Mean Absolute Percentage Error (MAPE)¶

Module Interface¶

class torchmetrics.MeanAbsolutePercentageError(compute_on_step=None, **kwargs)[source]

Computes 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.

Parameters

compute_on_step¶ (Optional[bool]) –
Forward only calls update() and returns None if this is set to False.

Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.
kwargs¶ (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Note

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

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.

compute()[source]

Computes mean absolute percentage error over state.

Return type: Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters

preds¶ (Tensor) – Predictions from model
target¶ (Tensor) – Ground truth values

Return type

None

Functional Interface¶

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

Computes mean absolute percentage error.

Parameters

preds¶ (Tensor) – estimated labels
target¶ (Tensor) – ground truth labels

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)