Automated Hyperparameter Optimization Competition

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. my_algo目录下新建searcher.py文件
  3. 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__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/
Deep learning algorithms for muon momentum estimation in the CMS Trigger System

Deep learning algorithms for muon momentum estimation in the CMS Trigger System The Compact Muon Solenoid (CMS) is a general-purpose detector at the L

anuragB 2 Oct 06, 2021
Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

Google Research 27 Dec 12, 2022
Official code implementation for "Personalized Federated Learning using Hypernetworks"

Personalized Federated Learning using Hypernetworks This is an official implementation of Personalized Federated Learning using Hypernetworks paper. [

Aviv Shamsian 121 Dec 25, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
Personal implementation of paper "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval"

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval This repo provides personal implementation of paper Approximate Ne

John 8 Oct 07, 2022
Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

ARM-Net Dependencies Python 3.6 Pytorch 1.7 Results Train Data preprocessing cd data_scripts python extract_subimages_test.py python data_augmentation

Bohong Chen 55 Nov 24, 2022
[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

[NeurIPS 2021] Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects YouTube | arXiv Prerequisites Kaolin is available here:

Denys Rozumnyi 107 Dec 26, 2022
Skipgram Negative Sampling in PyTorch

PyTorch SGNS Word2Vec's SkipGramNegativeSampling in Python. Yet another but quite general negative sampling loss implemented in PyTorch. It can be use

Jamie J. Seol 287 Dec 14, 2022
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.

Aditya M. Deshpande 19 Dec 29, 2022
Link prediction using Multiple Order Local Information (MOLI)

Understanding the network formation pattern for better link prediction Authors: [e

Wu Lab 0 Oct 18, 2021
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

Clairvoyance: A Pipeline Toolkit for Medical Time Series Authors: van der Schaar Lab This repository contains implementations of Clairvoyance: A Pipel

van_der_Schaar \LAB 89 Dec 07, 2022
A unified 3D Transformer Pipeline for visual synthesis

Overview This is the official repo for the paper: "NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion". NÜWA is a unified multimodal

Microsoft 2.6k Jan 03, 2023
TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction.

TalkNet 2 [WIP] TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Predictio

Rishikesh (ऋषिकेश) 69 Dec 17, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Introduction This repository includes the source code for "Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks", which is pu

machen 11 Nov 27, 2022
The most simple and minimalistic navigation dashboard.

Navigation This project follows a goal to have simple and lightweight dashboard with different links. I use it to have my own self-hosted service dash

Yaroslav 23 Dec 23, 2022
This repo contains the code required to train the multivariate time-series Transformer.

Multi-Variate Time-Series Transformer This repo contains the code required to train the multivariate time-series Transformer. Download the data The No

Gregory Duthé 4 Nov 24, 2022
Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle.

Paddle-Adversarial-Toolbox Paddle-Adversarial-Toolbox (PAT) is a Python library for Deep Learning Security based on PaddlePaddle. Model Zoo Common FGS

AgentMaker 17 Nov 08, 2022
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

Neural Architecture Search with Random Labels(RLNAS) Introduction This project provides an implementation for Neural Architecture Search with Random L

18 Nov 08, 2022
mmfewshot is an open source few shot learning toolbox based on PyTorch

OpenMMLab FewShot Learning Toolbox and Benchmark

OpenMMLab 514 Dec 28, 2022
DiSECt: Differentiable Simulator for Robotic Cutting

DiSECt: Differentiable Simulator for Robotic Cutting Website | Paper | Dataset | Video | Blog post DiSECt is a simulator for the cutting of deformable

NVIDIA Research Projects 73 Oct 29, 2022