Anderson Ksamp

anderson-ksamp

Metric anderson_ksamp from scipy.stats (scipy.stats.anderson_ksamp)

When to invoke this skill

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

Reference signature

from scipy.stats import anderson_ksamp

# anderson_ksamp(samples, midrank=<object object at 0x72cab25435f0>, *, variant=<object object at 0x72cab25435f0>, method=None)

Library docstring

The Anderson-Darling test for k-samples.

The k-sample Anderson-Darling test is a modification of the
one-sample Anderson-Darling test. It tests the null hypothesis
that k-samples are drawn from the same population without having
to specify the distribution function of that population. The
critical values depend on the number of samples.

Parameters
----------
samples : sequence of 1-D array_like
    Array of sample data in arrays.
midrank : bool, optional
    Variant of Anderson-Darling test which is computed. Default
    (True) is the midrank test applicable to continuous and
    discrete populations. If False, the right side empirical
    distribution is used.

    .. deprecated::1.17.0
        Use parameter `variant` instead.
variant : {'midrank', 'right', 'continuous'}
    Variant of Anderson-Darling test to be computed. ``'midrank'`` is applicable
    to both continuous and discrete populations. ``'discrete'`` and ``'continuous'``
    perform alternative versions of the test for discrete  and continuous
    populations, respectively.
    When `variant` is specified, the return object will not be unpackable as a
    tuple, and only attributes ``statistic`` and ``pvalue`` will be present.
method : PermutationMethod, optional
    Defines the method used to compute the p-value. If `method` is an
    instance of `PermutationMethod`, the p-value is computed using
    `scipy.stats.permutation_test` with the provided configuration options
    and other appropriate settings. Otherwise, the p-value is interpolated
    from tabulated values.

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

    statistic : float
        Normalized k-sample Anderson-Darling test statistic.
    critical_values : array
        The critical values for significance levels 25%, 10%, 5%, 2.5%, 1%,
        0.5%, 0.1%.

        .. deprecated::1.17.0
             Present only when `variant` is unspecified.

    pvalue : float
        The approximate p-value of the test. If `method` is not
        provided, the value is floored / capped at 0.1% / 25%.

Raises
------
ValueError
    If fewer than 2 samples are provided, a sample is empty, or no
    distinct observa

Quick recipe

import scipy.stats as _m
score = _m.anderson_ksamp(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).