GraphClassificationData

class flash.graph.classification.data.GraphClassificationData(train_input=None, val_input=None, test_input=None, predict_input=None, data_fetcher=None, transform=<class 'flash.core.data.io.input_transform.InputTransform'>, transform_kwargs=None, val_split=None, batch_size=None, num_workers=0, sampler=None, pin_memory=True, persistent_workers=False)

The GraphClassificationData class is a DataModule with a set of classmethods for loading data for graph classification.

classmethod from_datasets(train_dataset=None, val_dataset=None, test_dataset=None, predict_dataset=None, input_cls=<class 'flash.graph.classification.input.GraphClassificationDatasetInput'>, transform=<class 'flash.graph.classification.input_transform.GraphClassificationInputTransform'>, transform_kwargs=None, target_formatter=None, **data_module_kwargs)

Load the GraphClassificationData from PyTorch Dataset objects.

The Dataset objects should be one of the following:

• A PyTorch Dataset where the __getitem__ returns a tuple: (PyTorch Geometric Data object, target)

• A PyTorch Dataset where the __getitem__ returns a dict:

  {"input": PyTorch Geometric Data object, "target": target}

To learn how to customize the transforms applied for each stage, read our customizing transforms guide.

Parameters

• train_dataset (Optional[Dataset]) – The Dataset to use when training.

• val_dataset (Optional[Dataset]) – The Dataset to use when validating.

• test_dataset (Optional[Dataset]) – The Dataset to use when testing.

• predict_dataset (Optional[Dataset]) – The Dataset to use when predicting.

• target_formatter (Optional[TargetFormatter]) – Optionally provide a TargetFormatter to control how targets are handled. If None then no formatting will be applied to targets.

• input_cls (Type[Input]) – The Input type to use for loading the data.

• transform (TypeVar(INPUT_TRANSFORM_TYPE, Type[flash.core.data.io.input_transform.InputTransform], Callable, Tuple[Union[StrEnum, str], Dict[str, Any]], Union[StrEnum, str], None)) – The InputTransform type to use.

• transform_kwargs (Optional[Dict]) – Dict of keyword arguments to be provided when instantiating the transforms.

• data_module_kwargs – Additional keyword arguments to provide to the DataModule constructor.

Return type

GraphClassificationData

Returns

The constructed GraphClassificationData.

Examples

A PyTorch Dataset where the __getitem__ returns a tuple: (PyTorch Geometric Data object, target):

>>> import torch
>>> from torch.utils.data import Dataset
>>> from torch_geometric.data import Data
>>> from flash import Trainer
>>> from flash.graph import GraphClassificationData, GraphClassifier
>>> from flash.core.data.utilities.classification import SingleLabelTargetFormatter
>>>
>>> class CustomDataset(Dataset):
...     def __init__(self, targets=None):
...         self.targets = targets
...     def __getitem__(self, index):
...         edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)
...         x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
...         data = Data(x=x, edge_index=edge_index)
...         if self.targets is not None:
...             return data, self.targets[index]
...         return data
...     def __len__(self):
...         return len(self.targets) if self.targets is not None else 3
...
>>> datamodule = GraphClassificationData.from_datasets(
...     train_dataset=CustomDataset(["cat", "dog", "cat"]),
...     predict_dataset=CustomDataset(),
...     target_formatter=SingleLabelTargetFormatter(labels=["cat", "dog"]),
...     batch_size=2,
... )
>>> datamodule.num_features
1
>>> datamodule.num_classes
2
>>> datamodule.labels
['cat', 'dog']
>>> model = GraphClassifier(num_features=datamodule.num_features, num_classes=datamodule.num_classes)
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule)  
Training...
>>> trainer.predict(model, datamodule=datamodule)  
Predicting...

A PyTorch Dataset where the __getitem__ returns a dict: {"input": PyTorch Geometric Data object, "target": target}:

>>> import torch  # noqa: F811
>>> from torch.utils.data import Dataset
>>> from torch_geometric.data import Data  # noqa: F811
>>> from flash import Trainer
>>> from flash.graph import GraphClassificationData, GraphClassifier
>>> from flash.core.data.utilities.classification import SingleLabelTargetFormatter
>>>
>>> class CustomDataset(Dataset):
...     def __init__(self, targets=None):
...         self.targets = targets
...     def __getitem__(self, index):
...         edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)
...         x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
...         data = Data(x=x, edge_index=edge_index)
...         if self.targets is not None:
...             return {"input": data, "target": self.targets[index]}
...         return {"input": data}
...     def __len__(self):
...         return len(self.targets) if self.targets is not None else 3
...
>>> datamodule = GraphClassificationData.from_datasets(
...     train_dataset=CustomDataset(["cat", "dog", "cat"]),
...     predict_dataset=CustomDataset(),
...     target_formatter=SingleLabelTargetFormatter(labels=["cat", "dog"]),
...     batch_size=2,
... )
>>> datamodule.num_features
1
>>> datamodule.num_classes
2
>>> datamodule.labels
['cat', 'dog']
>>> model = GraphClassifier(num_features=datamodule.num_features, num_classes=datamodule.num_classes)
>>> trainer = Trainer(fast_dev_run=True)
>>> trainer.fit(model, datamodule=datamodule)  
Training...
>>> trainer.predict(model, datamodule=datamodule)  
Predicting...

property num_features

The number of features per node in the graphs contained in this GraphClassificationData.