Automated Hyperparameter Optimization Competition

Related tags

Deep LearningTHPO_Kit_2021
Overview

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

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

  1. 参照 example_randon_searcher，新建一个自己算法的目录my_algo
  2. 在my_algo目录下新建searcher.py文件
  3. 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）
  4. 实现 __init__ 和 suggest 函数
  5. 修改 local_test.sh，将SEARCHER修改为my_algo
  6. 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

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

  1. Refer to example_randon_searcher, create a new directory my_algo for your algorithm
  2. Create a new searcher.py file in the my_algo directory
  3. Implement your own Searcher class in the searcher.py file (the file name and class name are not allowed to be customized)
  4. Implement __init__ and suggest functions
  5. Modify local_test.sh and change SEARCHER to my_algo
  6. 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/
Owner
GitHub Repository
RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

RL-GAN: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation RL-GAN is an official implementation of the paper: T

42 Nov 10, 2022
PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Xingdong Zuo 215 Dec 07, 2022
商品推荐系统

商品top50推荐系统 问题建模 本项目的数据集给出了15万左右的用户以及12万左右的商品, 以及对应的经过脱敏处理的用户特征和经过预处理的商品特征，旨在为用户推荐50个其可能购买的商品。 推荐系统架构方案 本项目采用传统的召回＋排序的方案。

107 Dec 29, 2022
From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials You can find python source code under the python directory, and associated notebooks under notebooks. Source code Description 1 b

Parag K Mital 5.6k Jan 09, 2023
Generating Radiology Reports via Memory-driven Transformer

R2Gen This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020. Citations If you use or extend our work,

CUHK-SZ NLP Group 101 Dec 13, 2022
[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

BNN - BN = ? Training Binary Neural Networks without Batch Normalization Codes for this paper BNN - BN = ? Training Binary Neural Networks without Bat

VITA 40 Dec 30, 2022
Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022
Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF)

Graph Convolutional Gated Recurrent Neural Network (GCGRNN) Improved from Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF

Lei Lin 21 Dec 18, 2022
A python script to dump all the challenges locally of a CTFd-based Capture the Flag.

A python script to dump all the challenges locally of a CTFd-based Capture the Flag. Features Connects and logins to a remote CTFd instance. Dumps all

Podalirius 77 Dec 07, 2022
Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Deep Causal Reasoning for Recommender Systems The codes are associated with the following paper: Deep Causal Reasoning for Recommendations, Yaochen Zh

Yaochen Zhu 22 Oct 15, 2022
BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins Deep learning has brought most profound contributio

Narinder Singh Punn 12 Dec 04, 2022
BTC-Generator - BTC Generator With Python

Что такое BTC-Generator? Это генератор чеков всеми любимого @BTC_BANKER_BOT Для

DoomGod 3 Aug 24, 2022
Gapmm2: gapped alignment using minimap2 (align transcripts to genome)

gapmm2: gapped alignment using minimap2 This tool is a wrapper for minimap2 to r

Jon Palmer 2 Jan 27, 2022
[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

FaPN: Feature-aligned Pyramid Network for Dense Image Prediction [arXiv] [Project Page] @inproceedings{ huang2021fapn, title={{FaPN}: Feature-alig

EMI-Group 175 Dec 30, 2022
Fully Convolutional Refined Auto Encoding Generative Adversarial Networks for 3D Multi Object Scenes

Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes This repository contains the source code for Full

Yu Nishimura 106 Nov 21, 2022
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries From the makers of spaCy, Prodigy and FastAPI Thinc is a

Explosion 2.6k Dec 30, 2022
Simple Dynamic Batching Inference

Simple Dynamic Batching Inference 解决了什么问题？ 众所周知，Batch对于GPU上深度学习模型的运行效率影响很大。。。 是在Inference时。搜索、推荐等场景自带比较大的batch，问题不大。但更多场景面临的往往是稀碎的请求(比如图片服务里一次一张图)。 如果

116 Jan 01, 2023
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
Social Network Ads Prediction

Social network advertising, also social media targeting, is a group of terms that are used to describe forms of online advertising that focus on social networking services.

Khazar 2 Jan 28, 2022
Code for the TASLP paper "PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation".

PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation Introduction Getting Started FSD50K Recipe AudioSet Recipe Label E

Yuan Gong 84 Dec 27, 2022