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# 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:

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 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)
tensor(0.2667)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

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.

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.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)
tensor(0.2667)


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