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How to use test_z1 method in hypothesis

Best Python code snippet using hypothesis

experiment.py

Source:experiment.py

1import os2import pickle5 as pickle3import pandas as pd4import numpy as np5from sklearn.linear_model import LogisticRegression6from sklearn.ensemble import RandomForestClassifier7def preprocess(tp, dir_path):8    """9    :param dir_path: ~/VFAE10    :param tp: 0 -> german, 1 -> adult, 2 -> health11    :return: scores of logistic, random forest, random choice & accuracy on y12    """13    if tp == 0:14        data = 'german'15        sensitive = 'sex'16        target_dir = dir_path + '/dataset/german/'17        with open(os.path.join(target_dir, 'german_train.pkl'), 'rb') as f:18            train = pickle.load(f)19        with open(os.path.join(target_dir, 'german_test.pkl'), 'rb') as f:20            test = pickle.load(f)21    elif tp == 1:22        data = 'adult'23        sensitive = 'sex'24        target_dir = dir_path + '/dataset/adult/'25        with open(os.path.join(target_dir, 'adult_train.pkl'), 'rb') as f:26            train = pickle.load(f)27        with open(os.path.join(target_dir, 'adult_test.pkl'), 'rb') as f:28            test = pickle.load(f)29    else:30        data = 'health'31        target_dir = dir_path + '/dataset/health/'32        sensitive = 'age'33        with open(os.path.join(target_dir, 'health_train.pkl'), 'rb') as f:34            train = pickle.load(f)35        with open(os.path.join(target_dir, 'health_test.pkl'), 'rb') as f:36            test = pickle.load(f)37    train_z1_vae = np.load(os.path.join(dir_path + '/exp_%s/' % data, "%s_train_z1_vae.npy" % data))38    test_z1_vae = np.load(os.path.join(dir_path + '/exp_%s/' % data, "%s_test_z1_vae.npy" % data))39    train_z1_vfae = np.load(os.path.join(dir_path + '/exp_%s/' % data, "%s_train_z1_vfae.npy" % data))40    test_z1_vfae = np.load(os.path.join(dir_path + '/exp_%s/' % data, "%s_test_z1_vfae.npy" % data))41    train_s = train.pop(sensitive).astype('category')42    train_y = train.pop('label').astype('category')43    train_x = train.astype('category')44    test_s = test.pop(sensitive).astype('category')45    test_y = test.pop('label').astype('category')46    test_x = test.astype('category')47    train_z1 = [train_x, train_z1_vae, train_z1_vfae]48    test_z1 = [test_x, test_z1_vae, test_z1_vfae]49    return [train_z1, test_z1], [train_s, test_s], [train_y, test_y]50def calculate(z, s, y):51    [train_z1, test_z1] = z52    [train_s, test_s] = s53    [train_y, test_y] = y54    log_scores = []55    rf_scores = []56    log_y_scores = []57    disc_scores = []58    disc_prob_scores = []59    for i in range(3):60        # logistic regression & random forest61        log = LogisticRegression(random_state=1).fit(train_z1[i], train_s)62        log_scores.append(log.score(test_z1[i], test_s))63        rf = RandomForestClassifier(random_state=1).fit(train_z1[i], train_s)64        rf_scores.append(rf.score(test_z1[i], test_s))65        log_y = LogisticRegression(random_state=1).fit(train_z1[i], train_y)66        log_y_scores.append(log_y.score(test_z1[i], test_y))67        # discrimination on s68        pred = log_y.predict(test_z1[i])69        s_zero = test_s == 070        s_one = test_s == 171        disc_scores.append(np.abs(((pred[s_zero] == test_y[s_zero]).sum()) / s_zero.sum() -72                                  ((pred[s_one] == test_y[s_one]).sum()) / s_one.sum()))73        # discrimination prob on s74        pred_probs = log_y.predict_proba(test_z1[i])75        pred_prob = pd.Series(np.apply_along_axis(lambda x: x[0] if x[0] > x[1] else x[1], 1, pred_probs), dtype=float)76        disc_prob_scores.append(np.abs((pred_prob[s_zero].sum()) / s_zero.sum() -77                                       (pred_prob[s_one].sum()) / s_one.sum()))...

