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# Mean Absolute Percentage Error (MAPE)¶

## Module Interface¶

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

Computes Mean Absolute Percentage Error (MAPE):

Where is a tensor of target values, and is a tensor of predictions.

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

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

Parameters

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

Note

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 arbitarily large values. Especially when some target values are close to 0. This MAPE implementation returns a very large number instead of inf.

Example

>>> from torchmetrics import MeanAbsolutePercentageError
>>> target = torch.tensor([1, 10, 1e6])
>>> preds = torch.tensor([0.9, 15, 1.2e6])
>>> mean_abs_percentage_error = MeanAbsolutePercentageError()
>>> mean_abs_percentage_error(preds, target)
tensor(0.2667)


Initializes internal Module state, shared by both nn.Module and ScriptModule.

## Functional Interface¶

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

Computes mean absolute percentage error.

Parameters
Return type

Tensor

Returns

Tensor with MAPE

Note

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

Example

>>> from torchmetrics.functional 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)
tensor(0.2667)


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