Evaluator

Evaluator

bigdl.chronos.metric.forecast_metrics.symmetric_mean_absolute_percentage_error(preds: torch.Tensor, target: torch.Tensor) → torch.Tensor

class bigdl.chronos.metric.forecast_metrics.Evaluator

Bases: object

Evaluate metrics for y_true and y_pred.

static evaluate(metrics, y_true, y_pred, aggregate='mean')

Evaluate a specific metrics for y_true and y_pred.

Parameters

• metrics – String or list in [‘mae’, ‘mse’, ‘rmse’, ‘r2’, ‘mape’, ‘smape’] for built-in metrics. If callable function, it signature should be func(y_true, y_pred), where y_true and y_pred are numpy ndarray.

• y_true – Array-like of shape = (n_samples, *). Ground truth (correct) target values.

• y_pred – Array-like of shape = (n_samples, *). Estimated target values.

• aggregate – aggregation method. Currently, “mean” and None are supported, ‘mean’ represents aggregating by mean, while None will return the element-wise result. The value defaults to ‘mean’.

Returns

Float or ndarray of floats. A floating point value, or an array of floating point values, one for each individual target.