def main():
x1 = []
x2 = []
for line in sys.stdin:
s = map(float, line.strip().split())
x1.append(s[0])
x2.append(s[1])
w_tester = WilcoxonTest(x1, x2)
print "Nr:", w_tester.get_N(), " (it should be more than 10)"
print "W:", w_tester.get_W()
print "Result: ", w_tester.get_result()
def main():
x1 = []
x2 = []
for line in sys.stdin:
s = map(float, line.strip().split())
x1.append(s[0])
x2.append(s[1])
w_tester = WilcoxonTest(x1, x2)
print "Nr:", w_tester.get_N(), " (it should be more than 10)"
print "W:", w_tester.get_W()
print "Result: ", w_tester.get_result()
def main():
optparser = OptionParser(usage="""
%prog [OPTIONS] features.tsv[.gz]
Evaluate features score on features.tsv[.gz] pool""")
optparser.add_option('-f', '--feature', dest='feature_to_test',
type='int', default=None,
help='Feature index (0-based) to test score')
optparser.add_option('--folds', dest='folds',
type='int', default=200,
help='Number of folds to test')
optparser.add_option('--fold-size', dest='fold_size',
type='int', default=10000,
help='Each fold (learn) size in percents of original')
optparser.add_option('--training script', dest='training_script',
type='string', default='learn_and_predict_default.sh',
help='Script to learn on pool and predict. Should use -i option to ignore some features')
opts, args = optparser.parse_args()
tmp_learn_filepath = "eval.learn.%d.tsv" % os.getpid()
tmp_test_filepath = "eval.test.%d.tsv" % os.getpid()
tmp_result_filepath = "eval.result.%d.tsv" % os.getpid()
tmp_learn_file = SmartWriter().open(tmp_learn_filepath)
tmp_test_file = SmartWriter().open(tmp_test_filepath)
x1 = []
x2 = []
for iteration in xrange(opts.folds):
for line in SmartReader().open(args[0]):
if random.randint(0, 99) <= opts.fold_size:
print >> tmp_learn_file, line.strip()
else:
print >> tmp_test_file, line.strip()
tmp_learn_file.close()
tmp_test_file.close()
os.system('%s %s %s > %s' % (opts.training_script, tmp_learn_filepath, tmp_test_filepath, tmp_result_filepath))
x1.append(ScoreCalcer(tmp_test_filepath, tmp_result_filepath, delimiter='\t').score_AUC())
os.system('%s -i %d %s %s > %s' % (opts.training_script, opts.feature, tmp_learn_filepath, tmp_test_filepath, tmp_result_filepath))
x2.append(ScoreCalcer(tmp_test_filepath, tmp_result_filepath, delimiter='\t').score_AUC())
os.system('rm %s %s %s' % (tmp_result_filepath, tmp_learn_filepath, tmp_test_filepath))
w_tester = WilcoxonTest(x1, x2)
print w_tester.get_result()
print w_tester.get_W()
