Spearmanr

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

# spearmanr

> Metric `spearmanr` from `scipy.stats` (scipy.stats.spearmanr)

## When to invoke this skill

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

## Reference signature

```python
from scipy.stats import spearmanr

# spearmanr(a, b=None, axis=0, nan_policy='propagate', alternative='two-sided')
```

## Library docstring

```
Calculate a Spearman correlation coefficient with associated p-value.

The Spearman rank-order correlation coefficient is a nonparametric measure
of the monotonicity of the relationship between two datasets.
Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Correlations of -1 or +1 imply an exact monotonic relationship. Positive
correlations imply that as x increases, so does y. Negative correlations
imply that as x increases, y decreases.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. Although calculation of the
p-value does not make strong assumptions about the distributions underlying
the samples, it is only accurate for very large samples (>500
observations). For smaller sample sizes, consider a permutation test (see
Examples section below).

Parameters
----------
a, b : 1D or 2D array_like, b is optional
    One or two 1-D or 2-D arrays containing multiple variables and
    observations. When these are 1-D, each represents a vector of
    observations of a single variable. For the behavior in the 2-D case,
    see under ``axis``, below.
    Both arrays need to have the same length in the ``axis`` dimension.
axis : int or None, optional
    If axis=0 (default), then each column represents a variable, with
    observations in the rows. If axis=1, the relationship is transposed:
    each row represents a variable, while the columns contain observations.
    If axis=None, then both arrays will be raveled.
nan_policy : {'propagate', 'raise', 'omit'}, optional
    Defines how to handle when input contains nan.
    The following options are available (default is 'propagate'):

    * 'propagate': returns nan
    * 'raise': throws an error
    * 'omit': performs the calculations ignoring nan values
alternative : {'two-sided', 'less', 'greater'}, optional
    Defines the alternative hypothesis. Default is 'two-sided'.
    The following options are available:

    * 'two-sided': the correlation is nonzero
    * 'less': the correlation is negative (less than zero)
    * 'greater':
```

## Quick recipe

```python
import scipy.stats as _m
score = _m.spearmanr(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)`.