Team: QuMantumPhysicists
.
├── input/
├── train.csv
└── Track 1
└── test.csv
├── requirements.txt
├── Makefile
├── README.md
├── model_description.PNG
├── fit_param_dictAA.dump
├── fit_param_dictB2.dump
├── lr_hyperparam.csv
├── main.sh
├── trackA_predict.py
├── trackB_predict.py
└── train.py
- common
input/
- input data folder from https://disk.yandex.ru/d/0zYx00gSraxZ3w
requirements.txt
- development environment
train.py
- fit by linear regression and save results
- generate
fit_param_dictAA.dump
andfit_param_dictB2.dump
lr_hyperparam.csv
- list of hyper parameters
- for track A
fit_param_dictAA.dump
trackA_predict.py
- make prediction
- for track B
fit_param_dictB2.dump
trackB_predict.py
- make prediction
Makefile
main.sh
- Place input data in
input/
(shown in Directory section). - If necessary, install packages in
requirements.txt
. - Train models for both tracks:
python train.py
.- Pretrained models(
fit_param_dictAA.dump
andfit_param_dictB2.dump
) will be generated. - It takes around 1 hour with 4 cores.
- Pretrained models(
- Make submission:
python trackA_predict.py
.submission_trackA.csv
will be generated.
- Zip current working directory.
All you need is linear regression