usage:
python main.py [-h] [-predict] [-watsoneq] [-K_fold] [-maxatoms MAXATOMS]
[-lr LR] [-epoch EPOCH] [-batchsize BATCHSIZE] [-layers LAYERS]
[-heads HEADS] [-residcon] [-explicitH] [-dropout DROPOUT]
[-modelname MODELNAME] [-num_hidden NUM_HIDDEN] [-train_only]
[-loss LOSS] [-sw_thr SW_THR] [-sw_decay SW_DECAY]optional arguments:
-h, --help show this help message and exit
-predict If specified, prediction is carried out
(default=False)
-watsoneq whether to use watson equation (default=False)
-K_fold whether to run KFoldCV (default=False)
-maxatoms MAXATOMS Maximum number of atoms in a molecule (default=64)
-lr LR Learning rate (default=5.0e-4)
-epoch EPOCH epoch (default=200)
-batchsize BATCHSIZE batch_size (default=256)
-layers LAYERS number of gnn layers (default=5)
-heads HEADS number of gat heads (default=5)
-residcon whether to use residual connection (default=True)
-explicitH whether to use explicit hydrogens (default=False)
-dropout DROPOUT dropout rate (default=0.0)
-modelname MODELNAME model name (default=an array of hyperparameter values)
-num_hidden NUM_HIDDEN
number of nodes in hidden layers (default=32)
-train_only If specified, no 8:1:1 split is carried out, the whole
database is used for training (default=False)
-loss LOSS loss function (default=mse). Options - mae, mse,
kl_div_normalexample commands:
# HoV prediction with uncertainty quantification - 'molecules_to_predict.csv' file is needed as an input which contains two columns: 'smiles' and 'temperature'
python main.py -predict -modelname best_211007 -loss kl_div_normal
# model training
python main.py -modelname test_model -loss kl_div_normal
# non-default hyperparameters can also be tested by adding more arguments