easygraph.experiments.hypergraphs.hypergraph module

class easygraph.experiments.hypergraphs.hypergraph.HypergraphVertexClassificationTask(work_root: str | Path | None, data: dict, model_builder: Callable, train_builder: Callable, evaluator: BaseEvaluator, device: device, structure_builder: Callable | None = None, study_name: str | None = None, overwrite: bool = True)

Bases: VertexClassificationTask

The auto-experiment class for the vertex classification task on hypergraph.

Parameters:

• work_root (Optional[Union[str, Path]]) – User’s work root to store all studies.

• data (dict) – The dictionary to store input data that used in the experiment.

• model_builder (Callable) – The function to build a model with a fixed parameter trial.

• train_builder (Callable) – The function to build a training configuration with two fixed parameters trial and model.

• evaluator (easygraph.ml_metrics.BaseEvaluator) – The DHG evaluator object to evaluate performance of the model in the experiment.

• device (torch.device) – The target device to run the experiment.

• structure_builder (Optional[Callable]) – The function to build a structure with a fixed parameter trial. The structure should be easygraph.Hypergraph.

• study_name (Optional[str]) – The name of this study. If set to None, the study name will be generated automatically according to current time. Defaults to None.

• overwrite (bool) – The flag that whether to overwrite the existing study. Different studies are identified by the study_name. Defaults to True.

Attributes:

vars_for_DL

Return a name list for available variables for deep learning in the vertex classification on hypergraph.

Methods

experiment(trial)	Run the experiment for a given trial.
run(max_epoch[, num_trials, direction])	Run experiments with automatically hyper-parameter tuning.
test([data, model])	Test the model.
to(device)	Move the input data to the target device.
train(data, model, optimizer, criterion)	Train model for one epoch.
validate(data, model)	Validate the model.

experiment(trial: Trial)

Run the experiment for a given trial.

Parameters:

trial (optuna.Trial) – The optuna.Trial object.

run(max_epoch: int, num_trials: int = 1, direction: str = 'maximize')

Run experiments with automatically hyper-parameter tuning.

Parameters:

• max_epoch (int) – The maximum number of epochs to train for each experiment.

• num_trials (int) – The number of trials to run. Defaults to 1.

• direction (str) – The direction to optimize. Defaults to "maximize".

test(data: dict | None = None, model: Module | None = None)

Test the model.

Parameters:

• data (dict, optional) – The input data if set to None, the specified data in the intialization of the experiments will be used. Defaults to None.

• model (nn.Module, optional) – The model if set to None, the trained best model will be used. Defaults to None.

to(device: device)

Move the input data to the target device.

Parameters:

device (torch.device) – The specified target device to store the input data.

train(data: dict, model: Module, optimizer: Optimizer, criterion: Module)

Train model for one epoch.

Parameters:

• data (dict) – The input data.

• model (nn.Module) – The model.

• optimizer (torch.optim.Optimizer) – The model optimizer.

• criterion (nn.Module) – The loss function.

validate(data: dict, model: Module)

Validate the model.

Parameters:

• data (dict) – The input data.

• model (nn.Module) – The model.

property vars_for_DL

Return a name list for available variables for deep learning in the vertex classification on hypergraph. The name list includes features, structure, labels, train_mask, val_mask, and test_mask.