83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
|
# Sebastian Raschka 2014-2020
|
||
|
|
# mlxtend Machine Learning Library Extensions
|
||
|
|
#
|
||
|
|
# A class for transforming sparse numpy arrays into dense arrays.
|
||
|
|
# Author: Sebastian Raschka <sebastianraschka.com>
|
||
|
|
#
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
|
||
|
|
from sklearn.base import BaseEstimator
|
||
|
|
from scipy.sparse import issparse
|
||
|
|
|
||
|
|
|
||
|
|
class DenseTransformer(BaseEstimator):
|
||
|
|
"""
|
||
|
|
Convert a sparse array into a dense array.
|
||
|
|
|
||
|
|
For usage examples, please see
|
||
|
|
http://rasbt.github.io/mlxtend/user_guide/preprocessing/DenseTransformer/
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, return_copy=True):
|
||
|
|
self.return_copy = return_copy
|
||
|
|
self.is_fitted = False
|
||
|
|
|
||
|
|
def transform(self, X, y=None):
|
||
|
|
""" Return a dense version of the input array.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
||
|
|
Training vectors, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y : array-like, shape = [n_samples] (default: None)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
---------
|
||
|
|
X_dense : dense version of the input X array.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if issparse(X):
|
||
|
|
return X.toarray()
|
||
|
|
elif self.return_copy:
|
||
|
|
return X.copy()
|
||
|
|
else:
|
||
|
|
return X
|
||
|
|
|
||
|
|
def fit(self, X, y=None):
|
||
|
|
""" Mock method. Does nothing.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
||
|
|
Training vectors, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y : array-like, shape = [n_samples] (default: None)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
---------
|
||
|
|
self
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.is_fitted = True
|
||
|
|
return self
|
||
|
|
|
||
|
|
def fit_transform(self, X, y=None):
|
||
|
|
""" Return a dense version of the input array.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
||
|
|
Training vectors, where n_samples is the number of samples and
|
||
|
|
n_features is the number of features.
|
||
|
|
y : array-like, shape = [n_samples] (default: None)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
---------
|
||
|
|
X_dense : dense version of the input X array.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return self.transform(X=X, y=y)
|