# ElasticNet y_predicted_en = algorithm.elastic_net2(x_train, y_train, x_train) rmse = compare(y_train, y_predicted_en) print('RMSE Elastic %.3f' % (rmse)) print('------- Test --------') # No Prediction y_hat_predicted = y_test rmse = compare(y_test, y_hat_predicted) print('RMSE NoPredic %.3f' % (rmse)) # Dummy y_predicted_dummy = x_test[:, 0] rmse = compare(y_test, y_predicted_dummy) print('RMSE Dummy %.3f' % (rmse)) # ElasticNet y_predicted_en, y_future_en = algorithm.elastic_net( x_train, y_train, x_test, y_test) rmse = compare(y_test, y_predicted_en) print('RMSE Elastic %.3f' % (rmse)) rmse = compare(y_test, y_future_en) print('RMSE Elastic Future %.3f' % (rmse)) print(' ') titles = ['Y test', 'ElasticNet', 'ElasticNet Future'] data = [y_test, y_predicted_en, y_future_en] date_test = date[split:] misc.plot_lines_graph('Raw Data, Test Data, Window size ' + str(x), date_test, titles, data)
rmse, y_predicted = compare_train(x_test, y_hat_predicted) print('RMSE LSTM %.3f' % (rmse)) print('------- Test --------') # No Prediction y_hat_predicted = y_test rmse, y_hat_predicted = compare_test(y_test, y_hat_predicted) print('RMSE NoPredic %.3f' % (rmse)) # Dummy y_predicted_dummy_es = x_test[:, 0] rmse, y_predicted_dummy = compare_test(y_test, y_predicted_dummy_es) print('RMSE Dummy %.3f' % (rmse)) # ElasticNet y_predicted_en_es, y_future_en_es = algorithm.elastic_net(x_train, y_train, x_test, y_test, normalize=False) rmse, y_predicted_en = compare_test(y_test, y_predicted_en_es) print('RMSE Elastic %.3f' % (rmse)) # y_future_en = compare_test(y_test, y_future_en_es) # KNN5 y_predicted_knn5_es = algorithm.knn_regressor(x_train, y_train, x_test, 5) rmse, y_predicted_knn5 = compare_test(y_test, y_predicted_knn5_es) print('RMSE KNN(5) %.3f' % (rmse)) # KNN10 y_predicted_knn10_es = algorithm.knn_regressor(x_train, y_train, x_test, 10) rmse, y_predicted_knn10 = compare_test(y_test, y_predicted_knn10_es) print('RMSE KNN(10) %.3f' % (rmse))
print('Size raw_values %i' % (size_raw_values)) print('------- Test --------') # No Prediction y_hat_predicted = y_test rmse = compare(y_test, y_hat_predicted) print('RMSE NoPredic %.3f' % (rmse)) # Dummy y_predicted_dummy = x_test[:, -1] rmse = compare(y_test, y_predicted_dummy) print('RMSE Dummy %.3f' % (rmse)) # ElasticNet y_predicted_en, y_future_en = algorithm.elastic_net(x_train, y_train, x_test, y_test,normalize=True) rmse = compare(y_test, y_predicted_en) print('RMSE Elastic %.3f' % (rmse)) # Lasso y_predicted_en = algorithm.lasso(x_train, y_train, x_test, normalize=True) rmse = compare(y_test, y_predicted_en) print('RMSE Lasso %.3f' % (rmse)) titles = ['Y', 'ElasticNet'] data = [y_test, y_predicted_en] date_test = date[split:] print('Length date test:' + str(len(date_test))) print('Length data test:' + str(len(y_test)))
# transform the scale of the data scaler, train_scaled, test_scaled = data_misc.scale(train, test) x_train, y_train = train_scaled[:, 0:-1], train_scaled[:, -1] x_test, y_test = test_scaled[:, 0:-1], test_scaled[:, -1] x_test = [x_test[i] for i in range(len(x_test))] print('------- Train -------') # No Prediction y_hat_predicted = y_train rmse = compare_train(train_scaled, y_hat_predicted) print('RMSE NoPredic %.3f' % (rmse)) # ElasticNet y_hat_predicted = algorithm.elastic_net(x_train, y_train, x_train) rmse = compare_train(train_scaled, y_hat_predicted) print('RMSE Elastic %.3f' % (rmse)) # KNN5 y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_train, 5) rmse = compare_train(train_scaled, y_hat_predicted) print('RMSE KNN(5) %.3f' % (rmse)) # KNN10 y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_train, 10) rmse = compare_train(train_scaled, y_hat_predicted) print('RMSE KNN(10) %.3f' % (rmse)) # SGD y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_train)