topobench.data.utils package

Init file for data/utils module.

data2simplicial(data)

Convert a data dictionary into a SimplicialComplex object.

Parameters:

datadict

A dictionary containing at least ‘incidence_0’, ‘adjacency_0’, ‘incidence_1’, ‘incidence_2’, and optionally ‘incidence_3’ tensors.

Returns:

SimplicialComplex

A SimplicialComplex object constructed from nodes, edges, triangles, and tetrahedrons.

download_file_from_drive(file_link, path_to_save, dataset_name, file_format='tar.gz')

Download a file from a Google Drive link and saves it to the specified path.

Parameters:

file_linkstr

The Google Drive link of the file to download.

path_to_savestr

The path where the downloaded file will be saved.

dataset_namestr

The name of the dataset.

file_formatstr, optional

The format of the downloaded file. Defaults to “tar.gz”.

Raises:

None

ensure_serializable(obj)

Ensure that the object is serializable.

Parameters:

objobject

Object to ensure serializability.

Returns:

object

Object that is serializable.

generate_zero_sparse_connectivity(m, n)

Generate a zero sparse connectivity matrix.

Parameters:

mint

Number of rows.

nint

Number of columns.

Returns:

torch.sparse_coo_tensor

Zero sparse connectivity matrix.

get_combinatorial_complex_connectivity(complex, max_rank, neighborhoods=None)

Get the connectivity matrices for the Combinatorial Complex.

Parameters:

complextopnetx.CombinatorialComplex

Cell complex.

max_rankint

Maximum rank of the complex.

neighborhoodslist, optional

List of neighborhoods of interest.

Returns:

dict

Dictionary containing the connectivity matrices.

get_complex_connectivity(complex, max_rank, neighborhoods=None, signed=False)

Get the connectivity matrices for the complex.

Parameters:

complextoponetx.CellComplex or toponetx.SimplicialComplex

Cell complex.

max_rankint

Maximum rank of the complex.

neighborhoodslist, optional

List of neighborhoods of interest.

signedbool, optional

If True, returns signed connectivity matrices.

Returns:

dict

Dictionary containing the connectivity matrices.

get_routes_from_neighborhoods(neighborhoods)

Get the routes from the neighborhoods.

Combination of src_rank, dst_rank. ex: [[0, 0], [1, 0], [1, 1], [1, 1], [2, 1]].

Parameters:

neighborhoodslist

List of neighborhoods of interest.

Returns:

list

List of routes.

load_cell_complex_dataset(cfg)

Load cell complex datasets.

Parameters:

cfgDictConfig

Configuration parameters.

load_coauthorship_hypergraph_splits(data, parameters, train_prop=0.5)

Load the split generated by rand_train_test_idx function.

Parameters:

datatorch_geometric.data.Data

Graph dataset.

parametersDictConfig

Configuration parameters.

train_propfloat

Proportion of training data.

Returns:

torch_geometric.data.Data:

Graph dataset with the specified split.

load_hypergraph_content_dataset(data_dir, data_name)

Load hypergraph datasets from pickle files.

Parameters:

data_dirstr

Path to data.

data_namestr

Name of the dataset.

Returns:

torch_geometric.data.Data

Hypergraph dataset.

load_hypergraph_pickle_dataset(data_dir, data_name)

Load hypergraph datasets from pickle files.

Parameters:

data_dirstr

Path to data.

data_namestr

Name of the dataset.

Returns:

torch_geometric.data.Data

Hypergraph dataset.

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_manual_graph()

Create a manual graph for testing purposes.

Returns:

torch_geometric.data.Data

Manual graph.

load_simplicial_dataset(cfg)

Load simplicial datasets.

Parameters:

cfgDictConfig

Configuration parameters.

Returns:

torch_geometric.data.Data

Simplicial dataset.

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.

read_us_county_demos(path, year=2012, y_col='Election')

Load US County Demos dataset.

Parameters:

pathstr

Path to the dataset.

yearint, optional

Year to load the features (default: 2012).

y_colstr, optional

Column to use as label. Can be one of [‘Election’, ‘MedianIncome’, ‘MigraRate’, ‘BirthRate’, ‘DeathRate’, ‘BachelorRate’, ‘UnemploymentRate’] (default: “Election”).

