How to use test_z2 method in hypothesis

Best Python code snippet using hypothesis

10_10cv.py

Source:10_10cv.py

1#knn 2from sklearn.neighbors import KNeighborsClassifier3from sklearn.model_selection import StratifiedKFold4import csv5import numpy as np 6with open("dataset_dt.txt", "r") as f:7    reader = csv.reader(f)8    line = [row for row in reader]9with open("dataset_rf.txt", "r") as f:10    reader = csv.reader(f)11    line3 = [row for row in reader]12line2 = []13with open("t_test_dt_rf.txt", "r") as f:14    reader = csv.reader(f)15    for row in reader:16       if len(row) != 0:17         line2.append(row)18'''19with open("train_x_0_0.txt","r") as f:20    reader = csv.reader(f)21    line4 = [row for row in reader]22for i in range(len(line)):23    for j in range(len(line[0])):24        if line[i][j] == "":25            print(i)26            print(line[i])27'''28'''29with open("dataset_raw.txt","r") as f:30    reader = csv.reader(f)31    line5 = [row for row in reader]32print(line5[28612])33'''34n = len(line)35m = len(line[0])36#print(n,m)37#print(len(line2))38#X attributes; Y class labels; Z accuracies39X = [[None for i in range(m-1)] for j in range(n)]40#Y = [None for i in range(n)]41Z = [None for i in range(n)]42Z2 = [None for i in range(n)]43Y = []44for row in line2:45    Y.append(int(row[0]))46        47for i in range(n):48    for j in range(m-1):49        X[i][j] = line[i][j]50    Z[i] = line[i][m-1]51    Z2[i] = line3[i][m-1]52#print(len(X),len(Y),len(Z))53#dividing on the basis of class54label_wise = [[] for i in range(m-1)]55for i in range(n):56    index = Y[i]57    label_wise[index].append(i)58print(len(label_wise[0]),len(label_wise[1]),len(label_wise[2]))59accuracy = [[] for i in range(15)]60for k in range(2,3):61    print(k)62    #start of 10 x 10 cv63    for n0 in range(10):64        label_wise_all = []65        for i in label_wise:66            np.random.shuffle(i)67            for j in i:68                label_wise_all.append(j)69        one,two,three,four,five,six,seven,eight,nine,ten = [[] for i in range(10)]70        folds = [one,two,three,four,five,six,seven,eight,nine,ten]71        for j in range(len(label_wise_all)):72            temp = j % 1073            temp = folds[temp]74            temp.append(label_wise_all[j])75        for test_fold in range(10):76            train_x = []77            train_y = []78            train_z = []79            train_z2 = []80            test_x = []81            test_y = []82            test_z = []83            test_z2 = []84            for fold in range(10):85                temp = folds[fold]86                if fold == test_fold:87                    for i in temp:88                        test_x.append(X[i])89                        test_y.append(Y[i])90                        test_z.append(Z[i])91                        test_z2.append(Z2[i])92                else:93                    for i in temp:94                        train_x.append(X[i])95                        train_y.append(Y[i])96                        train_z.append(Z[i])97                        train_z2.append(Z2[i])98            train_y = np.array(train_y).reshape(-1,1)99            test_y = np.array(test_y).reshape(-1,1)100            train_z = np.array(train_z).reshape(-1,1)101            train_z2 = np.array(train_z2).reshape(-1,1)102            test_z = np.array(test_z).reshape(-1,1)103            test_z2 = np.array(test_z2).reshape(-1,1)104            file_name = "train_x_" + str(n0) + '_' + str(test_fold) + ".txt"105            with open(file_name,"w") as f:106                writer = csv.writer(f)107                writer.writerows(train_x)108            file_name = "train_y_" + str(n0) + '_' + str(test_fold) + ".txt"109            with open(file_name,"w") as f:110                writer = csv.writer(f)111                writer.writerows(train_y)112            file_name = "train_dt_" + str(n0) + '_' + str(test_fold) + ".txt"113            with open(file_name,"w") as f:114                writer = csv.writer(f)115                writer.writerows(train_z)116            file_name = "train_rf_" + str(n0) + '_' + str(test_fold) + ".txt"117            with open(file_name,"w") as f:118                writer = csv.writer(f)119                writer.writerows(train_z2)120            file_name = "test_x_" + str(n0) + '_' + str(test_fold) + ".txt"121            with open(file_name,"w") as f:122                writer = csv.writer(f)123                writer.writerows(test_x)124            file_name = "test_y_" + str(n0) + '_' + str(test_fold) + ".txt"125            with open(file_name,"w") as f:126                writer = csv.writer(f)127                writer.writerows(test_y)128            file_name = "test_dt_" + str(n0) + '_' + str(test_fold) + ".txt"129            with open(file_name,"w") as f:130                writer = csv.writer(f)131                writer.writerows(test_z)132            file_name = "test_rf_" + str(n0) + '_' + str(test_fold) + ".txt"133            with open(file_name,"w") as f:134                writer = csv.writer(f)135                writer.writerows(test_z2)...

