PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms.
Project description
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
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 git, run the following lines of code
git clone https://github.com/WilliamLwj/PyXAB.git
cd PyXAB
pip install .
To install via pip, run the following lines of code
pip install PyXAB # normal install
pip install --upgrade PyXAB # or update if needed
Features:
Stochastic X-armed bandit algorithms
- Algorithm starred are meta-algorithms (wrappers)
Hierarchical partition
Partition | Description |
---|---|
BinaryPartition | Equal-size binary partition of the parameter space, the split dimension is chosen uniform randomly |
RandomBinaryPartition | The same as BinaryPartition but with a randomly chosen split point |
DimensionBinaryPartition | Equal-size partition of the space with a binary split on each dimension, the number of children of one node is 2^d |
Synthetic objectives
- Some of these objectives can be found on Wikipedia
Objectives | Image |
---|---|
Garland | |
DoubleSine | |
DifficultFunc | |
Ackley | |
Himmelblau | |
Rastrigin |
Contributing
PyXAB is still under active development, and 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
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}
}
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