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import math
import torch
import torch_geometric
from torch_geometric.deprecation import deprecated
from torch_geometric.utils import to_undirected
@deprecated("use 'transforms.RandomLinkSplit' instead")
def train_test_split_edges(
data: 'torch_geometric.data.Data',
val_ratio: float = 0.05,
test_ratio: float = 0.1,
) -> 'torch_geometric.data.Data':
r"""Splits the edges of a :class:`torch_geometric.data.Data` object
into positive and negative train/val/test edges.
As such, it will replace the :obj:`edge_index` attribute with
:obj:`train_pos_edge_index`, :obj:`train_pos_neg_adj_mask`,
:obj:`val_pos_edge_index`, :obj:`val_neg_edge_index` and
:obj:`test_pos_edge_index` attributes.
If :obj:`data` has edge features named :obj:`edge_attr`, then
:obj:`train_pos_edge_attr`, :obj:`val_pos_edge_attr` and
:obj:`test_pos_edge_attr` will be added as well.
.. warning::
:meth:`~torch_geometric.utils.train_test_split_edges` is deprecated and
will be removed in a future release.
Use :class:`torch_geometric.transforms.RandomLinkSplit` instead.
Args:
data (Data): The data object.
val_ratio (float, optional): The ratio of positive validation edges.
(default: :obj:`0.05`)
test_ratio (float, optional): The ratio of positive test edges.
(default: :obj:`0.1`)
:rtype: :class:`torch_geometric.data.Data`
"""
assert 'batch' not in data # No batch-mode.
assert data.num_nodes is not None
assert data.edge_index is not None
num_nodes = data.num_nodes
row, col = data.edge_index
edge_attr = data.edge_attr
del data.edge_index
del data.edge_attr
# Return upper triangular portion.
mask = row < col
row, col = row[mask], col[mask]
if edge_attr is not None:
edge_attr = edge_attr[mask]
n_v = int(math.floor(val_ratio * row.size(0)))
n_t = int(math.floor(test_ratio * row.size(0)))
# Positive edges.
perm = torch.randperm(row.size(0))
row, col = row[perm], col[perm]
if edge_attr is not None:
edge_attr = edge_attr[perm]
r, c = row[:n_v], col[:n_v]
data.val_pos_edge_index = torch.stack([r, c], dim=0)
if edge_attr is not None:
data.val_pos_edge_attr = edge_attr[:n_v]
r, c = row[n_v:n_v + n_t], col[n_v:n_v + n_t]
data.test_pos_edge_index = torch.stack([r, c], dim=0)
if edge_attr is not None:
data.test_pos_edge_attr = edge_attr[n_v:n_v + n_t]
r, c = row[n_v + n_t:], col[n_v + n_t:]
data.train_pos_edge_index = torch.stack([r, c], dim=0)
if edge_attr is not None:
out = to_undirected(data.train_pos_edge_index, edge_attr[n_v + n_t:])
data.train_pos_edge_index, data.train_pos_edge_attr = out
else:
data.train_pos_edge_index = to_undirected(data.train_pos_edge_index)
# Negative edges.
neg_adj_mask = torch.ones(num_nodes, num_nodes, dtype=torch.uint8)
neg_adj_mask = neg_adj_mask.triu(diagonal=1).to(torch.bool)
neg_adj_mask[row, col] = 0
neg_row, neg_col = neg_adj_mask.nonzero(as_tuple=False).t()
perm = torch.randperm(neg_row.size(0))[:n_v + n_t]
neg_row, neg_col = neg_row[perm], neg_col[perm]
neg_adj_mask[neg_row, neg_col] = 0
data.train_neg_adj_mask = neg_adj_mask
row, col = neg_row[:n_v], neg_col[:n_v]
data.val_neg_edge_index = torch.stack([row, col], dim=0)
row, col = neg_row[n_v:n_v + n_t], neg_col[n_v:n_v + n_t]
data.test_neg_edge_index = torch.stack([row, col], dim=0)
return data
|
