Rand Score

Module Interface

class torchmetrics.clustering.RandScore(**kwargs)

Compute Rand Score (alternatively known as Rand Index).

\[RS(U, V) = \text{number of agreeing pairs} / \text{number of pairs}\]

The number of agreeing pairs is every \((i, j)\) pair of samples where \(i \in U\) and \(j \in V\) (the predicted and true clusterings, respectively) that are in the same cluster for both clusterings. The metric is symmetric, therefore swapping \(U\) and \(V\) yields the same rand score.

This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not be available in practice since clustering in generally is used for unsupervised learning.

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

• preds (Tensor): single integer tensor with shape (N,) with predicted cluster labels

• target (Tensor): single integer tensor with shape (N,) with ground truth cluster labels

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

• rand_score (Tensor): A tensor with the Rand Score

Parameters:

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

Example::

>>> import torch
>>> from torchmetrics.clustering import RandScore
>>> preds = torch.tensor([2, 1, 0, 1, 0])
>>> target = torch.tensor([0, 2, 1, 1, 0])
>>> metric = RandScore()
>>> metric(preds, target)
tensor(0.6000)

plot(val=None, ax=None)

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.clustering import RandScore
>>> metric = RandScore()
>>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
>>> fig_, ax_ = metric.plot(metric.compute())

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

Functional Interface

torchmetrics.functional.clustering.rand_score(preds, target)

Compute the Rand score between two clusterings.

Parameters:

• preds (Tensor) – predicted cluster labels

• target (Tensor) – ground truth cluster labels

Return type:

Tensor

Returns:

scalar tensor with the rand score

Example

>>> from torchmetrics.functional.clustering import rand_score
>>> import torch
>>> rand_score(torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 0, 0]))
tensor(1.)
>>> rand_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1]))
tensor(0.8333)