How to use get_test_label method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

varyParameters.py

Source:varyParameters.py

...56    while (epoch < training_epochs):57        epoch = epoch + 158        for minibatch_index in xrange(n_train_batches):59            minibatch_avg_cost1 = r_train_fn(minibatch_index)60    predict_y, origin_y = r_get_test_label()  61    print 'first results from right DBN , presion, recall, F1, accuracy: '62    print >> saveFile,'origin_y: ',origin_y63    print >> saveFile,'predict_y: ',predict_y64    predict_y = change2PrimaryC(predict_y)65    origin_y = change2PrimaryC(origin_y)66    print >> saveFile,'first results from right DBN , presion, recall, F1, accuracy: '67    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)68    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)69          70    r_sigmoid_layers,r_output,r_params = rDBN.getParams(train_set_x)71    r_sigmoid_layersT,r_outputT,r_paramsT = rDBN.getParams(test_set_x)72    print shape(r_sigmoid_layers())73           74    new_features = []75    l_features = []76    new_featuresT = []77    for x in topic_x:78        l_features = append(l_features,x)79#     l_features = [l_sigmoid_layers()[i] for i in xrange(len_left)]80# new_features = map(list,zip(*(map(list,zip(*a))+map(list,zip(*b)))))81    print shape(l_features)82    for r_feature in r_sigmoid_layers():83        new_features.append(append(l_features,r_feature))84    for r_feature in r_sigmoid_layersT():85        new_featuresT.append(append(l_features,r_feature))86    print shape(new_features),shape(new_featuresT)87           88#     sim_result =  similarity(r_sigmoid_layers(),topic_x)89#     new_features = []90#     for i in xrange(len(sim_result)):91#         feature = append(topic_x[sim_result[i]],(r_sigmoid_layers()[i]))92#         new_features.append(feature)93#     sim_result =  similarity(r_sigmoid_layersT(),topic_x)94#     new_featuresT = []95#     for i in xrange(len(sim_result)):96#         feature = append(topic_x[sim_result[i]],(r_sigmoid_layersT()[i]))97#         new_featuresT.append(feature)98#     print shape(new_features),shape(new_featuresT)99           100            101    n_dim = shape(new_features)[1]102    shared_x = theano.shared(numpy.asarray(new_features,dtype=theano.config.floatX),borrow=True)103    shared_y = theano.shared(numpy.asarray(new_featuresT,dtype=theano.config.floatX),borrow=True)104    105    layers=[100]106    print  >> saveFile,'top layer hidden units: ',layers107    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)108    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)109    for i in xrange(dbn.n_layers):110        for epoch in xrange(pretraining_epochs):111            c = []112            for batch_index in xrange(n_train_batches):113                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))114#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),115#             print numpy.mean(c)116    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]117    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(118                datasets=datasets, batch_size=batch_size,119                learning_rate=finetune_lr)120    epoch = 0121    while (epoch < training_epochs):122        epoch = epoch + 1123        for minibatch_index in xrange(n_train_batches):124            minibatch_avg_cost = train_fn(minibatch_index)125    predict_y, origin_y = get_test_label()  126    print 'second results from LDADBN, presion, recall, F1, accuracy: '127    print >> saveFile,'predict_y: ',predict_y128    predict_y = change2PrimaryC(predict_y)129    origin_y = change2PrimaryC(origin_y)130    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '131    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)132    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)133    134    layers=[200]135    print  >> saveFile,'top layer hidden units: ',layers136    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)137    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)138    for i in xrange(dbn.n_layers):139        for epoch in xrange(pretraining_epochs):140            c = []141            for batch_index in xrange(n_train_batches):142                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))143#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),144#             print numpy.mean(c)145    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]146    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(147                datasets=datasets, batch_size=batch_size,148                learning_rate=finetune_lr)149    epoch = 0150    while (epoch < training_epochs):151        epoch = epoch + 1152        for minibatch_index in xrange(n_train_batches):153            minibatch_avg_cost = train_fn(minibatch_index)154    predict_y, origin_y = get_test_label()  155    print 'second results from LDADBN, presion, recall, F1, accuracy: '156    print >> saveFile,'predict_y: ',predict_y157    predict_y = change2PrimaryC(predict_y)158    origin_y = change2PrimaryC(origin_y)159    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '160    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)161    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)162 163    layers=[300]164    print  >> saveFile,'top layer hidden units: ',layers165    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)166    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)167    for i in xrange(dbn.n_layers):168        for epoch in xrange(pretraining_epochs):169            c = []170            for batch_index in xrange(n_train_batches):171                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))172#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),173#             print numpy.mean(c)174    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]175    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(176                datasets=datasets, batch_size=batch_size,177                learning_rate=finetune_lr)178    epoch = 0179    while (epoch < training_epochs):180        epoch = epoch + 1181        for minibatch_index in