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 π«
- Multi-group split π³
- Matching of new control group to the existing pilot π
- Experiments result evaluation as p-value, point estimate of effect and confidence interval π
- Data preprocessing βοΈ
- Experiments acceleration π’
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 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.
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 install
You must have python3
and poetry
installed.
For autoformatting run
make autoformat
For linters check run
make lint
For tests run
make test
For coverage run
make coverage
To remove virtual environment run
make clean
Developers and evangelists: