update lost config fp

Author: aditya0by0

README.md

@@ -73,7 +73,7 @@ The dataset has a customizable list of properties for atoms, bonds and molecules
 The list can be found in the `configs/data/chebi50_graph_properties.yml` file.
 
 ```bash
-python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/csv_logger.yml --model=../python-chebai-graph/configs/model/gnn_res_gated.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml
+python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/csv_logger.yml --model=../python-chebai-graph/configs/model/gnn_res_gated.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce_weighted.yml
 ```
 
 ## Augmented Graphs
@@ -94,7 +94,7 @@ Among all the connection schemes we evaluated, this configuration delivered the
 Below is the command for the model and data configuration that achieved the best classification performance using augmented graphs.
 
 ```bash
-python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/gat_aug_amgpool.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_aug_prop_as_per_node.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml --trainer.logger.init_args.name=gatv2_amg_s0
+python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/gat_aug_amgpool.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_aug_prop_as_per_node.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce_weighted.yml --trainer.logger.init_args.name=gatv2_amg_s0
 ```
 
 ### Model Hyperparameters
@@ -146,7 +146,7 @@ These can be used for both GAT and ResGated architectures:
 In this type of node initialization, the node features (and/or edge features) of the given molecular graph are initialized only once during dataset creation with the given initialization scheme.
 
 ```bash
-python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/resgated.yml --model.config.in_channels=203 --model.config.edge_dim=11 --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.pad_node_features=45 --data.pad_edge_features=4 --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --data.init_args.persistent_workers=False --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml --trainer.logger.init_args.name=gni_res_props+zeros_s0
+python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/resgated.yml --model.config.in_channels=203 --model.config.edge_dim=11 --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.pad_node_features=45 --data.pad_edge_features=4 --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --data.init_args.persistent_workers=False --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce_weighted.yml --trainer.logger.init_args.name=gni_res_props+zeros_s0
 ```
 
 In the above command, for each node we use the 158 node features (corresponding the node properties defined in `chebi50_graph_properties.yml`) which are retrieved from RDKit and additional 45 additional features (specified by `--data.pad_node_features=45`) drawn from a normal distribution (default).
@@ -188,5 +188,5 @@ If all features should be initialized from the given distribution, remove the co
 Please find below the command for a typical dynamic node initialization:
 
 ```bash
-python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/resgated_dynamic_gni.yml --model.config.in_channels=203 --model.config.edge_dim=11 --model.config.complete_randomness=False --model.config.pad_node_features=45 --model.config.pad_edge_features=4 --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --data.init_args.persistent_workers=False --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce.yml --trainer.logger.init_args.name=gni_dres_props+rand_s0
+python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/wandb_logger.yml --model=../python-chebai-graph/configs/model/resgated_dynamic_gni.yml --model.config.in_channels=203 --model.config.edge_dim=11 --model.config.complete_randomness=False --model.config.pad_node_features=45 --model.config.pad_edge_features=4 --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --data.init_args.persistent_workers=False --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce_weighted.yml --trainer.logger.init_args.name=gni_dres_props+rand_s0
 ```