+            return LinearRegression()
+
+        return clone(self.regressor) if get_clone else self.regressor
diff --git a/sklearn/tests/test_metaestimators_metadata_routing.py b/sklearn/tests/test_metaestimators_metadata_routing.py
@@ -382,6 +382,14 @@ def enable_slep006():
         "cv_name": "cv",
         "cv_routing_methods": ["fit"],
     },
+    {
+        "metaestimator": TransformedTargetRegressor,
+        "estimator": "regressor",
+        "estimator_name": "regressor",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit", "predict"],
+    },
 ]
 """List containing all metaestimators to be tested and their settings
 
@@ -427,7 +435,6 @@ def enable_slep006():
     RFECV(ConsumingClassifier()),
     SelfTrainingClassifier(ConsumingClassifier()),
     SequentialFeatureSelector(ConsumingClassifier()),
-    TransformedTargetRegressor(),
 ]