ABexp is a Python library which aims to support users along the entire end-to-end A/B test experiment flow
(see picture below). It contains A/B testing modules which use both frequentist and bayesian statistical approaches
including bayesian generalized linear model (GLM).
This library is distributed on PyPI and
can be installed with pip. The latest release is version 0.0.1.
$ pip install abexp
The command above will automatically install all the dependencies listed in requirements.txt. Please visit the
installation
page for more details.
A short example, illustrating it use:
import abexp
Compute the minimum sample size needed for an A/B test experiment with two variants, so called control and treatment groups.
from abexp.core.design import SampleSize
c = 0.33 # conversion rate control group
t = 0.31 # conversion rate treatment group
sample_size = SampleSize.ssd_prop(prop_contr=c, prop_treat=t) # minimum sample size per each group
For more information please read the full documentation and tutorials.
The source code of the project is available on GitHub.
$ git clone https://github.com/PlaytikaOSS/abexp.git
You can install the library and the dependencies with one of the following commands:
$ pip install . # install library + dependencies
$ pip install .[develop] # install library + dependencies + developer-dependencies
$ pip install -r requirements.txt # install dependencies
$ pip install -r requirements-dev.txt # install developer-dependencies
As suggested by the authors of pymc3 and pandoc, we highly recommend to install these dependencies with
conda:
$ conda install -c conda-forge pandoc
$ conda install -c conda-forge pymc3
To create the file abexp.whl for the installation with pip run the following command:
$ python setup.py sdist bdist_wheel
To create the HTML documentation run the following commands:
$ cd docs
$ make html
Tests can be executed with pytest running the following commands:
$ cd tests
$ pytest # run all tests
$ pytest test_testmodule.py # run all tests within a module
$ pytest test_testmodule.py -k test_testname # run only 1 test