QQ浏览器2021AI算法大赛 - 自动超参数优化竞赛

ACM CIKM 2021 AnalyticCup

在信息流推荐业务场景中普遍存在模型或策略效果依赖于“超参数”的问题，而“超参数"的设定往往依赖人工经验调参，不仅效率低下维护成本高，而且难以实现更优效果。因此，本次赛题以超参数优化为主题，从真实业务场景问题出发，并基于脱敏后的数据集来评测各个参赛队伍的超参数优化算法。本赛题为超参数优化问题或黑盒优化问题：给定超参数的取值空间，每一轮可以获取一组超参数对应的Reward，要求超参数优化算法在限定的迭代轮次内找到Reward尽可能大的一组超参数，最终按照找到的最大Reward来计算排名。

1. 重要资源

比赛官网：QQ浏览器2021AI算法大赛
参赛手册：自动超参数优化参赛手册
代码仓库：THPO-Kit Github
实现和评分参考：THPO-Kit 介绍文档
代码提交：比赛代码提交入口
比赛FAQ：比赛常问问题

2.代码结构

|--example_random_searcher  随机算法代码提交示例
|  `--searcher.py
|
|--example_bayesian_optimization 贝叶斯优化算法提交示例
|  |--requirements.txt     提交附加程序包示例
|  `--searcher.py
|
|--input                   测试评估函数数据
|  |--data-2
|  `--data-30
|
|--thpo                    thpo比赛工具包
|  |--__init__.py
|  |--abstract_searcher.py
|  |--common.py
|  |--evaluate_function.py
|  |--reward_calculation.py
|  |--run_search_one_time.py
|  `--run_search.py
|
|--main.py                 测试主程序文件
|--local_test.sh           本地测试脚本
|--prepare_submission.sh   提交代码前打包脚本
|--environments.txt        评测环境已经安装的包
`--requirements.txt        demo程序依赖的包环境

3. 快速入门

3.1 环境搭建

THPO-Kit程序工具包使用python3编写，程序依赖包在requirements.txt中，需要安装依赖包才能执行，使用pip3安装依赖包：

pip3 install -r requirements.txt

3.2 算法创建

参照 example_randon_searcher，新建一个自己算法的目录my_algo
在my_algo目录下新建searcher.py文件
在searcher.py文件里实现自己的Searcher类（文件名和类名不允许自定义）
实现 __init__ 和 suggest 函数
修改 local_test.sh，将SEARCHER修改为my_algo
执行 local_test.sh 脚本，将得到算法的执行结果

Step 1 - Step 2:[root folder]

|--my_algo
|  |--requirements.txt
|  `--searcher.py 
|--local_test.sh

Step 3 - Step 4:[searcher.py]

# 必须引入searcher抽象类，必不可少
from thpo.abstract_searcher import AbstractSearcher
from random import randint

class Searcher(AbstractSearcher):
    searcher_name = "RandomSearcher"

    def __init__(self, parameters_config, n_iter, n_suggestion):
        AbstractSearcher.__init__(self, 
                                  parameters_config, 
                                  n_iter,
                                  n_suggestion)

    def suggest(self, suggestion_history, n_suggestions=1):
        next_suggestions = []
        for i in range(n_suggestions):
            next_suggest = {
                name: 
                conf["coords"][randint(0,len(conf["coords"])-1)]
                for name, conf in self.parameters_config.items()
            }
            next_suggestions.append(next_suggest)
        return next_suggestions

Step 5:[local_test.sh]

SEARCHER="my_algo"

3.3 本地运行

执行脚本local_test.sh进行本地评测

./local_test.sh

执行结果：

====================== run search result ========================
 err_code:  0  err_msg:  
========================= iters means ===========================
func: data-2 iteration best: [25.24271821 26.36435157 12.77928619 10.19180929 11.3147711  10.17430656
 12.77928619 27.79752169 26.36793589 11.12007615]
func: data-30 iteration best: [-0.95264345 -0.27725879 -0.36873091 -0.68088963 -0.28840479 -0.50006427
 -0.32088949 -0.78627201 -0.53204227 -0.98427191]
========================= fianl score ============================
example_bayesian_optimization final score:  0.47173337831255463
==================================================================

3.4 提交比赛代码

使用prepare_submission.sh 脚本打包，提交打包后的searcher程序包到比赛代码提交入口。

./prepare_submission.sh example_random_searcher

执行结果：

upload_example_random_searcher_08131917
  adding: requirements.txt (stored 0%)
  adding: searcher.py (deflated 66%)
----------------------------------------------------------------
Built achive for upload
Archive:  ./upload_example_random_searcher_08131917.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
        0  08-13-2021 19:17   requirements.txt
     3767  08-13-2021 19:17   searcher.py
---------                     -------
     3767                     2 files
For scoring, upload upload_example_random_searcher_08131917.zip at address:
https://algo.browser.qq.com/

QQ Browser 2021 AI Algorithm Competiton - Automated Hyperparameter Optimization Contest

ACM CIKM 2021 AnalyticCup

The choices of hyperparameters have critical effects on models or strategies in recommendation systems. But the hyperparameters are mostly chosen based on experience, which brings high maintenance costs and sub-optimal results. Thus, this track aims at automated hyperparameters optimization based on anonymized realistic industrial tasks and datasets. Given the space of all possible hyperparameters' values, a reward could be achieved with a set of hyperparameters in each iteration. The participants are asked to maximize the reward within a given limit of iterations with a hyperparameters optimization algorithm. The final rank of the participants will be the rank of their maximum reward.

