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Pearson Corr. Coef.

Module Interface

class torchmetrics.PearsonCorrCoef(compute_on_step=None, **kwargs)[source]

Computes 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.

Forward accepts

  • preds (float tensor): (N,)

  • target``(float tensor): ``(N,)

Parameters
  • compute_on_step (Optional[bool]) –

    Forward only calls update() and returns None if this is set to False.

    Deprecated since version v0.8: Argument has no use anymore and will be removed v0.9.

  • kwargs (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> 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)

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

compute()[source]

Computes pearson correlation coefficient over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

Functional Interface

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

Computes pearson correlation coefficient.

Parameters
  • preds (Tensor) – estimated scores

  • target (Tensor) – ground truth scores

Example

>>> 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)
Return type

Tensor

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