GOFevaluation

Evaluate the Goodness-of-Fit (GoF) for binned or unbinned data.

This GoF suite comprises the possibility to calculate different 1D / nD, binned / two-sample (unbinned) GoF measures and the corresponding p-value. A list of implemented measures is given below.

Implemented GoF measures

Installation and Set-Up

Clone the repository:

git clone https://github.com/XENONnT/GOFevaluation
cd GOFevaluation

Install the requirements in your environment:

pip install -r requirements.txt

Then install the package:

python setup.py install --user

You are now good to go!

Usage

Individual GoF Measures

Depending on your data and reference input you can initialise a gof_object in one of the following ways:

import GOFevaluation as ge

# Data Sample + Binned PDF
gof_object = ge.BinnedPoissonChi2GOF(data_sample, pdf, bin_edges, nevents_expected)

# Binned Data + Binned PDF
gof_object = ge.BinnedPoissonChi2GOF.from_binned(binned_data, binned_reference)

# Data Sample + Reference Sample
gof_object = ge.PointToPointGOF(data_sample, reference_sample)

With any gof_object you can calculate the GoF and the corresponding p-value as follows:

gof = gof_object.get_gof()
p_value = gof_object.get_pvalue()

Multiple GoF Measures at once

You can compute GoF and p-values for multiple measures at once with the GOFTest class.

Example:

import GOFevaluation as ge
import scipy.stats as sps

# random_state makes sure the gof values are reproducible.
# For the p-values, a slight variation is expected due to
# the random re-sampling method that is used.
data_sample = sps.uniform.rvs(size=100, random_state=200)
reference_sample = sps.uniform.rvs(size=300, random_state=201)

# Initialise all two-sample GoF measures:
gof_object = ge.GOFTest(data_sample=data_sample, 
                        reference_sample=reference_sample,
                        gof_list=['ADTestTwoSampleGOF', 
                                  'KSTestTwoSampleGOF', 
                                  'PointToPointGOF'])
# Calculate GoFs and p-values:
d_min = 0.01
gof_object.get_gofs(d_min=d_min)
# OUTPUT:
# OrderedDict([('ADTestTwoSampleGOF', 1.6301454042304904),
#              ('KSTestTwoSampleGOF', 0.14),
#              ('PointToPointGOF', 0.00048491049630050576)])

gof_object.get_pvalues(d_min=d_min)
# OUTPUT:
# OrderedDict([('ADTestTwoSampleGOF', 0.08699999999999997),
#              ('KSTestTwoSampleGOF', 0.10699999999999998),
#              ('PointToPointGOF', 0.14300000000000002)])

# Re-calculate p-value only for one measure:
gof_object.get_pvalues(d_min=.3, gof_list=['PointToPointGOF'])
# OUTPUT:
# OrderedDict([('ADTestTwoSampleGOF', 0.08699999999999997),
#              ('KSTestTwoSampleGOF', 0.10699999999999998),
#              ('PointToPointGOF', 0.03400000000000003)])

print(gof_object)
# OUTPUT:
# GOFevaluation.gof_test
# GoF measures: ADTestTwoSampleGOF, KSTestTwoSampleGOF, PointToPointGOF
# gofs = 1.6301454042304904, 0.14, 0.00048491049630050576
# p-values = 0.08699999999999997, 0.10699999999999998, 0.03400000000000003

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

v0.1.0

• Release as a python package