PyBandits
PyBandits is a Python library for Multi-Armed Bandit. It provides an implementation of stochastic Multi-Armed Bandit (sMAB) and contextual Multi-Armed Bandit (cMAB) based on Thompson Sampling.
For the sMAB, we implemented a Bernoulli multi-armed bandit based on Thompson Sampling algorithm Agrawal and Goyal, 2012. If context information is available we provide a generalisation of Thompson Sampling for cMAB Agrawal and Goyal, 2014 implemented with PyMC, an open source probabilistic programming framework for automatic Bayesian inference on user-defined probabilistic models.
Installation
This library is distributed on PyPI and can be installed with pip.
pip install pybandits
Based on the guidelines of pymc authors, it is highly recommended to install the library in a conda environment via the following.
conda install -c conda-forge pymc
pip install pybandits
The command above will automatically install all the dependencies listed in pyproject.toml. Please visit the
installation
page for more details.
Getting started
A short example, illustrating it use. Use the sMAB model to predict actions and update the model based on rewards from the environment.
import numpy as np
from pybandits.model import Beta
from pybandits.smab import SmabBernoulli
n_samples=100
# define action model
actions = {
"a1": Beta(),
"a2": Beta(),
}
# init stochastic Multi-Armed Bandit model
smab = SmabBernoulli(actions=actions)
# predict actions
pred_actions, _ = smab.predict(n_samples=n_samples)
simulated_rewards = np.random.randint(2, size=n_samples)
# update model
smab.update(actions=pred_actions, rewards=simulated_rewards)
Documentation
For more information please read the full documentation and tutorials.
You can also observe on DeepWiki.
Info for developers
The source code of the project is available on GitHub.
git clone https://github.com/playtikaoss/pybandits.git
You can install the library and the dependencies from the source code with one of the following commands:
poetry install # install library + dependencies
poetry install --without dev # install library + dependencies, excluding developer-dependencies
To create the HTML documentation run the following commands:
cd docs/src
make html
Run tests
Tests can be executed with pytest running the following commands. Make sure to have the library installed before to
run any tests.
cd tests
pytest -vv # run all tests
pytest -vv test_testmodule.py # run all tests within a module
pytest -vv test_testmodule.py -k test_testname # run only 1 test
pytest -vv -k 'not time' # run all tests but not exec time
License
3 maintainers
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pybandits-7.5.1.tar.gz.
File metadata
- Download URL: pybandits-7.5.1.tar.gz
- Upload date:
- Size: 135.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
poetry/2.2.1 CPython/3.10.20 Linux/6.17.0-1018-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
de4412ce0af7eb08ccdd39a18cc312161f830f2c94acdc134c0cfe961ac3e357
|
|
| MD5 |
70cb8670f5fe4e05c92454c04fcb4443
|
|
| BLAKE2b-256 |
d1a5504d5cd77f266e4dc6135b1620b49ddfbb4c0db2d365740455ae1864f9cb
|
File details
Details for the file pybandits-7.5.1-py3-none-any.whl.
File metadata
- Download URL: pybandits-7.5.1-py3-none-any.whl
- Upload date:
- Size: 170.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
poetry/2.2.1 CPython/3.10.20 Linux/6.17.0-1018-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
bb35879c2006109773e847f2cd8712aab9a16a79b3d1029425d0f3b4cec5f6a4
|
|
| MD5 |
8998949328c058f312bb74be6a68cbb4
|
|
| BLAKE2b-256 |
9a3fcaa5e524688cffe62566581818b1e6418f77b49d196a014904bbdadc40b8
|
