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Ambrosia

|PyPI| |PyPI License| |ReadTheDocs| |Tests| |Coverage| |Black| |Python Versions| |Telegram Channel|

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.. title

Ambrosia is a Python library for A/B tests design, split and effect measurement. It provides rich set of methods for conducting full A/B testing pipeline.

The project is intended for use in research and production environments based on data in pandas and Spark format.

.. functional

Key functionality

• Pilots design 🛫
• Multi-group split 🎳
• Matching of new control group to the existing pilot 🎏
• Experiments result evaluation as p-value, point estimate of effect and confidence interval 🎞
• Data preprocessing ✂️
• Experiments acceleration 🎢

.. documentation

Documentation

For more details, see the Documentation <https://ambrosia.readthedocs.io/>_ and Tutorials <https://github.com/MobileTeleSystems/Ambrosia/tree/main/examples>_.

.. install

Installation

You can always get the newest Ambrosia release using pip. Stable version is released on every tag to main branch.

.. code:: bash

pip install ambrosia 

Starting from version 0.4.0, the ability to process PySpark data is optional and can be enabled using pip extras during the installation.

.. code:: bash

pip install ambrosia[spark]

.. usage

Usage

The main functionality of Ambrosia is contained in several core classes and methods, which are autonomic for each stage of an experiment and have very intuitive interface.

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Below is a brief overview example of using a set of three classes to conduct some simple experiment.

Designer

.. code:: python

from ambrosia.designer import Designer
designer = Designer(dataframe=df, effects=1.2, metrics='portfel_clc') # 20% effect, and loaded data frame df
designer.run('size') 

Splitter

.. code:: python

from ambrosia.splitter import Splitter
splitter = Splitter(dataframe=df, id_column='id') # loaded data frame df with column with id - 'id'
splitter.run(groups_size=500, method='simple') 

Tester

.. code:: python

from ambrosia.tester import Tester
tester = Tester(dataframe=df, column_groups='group') # loaded data frame df with groups info 'group'
tester.run(metrics='retention', method='theory', criterion='ttest')

.. develop

Development

To install all requirements run

.. code:: bash

make install

You must have python3 and poetry installed.

For autoformatting run

.. code:: bash

make autoformat

For linters check run

.. code:: bash

make lint

For tests run

.. code:: bash

make test

For coverage run

.. code:: bash

make coverage

To remove virtual environment run

.. code:: bash

make clean

.. contributors

Authors

Developers and evangelists:

• Bayramkulov Aslan <https://github.com/aslanbm>_
• Khakimov Artem <https://github.com/xandaau>_
• Vasin Artem <https://github.com/VictorFromChoback>_