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

download data from Kaggle by /src/01_downlaod/download.py
create a dataset by /src/02_[regressor]/preprocess.py
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

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

Related tags

Overview

Santa 2020 - The Candy Cane Contest

Basic Strategy

Features

Software

Usage

Top Agents

Owner

toshi_k

tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

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