scikit-learn · raghavrv · Jun 22, 2017 · Jun 9, 2017 · Jun 9, 2017 · Feb 19, 2017
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -539,6 +539,12 @@ API changes summary
      - ``utils.stats.rankdata``
      - ``neighbors.approximate.LSHForest``
 
+    - Deprecate the ``y`` parameter in `transform` and `inverse_transform`.
+      The method  should not accept ``y`` parameter, as it's used at the prediction time.
+      :issue:`8174` by :user:`Tahar Zanouda <tzano>`, `Alexandre Gramfort`_
+      and `Raghav RV`_.
+
+
 .. _changes_0_18_1:
 
 Version 0.18.1

diff --git a/sklearn/decomposition/base.py b/sklearn/decomposition/base.py
@@ -96,8 +96,7 @@ def fit(X, y=None):
             Returns the instance itself.
         """
 
-
-    def transform(self, X, y=None):
+    def transform(self, X):
         """Apply dimensionality reduction to X.
 
         X is projected on the first principal components previously extracted
@@ -134,7 +133,7 @@ def transform(self, X, y=None):
             X_transformed /= np.sqrt(self.explained_variance_)
         return X_transformed
 
-    def inverse_transform(self, X, y=None):
+    def inverse_transform(self, X):
         """Transform data back to its original space.
 
         In other words, return an input X_original whose transform would be X.

diff --git a/sklearn/decomposition/fastica_.py b/sklearn/decomposition/fastica_.py
@@ -8,13 +8,16 @@
 # Authors: Pierre Lafaye de Micheaux, Stefan van der Walt, Gael Varoquaux,
 #          Bertrand Thirion, Alexandre Gramfort, Denis A. Engemann
 # License: BSD 3 clause
+
 import warnings
+
 import numpy as np
 from scipy import linalg
 
 from ..base import BaseEstimator, TransformerMixin
 from ..externals import six
 from ..externals.six import moves
+from ..externals.six import string_types
 from ..utils import check_array, as_float_array, check_random_state
 from ..utils.validation import check_is_fitted
 from ..utils.validation import FLOAT_DTYPES
@@ -528,22 +531,29 @@ def fit(self, X, y=None):
         self._fit(X, compute_sources=False)
         return self
 
-    def transform(self, X, y=None, copy=True):
+    def transform(self, X, y='deprecated', copy=True):
         """Recover the sources from X (apply the unmixing matrix).
 
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
             Data to transform, where n_samples is the number of samples
             and n_features is the number of features.
-
         copy : bool (optional)
             If False, data passed to fit are overwritten. Defaults to True.
+        y : (ignored)
+            .. deprecated:: 0.19
+               This parameter will be removed in 0.21.
 
         Returns
         -------
         X_new : array-like, shape (n_samples, n_components)
         """
+        if not isinstance(y, string_types) or y != 'deprecated':
+            warnings.warn("The parameter y on transform() is "
+                          "deprecated since 0.19 and will be removed in 0.21",
+                          DeprecationWarning)
+
         check_is_fitted(self, 'mixing_')
 
         X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)

diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py
@@ -770,7 +770,7 @@ def fit_transform(self, X, y=None):
         X = self._fit(X)
         return np.dot(X, self.components_.T)
 
-    def inverse_transform(self, X, y=None):
+    def inverse_transform(self, X):
         """Transform data back to its original space.
 
         Returns an array X_original whose transform would be X.

diff --git a/sklearn/feature_extraction/dict_vectorizer.py b/sklearn/feature_extraction/dict_vectorizer.py
@@ -270,7 +270,7 @@ def inverse_transform(self, X, dict_type=dict):
 
         return dicts
 
-    def transform(self, X, y=None):
+    def transform(self, X):
         """Transform feature->value dicts to array or sparse matrix.
 
         Named features not encountered during fit or fit_transform will be
@@ -281,7 +281,6 @@ def transform(self, X, y=None):
         X : Mapping or iterable over Mappings, length = n_samples
             Dict(s) or Mapping(s) from feature names (arbitrary Python
             objects) to feature values (strings or convertible to dtype).
-        y : (ignored)
 
         Returns
         -------

diff --git a/sklearn/feature_extraction/hashing.py b/sklearn/feature_extraction/hashing.py
@@ -126,7 +126,7 @@ def fit(self, X=None, y=None):
         self._validate_params(self.n_features, self.input_type)
         return self
 
-    def transform(self, raw_X, y=None):
+    def transform(self, raw_X):
         """Transform a sequence of instances to a scipy.sparse matrix.
 
         Parameters
@@ -137,7 +137,6 @@ def transform(self, raw_X, y=None):
             the input_type constructor argument) which will be hashed.
             raw_X need not support the len function, so it can be the result
             of a generator; n_samples is determined on the fly.
-        y : (ignored)
 
         Returns
         -------

diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -509,7 +509,6 @@ def transform(self, X):
         -------
         X : scipy.sparse matrix, shape = (n_samples, self.n_features)
             Document-term matrix.
-
         """
         if isinstance(X, six.string_types):
             raise ValueError(

diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py
@@ -88,7 +88,7 @@ def fit(self, X, y=None):
                                                    size=self.n_components)
         return self
 
-    def transform(self, X, y=None):
+    def transform(self, X):
         """Apply the approximate feature map to X.
 
