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#!/usr/bin/env python
from feast import *
import numpy as np
import csv
def check_result(selected_features, n_relevant):
selected_features = sorted(selected_features)
success = True
for k in range(n_relevant):
if k != selected_features[k]:
success = False
return success
def read_digits(fname='digit.txt'):
'''
read_digits(fname='digit.txt')
read a data file that contains the features and class labels.
each row of the file is a feature vector with the class
label appended.
'''
fw = csv.reader(open(fname,'rb'), delimiter='\t')
data = []
for line in fw:
data.append( [float(x) for x in line] )
data = np.array(data, order="F")
labels = data[:,len(data.transpose())-1]
data = data[:,:len(data.transpose())-1]
return data, labels
def uniform_data(n_observations = 1000, n_features = 50, n_relevant = 5):
import numpy as np
xmax = 10
xmin = 0
data = 1.0*np.random.randint(xmax + 1, size = (n_features, n_observations))
labels = np.zeros(n_observations)
delta = n_relevant * (xmax - xmin) / 2.0
for m in range(n_observations):
zz = 0.0
for k in range(n_relevant):
zz += data[k, m]
if zz > delta:
labels[m] = 1
else:
labels[m] = 2
data = data.transpose()
return data, labels
n_relevant = 5
data_source = 'uniform' # set the data set we want to test
if data_source == 'uniform':
data, labels = uniform_data(n_relevant = n_relevant)
elif data_source == 'digits':
data, labels = read_digits('digit.txt')
n_observations = len(data) # number of samples in the data set
n_features = len(data.transpose()) # number of features in the data set
n_select = 15 # how many features to select
method = 'MIM' # feature selection algorithm
print '---> Information'
print ' :n_observations - ' + str(n_observations)
print ' :n_features - ' + str(n_features)
print ' :n_select - ' + str(n_select)
print ' :algorithm - ' + str(method)
print ' '
print '---> Running unit tests on FEAST 4 Python... '
#################################################################
#################################################################
print ' Running BetaGamma... '
sf = BetaGamma(data, labels, n_select, beta=0.5, gamma=0.5)
if check_result(sf, n_relevant) == True:
print ' BetaGamma passed!'
else:
print ' BetaGamma failed!'
#################################################################
#################################################################
print ' Running CMIM... '
sf = CMIM(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' CMIM passed!'
else:
print ' CMIM failed!'
#################################################################
#################################################################
print ' Running CondMI... '
sf = CondMI(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' CondMI passed!'
else:
print ' CondMI failed!'
#################################################################
#################################################################
print ' Running DISR... '
sf = DISR(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' DISR passed!'
else:
print ' DISR failed!'
#################################################################
#################################################################
print ' Running ICAP... '
sf = ICAP(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' ICAP passed!'
else:
print ' ICAP failed!'
#################################################################
#################################################################
print ' Running JMI... '
sf = JMI(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' JMI passed!'
else:
print ' JMI failed!'
#################################################################
#################################################################
print ' Running mRMR... '
sf = mRMR(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' mRMR passed!'
else:
print ' mRMR failed!'
#################################################################
#################################################################
print ' Running MIM...'
sf = MIM(data, labels, n_select)
if check_result(sf, n_relevant) == True:
print ' MIM passed!'
else:
print ' MIM failed!'
print '---> Done unit tests!'
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