print('Size raw_values %i' % (size_raw_values)) print('------- Train -------') # No Prediction y_hat_predicted = y_test rmse = compare(y_test, y_hat_predicted) print('RMSE NoPredic %.3f' % (rmse)) # Dummy y_predicted_dummy = x_train[:, 0] rmse = compare(y_train, y_predicted_dummy) print('RMSE Dummy %.3f' % (rmse)) # 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
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)) # Dummy y_hat_predicted = x_train rmse = compare_train(train_scaled, y_hat_predicted) print('RMSE Dummy %.3f' % (rmse)) # ElasticNet y_hat_predicted = algorithm.elastic_net2(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)
print('Size supervised %i' % (size_supervised)) print('------- Train -------') # No Prediction y_hat_predicted = y_train rmse, y_predicted = compare_train(x_test, y_hat_predicted) print('RMSE NoPredic %.3f' % (rmse)) # Dummy y_hat_predicted = x_train[:, 0] rmse, y_predicted = compare_train(x_test, y_hat_predicted) print('RMSE Dummy %.3f' % (rmse)) # ElasticNet y_hat_predicted = algorithm.elastic_net2(x_train, y_train, x_train, normalize=False) rmse, y_predicted = compare_train(x_test, y_hat_predicted) print('RMSE Elastic %.3f' % (rmse)) # KNN5 y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_train, 5) rmse, y_predicted = compare_train(x_test, y_hat_predicted) print('RMSE KNN(5) %.3f' % (rmse)) # KNN10 y_hat_predicted = algorithm.knn_regressor(x_train, y_train, x_train, 10) rmse, y_predicted = compare_train(x_test, y_hat_predicted) print('RMSE KNN(10) %.3f' % (rmse)) # SGD y_hat_predicted = algorithm.sgd_regressor(x_train, y_train, x_train)
print('Size supervised %i' % (size_supervised)) print('------- Test --------') # No Prediction y_hat_predicted_sc = y_test rmse, y_hat_predicted = compare_test(y_test, y_hat_predicted_sc) #y_test = y_hat_predicted #print('RMSE NoPredic %.3f' % (rmse)) # Dummy y_predicted_dummy_sc = x_test[:, 0] #rmse, y_predicted_dummy = compare_test(y_test, y_predicted_dummy_sc) #print('RMSE Dummy %.3f' % (rmse)) # ElasticNet y_predicted_en_sc = algorithm.elastic_net2(x_train, y_train, x_test, normalize=False) rmse, y_predicted_en = compare_test(y_test, y_predicted_en_sc) print('RMSE Elastic %.3f' % (rmse)) # rmse, y_future_en = compare_test(y_test, y_future_en_sc) # print('RMSE Fut Els %.3f' % (rmse)) # KNN5 #y_predicted_knn5_sc = algorithm.knn_regressor(x_train, y_train, x_test, 5) #rmse, y_predicted_knn5 = compare_test(y_test, y_predicted_knn5_sc) #print('RMSE KNN(5) %.3f' % (rmse)) # KNN10 #y_predicted_knn10_sc = algorithm.knn_regressor(x_train, y_train, x_test, 10) #rmse, y_predicted_knn10 = compare_test(y_test, y_predicted_knn10_sc) #print('RMSE KNN(10) %.3f' % (rmse))