Pushing the docs to dev/ for branch: main, commit 4bcd6ea976a485f479ab4a329f8ea06f3615dca3

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: a42bb7a3aa546e8e74c975ecf23d7839
+config: 2395bb72786bda0d07b08b4066341e5a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -492,8 +492,17 @@ def transform(self, X, groups=None):
         check_metadata(self, groups=groups)
         return X
 
+    def fit_transform(self, X, y, sample_weight=None, groups=None):
+        return self.fit(X, y, sample_weight).transform(X, groups)
+
 
 # %%
+# Note that in the above example, we have implemented ``fit_transform`` which
+# calls ``fit`` and ``transform`` with the appropriate metadata. This is only
+# required if ``transform`` accepts metadata, since the default ``fit_transform``
+# implementation in :class:`~base.TransformerMixin` doesn't pass metadata to
+# ``transform``.
+#
 # Now we can test our pipeline, and see if metadata is correctly passed around.
 # This example uses our simple pipeline, and our transformer, and our
 # consumer+router estimator which uses our simple classifier.

dev/_downloads/35686e86ab04c9899b73acd07be52bc7/plot_metadata_routing.py

@@ -497,14 +497,14 @@
       },
       "outputs": [],
       "source": [
-        "class ExampleTransformer(TransformerMixin, BaseEstimator):\n    def fit(self, X, y, sample_weight=None):\n        check_metadata(self, sample_weight=sample_weight)\n        return self\n\n    def transform(self, X, groups=None):\n        check_metadata(self, groups=groups)\n        return X"
+        "class ExampleTransformer(TransformerMixin, BaseEstimator):\n    def fit(self, X, y, sample_weight=None):\n        check_metadata(self, sample_weight=sample_weight)\n        return self\n\n    def transform(self, X, groups=None):\n        check_metadata(self, groups=groups)\n        return X\n\n    def fit_transform(self, X, y, sample_weight=None, groups=None):\n        return self.fit(X, y, sample_weight).transform(X, groups)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now we can test our pipeline, and see if metadata is correctly passed around.\nThis example uses our simple pipeline, and our transformer, and our\nconsumer+router estimator which uses our simple classifier.\n\n"
+        "Note that in the above example, we have implemented ``fit_transform`` which\ncalls ``fit`` and ``transform`` with the appropriate metadata. This is only\nrequired if ``transform`` accepts metadata, since the default ``fit_transform``\nimplementation in :class:`~base.TransformerMixin` doesn't pass metadata to\n``transform``.\n\nNow we can test our pipeline, and see if metadata is correctly passed around.\nThis example uses our simple pipeline, and our transformer, and our\nconsumer+router estimator which uses our simple classifier.\n\n"
       ]
     },
     {