# Spearmancorrcoef

> Compute the SpearmanCorrCoef metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute SpearmanCorrCoef, or asks how to score with SpearmanCorrCoef.

- Skill: `qhjqhj00/spearmancorrcoef` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/spearmancorrcoef`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/spearmancorrcoef/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/spearmancorrcoef

---


# spearmancorrcoef

> Metric `SpearmanCorrCoef` from `torchmetrics` (torchmetrics.SpearmanCorrCoef)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import SpearmanCorrCoef

# SpearmanCorrCoef(num_outputs: int = 1, **kwargs: Any) -> None
```

## Library docstring

```
Compute `spearmans rank correlation coefficient`_.

.. math:
    r_s = = \frac{cov(rg_x, rg_y)}{\sigma_{rg_x} * \sigma_{rg_y}}

where :math:`rg_x` and :math:`rg_y` are the rank associated to the variables :math:`x` and :math:`y`.
Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated
on the rank variables.

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

- ``preds`` (:class:`~torch.Tensor`): Predictions from model in float tensor with shape ``(N,d)``
- ``target`` (:class:`~torch.Tensor`): Ground truth values in float tensor with shape ``(N,d)``

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

- ``spearman`` (:class:`~torch.Tensor`): A tensor with the spearman correlation(s)

Args:
    num_outputs: Number of outputs in multioutput setting
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example (single output regression):
    >>> from torch import tensor
    >>> from torchmetrics.regression import SpearmanCorrCoef
    >>> target = tensor([3, -0.5, 2, 7])
    >>> preds = tensor([2.5, 0.0, 2, 8])
    >>> spearman = SpearmanCorrCoef()
    >>> spearman(preds, target)
    tensor(1.0000)

Example (multi output regression):
    >>> from torchmetrics.regression import SpearmanCorrCoef
    >>> target = tensor([[3, -0.5], [2, 7]])
    >>> preds = tensor([[2.5, 0.0], [2, 8]])
    >>> spearman = SpearmanCorrCoef(num_outputs=2)
    >>> spearman(preds, target)
    tensor([1.0000, 1.0000])
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.SpearmanCorrCoef(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)`.