scikit-learn · tzano · Jan 8, 2017 · Jan 11, 2017 · Jan 18, 2017 · Jan 19, 2017
diff --git a/sklearn/cluster/birch.py b/sklearn/cluster/birch.py
@@ -88,6 +88,7 @@ def _split_node(node, threshold, branching_factor):
 
 
 class _CFNode(object):
+
     """Each node in a CFTree is called a CFNode.
 
     The CFNode can have a maximum of branching_factor
@@ -134,6 +135,7 @@ class _CFNode(object):
         view of ``init_sq_norm_``.
 
     """
+
     def __init__(self, threshold, branching_factor, is_leaf, n_features):
         self.threshold = threshold
         self.branching_factor = branching_factor
@@ -241,6 +243,7 @@ def insert_cf_subcluster(self, subcluster):
 
 
 class _CFSubcluster(object):
+
     """Each subcluster in a CFNode is called a CFSubcluster.
 
     A CFSubcluster can have a CFNode has its child.
@@ -275,6 +278,7 @@ class _CFSubcluster(object):
         Squared norm of the subcluster. Used to prevent recomputing when
         pairwise minimum distances are computed.
     """
+
     def __init__(self, linear_sum=None):
         if linear_sum is None:
             self.n_samples_ = 0
@@ -322,6 +326,7 @@ def radius(self):
 
 
 class Birch(BaseEstimator, TransformerMixin, ClusterMixin):
+
     """Implements the Birch clustering algorithm.
 
     Every new sample is inserted into the root of the Clustering Feature
@@ -565,6 +570,11 @@ def transform(self, X, y=None):
         X_trans : {array-like, sparse matrix}, shape (n_samples, n_clusters)
             Transformed data.
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
+
         check_is_fitted(self, 'subcluster_centers_')
         return euclidean_distances(X, self.subcluster_centers_)
 

diff --git a/sklearn/cluster/k_means_.py b/sklearn/cluster/k_means_.py
@@ -704,6 +704,7 @@ def _init_centroids(X, k, init, random_state=None, x_squared_norms=None,
 
 
 class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
+
     """K-Means clustering
 
     Read more in the :ref:`User Guide <k_means>`.
@@ -928,8 +929,12 @@ def transform(self, X, y=None):
         X_new : array, shape [n_samples, k]
             X transformed in the new space.
         """
-        check_is_fitted(self, 'cluster_centers_')
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
 
+        check_is_fitted(self, 'cluster_centers_')
         X = self._check_test_data(X)
         return self._transform(X)
 
@@ -1183,6 +1188,7 @@ def _mini_batch_convergence(model, iteration_idx, n_iter, tol,
 
 
 class MiniBatchKMeans(KMeans):
+
     """Mini-Batch K-Means clustering
 
     Read more in the :ref:`User Guide <mini_batch_kmeans>`.

diff --git a/sklearn/decomposition/base.py b/sklearn/decomposition/base.py
@@ -8,6 +8,8 @@
 #
 # License: BSD 3 clause
 
+import warnings
+
 import numpy as np
 from scipy import linalg
 
@@ -20,11 +22,13 @@
 
 
 class _BasePCA(six.with_metaclass(ABCMeta, BaseEstimator, TransformerMixin)):
+
     """Base class for PCA methods.
 
     Warning: This class should not be used directly.
     Use derived classes instead.
     """
+
     def get_covariance(self):
         """Compute data covariance with the generative model.
 
@@ -97,7 +101,6 @@ def fit(X, y=None):
             Returns the instance itself.
         """
 
-
     def transform(self, X, y=None):
         """Apply dimensionality reduction to X.
 
