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version 0.1.3

Variations on goodness of fit tests for SciPy.

People: Wojciech Ruszczewski



Provides variants of Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling
goodness of fit tests for fully specified continuous distributions.


.. code:: python

>>> from scipy.stats import norm, uniform
>>> from skgof import ks_test, cvm_test, ad_test

>>> ks_test((1, 2, 3), uniform(0, 4))
GofResult(statistic=0.25, pvalue=0.97...)

>>> cvm_test((1, 2, 3), uniform(0, 4))
GofResult(statistic=0.04..., pvalue=0.95...)

>>> data = norm(0, 1).rvs(random_state=1, size=100)
>>> ad_test(data, norm(0, 1))
GofResult(statistic=0.75..., pvalue=0.51...)
>>> ad_test(data, norm(.3, 1))
GofResult(statistic=3.52..., pvalue=0.01...)

Simple tests

Scikit-gof currently only offers three nonparametric tests that let you
compare a sample with a reference probability distribution. These are:

Kolmogorov-Smirnov supremum statistic; almost the same as
``scipy.stats.kstest()`` with ``alternative=\'two-sided\'`` but with
(hopefully) somewhat more precise p-value calculation;

Cramer-von Mises L2 statistic, with a rather crude estimation of the
statistic distribution (but seemingly the best available);

Anderson-Darling statistic with a fair approximation of its distribution;
unlike the composite ``scipy.stats.anderson()`` this one needs a fully
specified hypothesized distribution.

Simple test functions use a common interface, taking as the first argument the
data (sample) to be compared and as the second argument a frozen ``scipy.stats``
They return a named tuple with two fields: ``statistic`` and ``pvalue``.

For a simple example consider the hypothesis that the sample (.4, .1, .7) comes
from the uniform distribution on [0, 1]:

.. code:: python

if ks_test((.4, .1, .7), unif(0, 1)).pvalue < .05:
print(\"Hypothesis rejected with 5% significance.\")

If your samples are very large and you have them sorted ahead of time, pass
``assume_sorted=True`` to save some time that would be wasted resorting.


Simple tests are composed of two phases: calculating the test statistic and
determining how likely is the resulting value (under the hypothesis).
New tests may be defined by providing a new statistic calculation routine or an
alternative distribution for a statistic.

Functions calculating statistics are given evaluations of the reference
cumulative distribution function on sorted data and are expected to return
a single number.
For a simple test, if the sample indeed comes from the hypothesized (continuous)
distribution, the values passed to the function should be uniformly distributed
over [0, 1].

Here is a simplistic example of how a statistic function might look like:

.. code:: python

def ex_stat(data):
return abs(data.sum() - data.size / 2)

Statistic functions for the provided tests, ``ks_stat()``, ``cvm_stat()``,
and ``ad_stat()``, can be imported from ``skgof.ecdfgof``.

Statistic distributions should derive from ``rv_continuous`` and implement
at least one of the abstract ``_cdf()`` or ``_pdf()`` methods (you might
also consider directly coding ``_sf()`` for increased precision of results
close to 1). For example:

.. code:: python

from numpy import sqrt
from scipy.stats import norm, rv_continuous

class ex_unif_gen(rv_continuous):
def _cdf(self, statistic, samples):
return 1 - 2 * norm.cdf(-statistic, scale=sqrt(samples / 12))

ex_unif = ex_unif_gen(a=0, name=\'ex-unif\', shapes=\'samples\')

The provided distributions live in separate modules, respectively ``ksdist``,
``cvmdist``, and ``addist``.

Once you have a statistic calculation function and a statistic distribution the
two parts can be combined using ``simple_test``:

.. code:: python

from functools import partial
from skgof.ecdfgof import simple_test

ex_test = partial(simple_test, stat=ex_stat, pdist=ex_unif)

**Exercise**: The example test has a fundamental flaw. Can you point it out?

.. The test is not consistent under all alternatives. For instance, if the
hypothesis was that samples come from the uniform distribution on [0, 1],
but they really were \"drawn\" from the degenerate distribution at .5, the
test would never notice, even for arbitrarily large sample sizes.

Moreover, the asymptotic distribution is not a good approximation of the
actual statistic distribution for small sample sizes.


.. code:: bash

pip install scikit-gof

Requires recent versions of Python (> 3), NumPy (>= 1.10) and SciPy.

Please fix or point out any errors, inaccuracies or typos you notice.



You can download the latest distribution from PyPI here:

Using pip

You can install scikit-gof for yourself from the terminal by running:

pip install --user scikit-gof

If you want to install it for all users on your machine, do:

pip install scikit-gof
On Linux, do sudo pip install scikit-gof.

If you don't yet have the pip tool, you can get it following these instructions.

This package was discovered in PyPI.