data

class autogl.data.Batch(batch=None, **kwargs)

A plain old python object modeling a batch of graphs as one big (dicconnected) graph. With cogdl.data.Data being the base class, all its methods can also be used here. In addition, single graphs can be reconstructed via the assignment vector batch, which maps each node to its respective graph identifier.

cumsum(key, item)

If True, the attribute key with content item should be added up cumulatively before concatenated together.

Note

This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.

static from_data_list(data_list, follow_batch=[])

Constructs a batch object from a python list holding torch_geometric.data.Data objects. The assignment vector batch is created on the fly. Additionally, creates assignment batch vectors for each key in follow_batch.

property num_graphs

Returns the number of graphs in the batch.

to_data_list()

Reconstructs the list of torch_geometric.data.Data objects from the batch object. The batch object must have been created via from_data_list() in order to be able reconstruct the initial objects.

class autogl.data.Data(x=None, edge_index=None, edge_attr=None, y=None, pos=None)

A plain old python object modeling a single graph with various (optional) attributes:

Parameters

• x (Tensor, optional) – Node feature matrix with shape [num_nodes, num_node_features]. (default: None)

• edge_index (LongTensor, optional) – Graph connectivity in COO format with shape [2, num_edges]. (default: None)

• edge_attr (Tensor, optional) – Edge feature matrix with shape [num_edges, num_edge_features]. (default: None)

• y (Tensor, optional) – Graph or node targets with arbitrary shape. (default: None)

• pos (Tensor, optional) – Node position matrix with shape [num_nodes, num_dimensions]. (default: None)

The data object is not restricted to these attributes and can be extented by any other additional data.

__call__(*keys)

Iterates over all attributes *keys in the data, yielding their attribute names and content. If *keys is not given this method will iterative over all present attributes.

__contains__(key)

Returns True, if the attribute key is present in the data.

__getitem__(key)

Gets the data of the attribute key.

__inc__(key, value)

“Returns the incremental count to cumulatively increase the value of the next attribute of key when creating batches.

Note

This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.

__iter__()

Iterates over all present attributes in the data, yielding their attribute names and content.

__len__()

Returns the number of all present attributes.

__setitem__(key, value)

Sets the attribute key to value.

apply(func, *keys)

Applies the function func to all attributes *keys. If *keys is not given, func is applied to all present attributes.

cat_dim(key, value)

Returns the dimension in which the attribute key with content value gets concatenated when creating batches.

Note

This method is for internal use only, and should only be overridden if the batch concatenation process is corrupted for a specific data attribute.

contiguous(*keys)

Ensures a contiguous memory layout for all attributes *keys. If *keys is not given, all present attributes are ensured to have a contiguous memory layout.

static from_dict(dictionary)

Creates a data object from a python dictionary.

get_label_number()

Get the number of labels in this dataset as dict.

is_coalesced()

Returns True, if edge indices are ordered and do not contain duplicate entries.

property keys

Returns all names of graph attributes.

property num_edges

Returns the number of edges in the graph.

property num_features

Returns the number of features per node in the graph.

random_splits_mask(train_ratio, val_ratio, seed=None)

If the data has masks for train/val/test, return the splits with specific ratio.

Parameters

• train_ratio (float) – the portion of data that used for training.

• val_ratio (float) – the portion of data that used for validation.

• seed (int) – random seed for splitting dataset.

random_splits_mask_class(num_train_per_class, num_val, num_test, seed=None)

If the data has masks for train/val/test, return the splits with specific number of samples from every class for training.

Parameters

• num_train_per_class (int) – the number of samples from every class used for training.

• num_val (int) – the total number of nodes that used for validation.

• num_test (int) – the total number of nodes that used for testing.

• seed (int) – random seed for splitting dataset.

random_splits_nodes(train_ratio, val_ratio, seed=None)

If the data uses id of nodes for train/val/test, return the splits with specific ratio.

