pareto-citeseer / link_gen.py
SauravMaheshkar's picture
feat: add link generation script
46a85c5 verified
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")