class ignite.contrib.metrics.regression.MedianAbsolutePercentageError(output_transform=<function MedianAbsolutePercentageError.<lambda>>, device=device(type='cpu'))[source]#

Calculates the Median Absolute Percentage Error.

MdAPE=100MDj=1,n(AjPjAj)\text{MdAPE} = 100 \cdot \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_j|} \right)

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1) and of type float32.


Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM.

  • output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • device (Union[str, torch.device]) – optional device specification for internal storage.