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