| Name | kolmogorov-abacus JSON |
| Version |
0.0.5
JSON |
| download |
| home_page | |
| Summary | A/B experiments planning and evaluation tool |
| upload_time | 2024-02-07 19:42:13 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2023 Kolmogorov AI Lab Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
a/b
experiments
oce
online controlled experiment
ab test
experiment design
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img alt="Experiment report" src="https://raw.githubusercontent.com/kolmogorov-lab/abacus/main/docs/source/_static/abacus.png?raw=true" width="320px" height="320px">
[](https://pypi.org/project/kolmogorov-abacus/)
[](https://pypi.org/project/kolmogorov-abacus/)
[](https://t.me/kolmogorov_abacus_chat)
[](https://github.com/kolmogorov-lab/abacus/blob/main/LICENSE)
# ABacus: fast hypothesis testing and experiment design solution
**ABacus** is a Python library developed for A/B experimentation and testing.
It includes versatile instruments for different experimentation tasks like
prepilot, sample size determination, results calculation, visualisations and reporting.
## Important features
* Experiment design: type I and II errors, effect size, sample size simulations.
* Groups splitting with flexible configuration and stratification.
* A/A test and evaluation of splitter accuracy.
* Evaluation of experiment results with various statistical tests and approaches.
* Sensitivity increasing techniques like stratification, CUPED and CUPAC.
* Visualisation of experiment.
* Reporting in a human-readable format.
## Installation
You can use **pip** to install **ABacus** directly from PyPI:
```shell
pip install kolmogorov-abacus
```
or right from GitHub:
```shell
pip install pip+https://github.com/kolmogorov-lab/abacus
```
Note the requirement of Python 3.8+.
## Quick example
To define an experiment and analyse it is as easy as to describe your experiment and data:
```shell
from abacus.auto_ab.abtest import ABTest
from abacus.auto_ab.params import ABTestParams, DataParams, HypothesisParams
data_params = DataParams(...)
hypothesis_params = HypothesisParams(...)
ab_params = ABTestParams(data_params, hypothesis_params)
data = pd.read_csv('abtest_data.csv')
ab_test = ABTest(data, ab_params)
ab_test.report()
```
The result of code execution is the following:
<img alt="Experiment report" src="https://raw.githubusercontent.com/kolmogorov-lab/abacus/main/docs/source/_static/report_example.png?raw=true" width="400px">
## Documentation and Examples
Detailed [documentation](https://kolmogorov-abacus.readthedocs.io/en/latest/) and [examples](https://github.com/kolmogorov-lab/abacus/tree/main/examples) are available for your usage.
## Communication
Authors and developers:
* [Vadim Glukhov](https://github.com/educauchy)
* [Egor Shishkovets](https://github.com/egorshishkovets)
* [Dmitry Zabavin](https://github.com/dmitryzabavin)
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"author_email": "Vadim Glukhov <educauchy@gmail.com>, Egor Shishkovets <egor.shishkovets@glowbyteconsulting.com>, Dmitry Zabavin <dmitriy.zabavin@glowbyteconsulting.com>",
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