# 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)
