dangnh0611/kaggle_leash_belka

11th place solution of NeurIPS 2024 - Predict New Medicines with BELKA competition on Kaggle: https://www.kaggle.com/competitions/leash-BELKA

11th place solution (gold medal) of NeurIPS 2024 - Predict New Medicines with BELKA competition on Kaggle

Solution writeup: https://www.kaggle.com/competitions/leash-BELKA/discussion/518993

This repo use Pytorch Lightning and Hydra for flexible configuration system. One could add more tasks, models, datasets, input representations while keeping all in this single flexible codebase.

If you have any requests/questions, feel free to raise those with me in Github Issues.

Installation

Recommended to use this Dockerfile

Some small tricks

• Use container object without reference count for each element (see this) in dataset/dataloader to reduce memory usage. For example, if dataset contain a large list of SMILES strings, it's better to stored it in a Polars dataframe, or a Huggingface's dataset. I found the later Huggingface's dataset show better performance while allow to be on-disk loading, so it's probably the best choice. All you need is about 8GB or disk space and 20 GB of RAM is enough to train a big model on all 100M data
• Use numpy memmap to stored pre-generated features on disk as well
• Make Torch's Dataset's __get_item__() accept a list of indices as input, e.g in src/data/datasets/pretrain_tokenize_mtr_mlm.py. This allow to use on-the-fly tokenization, which is needed for SMILES Enumeration (Chem.MolToSmiles(doRandom=True)). Combine with the above tricks, dataloading work blazy fast with flexible modifications (e.g, you need not to tokenize all data before training)

Utilities

For this large amount of data, one need to transform dataset to another optimized format for faster dataloader/memory saving, create tokenizers, extract useful features, etc.

The notebooks directory contains some useful notebooks which is not fully ready, but easy to modify suit for need, e.g just change some paths which are specific to your local environment.

• Pre-generate features (e.g, Fingerprints,..): src/tools/extract_features.py, e.g python3 src/tools/extract_features.py --feature ecfp6 --num-chunks 1 --chunk-idx 0 --batch-size 1000 --subset test
• Simulate LB Cross-Validation split: full_final_cv_split.ipynb
• Random Split: random_split.ipynb
• Save as HuggingFace's datasets for optimized on-disk dataloader: save_hf_datasets.ipynb
• Create and save tokenizers (support SMILES chars, AIS, SELFIES, DeepSMILES): save_tokenizers.ipynb. Pre-generated tokenizers also included in this repo in assets/tokenizer_v2
• CatBoost training: catboost-final.ipynb

Training

Just change the configuration files and start training with multiple Task/Model/Dataset. I will update more example training commands to different Tasks, Models and Datasets later. Many training commands are tracked in docs/exp_tracking.xlsx and readily to be tried as well.

Starting by

cd src/

Reproduce Roberta + Join MLM+MTR training

Prerequisite

Refer to Utilities for more details

• Saved SMILES char Tokenizer, also included in assets/tokenizer_v2/smiles_char
• Train/Test Dataset in Huggingface's dataset format
• Cross validation metadata: skf20 means StratifiedKFold(n_splits=20)

Pretraining with MLM + MTR:

cd src
python3 train.py exp=pretrain_mtr_mlm exp_name=pretrain-mtr-mlm_roberta-depth8-dim256-abs optim.lr=5e-4 optim.weight_decay=0.0 optim.name='timm@adam' data.tokenizer.name=smiles_char model=roberta model.vocab_size=44 model.encoder.num_hidden_layers=8 model.encoder.hidden_size=256 model.encoder.intermediate_size=512 model.pool_type=concat_attn model.head.dropout=0.1 model.head.type=mtr_mlm loader.train_batch_size=2048 trainer.max_epochs=10 trainer.precision=16-mixed loader.num_workers=16

Finetune on competition task

replace ckpt_path to your pretrained checkpoint path, e.g

python3 train.py exp=finetune_tokenize exp_name=finetunemtr-smiles_char_roberta-depth8-dim256-lr5e-4 optim.lr=5e-4 optim.weight_decay=0.0 optim.name='timm@adam' data.tokenizer.name=smiles_char model=roberta model.vocab_size=44 model.encoder.num_hidden_layers=8 model.encoder.hidden_size=256 model.encoder.intermediate_size=512 model.pool_type=concat_attn 'model.head.mlp_chans=[1024,1024]' model.head.dropout=0.3 loader.train_batch_size=2048 trainer.max_epochs=12 trainer.precision=16-mixed loader.num_workers=16 cv.strategy=skf20 cv.fold_idx=0 cv.train_on_all=True 'ckpt_path="outputs/train/run/06-26/08-16-02.947187_pretrain-mtr-mlm_roberta-depth8-dim256-abs/fold_0/ckpts/ep=3_step=215000_train|loss=0.000781.ckpt"'

Submit

Just add flags train=False predict=True ckpt_path={YOUR CHECKPOINT PATH HERE} to your training command.