BUG Fixes FunctionTransformer validation in inverse_transform (#20961)

thomasjpfan · adrinjalali · commit c42edd3ef2ad · 2021-09-14T13:28:03.000+02:00
diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst
@@ -824,6 +824,9 @@ Changelog
   `n_features_in_` and will be removed in 1.2. :pr:`20240` by
   :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
+- |Fix| :class:`preprocessing.FunctionTransformer` does not set `n_features_in_`
+  based on the input to `inverse_transform`. :pr:`20961` by `Thomas Fan`_.
+
 :mod:`sklearn.svm`
 ...................
 
diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py
@@ -1,7 +1,7 @@
 import warnings
 
 from ..base import BaseEstimator, TransformerMixin
-from ..utils.validation import _allclose_dense_sparse
+from ..utils.validation import _allclose_dense_sparse, check_array
 
 
 def _identity(X):
@@ -71,6 +71,20 @@ class FunctionTransformer(TransformerMixin, BaseEstimator):
 
         .. versionadded:: 0.18
 
+    Attributes
+    ----------
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Defined only when
+        `validate=True`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `validate=True`
+        and `X` has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     MaxAbsScaler : Scale each feature by its maximum absolute value.
@@ -110,9 +124,9 @@ def __init__(
         self.kw_args = kw_args
         self.inv_kw_args = inv_kw_args
 
-    def _check_input(self, X):
+    def _check_input(self, X, *, reset):
         if self.validate:
-            return self._validate_data(X, accept_sparse=self.accept_sparse)
+            return self._validate_data(X, accept_sparse=self.accept_sparse, reset=reset)
         return X
 
     def _check_inverse_transform(self, X):
@@ -146,7 +160,7 @@ def fit(self, X, y=None):
         self : object
             FunctionTransformer class instance.
         """
-        X = self._check_input(X)
+        X = self._check_input(X, reset=True)
         if self.check_inverse and not (self.func is None or self.inverse_func is None):
             self._check_inverse_transform(X)
         return self
@@ -164,6 +178,7 @@ def transform(self, X):
         X_out : array-like, shape (n_samples, n_features)
             Transformed input.
         """
+        X = self._check_input(X, reset=False)
         return self._transform(X, func=self.func, kw_args=self.kw_args)
 
     def inverse_transform(self, X):
@@ -179,11 +194,11 @@ def inverse_transform(self, X):
         X_out : array-like, shape (n_samples, n_features)
             Transformed input.
         """
+        if self.validate:
+            X = check_array(X, accept_sparse=self.accept_sparse)
         return self._transform(X, func=self.inverse_func, kw_args=self.inv_kw_args)
 
     def _transform(self, X, func=None, kw_args=None):
-        X = self._check_input(X)
-
         if func is None:
             func = _identity
 
diff --git a/sklearn/preprocessing/tests/test_function_transformer.py b/sklearn/preprocessing/tests/test_function_transformer.py
@@ -174,3 +174,27 @@ def test_function_transformer_frame():
     transformer = FunctionTransformer()
     X_df_trans = transformer.fit_transform(X_df)
     assert hasattr(X_df_trans, "loc")
+
+
+def test_function_transformer_validate_inverse():
+    """Test that function transformer does not reset estimator in
+    `inverse_transform`."""
+
+    def add_constant_feature(X):
+        X_one = np.ones((X.shape[0], 1))
+        return np.concatenate((X, X_one), axis=1)
+
+    def inverse_add_constant(X):
+        return X[:, :-1]
+
+    X = np.array([[1, 2], [3, 4], [3, 4]])
+    trans = FunctionTransformer(
+        func=add_constant_feature,
+        inverse_func=inverse_add_constant,
+        validate=True,
+    )
+    X_trans = trans.fit_transform(X)
+    assert trans.n_features_in_ == X.shape[1]
+
+    trans.inverse_transform(X_trans)
+    assert trans.n_features_in_ == X.shape[1]
