scikit-learn · jeremiedbb · Apr 29, 2024 · Apr 2, 2024 · Apr 2, 2024 · Apr 3, 2024
diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst
@@ -56,6 +56,17 @@ Changed models
   signs across all `PCA` solvers, including the new
   `svd_solver="covariance_eigh"` option introduced in this release.
 
+Changes impacting many modules
+------------------------------
+
+- |API| The name of the input of the `inverse_transform` method of estimators has been
+  standardized to `X`. As a consequence, `Xt` is deprecated and will be removed in
+  version 1.7 in the following estimators: :class:`cluster.FeatureAgglomeration`,
+  :class:`decomposition.MiniBatchNMF`, :class:`decomposition.NMF`,
+  :class:`model_selection.GridSearchCV`, :class:`model_selection.RandomizedSearchCV`,
+  :class:`pipeline.Pipeline` and :class:`preprocessing.KBinsDiscretizer`.
+  :pr:`28756` by :user:`Will Dean <wd60622>`.
+
 Support for Array API
 ---------------------
 

diff --git a/sklearn/cluster/_feature_agglomeration.py b/sklearn/cluster/_feature_agglomeration.py
@@ -6,13 +6,13 @@
 # Author: V. Michel, A. Gramfort
 # License: BSD 3 clause
 
-import warnings
 
 import numpy as np
 from scipy.sparse import issparse
 
 from ..base import TransformerMixin
 from ..utils import metadata_routing
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.validation import check_is_fitted
 
 ###############################################################################
@@ -25,9 +25,9 @@ class AgglomerationTransform(TransformerMixin):
     """
 
     # This prevents ``set_split_inverse_transform`` to be generated for the
-    # non-standard ``Xred`` arg on ``inverse_transform``.
-    # TODO(1.5): remove when Xred is removed for inverse_transform.
-    __metadata_request__inverse_transform = {"Xred": metadata_routing.UNUSED}
+    # non-standard ``Xt`` arg on ``inverse_transform``.
+    # TODO(1.7): remove when Xt is removed for inverse_transform.
+    __metadata_request__inverse_transform = {"Xt": metadata_routing.UNUSED}
 
     def transform(self, X):
         """
@@ -63,43 +63,30 @@ def transform(self, X):
             nX = np.array(nX).T
         return nX
 
-    def inverse_transform(self, Xt=None, Xred=None):
+    def inverse_transform(self, X=None, *, Xt=None):
         """
         Inverse the transformation and return a vector of size `n_features`.
 
         Parameters
         ----------
-        Xt : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+        X : array-like of shape (n_samples, n_clusters) or (n_clusters,)
             The values to be assigned to each cluster of samples.
 
-        Xred : deprecated
-            Use `Xt` instead.
+        Xt : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+            The values to be assigned to each cluster of samples.
 
-            .. deprecated:: 1.3
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
 
         Returns
         -------
         X : ndarray of shape (n_samples, n_features) or (n_features,)
             A vector of size `n_samples` with the values of `Xred` assigned to
             each of the cluster of samples.
         """
-        if Xt is None and Xred is None:
-            raise TypeError("Missing required positional argument: Xt")
-
-        if Xred is not None and Xt is not None:
-            raise ValueError("Please provide only `Xt`, and not `Xred`.")
-
-        if Xred is not None:
-            warnings.warn(
-                (
-                    "Input argument `Xred` was renamed to `Xt` in v1.3 and will be"
-                    " removed in v1.5."
-                ),
-                FutureWarning,
-            )
-            Xt = Xred
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
 
         check_is_fitted(self)
 
         unil, inverse = np.unique(self.labels_, return_inverse=True)
-        return Xt[..., inverse]
+        return X[..., inverse]
diff --git a/sklearn/cluster/tests/test_feature_agglomeration.py b/sklearn/cluster/tests/test_feature_agglomeration.py
@@ -59,23 +59,23 @@ def test_feature_agglomeration_feature_names_out():
     )
 
 
-# TODO(1.5): remove this test
-def test_inverse_transform_Xred_deprecation():
+# TODO(1.7): remove this test
+def test_inverse_transform_Xt_deprecation():
     X = np.array([0, 0, 1]).reshape(1, 3)  # (n_samples, n_features)
 
     est = FeatureAgglomeration(n_clusters=1, pooling_func=np.mean)
     est.fit(X)
-    Xt = est.transform(X)
+    X = est.transform(X)
 
     with pytest.raises(TypeError, match="Missing required positional argument"):
         est.inverse_transform()
 
