Metadata-Version: 2.1
Name: PyXAB
Version: 0.1.2
Summary: PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms.
Home-page: https://github.com/WilliamLwj/PyXAB
Author: Wenjie Li, Haoze Li
Author-email: lil3549@purdue.edu
License: MIT
Description: 
        
        # PyXAB - Python *X*-Armed Bandit 
        
        
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        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.
        
        
        <p align='center'>
          <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/HCT_trajectory.gif" alt="trajectory" width="48%"/>  
          <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/HCT_heatmap.gif" alt="heatmap" width="48%"/>  
        </p>
        
        PyXAB includes implementations of different *X*-armed bandit algorithms, including the classic ones such as [HOO (Bubeck et al., 2011)](https://jmlr.org/papers/v12/bubeck11a.html), 
         [StoSOO (Valko et al., 2013)](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/StoSOO.py), and [HCT (Azar et al., 2014)](https://proceedings.mlr.press/v32/azar14.html), and the most
        recent works such as [GPO (Shang et al., 2019)](https://proceedings.mlr.press/v98/xuedong19a.html) and [VHCT (Li et al, 2021)](https://arxiv.org/abs/2106.09215).
        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](#Quick-Example)
        - [Documentations](#Documentations)
        - [Installation](#Installation)
        - [Features](#Features)
          * [*X*-armed bandit algorithms](#Stochastic-X-armed-bandit-algorithms)
          * [Hierarchical partition ](#Hierarchical-partition)
          * [Synthetic objectives](#Synthetic-objectives)
        - [Contributing](#Contributing)
        - [Citations](#Citations)
        
        ## Quick Example
        First define the blackbox objective, the parameter domain, the partition of the space, and the algorithm, e.g.
        
        ```python3
        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
        
        ```python3
        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](https://pyxab.readthedocs.io/) for PyXAB
        
          * The [roadmap](https://github.com/users/WilliamLwj/projects/1) for our project    
        
        ## Installation
        
        To install via pip, run the following lines of code
        ```bash
        pip install PyXAB                 # normal install
        pip install --upgrade PyXAB       # or update if needed
        ```
        
        
        To install via git, run the following lines of code
        ```bash
        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](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/HOO.py)           | [*X*-Armed Bandit](https://jmlr.org/papers/v12/bubeck11a.html)                                                                                                                           | 2011 |
        | [DOO](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/DOO.py)             | [Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness](https://proceedings.neurips.cc/paper/2011/file/7e889fb76e0e07c11733550f2a6c7a5a-Paper.pdf) | 2011 |
        | [SOO](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/SOO.py)             | [Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness](https://proceedings.neurips.cc/paper/2011/file/7e889fb76e0e07c11733550f2a6c7a5a-Paper.pdf) | 2011 |
        | [StoSOO](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/StoSOO.py)       | [Stochastic Simultaneous Optimistic Optimization](http://proceedings.mlr.press/v28/valko13.pdf)                                                                                          | 2013 |
        | [HCT](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/HCT.py)             | [Online Stochastic Optimization Under Correlated Bandit Feedback](https://proceedings.mlr.press/v32/azar14.html)                                                                         | 2014 |
        | [POO*](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/POO.py)            | [Black-box optimization of noisy functions with unknown smoothness](https://papers.nips.cc/paper/2015/hash/ab817c9349cf9c4f6877e1894a1faa00-Abstract.html)                               | 2015 |
        | [GPO*](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/GPO.py)            | [General Parallel Optimization Without A Metric](https://proceedings.mlr.press/v98/xuedong19a.html)                                                                                      | 2019 |
        | [PCT](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/PCT.py)             | [General Parallel Optimization Without A Metric](https://proceedings.mlr.press/v98/xuedong19a.html)                                                                                      | 2019 |
        | [SequOOL](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/SequOOL.py)     | [A Simple Parameter-free And Adaptive Approach to Optimization Under A Minimal Local Smoothness Assumption](https://arxiv.org/pdf/1810.00997.pdf)                                        | 2019 |
        | [StroquOOL](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/StroquOOL.py) | [A Simple Parameter-free And Adaptive Approach to Optimization Under A Minimal Local Smoothness Assumption](https://arxiv.org/pdf/1810.00997.pdf)                                        | 2019 |
        | [VHCT](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/VHCT.py)           | [Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization](https://arxiv.org/abs/2106.09215)                                                               | 2021 |
        | [VPCT](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/VPCT.py)           | N.A. ([GPO](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/GPO.py) + [VHCT](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/algos/VHCT.py))                               | N.A. |
        
        
        ### Hierarchical partition 
        
        | Partition                                                                                                             | Description                                                                                                        |
        |-----------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------|
        | [BinaryPartition](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/partition/BinaryPartition.py)                   | Equal-size binary partition of the parameter space, the split dimension is chosen uniform randomly                 |
        | [RandomBinaryPartition](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/partition/RandomBinaryPartition.py)       | Random-size binary partition of the parameter space, the split dimension is chosen uniform randomly                |
        | [DimensionBinaryPartition](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/partition/DimensionBinaryPartition.py) | Equal-size partition of the space with a binary split on each dimension, the number of children of one node is 2^d |
        | [KaryPartition](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/partition/KaryPartition.py)       | Equal-size K-ary partition of the parameter space, the split dimension is chosen uniform randomly                  |
        | [RandomKaryPartition](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/partition/RandomKaryPartition.py) | 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](https://en.wikipedia.org/wiki/Test_functions_for_optimization)
        
        | Objectives <img width=200/>| Image | 
        | --- |--- |
        | [Garland](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/synthetic_obj/Garland.py) | <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/synthetic/Garland.png" alt="Garland" width="100"/> |
        | [DoubleSine](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/synthetic_obj/DoubleSine.py)  | <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/synthetic/DoubleSine.png" alt="DoubleSine" width="100"/>  |
        | [DifficultFunc](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/synthetic_obj/DifficultFunc.py) | <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/synthetic/DifficultFunc.png" alt="DifficultFunc" width="100"/>  |
        | [Ackley](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/synthetic_obj/Ackley.py) | <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/synthetic/Ackley.png" alt="Ackley" width="100"/>  |
        | [Himmelblau](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/synthetic_obj/Himmelblau.py) | <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/synthetic/Himmelblau.png" alt="Himmelblau" width="100"/>  |
        | [Rastrigin](https://github.com/WilliamLwj/PyXAB/blob/main/PyXAB/synthetic_obj/Rastrigin.py) |  <img src="https://raw.githubusercontent.com/WilliamLwj/PyXAB/main/figs/synthetic/Rastrigin.png" alt="Rastrigin" width="100"/>  |
        
        
        ## 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](https://pyxab.readthedocs.io/en/latest/info/contributing.html) before submitting
        a pull request.
        
        
        ## Citations
        If you use our package in your research or projects, we kindly ask you to cite our work
        ```text
        @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}
        }
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
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