topobench.data.loaders.graph.hetero_datasets module#

Loaders for Heterophilous Graph datasets.

class AbstractLoader(parameters)#

Bases: ABC

Abstract class that provides an interface to load data.

Parameters:
parametersDictConfig

Configuration parameters.

__init__(parameters)#
get_data_dir()#

Get the data directory.

Returns:
Path

The path to the dataset directory.

load(**kwargs)#

Load data.

Parameters:
**kwargsdict

Additional keyword arguments.

Returns:
tuple[torch_geometric.data.Data, str]

Tuple containing the loaded data and the data directory.

abstract load_dataset()#

Load data into a dataset.

Returns:
Union[torch_geometric.data.Dataset, torch.utils.data.Dataset]

The loaded dataset, which could be a PyG or PyTorch dataset.

Raises:
NotImplementedError

If the method is not implemented.

class Dataset(root=None, transform=None, pre_transform=None, pre_filter=None, log=True, force_reload=False)#

Bases: Dataset

Dataset base class for creating graph datasets. See here for the accompanying tutorial.

Parameters:

  • root (str, optional) – Root directory where the dataset should be saved. (optional: None)

  • transform (callable, optional) – A function/transform that takes in a Data or HeteroData object and returns a transformed version. The data object will be transformed before every access. (default: None)

  • pre_transform (callable, optional) – A function/transform that takes in a Data or HeteroData object and returns a transformed version. The data object will be transformed before being saved to disk. (default: None)

  • pre_filter (callable, optional) – A function that takes in a Data or HeteroData object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default: None)

  • log (bool, optional) – Whether to print any console output while downloading and processing the dataset. (default: True)

  • force_reload (bool, optional) – Whether to re-process the dataset. (default: False)

__init__(root=None, transform=None, pre_transform=None, pre_filter=None, log=True, force_reload=False)#
download()#

Downloads the dataset to the self.raw_dir folder.

get(idx)#

Gets the data object at index idx.

get_summary()#

Collects summary statistics for the dataset.

index_select(idx)#

Creates a subset of the dataset from specified indices idx. Indices idx can be a slicing object, e.g., [2:5], a list, a tuple, or a torch.Tensor or np.ndarray of type long or bool.

indices()#
len()#

Returns the number of data objects stored in the dataset.

print_summary(fmt='psql')#

Prints summary statistics of the dataset to the console.

Parameters:

fmt (str, optional) – Summary tables format. Available table formats can be found here. (default: "psql")

process()#

Processes the dataset to the self.processed_dir folder.

shuffle(return_perm=False)#

Randomly shuffles the examples in the dataset.

Parameters:

return_perm (bool, optional) – If set to True, will also return the random permutation used to shuffle the dataset. (default: False)

to_datapipe()#

Converts the dataset into a torch.utils.data.DataPipe.

The returned instance can then be used with :pyg:`PyG's` built-in DataPipes for batching graphs as follows:

from torch_geometric.datasets import QM9

dp = QM9(root='./data/QM9/').to_datapipe()
dp = dp.batch_graphs(batch_size=2, drop_last=True)

for batch in dp:
    pass

See the PyTorch tutorial for further background on DataPipes.

property has_download: bool#

Checks whether the dataset defines a download() method.

property has_process: bool#

Checks whether the dataset defines a process() method.

property num_classes: int#

Returns the number of classes in the dataset.

property num_edge_features: int#

Returns the number of features per edge in the dataset.

property num_features: int#

Returns the number of features per node in the dataset. Alias for num_node_features.

property num_node_features: int#

Returns the number of features per node in the dataset.

property processed_dir: str#

!! processed by numpydoc !!

property processed_file_names: str | List[str] | Tuple[str, ...]#

The name of the files in the self.processed_dir folder that must be present in order to skip processing.

property processed_paths: List[str]#

The absolute filepaths that must be present in order to skip processing.

property raw_dir: str#

!! processed by numpydoc !!

property raw_file_names: str | List[str] | Tuple[str, ...]#

The name of the files in the self.raw_dir folder that must be present in order to skip downloading.

property raw_paths: List[str]#

The absolute filepaths that must be present in order to skip downloading.

class DictConfig(content, key=None, parent=None, ref_type=typing.Any, key_type=typing.Any, element_type=typing.Any, is_optional=True, flags=None)#

Bases: BaseContainer, MutableMapping[Any, Any]

__init__(content, key=None, parent=None, ref_type=typing.Any, key_type=typing.Any, element_type=typing.Any, is_optional=True, flags=None)#
copy()#
get(key, default_value=None)#

Return the value for key if key is in the dictionary, else default_value (defaulting to None).

items() → a set-like object providing a view on D's items#
items_ex(resolve=True, keys=None)#
keys() → a set-like object providing a view on D's keys#
pop(k[, d]) → v, remove specified key and return the corresponding value.#

If key is not found, d is returned if given, otherwise KeyError is raised.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D#
class HeterophilousGraphDataset(root, name, transform=None, pre_transform=None, force_reload=False)#

Bases: InMemoryDataset

The heterophilous graphs "Roman-empire", "Amazon-ratings", "Minesweeper", "Tolokers" and "Questions" from the “A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?” paper.

Parameters:

  • root (str) – Root directory where the dataset should be saved.

  • name (str) – The name of the dataset ("Roman-empire", "Amazon-ratings", "Minesweeper", "Tolokers", "Questions").

  • transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data object and returns a transformed version. The data object will be transformed before every access. (default: None)

  • pre_transform (callable, optional) – A function/transform that takes in an torch_geometric.data.Data object and returns a transformed version. The data object will be transformed before being saved to disk. (default: None)

  • force_reload (bool, optional) – Whether to re-process the dataset. (default: False)

STATS:

Name

#nodes

#edges

#features

#classes

Roman-empire

22,662

32,927

300

18

Amazon-ratings

24,492

93,050

300

5

Minesweeper

10,000

39,402

7

2

Tolokers

11,758

519,000

10

2

Questions

48,921

153,540

301

2
__init__(root, name, transform=None, pre_transform=None, force_reload=False)#
download()#

Downloads the dataset to the self.raw_dir folder.

process()#

Processes the dataset to the self.processed_dir folder.

property processed_dir: str#

!! processed by numpydoc !!

property processed_file_names: str#

The name of the files in the self.processed_dir folder that must be present in order to skip processing.

property raw_dir: str#

!! processed by numpydoc !!

property raw_file_names: str#

The name of the files in the self.raw_dir folder that must be present in order to skip downloading.

url = 'https://github.com/yandex-research/heterophilous-graphs/raw/main/data'#
class HeterophilousGraphDatasetLoader(parameters)#

Bases: AbstractLoader

Load Heterophilous Graph datasets.

Parameters:
parametersDictConfig
Configuration parameters containing:

  • data_dir: Root directory for data

  • data_name: Name of the dataset

  • data_type: Type of the dataset (e.g., “heterophilous”)

__init__(parameters)#
load_dataset()#

Load Heterophilous Graph dataset.

Returns:
Dataset

The loaded Heterophilous Graph dataset.

Raises:
RuntimeError

If dataset loading fails.