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 approximate p-value. A list of implemented measures is given below.
| GoF measure | Class | data input | reference input | dim |
|---|---|---|---|---|
| Kolmogorov-Smirnov | KSTestGOF |
sample | binned | 1D |
| Two-Sample Kolmogorov-Smirnov | KSTestTwoSampleGOF |
sample | sample | 1D |
| Two-Sample Anderson-Darling | ADTestTwoSampleGOF |
sample | sample | 1D |
| Poisson Chi2 | BinnedPoissonChi2GOF |
binned / sample | binned | nD |
| Chi2 | BinnedChi2GOF |
binned / sample | binned | nD |
| Point-to-point | PointToPointGOF |
sample | sample | nD |
pip install GOFevaluation
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!
The best way to start with the GOFevaluation package is to have a look at the tutorial notebook. If you click on the mybinder badge, you can execute the interactive notebook and give it a try yourself without the need of a local installation.
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()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', -0.7324060759792504)])
gof_object.get_pvalues(d_min=d_min)
# OUTPUT:
# OrderedDict([('ADTestTwoSampleGOF', 0.08699999999999997),
# ('KSTestTwoSampleGOF', 0.10699999999999998),
# ('PointToPointGOF', 0.31200000000000006)])
# Re-calculate p-value only for one measure:
gof_object.get_pvalues(d_min=.001, gof_list=['PointToPointGOF'])
# OUTPUT:
# OrderedDict([('ADTestTwoSampleGOF', 0.08699999999999997),
# ('KSTestTwoSampleGOF', 0.10699999999999998),
# ('PointToPointGOF', 0.128)])
print(gof_object)
# OUTPUT:
# GOFevaluation.gof_test
# GOF measures: ADTestTwoSampleGOF, KSTestTwoSampleGOF, PointToPointGOF
# ADTestTwoSampleGOF
# gof = 1.6301454042304904
# p-value = 0.08499999999999996
# KSTestTwoSampleGOF
# gof = 0.13999999999999996
# p-value = 0.09799999999999998
# PointToPointGOF
# gof = -0.7324060759792504
# p-value = 0.128Pull 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.
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