main.py

Source:main.py

1#!/usr/bin/env python22# -*- coding: utf-8 -*-3"""4Created on Sun Apr  8 21:49:30 20185@author: abinaya6"""7import numpy as np8import matplotlib.pyplot as plt9import math10from scipy.stats import norm, uniform11def function_f(x):12    z = (3 - 0.8) * (1/ (1 + (np.sinh(2*x) * np.log(x))))13    return z14 15def function_p(x):16    z = uniform.pdf(x, loc=0.8, scale=3)17    return z18def function_g(x, mu, std):19    z = norm.pdf(x, loc=mu, scale=std)20    return z21print "MONTE CARLO ------------------------"22max_iterations = 5023monte_carlo_estimates = []24for i in range(0,max_iterations):    25    test_x1 = np.random.uniform(0.8,3,size=1000)26    #test_z1 = map(lambda t: function_f(t),test_x1)27    test_z1 = (3 - 0.8) * (1/ (1 + (np.sinh(2*test_x1) * np.log(test_x1))))28    monte_carlo_estimates.append(np.mean(test_z1))29    30print "Mean Integration Value- Simple Monte Carlo: ", np.mean(monte_carlo_estimates)31print "Variance Integration Value- Simple Monte Carlo: ", np.var(monte_carlo_estimates)32'''33test_x1 = np.linspace(0.8,3,1000)34test_z1 = (3 - 0.8) * (1/ (1 + (np.sinh(2*test_x1) * np.log(test_x1))))35plt.figure()36plt.plot(test_x1,test_z1)37plt.xlabel('Samples')38plt.ylabel('Function Value')39plt.title('Integration Function Plot - Stratification')40'''41print "\nMONTE CARLO USING STRATIFICATION ------------------------"42n=100043n1=70044n2=30045value1 = []46value2 = []47for i in range (0,50):48    X1_1 = np.random.uniform(0.8, 1.24, n1) 49    Fnc1_1 = (1.24-0.8) * pow((1 + (np.sinh(2*X1_1)*np.log(X1_1))),-1) 50    value1_1 = (np.sum(Fnc1_1))/ n151    52    X1_2 = np.random.uniform(1.24, 3, n2) 53    Fnc1_2 = (3-1.24)*pow((1 + (np.sinh(2*X1_2)*np.log(X1_2))),-1) 54    value1_2 = (np.sum(Fnc1_2))/ n255    56    value2.append(value1_1 + value1_2)57variance_stratified_Fnc1 = np.var(value2)58Mean_stratified_Fnc1 = np.mean(value2)59print  "Mean Integration Value- Stratification: ", Mean_stratified_Fnc160print  "Variance Integration Value- Stratification: ", variance_stratified_Fnc161'''62plt.scatter(X1_1,Fnc1_1, color='r')63plt.scatter(X1_2,Fnc1_2, color='g')64'''65print "\nMONTE CARLO USING IMPORTANCE SAMPLING ------------------------"66importance_sampling_estimates = []67mu = 0.568std = 0.769for i in range(0,max_iterations):70    test_x3 = np.random.normal(loc=mu, scale=std, size=1000)71    72    fx3 =  (3 - 0.8) * (1/ (1 + (np.sinh(2*test_x3) * np.log(test_x3))))73    px3 = uniform.pdf(test_x3, loc=0.8, scale=3)74    gx3 = norm.pdf(test_x3, loc=mu, scale=std)75    76    z3 = (np.array(fx3)) * (np.array(px3) / np.array(gx3))77    z3 = z3[~np.isnan(z3)]78    importance_sampling_estimates.append(np.mean(z3))79    80print  "Mean Integration Value- Importance sampling: ", np.mean(importance_sampling_estimates)...

test_given_reuse.py

Source:test_given_reuse.py

...23    pass24given_named_booleans = given(z=st.text())25def test_fail_independently():26    @given_named_booleans27    def test_z1(z):28        assert False29    @given_named_booleans30    def test_z2(z):31        pass32    with pytest.raises(AssertionError):33        test_z1()...

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