readme - minor clarification

Author: aditya0by0

README.md

@@ -78,6 +78,9 @@ python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.lo
 
 ## Augmented Graphs
 
+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 --model.config.v2=True --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
 ```
@@ -95,7 +98,7 @@ To use a GAT-based model, choose **one** of the following configs:
 #### GAT-specific hyperparameters
 
 - **Number of message-passing layers**: `--model.config.num_layers=5`        (default: 4)
-- **Attention heads**: `--model.config.heads=4`             (Default: 8)  
+- **Attention heads**: `--model.config.heads=4`             (default: 8)
   > Note: The number of heads should be divisible by the output channels (or hidden channels if output channels are not specified).
 - **Use GATv2**: `--model.config.v2=True`             (default: False)
 
@@ -118,62 +121,50 @@ These can be used for both GAT and ResGated architectures:
 
 ## Static Node Initialization
 
-In this type of node initialization, the node properties (and/or edge properties) of the given molecular graph are initialized only once during dataset creation with the given initialization scheme.
-
+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
 ```
 
-In the above config, for each node we use the 158 node properties retrieved from RDKit and add 45 additional features (specified by `--data.pad_node_features=45`) drawn from a normal distribution (default). You can change the distribution using:
+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 add 45 additional features (specified by `--data.pad_node_features=45`) drawn from a normal distribution (default).
 
-```
---data.distribution=zeros
-```
+You can change the distribution using the following config in above command: `--data.distribution=zeros`
 
-Available distributions:
+Available distributions: `"normal", "uniform", "xavier_normal", "xavier_uniform", "zeros"`
 
-```
-["normal", "uniform", "xavier_normal", "xavier_uniform", "zeros"]
-```
 
-Similarly, each edge is initialized with 7 RDKit properties and 4 additional features drawn from the given distribution.
+Similarly, each edge is initialized with 7 RDKit features and 4 additional features drawn from the given distribution.
 
 
-If you want all node (and edge) features to be drawn from a given distribution (i.e., ignore RDKit features), use:
+If you want all node (and edge) features to be drawn from a given distribution (i.e., ignore RDKit features), use: `--data=../python-chebai-graph/configs/data/chebi50_static_gni.yml`
 
-```
---data=../python-chebai-graph/configs/data/chebi50_static_gni.yml
-```
 
 Refer to the data class code for details.
 
 
 ## Dynamic Node Initialization
 
-In this type of node initialization, the node properties (and/or edge properties) of the molecular graph are initialized at **each forward pass** of the model using the given initialization scheme.
+In this type of node initialization, the node features (and/or edge features) of the molecular graph are initialized at **each forward pass** of the model using the given initialization scheme.
 
-Currently, dynamic node initialization is implemented only for the **resgated** architecture by specifying:
 
-```
---model=../python-chebai-graph/configs/model/resgated_dynamic_gni.yml
-```
 
-To keep RDKit features and *add* dynamically initialized features:
+Currently, dynamic node initialization is implemented only for the **resgated** architecture by specifying: `--model=../python-chebai-graph/configs/model/resgated_dynamic_gni.yml`
+
+To keep RDKit features and *add* dynamically initialized features use the following config in the command:
 
 ```
 --model.config.complete_randomness=False
 --model.config.pad_node_features=45
 ```
 
-The additional features are drawn from normal distribution (default). You can change it using:
-
-```
---model.config.distribution=uniform
-```
+The additional features are drawn from normal distribution (default). You can change it using:`--model.config.distribution=uniform`
 
 If all features should be initialized from the given distribution, remove the complete_randomness flag (default is True).
 
-```
+
+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
 ```