/dsb2018_topcoders

DSB2018 [ods.ai] topcoders

Primary LanguagePythonMIT LicenseMIT

DSB2018 [ods.ai] topcoders 1st place solution

Model weights/annotated data

You can download the whole package (7.5G) that contains training data, nn models weights, GBT models.

See also solution description on Kaggle

You need to setup your environment first. Please install latest nvidia drivers, cuda 9 and cudnn 7. After that run setup_env.sh script

How to run predict

unzip test data into data_test folder and

./predict_test.sh

Submission files will be in predictions folder (submission_0.csv, submission_1.csv). Individual model predictions will be also in predictions folder.

How to run training

Before training please remove models from:

  • albu/weights
  • selim/nn_models
  • victor/nn_models
  • victor/lgbm_models

after it run:

./train_all.sh
./tune_all.sh
./predict_oof_trees.sh

We use two stage training because we want to tune models on the stage1 data released 11.04. Every script goes into every folder and runs scripts to train models.