| import torch |
|
|
| |
| def explained_variance( |
| y_pred: torch.tensor, y_true: torch.tensor |
| ) -> torch.tensor: |
| """ |
| Computes fraction of variance that ypred explains about y. |
| Returns 1 - Var[y-ypred] / Var[y] |
| |
| interpretation: |
| ev=0 => might as well have predicted zero |
| ev=1 => perfect prediction |
| ev<0 => worse than just predicting zero |
| |
| :param y_pred: the prediction |
| :param y_true: the expected value |
| :return: explained variance of ypred and y |
| """ |
| assert y_true.ndim == 1 and y_pred.ndim == 1 |
| var_y = torch.var(y_true) |
| return torch.nan if var_y == 0 else 1 - torch.var(y_true - y_pred) / var_y |
|
|