-    with pytest.raises(ValueError, match="Please provide only"):
-        est.inverse_transform(Xt=Xt, Xred=Xt)
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only."):
+        est.inverse_transform(X=X, Xt=X)
 
     with warnings.catch_warnings(record=True):
         warnings.simplefilter("error")
-        est.inverse_transform(Xt)
+        est.inverse_transform(X)
 
-    with pytest.warns(FutureWarning, match="Input argument `Xred` was renamed to `Xt`"):
-        est.inverse_transform(Xred=Xt)
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        est.inverse_transform(Xt=X)
diff --git a/sklearn/decomposition/_nmf.py b/sklearn/decomposition/_nmf.py
@@ -32,6 +32,7 @@
     StrOptions,
     validate_params,
 )
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
 from ..utils.validation import (
     check_is_fitted,
@@ -1310,44 +1311,32 @@ def fit(self, X, y=None, **params):
         self.fit_transform(X, **params)
         return self
 
-    def inverse_transform(self, Xt=None, W=None):
+    def inverse_transform(self, X=None, *, Xt=None):
         """Transform data back to its original space.
 
         .. versionadded:: 0.18
 
         Parameters
         ----------
-        Xt : {ndarray, sparse matrix} of shape (n_samples, n_components)
+        X : {ndarray, sparse matrix} of shape (n_samples, n_components)
             Transformed data matrix.
 
-        W : deprecated
-            Use `Xt` instead.
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_components)
+            Transformed data matrix.
 
-            .. deprecated:: 1.3
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
 
         Returns
         -------
         X : ndarray of shape (n_samples, n_features)
             Returns a data matrix of the original shape.
         """
-        if Xt is None and W is None:
-            raise TypeError("Missing required positional argument: Xt")
 
-        if W is not None and Xt is not None:
-            raise ValueError("Please provide only `Xt`, and not `W`.")
-
-        if W is not None:
-            warnings.warn(
-                (
-                    "Input argument `W` was renamed to `Xt` in v1.3 and will be removed"
-                    " in v1.5."
-                ),
-                FutureWarning,
-            )
-            Xt = W
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
 
         check_is_fitted(self)
-        return Xt @ self.components_
+        return X @ self.components_
 
     @property
     def _n_features_out(self):

diff --git a/sklearn/decomposition/tests/test_nmf.py b/sklearn/decomposition/tests/test_nmf.py
@@ -933,30 +933,31 @@ def test_minibatch_nmf_verbose():
         sys.stdout = old_stdout
 
 
-# TODO(1.5): remove this test
-def test_NMF_inverse_transform_W_deprecation():
-    rng = np.random.mtrand.RandomState(42)
+# TODO(1.7): remove this test
+@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
+def test_NMF_inverse_transform_Xt_deprecation(Estimator):
+    rng = np.random.RandomState(42)
     A = np.abs(rng.randn(6, 5))
-    est = NMF(
+    est = Estimator(
         n_components=3,
         init="random",
         random_state=0,
         tol=1e-6,
     )
-    Xt = est.fit_transform(A)
+    X = est.fit_transform(A)
 
     with pytest.raises(TypeError, match="Missing required positional argument"):
         est.inverse_transform()
 
-    with pytest.raises(ValueError, match="Please provide only"):
-        est.inverse_transform(Xt=Xt, W=Xt)
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
+        est.inverse_transform(X=X, Xt=X)
 
     with warnings.catch_warnings(record=True):
         warnings.simplefilter("error")
-        est.inverse_transform(Xt)
+        est.inverse_transform(X)
 
-    with pytest.warns(FutureWarning, match="Input argument `W` was renamed to `Xt`"):
-        est.inverse_transform(W=Xt)
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        est.inverse_transform(Xt=X)
 
 
 @pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -36,6 +36,7 @@
 from ..utils._estimator_html_repr import _VisualBlock
 from ..utils._param_validation import HasMethods, Interval, StrOptions
 from ..utils._tags import _safe_tags
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.metadata_routing import (
     MetadataRouter,
     MethodMapping,
@@ -637,26 +638,34 @@ def transform(self, X):
         return self.best_estimator_.transform(X)
 
     @available_if(_estimator_has("inverse_transform"))
-    def inverse_transform(self, Xt):
+    def inverse_transform(self, X=None, Xt=None):
         """Call inverse_transform on the estimator with the best found params.
 
