Source code for torch_geometric.utils.undirected

from typing import List, Optional, Tuple, Union

import torch
from torch import Tensor

from torch_geometric.utils import coalesce, sort_edge_index

from .num_nodes import maybe_num_nodes


[docs]def is_undirected( edge_index: Tensor, edge_attr: Optional[Union[Tensor, List[Tensor]]] = None, num_nodes: Optional[int] = None, ) -> bool: r"""Returns :obj:`True` if the graph given by :attr:`edge_index` is undirected. Args: edge_index (LongTensor): The edge indices. edge_attr (Tensor or List[Tensor], optional): Edge weights or multi- dimensional edge features. If given as a list, will check for equivalence in all its entries. (default: :obj:`None`) num_nodes (int, optional): The number of nodes, *i.e.* :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`) :rtype: bool Examples: >>> edge_index = torch.tensor([[0, 1, 0], ... [1, 0, 0]]) >>> weight = torch.tensor([0, 0, 1]) >>> is_undirected(edge_index, weight) True >>> weight = torch.tensor([0, 1, 1]) >>> is_undirected(edge_index, weight) False """ num_nodes = maybe_num_nodes(edge_index, num_nodes) edge_attr = [] if edge_attr is None else edge_attr edge_attr = [edge_attr] if isinstance(edge_attr, Tensor) else edge_attr edge_index1, edge_attr1 = sort_edge_index( edge_index, edge_attr, num_nodes=num_nodes, sort_by_row=True, ) edge_index2, edge_attr2 = sort_edge_index( edge_index1, edge_attr1, num_nodes=num_nodes, sort_by_row=False, ) return (bool(torch.all(edge_index1[0] == edge_index2[1])) and bool(torch.all(edge_index1[1] == edge_index2[0])) and all([ torch.all(e == e_T) for e, e_T in zip(edge_attr1, edge_attr2) ]))
[docs]def to_undirected( edge_index: Tensor, edge_attr: Optional[Union[Tensor, List[Tensor]]] = None, num_nodes: Optional[int] = None, reduce: str = "add", ) -> Union[Tensor, Tuple[Tensor, Tensor], Tuple[Tensor, List[Tensor]]]: r"""Converts the graph given by :attr:`edge_index` to an undirected graph such that :math:`(j,i) \in \mathcal{E}` for every edge :math:`(i,j) \in \mathcal{E}`. Args: edge_index (LongTensor): The edge indices. edge_attr (Tensor or List[Tensor], optional): Edge weights or multi- dimensional edge features. If given as a list, will remove duplicates for all its entries. (default: :obj:`None`) num_nodes (int, optional): The number of nodes, *i.e.* :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`) reduce (string, optional): The reduce operation to use for merging edge features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`, :obj:`"mul"`). (default: :obj:`"add"`) :rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else (:class:`LongTensor`, :obj:`Tensor` or :obj:`List[Tensor]]`) Examples: >>> edge_index = torch.tensor([[0, 1, 1], ... [1, 0, 2]]) >>> to_undirected(edge_index) tensor([[0, 1, 1, 2], [1, 0, 2, 1]]) >>> edge_index = torch.tensor([[0, 1, 1], ... [1, 0, 2]]) >>> edge_weight = torch.tensor([1., 1., 1.]) >>> to_undirected(edge_index, edge_weight) (tensor([[0, 1, 1, 2], [1, 0, 2, 1]]), tensor([2., 2., 1., 1.])) >>> # Use 'mean' operation to merge edge features >>> to_undirected(edge_index, edge_weight, reduce='mean') (tensor([[0, 1, 1, 2], [1, 0, 2, 1]]), tensor([1., 1., 1., 1.])) """ # Maintain backward compatibility to `to_undirected(edge_index, num_nodes)` if isinstance(edge_attr, int): edge_attr = None num_nodes = edge_attr row, col = edge_index row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0) edge_index = torch.stack([row, col], dim=0) if edge_attr is not None and isinstance(edge_attr, Tensor): edge_attr = torch.cat([edge_attr, edge_attr], dim=0) elif edge_attr is not None: edge_attr = [torch.cat([e, e], dim=0) for e in edge_attr] return coalesce(edge_index, edge_attr, num_nodes, reduce)