Pearson Corr. Coef.

Module Interface

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

Compute Pearson Correlation Coefficient.

Pcorr(x,y)=cov(x,y)σxσy

Where y is a tensor of target values, and x 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:

  • pearson (Tensor): A tensor with the Pearson Correlation Coefficient

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

Plot a single or multiple values from the metric.

Parameters:
  • val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

  • ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

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 PearsonCorrCoef
>>> metric = PearsonCorrCoef()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()
../_images/pearson_corr_coef-1.png
>>>
>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import PearsonCorrCoef
>>> metric = PearsonCorrCoef()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
../_images/pearson_corr_coef-2.png

Functional Interface

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

Compute pearson correlation coefficient.

Parameters:
  • preds (Tensor) – estimated scores

  • target (Tensor) – ground truth scores

Return type:

Tensor

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