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