7 models that use the fingerprint/atom/SMILES-level features for representating molecules.
python train_BELKA.py --modelType FprMLP --EMA True # for 7 fingerprint-based MLP model
python train_BELKA.py --modelType DeepFM/DeepFM2 --EMA True # for 4 fingerprint-based DeepFM model
python train_BELKA.py --modelType PseLabAttn --EMA True # for SMILES/ECFP/atom features-based RNN-Transformer model
python train_BELKA.py --modelType GraphMLP/GraphMLP2 --EMA True# for SMILES/FCFP/atom features-based GNN model
python train_BELKA_lgb.py # for 7 fingerprint-based lgb model
FprMLP/DeepFM/DeepFM2 are all based on the molecular fingerprint features only, and achieve 0.620~0.645 in validation(15-fold) and 0.432 in public LB;
PseLabAttn/GraphMLP are feature-mixture model, achieving 0.650 in validation(15-fold) and 0.458/0.398 in public LB; (this GNN didn't consider the bond type)
lgb is fingerprint-based lightgbm model, achieving 0.615 in validation(15-fold) and 0.377 in public LB;
ensemble all of them can lead to about 0.480 in public LB.