Completenessscore

---
name: completenessscore
description: Compute the CompletenessScore metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute CompletenessScore, or asks how to score with CompletenessScore.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.clustering.CompletenessScore
  source: library_introspection
---

# completenessscore

> Metric `CompletenessScore` from `torchmetrics` (torchmetrics.clustering.CompletenessScore)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with CompletenessScore, or
mentions `torchmetrics.clustering.CompletenessScore` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics.clustering import CompletenessScore

# CompletenessScore(**kwargs: Any) -> None
```

## Library docstring

```
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`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
- ``target`` (:class:`~torch.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`` (:class:`~torch.Tensor`): A tensor with the Rand Score

Args:
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` 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)
```

## Quick recipe

```python
import torchmetrics.clustering as _m
score = _m.CompletenessScore(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.