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)