xrange(n_train_batches):182            minibatch_avg_cost = train_fn(minibatch_index)183    predict_y, origin_y = get_test_label()  184    print 'second results from LDADBN, presion, recall, F1, accuracy: '185    print >> saveFile,'predict_y: ',predict_y186    predict_y = change2PrimaryC(predict_y)187    origin_y = change2PrimaryC(origin_y)188    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '189    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)190    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)191    192    layers=[400]193    print  >> saveFile,'top layer hidden units: ',layers194    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)195    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)196    for i in xrange(dbn.n_layers):197        for epoch in xrange(pretraining_epochs):198            c = []199            for batch_index in xrange(n_train_batches):200                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))201#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),202#             print numpy.mean(c)203    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]204    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(205                datasets=datasets, batch_size=batch_size,206                learning_rate=finetune_lr)207    epoch = 0208    while (epoch < training_epochs):209        epoch = epoch + 1210        for minibatch_index in xrange(n_train_batches):211            minibatch_avg_cost = train_fn(minibatch_index)212    predict_y, origin_y = get_test_label()  213    print 'second results from LDADBN, presion, recall, F1, accuracy: '214    print >> saveFile,'predict_y: ',predict_y215    predict_y = change2PrimaryC(predict_y)216    origin_y = change2PrimaryC(origin_y)217    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '218    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)219    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)220    221    layers=[500]222    print  >> saveFile,'top layer hidden units: ',layers223    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)224    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)225    for i in xrange(dbn.n_layers):226        for epoch in xrange(pretraining_epochs):227            c = []228            for batch_index in xrange(n_train_batches):229                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))230#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),231#             print numpy.mean(c)232    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]233    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(234                datasets=datasets, batch_size=batch_size,235                learning_rate=finetune_lr)236    epoch = 0237    while (epoch < training_epochs):238        epoch = epoch + 1239        for minibatch_index in xrange(n_train_batches):240            minibatch_avg_cost = train_fn(minibatch_index)241    predict_y, origin_y = get_test_label()  242    print 'second results from LDADBN, presion, recall, F1, accuracy: '243    print >> saveFile,'predict_y: ',predict_y244    predict_y = change2PrimaryC(predict_y)245    origin_y = change2PrimaryC(origin_y)246    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '247    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)248    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)249    250    layers=[600]251    print  >> saveFile,'top layer hidden units: ',layers252    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)253    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)254    for i in xrange(dbn.n_layers):255        for epoch in xrange(pretraining_epochs):256            c = []257            for batch_index in xrange(n_train_batches):258                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))259#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),260#             print numpy.mean(c)261    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]262    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(263                datasets=datasets, batch_size=batch_size,264                learning_rate=finetune_lr)265    epoch = 0266    while (epoch < training_epochs):267        epoch = epoch + 1268        for minibatch_index in xrange(n_train_batches):269            minibatch_avg_cost = train_fn(minibatch_index)270    predict_y, origin_y = get_test_label()  271    print 'second results from LDADBN, presion, recall, F1, accuracy: '272    print >> saveFile,'predict_y: ',predict_y273    predict_y = change2PrimaryC(predict_y)274    origin_y = change2PrimaryC(origin_y)275    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '276    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)277    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)278    279    layers=[700]280    print  >> saveFile,'top layer hidden units: ',layers281    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)282    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)283    for i in xrange(dbn.n_layers):284        for epoch in xrange(pretraining_epochs):285            c = []286            for batch_index in xrange(n_train_batches):287                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))288#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),289#             print numpy.mean(c)290    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]291    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(292                datasets=datasets, batch_size=batch_size,293                learning_rate=finetune_lr)294    epoch = 0295    while (epoch < training_epochs):296        epoch = epoch + 1297        for minibatch_index in xrange(n_train_batches):298            minibatch_avg_cost = train_fn(minibatch_index)299    predict_y, origin_y = get_test_label()  300    print 'second results from LDADBN, presion, recall, F1, accuracy: '301    print >> saveFile,'predict_y: ',predict_y302    predict_y = change2PrimaryC(predict_y)303    origin_y = change2PrimaryC(origin_y)304    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '305    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)306    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)307    