def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.output_path != None:
outf = open(opts.output_path, 'w')
else:
outf = sys.stdout
dists = parse_distmat(open(opts.input_path, 'U'))
map_data = parse_mapping_file_to_dict(open(opts.map, 'U'))
diff_dists, same_dists = clust_qual_ratio(dists, map_data, opts.category)
if opts.short:
print >> outf, numpy.mean(diff_dists) / numpy.mean(same_dists)
else:
print >> outf, "dissimilarity ratio between/within (large for clustered data):"
print >> outf, numpy.mean(diff_dists) / numpy.mean(same_dists)
print >> outf, "dissimilarities between clusters: mean, std, num:"
print >> outf, '\t'.join(
map(str, [
numpy.mean(diff_dists),
numpy.std(diff_dists),
len(diff_dists)
]))
print >> outf, "dissimilarities within clusters: mean, std, num:"
print >> outf, '\t'.join(
map(str, [
numpy.mean(same_dists),
numpy.std(same_dists),
len(same_dists)
]))
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
if opts.output_path != None:
outf = open(opts.output_path,'w')
else:
outf = sys.stdout
dists = parse_distmat(open(opts.input_path,'U'))
map_data = parse_mapping_file_to_dict(open(opts.map,'U'))
diff_dists, same_dists = clust_qual_ratio(dists, map_data, opts.category)
if opts.short:
print >> outf, numpy.mean(diff_dists)/numpy.mean(same_dists)
else:
print >> outf, "dissimilarity ratio between/within (large for clustered data):"
print >> outf, numpy.mean(diff_dists)/numpy.mean(same_dists)
print >> outf, "dissimilarities between clusters: mean, std, num:"
print >> outf, '\t'.join(map(str,[numpy.mean(diff_dists), numpy.std(diff_dists),
len(diff_dists)]))
print >> outf, "dissimilarities within clusters: mean, std, num:"
print >> outf, '\t'.join(map(str,[numpy.mean(same_dists), numpy.std(same_dists),
len(same_dists)]))
def test_clust_qual_ratio(self):
dist_labels = ["s1", "s2", "s3"]
dmtx = numpy.array([[0, 2.1, 3], [2.1, 0, 1], [3, 1, 0]])
dists = [dist_labels, dmtx]
map_data = {"s1": {"color": "red"}, "s2": {"color": "blue"}, "s3": {"color": "red"}}
bet, within = clust_qual_ratio(dists, [map_data, []], "color")
assert_almost_equal(sorted(bet), [1, 2.1])
assert_almost_equal(within, [3.0])
def test_clust_qual_ratio(self):
dist_labels = ['s1','s2','s3']
dmtx = numpy.array([[0,2.1,3],[2.1,0,1],[3,1,0]])
dists = [dist_labels,dmtx]
map_data = \
{'s1':{'color':'red'},'s2':{'color':'blue'},'s3':{'color':'red'}}
bet, within = clust_qual_ratio(dists, [map_data,[]], 'color')
self.assertFloatEqual( sorted(bet), [1,2.1])
self.assertFloatEqual(within, [3.])
def test_clust_qual_ratio(self):
dist_labels = ['s1','s2','s3']
dmtx = numpy.array([[0,2.1,3],[2.1,0,1],[3,1,0]])
dists = [dist_labels,dmtx]
map_data = \
{'s1':{'color':'red'},'s2':{'color':'blue'},'s3':{'color':'red'}}
bet, within = clust_qual_ratio(dists, [map_data,[]], 'color')
self.assertFloatEqual( sorted(bet), [1,2.1])
self.assertFloatEqual(within, [3.])