graphtoolbox.training.metrics¶
- graphtoolbox.training.metrics.MAE(preds: Tensor | ndarray, targets: Tensor | ndarray) Tensor | float[source][source]¶
Mean Absolute Error (MAE) between predictions and targets.
- Parameters:
preds (Union[torch.Tensor, np.ndarray]) – Predicted values.
targets (Union[torch.Tensor, np.ndarray]) – True values.
- Returns:
The mean absolute error.
- Return type:
Union[torch.Tensor, float]
- graphtoolbox.training.metrics.NMAE(preds: Tensor | ndarray, targets: Tensor | ndarray) Tensor | float[source][source]¶
Normalized Mean Absolute Error (NMAE) between predictions and targets.
- Parameters:
preds (Union[torch.Tensor, np.ndarray]) – Predicted values.
targets (Union[torch.Tensor, np.ndarray]) – True values.
- Returns:
The normalized mean absolute error.
- Return type:
Union[torch.Tensor, float]
- graphtoolbox.training.metrics.MAPE(preds: Tensor | ndarray, targets: Tensor | ndarray) Tensor | float[source][source]¶
Mean Absolute Percentage Error (MAPE) between predictions and targets.
- Parameters:
preds (Union[torch.Tensor, np.ndarray]) – Predicted values.
targets (Union[torch.Tensor, np.ndarray]) – True values.
- Returns:
The mean absolute percentage error.
- Return type:
Union[torch.Tensor, float]
- graphtoolbox.training.metrics.RMSE(preds: Tensor | ndarray, targets: Tensor | ndarray) Tensor | float[source][source]¶
Root Mean Square Error (RMSE) between predictions and targets.
- Parameters:
preds (Union[torch.Tensor, np.ndarray]) – Predicted values.
targets (Union[torch.Tensor, np.ndarray]) – True values.
- Returns:
The root mean square error.
- Return type:
Union[torch.Tensor, float]
- graphtoolbox.training.metrics.BIAS(preds: Tensor | ndarray, targets: Tensor | ndarray) Tensor | float[source][source]¶
Bias between predictions and targets.
- Parameters:
preds (Union[torch.Tensor, np.ndarray]) – Predicted values.
targets (Union[torch.Tensor, np.ndarray]) – True values.
- Returns:
The bias (mean error).
- Return type:
Union[torch.Tensor, float]