308    layers=[800]309    print  >> saveFile,'top layer hidden units: ',layers310    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)311    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)312    for i in xrange(dbn.n_layers):313        for epoch in xrange(pretraining_epochs):314            c = []315            for batch_index in xrange(n_train_batches):316                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))317#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),318#             print numpy.mean(c)319    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]320    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(321                datasets=datasets, batch_size=batch_size,322                learning_rate=finetune_lr)323    epoch = 0324    while (epoch < training_epochs):325        epoch = epoch + 1326        for minibatch_index in xrange(n_train_batches):327            minibatch_avg_cost = train_fn(minibatch_index)328    predict_y, origin_y = get_test_label()  329    print 'second results from LDADBN, presion, recall, F1, accuracy: '330    print >> saveFile,'predict_y: ',predict_y331    predict_y = change2PrimaryC(predict_y)332    origin_y = change2PrimaryC(origin_y)333    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '334    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)335    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)336    337    layers=[900]338    print  >> saveFile,'top layer hidden units: ',layers339    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)340    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)341    for i in xrange(dbn.n_layers):342        for epoch in xrange(pretraining_epochs):343            c = []344            for batch_index in xrange(n_train_batches):345                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))346#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),347#             print numpy.mean(c)348    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]349    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(350                datasets=datasets, batch_size=batch_size,351                learning_rate=finetune_lr)352    epoch = 0353    while (epoch < training_epochs):354        epoch = epoch + 1355        for minibatch_index in xrange(n_train_batches):356            minibatch_avg_cost = train_fn(minibatch_index)357    predict_y, origin_y = get_test_label()  358    print 'second results from LDADBN, presion, recall, F1, accuracy: '359    print >> saveFile,'predict_y: ',predict_y360    predict_y = change2PrimaryC(predict_y)361    origin_y = change2PrimaryC(origin_y)362    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '363    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)364    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)365    366    layers=[1000]367    print  >> saveFile,'top layer hidden units: ',layers368    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)369    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)370    for i in xrange(dbn.n_layers):371        for epoch in xrange(pretraining_epochs):372            c = []373            for batch_index in xrange(n_train_batches):374                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))375#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),376#             print numpy.mean(c)377    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]378    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(379                datasets=datasets, batch_size=batch_size,380                learning_rate=finetune_lr)381    epoch = 0382    while (epoch < training_epochs):383        epoch = epoch + 1384        for minibatch_index in xrange(n_train_batches):385            minibatch_avg_cost = train_fn(minibatch_index)386    predict_y, origin_y = get_test_label()  387    print 'second results from LDADBN, presion, recall, F1, accuracy: '388    print >> saveFile,'predict_y: ',predict_y389    predict_y = change2PrimaryC(predict_y)390    origin_y = change2PrimaryC(origin_y)391    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '392    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)393    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)394    395    layers=[1100]396    print  >> saveFile,'top layer hidden units: ',layers397    dbn = DBN(n_ins=n_dim, hidden_layers_sizes=layers,n_outs=26)398    pretraining_fns = dbn.pretraining_function(shared_x, batch_size, k)399    for i in xrange(dbn.n_layers):400        for epoch in xrange(pretraining_epochs):401            c = []402            for batch_index in xrange(n_train_batches):403                c.append(pretraining_fns[i](index=batch_index,lr=pretrain_lr))404#             print 'Final Pre-training layer %i, epoch %d, cost ' % (i, epoch),405#             print numpy.mean(c)406    datasets = [(shared_x,train_set_y),(shared_y,test_set_y)]407    train_fn, test_score, get_test_label, features = dbn.build_finetune_functions(408                datasets=datasets, batch_size=batch_size,409                learning_rate=finetune_lr)410    epoch = 0411    while (epoch < training_epochs):412        epoch = epoch + 1413        for minibatch_index in xrange(n_train_batches):414            minibatch_avg_cost = train_fn(minibatch_index)415    predict_y, origin_y = get_test_label()  416    print 'second results from LDADBN, presion, recall, F1, accuracy: '417    print >> saveFile,'predict_y: ',predict_y418    predict_y = change2PrimaryC(predict_y)419    origin_y = change2PrimaryC(origin_y)420    print  >> saveFile,'second results from LDADBN, presion, recall, F1, accuracy: '421    print evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)422    print >> saveFile,evaluation(predict_y, origin_y),getAccuracy(predict_y, origin_y)423 424    425    end_time = time.clock()426    print >> saveFile,'Finish all using  %.2f mins' % ((end_time - start_time) / 60.)427    print 'Finish all using  %.2f mins' % ((end_time - start_time) / 60.)428    print >>saveFile,'------------------------------------------------------------------------------'429# layers = []...

