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# Pearson Corr. Coef.¶

## Module Interface¶

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

Computes Pearson Correlation Coefficient:

Where is a tensor of target values, and is a tensor of predictions.

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 tensor with shape (N,) or multioutput tensor of shape (N,d)

As output of forward and compute the metric returns the following output:

Parameters
Example (single output regression):
>>> from torchmetrics import PearsonCorrCoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> pearson = PearsonCorrCoef()
>>> pearson(preds, target)
tensor(0.9849)

Example (multi output regression):
>>> from torchmetrics import PearsonCorrCoef
>>> target = torch.tensor([[3, -0.5], [2, 7]])
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> pearson = PearsonCorrCoef(num_outputs=2)
>>> pearson(preds, target)
tensor([1., 1.])


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

## Functional Interface¶

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

Computes pearson correlation coefficient.

Parameters
Example (single output regression):
>>> from torchmetrics.functional import pearson_corrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> pearson_corrcoef(preds, target)
tensor(0.9849)

Example (multi output regression):
>>> from torchmetrics.functional import pearson_corrcoef
>>> target = torch.tensor([[3, -0.5], [2, 7]])
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> pearson_corrcoef(preds, target)
tensor([1., 1.])

Return type

Tensor

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