Returns:

torch_geometric.data.Data

Data object of the graph for the US County Demos dataset.

select_neighborhoods_of_interest(connectivity, neighborhoods)

Select the neighborhoods of interest.

Parameters:

connectivitydict

Connectivity matrices generated by default.

neighborhoodslist

List of neighborhoods of interest.

Returns:

dict

Connectivity matrices of interest.

Submodules

• topobench.data.utils.io_utils module
  • Data
    • Data.__init__()
    • Data.connected_components()
    • Data.debug()
    • Data.edge_subgraph()
    • Data.from_dict()
    • Data.get_all_edge_attrs()
    • Data.get_all_tensor_attrs()
    • Data.is_edge_attr()
    • Data.is_node_attr()
    • Data.stores_as()
    • Data.subgraph()
    • Data.to_dict()
    • Data.to_heterogeneous()
    • Data.to_namedtuple()
    • Data.update()
    • Data.validate()
    • Data.batch
    • Data.edge_attr
    • Data.edge_index
    • Data.edge_stores
    • Data.edge_weight
    • Data.face
    • Data.node_stores
    • Data.num_edge_features
    • Data.num_edge_types
    • Data.num_faces
    • Data.num_features
    • Data.num_node_features
    • Data.num_node_types
    • Data.num_nodes
    • Data.pos
    • Data.stores
    • Data.time
    • Data.x
    • Data.y
  • SimplicialComplex
    • SimplicialComplex.__init__()
    • SimplicialComplex.add_elements_from_nx_graph()
    • SimplicialComplex.add_node()
    • SimplicialComplex.add_simplex()
    • SimplicialComplex.add_simplices_from()
    • SimplicialComplex.adjacency_matrix()
    • SimplicialComplex.clone()
    • SimplicialComplex.coadjacency_matrix()
    • SimplicialComplex.coincidence_matrix()
    • SimplicialComplex.dirac_operator_matrix()
    • SimplicialComplex.down_laplacian_matrix()
    • SimplicialComplex.from_gudhi()
    • SimplicialComplex.from_nx()
    • SimplicialComplex.from_spharapy()
    • SimplicialComplex.from_spharpy()
    • SimplicialComplex.from_trimesh()
    • SimplicialComplex.get_all_maximal_simplices()
    • SimplicialComplex.get_boundaries()
    • SimplicialComplex.get_cofaces()
    • SimplicialComplex.get_edges_from_matrix()
    • SimplicialComplex.get_maximal_simplices_of_simplex()
    • SimplicialComplex.get_node_attributes()
    • SimplicialComplex.get_simplex_attributes()
    • SimplicialComplex.get_star()
    • SimplicialComplex.graph_skeleton()
    • SimplicialComplex.hodge_laplacian_matrix()
    • SimplicialComplex.incidence_matrix()
    • SimplicialComplex.is_connected()
    • SimplicialComplex.is_maximal()
    • SimplicialComplex.is_triangular_mesh()
    • SimplicialComplex.laplace_beltrami_operator()
    • SimplicialComplex.load_mesh()
    • SimplicialComplex.normalized_laplacian_matrix()
    • SimplicialComplex.remove_maximal_simplex()
    • SimplicialComplex.remove_nodes()
    • SimplicialComplex.restrict_to_nodes()
    • SimplicialComplex.restrict_to_simplices()
    • SimplicialComplex.set_simplex_attributes()
    • SimplicialComplex.simplicial_closure_of_hypergraph()
    • SimplicialComplex.skeleton()
    • SimplicialComplex.to_cell_complex()
    • SimplicialComplex.to_combinatorial_complex()
    • SimplicialComplex.to_hasse_graph()
    • SimplicialComplex.to_hypergraph()
    • SimplicialComplex.to_spharapy()
    • SimplicialComplex.to_trimesh()
    • SimplicialComplex.up_laplacian_matrix()
    • SimplicialComplex.dim
    • SimplicialComplex.maxdim
    • SimplicialComplex.nodes
    • SimplicialComplex.shape
    • SimplicialComplex.simplices
  • coalesce()
  • download_file_from_drive()
