Shortcuts

Kendall Rank Corr. Coef.

Module Interface

class torchmetrics.KendallRankCorrCoef(variant='b', t_test=False, alternative='two-sided', num_outputs=1, **kwargs)[source]

Compute Kendall Rank Correlation Coefficient:

tau_a = \frac{C - D}{C + D}

where C represents concordant pairs, D stands for discordant pairs.

tau_b = \frac{C - D}{\sqrt{(C + D + T_{preds}) * (C + D + T_{target})}}

where C represents concordant pairs, D stands for discordant pairs and T represents a total number of ties.

tau_c = 2 * \frac{C - D}{n^2 * \frac{m - 1}{m}}

where C represents concordant pairs, D stands for discordant pairs, n is a total number of observations and m is a min of unique values in preds and target sequence.

Definitions according to Definition according to The Treatment of Ties in Ranking Problems.

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

  • preds (Tensor): Sequence of data in float tensor of either shape (N,) or (N,d)

  • target (Tensor): Sequence of data in float tensor of either shape (N,) or (N,d)

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

  • kendall (Tensor): A tensor with the correlation tau statistic, and if it is not None, the p-value of corresponding statistical test.

Parameters
  • variant (Literal[‘a’, ‘b’, ‘c’]) – Indication of which variant of Kendall’s tau to be used

  • t_test (bool) – Indication whether to run t-test

  • alternative (Optional[Literal[‘two-sided’, ‘less’, ‘greater’]]) – Alternative hypothesis for t-test. Possible values: - ‘two-sided’: the rank correlation is nonzero - ‘less’: the rank correlation is negative (less than zero) - ‘greater’: the rank correlation is positive (greater than zero)

  • num_outputs (int) – Number of outputs in multioutput setting

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

Raises
Example (single output regression):
>>> from torch import tensor
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> target = tensor([3, -0.5, 2, 1])
>>> kendall = KendallRankCorrCoef()
>>> kendall(preds, target)
tensor(0.3333)
Example (multi output regression):
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([[2.5, 0.0], [2, 8]])
>>> target = tensor([[3, -0.5], [2, 1]])
>>> kendall = KendallRankCorrCoef(num_outputs=2)
>>> kendall(preds, target)
tensor([1., 1.])
Example (single output regression with t-test):
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> target = tensor([3, -0.5, 2, 1])
>>> kendall = KendallRankCorrCoef(t_test=True, alternative='two-sided')
>>> kendall(preds, target)
(tensor(0.3333), tensor(0.4969))
Example (multi output regression with t-test):
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([[2.5, 0.0], [2, 8]])
>>> target = tensor([[3, -0.5], [2, 1]])
>>> kendall = KendallRankCorrCoef(t_test=True, alternative='two-sided', num_outputs=2)
>>> kendall(preds, target)
(tensor([1., 1.]), tensor([nan, nan]))

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

Functional Interface

torchmetrics.functional.kendall_rank_corrcoef(preds, target, variant='b', t_test=False, alternative='two-sided')[source]

Compute Kendall Rank Correlation Coefficient.

tau_a = \frac{C - D}{C + D}

where C represents concordant pairs, D stands for discordant pairs.

tau_b = \frac{C - D}{\sqrt{(C + D + T_{preds}) * (C + D + T_{target})}}

where C represents concordant pairs, D stands for discordant pairs and T represents a total number of ties.

tau_c = 2 * \frac{C - D}{n^2 * \frac{m - 1}{m}}

where C represents concordant pairs, D stands for discordant pairs, n is a total number of observations and m is a min of unique values in preds and target sequence.

Definitions according to Definition according to The Treatment of Ties in Ranking Problems.

Parameters
  • preds (Tensor) – Sequence of data of either shape (N,) or (N,d)

  • target (Tensor) – Sequence of data of either shape (N,) or (N,d)

  • variant (Literal[‘a’, ‘b’, ‘c’]) – Indication of which variant of Kendall’s tau to be used

  • t_test (bool) – Indication whether to run t-test

  • alternative (Optional[Literal[‘two-sided’, ‘less’, ‘greater’]]) – Alternative hypothesis for t-test. Possible values: - ‘two-sided’: the rank correlation is nonzero - ‘less’: the rank correlation is negative (less than zero) - ‘greater’: the rank correlation is positive (greater than zero)

Return type

Union[Tensor, Tuple[Tensor, Tensor]]

Returns

Correlation tau statistic (Optional) p-value of corresponding statistical test (asymptotic)

Raises
Example (single output regression):
>>> from torchmetrics.functional.regression import kendall_rank_corrcoef
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> target = torch.tensor([3, -0.5, 2, 1])
>>> kendall_rank_corrcoef(preds, target)
tensor(0.3333)
Example (multi output regression):
>>> from torchmetrics.functional.regression import kendall_rank_corrcoef
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> target = torch.tensor([[3, -0.5], [2, 1]])
>>> kendall_rank_corrcoef(preds, target)
tensor([1., 1.])
Example (single output regression with t-test)
>>> from torchmetrics.functional.regression import kendall_rank_corrcoef
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> target = torch.tensor([3, -0.5, 2, 1])
>>> kendall_rank_corrcoef(preds, target, t_test=True, alternative='two-sided')
(tensor(0.3333), tensor(0.4969))
Example (multi output regression with t-test):
>>> from torchmetrics.functional.regression import kendall_rank_corrcoef
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> target = torch.tensor([[3, -0.5], [2, 1]])
>>> kendall_rank_corrcoef(preds, target, t_test=True, alternative='two-sided')
    (tensor([1., 1.]), tensor([nan, nan]))
Read the Docs v: latest
Versions
latest
stable
v0.11.1
v0.11.0
v0.10.3
v0.10.2
v0.10.1
v0.10.0
v0.9.3
v0.9.2
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
Downloads
pdf
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.