classifier.py

Source:classifier.py

1import tensorflow as tf2import numpy as np3from sklearn.metrics import roc_auc_score4import matplotlib.pyplot as plt5from sklearn.svm import SVC6plt.switch_backend("agg")7inp_dim = 1008num_classes = 29niter = 200010reg  = .0511def get_indices(raw_labels):12#    i2 = (raw_labels==2).nonzero()[0]13    i1 = (raw_labels==1).nonzero()[0]14    i0 = (raw_labels==0).nonzero()[0]15    a = np.amin([i1.shape[0],i0.shape[0]])16#    np.random.shuffle(i2) 17    np.random.shuffle(i1) 18    np.random.shuffle(i0)19    r = np.concatenate((i1[0:a], i0[0:a]))20    np.random.shuffle(r)21    return r22#def SVM_C():23#    train_z2 = np.load("/opt/data/saket/gene_data/data/train_z2.npy")24#    test_z2 = np.load("/opt/data/saket/gene_data/data/test_z2.npy")25#    labels = np.load("/opt/data/saket/gene_data/data/data_label.npy")26#    i2 = (labels==2).nonzero()[0]27#    i1 = (labels==1).nonzero()[0]28#    i0 = (labels==0).nonzero()[0]29#    labels[i1] = 130#    labels[i2] = 031#    labels[i0] = 032#    train_labels = labels[0:160]33#    test_labels = labels[160:]34#    clf = SVC(probability=True)35#    train_z2 = np.mean(train_z2, axis=0)36#    clf.fit(train_z2, train_labels)37#    test_z2 = np.mean(test_z2, axis=0)38#    metrics = clf.predict(test_z2)39#    Accuracy = 040#    n = metrics.shape[0]41#    for i in range(n):42#        if metrics[i] == test_labels[i]:43#            Accuracy += 144#    print ("Accuracy:",Accuracy)45def normalize(x):46    m,v = tf.nn.moments(x, [0])47    y = (x-m)/tf.sqrt(v)48    return y49def main():50    51    x = tf.placeholder(tf.float32, [None, inp_dim])52    y1 = normalize(x)53    W = tf.Variable(tf.ones([inp_dim, num_classes]))54    b = tf.Variable(tf.zeros([num_classes]))55    y = tf.matmul(y1,W)+b56    ys = tf.nn.softmax(y)57    y_ = tf.placeholder(tf.int64, [None])58    cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_, logits=y))59    train_step = tf.train.GradientDescentOptimizer(0.005).minimize(cross_entropy+reg*tf.nn.l2_loss(W))60    61    sess = tf.InteractiveSession()62    sess.run(tf.global_variables_initializer())63    64    train_z2 = np.load("/opt/data/saket/gene_data/data/train_z2.npy")65    test_z2 = np.load("/opt/data/saket/gene_data/data/test_z2.npy")66    labels = np.load("/opt/data/saket/gene_data/data/data_label.npy")67    #train_z2 = train_z2*(10**5)68    #test_z2 = test_z2*(10**5)69    print ("Class 0 Vs all")70    i2 = (labels==2).nonzero()[0]71    i1 = (labels==1).nonzero()[0]72    i0 = (labels==0).nonzero()[0]73    labels[i1] = 074    labels[i2] = 175    labels[i0] = 076    train_labels = labels[0:160]77    test_labels = labels[160:]78        79    np.random.shuffle(train_z2)80    closs = []81    correct_prediction = tf.equal(tf.argmax(y,1), y_)82    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))83    for i in range(niter):84        z2 = train_z2[i%train_z2.shape[0]]85        #print ("z2:",z2)86        indices = get_indices(train_labels)87#        print("normalized data:",sess.run(y1, feed_dict={x:z2[indices], y_:train_labels[indices]}))88        l, train_a, _ = sess.run([cross_entropy, accuracy, train_step], feed_dict = {x:z2[indices], y_:train_labels[indices]})89        closs.append((l, train_a))90     #   print ("Step:%d accuracy:%f loss:%f"%(i, train_a, l))91    # Test the trained model92    testylist = []93    for i in range(test_z2.shape[0]):94        y = sess.run(ys, feed_dict={x:test_z2[i], y_:test_labels})95        testylist.append(y)96        #print(sess.run(y1, feed_dict={x:test_z2[i], y_:test_labels}))97    testy = np.array(testylist)98    testy = np.mean(testy, axis=0)99    pred_labels = np.argmax(testy, axis=1)100    print ("pred_labels:", pred_labels)101#    indices = np.concatenate((np.expand_dims(np.arange(0,pred_labels.shape[0]), axis=1), np.expand_dims(pred_labels, axis=1)), axis=1)102    score = testy[np.arange(0,pred_labels.shape[0]),pred_labels]103    print("indices:", indices.shape)104    print("score shape:",score.shape)105    print("test_labels_shape:", test_labels.shape)106    print("ROC Score:", roc_auc_score(test_labels, score))    107    n = pred_labels.shape[0]108    TP = 0109    FP = 0110    TN = 0111    FN = 0112    for i in range(n):113        if test_labels[i] == 0:114            if pred_labels[i] == 0:115                TN += 1116            else:117                FP += 1118        else:119            if pred_labels[i] == 0:120                FN += 1121            else:122                TP += 1123    test_accuracy = pred_labels==test_labels124    test_accuracy = np.sum(test_accuracy)125    print("Test classification accuracy:",test_accuracy)126    closs = np.array(closs)127    plt.plot(closs[:,0])128    plt.savefig("closs.png")129    plt.close()130    print("Test FP:%d TP:%d FN:%d TN:%d"%(FP,TP,FN,TN))...