1.Resource

Official website：QQ Browser 2021 AI Algorithm Competiton
Contest manual：Automated Hyperparameter Optimization Contest manual
Code repository：THPO-Kit Github
API reference & ranking rules：Introduction to THPO-Kit
Code submission：Code submission entry
Competition FAQ：Frequently asked questions

2.Repo structure

|--example_random_searcher   	    # example of random search
|  `--searcher.py
|
|--example_bayesian_optimization    # example of bayesian optimization
|  |--requirements.txt              # extra paackge requirement
|  `--searcher.py
|
|--input                            # testcases
|  |--data-2
|  `--data-30
|
|--thpo                             # thpo-kit
|  |--__init__.py
|  |--abstract_searcher.py
|  |--common.py
|  |--evaluate_function.py
|  |--reward_calculation.py
|  |--run_search_one_time.py
|  `--run_search.py
|
|--main.py                          # main
|--local_test.sh                    # script for local test
|--prepare_submission.sh            # script for submission
|--environments.txt                 # packages installed in remote envrionment
`--requirements.txt                 # demo requirements

3. Quick start

3.1 Environment setup

The THPO-Kit program toolkit is written in python3. The program dependency packages are in requirements.txt, and the dependency packages needs to be installed to execute scripts. Use pip3 to install the dependency package:

pip3 install -r requirements.txt

3.2 Create a searcher

Refer to example_randon_searcher, create a new directory my_algo for your algorithm
Create a new searcher.py file in the my_algo directory
Implement your own Searcher class in the searcher.py file (the file name and class name are not allowed to be customized)
Implement __init__ and suggest functions
Modify local_test.sh and change SEARCHER to my_algo
Execute the local_test.sh script to get the results of the algorithm

Step 1 - Step 2:[root folder]

|--my_algo
|  |--requirements.txt
|  `--searcher.py 
|--local_test.sh

Step 3 - Step 4:[searcher.py]

# MUST import AbstractSearcher from thpo.abstract_searcher
from thpo.abstract_searcher import AbstractSearcher
from random import randint

class Searcher(AbstractSearcher):
    searcher_name = "RandomSearcher"

    def __init__(self, parameters_config, n_iter, n_suggestion):
        AbstractSearcher.__init__(self, 
                                  parameters_config, 
                                  n_iter,
                                  n_suggestion)

    def suggest(self, suggestion_history, n_suggestions=1):
        next_suggestions = []
        for i in range(n_suggestions):
            next_suggest = {
                name: 
                conf["coords"][randint(0,len(conf["coords"])-1)]
                for name, conf in self.parameters_config.items()
            }
            next_suggestions.append(next_suggest)
        return next_suggestions

Step 5:[local_test.sh]

SEARCHER="my_algo"

3.3 Local test

Execute the script local_test.sh for local evaluation

./local_test.sh

Execution output：

====================== run search result ========================
 err_code:  0  err_msg:  
========================= iters means ===========================
func: data-2 iteration best: [25.24271821 26.36435157 12.77928619 10.19180929 11.3147711  10.17430656
 12.77928619 27.79752169 26.36793589 11.12007615]
func: data-30 iteration best: [-0.95264345 -0.27725879 -0.36873091 -0.68088963 -0.28840479 -0.50006427
 -0.32088949 -0.78627201 -0.53204227 -0.98427191]
========================= fianl score ============================
example_bayesian_optimization final score:  0.47173337831255463
==================================================================

3.4 Submission

Use prepare_submission.sh script to create a zip file, and submit the zip file to competition website Code submission entry.

./prepare_submission.sh example_random_searcher

Execution output:

upload_example_random_searcher_08131917
  adding: requirements.txt (stored 0%)
  adding: searcher.py (deflated 66%)
----------------------------------------------------------------
Built achive for upload
Archive:  ./upload_example_random_searcher_08131917.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
        0  08-13-2021 19:17   requirements.txt
     3767  08-13-2021 19:17   searcher.py
---------                     -------
     3767                     2 files
For scoring, upload upload_example_random_searcher_08131917.zip at address:
https://algo.browser.qq.com/

Automated Hyperparameter Optimization Competition

Related tags

Overview

QQ浏览器2021AI算法大赛 - 自动超参数优化竞赛

ACM CIKM 2021 AnalyticCup

1. 重要资源

2.代码结构

3. 快速入门

3.1 环境搭建

3.2 算法创建

3.3 本地运行

3.4 提交比赛代码

QQ Browser 2021 AI Algorithm Competiton - Automated Hyperparameter Optimization Contest

ACM CIKM 2021 AnalyticCup

1.Resource

2.Repo structure

3. Quick start

3.1 Environment setup

3.2 Create a searcher

3.3 Local test

3.4 Submission

Owner

Source code to accompany Defunctland's video "FASTPASS: A Complicated Legacy"

Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

A framework for Quantification written in Python

A Python framework for developing parallelized Computational Fluid Dynamics software to solve the hyperbolic 2D Euler equations on distributed, multi-block structured grids.

RealFormer-Pytorch Implementation of RealFormer using pytorch

This tool uses Deep Learning to help you draw and write with your hand and webcam.

Everything's Talkin': Pareidolia Face Reenactment (CVPR2021)

Synthetic Humans for Action Recognition, IJCV 2021

I-BERT: Integer-only BERT Quantization

Regulatory Instruments for Fair Personalized Pricing.

This implementation contains the application of GPlearn's symbolic transformer on a commodity futures sector of the financial market.

Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Self-training with Weak Supervision (NAACL 2021)

Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently.

PyTorch Lightning implementation of Automatic Speech Recognition

MNIST, but with Bezier curves instead of pixels

The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

Code for PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Relighting and Material Editing

An Easy-to-use, Modular and Prolongable package of deep-learning based Named Entity Recognition Models.