def cforange_filter_relieff(input_dict):
import orange
import orngFSS
data = input_dict['dataset']
measure = orange.MeasureAttribute_relief(k=int(input_dict['k']), m=int(input_dict['m']))
margin = float(input_dict['margin'])
new_dataset = orngFSS.filterRelieff(data,measure,margin)
output_dict = {}
output_dict['new_dataset'] = new_dataset
return output_dict
# Description: Recursively eliminates attributes using Relief measure, until
# the estimate relevants of all attributes is beyond certain threshold.
# Makes use of filterRelieff from orngFSS
# Category: preprocessing
# Uses: voting.tab
# Referenced: orngFSS.htm
import orange, orngFSS
def report_relevance(data):
m = orngFSS.attMeasure(data)
for i in m:
print "%5.3f %s" % (i[1], i[0])
data = orange.ExampleTable("../datasets/adult_sample")
print "Before feature subset selection:"; report_relevance(data)
marg = 0.01
ndata = orngFSS.filterRelieff(data, margin=marg)
print "\nAfter feature subset selection with margin %5.3f:" % marg
report_relevance(ndata)
# Description: Recursively eliminates attributes using Relief measure, until
# the estimate relevants of all attributes is beyond certain threshold.
# Makes use of filterRelieff from orngFSS
# Category: preprocessing
# Uses: voting.tab
# Referenced: orngFSS.htm
import orange, orngFSS
def report_relevance(data):
m = orngFSS.attMeasure(data)
for i in m:
print "%5.3f %s" % (i[1], i[0])
data = orange.ExampleTable("../datasets/adult_sample")
print "Before feature subset selection:"
report_relevance(data)
marg = 0.01
ndata = orngFSS.filterRelieff(data, margin=marg)
print "\nAfter feature subset selection with margin %5.3f:" % marg
report_relevance(ndata)