Mean Absolute Percentage Error (MAPE)

Module Interface

class torchmetrics.MeanAbsolutePercentageError(**kwargs)[source]

Compute Mean Absolute Percentage Error (MAPE).

\[\text{MAPE} = \frac{1}{n}\sum_{i=1}^n\frac{| y_i - \hat{y_i} |}{\max(\epsilon, | y_i |)}\]

Where \(y\) is a tensor of target values, and \(\hat{y}\) is a tensor of predictions.

As input to forward and update the metric accepts the following input:

  • preds (Tensor): Predictions from model

  • target (Tensor): Ground truth values

As output of forward and compute the metric returns the following output:

  • mean_abs_percentage_error (Tensor): A tensor with the mean absolute percentage error over state


kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.


MAPE output is a non-negative floating point. Best result is 0.0 . But it is important to note that, bad predictions, can lead to arbitrarily large values. Especially when some target values are close to 0. This MAPE implementation returns a very large number instead of inf.


>>> from torch import tensor
>>> from torchmetrics.regression import MeanAbsolutePercentageError
>>> target = tensor([1, 10, 1e6])
>>> preds = tensor([0.9, 15, 1.2e6])
>>> mean_abs_percentage_error = MeanAbsolutePercentageError()
>>> mean_abs_percentage_error(preds, target)
plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

  • val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

  • ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]


Figure and Axes object


ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import MeanAbsolutePercentageError
>>> metric = MeanAbsolutePercentageError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()
>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import MeanAbsolutePercentageError
>>> metric = MeanAbsolutePercentageError()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)

Functional Interface

torchmetrics.functional.mean_absolute_percentage_error(preds, target)[source]

Compute mean absolute percentage error.

  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

Return type:



Tensor with MAPE


The epsilon value is taken from scikit-learn’s implementation of MAPE.


>>> from torchmetrics.functional.regression import mean_absolute_percentage_error
>>> target = torch.tensor([1, 10, 1e6])
>>> preds = torch.tensor([0.9, 15, 1.2e6])
>>> mean_absolute_percentage_error(preds, target)