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import dgl
from dgl.data import CiteseerGraphDataset
import torch
import pickle
from copy import deepcopy
import scipy.sparse as sp
import numpy as np
import os
def mask_test_edges(adj_orig, val_frac, test_frac):
# Remove diagonal elements
adj = deepcopy(adj_orig)
# set diag as all zero
adj.setdiag(0)
adj.eliminate_zeros()
# Check that diag is zero:
# assert np.diag(adj.todense()).sum() == 0
adj_triu = sp.triu(adj, 1)
edges = sparse_to_tuple(adj_triu)[0]
num_test = int(np.floor(edges.shape[0] * test_frac))
num_val = int(np.floor(edges.shape[0] * val_frac))
all_edge_idx = list(range(edges.shape[0]))
np.random.shuffle(all_edge_idx)
val_edge_idx = all_edge_idx[:num_val]
test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
test_edges = edges[test_edge_idx]
val_edges = edges[val_edge_idx]
train_edges = edges[all_edge_idx[num_val + num_test :]]
noedge_mask = np.ones(adj.shape) - adj
noedges = np.asarray(sp.triu(noedge_mask, 1).nonzero()).T
all_edge_idx = list(range(noedges.shape[0]))
np.random.shuffle(all_edge_idx)
val_edge_idx = all_edge_idx[:num_val]
test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
test_edges_false = noedges[test_edge_idx]
val_edges_false = noedges[val_edge_idx]
data = np.ones(train_edges.shape[0])
adj_train = sp.csr_matrix(
(data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape
)
adj_train = adj_train + adj_train.T
train_mask = np.ones(adj_train.shape)
for edges_tmp in [val_edges, val_edges_false, test_edges, test_edges_false]:
for e in edges_tmp:
assert e[0] < e[1]
train_mask[edges_tmp.T[0], edges_tmp.T[1]] = 0
train_mask[edges_tmp.T[1], edges_tmp.T[0]] = 0
train_edges = np.asarray(sp.triu(adj_train, 1).nonzero()).T
train_edges_false = np.asarray(
(sp.triu(train_mask, 1) - sp.triu(adj_train, 1)).nonzero()
).T
# NOTE: all these edge lists only contain single direction of edge!
return (
train_edges,
train_edges_false,
val_edges,
val_edges_false,
test_edges,
test_edges_false,
)
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
sparse_mx = sparse_mx.tocoo()
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
values = sparse_mx.data
shape = sparse_mx.shape
return coords, values, shape
if __name__ == "__main__":
os.mkdir("links")
os.mkdir("pretrain_labels")
g = CiteseerGraphDataset()[0]
total_pos_edges = torch.randperm(g.num_edges())
adj_train = g.adjacency_matrix(scipy_fmt="csr")
(
train_edges,
train_edges_false,
val_edges,
val_edges_false,
test_edges,
test_edges_false,
) = mask_test_edges(adj_train, 0.1, 0.2)
tvt_edges_file = "links/citeseer_tvtEdges.pkl"
pickle.dump(
(
train_edges,
train_edges_false,
val_edges,
val_edges_false,
test_edges,
test_edges_false,
),
open(tvt_edges_file, "wb"),
)
node_assignment = dgl.metis_partition_assignment(g, 10)
torch.save(node_assignment, "pretrain_labels/metis_label_citeseer.pt") |