Concordance Corr. Coef.¶
Module Interface¶
- class torchmetrics.ConcordanceCorrCoef(num_outputs=1, **kwargs)[source]
Computes concordance correlation coefficient that measures the agreement between two variables. It is defined as.
where
is the means for the two variables,
are the corresponding variances and rho is the pearson correlation coefficient between the two variables.
As input to
forward
andupdate
the metric accepts the following input:preds
(Tensor
): either single output float tensor with shape(N,)
or multioutput float tensor of shape(N,d)
target
(Tensor
): either single output float tensor with shape(N,)
or multioutput float tensor of shape(N,d)
As output of
forward
andcompute
the metric returns the following output:concordance
(Tensor
): A scalar float tensor with the concordance coefficient(s) for non-multioutput input or a float tensor with shape(d,)
for multioutput input
- Parameters
num_outputs¶ (
int
) – Number of outputs in multioutput settingkwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Example (single output regression):
>>> from torchmetrics import ConcordanceCorrCoef >>> import torch >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> concordance = ConcordanceCorrCoef() >>> concordance(preds, target) tensor(0.9777)
- Example (multi output regression):
>>> from torchmetrics import ConcordanceCorrCoef >>> import torch >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> concordance = ConcordanceCorrCoef(num_outputs=2) >>> concordance(preds, target) tensor([0.7273, 0.9887])
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.concordance_corrcoef(preds, target)[source]
Computes concordance correlation coefficient that measures the agreement between two variables. It is defined as.
where
is the means for the two variables,
are the corresponding variances and rho is the pearson correlation coefficient between the two variables.
- Example (single output regression):
>>> from torchmetrics.functional import concordance_corrcoef >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> concordance_corrcoef(preds, target) tensor([0.9777])
- Example (multi output regression):
>>> from torchmetrics.functional import concordance_corrcoef >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> concordance_corrcoef(preds, target) tensor([0.7273, 0.9887])
- Return type