deprecate Xred and W in inverse_transforms

adrinjalali · Jun 8, 2023 · addf76e · addf76e
1 parent 253e1af
commit addf76e
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Showing 4 changed files with 110 additions and 6 deletions.
diff --git a/sklearn/cluster/_feature_agglomeration.py b/sklearn/cluster/_feature_agglomeration.py
@@ -5,6 +5,7 @@
 # Author: V. Michel, A. Gramfort
 # License: BSD 3 clause
 
+import warnings
 import numpy as np
 
 from ..base import TransformerMixin
@@ -54,22 +55,40 @@ def transform(self, X):
             nX = np.array(nX).T
         return nX
 
-    def inverse_transform(self, Xred):
+    def inverse_transform(self, Xt=None, Xred=None):
         """
         Inverse the transformation and return a vector of size `n_features`.
 
         Parameters
         ----------
-        Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+        Xt : 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.
+
+            .. deprecated:: 1.3
+
         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` renamed to `Xt` and will be removed in v1.5.",
+                FutureWarning,
+            )
+            Xt = Xred
+
         check_is_fitted(self)
 
         unil, inverse = np.unique(self.labels_, return_inverse=True)
-        return Xred[..., inverse]
+        return Xt[..., inverse]
diff --git a/sklearn/cluster/tests/test_feature_agglomeration.py b/sklearn/cluster/tests/test_feature_agglomeration.py
@@ -2,9 +2,11 @@
 Tests for sklearn.cluster._feature_agglomeration
 """
 # Authors: Sergul Aydore 2017
+import warnings
 import numpy as np
 
 from numpy.testing import assert_array_equal
+import pytest
 from sklearn.cluster import FeatureAgglomeration
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.datasets import make_blobs
@@ -53,3 +55,33 @@ def test_feature_agglomeration_feature_names_out():
     assert_array_equal(
         [f"featureagglomeration{i}" for i in range(n_clusters)], names_out
     )
+
+
+def test_inverse_transform_Xred_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)
+
+    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 warnings.catch_warnings(record=True) as w:
+        est.inverse_transform(Xt)
+
+        assert len(w) == 0
+
+    # This try / catch is to make sure passing -Werror::FutureWarning doesn't
+    # make this test to raise / fail.
+    try:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("ignore")
+            est.inverse_transform(Xred=Xt)
+
+            assert len(w) == 1
+    except Exception:
+        pass
diff --git a/sklearn/decomposition/_nmf.py b/sklearn/decomposition/_nmf.py
@@ -1245,23 +1245,41 @@ def fit(self, X, y=None, **params):
         self.fit_transform(X, **params)
         return self
 
-    def inverse_transform(self, W):
+    def inverse_transform(self, Xt=None, W=None):
         """Transform data back to its original space.
 
         .. versionadded:: 0.18
 
         Parameters
         ----------
-        W : {ndarray, sparse matrix} of shape (n_samples, n_components)
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_components)
             Transformed data matrix.
 
+        W : deprecated
+            Use `Xt` instead.
+
+            .. deprecated:: 1.3
+
         Returns
         -------
         X : {ndarray, sparse matrix} 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` renamed to `Xt` and will be removed in v1.5.",
+                FutureWarning,
+            )
+            Xt = W
+
         check_is_fitted(self)
-        return W @ self.components_
+        return Xt @ self.components_
 
     @property
     def _n_features_out(self):

diff --git a/sklearn/decomposition/tests/test_nmf.py b/sklearn/decomposition/tests/test_nmf.py
@@ -1,6 +1,7 @@
 import re
 import sys
 from io import StringIO
+import warnings
 
 import numpy as np
 import scipy.sparse as sp
@@ -906,3 +907,37 @@ def test_minibatch_nmf_verbose():
         nmf.fit(A)
     finally:
         sys.stdout = old_stdout
+
+
+def test_NMF_inverse_transform_W_deprecation():
+    rng = np.random.mtrand.RandomState(42)
+    A = np.abs(rng.randn(6, 5))
+    est = NMF(
+        n_components=3,
+        init="random",
+        random_state=0,
+        tol=1e-6,
+    )
+    Xt = 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 warnings.catch_warnings(record=True) as w:
+        est.inverse_transform(Xt)
+
+        assert len(w) == 0
+
+    # This try / catch is to make sure passing -Werror::FutureWarning doesn't
+    # make this test to raise / fail.
+    try:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("ignore")
+            est.inverse_transform(W=Xt)
+
+            assert len(w) == 1
+    except Exception:
+        pass