ChEBai is a deep learning library that allows the combination of deep learning methods with chemical ontologies (especially ChEBI). Special attention is given to the integration of the semantic qualities of the ontology into the learning process. This is done in two different ways:
python -m chebai fit --data.class_path=chebai.preprocessing.datasets.pubchem.SWJChem --model=configs/model/electra-for-pretraining.yml --trainer=configs/training/default_trainer.yml --trainer.callbacks=configs/training/default_callbacks.yml
python -m chebai fit --config=[path-to-your-electra_chebi100-config] --trainer.callbacks=configs/training/default_callbacks.yml --model.pretrained_checkpoint=[path-to-pretrained-model] --model.load_prefix=generator.
python -m chebai fit --config=[path-to-your-tox21-config] --trainer.callbacks=configs/training/default_callbacks.yml --model.pretrained_checkpoint=[path-to-pretrained-model] --model.load_prefix=generator.
python -m chebai train --config=[path-to-your-tox21-config] --trainer.callbacks=configs/training/default_callbacks.yml --ckpt_path=[path-to-model-with-ontology-pretraining]
python3 -m chebai predict_from_file --model=[path-to-model-config] --checkpoint_path=[path-to-model] --input_path={path-to-file-containing-smiles] [--classes_path=[path-to-classes-file]] [--save_to=[path-to-output]]
The input files should contain a list of line-separated SMILES strings. This generates a CSV file that contains the one row for each SMILES string and one column for each class.
You can do inner k-fold cross-validation, i.e., train models on k train-validation splits that all use the same test set. For that, you need to specify the total_number of folds as
--data.init_args.inner_k_folds=K
and the fold to be used in the current optimisation run as
--data.init_args.fold_index=I
To train K models, you need to do K such calls, each with a different fold_index. On the first call with a given
inner_k_folds, all folds will be created and stored in the data directory
Change the chebi version used for all sets (default: 200):
--data.init_args.chebi_version=VERSION
To change only the version of the train and validation sets independently of the test set, use
--data.init_args.chebi_version_train=VERSION
Data is stored in and retrieved from the raw and processed folders
data/${dataset_name}/${chebi_version}/raw/
and
data/${dataset_name}/${chebi_version}/processed/${reader_name}/
where ${dataset_name} is the _name-attribute of the DataModule used,
${chebi_version} refers to the ChEBI version used (only for ChEBI-datasets) and
${reader_name} is the name-attribute of the Reader class associated with the dataset.
For cross-validation, the folds are stored as cv_${n_folds}_fold/fold_{fold_index}_train.pkl
and cv_${n_folds}_fold/fold_{fold_index}_validation.pkl in the raw directory.
In the processed directory, .pt is used instead of .pkl.