import scipy
import numpy as np
from sklearn import linear_model
from skfeature.utility.construct_W import construct_W
def mcfs(X, n_selected_features, **kwargs):
"""
This function implements unsupervised feature selection for multi-cluster data.
Input
-----
X: {numpy array}, shape (n_samples, n_features)
input data
n_selected_features: {int}
number of features to select
kwargs: {dictionary}
W: {sparse matrix}, shape (n_samples, n_samples)
affinity matrix
n_clusters: {int}
number of clusters (default is 5)
Output
------
W: {numpy array}, shape(n_features, n_clusters)
feature weight matrix
Reference
---------
Cai, Deng et al. "Unsupervised Feature Selection for Multi-Cluster Data." KDD 2010.
"""
# use the default affinity matrix
if 'W' not in kwargs:
W = construct_W(X)
else:
W = kwargs['W']
# default number of clusters is 5
if 'n_clusters' not in kwargs:
n_clusters = 5
else:
n_clusters = kwargs['n_clusters']
# solve the generalized eigen-decomposition problem and get the top K
# eigen-vectors with respect to the smallest eigenvalues
W = W.toarray()
W = (W + W.T) / 2
W_norm = np.diag(np.sqrt(1 / W.sum(1)))
W = np.dot(W_norm, np.dot(W, W_norm))
WT = W.T
W[W < WT] = WT[W < WT]
eigen_value, ul = scipy.linalg.eigh(a=W)
Y = np.dot(W_norm, ul[:, -1*n_clusters-1:-1])
# solve K L1-regularized regression problem using LARs algorithm with cardinality constraint being d
n_sample, n_feature = X.shape
W = np.zeros((n_feature, n_clusters))
for i in range(n_clusters):
clf = linear_model.Lars(n_nonzero_coefs=n_selected_features)
clf.fit(X, Y[:, i])
W[:, i] = clf.coef_
return W
def feature_ranking(W):
"""
This function computes MCFS score and ranking features according to feature weights matrix W
"""
mcfs_score = W.max(1)
idx = np.argsort(mcfs_score, 0)
idx = idx[::-1]
return idx
