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# Concordance Corr. Coef.¶

## Module Interface¶

class torchmetrics.ConcordanceCorrCoef(num_outputs=1, **kwargs)[source]

Compute concordance correlation coefficient that measures the agreement between two variables.

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 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
Example (single output regression):
>>> from torchmetrics.regression import ConcordanceCorrCoef
>>> from torch import tensor
>>> target = tensor([3, -0.5, 2, 7])
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> concordance = ConcordanceCorrCoef()
>>> concordance(preds, target)
tensor(0.9777)

Example (multi output regression):
>>> from torchmetrics.regression import ConcordanceCorrCoef
>>> target = tensor([[3, -0.5], [2, 7]])
>>> preds = 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.

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters
Return type

Tuple[Figure, Union[Axes, ndarray]]

Returns

Figure and Axes object

Raises

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import ConcordanceCorrCoef
>>> metric = ConcordanceCorrCoef()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()

>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import ConcordanceCorrCoef
>>> metric = ConcordanceCorrCoef()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)


## Functional Interface¶

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

Compute concordance correlation coefficient that measures the agreement between two variables.

where is the means for the two variables, are the corresponding variances and rho is the pearson correlation coefficient between the two variables.

Parameters
Example (single output regression):
>>> from torchmetrics.functional.regression 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.regression 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

Tensor

© Copyright Copyright (c) 2020-2023, Lightning-AI et al... Revision 1edf6a11.

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