topobench.data.preprocessor.preprocessor module

Preprocessor for datasets.

class DataTransform(transform_name, **kwargs)

Bases: BaseTransform

Abstract class to define a custom data lifting.

Parameters:

transform_namestr

The name of the transform to be used.

**kwargsdict

Additional arguments for the class. Should contain “transform_name”.

__init__(transform_name, **kwargs)

forward(data)

Forward pass of the lifting.

Parameters:

datatorch_geometric.data.Data

The input data to be lifted.

Returns:

torch_geometric.data.Data

The lifted data.

class DataloadDataset(data_lst)

Bases: Dataset

Custom dataset to return all the values added to the dataset object.

Parameters:

data_lstlist[torch_geometric.data.Data]

List of torch_geometric.data.Data objects.

__init__(data_lst)

get(idx)

Get data object from data list.

Parameters:

idxint

Index of the data object to get.

Returns:

tuple

Tuple containing a list of all the values for the data and the corresponding keys.

len()

Return the length of the dataset.

Returns:

int

Length of the dataset.

class PreProcessor(dataset, data_dir, transforms_config=None, **kwargs)

Bases: InMemoryDataset

Preprocessor for datasets.

Parameters:

datasetlist

List of data objects.

data_dirstr

Path to the directory containing the data.

transforms_configDictConfig, optional

Configuration parameters for the transforms (default: None).

**kwargsoptional

Optional additional arguments.

__init__(dataset, data_dir, transforms_config=None, **kwargs)

instantiate_pre_transform(data_dir, transforms_config)

Instantiate the pre-transforms.

Parameters:

data_dirstr

Path to the directory containing the data.

transforms_configDictConfig

Configuration parameters for the transforms.

Returns:

torch_geometric.transforms.Compose

Pre-transform object.

load(path)

Load the dataset from the file path path.

Parameters:

pathstr

The path to the processed data.

load_dataset_splits(split_params)

Load the dataset splits.

Parameters:

split_paramsdict

Parameters for loading the dataset splits.

Returns:

tuple

A tuple containing the train, validation, and test datasets.

process()

Method that processes the data.

save_transform_parameters()

Save the transform parameters.

set_processed_data_dir(pre_transforms_dict, data_dir, transforms_config)

Set the processed data directory.

Parameters:

pre_transforms_dictdict

Dictionary containing the pre-transforms.

data_dirstr

Path to the directory containing the data.

transforms_configDictConfig

Configuration parameters for the transforms.

property processed_dir: str

Return the path to the processed directory.

Returns:

str

Path to the processed directory.

property processed_file_names: str

Return the name of the processed file.

Returns:

str

Name of the processed file.

ensure_serializable(obj)

Ensure that the object is serializable.

Parameters:

objobject

Object to ensure serializability.

Returns:

object

Object that is serializable.

load_inductive_splits(dataset, parameters)

Load multiple-graph datasets with the specified split.

Parameters:

datasettorch_geometric.data.Dataset

Graph dataset.

parametersDictConfig

Configuration parameters.

Returns:

list:

List containing the train, validation, and test splits.

load_transductive_splits(dataset, parameters)

Load the graph dataset with the specified split.

Parameters:

datasettorch_geometric.data.Dataset

Graph dataset.

parametersDictConfig

Configuration parameters.

Returns:

list:

List containing the train, validation, and test splits.

make_hash(o)

Make a hash from a dictionary, list, tuple or set to any level, that contains only other hashable types.

Parameters:

odict, list, tuple, set

Object to hash.

Returns:

int

Hash of the object.