from pathlib import Path
from typing import Callable
from typing import Optional
from typing import Union
import optuna
import torch
import torch.nn as nn
from easygraph.ml_metrics import BaseEvaluator
from ..vertex_classification import VertexClassificationTask
[docs]class HypergraphVertexClassificationTask(VertexClassificationTask):
r"""The auto-experiment class for the vertex classification task on hypergraph.
Args:
``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``.
"""
def __init__(
self,
work_root: Optional[Union[str, Path]],
data: dict,
model_builder: Callable,
train_builder: Callable,
evaluator: BaseEvaluator,
device: torch.device,
structure_builder: Optional[Callable] = None,
study_name: Optional[str] = None,
overwrite: bool = True,
):
super().__init__(
work_root,
data,
model_builder,
train_builder,
evaluator,
device,
structure_builder=structure_builder,
study_name=study_name,
overwrite=overwrite,
)
[docs] def to(self, device: torch.device):
r"""Move the input data to the target device.
Args:
``device`` (``torch.device``): The specified target device to store the input data.
"""
return super().to(device)
@property
def vars_for_DL(self):
r"""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``.
"""
return super().vars_for_DL
[docs] def experiment(self, trial: optuna.Trial):
r"""Run the experiment for a given trial.
Args:
``trial`` (``optuna.Trial``): The ``optuna.Trial`` object.
"""
return super().experiment(trial)
[docs] def run(self, max_epoch: int, num_trials: int = 1, direction: str = "maximize"):
r"""Run experiments with automatically hyper-parameter tuning.
Args:
``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"``.
"""
return super().run(max_epoch, num_trials, direction)
[docs] def train(
self,
data: dict,
model: nn.Module,
optimizer: torch.optim.Optimizer,
criterion: nn.Module,
):
r"""Train model for one epoch.
Args:
``data`` (``dict``): The input data.
``model`` (``nn.Module``): The model.
``optimizer`` (``torch.optim.Optimizer``): The model optimizer.
``criterion`` (``nn.Module``): The loss function.
"""
return super().train(data, model, optimizer, criterion)
[docs] @torch.no_grad()
def validate(self, data: dict, model: nn.Module):
r"""Validate the model.
Args:
``data`` (``dict``): The input data.
``model`` (``nn.Module``): The model.
"""
return super().validate(data, model)
[docs] @torch.no_grad()
def test(self, data: Optional[dict] = None, model: Optional[nn.Module] = None):
r"""Test the model.
Args:
``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``.
"""
return super().test(data, model)