         Only available if the underlying estimator implements
         ``inverse_transform`` and ``refit=True``.
 
         Parameters
         ----------
+        X : indexable, length n_samples
+            Must fulfill the input assumptions of the
+            underlying estimator.
+
         Xt : indexable, length n_samples
             Must fulfill the input assumptions of the
             underlying estimator.
 
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
+
         Returns
         -------
         X : {ndarray, sparse matrix} of shape (n_samples, n_features)
             Result of the `inverse_transform` function for `Xt` based on the
             estimator with the best found parameters.
         """
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
         check_is_fitted(self)
-        return self.best_estimator_.inverse_transform(Xt)
+        return self.best_estimator_.inverse_transform(X)
 
     @property
     def n_features_in_(self):

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
@@ -3,6 +3,7 @@
 import pickle
 import re
 import sys
+import warnings
 from collections.abc import Iterable, Sized
 from functools import partial
 from io import StringIO
@@ -2553,6 +2554,28 @@ def test_search_html_repr():
         assert "<pre>LogisticRegression()</pre>" in repr_html
 
 
+# TODO(1.7): remove this test
+@pytest.mark.parametrize("SearchCV", [GridSearchCV, RandomizedSearchCV])
+def test_inverse_transform_Xt_deprecation(SearchCV):
+    clf = MockClassifier()
+    search = SearchCV(clf, {"foo_param": [1, 2, 3]}, cv=3, verbose=3)
+
+    X2 = search.fit(X, y).transform(X)
+
+    with pytest.raises(TypeError, match="Missing required positional argument"):
+        search.inverse_transform()
+
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
+        search.inverse_transform(X=X2, Xt=X2)
+
+    with warnings.catch_warnings(record=True):
+        warnings.simplefilter("error")
+        search.inverse_transform(X2)
+
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        search.inverse_transform(Xt=X2)
+
+
 # Metadata Routing Tests
 # ======================
 

diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
@@ -29,6 +29,7 @@
 )
 from .utils._tags import _safe_tags
 from .utils._user_interface import _print_elapsed_time
+from .utils.deprecation import _deprecate_Xt_in_inverse_transform
 from .utils.metadata_routing import (
     MetadataRouter,
     MethodMapping,
@@ -909,19 +910,28 @@ def _can_inverse_transform(self):
         return all(hasattr(t, "inverse_transform") for _, _, t in self._iter())
 
     @available_if(_can_inverse_transform)
-    def inverse_transform(self, Xt, **params):
+    def inverse_transform(self, X=None, *, Xt=None, **params):
         """Apply `inverse_transform` for each step in a reverse order.
 
         All estimators in the pipeline must support `inverse_transform`.
 
         Parameters
         ----------
+        X : array-like of shape (n_samples, n_transformed_features)
+            Data samples, where ``n_samples`` is the number of samples and
+            ``n_features`` is the number of features. Must fulfill
+            input requirements of last step of pipeline's
+            ``inverse_transform`` method.
+
         Xt : array-like of shape (n_samples, n_transformed_features)
             Data samples, where ``n_samples`` is the number of samples and
             ``n_features`` is the number of features. Must fulfill
             input requirements of last step of pipeline's
             ``inverse_transform`` method.
 
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
+
         **params : dict of str -> object
             Parameters requested and accepted by steps. Each step must have
             requested certain metadata for these parameters to be forwarded to
@@ -940,15 +950,15 @@ def inverse_transform(self, Xt, **params):
         """
         _raise_for_params(params, self, "inverse_transform")
 
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
+
         # we don't have to branch here, since params is only non-empty if
         # enable_metadata_routing=True.
         routed_params = process_routing(self, "inverse_transform", **params)
         reverse_iter = reversed(list(self._iter()))
         for _, name, transform in reverse_iter:
-            Xt = transform.inverse_transform(
-                Xt, **routed_params[name].inverse_transform
-            )
-        return Xt
+            X = transform.inverse_transform(X, **routed_params[name].inverse_transform)
+        return X
 
     @available_if(_final_estimator_has("score"))
     def score(self, X, y=None, sample_weight=None, **params):