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How to use test_avg method in locust

Best Python code snippet using locust

overlapped_count_episode_time_constraint.py

Source:overlapped_count_episode_time_constraint.py

...139        time_cost = time.time() - time_start140        # print("countIns cost:", (time.time() - time_start))141        print(l_last, file=open("%s_%d_%.3f_%d.%s" % (filename, 33, min_sup, len(l_last), 'exp1'), 'w'))142        return l_last, time_cost143def test_avg(s, file, ck):144    time_constraint = 1000000145    sum_time = 0146    REPEAT = 3147    for i in range(REPEAT):148        ap = count_episode_time_constraint(time_constraint, 0, s)149        cnt, time_cost = ap.do(file, ck)150        sum_time += time_cost151    print('target episdoe size: %d' % len(list(ck)[0]))152    print(time_constraint, ck, cnt, ', avg_cost_time=', '%.7f' % (sum_time / REPEAT))153def test():154    folder = 'exp_settings&data/real-data/'155    names = list(os.walk(folder))[0][2]156    files = [folder + i for i in names if i.endswith('.txt')]157    files = [u'exp_settings&data/syn-data/æ±æ».txt']158    for file in files:159        print('\n', file)160        f = open(file)161        s = f.readline()162        f.close()163        s = eval(s)164        # 2_real165        # test_avg(s, file, {(67,)})166        # test_avg(s, file, {(68, 67, 68)})167        # test_avg(s, file, {(68, 68, 67, 67, 68)})168        # test_avg(s, file, {(67, 68, 67, 68, 67, 68, 68)})169        # 3_real170        # test_avg(s, file, {(68, 22, 68)})171        # test_avg(s, file, {(68, 68, 67, 67, 22)})172        # test_avg(s, file, {(67, 68, 67, 68, 67, 68, 68)})173        # 1_syn174        # test_avg(s, file, {('AI', 'AI', 'S')})175        # test_avg(s, file, {('P', 'Q', 'P')})176        # test_avg(s, file, {('B', 'Q', 'J')})177        # test_avg(s, file, {('AA', 'AA', 'J')})178        #179        # test_avg(s, file, {('V', 'P', 'Q', 'V', 'Q')})180        # test_avg(s, file, {('B', 'AA', 'P', 'B', 'AA')})181        # test_avg(s, file, {('J', 'AA', 'AA', 'J', 'J')})182        # test_avg(s, file, {('J', 'J', 'J', 'J', 'AA')})183        #184        # test_avg(s, file, {('V', 'B', 'B', 'AA', 'V', 'B', 'V')})185        # test_avg(s, file, {('J', 'J', 'AA', 'P', 'B', 'B', 'J')})186        # 2_syn187        # test_avg(s, file, {('G',)})188        # test_avg(s, file, {('AH', 'J', 'AA')})189        # test_avg(s, file, {('AA', 'J', 'B', 'J', 'J')})190        # test_avg(s, file, {('J', 'AA', 'J', 'C', 'B', 'Y', 'J')})191        # 3_syn192        test_avg(s, file, {('AH', 'J', 'AA')})193        test_avg(s, file, {('J', 'AA', 'J', 'C', 'V')})194        test_avg(s, file, {('J', 'AA', 'J', 'C', 'V', 'AA', 'J')})195        #(2, 68, 65, 44, 22, 12, 44, 22, 8),(2, 68, 65, 44, 22, 12, 44),(2, 68,196        #65, 44, 59),(2, 68, 65)197if __name__ == '__main__':...

