Metadata-Version: 2.1
Name: abexp
Version: 0.0.3
Summary: Python A/B testing experiment library
Home-page: UNKNOWN
Author: 
Author-email: 
License: UNKNOWN
Description: [comment]: <> (Modify also docs/installation.rst if change the README.md)
        [comment]: <> (Modify also LICENSE.rst if change the README.md)
        
        ABexp
        =====
        
        [comment]: <> (Modify also docs/badges.rst if you change the badges)
        [comment]: <> (Modify also LICENSE.rst if you change the license)
        ![alt text](https://img.shields.io/badge/build-passing-brightgreen)
        ![alt text](https://img.shields.io/badge/docs-passing-brightgreen)
        ![alt text](https://img.shields.io/badge/coverage-95%25-green)
        ![alt text](https://img.shields.io/badge/version-0.0.1-blue)
        ![alt text](https://img.shields.io/badge/license-MIT-blue)
        
        **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).
        
        <br/>
        
        ![A/B testing experiment flow](https://github.com/PlaytikaResearch/abexp/blob/main/docs/src/img/experiment_flow.png)
        
        <br/>
        
        
        Installation
        ------------
        
        This library is distributed on [PyPI](https://pypi.org/project/abexp/) 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](https://playtikaresearch.github.io/abexp/installation.html)
        page for more details.
        
        <br/>
        
        Getting started
        ---------------
        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
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        <br/>
        
        Documentation
        -------------
        For more information please read the full
        [documentation](https://playtikaresearch.github.io/abexp/abexp.html)
        and
        [tutorials](https://playtikaresearch.github.io/abexp/tutorials.html).
        
        <br/>
        
        Info for developers
        -------------------
        
        The source code of the project is available on [GitHub](https://github.com/PlaytikaResearch/abexp).
        
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        $ git clone https://github.com/PlaytikaResearch/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
        ~~~~~~~~~~~
        
        <br/>
        
        Run tests
        ---------
        
        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
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        <br/>
        
        License
        -------
        
        [MIT License](LICENSE)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: develop
