Vmeasurescore

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

# vmeasurescore

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.clustering import VMeasureScore

# VMeasureScore(beta: float = 1.0, **kwargs: Any) -> None
```

## Library docstring

```
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 :math:`\beta` is a weight parameter that defines the weight of homogeneity in the harmonic mean, with the
default value :math:`\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`` (: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:
    beta: Weight parameter that defines the weight of homogeneity in the harmonic mean
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` 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)
```

## Quick recipe

```python
import torchmetrics.clustering as _m
score = _m.VMeasureScore(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)`.