Spearman Corr. Coef.¶
Module Interface¶
- class torchmetrics.SpearmanCorrCoef(num_outputs=1, **kwargs)[source]
Computes spearmans rank correlation coefficient.
where and are the rank associated to the variables and . Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables.
Forward accepts
preds
(float tensor):(N,d)
target``(float tensor): ``(N,d)
- 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 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 and 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.
- 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