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
andupdate
the metric accepts the following input:As output of
forward
andcompute
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 arbitrarily large values. Especially when sometarget
values are close to 0. This MAPE implementation returns a very large number instead ofinf
.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:
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:
- 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:
- 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)