         Parameters
@@ -178,7 +178,7 @@ def fit(self, X, y=None):
                                                    size=self.n_components)
         return self
 
-    def transform(self, X, y=None):
+    def transform(self, X):
         """Apply the approximate feature map to X.
 
         Parameters
@@ -278,7 +278,7 @@ def fit(self, X, y=None):
             self.sample_interval_ = self.sample_interval
         return self
 
-    def transform(self, X, y=None):
+    def transform(self, X):
         """Apply approximate feature map to X.
 
         Parameters

diff --git a/sklearn/mixture/base.py b/sklearn/mixture/base.py
@@ -321,7 +321,7 @@ def score(self, X, y=None):
         """
         return self.score_samples(X).mean()
 
-    def predict(self, X, y=None):
+    def predict(self, X):
         """Predict the labels for the data samples in X using trained model.
 
         Parameters

diff --git a/sklearn/neighbors/approximate.py b/sklearn/neighbors/approximate.py
@@ -85,7 +85,7 @@ def fit_transform(self, X, y=None):
         self.fit(X)
         return self.transform(X)
 
-    def transform(self, X, y=None):
+    def transform(self, X):
         return self._to_hash(super(ProjectionToHashMixin, self).transform(X))
 
 

diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py
@@ -1,5 +1,8 @@
+import warnings
+
 from ..base import BaseEstimator, TransformerMixin
 from ..utils import check_array
+from ..externals.six import string_types
 
 
 def _identity(X):
@@ -54,6 +57,8 @@ class FunctionTransformer(BaseEstimator, TransformerMixin):
         Indicate that transform should forward the y argument to the
         inner callable.
 
+        .. deprecated::0.19
+
     kw_args : dict, optional
         Dictionary of additional keyword arguments to pass to func.
 
@@ -62,7 +67,7 @@ class FunctionTransformer(BaseEstimator, TransformerMixin):
 
     """
     def __init__(self, func=None, inverse_func=None, validate=True,
-                 accept_sparse=False, pass_y=False,
+                 accept_sparse=False, pass_y='deprecated',
                  kw_args=None, inv_kw_args=None):
         self.func = func
         self.inverse_func = inverse_func
@@ -90,35 +95,51 @@ def fit(self, X, y=None):
             check_array(X, self.accept_sparse)
         return self
 
-    def transform(self, X, y=None):
+    def transform(self, X, y='deprecated'):
         """Transform X using the forward function.
 
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
             Input array.
 
+        y : (ignored)
+            .. deprecated::0.19
+
         Returns
         -------
         X_out : array-like, shape (n_samples, n_features)
             Transformed input.
         """
-        return self._transform(X, y, self.func, self.kw_args)
+        if not isinstance(y, string_types) or y != 'deprecated':
+            warnings.warn("The parameter y on transform() is "
+                          "deprecated since 0.19 and will be removed in 0.21",
+                          DeprecationWarning)
 
-    def inverse_transform(self, X, y=None):
+        return self._transform(X, y=y, func=self.func, kw_args=self.kw_args)
+
+    def inverse_transform(self, X, y='deprecated'):
         """Transform X using the inverse function.
 
         Parameters
         ----------
         X : array-like, shape (n_samples, n_features)
             Input array.
 
+        y : (ignored)
+            .. deprecated::0.19
+
         Returns
         -------
         X_out : array-like, shape (n_samples, n_features)
             Transformed input.
         """
-        return self._transform(X, y, self.inverse_func, self.inv_kw_args)
+        if not isinstance(y, string_types) or y != 'deprecated':
+            warnings.warn("The parameter y on inverse_transform() is "
+                          "deprecated since 0.19 and will be removed in 0.21",
+                          DeprecationWarning)
+        return self._transform(X, y=y, func=self.inverse_func,
+                               kw_args=self.inv_kw_args)
 
     def _transform(self, X, y=None, func=None, kw_args=None):
         if self.validate:
@@ -127,5 +148,14 @@ def _transform(self, X, y=None, func=None, kw_args=None):
         if func is None:
             func = _identity
 
-        return func(X, *((y,) if self.pass_y else ()),
+        if (not isinstance(self.pass_y, string_types) or
+                self.pass_y != 'deprecated'):
+            # We do this to know if pass_y was set to False / True
+            pass_y = self.pass_y
+            warnings.warn("The parameter pass_y is deprecated since 0.19 and "
+                          "will be removed in 0.21", DeprecationWarning)
+        else:
+            pass_y = False
+
+        return func(X, *((y,) if pass_y else ()),
                     **(kw_args if kw_args else {}))