@@ -125,6 +128,11 @@ def transform(self, X, y=None):
         IncrementalPCA(batch_size=3, copy=True, n_components=2, whiten=False)
         >>> ipca.transform(X) # doctest: +SKIP
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
+
         check_is_fitted(self, ['mean_', 'components_'], all_or_any=all)
 
         X = check_array(X)

diff --git a/sklearn/decomposition/dict_learning.py b/sklearn/decomposition/dict_learning.py
@@ -4,6 +4,8 @@
 # Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
 # License: BSD 3 clause
 
+import warnings
+
 import time
 import sys
 import itertools
@@ -145,7 +147,7 @@ def _sparse_encode(X, dictionary, gram, cov=None, algorithm='lasso_lars',
 
     elif algorithm == 'threshold':
         new_code = ((np.sign(cov) *
-                    np.maximum(np.abs(cov) - regularization, 0)).T)
+                     np.maximum(np.abs(cov) - regularization, 0)).T)
 
     elif algorithm == 'omp':
         # TODO: Should verbose argument be passed to this?
@@ -722,8 +724,8 @@ def dict_learning_online(X, n_components=2, alpha=1, n_iter=100,
             sys.stdout.flush()
         elif verbose:
             if verbose > 10 or ii % ceil(100. / verbose) == 0:
-                print ("Iteration % 3i (elapsed time: % 3is, % 4.1fmn)"
-                       % (ii, dt, dt / 60))
+                print("Iteration % 3i (elapsed time: % 3is, % 4.1fmn)"
+                      % (ii, dt, dt / 60))
 
         this_code = sparse_encode(this_X, dictionary.T, algorithm=method,
                                   alpha=alpha, n_jobs=n_jobs).T
@@ -777,6 +779,7 @@ def dict_learning_online(X, n_components=2, alpha=1, n_iter=100,
 
 
 class SparseCodingMixin(TransformerMixin):
+
     """Sparse coding mixin"""
 
     def _set_sparse_coding_params(self, n_components,
@@ -809,6 +812,11 @@ def transform(self, X, y=None):
             Transformed data
 
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
+
         check_is_fitted(self, 'components_')
 
         # XXX : kwargs is not documented
@@ -832,6 +840,7 @@ def transform(self, X, y=None):
 
 
 class SparseCoder(BaseEstimator, SparseCodingMixin):
+
     """Sparse coding
 
     Finds a sparse representation of data against a fixed, precomputed
@@ -917,6 +926,7 @@ def fit(self, X, y=None):
 
 
 class DictionaryLearning(BaseEstimator, SparseCodingMixin):
+
     """Dictionary learning
 
     Finds a dictionary (a set of atoms) that can best be used to represent data
@@ -1028,6 +1038,7 @@ class DictionaryLearning(BaseEstimator, SparseCodingMixin):
     SparsePCA
     MiniBatchSparsePCA
     """
+
     def __init__(self, n_components=None, alpha=1, max_iter=1000, tol=1e-8,
                  fit_algorithm='lars', transform_algorithm='omp',
                  transform_n_nonzero_coefs=None, transform_alpha=None,
@@ -1083,6 +1094,7 @@ def fit(self, X, y=None):
 
 
 class MiniBatchDictionaryLearning(BaseEstimator, SparseCodingMixin):
+
     """Mini-batch dictionary learning
 
     Finds a dictionary (a set of atoms) that can best be used to represent data
@@ -1194,6 +1206,7 @@ class MiniBatchDictionaryLearning(BaseEstimator, SparseCodingMixin):
     MiniBatchSparsePCA
 
     """
+
     def __init__(self, n_components=None, alpha=1, n_iter=1000,
                  fit_algorithm='lars', n_jobs=1, batch_size=3,
                  shuffle=True, dict_init=None, transform_algorithm='omp',

diff --git a/sklearn/decomposition/fastica_.py b/sklearn/decomposition/fastica_.py
@@ -293,7 +293,8 @@ def g(x, fun_args):
         n_components = min(n, p)
     if (n_components > min(n, p)):
         n_components = min(n, p)
-        warnings.warn('n_components is too large: it will be set to %s' % n_components)
+        warnings.warn(
+            'n_components is too large: it will be set to %s' % n_components)
 
