77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""
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Feature agglomeration. Base classes and functions for performing feature
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agglomeration.
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"""
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# Author: V. Michel, A. Gramfort
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# License: BSD 3 clause
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import numpy as np
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from ..base import TransformerMixin
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from ..utils import check_array
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from ..utils.validation import check_is_fitted
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from scipy.sparse import issparse
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###############################################################################
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# Mixin class for feature agglomeration.
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class AgglomerationTransform(TransformerMixin):
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"""
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A class for feature agglomeration via the transform interface
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"""
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def transform(self, X):
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"""
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Transform a new matrix using the built clustering
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Parameters
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----------
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X : array-like of shape (n_samples, n_features) or (n_samples,)
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A M by N array of M observations in N dimensions or a length
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M array of M one-dimensional observations.
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Returns
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-------
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Y : array, shape = [n_samples, n_clusters] or [n_clusters]
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The pooled values for each feature cluster.
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"""
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check_is_fitted(self)
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X = check_array(X)
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if len(self.labels_) != X.shape[1]:
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raise ValueError("X has a different number of features than "
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"during fitting.")
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if self.pooling_func == np.mean and not issparse(X):
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size = np.bincount(self.labels_)
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n_samples = X.shape[0]
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# a fast way to compute the mean of grouped features
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nX = np.array([np.bincount(self.labels_, X[i, :]) / size
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for i in range(n_samples)])
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else:
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nX = [self.pooling_func(X[:, self.labels_ == l], axis=1)
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for l in np.unique(self.labels_)]
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nX = np.array(nX).T
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return nX
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def inverse_transform(self, Xred):
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"""
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Inverse the transformation.
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Return a vector of size nb_features with the values of Xred assigned
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to each group of features
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Parameters
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----------
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Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,)
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The values to be assigned to each cluster of samples
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Returns
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-------
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X : array, shape=[n_samples, n_features] or [n_features]
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A vector of size n_samples with the values of Xred assigned to
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each of the cluster of samples.
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"""
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check_is_fitted(self)
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unil, inverse = np.unique(self.labels_, return_inverse=True)
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return Xred[..., inverse]
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