9th place solution in "Santa 2020 - The Candy Cane Contest"

Related tags

Deep Learningkaggle-santa-2020
Overview

Santa 2020 - The Candy Cane Contest

My solution in this Kaggle competition "Santa 2020 - The Candy Cane Contest", 9th place.

Basic Strategy

In this competition, the reward was decided by comparing the threshold and random generated number. It was easy to calculate the probability of getting reward if we knew the thresholds. But the agents can't see the threshold during the game, we had to estimate it.

Like other teams, I also downloaded the history by Kaggle API and created a dataset for supervised learning. We can see the true value of threshold at each round in the response of API. So, I used it as the target variable.

In the middle of the competition, I found out that quantile regression is much better than conventional L2 regression. I think it can adjust the balance between Explore and Exploit by the percentile parameter.

Features

        #         Name Explanation
#1 round number of round in the game (0-1999)
#2 last_opponent_chosen whether the opponent agent chose this machine in the last step or not
#3 second_last_opponent_chosen whether the opponent agent chose this machine in the second last step or not
#4 third_last_opponent_chosen whether the opponent agent chose this machine in the third last step or not
#5 opponent_repeat_twice whether the opponent agent continued to choose this machine in the last two rounds (#2 x #3)
#6 opponent_repeat_three_times whether the opponent agent continued to choose this machine in the last three rounds (#2 x #3 x #4)
#7 num_chosen how many times the opponent and my agent chose this machine
#8 num_chosen_mine how many times my agent chose this machine
#9 num_chosen_opponent how many time the opponent agent chose this machine (#7 - #8)
#10 num_get_reward how many time my agent got rewards from this machine
#11 num_non_reward how many time my agent didn't get rewarded from this machine
#12 rate_mine ratio of my choices against the total number of choices (#8 / #7)
#13 rate_opponent ratio of opponent choices against the total number of choices (#9 / #7)
#14 rate_get_reward ratio of my rewarded choices against the total number of choices (#10 / #7)
#15 empirical_win_rate posterior expectation of threshold value based on my choices and rewords
#16 quantile_10 10% point of posterior distribution of threshold based on my choices and rewords
#17 quantile_20 20% point of posterior distribution of threshold based on my choices and rewords
#18 quantile_30 30% point of posterior distribution of threshold based on my choices and rewords
#19 quantile_40 40% point of posterior distribution of threshold based on my choices and rewords
#20 quantile_50 50% point of posterior distribution of threshold based on my choices and rewords
#21 quantile_60 60% point of posterior distribution of threshold based on my choices and rewords
#22 quantile_70 70% point of posterior distribution of threshold based on my choices and rewords
#23 quantile_80 80% point of posterior distribution of threshold based on my choices and rewords
#24 quantile_90 90% point of posterior distribution of threshold based on my choices and rewords
#25 repeat_head how many times my agent chose this machine before the opponent agent chose this agent for the first time
#26 repeat_tail how many times my agent chose this machine after the opponent agent chose this agent last time
#27 repeat_get_reward_head how many times my agent got reward from this machine before my agent didn't get rewarded or the opponent agent chose this agent for the first time
#28 repeat_get_reward_tail how many times my agent got reward from this machine after my agent didn't get rewarded or the opponent agent chose this agent last time
#29 repeat_non_reward_head how many times my agent didn't get rewarded from this machine before my agent got reward or the opponent agent chose this agent for the first time
#30 repeat_non_reward_tail how many times my agent didn't get rewarded from this machine after my agent got reward or the opponent agent chose this agent last time
#31 opponent_repeat_head how many times the opponent agent chose this machine before my agent chose this machine for the first time
#32 opponent_repeat_tail how many times the opponent agent chose this machine after my agent chose this machine last time

Software

  • Python 3.7.8
  • numpy==1.18.5
  • pandas==1.0.5
  • matplotlib==3.2.2
  • lightgbm==3.1.1
  • catboost==0.24.4
  • xgboost==1.2.1
  • tqdm==4.47.0