SVMModel.py

Source:SVMModel.py

...30  clf, predicted = svm_imbalanced.train_and_predict()31  svm_imbalanced.display_confusion_matrix(predicted)32  svm_imbalanced.print_accuracy(predicted)33  print(f"Classification report for classifier {clf}:\n"34      f"{metrics.classification_report(svm_imbalanced.get_test_label(), predicted)}\n")35  # test_image, actual_label, predicted_label = svm_imbalanced.get_misclassified(predicted)36  # svm_imbalanced.display_samples(test_image, actual_label, predicted_label, False)37  print('Imbalanced with Symmetrical Noise')38  sym_non_balanced = SVMModel(balanced=False, noise_type='sym', noise_ratio=40)39  sym_non_balanced.scale_pixels()40  sym_non_balanced.show_class_distribution(False)41  clf2, predicted2 = sym_non_balanced.train_and_predict()42  sym_non_balanced.display_confusion_matrix(predicted2)43  sym_non_balanced.print_accuracy(predicted2)44  print(f"Classification report for classifier {clf2}:\n"45    f"{metrics.classification_report(sym_non_balanced.get_test_label(), predicted2)}\n")46  print('Imbalanced with Asymmetrical Noise')47  asym_non_balanced = SVMModel(balanced=False, noise_type='asym', noise_ratio=40)48  asym_non_balanced.scale_pixels()49  asym_non_balanced.show_class_distribution(False)50  clf3, predicted3 = asym_non_balanced.train_and_predict()51  asym_non_balanced.display_confusion_matrix(predicted3)52  asym_non_balanced.print_accuracy(predicted3)53  print(f"Classification report for classifier {clf3}:\n"54    f"{metrics.classification_report(asym_non_balanced.get_test_label(), predicted3)}\n")55  print('Balanced with No Noise')56  svm_balanced = SVMModel(balanced=True, noise_type='No Noise', noise_ratio=0)57  svm_balanced.scale_pixels()58  svm_balanced.show_class_distribution(False)59  clf6, predicted6 = svm_balanced.train_and_predict()60  svm_balanced.display_confusion_matrix(predicted6)61  svm_balanced.print_accuracy(predicted6)62  print(f"Classification report for classifier {clf6}:\n"63    f"{metrics.classification_report(svm_balanced.get_test_label(), predicted6)}\n")64  print('Balanced with Symmetrical Noise')65  sym_balanced = SVMModel(balanced=True, noise_type='sym', noise_ratio=40)66  sym_balanced.scale_pixels()67  sym_balanced.show_class_distribution(False)68  clf4, predicted4 = sym_balanced.train_and_predict()69  sym_balanced.display_confusion_matrix(predicted4)70  sym_balanced.print_accuracy(predicted4)71  print(f"Classification report for classifier {clf4}:\n"72    f"{metrics.classification_report(sym_balanced.get_test_label(), predicted4)}\n")73  print('Balanced with Asymmetrical Noise')74  asym_balanced = SVMModel(balanced=True, noise_type='asym', noise_ratio=40)75  asym_balanced.scale_pixels()76  asym_balanced.show_class_distribution(False)77  clf5, predicted5 = asym_balanced.train_and_predict()78  asym_balanced.display_confusion_matrix(predicted5)79  asym_balanced.print_accuracy(predicted5)80  print(f"Classification report for classifier {clf5}:\n"...

train_classifier.py

Source:train_classifier.py

...33    print("The length of image are train: {} test: {}".format(len(train_image_paths),len(test_image_paths)))34    print("Ambiguity will be filtered")35    #load label file36    meta = pd.read_csv(meta)37    train_X, train_y = get_test_label(meta,train_image_paths)38    val_X, val_y = train_test_split(train_X,train_y,validation_proportion)39    test_X, test_y = get_test_label(meta,test_image_paths)40    print("*"*50)41    print("Train: {}  Validation: {}  Test: {}".format(len(train_X),len(val_X),len(test_X)))42    # Create Dataset43    train_dataset = ClassifierDataset(train_X,train_y,config['augmentation'])44    val_dataset = ClassifierDataset(val_X,val_y,config['augmentation'])...

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.