Pearson Corr. Coef.¶

Module Interface¶

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

Compute Pearson Correlation Coefficient.

$P_{corr}(x,y) = \frac{cov(x,y)}{\sigma_x \sigma_y}$

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.])

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

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()

(Source code, png, hires.png, pdf)

>>> 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)

(Source code, png, hires.png, pdf)

Functional Interface¶

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

Compute pearson correlation coefficient.

Parameters

preds¶ (Tensor) – estimated scores
target¶ (Tensor) – ground truth scores

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.])

Return type: Tensor