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
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

Yuan Yin 24 Oct 24, 2022
A script that trains a model to recognize handwritten digits using the MNIST data set.

handwritten-digits-recognition A script that trains a model to recognize handwritten digits using the MNIST data set. Then it loads external files and

Hamza Sayih 1 Oct 30, 2021
LoFTR:Detector-Free Local Feature Matching with Transformers CVPR 2021

LoFTR-with-train-script LoFTR:Detector-Free Local Feature Matching with Transformers CVPR 2021 (with train script --- unofficial ---). About Megadepth

Nan Xiaohu 15 Nov 04, 2022
Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

Keyword Spotting Transformer This is the unofficial TensorFlow implementation of the Keyword Spotting Transformer model. This model is used to train o

Intelligent Machines Limited 8 May 11, 2022
Implementation of SwinTransformerV2 in TensorFlow.

SwinTransformerV2-TensorFlow A TensorFlow implementation of SwinTransformerV2 by Microsoft Research Asia, based on their official implementation of Sw

Phan Nguyen 2 May 30, 2022
Dense Prediction Transformers

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

Intelligent Systems Lab Org 1.3k Jan 02, 2023
The Official TensorFlow Implementation for SPatchGAN (ICCV2021)

SPatchGAN: Official TensorFlow Implementation Paper "SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation"

39 Dec 30, 2022
Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

Adversarial Neuron Pruning Purifies Backdoored Deep Models Code for NeurIPS 2021 "Adversarial Neuron Pruning Purifies Backdoored Deep Models" by Dongx

Dongxian Wu 31 Dec 11, 2022
This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark

SILG This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark. If you find this work helpful, please cons

Victor Zhong 17 Nov 27, 2022
A Moonraker plug-in for real-time compensation of frame thermal expansion

Frame Expansion Compensation A Moonraker plug-in for real-time compensation of frame thermal expansion. Installation Credit to protoloft, from whom I

58 Jan 02, 2023
Multi-Glimpse Network With Python

Multi-Glimpse Network Our code requires Python ≥ 3.8 Installation For example, venv + pip: $ python3 -m venv env $ source env/bin/activate (env) $ pyt

9 May 10, 2022
COPA-SSE contains crowdsourced explanations for the Balanced COPA dataset

COPA-SSE Repository for COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning. COPA-SSE contains crowdsourced explanations for the Balanced

Ana Brassard 5 Jul 31, 2022
[Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021

Pedestron Pedestron is a MMdetection based repository, that focuses on the advancement of research on pedestrian detection. We provide a list of detec

Irtiza Hasan 594 Jan 05, 2023
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

Real-Time Seizure Detection using Electroencephalogram (EEG) This is the repository for "Real-Time Seizure Detection using EEG: A Comprehensive Compar

AITRICS 30 Dec 17, 2022
Transfer-Learn is an open-source and well-documented library for Transfer Learning.

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consist

THUML @ Tsinghua University 2.2k Jan 03, 2023
Training deep models using anime, illustration images.

animeface deep models for anime images. Datasets anime-face-dataset Anime faces collected from Getchu.com. Based on Mckinsey666's dataset. 63.6K image

Tomoya Sawada 61 Dec 25, 2022
Discovering Interpretable GAN Controls [NeurIPS 2020]

GANSpace: Discovering Interpretable GAN Controls Figure 1: Sequences of image edits performed using control discovered with our method, applied to thr

Erik Härkönen 1.7k Jan 03, 2023
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021

Disentangled Cycle Consistency for Highly-realistic Virtual Try-On, CVPR 2021 [WIP] The code for CVPR 2021 paper 'Disentangled Cycle Consistency for H

ChongjianGE 94 Dec 11, 2022
An end-to-end image translation model with weight-map for color constancy

CCUnet An end-to-end image translation model with weight-map for color constancy 1. Download the dataset (take Colorchecker_recommended dataset as an

Jianhui Qiu 1 Dec 21, 2021
Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL" This is the official codebase for Pessimism Meets I

3 Sep 19, 2022