PyXAB 0.1.2

PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms.

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, i.e., optimize an objective without gradients, also known as the continuous-arm bandit (CAB), Lipschitz bandit, global optimization (GO) and bandit-based black-box optimization problems.

PyXAB includes implementations of different X-armed bandit algorithms, including the classic ones such as HOO (Bubeck et al., 2011), StoSOO (Valko et al., 2013), and HCT (Azar et al., 2014), and the most recent works such as GPO (Shang et al., 2019) and VHCT (Li et al, 2021). PyXAB also provides the most commonly-used synthetic objectives to evaluate the performance of different algorithms and the implementations for different hierarchical partitions

Quick Links

• Quick Example
• Documentations
• Installation
• Features
  • X-armed bandit algorithms
  • Hierarchical partition
  • Synthetic objectives
• Contributing
• Citations

Quick Example

First define the blackbox objective, the parameter domain, the partition of the space, and the algorithm, e.g.

target = Garland()
domain = [[0, 1]]
partition = BinaryPartition
algo = T_HOO(rounds=1000, domain=domain, partition=partition)

At every round t, call algo.pull(t) to get a point. After receiving the (stochastic) reward for the point, call algo.receive_reward(t, reward) to give the algorithm the feedback

point = algo.pull(t)
reward = target.f(point) + np.random.uniform(-0.1, 0.1) # Uniform noise example
algo.receive_reward(t, reward)

Documentations

• The most up-to-date documentations for PyXAB

• The roadmap for our project

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
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
VHCT	Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization	2021
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

@article{li2021optimum,
  title={Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization},
  author={Li, Wenjie and Wang, Chi-Hua, Qifan Song and Cheng, Guang},
  journal={arXiv preprint arXiv:2106.09215},
  year={2021}
}