Cramer’s V

Compute Cramer’s V statistic measuring the association between two categorical (nominal) data series.

where

where $n_{ij}$ denotes the number of times the values $(A_i,B_j)$ are observed with $A_i,B_j$ represent frequencies of values in preds and target, respectively. Cramer’s V is a symmetric coefficient, i.e. $V(preds,target)=V(target,preds)$, so order of input arguments does not matter. The output values lies in [0, 1] with 1 meaning the perfect association.

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

• preds (Tensor): Either 1D or 2D tensor of categorical (nominal) data from the first data series with shape (batch_size,) or (batch_size, num_classes), respectively.

• target (Tensor): Either 1D or 2D tensor of categorical (nominal) data from the second data series with shape (batch_size,) or (batch_size, num_classes), respectively.

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

• cramers_v (Tensor): Scalar tensor containing the Cramer’s V statistic.

Parameters:

• num_classes (int) – Integer specifying the number of classes

• bias_correction (bool) – Indication of whether to use bias correction.

• nan_strategy (Literal['replace', 'drop']) – Indication of whether to replace or drop NaN values

• nan_replace_value (Optional[float]) – Value to replace NaN``s when ``nan_strategy = 'replace'

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

Raises:

• ValueError – If nan_strategy is not one of ‘replace’ and ‘drop’

• ValueError – If nan_strategy is equal to ‘replace’ and nan_replace_value is not an int or float

Example:

>>> from torch import randint, randn
>>> from torchmetrics.nominal import CramersV
>>> preds = randint(0, 4, (100,))
>>> target = (preds + randn(100)).round().clamp(0, 4)
>>> cramers_v = CramersV(num_classes=5)
>>> cramers_v(preds, target)
tensor(0.5284)

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:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>>

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.nominal import CramersV
>>> metric = CramersV(num_classes=5)
>>> metric.update(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,)))
>>> fig_, ax_ = metric.plot()

>>>

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.nominal import CramersV
>>> metric = CramersV(num_classes=5)
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,))))
>>> fig_, ax_ = metric.plot(values)