configs

RxnRep configuration

The directory contains the configuration files for the supervised training, pretriaining, and finetuning tasks. We use hydra for all the configurations.

It is already discussed in the main README about how to run the experiments for classification tasks and here are brief notes on how to run the experiments for regression tasks.

Train regression models

Direct supervised training

To train for the Heid dataset:

python run.py --config-name config.yaml  datamodule=regression/heid.yaml \
       model/decoder=regressor.yaml

Pretraining & finetuning

To pretrain the model using contrastive learning:

python run.py --config-name config_contrastive.yaml datamodule=contrastive/heid.yaml

Note, you may need to set functional_group_smarts_filenames in subgraph.yaml and transform_or_identity.yaml to the path of smarts_daylight.tsv before running the above command, if you want to use the subgraph augmentation method.

To finetune the pretrained model:

python run.py --config-name config_finetune.yaml datamodule=regression/heid.yaml \
       datamodule.regression.allow_label_scaler_none=true \
       model/finetuner=regressor.yaml  pretrained_wandb_id=<wandb_id>

where wandb_id is the W&B id for the pretraining run, an eight-character alphanumeric (e.g. 3oep187z). By providing the wandb_id, the finetuning script will automatically search for the pretrained model.

Alternatively, the pretrained model info can be passed in manually:

python run.py --config-name config_finetune.yaml datamodule=regression/heid.yaml \
       datamodule.regression.allow_label_scaler_none=true \
       model/finetuner=regressor.yaml \
       model.finetuner.model_class.pretrained_checkpoint_filename=<checkpoint> \
       model.finetuner.model_class.pretrained_dataset_state_dict_filename=<dataset_state_dict> \
       model.finetuner.model_class.pretrained_config_filename=<config>

where

• checkpoint is the path to the pretrained model checkpoint, e.g. epoch=9-step=39.ckpt
• dataset_state_dict is the path to the dataset state dict of the pretrained model, e.g. dataset_state_dict.yaml
• config is the path to the config file of the pretrained model, e.g. hydra_cfg_final.yaml