# keentune_brain

**Repository Path**: qinglong_wang/keentune_brain

## Basic Information

- **Project Name**: keentune_brain
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 12
- **Created**: 2022-02-22
- **Last Updated**: 2022-02-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[English](./keentune-brain/README.md)| [简体中文](./keentune-brain/README.md) 

# KeenTune Brain  
## Introduction
---  
KeenTune-brain is an AI tuning Engine of 'KeenTune' system parameter optimization system. KeenTune-brain implements a variety of intelligent tuning algorithms. It generates a candidate configuration for KeenTune system, obtains an evaluation from Keentune-bench, and gives the optimal parameter configuration.

## Installation
---  
### 1. install python-setuptools  
```sh
$ sudo apt-get install python-setuptools
or
$ sudo yum install python-setuptools
```

### 2. install keentune-brain  
```shell
$ sudo python3 setup.py install
```

### 3. install requirements  
```shell
$ pip3 install -r requirements.txt
```

### 4. run  
```shell
$ keentune-brain
```

## Algorithm
---   
### Sensitive Parameter Detection Algorithm
### 线性回归模型 ElasticNet
用于捕捉参数与调优结果之间明显的线性相关性。ElasticnNet可以快速训练完成，结合多轮次识别部分介绍，选择线性模型可以保障在多轮次识别的情况下，整体算法执行效率仍然较高。
### 单变量互信息 Mutual Information
用于捕捉单一参数和调优结果之间的线性/非线性相关性，避免由于参数冗余造成敏感参数的漏报。
### 非线性模型XGBoost+可解释AI算法-SHAP
用于捕捉参数与调有结果之间复杂的非线性关系，并通过可解释AI算法量化黑盒非线性模型捕捉到的相关性。

### Tuning Algorithm
#### TPE(Tree-structured Parzen Estimator)
基于GP代理模型和SMBO框架实现的参数调优算法，KeenTune中使用[hyperopt](https://github.com/hyperopt/hyperopt)实现TPE算法   
[Algorithms for Hyper-Parameter Optimization](https://proceedings.neurips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf)

#### HORD(Radial Basis Function and Dynamic coordinate search)
基于RBF代理模型和Dycors的参数调优算法，KeenTune中使用[pySOT](https://github.com/dme65/pySOT)实现了HORD算法  
[HORD](https://github.com/ilija139/HORD)

## 代码结构
---  
+ algorithm: 算法模块，包括Tuning Algorithm和Sensitive Parameter Detection Algorithm  
+ common: 通用方法模块
+ controller: Web通信模块
+ visualization: 可视化模块

## Documentation
---