summary.py

Source:summary.py

1# -*- coding: utf-8 -*-2'''3print summary4'''5from __future__ import print_function6from collections import Counter, OrderedDict7import string8import re9import argparse10import json11import sys12reload(sys)13sys.setdefaultencoding('utf-8')14import pdb15import os16import math17import numpy as np18import collections19from prettytable import PrettyTable20def print_summary():21	lscmd = os.popen('ls '+sys.argv[1]+'/result.*').read()22	result_list = lscmd.split()23	num_args = len(result_list)24	assert num_args==2 or num_args==325	dev_input_file = open(sys.argv[1]+'/result.dev', 'rb')26	test_input_file = open(sys.argv[1]+'/result.test', 'rb')27	if num_args==2:28		print_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','FILE'])29	elif num_args==3:30		chl_input_file = open(sys.argv[1]+'/result.challenge', 'rb')31		print_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','CHL-AVG','CHL-EM','CHL-F1','FILE'])32	# style set33	print_table.align['FILE'] = 'l'34	print_table.float_format = '2.3'35	# data fill36	dev_avg = []37	dev_em = []38	dev_f1 = []39	dev_file = []40	for dline in dev_input_file.readlines():41		dline = dline.strip()42		if re.search('^{', dline):43			ddict = json.loads(dline)44			dev_avg.append(float(ddict['AVERAGE']))45			dev_em.append(float(ddict['EM']))46			dev_f1.append(float(ddict['F1']))47			dev_file.append(ddict['FILE'])48	test_avg = []49	test_em = []50	test_f1 = []51	test_file = []52	for dline in test_input_file.readlines():53		dline = dline.strip()54		if re.search('^{', dline):55			ddict = json.loads(dline)56			test_avg.append(float(ddict['AVERAGE']))57			test_em.append(float(ddict['EM']))58			test_f1.append(float(ddict['F1']))59			test_file.append(ddict['FILE'])60	if num_args==3:61		chl_avg = []62		chl_em = []63		chl_f1 = []64		chl_file = []65		for dline in chl_input_file.readlines():66			dline = dline.strip()67			if re.search('^{', dline):68				ddict = json.loads(dline)69				chl_avg.append(float(ddict['AVERAGE']))70				chl_em.append(float(ddict['EM']))71				chl_f1.append(float(ddict['F1']))72				chl_file.append(ddict['FILE'])73	# print74	if num_args == 2:75		min_len = min(len(dev_avg),len(test_avg))76		for k in range(min_len):77			print_table.add_row([k+1, dev_avg[k], dev_em[k], dev_f1[k], test_avg[k], test_em[k], test_f1[k], dev_file[k]])78	elif num_args == 3:79		min_len = min(len(dev_avg),len(test_avg),len(chl_avg))80		for k in range(min_len):81			print_table.add_row([k+1, dev_avg[k], dev_em[k], dev_f1[k], test_avg[k], test_em[k], test_f1[k], chl_avg[k], chl_em[k], chl_f1[k], dev_file[k]])82	if len(sys.argv)==3:83		sk = sys.argv[2].upper()84		print('sort key detected: {}'.format(sk))85		print(print_table.get_string(sortby=sk, reversesort=True))86	else:87		print(print_table)88	89	if num_args == 2:90		summary_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','FILE'])91		summary_table.add_row(["M", np.max(dev_avg), np.max(dev_em), np.max(dev_f1), 92								  np.max(test_avg), np.max(test_em), np.max(test_f1),"-"])93		summary_table.add_row(["A", np.mean(dev_avg), np.mean(dev_em), np.mean(dev_f1), 94								  np.mean(test_avg), np.mean(test_em), np.mean(test_f1),"-"])95		summary_table.add_row(["D", np.std(dev_avg), np.std(dev_em), np.std(dev_f1), 96								  np.std(test_avg), np.std(test_em), np.std(test_f1),"-"])97	elif num_args == 3:98		summary_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','CHL-AVG','CHL-EM','CHL-F1','FILE'])99		summary_table.add_row(["M", np.max(dev_avg), np.max(dev_em), np.max(dev_f1), 100								  np.max(test_avg), np.max(test_em), np.max(test_f1),101								  np.max(chl_avg), np.max(chl_em), np.max(chl_f1), "-"])102		summary_table.add_row(["A", np.mean(dev_avg), np.mean(dev_em), np.mean(dev_f1), 103								  np.mean(test_avg), np.mean(test_em), np.mean(test_f1),104								  np.mean(chl_avg), np.mean(chl_em), np.mean(chl_f1), "-"])105		summary_table.add_row(["D", np.std(dev_avg), np.std(dev_em), np.std(dev_f1), 106								  np.std(test_avg), np.std(test_em), np.std(test_f1),107								  np.std(chl_avg), np.std(chl_em), np.std(chl_f1), "-"])108	# style set109	summary_table.align['FILE'] = 'l'110	summary_table.float_format = '2.3'111	print(summary_table)112	return 0113if __name__ == '__main__':...

naive_bayes_tester.py

Source:naive_bayes_tester.py

1import naive_bayes_classifier2import pandas as pd3import numpy as np4df = pd.read_csv('../data/weather_data.csv')5# Split up X and y6y = df['RainTomorrow']7X = df.drop(columns=['RainTomorrow']) # Get rid of prediction8# containers9testing_outputs = []10train_outputs = []11alphas = int(input("Enter test number: "))12num_test = int(input("Enter number of tests per Hyperparameter: "))13step = float(input("Enter step value: "))14print(f'Hyperparameter Range being Run: ({(alphas * 0.1) - 0.1}, {alphas * 0.1}), step={step}.')15# test16alphas = np.arange((0.1 * alphas) - 0.1, (alphas * 0.1), step)17# Testing for alpha18for alpha in alphas:19    test_avg = 020    train_avg = 021    for i in range(num_test):22        y_pred, test_score, train_score = naive_bayes_classifier.run(X, y, alph=alpha)23        test_avg += test_score24        train_avg += train_score25    test_avg = test_avg / num_test26    train_avg = train_avg / num_test27    testing_outputs.append(test_avg)28    train_outputs.append(train_avg)29# to df30df = pd.DataFrame(list(zip(alphas, train_outputs, testing_outputs)))31print(df)32compression_opts = dict(method='zip', archive_name=f'bayes_output_data.csv')...

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