GitLocker: The Coding Marketplace

Description:

PyXAB 0.3.0

PyXAB - Python X-Armed Bandit

PyXAB is a Python open-source library for X-armed bandit algorithms, a prestigious set of optimizers for online black-box optimization and
hyperparameter optimization.

PyXAB contains the implementations of 10+ optimization algorithms, including the classic ones such as Zooming,
StoSOO, and HCT, and the most
recent works such as GPO, StroquOOL and VHCT.
PyXAB also provides the most commonly-used synthetic objectives to evaluate the performance of different algorithms and the implementations for different hierarchical partitions
PyXAB is featured for:

User-friendly APIs, clear documentation, and detailed examples
Comprehensive library of optimization algorithms, partitions and synthetic objectives
High standard code quality and high testing coverage
Low dependency for flexible combination with other packages such as PyTorch, Scikit-Learn

Reminder: The algorithms are maximization algorithms!
Quick Links

Quick Example
Documentations
Installation
Features

X-armed bandit algorithms
Hierarchical partition
Synthetic objectives

Contributing
Citations

Quick Example
PyXAB follows a natural and straightforward API design completely aligned with the online blackbox
optimization paradigm. The following is a simple 6-line usage example.
First, we define the parameter domain and the algorithm to run.
At every round t, call algo.pull(t) to get a point and call
algo.receive_reward(t, reward) to give the algorithm the objective evaluation (reward)
domain = [[0, 1]] # Parameter is 1-D and between 0 and 1
algo = T_HOO(rounds=1000, domain=domain)
for t in range(1000):
point = algo.pull(t)
reward = 1 #TODO: User-defined objective returns the reward
algo.receive_reward(t, reward)

More detailed examples can be found here
Documentations

The most up-to-date documentations

The roadmap for our project

Our manuscript for the library

Installation
To install via pip, run the following lines of code
pip install PyXAB # normal install
pip install --upgrade PyXAB # or update if needed

To install via git, run the following lines of code
git clone https://github.com/WilliamLwj/PyXAB.git
cd PyXAB
pip install .

Features:
X-armed bandit algorithms

Algorithm starred are meta-algorithms (wrappers)

Algorithm
Research Paper
Year

Zooming
Multi-Armed Bandits in Metric Spaces
2008

T-HOO
X-Armed Bandit
2011

DOO
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness
2011

SOO
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness
2011

StoSOO
Stochastic Simultaneous Optimistic Optimization
2013

HCT
Online Stochastic Optimization Under Correlated Bandit Feedback
2014

POO*
Black-box optimization of noisy functions with unknown smoothness
2015

GPO*
General Parallel Optimization Without A Metric
2019

PCT
General Parallel Optimization Without A Metric
2019

SequOOL
A Simple Parameter-free And Adaptive Approach to Optimization Under A Minimal Local Smoothness Assumption
2019

StroquOOL
A Simple Parameter-free And Adaptive Approach to Optimization Under A Minimal Local Smoothness Assumption
2019

VROOM
Derivative-Free & Order-Robust Optimisation
2020

VHCT
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
2023

VPCT
N.A. (GPO + VHCT)
N.A.

Hierarchical partition

Partition
Description

BinaryPartition
Equal-size binary partition of the parameter space, the split dimension is chosen uniform randomly

RandomBinaryPartition
Random-size binary partition of the parameter space, the split dimension is chosen uniform randomly

DimensionBinaryPartition
Equal-size partition of the space with a binary split on each dimension, the number of children of one node is 2^d

KaryPartition
Equal-size K-ary partition of the parameter space, the split dimension is chosen uniform randomly

RandomKaryPartition
Random-size K-ary partition of the parameter space, the split dimension is chosen uniform randomly

Synthetic objectives

Some of these objectives can be found on Wikipedia

Objectives
Image

Garland

DoubleSine

DifficultFunc

Ackley

Himmelblau

Rastrigin

Contributing
We appreciate all forms of help and contributions, including but not limited to

Star and watch our project
Open an issue for any bugs you find or features you want to add to our library
Fork our project and submit a pull request with your valuable codes

Please read the contributing instructions before submitting
a pull request.
Citations
If you use our package in your research or projects, we kindly ask you to cite our work
@misc{Li2023PyXAB,
doi = {10.48550/ARXIV.2303.04030},
url = {https://arxiv.org/abs/2303.04030},
author = {Li, Wenjie and Li, Haoze and Honorio, Jean and Song, Qifan},
title = {PyXAB -- A Python Library for $\mathcal{X}$-Armed Bandit and Online Blackbox Optimization Algorithms},
publisher = {arXiv},
year = {2023},
}

We would also appreciate it if you could cite our related works.
@article{li2023optimumstatistical,
title={Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization},
author={Wenjie Li and Chi-Hua Wang and Guang Cheng and Qifan Song},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=ClIcmwdlxn},
note={}
}

@misc{li2022Federated,
doi = {10.48550/ARXIV.2205.15268},
url = {https://arxiv.org/abs/2205.15268},
author = {Li, Wenjie and Song, Qifan and Honorio, Jean and Lin, Guang},
title = {Federated X-Armed Bandit},
publisher = {arXiv},
year = {2022},
}