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Explained Variance

Module Interface

class torchmetrics.ExplainedVariance(multioutput='uniform_average', compute_on_step=None, **kwargs)[source]

Computes explained variance:

\text{ExplainedVariance} = 1 - \frac{\text{Var}(y - \hat{y})}{\text{Var}(y)}

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

Forward accepts

  • preds (float tensor): (N,) or (N, ...) (multioutput)

  • target (long tensor): (N,) or (N, ...) (multioutput)

In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions. Please see argument multioutput for changing this behavior.

Parameters
  • multioutput (str) –

    Defines aggregation in the case of multiple output scores. Can be one of the following strings (default is 'uniform_average'.):

    • 'raw_values' returns full set of scores

    • 'uniform_average' scores are uniformly averaged

    • 'variance_weighted' scores are weighted by their individual variances

  • 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.

Raises

ValueError – If multioutput is not one of "raw_values", "uniform_average" or "variance_weighted".

Example

>>> from torchmetrics import ExplainedVariance
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> explained_variance = ExplainedVariance()
>>> explained_variance(preds, target)
tensor(0.9572)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> explained_variance = ExplainedVariance(multioutput='raw_values')
>>> explained_variance(preds, target)
tensor([0.9677, 1.0000])

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

compute()[source]

Computes explained variance over state.

Return type

Union[Tensor, Sequence[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.explained_variance(preds, target, multioutput='uniform_average')[source]

Computes explained variance.

Parameters
  • preds (Tensor) – estimated labels

  • target (Tensor) – ground truth labels

  • multioutput (str) –

    Defines aggregation in the case of multiple output scores. Can be one of the following strings):

    • 'raw_values' returns full set of scores

    • 'uniform_average' scores are uniformly averaged

    • 'variance_weighted' scores are weighted by their individual variances

Example

>>> from torchmetrics.functional import explained_variance
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> explained_variance(preds, target)
tensor(0.9572)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> explained_variance(preds, target, multioutput='raw_values')
tensor([0.9677, 1.0000])
Return type

Union[Tensor, Sequence[Tensor]]

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