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# About This Repository

This repository contains part of resources in 4th place solution (bilzard part) on the competition [HMS - Harmful Brain Activity Classification](https://www.kaggle.com/competitions/hms-harmful-brain-activity-classification)

Details of this solution is described [here](https://github.com/bilzard/kaggle-hms-public/blob/main/doc/02_solution_bilzard.pdf).

## LICENSE

Apache 2.0

## Environment

- Graphic card: Nvidia RTX4090
- CUDA: 12.1

## Setup

### Install required packages

```bash
pip install -r requirements.txt
```

### Install local packages with additional requirements

Below command installs this repository in your local python environment with dependent packages.

```bash
pip install --editable .
```

### Configure Local Environment Settings

You should edit `conf/env/local.yaml` to meet configuration of your local environment.

```yaml
name: local
num_workers: 24
infer_batch_size: 32
grad_checkpointing: false

data_dir: (path to competition data)
working_dir: (path to your working directory)
output_dir: ${env.working_dir}/${job_name}/${phase}
checkpoint_dir: ${env.working_dir}/train
submission_dir: .
```

## How to Reproduce

To reproduce final submission, execute the following commands:

```bash
python -m run.preprocess job_name=preprocess phase=train
python -m run.fold_split job_name=fold_split phase=train
python schedule.py train --config_names=v5_eeg_24ep_cutmix --folds=0,1,2,3,4 --seeds=0,1,2
```

These commands will train the models which is contained in the ensemble of final submissions. Trained model checkpoints will saved in `./data/train`.

Training is executed per each fold and random seed, so this generates the 15 model (5-folds and 3-seeds) checkpoints per `config_name`.

## Performance

These are the seed & fold ensemble result per each experiments.

|exp_name|CV(n_votes>8.4)|Private LB|Public LB
|--|--|--|--|
|v5_eeg_24ep_cutmix|0.2477|0.327657|0.256772|

## Detailed Explanation of Entry Points (Optional)

Usage of major entry points in this repository is explained in this section. You don't have to fully understand the details to reproduce the final submission, but it will help you to understand how to train & evaluate new models using resources in this repository.

### Pre-Process

This command produces EEGs, Channel Quality Masks(CQM) and Kaggle spectrograms in numpy's ndarray format. By default EEGs are sub-sampled with 40Hz.

```bash
python -m run.preprocess job_name=preprocess phase=train
```

### Fold-split

This command generates 5-fold train/validation splits. Each folds are generated by GroupKFold group by `patient_id`.

```bash
python -m run.fold_split job_name=fold_split phase=train
```

### Train

This is useful to train multiple single models at the same time.

```bash
python schedule.py train --config_names=v5_eeg_24ep_cutmix --folds=0,1,2,3,4 --seeds=0,1,2
```

### Batch Inference (Infer & Ensemble)

This is useful to make inference and mean ensemble with all the model specified in the specified ensemble entities at the same time.
It makes mean ensemble of all models specified in ensemble_entity (see `run/conf/ensemble_entity`), and outputs `submission.csv` to working directory.

```bash
python -m run.batch_infer job_name=ensemble ensemble_entity=f01234_s012 ensemble_entity.name=v5_eeg_24ep_cutmix
```

## Acknowledgements

The architecture of the resources in this repository was inspired by the following repositories. Thanks to the authors.

- [tubo213/kaggle-child-mind-institute-detect-sleep-states](https://github.com/tubo213/kaggle-child-mind-institute-detect-sleep-states)

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