| import numpy as np | |
| from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score | |
| def calculate_metrics(y, y_hat, y_train=None): | |
| def smape(a, f): | |
| return 1/len(a) * np.sum(2 * np.abs(f - a) / (np.abs(a) + np.abs(f) + np.finfo(float).eps)) | |
| def mase(y_actual, y_pred, y_train): | |
| n = y_train.shape[1] | |
| d = np.abs(np.diff(y_train)).sum() / (n - 1) | |
| errors = np.abs(y_actual - y_pred) | |
| return errors.mean() / d | |
| def phase_space_distance(y_actual, y_pred): | |
| return np.sqrt(np.sum(np.square(y_actual - y_pred))) | |
| SMAPE = np.mean([smape(yi.reshape(-1), y_hati.reshape(-1)) for yi, y_hati in zip(y, y_hat)]) | |
| MSE = np.mean([mean_squared_error(yi.reshape(-1), y_hati.reshape(-1)) for yi, y_hati in zip(y, y_hat)]) | |
| RMSE = np.mean([np.sqrt(mean_squared_error(yi.reshape(-1), y_hati.reshape(-1))) for yi, y_hati in zip(y, y_hat)]) | |
| MAE = np.mean([mean_absolute_error(yi.reshape(-1), y_hati.reshape(-1)) for yi, y_hati in zip(y, y_hat)]) | |
| R2 = np.mean([r2_score(yi.reshape(-1), y_hati.reshape(-1)) for yi, y_hati in zip(y, y_hat)]) | |
| PSD = np.mean([phase_space_distance(yi.reshape(-1), y_hati.reshape(-1)) for yi, y_hati in zip(y, y_hat)]) | |
| if y_train is None: | |
| return SMAPE, MSE, RMSE, MAE, R2, PSD | |
| else: | |
| MASE = np.mean([mase(yi, y_hati, yt) for yi, y_hati, yt in zip(y, y_hat, y_train)]) | |
| return SMAPE, MSE, RMSE, MAE, R2, MASE, PSD |