def fetch_results(self):
men_data = []
women_data = []
men_append = men_data.append
women_append = women_data.append
for level in range(0, self.num_levels):
men_averager = Averager()
women_averager = Averager()
for result in self.results:
men_averager.add(result.men[level])
women_averager.add(result.women[level])
total_employees = men_averager.get_total(
) + women_averager.get_total()
men_avg = men_averager.get_average()
men_percentage = 100 * men_averager.get_total() / total_employees
women_avg = women_averager.get_average()
women_percentage = 100 * women_averager.get_total(
) / total_employees
men_append(men_percentage)
women_append(women_percentage)
return [men_data, women_data]
def print_summary(self):
"""Print summary"""
print ("Level\tMen\t\t\tWomen")
print ("\tavg\tmedian\t%\tavg\tmedian\t%")
print ("-----\t-----------------\t-----------------")
for level in range(0, self.num_levels):
men_averager = Averager()
women_averager = Averager()
for result in self.results:
men_averager.add(result.men[level])
women_averager.add(result.women[level])
total_employees = men_averager.get_total() + women_averager.get_total()
men_avg = men_averager.get_average()
men_median = men_averager.get_median()
men_percentage = 100 * men_averager.get_total() / total_employees
women_avg = women_averager.get_average()
women_median = women_averager.get_median()
women_percentage = 100 * women_averager.get_total() / total_employees
summary = "%d\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f\t%.2f" % (
level + 1,
men_avg,
men_median,
men_percentage,
women_avg,
women_median,
women_percentage,
)
print summary
def fetch_results(self):
men_data = []
women_data = []
men_append = men_data.append
women_append = women_data.append
for level in range(0, self.num_levels):
men_averager = Averager()
women_averager = Averager()
for result in self.results:
men_averager.add(result.men[level])
women_averager.add(result.women[level])
total_employees = men_averager.get_total() + women_averager.get_total()
men_avg = men_averager.get_average()
men_percentage = 100 * men_averager.get_total() / total_employees
women_avg = women_averager.get_average()
women_percentage = 100 * women_averager.get_total() / total_employees
men_append(men_percentage)
women_append(women_percentage)
return [men_data, women_data]
correct += a_correct
total += len(p_lines)
print 'correct = ', correct, ' total = ', total, ' percentage = ', float(correct) / float(total)
print 'final results:'
print 'correct = ', correct, ' total = ', total, ' percentage = ', float(correct)/float(total)
elif train_or_test == 'train':
with open(data_filename) as data_file:
with tf.Session() as session:
session.run(init_op)
av = Averager(50)
for batch_index in range(number_of_batches):
lines = helper.read_file_in_loop(data_file, batch_size)
input_header_text_vectors = helper.convert_lines_to_matrix(lines, word_to_index, header_text_length, 'h')
input_content_text_vectors = helper.convert_lines_to_matrix(lines, word_to_index, content_text_length, 'c')
input_label_vector = helper.convert_lines_to_labels(lines)
feed_dict = {input_header_text: input_header_text_vectors, input_content_text: input_content_text_vectors, input_labels: input_label_vector}
(_loss, _, _predicted) = session.run([loss, train_op, predicted_as_vector], feed_dict=feed_dict)
assessment = helper.prediction_assessment(input_label_vector, _predicted)
(_, _percent) = assessment
av.add(_percent)
if (batch_index % 50) == 0 or batch_index == (number_of_batches-1):
print 'batch: ', batch_index, ' loss: ', _loss
print 'assessment: ', assessment
print 'Last 50 iterations average: ', av.average()
print ''
saver.save(session, save_file)
else:
raise ValueError('Bad argument: ' + train_or_test)
