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.

$\rho_c = \frac{2 \rho \sigma_x \sigma_y}{\sigma_x^2 + \sigma_y^2 + (\mu_x - \mu_y)^2}$

where $\mu_x, \mu_y$ is the means for the two variables, $\sigma_x^2, \sigma_y^2$ are the corresponding variances and rho is the pearson correlation coefficient between the two variables.

As input to forward and update 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 and compute 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 setting
kwargs¶ (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]