gan_tester.py

Source:gan_tester.py

1import random as rand2import time3#description Generative Adverserial Networks trainer4#icon fa fa-retweet5#MAIN=Run6#param object7_input = None8#param object9Generator = None10#param object11Discriminator = None12#param int13latent_size = 1014#param folder15save_path = ""16#param list: Image,Sound,Text,3D Model,Raw17preview = "Image"18#editor param float19#preview_state = 0.520def Run(self):21	self.X = tf.placeholder(tf.float32, shape=[None, self._input.input_size], name="x_input")	22	self.Z = tf.placeholder(tf.float32, shape=[None, self.latent_size], name="z_input")		23	fakeX = self.Generator.Run(self.Z)24	25	#initialize everything26	instance = AIBlocks.InitModel(load_path=self.save_path)27	Log("Model initialized!")28			29	acc_log = []30	resetRand = 031	test_Z = np.random.uniform(-1., 1., size=[self.latent_size])32	test_Z1 = np.random.uniform(-1., 1., size=[self.latent_size])33	test_Z2 = np.random.uniform(-1., 1., size=[self.latent_size])34	targetRand = np.random.uniform(-1., 1., size=[self.latent_size])35	targetRand1 = np.random.uniform(-1., 1., size=[self.latent_size])36	targetRand2 = np.random.uniform(-1., 1., size=[self.latent_size])37	38	while 1:39		Math.LerpVec(test_Z, targetRand, 0.1)40		Math.LerpVec(test_Z1, targetRand1, 0.1)41		Math.LerpVec(test_Z2, targetRand2, 0.1)42		if(resetRand>10):43			targetRand = np.random.uniform(-1., 1., size=[self.latent_size])44			targetRand1 = np.random.uniform(-1., 1., size=[self.latent_size])45			targetRand2 = np.random.uniform(-1., 1., size=[self.latent_size])46			resetRand = 047		resetRand += 148		49		imagined = instance.Run(fakeX, feed_dict = {self.Z: [test_Z]})[0]50		imagined1 = instance.Run(fakeX, feed_dict = {self.Z: [test_Z1]})[0]51		imagined2 = instance.Run(fakeX, feed_dict = {self.Z: [test_Z2]})[0]52		if self.preview=="Image":53			SendImageData(self.id, imagined, self._input.image_size[0], self._input.image_size[1], "1")54			SendImageData(self.id, imagined1, self._input.image_size[0], self._input.image_size[1], "2")55			SendImageData(self.id, imagined2, self._input.image_size[0], self._input.image_size[1], "3")56		elif self.preview=="Sound":57			pass58		else:59			pass60		time.sleep(0.01)61	AIBlocks.SaveModel(instance)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.