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