Ks 2samp

---
name: ks-2samp
description: Compute the ks_2samp metric — provided by scipy.stats. Use when the user has predictions and ground-truth and needs to compute ks_2samp, or asks how to score with ks_2samp.
metadata:
  skill_kind: metric
  source_lib: scipy.stats
  import_path: scipy.stats.ks_2samp
  source: library_introspection
---

# ks-2samp

> Metric `ks_2samp` from `scipy.stats` (scipy.stats.ks_2samp)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with ks_2samp, or
mentions `scipy.stats.ks_2samp` directly, or wants the standard scipy.stats implementation.

## Reference signature

```python
from scipy.stats import ks_2samp

# ks_2samp(data1, data2, alternative='two-sided', method='auto', *, axis=0, nan_policy='propagate', keepdims=False)
```

## Library docstring

```
Performs the two-sample Kolmogorov-Smirnov test for goodness of fit.

This test compares the underlying continuous distributions F(x) and G(x)
of two independent samples.  See Notes for a description of the available
null and alternative hypotheses.

Parameters
----------
data1, data2 : array_like, 1-Dimensional
    Two arrays of sample observations assumed to be drawn from a continuous
    distribution, sample sizes can be different.
alternative : {'two-sided', 'less', 'greater'}, optional
    Defines the null and alternative hypotheses. Default is 'two-sided'.
    Please see explanations in the Notes below.
method : {'auto', 'exact', 'asymp'}, optional
    Defines the method used for calculating the p-value.
    The following options are available (default is 'auto'):

      * 'auto' : use 'exact' for small size arrays, 'asymp' for large
      * 'exact' : use exact distribution of test statistic
      * 'asymp' : use asymptotic distribution of test statistic
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.

    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

Returns
-------
res: KstestResult
    An object containing attributes:

    statistic : float
        KS test statistic.
    pvalue : float
      
```

## Quick recipe

```python
import scipy.stats as _m
score = _m.ks_2samp(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)`.