     if whiten:
         # Centering the columns (ie the variables)
@@ -316,8 +317,8 @@ def g(x, fun_args):
         X1 = as_float_array(X, copy=False)  # copy has been taken care of
 
     if w_init is None:
-        w_init = np.asarray(random_state.normal(size=(n_components,
-                            n_components)), dtype=X1.dtype)
+        w_init = np.asarray(random_state.normal(
+                            size=(n_components, n_components)), dtype=X1.dtype)
 
     else:
         w_init = np.asarray(w_init)
@@ -374,6 +375,7 @@ def g(x, fun_args):
 
 
 class FastICA(BaseEstimator, TransformerMixin):
+
     """FastICA: a fast algorithm for Independent Component Analysis.
 
     Read more in the :ref:`User Guide <ICA>`.
@@ -439,6 +441,7 @@ def my_g(x):
     pp. 411-430`
 
     """
+
     def __init__(self, n_components=None, algorithm='parallel', whiten=True,
                  fun='logcosh', fun_args=None, max_iter=200, tol=1e-4,
                  w_init=None, random_state=None):
@@ -539,6 +542,11 @@ def transform(self, X, y=None, copy=True):
         -------
         X_new : array-like, shape (n_samples, n_components)
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          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
@@ -10,6 +10,7 @@
 #
 # License: BSD 3 clause
 
+import warnings
 from math import log, sqrt
 
 import numpy as np
@@ -105,6 +106,7 @@ def _infer_dimension_(spectrum, n_samples, n_features):
 
 
 class PCA(_BasePCA):
+
     """Principal component analysis (PCA)
 
     Linear dimensionality reduction using Singular Value Decomposition of the
@@ -558,6 +560,7 @@ def score(self, X, y=None):
             "DOES NOT store whiten ``components_``. Apply transform to get "
             "them.")
 class RandomizedPCA(BaseEstimator, TransformerMixin):
+
     """Principal component analysis (PCA) using randomized SVD
 
     .. deprecated:: 0.18
@@ -736,6 +739,11 @@ def transform(self, X, y=None):
         X_new : array-like, shape (n_samples, n_components)
 
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
+
         check_is_fitted(self, 'mean_')
 
         X = check_array(X)

diff --git a/sklearn/feature_extraction/dict_vectorizer.py b/sklearn/feature_extraction/dict_vectorizer.py
@@ -2,6 +2,7 @@
 #          Dan Blanchard <dblanchard@ets.org>
 # License: BSD 3 clause
 
+import warnings
 from array import array
 from collections import Mapping
 from operator import itemgetter
@@ -25,6 +26,7 @@ def _tosequence(X):
 
 
 class DictVectorizer(BaseEstimator, TransformerMixin):
+
     """Transforms lists of feature-value mappings to vectors.
 
     This transformer turns lists of mappings (dict-like objects) of feature
@@ -289,6 +291,11 @@ def transform(self, X, y=None):
         Xa : {array, sparse matrix}
             Feature vectors; always 2-d.
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
+
         if self.sparse:
             return self._transform(X, fitting=False)
 

diff --git a/sklearn/feature_extraction/hashing.py b/sklearn/feature_extraction/hashing.py
@@ -1,6 +1,7 @@
 # Author: Lars Buitinck
 # License: BSD 3 clause
 
+import warnings
 import numbers
 
 import numpy as np
@@ -16,6 +17,7 @@ def _iteritems(d):
 
 
 class FeatureHasher(BaseEstimator, TransformerMixin):
+
     """Implements feature hashing, aka the hashing trick.
 
     This class turns sequences of symbolic feature names (strings) into
@@ -133,6 +135,11 @@ def transform(self, raw_X, y=None):
             Feature matrix, for use with estimators or further transformers.
 
         """
+        if y is not None:
+            warnings.warn('y is deprecated and will be'
+                          ' removed in a future version',
+                          DeprecationWarning)
+
         raw_X = iter(raw_X)
         if self.input_type == "dict":
             raw_X = (_iteritems(d) for d in raw_X)