81 lines
2.3 KiB
Python
81 lines
2.3 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 that returns a copy of a dataset in a scikit-learn pipeline.
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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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import numpy as np
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from sklearn.base import BaseEstimator
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from scipy.sparse import issparse
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class CopyTransformer(BaseEstimator):
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"""Transformer that returns a copy of the input array
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/preprocessing/CopyTransformer/
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"""
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def __init__(self):
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pass
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def transform(self, X, y=None):
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""" Return a copy 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_copy : copy of the input X array.
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"""
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if isinstance(X, list):
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return np.asarray(X)
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elif isinstance(X, np.ndarray) or issparse(X):
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return X.copy()
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else:
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raise ValueError('X must be a list or NumPy array'
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' or SciPy sparse array. Found %s'
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% type(X))
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def fit_transform(self, X, y=None):
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""" Return a copy 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_copy : copy of the input X array.
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"""
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return self.transform(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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return self
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