PLAsTiCC Astronomical Classification 3rd-place solution

Overview of solution

https://www.kaggle.com/c/PLAsTiCC-2018/discussion/75131

environment

I used pyenv virtualenv to set up the environment. I think catboost==0.10.4.1 is important, but the versions of the other libraries won't affect the score so much.

$ pyenv install 3.5.1
$ pyenv virtualenv 3.5.1 plasticc
$ pyenv activate plasticc
$ pip install --upgrade pip
$ pip install cython==0.27.3
$ pip install numpy==1.13.0
$ pip install PyYAML==3.12
$ pip install -r requirements.txt

I used n1-standard-64 in Google Cloud Engine, which has 240GB RAM and 64 CPUs.
OS/Platform : Ubuntu 16.04

datasets & result files

I will upload prepare.zip for the host, which contains these directories.

• buckets:
  It contains nyanp's train & test features.

• data:
  It contains kaggle datasets. you can also download them via

kaggle competitions download -c PLAsTiCC-2018

• features:
  It contains all of my train & test features.

• fi:
  It contains feature names and the number of rounds used for training.

  • exp_*.npy
    numpy array that contains feature names.
  • exp_*rounds.pkl
    pickle object that contains the number of rounds.
  • whole_fn_s.npy
    numpy array that contains all feature names.
  • mamas_feature_names_*.npy
    the names of features that yuval used.

• models:
  It contains trained models.

  • exp*.cbm
    trained catboost model.

• others:
  It contains class weights.

  • W.npy
    numpy array that contains class weights.

• sub:
  It contains submission files

  • experiment57_59(th985)_61_62.csv
    nyanp's averaged submission file.
  • pred*.csv
    yuval's submission file.

scripts

• utils.py :
  It contains utility functions.
• preprocess_*.py :
  I did easy preprocessing here, like converting .csv files into .feather files.
• save_features_train_*.py :
  I saved test features here.
• save_features_test_*.py :
  I saved train features here.
• save_features_nyanp.py :
  I saved nyanp's train & test features here.
• train.py :
  I trained models here.
• predict.py :
  I made predictions here.
• postprocess.py :
  I did postprocessing like ensembling and class99 handling here.

usage

• full version :
  It will take a few months to run with a single machine (64 core, 240GB RAM).
  I never recommend you to run it.

cd mamas/
unzip prepare.zip
cp -r prepare/* .
rm features/*
rm models/*
cd ../scripts
python preprocess_01.py
python preprocess_02.py
python save_features_train_01.py
python save_features_train_02.py
python save_features_train_03.py
python save_features_train_04.py
python save_features_train_05.py
python save_features_train_06.py
python save_features_test_01.py
python save_features_test_02.py
python save_features_test_03.py
python save_features_test_04.py
python save_features_test_05.py
python save_features_test_06.py
python save_features_nyanp.py
python save_features_for_yuval.py
python train.py
python predict.py
python postprocess.py

Then, mamas/sub/host_sub.csv.gz will be generated.

• short version :
  It's a short version, which will take about 4 hours. I use extracted features and trained model here.

cd mamas/
unzip prepare.zip
cp -r prepare/* .
cd scripts
python preprocess_01.py
python preprocess_02.py
python predict.py
python postprocess.py

Then, mamas/sub/host_sub.csv.gz will be generated.
It should score 0.680 on public LB, 0.700 on private LB.

directory

• preds/ :
  prediction files.
• curve/ :
  linear interpolated curve files with yuval's method.
• fe_extract/ :
  feature extraction library.
• notebook/:
  It contains .ipynb files.
• scripts/:
  It contains scripts.