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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)...

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