Usage

  1. download data from Kaggle by /src/01_downlaod/download.py

  2. create a dataset by /src/02_[regressor]/preprocess.py

  3. train a model by /src/02_[regressor]/train.py

Top Agents

Regressor Loss NumRound LearningRate LB Score SubmissionID
LightBGM Quantile (0.65) 4000 0.05 1449.4 19318812
LightBGM Quantile (0.65) 4000 0.10 1442.1 19182047
LightBGM Quantile (0.65) 3000 0.03 1438.8 19042049
LightBGM Quantile (0.66) 3500 0.04 1433.9 19137024
CatBoost Quantile (0.65) 4000 0.05 1417.6 19153745
CatBoost Quantile (0.67) 3000 0.10 1344.5 19170829
LightGBM MSE 4000 0.03 1313.3 19093039
XGBoost Pairwised 1500 0.10 1173.5 19269952
Owner
toshi_k
toshi_k
GitHub Repository
[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

OpenCOOD OpenCOOD is an Open COOperative Detection framework for autonomous driving. It is also the official implementation of the ICRA 2022 paper OPV

Runsheng Xu 322 Dec 23, 2022
Deep Learning Based Fasion Recommendation System for Ecommerce

Project Name: Fasion Recommendation System for Ecommerce A Deep learning based streamlit web app which can recommened you various types of fasion prod

BAPPY AHMED 13 Dec 13, 2022
Racing line optimization algorithm in python that uses Particle Swarm Optimization.

Racing Line Optimization with PSO This repository contains a racing line optimization algorithm in python that uses Particle Swarm Optimization. Requi

Parsa Dahesh 6 Dec 14, 2022
TensorFlow implementation of the paper "Hierarchical Attention Networks for Document Classification"

Hierarchical Attention Networks for Document Classification This is an implementation of the paper Hierarchical Attention Networks for Document Classi

Quoc-Tuan Truong 83 Dec 05, 2022
A Number Recognition algorithm

Paddle-VisualAttention Results_Compared SVHN Dataset Methods Steps GPU Batch Size Learning Rate Patience Decay Step Decay Rate Training Speed (FPS) Ac

1 Nov 12, 2021
Shape-Adaptive Selection and Measurement for Oriented Object Detection

Source Code of AAAI22-2171 Introduction The source code includes training and inference procedures for the proposed method of the paper submitted to t

houliping 24 Nov 29, 2022
MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project developed by MMLab.

OpenMMLab 3.2k Jan 05, 2023
scikit-learn inspired API for CRFsuite

sklearn-crfsuite sklearn-crfsuite is a thin CRFsuite (python-crfsuite) wrapper which provides interface simlar to scikit-learn. sklearn_crfsuite.CRF i

417 Dec 20, 2022
This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution

Trajectory Prediction using Equivariant Continuous Convolution (ECCO) This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivar

Spatiotemporal Machine Learning 45 Jul 22, 2022
An implementation of EWC with PyTorch

EWC.pytorch An implementation of Elastic Weight Consolidation (EWC), proposed in James Kirkpatrick et al. Overcoming catastrophic forgetting in neural

Ryuichiro Hataya 166 Dec 22, 2022
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions Accepted by AAAI 2022 [arxiv] Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jia

liuwenyu 245 Dec 16, 2022
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing

Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing Paper Introduction Multi-task indoor scene understanding is widely considered a

62 Dec 05, 2022
Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC.

Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC. Para los Laboratorios de la materia, vamos a utilizar el len

Luis Biedma 18 Dec 12, 2022
Container : Context Aggregation Network

Container : Context Aggregation Network If you use this code for a paper please cite: @article{gao2021container, title={Container: Context Aggregati

AI2 47 Dec 16, 2022
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
Simple and ready-to-use tutorials for TensorFlow

TensorFlow World To support maintaining and upgrading this project, please kindly consider Sponsoring the project developer. Any level of support is a

Amirsina Torfi 4.5k Dec 23, 2022
Python library containing BART query generation and BERT-based Siamese models for neural retrieval.

Neural Retrieval Embedding-based Zero-shot Retrieval through Query Generation leverages query synthesis over large corpuses of unlabeled text (such as

Amazon Web Services - Labs 35 Apr 14, 2022
MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.

Front-end View Backend View Table of Contents Description Prerequisites Running Basic Information Measurements User Interface Feedback and usage Descr

Center for Computational Imaging and Personalized Diagnostics 58 Dec 02, 2022
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c

Phil Wang 272 Dec 23, 2022
Learning cell communication from spatial graphs of cells

ncem Features Repository for the manuscript Fischer, D. S., Schaar, A. C. and Theis, F. Learning cell communication from spatial graphs of cells. 2021

Theis Lab 77 Dec 30, 2022