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How to use test_yes method in green

Best Python code snippet using green

naive_bayes.py

Source:naive_bayes.py

1import numpy as np2"""Data: 57 features, 4601 data points"""3yes = []; no = []; outcome = []4ratio = 0.85#Reading the data from data.txt:6with open('data.txt', 'r') as data:7	for line in data.readlines():8		#train_cat.append(map(lambda x: np.float64(x), line.split(',')))9		point = (map(lambda x: np.float64(x), line.split(' ')))10		outcome.append(point[-1])11		if point[-1] == 1:12			yes.append(point[:len(point)-1])13		else:14			no.append(point[:len(point)-1])15"""print(0, int(round(ratio*len(yes), 0))-1)16print(int(round(ratio*len(yes), 0)), int(round(len(yes), 0))-1)17print(len(yes), int(round(len(yes) + ratio*len(no), 0))-1)18print(int(round(len(yes) + ratio*len(no), 0)), int(round(len(yes) + len(no), 0))-1)"""19#Indices:20ind1 = 021ind2 = int(round(ratio*len(yes), 0))22ind3 = int(round(len(yes), 0))23ind4 = int(round(len(yes) + ratio*len(no), 0))24ind5 = int(round(len(yes) + len(no), 0))25train_yes = yes[:ind2]; train_yes = np.transpose(train_yes)26train_no  = no [:int(round(ratio*len(no),0))]; train_no = np.transpose(train_no)27outcome_test = outcome[ind2:ind3] + outcome[ind4:ind5]28test_yes = yes[ind2:]; test_no = no [int(round(ratio*len(no),0)):]29"""train_yes = yes[:int(round(.8*len(yes), 0))]; train_yes = np.transpose(train_yes)30train_no  = no [:int(round(.8*len(no ), 0))]; train_no  = np.transpose(train_no )31outcome_test = outcome[1450:1813] + outcome[4043:4601]32test_yes = yes[int(round(.8*len(yes), 0)):] ; test_no  = no [int(round(.8*len(no ), 0)):]"""33print("len(yes) = {}, len(train_yes) = {}, len(test_yes) = {}\nlen(no ) = {}, len(train_no ) = {}, len(test_no ) = {}".format(len(yes), np.shape(train_yes)[1], len(test_yes), len(no), np.shape(train_no)[1], len(test_no)))34mean_yes = np.mean(train_yes, 1); mean_no  = np.mean(train_no, 1)35cov_yes = np.cov(train_yes); cov_no  = np.cov(train_no)36Y = test_yes + test_no37#print("Y.shape = {}x{}".format(len(Y), len(Y[0])))38output = []39p_yes = float(len(yes))/(len(yes) + len(no)); p_no  = float(len(no ))/(len(yes) + len(no))40term1 = -1*(len(Y[0])/2) * np.log10(2*np.pi)41term2_yes = -0.5* np.log10(np.linalg.det(cov_yes)); term12_yes = term1 + term2_yes42term2_no  = -0.5* np.log10(np.linalg.det(cov_no)) ; term12_no  = term1 + term2_no43cov_inv_yes = np.linalg.inv(cov_yes)44cov_inv_no  = np.linalg.inv(cov_no )45for point in Y:46	term3_yes = np.matmul(np.transpose(point - mean_yes), cov_inv_yes)47	term3_yes = -0.5 * np.matmul(term3_yes, point - mean_yes) + p_yes48	term3_no  = np.matmul(np.transpose(point - mean_no ), cov_inv_no )49	term3_no  = -0.5 * np.matmul(term3_no, point - mean_no ) + p_no50	if (term12_yes + term3_yes >= term12_no + term3_no):51		output.append(1)52	else:53		output.append(0)54wrong_spam  = 0; wrong_non_spam = 055for i in range(len(output)):56	if outcome_test[i] == 1 and output[i] == 0:57		wrong_non_spam += 158	if outcome_test[i] == 0 and output[i] == 1:59		wrong_spam += 160error = np.float64(wrong_spam + wrong_non_spam)61print("Result: With training:testing ratio of {}, accuracy = 1 - {}/{} = {}".format(ratio, error, len(Y), 1-(error/len(Y))))62print("Fraction of non-spam incorrectly detected as spam = {}, fraction of spam incorrectly detected as non-spam = {}".format(np.float64(wrong_spam)/len(test_no), np.float64(wrong_non_spam)/len(test_yes)))63"""With ration = 0.8, 64len(yes) = 1813, len(train_yes) = 1450, len(test_yes) = 36365len(no ) = 2788, len(train_no ) = 2230, len(test_no ) = 55866cov_yes.shape = (57, 57)67Y.shape = 921x5768p_yes = 0.394044772875, p_no = 0.605955227125...

test_env.py

Source:test_env.py

1# -*- coding: utf-8 -*-2# Copyright (C) Duncan Macleod (2018-2020)3#4# This file is part of GWpy.5#6# GWpy is free software: you can redistribute it and/or modify7# it under the terms of the GNU General Public License as published by8# the Free Software Foundation, either version 3 of the License, or9# (at your option) any later version.10#11# GWpy is distributed in the hope that it will be useful,12# but WITHOUT ANY WARRANTY; without even the implied warranty of13# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the14# GNU General Public License for more details.15#16# You should have received a copy of the GNU General Public License17# along with GWpy.  If not, see <http://www.gnu.org/licenses/>.18"""Tests for :mod:`gwpy.utils.env`19"""20from unittest import mock21import pytest22from .. import env as utils_env23BOOL_TRUE = {24    'TEST_y': 'y',25    'TEST_Y': 'Y',26    'TEST_yes': 'yes',27    'TEST_Yes': 'Yes',28    'TEST_YES': 'YES',29    'TEST_ONE': '1',30    'TEST_true': 'true',31    'TEST_True': 'True',32    'TEST_TRUE': 'TRUE',33}34BOOL_FALSE = {35    'TEST_no': 'no',36    'TEST_No': 'No',37    'TEST_ZERO': '0',38    'TEST_false': 'false',39    'TEST_False': 'False',40    'TEST_FALSE': 'FALSE',41    'TEST_OTHER': 'blah',42}43BOOL_ENV = BOOL_TRUE.copy()44BOOL_ENV.update(BOOL_FALSE)45@mock.patch.dict('os.environ', values=BOOL_ENV)46@pytest.mark.parametrize(47    'env, result',48    [(k, True) for k in sorted(BOOL_TRUE)]49    + [(k, False) for k in sorted(BOOL_FALSE)],50)51def test_bool_env(env, result):52    """Test :meth:`gwpy.utils.env.bool_env` _without_ the `default` keyword53    """54    assert utils_env.bool_env(env) is result55@mock.patch.dict('os.environ', values=BOOL_TRUE)56@pytest.mark.parametrize('env, default, result', [57    ('TEST_YES', False, True),58    ('TEST_MISSING', True, True),59])60def test_bool_env_default(env, default, result):61    """Test :meth:`gwpy.utils.env.bool_env` _with_ the `default` keyword62    """...

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