V-Measure Score

Module Interface

class torchmetrics.clustering.VMeasureScore(beta=1.0, **kwargs)

Compute V-Measure Score.

The V-measure is the harmonic mean between homogeneity and completeness:

..math::

v = frac{(1 + beta) * homogeneity * completeness}{beta * homogeneity + completeness}

where \(\beta\) is a weight parameter that defines the weight of homogeneity in the harmonic mean, with the default value \(\beta=1\). The V-measure is symmetric, which means that swapping preds and target does not change the 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:

• beta (float) – Weight parameter that defines the weight of homogeneity in the harmonic mean

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

Example::

>>> import torch
>>> from torchmetrics.clustering import VMeasureScore
>>> preds = torch.tensor([2, 1, 0, 1, 0])
>>> target = torch.tensor([0, 2, 1, 1, 0])
>>> metric = VMeasureScore(beta=2.0)
>>> metric(preds, target)
tensor(0.4744)

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 VMeasureScore
>>> metric = VMeasureScore()
>>> 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 VMeasureScore
>>> metric = VMeasureScore()
>>> 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.v_measure_score(preds, target, beta=1.0)

Compute the V-measure score between two clusterings.

Parameters:

• preds (Tensor) – predicted cluster labels

• target (Tensor) – ground truth cluster labels

• beta (float) – weight of the harmonic mean between homogeneity and completeness

Return type:

Tensor

Returns:

scalar tensor with the rand score

Example

>>> from torchmetrics.functional.clustering import v_measure_score
>>> import torch
>>> v_measure_score(torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 0, 0]))
tensor(1.)
>>> v_measure_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1]))
tensor(0.8000)