autogl.data

class autogl.data.Data(x=None, edge_index=None, edge_attr=None, y=None, pos=None)[source]

A plain 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)[source]: 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)[source]: Returns True, if the attribute key is present in the data.

__inc__(key, value)[source]: “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__()[source]: Iterates over all present attributes in the data, yielding their attribute names and content.

__setitem__(key, value)[source]: Sets the attribute key to value.

apply(func, *keys)[source]: Applies the function func to all attributes *keys. If *keys is not given, func is applied to all present attributes.

cat_dim(key, value)[source]: 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)[source]: 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)[source]: Creates a data object from a python dictionary.

get_label_number()[source]: Get the number of labels in this dataset as dict.

is_coalesced()[source]: Returns True, if edge indices are ordered and do not contain duplicate entries.

random_splits_mask(train_ratio, val_ratio, seed=None)[source]

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)[source]

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)[source]

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)[source]

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)[source]: 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.InMemoryDataset(data: Iterable[_D], train_index: Optional[Iterable[int]] = Ellipsis, val_index: Optional[Iterable[int]] = Ellipsis, test_index: Optional[Iterable[int]] = Ellipsis, schema: Optional[autogl.data._dataset._dataset._Schema] = Ellipsis)[source]

class autogl.data.InMemoryStaticGraphSet(graphs: Iterable[autogl.data.graph._general_static_graph._general_static_graph.GeneralStaticGraph], train_index: Optional[Iterable[int]] = Ellipsis, val_index: Optional[Iterable[int]] = Ellipsis, test_index: Optional[Iterable[int]] = Ellipsis)[source]

autogl.data.download_url(url, folder, name=None, log=True)[source]

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)[source]

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)[source]

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`)