bayesianbandits 1.3.0

A Pythonic microframework for Multi-Armed Bandit algorithms.

bayesianbandits

Bayesian Multi-Armed Bandits for Python

Problem: Despite having a conceptually simple interface, putting together a multi-armed bandit in Python is a daunting task.

Solution: bayesianbandits is a Python package that provides a simple interface for creating and running Bayesian multi-armed bandits. It is built on top of scikit-learn and scipy, taking advantage of conjugate priors to provide fast and accurate inference.

While the API is still evolving, this library is already being used in production for marketing optimization, dynamic pricing, and other applications. Are you using bayesianbandits in your project? Let us know!

Features

• Simple API: bayesianbandits provides a simple interface - most users will only need to call pull and update to get started.
• Hybrid bandits with cross-arm learning: Share knowledge across similar arms for faster learning and better sample efficiency.
• Fast: bayesianbandits is built on top of already fast scientific Python libraries, but, if installed, will also use SuiteSparse to further speed up matrix operations on sparse matrices. Handling tens or even hundreds of thousands of features in a sparse model is no problem.
• sklearn pipeline integration: Use sklearn pipelines and transformers to preprocess data before feeding it into your bandit.
• Adversarial bandits (EXP3A): Robust performance in non-stationary and adversarial environments.
• Flexible: Pick from a variety of policy algorithms, including Thompson sampling, upper confidence bound, and epsilon-greedy. Pick from a variety of prior distributions, including beta, gamma, normal, and normal-inverse-gamma.
• Extensible: bayesianbandits provides simple interfaces for creating custom policies and priors.
• Well-tested: bayesianbandits is well-tested, with nearly 100% test coverage.

Compatibility

bayesianbandits is tested with Python 3.10, 3.11, 3.12, 3.13, and 3.14 with scikit-learn 1.5.2, 1.6.1, 1.7.2, 1.8.0.

Requires NumPy >= 2.0 and SciPy >= 1.14. For CHOLMOD sparse support, requires scikit-sparse >= 0.5.0, which in turn requires SuiteSparse >= 7.4.0.

Getting Started

Install this package from PyPI.

pip install -U bayesianbandits

Define a LinearUCB contextual bandit with a normal prior.

import numpy as np
from bayesianbandits import (
    Arm,
    NormalInverseGammaRegressor,
    ContextualAgent,
    UpperConfidenceBound,
)

arms = [
    Arm(1, learner=NormalInverseGammaRegressor()),
    Arm(2, learner=NormalInverseGammaRegressor()),
    Arm(3, learner=NormalInverseGammaRegressor()),
    Arm(4, learner=NormalInverseGammaRegressor()),
]

policy = UpperConfidenceBound(alpha=0.84)

Instantiate the agent and pull an arm with context.

agent = ContextualAgent(arms, policy)

context = np.array([[1, 0, 0, 0]])

# Can be constructed with sklearn, formulaic, patsy, etc...
# context = formulaic.Formula("1 + article_number").get_model_matrix(data)
# context = sklearn.preprocessing.OneHotEncoder().fit_transform(data)

agent.pull(context)

Update the bandit with the reward.

agent.update(context, np.array([15.0]))

For shared learning across arms via the design matrix:

from bayesianbandits import LipschitzContextualAgent, ArmColumnFeaturizer, NormalRegressor

# Single shared learner across all arms
agent = LipschitzContextualAgent(
    arms=[Arm(i) for i in range(100)],  # 100 arms sharing knowledge
    learner=NormalRegressor(),
    arm_featurizer=ArmColumnFeaturizer(column_name='article_id'),
    policy=ThompsonSampling()
)

That's it! Check out the documentation for more examples.