This repository implements the winning solution for the single cell perturbation problems

Make sure Anaconda3 is installed and execute the following:

1. Clone this repository git clone https://github.com/Jean-KOUAGOU/1st-place-solution-single-cell-pbs.git

2. First create and activate a conda environement conda create -n single_cell_env python==3.9.0 --y && conda activate single_cell_env

3. Install all required packages in the environment pip install -r requirements.txt

1. python 3.9.0
2. pandas 2.1.3
3. pyarrow 14.0.1
4. tqdm 4.66.1
5. scikit-learn 1.3.2
6. torch 2.1.1
7. transformers 4.35.2
8. matplotlib 3.8.2

• Ubuntu 20.04.6 LTS (Kaggle) AMD EPYC 7B12 CPU @ 2.25GHz (4 CPUs) 30GB RAM, 1xTesla GPU P100 16 GB (Kaggle), 73 GB disc
• Also tested on Debian GNU/Linux 11, 1xNvidia GPU rtx 3090 24 GB, 252 GB RAM, 500 GB disc

1. Create a folder called data/ in the main directory

2. Add the training data in parquet format, e.g., de_train.parquet as in the competition. Make sure to edit config/config_train.json by specifying the correct training data path. The same path should be entered in config/config_test as it is needed to read columns at inference time.

3. Also add the test data and a sample submission file (both should be csv files) in the same directory data/ and edit config/config_test.json accordingly. Note that paths to trained models and to the training data should be specified in config/config_test.json.

4. Run python prepare_data.py to complete all required preprocessing steps

Make sure to locate in the main directory

• Run python train.py to train models. This will automatically create a directory call trained_models and store the trained models. One can specify the number of folds in the K-fold cross-validation scheme using python train.py --kf_n_splis. Similarly, the number of epochs can be specified using --epochs
• Pretrained models can also be downloaded, see link on Kaggle to avoid training.

Check that there is a non-empty directory named trained_models and that its path is specified in config/config_test.json

• Run python predict.py to predict on the test data whose path is specified in config/config_test.json. This will automatically create a directory submissions and store predictions in submissions/submission.csv