# Predict
main_y_hat = np.zeros((dataset.test_X.shape[0], len(dataset.regions)))
for i in range(len(xgbs)):
print("Predicting XGB number {}".format(i + 1))
main_y_hat[:, i] = np.array(xgbs[i].predict(dataset.test_X)).flatten()
# Add predictions to DataFrame
dataset.test = util.add_preds(dataset.test, main_y_hat, "XGB",
dataset.regions)
# De-normalize data
dataset.test = util.denormalize_data(dataset.test, ['target', 'XGB'],
dataset.mean, dataset.std)
# # Round predictions
# dataset.test = util.round_values(dataset.test, ['XGB'])
# Calculate Errors
util.calculate_errors(dataset.test, target="target", pred="XGB")
# Write results out
util.result_file(dataset.test,
model_path=model_dir_path,
target="target",
model_names=["XGB"])
# Write predicted data out
util.predictions_to_csv(dataset.test,
model_dir_path=model_dir_path,
file_name="XGB")
print('Elapsed: {}'.format(elapsed))
# Add predictions to DataFrame
dataset.test = util.add_preds(dataset.test, ha_preds, "HA", dataset.regions)
dataset.test = util.add_preds(dataset.test, sma_preds, "SMA", dataset.regions)
dataset.test = util.add_preds(dataset.test, dema_preds, "DEMA", dataset.regions)
dataset.test = util.add_preds(dataset.test, olsr_preds, "OLSR", dataset.regions)
dataset.test = util.add_preds(dataset.test, ridge_preds, "Ridge", dataset.regions)
dataset.test = util.add_preds(dataset.test, lasso_preds, "Lasso", dataset.regions)
# De-normalize data
dataset.test = util.denormalize_data(dataset.test, ['target', 'SMA', 'DEMA', 'OLSR', 'Ridge', 'Lasso'],
dataset.mean, dataset.std)
# Round predictions
dataset.test = util.round_values(dataset.test, ['SMA', 'DEMA', 'OLSR', 'Ridge', 'Lasso'])
# Calculate Errors
util.calculate_errors(dataset.test, target="target", pred="HA")
util.calculate_errors(dataset.test, target="target", pred="SMA")
util.calculate_errors(dataset.test, target="target", pred="DEMA")
util.calculate_errors(dataset.test, target="target", pred="OLSR")
util.calculate_errors(dataset.test, target="target", pred="Ridge")
util.calculate_errors(dataset.test, target="target", pred="Lasso")
# Write results out
util.result_file(dataset.test, model_path=model_dir_path,
target="target", model_names=['HA', 'SMA', 'DEMA', 'OLSR', 'Ridge', 'Lasso'])
# Write predicted data out
util.predictions_to_csv(dataset.test, model_dir_path=model_dir_path, file_name="Other_methods")