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[MRG+1] y not needed in transform in Birch #9066

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Merged
merged 3 commits into from
Jun 8, 2017
Merged

[MRG+1] y not needed in transform in Birch #9066

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y not needed in transform
agramfort Jun 8, 2017
c630fb1
y not needed in transform
agramfort Jun 8, 2017
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agramfort Jun 8, 2017
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2 changes: 1 addition & 1 deletion sklearn/cluster/birch.py
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Expand Up @@ -569,7 +569,7 @@ def predict(self, X):
reduced_distance += self._subcluster_norms
return self.subcluster_labels_[np.argmin(reduced_distance, axis=1)]

def transform(self, X, y=None):
def transform(self, X):
"""
Transform X into subcluster centroids dimension.

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2 changes: 1 addition & 1 deletion sklearn/cluster/k_means_.py
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Expand Up @@ -913,7 +913,7 @@ def fit_transform(self, X, y=None):
X = self._check_fit_data(X)
return self.fit(X)._transform(X)

def transform(self, X, y=None):
def transform(self, X):
"""Transform X to a cluster-distance space.

In the new space, each dimension is the distance to the cluster
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2 changes: 1 addition & 1 deletion sklearn/decomposition/dict_learning.py
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Expand Up @@ -803,7 +803,7 @@ def _set_sparse_coding_params(self, n_components,
self.split_sign = split_sign
self.n_jobs = n_jobs

def transform(self, X, y=None):
def transform(self, X):
"""Encode the data as a sparse combination of the dictionary atoms.

Coding method is determined by the object parameter
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2 changes: 1 addition & 1 deletion sklearn/decomposition/pca.py
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Expand Up @@ -726,7 +726,7 @@ def _fit(self, X):

return X

def transform(self, X, y=None):
def transform(self, X):
"""Apply dimensionality reduction on X.

X is projected on the first principal components previous extracted
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4 changes: 1 addition & 3 deletions sklearn/feature_extraction/text.py
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Expand Up @@ -459,7 +459,7 @@ def fit(self, X, y=None):
self._get_hasher().fit(X, y=y)
return self

def transform(self, X, y=None):
def transform(self, X):
"""Transform a sequence of documents to a document-term matrix.

Parameters
Expand All @@ -469,8 +469,6 @@ def transform(self, X, y=None):
unicode strings, file name or file object depending on the
constructor argument) which will be tokenized and hashed.

y : (ignored)

Returns
-------
X : scipy.sparse matrix, shape = (n_samples, self.n_features)
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4 changes: 1 addition & 3 deletions sklearn/random_projection.py
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Expand Up @@ -392,16 +392,14 @@ def fit(self, X, y=None):

return self

def transform(self, X, y=None):
def transform(self, X):
"""Project the data by using matrix product with the random matrix

Parameters
----------
X : numpy array or scipy.sparse of shape [n_samples, n_features]
The input data to project into a smaller dimensional space.

y : is not used: placeholder to allow for usage in a Pipeline.

Returns
-------
X_new : numpy array or scipy sparse of shape [n_samples, n_components]
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