# Completeness Score¶

## Module Interface¶

class torchmetrics.clustering.CompletenessScore(**kwargs)[source]

Compute Completeness Score.

A clustering result satisfies completeness if all the data points that are members of a given class are elements of the same cluster. The metric is not symmetric, therefore swapping preds and target yields a different 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:

Parameters:

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

Example

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

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.clustering import CompletenessScore
>>> metric = CompletenessScore()
>>> 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 CompletenessScore
>>> metric = CompletenessScore()
>>> 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.completeness_score(preds, target)[source]

Compute the Completeness score between two clusterings.

Parameters:
Return type:

Tensor

Returns:

scalar tensor with the rand score

Example

>>> from torchmetrics.functional.clustering import completeness_score
>>> import torch
>>> completeness_score(torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 0, 0]))
tensor(1.)
>>> completeness_score(torch.tensor([0, 0, 1, 2]), torch.tensor([0, 0, 1, 1]))
tensor(0.6667)