def main():
"""
Command Line Inputs:
Data file with lines: PAGE URL LABEL_1 LABEL_2 ... LABEL_n and associated text for page
Input Model file with labels and array of weak learners
Output Model file with correction factors
Print the matthews correlation coefficient for each label
"""
model = read_model(sys.argv[2])
print 'model has {} labels and {} tuples'.format(len(model['labels']), len(model['learners']))
page_tuples, page_labels, page_num_labels = read_data_labels(sys.argv[1], model)
print 'read {} pages'.format(len(page_labels))
evaluate(model, page_tuples, page_labels, page_num_labels)
model = normalize(model, page_tuples, page_labels, page_num_labels)
write_model(sys.argv[3], model)
evaluate(model, page_tuples, page_labels, page_num_labels)
def main():
"""
Command Line Inputs:
Data file with lines: PAGE URL LABEL_1 LABEL_2 ... LABEL_n and associated text for page
Input Model file with labels and array of weak learners
Output the positive score TP / (TP + FP) for each label
"""
model = read_model(sys.argv[2])
# print 'model has {} labels and {} tuples'.format(len(model['labels']), len(model['learners']))
page_tuples, page_labels, page_num_labels = read_data_labels(sys.argv[1], model)
# print 'read {} pages'.format(len(page_labels))
TP, TN, FP, FN = score_errors(model, page_tuples, page_labels, page_num_labels, False, TVDBG_P, TVDBG_L)
score = score_positive(TP, TN, FP, FN, TVDBG_P, TVDBG_L)
print 'score = {}'.format(score)
page_total, page_count = get_page_count(page_labels)
wgt_avg = weighted_average(page_total, page_count, score)
print 'weighted average = {}'.format(wgt_avg)
def main():
"""
Command Line Inputs:
Data file with lines: PAGE URL LABEL_1 LABEL_2 ... LABEL_n and associated text for page
Input Model file with labels and array of weak learners
Output the matthews correlation coefficient for each label
"""
model = read_model(sys.argv[2])
# print 'model has {} labels and {} tuples'.format(len(model['labels']), len(model['learners']))
page_tuples, page_labels, page_num_labels = read_data_labels(sys.argv[1], model)
# print 'read {} pages'.format(len(page_labels))
TP, TN, FP, FN = score_errors(model, page_tuples, page_labels, page_num_labels, False)
matthews = score_matthews(TP, TN, FP, FN)
print 'score = {}'.format(matthews)
page_total, page_count = get_page_count(page_labels)
wgt_avg = weighted_average(page_total, page_count, matthews)
print 'weighted average = {}'.format(wgt_avg)
def main():
"""
Command Line Inputs:
Data file with lines: PAGE URL LABEL_1 LABEL_2 ... LABEL_n and associated text for page
Input Model file with labels and array of weak learners
Output the positive score TP / (TP + FP) for each label
"""
model = read_model(sys.argv[2])
# print 'model has {} labels and {} tuples'.format(len(model['labels']), len(model['learners']))
page_tuples, page_labels, page_num_labels = read_data_labels(sys.argv[1], model)
# print 'read {} pages'.format(len(page_labels))
TP, TN, FP, FN = score_errors(model, page_tuples, page_labels, page_num_labels, False)
score = score_positive(TP, TN, FP, FN)
print 'score = {}'.format(score)
page_total, page_count = get_page_count(page_labels)
wgt_avg = weighted_average(page_total, page_count, score)
print 'weighted average = {}'.format(wgt_avg)
def main():
"""
Command Line Inputs:
Data file with lines: PAGE URL LABEL_1 LABEL_2 ... LABEL_n and associated text for page
Input Model file with labels and array of weak learners
Output Model file with correction factors
Print the matthews correlation coefficient for each label
"""
model = read_model(sys.argv[2])
print 'model has {} labels and {} tuples'.format(len(model['labels']),
len(model['learners']))
page_tuples, page_labels, page_num_labels = read_data_labels(
sys.argv[1], model)
print 'read {} pages'.format(len(page_labels))
evaluate(model, page_tuples, page_labels, page_num_labels)
model = normalize(model, page_tuples, page_labels, page_num_labels)
write_model(sys.argv[3], model)
evaluate(model, page_tuples, page_labels, page_num_labels)
def main():
"""
Command Line Inputs:
Data file with lines: PAGE URL LABEL_1 LABEL_2 ... LABEL_n and associated text for page
Input Model file with labels and array of weak learners
Output the matthews correlation coefficient for each label
"""
model = read_model(sys.argv[2])
# print 'model has {} labels and {} tuples'.format(len(model['labels']), len(model['learners']))
page_tuples, page_labels, page_num_labels = read_data_labels(
sys.argv[1], model)
# print 'read {} pages'.format(len(page_labels))
TP, TN, FP, FN = score_errors(model, page_tuples, page_labels,
page_num_labels, True, TVDBG_P, TVDBG_L)
matthews = score_matthews(TP, TN, FP, FN, TVDBG_P, TVDBG_L)
print 'score = {}'.format(matthews)
page_total, page_count = get_page_count(page_labels)
wgt_avg = weighted_average(page_total, page_count, matthews)
print 'weighted average = {}'.format(wgt_avg)
