Python Multi-Armed Bandit Library
Project description
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 PyMC3, 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
.
The latest release is version 0.0.2
. pybandits
requires a Python version >= 3.8
.
pip install pybandits
The command above will automatically install all the dependencies listed in requirements.txt
. 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
import random
from pybandits.core.smab import Smab
# init stochastic Multi-Armed Bandit model
smab = Smab(action_ids=['Action A', 'Action B', 'Action C'])
# predict actions
pred_actions, _ = smab.predict(n_samples=100)
n_successes, n_failures = {}, {}
for a in set(pred_actions):
# simulate rewards from environment
n_successes[a] = random.randint(0, pred_actions.count(a))
n_failures[a] = pred_actions.count(a) - n_successes[a]
# update model
smab.update(action_id=a, n_successes=n_successes[a], n_failures=n_failures[a])
Documentation
For more information please read the full documentation and tutorials.
Info for developers
PyBandits is supported by the AI for gaming and entertainment apps community.
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 with one of the following commands:
pip install . # install library + dependencies
pip install .[develop] # install library + dependencies + developer-dependencies
pip install -r requirements.txt # install dependencies
pip install -r requirements-dev.txt # install developer-dependencies
As suggested by the authors of pymc3
and pandoc
, we highly recommend to install these dependencies with
conda
:
conda install -c conda-forge pandoc
conda install -c conda-forge pymc3
To create the file pybandits.whl
for the installation with pip
run the following command:
python setup.py sdist bdist_wheel
To create the HTML documentation run the following commands:
cd docs
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
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