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

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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
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