OpenHGNN/openhgnn/trainerflow

• TrainerFlow
• • • • Included Object:
      • Method:
      • Supported trainerflow
      • • Node classification flow
        • Link prediction
        • HetGNN trainerflow
        • NSHE trainerflow
    • How to build a new trainerflow

TrainerFlow

A trainerflow is an abstraction of a predesigned workflow that trains and evaluate a model on a given dataset for a specific use case. It must contain a unique training mechanism involving loss calculation and a specific sampler(sample something used in loss calculation) .

Once we select the model and the task, the func get_trainerflow will help us select the trainerflow. So the customized trainerflow needed be added in this func.

Included Object:

• task : Task
• model : Model (built through given args.model)
• optimizer : torch.optim.Optimizer
• dataloader(if mini_batch_flag is True) :
  • torch.utils.data.DataLoader
  • dgl.dataloading

Method:

• train()
  • decorated with @abstractmethod, so it must be overridden.
• _full_train_setp()
  • train with a full_batch graph
• _mini_train_step()
  • train with a mini_batch seed nodes graph
• _test_step()
  • evaluate in training/validation/testing

Supported trainerflow

• Node classification flow

  • Supported Model: HAN/MAGNN/GTN
  • The task: node classification
    • The task.dataset must include the splited[train/valid/test.] mask.
    • The task.dataset will give the value of input dimension to the args.in_dim.
  • The sampler in this flow is supported by dgl.dataloading.
  • The flow is the most common in the GNNs cause most GNNs model are involved in the task semi-supervised node classification. Here the task is to classify the nodes of HIN(Heterogeneous Information Network).
  • Note: we will set the args.out_dim with num_classes if they are not equivalent.

• Link prediction

  • The same with entity classification except that it is used for link prediction.
  • Supported Model: RGCN/CompGCN/RSHN
  • Supported Task: link prediction

• HetGNN trainerflow

• NSHE trainerflow

How to build a new trainerflow

1. Create a class your_trainerflow that inherits the BaseFlow and register the trainerflow with @register_flow(str).
2. We decorate the func train() with @abstractmethod. So the train() must be overridden, or the your_trainerflow cannot be instantiated. Besides train(), the init and _test_step() should both be implement. One of the _full_train_step() and _mini_train_step() must be implemented at least.
3. Add your_trainerflow into the func get_trainerflow.
4. Fill the dict SUPPORTED_FLOWS in trainerflow/init.py