xhluca
diff --git a/‎sklearn/compose/_target.py
Lines changed: 7 additions & 14 deletions b/‎sklearn/compose/_target.py
Lines changed: 7 additions & 14 deletions
diff --git a/‎sklearn/compose/tests/test_target.py
Lines changed: 0 additions & 28 deletions b/‎sklearn/compose/tests/test_target.py
Lines changed: 0 additions & 28 deletions
@@ -113,12 +113,6 @@ def __init__(self, regressor=None, transformer=None,
         self.check_inverse = check_inverse
 
     def _fit_transformer(self, y):
-        """Check transformer and fit transformer.
-
-        Create the default transformer, fit it and make additional inverse
-        check on a subset (optional).
-
-        """
         if (self.transformer is not None and
                 (self.func is not None or self.inverse_func is not None)):
             raise ValueError("'transformer' and functions 'func'/"
@@ -183,20 +177,19 @@ def fit(self, X, y, sample_weight=None):
             y_2d = y
         self._fit_transformer(y_2d)
 
-        # transform y and convert back to 1d array if needed
-        y_trans = self.transformer_.transform(y_2d)
-        # FIXME: a FunctionTransformer can return a 1D array even when validate
-        # is set to True. Therefore, we need to check the number of dimension
-        # first.
-        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()
         else:
             self.regressor_ = clone(self.regressor)
 
+        # transform y and convert back to 1d array if needed
+        y_trans = self.transformer_.fit_transform(y_2d)
+        # FIXME: a FunctionTransformer can return a 1D array even when validate
+        # is set to True. Therefore, we need to check the number of dimension
+        # first.
+        if y_trans.ndim == 2 and y_trans.shape[1] == 1:
+            y_trans = y_trans.squeeze(axis=1)
         if sample_weight is None:
             self.regressor_.fit(X, y_trans)
         else:
 
@@ -265,31 +265,3 @@ def test_transform_target_regressor_ensure_y_array():
     tt.predict(X.tolist())
     assert_raises(AssertionError, tt.fit, X, y.tolist())
     assert_raises(AssertionError, tt.predict, X)
-
-
-class DummyTransformer(BaseEstimator, TransformerMixin):
-    """Dummy transformer which count how many time fit was called."""
-    def __init__(self, fit_counter=0):
-        self.fit_counter = fit_counter
-
-    def fit(self, X, y=None):
-        self.fit_counter += 1
-        return self
-
-    def transform(self, X):
-        return X
-
-    def inverse_transform(self, X):
-        return X
-
-
-@pytest.mark.parametrize("check_inverse", [False, True])
-def test_transform_target_regressor_count_fit(check_inverse):
-    # regression test for gh-issue #11618
-    # check that we only call a single time fit for the transformer
-    X, y = friedman
-    ttr = TransformedTargetRegressor(
-        transformer=DummyTransformer(), check_inverse=check_inverse
-    )
-    ttr.fit(X, y)
-    assert ttr.transformer_.fit_counter == 1