FIX TransformedTargetRegressor warns when set_output expects data…

…frame (scikit-learn#29401)

doc/whats_new/v1.5.rst

@@ -13,6 +13,23 @@ For a short description of the main highlights of the release, please refer to
 
 .. include:: changelog_legend.inc
 
+.. _changes_1_5_2:
+
+Version 1.5.2
+=============
+
+**release date of 1.5.2**
+
+Changelog
+---------
+
+:mod:`sklearn.compose`
+......................
+
+- |Fix| Fixed :class:`compose.TransformedTargetRegressor` not to raise `UserWarning` if
+ transform output is set to `pandas` or `polars`, since it isn't a transformer.
+ :pr:`29401` by :user:`Stefanie Senger <StefanieSenger>`.
+
 .. _changes_1_5_1:
 
 Version 1.5.1

sklearn/compose/_target.py

@@ -193,6 +193,10 @@ def _fit_transformer(self, y):
  validate=True,
  check_inverse=self.check_inverse,
  )
+ # We are transforming the target here and not the features, so we set the
+ # output of FunctionTransformer() to be a numpy array (default) and to not
+ # depend on the global configuration:
+ self.transformer_.set_output(transform="default")
  # XXX: sample_weight is not currently passed to the
  # transformer. However, if transformer starts using sample_weight, the
  # code should be modified accordingly. At the time to consider the

sklearn/compose/tests/test_target.py

@@ -1,7 +1,9 @@
+import warnings
+
 import numpy as np
 import pytest
 
-from sklearn import datasets
+from sklearn import config_context, datasets
 from sklearn.base import BaseEstimator, TransformerMixin, clone
 from sklearn.compose import TransformedTargetRegressor
 from sklearn.dummy import DummyRegressor
@@ -393,3 +395,18 @@ def test_transform_target_regressor_pass_extra_predict_parameters():
  regr.fit(X, y)
  regr.predict(X, check_input=False)
  assert regr.regressor_.predict_called
+
+
+@pytest.mark.parametrize("output_format", ["pandas", "polars"])
+def test_transform_target_regressor_not_warns_with_global_output_set(output_format):
+ """Test that TransformedTargetRegressor will not raise warnings if
+ set_config(transform_output="pandas"/"polars") is set globally; regression test for
+ issue #29361."""
+ X, y = datasets.make_regression()
+ y = np.abs(y) + 1
+ with config_context(transform_output=output_format):
+ with warnings.catch_warnings():
+ warnings.simplefilter("error")
+ TransformedTargetRegressor(
+ regressor=LinearRegression(), func=np.log, inverse_func=np.exp
+ ).fit(X, y)