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.
- 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 🎢
For more details, see the Documentation and Tutorials.
You can always get the newest Ambrosia release using pip.
Stable version is released on every tag to main branch.
pip install ambrosiaStarting from version 0.4.0, the ability to process PySpark data is optional and can be enabled
using pip extras during the installation.
pip install ambrosia[spark]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.
Below is a brief overview example of using a set of three classes to conduct some simple experiment.
Designer
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
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
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')To install all requirements run
make installYou must have python3 and poetry installed.
For autoformatting run
make autoformatFor linters check run
make lintFor tests run
make testFor coverage run
make coverageTo remove virtual environment run
make cleanDevelopers and evangelists: