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.
Forward accepts -
preds
(float tensor): either single output tensor with shape(N,)
or multioutput tensor of shape(N,d)
-target``(float tensor): either single output tensor with shape ``(N,)
or multioutput tensor of shape(N,d)
- 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