Spearman Corr. Coef.¶

Module Interface¶

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

Computes spearmans rank correlation coefficient.

where $rg_x$ and $rg_y$ are the rank associated to the variables $x$ and $y$ . Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables.

As input to forward and update the metric accepts the following input:

preds (Tensor): Predictions from model in float tensor with shape (N,d)
target (Tensor): Ground truth values in float tensor with shape (N,d)

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

spearman (Tensor): A tensor with the spearman correlation(s)

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 import SpearmanCorrCoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> spearman = SpearmanCorrCoef()
>>> spearman(preds, target)
tensor(1.0000)

Example (multi output regression):

>>> from torchmetrics import SpearmanCorrCoef
>>> target = torch.tensor([[3, -0.5], [2, 7]])
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> spearman = SpearmanCorrCoef(num_outputs=2)
>>> spearman(preds, target)
tensor([1.0000, 1.0000])

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

Functional Interface¶

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

Computes spearmans rank correlation coefficient:

where $rg_x$ and $rg_y$ are the rank associated to the variables x and y. Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables.

Parameters

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

Example (single output regression):

>>> from torchmetrics.functional import spearman_corrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> spearman_corrcoef(preds, target)
tensor(1.0000)

Example (multi output regression):

>>> from torchmetrics.functional import spearman_corrcoef
>>> target = torch.tensor([[3, -0.5], [2, 7]])
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> spearman_corrcoef(preds, target)
tensor([1.0000, 1.0000])

Return type: Tensor