Pearson Corr. Coef.
Module Interface
- class torchmetrics.PearsonCorrCoef(num_outputs=1, **kwargs)[source]
Compute Pearson Correlation Coefficient.
Where
is a tensor of target values, and is a tensor of predictions.As input to
forward
andupdate
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
andcompute
the metric returns the following output:pearson
(Tensor
): A tensor with the Pearson Correlation Coefficient
- Parameters:
num_outputs (
int
) – Number of outputs in multioutput settingkwargs (
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:
- 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()
>>> 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)
Functional Interface
- torchmetrics.functional.pearson_corrcoef(preds, target)[source]
Compute pearson correlation coefficient.
- Parameters:
- Return type:
- 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.])