From ba698e6f8efcd882c0c9d070d8c9f62f41365aa7 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 8 Jun 2017 17:45:04 +0200 Subject: [PATCH 1/3] y not needed in transform --- sklearn/cluster/birch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/cluster/birch.py b/sklearn/cluster/birch.py index 63f2720d6d8fb..04d7726743b06 100644 --- a/sklearn/cluster/birch.py +++ b/sklearn/cluster/birch.py @@ -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. From c630fb1ad881196a66533f8c177d425ec835c4f4 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 8 Jun 2017 17:51:52 +0200 Subject: [PATCH 2/3] y not needed in transform --- sklearn/cluster/k_means_.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/cluster/k_means_.py b/sklearn/cluster/k_means_.py index d1b9f264fe759..5014279946637 100644 --- a/sklearn/cluster/k_means_.py +++ b/sklearn/cluster/k_means_.py @@ -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 From 905d59cddc8b93aeda59079b4d85f29b15683411 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 8 Jun 2017 21:18:43 +0200 Subject: [PATCH 3/3] more --- sklearn/decomposition/dict_learning.py | 2 +- sklearn/decomposition/pca.py | 2 +- sklearn/feature_extraction/text.py | 4 +--- sklearn/random_projection.py | 4 +--- 4 files changed, 4 insertions(+), 8 deletions(-) diff --git a/sklearn/decomposition/dict_learning.py b/sklearn/decomposition/dict_learning.py index b9bb0fcea864c..14ed2cf467309 100644 --- a/sklearn/decomposition/dict_learning.py +++ b/sklearn/decomposition/dict_learning.py @@ -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 diff --git a/sklearn/decomposition/pca.py b/sklearn/decomposition/pca.py index 89b9f67ee437c..9781efd57c71b 100644 --- a/sklearn/decomposition/pca.py +++ b/sklearn/decomposition/pca.py @@ -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 diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index 3cf76187350f6..e352d2a99abff 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -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 @@ -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) diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py index 1ec4d0d21e678..8d250add9f575 100644 --- a/sklearn/random_projection.py +++ b/sklearn/random_projection.py @@ -392,7 +392,7 @@ 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 @@ -400,8 +400,6 @@ def transform(self, X, y=None): 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]