# predict the results for the test data print("Generating probability prediction for the test data...\n") y_pred = LRC.predict(X_test) # print testing time time_test_end = time.clock() print("Testing finished, testing time: %g seconds \n" % (time_test_end - time_test_start)) ''' ### print the classification results ### print ("The probabilities for each instance in the test set are:\n") for prob in LRC.predict_proba(X_test): print (prob) ''' # print simple precision metric to the console print('Accuracy: ' + str(fipr.compute_accuracy(y_test, y_pred))) # predict the results for the test data print("Generating probability prediction for the EVEN test data...\n") y_pred_even = LRC.predict(X_test_even) # print simple precision metric to the console print('Accuracy: ' + str(fipr.compute_accuracy(y_test_even, y_pred_even))) # write the model to the model file # LRC.print_model(features, model_file) # Calculate total running time print("--- Total running time: %g seconds ---" % (time.clock() - start_time)) """ if __name__ == '__main__':
def main(): # open and load csv files time_load_start = time.clock() X_train, y_train = fipr.load_csv("train_file.csv", True) X_test, y_test = fipr.load_csv("test_file.csv", True) #y_train = y_train.flatten() #y_test = y_test.flatten() time_load_end = time.clock() print("Loading finished, loading time: %g seconds" % (time_load_end - time_load_start)) X_test_even, y_test_even = fipr.load_csv("test_file_even.csv", True) training_data = X_train training_labels = y_train test_data = X_test test_labels = y_test test_data_even = X_test_even test_labels_even = y_test_even # building the SDA sDA = StackedDA([100]) # start counting time for training time_train_start = time.clock() print('Pre-training...') # pre-trainning the SDA sDA.pre_train(training_data[:1000], noise_rate=0.3, epochs=100) print('Training Network...') # adding the final layer sDA.finalLayer(training_data, training_labels, epochs=500) # trainning the whole network sDA.fine_tune(training_data, training_labels, epochs=500) # print training time time_train_end = time.clock() print("Training finished, training time: %g seconds \n" % (time_train_end - time_train_start)) # start counting time for testing time_test_start = time.clock() print('Testing performance...') # predicting using the SDA y_pred = sDA.predict(test_data).argmax(1) # print simple precision metric to the console print('Accuracy: ' + str(fipr.compute_accuracy(y_test, y_pred))) # print testing time time_test_end = time.clock() print("Testing finished, testing time: %g seconds \n" % (time_test_end - time_test_start)) # Even set test y_pred_even = sDA.predict(test_data_even).argmax(1) # print simple precision metric to the console print('Accuracy on EVEN set: ' + str(fipr.compute_accuracy(y_test_even, y_pred_even))) return sDA