  • download_file_from_link()
  • get_complex_connectivity()
  • get_file_id_from_url()
  • load_hypergraph_content_dataset()
  • load_hypergraph_pickle_dataset()
  • parse_qs()
  • read_ndim_manifolds()
  • read_us_county_demos()
  • urlparse()
• topobench.data.utils.split_utils module
  • DataloadDataset
    • DataloadDataset.__init__()
    • DataloadDataset.get()
    • DataloadDataset.len()
  • StratifiedKFold
    • StratifiedKFold.__init__()
    • StratifiedKFold.split()
  • assign_train_val_test_mask_to_graphs()
  • k_fold_split()
  • load_coauthorship_hypergraph_splits()
  • load_inductive_splits()
  • load_transductive_splits()
  • random_splitting()
• topobench.data.utils.utils module
  • SimplicialComplex
    • SimplicialComplex.__init__()
    • SimplicialComplex.add_elements_from_nx_graph()
    • SimplicialComplex.add_node()
    • SimplicialComplex.add_simplex()
    • SimplicialComplex.add_simplices_from()
    • SimplicialComplex.adjacency_matrix()
    • SimplicialComplex.clone()
    • SimplicialComplex.coadjacency_matrix()
    • SimplicialComplex.coincidence_matrix()
    • SimplicialComplex.dirac_operator_matrix()
    • SimplicialComplex.down_laplacian_matrix()
    • SimplicialComplex.from_gudhi()
    • SimplicialComplex.from_nx()
    • SimplicialComplex.from_spharapy()
    • SimplicialComplex.from_spharpy()
    • SimplicialComplex.from_trimesh()
    • SimplicialComplex.get_all_maximal_simplices()
    • SimplicialComplex.get_boundaries()
    • SimplicialComplex.get_cofaces()
    • SimplicialComplex.get_edges_from_matrix()
    • SimplicialComplex.get_maximal_simplices_of_simplex()
    • SimplicialComplex.get_node_attributes()
    • SimplicialComplex.get_simplex_attributes()
    • SimplicialComplex.get_star()
    • SimplicialComplex.graph_skeleton()
    • SimplicialComplex.hodge_laplacian_matrix()
    • SimplicialComplex.incidence_matrix()
    • SimplicialComplex.is_connected()
    • SimplicialComplex.is_maximal()
    • SimplicialComplex.is_triangular_mesh()
    • SimplicialComplex.laplace_beltrami_operator()
    • SimplicialComplex.load_mesh()
    • SimplicialComplex.normalized_laplacian_matrix()
    • SimplicialComplex.remove_maximal_simplex()
    • SimplicialComplex.remove_nodes()
    • SimplicialComplex.restrict_to_nodes()
    • SimplicialComplex.restrict_to_simplices()
    • SimplicialComplex.set_simplex_attributes()
    • SimplicialComplex.simplicial_closure_of_hypergraph()
    • SimplicialComplex.skeleton()
    • SimplicialComplex.to_cell_complex()
    • SimplicialComplex.to_combinatorial_complex()
    • SimplicialComplex.to_hasse_graph()
    • SimplicialComplex.to_hypergraph()
    • SimplicialComplex.to_spharapy()
    • SimplicialComplex.to_trimesh()
    • SimplicialComplex.up_laplacian_matrix()
    • SimplicialComplex.dim
    • SimplicialComplex.maxdim
    • SimplicialComplex.nodes
    • SimplicialComplex.shape
    • SimplicialComplex.simplices
  • data2simplicial()
  • ensure_serializable()
  • find_tetrahedrons()
  • find_triangles()
  • from_sparse()
  • generate_zero_sparse_connectivity()
  • get_combinatorial_complex_connectivity()
  • get_complex_connectivity()
  • get_routes_from_neighborhoods()
  • load_cell_complex_dataset()
  • load_manual_graph()
  • load_manual_graph_second_structure()
  • load_manual_hypergraph()
  • load_manual_pointcloud()
  • load_manual_points()
  • load_manual_simplicial_complex()
  • load_simplicial_dataset()
  • make_hash()
  • select_neighborhoods_of_interest()