Source code for

import sys
import os.path as osp
from itertools import repeat

import torch
from torch_sparse import coalesce
from import Data
from import read_txt_array
from torch_geometric.utils import remove_self_loops

    import cPickle as pickle
except ImportError:
    import pickle

[docs]def read_planetoid_data(folder, prefix): names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index'] items = [read_file(folder, prefix, name) for name in names] x, tx, allx, y, ty, ally, graph, test_index = items train_index = torch.arange(y.size(0), dtype=torch.long) val_index = torch.arange(y.size(0), y.size(0) + 500, dtype=torch.long) sorted_test_index = test_index.sort()[0] if prefix.lower() == 'citeseer': # There are some isolated nodes in the Citeseer graph, resulting in # none consecutive test indices. We need to identify them and add them # as zero vectors to `tx` and `ty`. len_test_indices = (test_index.max() - test_index.min()).item() + 1 tx_ext = torch.zeros(len_test_indices, tx.size(1)) tx_ext[sorted_test_index - test_index.min(), :] = tx ty_ext = torch.zeros(len_test_indices, ty.size(1)) ty_ext[sorted_test_index - test_index.min(), :] = ty tx, ty = tx_ext, ty_ext x =[allx, tx], dim=0) y =[ally, ty], dim=0).max(dim=1)[1] x[test_index] = x[sorted_test_index] y[test_index] = y[sorted_test_index] train_mask = index_to_mask(train_index, size=y.size(0)) val_mask = index_to_mask(val_index, size=y.size(0)) test_mask = index_to_mask(test_index, size=y.size(0)) edge_index = edge_index_from_dict(graph, num_nodes=y.size(0)) data = Data(x=x, edge_index=edge_index, y=y) data.train_mask = train_mask data.val_mask = val_mask data.test_mask = test_mask return data
def read_file(folder, prefix, name): path = osp.join(folder, 'ind.{}.{}'.format(prefix.lower(), name)) if name == 'test.index': return read_txt_array(path, dtype=torch.long) with open(path, 'rb') as f: if sys.version_info > (3, 0): out = pickle.load(f, encoding='latin1') else: out = pickle.load(f) if name == 'graph': return out out = out.todense() if hasattr(out, 'todense') else out out = torch.Tensor(out) return out def edge_index_from_dict(graph_dict, num_nodes=None): row, col = [], [] for key, value in graph_dict.items(): row += repeat(key, len(value)) col += value edge_index = torch.stack([torch.tensor(row), torch.tensor(col)], dim=0) # NOTE: There are duplicated edges and self loops in the datasets. Other # implementations do not remove them! edge_index, _ = remove_self_loops(edge_index) edge_index, _ = coalesce(edge_index, None, num_nodes, num_nodes) return edge_index def index_to_mask(index, size): mask = torch.zeros((size, ), dtype=torch.bool) mask[index] = 1 return mask