Parameters

• train_ratio (float) – the portion of data that used for training.

• val_ratio (float) – the portion of data that used for validation.

• seed (int) – random seed for splitting dataset.

random_splits_nodes_class(num_train_per_class, num_val, num_test, seed=None)

If the data uses id of nodes for train/val/test, return the splits with specific number of samples from every class for training.

Parameters

• num_train_per_class (int) – the number of samples from every class used for training.

• num_val (int) – the total number of nodes that used for validation.

• num_test (int) – the total number of nodes that used for testing.

• seed (int) – random seed for splitting dataset.

to(device, *keys)

Performs tensor dtype and/or device conversion to all attributes *keys. If *keys is not given, the conversion is applied to all present attributes.

class autogl.data.DataListLoader(dataset, batch_size=1, shuffle=True, **kwargs)

Data loader which merges data objects from a cogdl.data.dataset to a python list.

Note

This data loader should be used for multi-gpu support via cogdl.nn.DataParallel.

Parameters

• dataset (Dataset) – The dataset from which to load the data.

• batch_size (int, optional) – How may samples per batch to load. (default: 1)

• shuffle (bool, optional) – If set to True, the data will be reshuffled at every epoch (default: True)

class autogl.data.DataLoader(dataset, batch_size=1, shuffle=True, **kwargs)

Data loader which merges data objects from a cogdl.data.dataset to a mini-batch.

Parameters

• dataset (Dataset) – The dataset from which to load the data.

• batch_size (int, optional) – How may samples per batch to load. (default: 1)

• shuffle (bool, optional) – If set to True, the data will be reshuffled at every epoch (default: True)

class autogl.data.Dataset(root, transform=None, pre_transform=None, pre_filter=None)

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

Parameters

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

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

__getitem__(idx)

Gets the data object at index idx and transforms it (in case a self.transform is given).

__len__()

The number of examples in the dataset.

download()

Downloads the dataset to the self.raw_dir folder.

get(idx)

Gets the data object at index idx.

property get_label_number

Get the number of labels in this dataset as dict.

property num_features

Returns the number of features per node in the graph.

process()

Processes the dataset to the self.processed_dir folder.

property processed_file_names

The name of the files to find in the self.processed_dir folder in order to skip the processing.

property processed_paths

The filepaths to find in the self.processed_dir folder in order to skip the processing.

property raw_file_names

The name of the files to find in the self.raw_dir folder in order to skip the download.

property raw_paths

The filepaths to find in order to skip the download.

class autogl.data.DenseDataLoader(dataset, batch_size=1, shuffle=True, **kwargs)

Data loader which merges data objects from a cogdl.data.dataset to a mini-batch.

Note

To make use of this data loader, all graphs in the dataset needs to have the same shape for each its attributes. Therefore, this data loader should only be used when working with dense adjacency matrices.

Parameters

• dataset (Dataset) – The dataset from which to load the data.

• batch_size (int, optional) – How may samples per batch to load. (default: 1)

• shuffle (bool, optional) – If set to True, the data will be reshuffled at every epoch (default: True)

autogl.data.download_url(url, folder, name=None, log=True)

Downloads the content of an URL to a specific folder.

Parameters

• url (string) – The url.

• folder (string) – The folder.

• log (bool, optional) – If False, will not print anything to the console. (default: True)

autogl.data.extract_tar(path, folder, mode='r:gz', log=True)

Extracts a tar archive to a specific folder.

Parameters

• path (string) – The path to the tar archive.

• folder (string) – The folder.

• mode (string, optional) – The compression mode. (default: "r:gz")

• log (bool, optional) – If False, will not print anything to the console. (default: True)

autogl.data.extract_zip(path, folder, log=True)

Extracts a zip archive to a specific folder.

Parameters

• path (string) – The path to the tar archive.

• folder (string) – The folder.

• log (bool, optional) – If False, will not print anything to the console. (default: True)