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122 lines
4.9 KiB
122 lines
4.9 KiB
import numpy as np |
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import pickle as pkl |
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import networkx as nx |
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import scipy.sparse as sp |
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import sys |
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def parse_index_file(filename): |
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index = [] |
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for line in open(filename): |
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index.append(int(line.strip())) |
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return index |
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def sample_mask(idx, l): |
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"""Create mask.""" |
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mask = np.zeros(l) |
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mask[idx] = 1 |
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return np.array(mask, dtype=np.bool) |
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def load_data(dataset): |
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# load the data: x, tx, allx, graph |
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# x => 训练实例的特征向量，如scipy.sparse.csr.csr_matrix类的实例 |
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# tx => 测试实例的特征向量，如scipy.sparse.csr.csr_matrix类的实例 |
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# allx => 有标签的+无无标签训练实例的特征向量，是ind.dataset_str.x的超集 |
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# y => 训练实例的标签，独热编码，numpy.ndarray类的实例 |
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# ty => 测试实例的标签，独热编码，numpy.ndarray类的实例 |
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# ally => 有标签的+无无标签训练实例的标签，独热编码，numpy.ndarray类的实例 |
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# graph => 图数据，collections.defaultdict类的实例，格式为 {index：[index_of_neighbor_nodes]} |
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# index => 测试实例的id |
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names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] |
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objects = [] |
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for i in range(len(names)): |
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with open("data/ind.{}.{}".format(dataset, names[i]), 'rb') as f: |
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if sys.version_info > (3, 0): |
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objects.append(pkl.load(f, encoding='latin1')) |
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else: |
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objects.append(pkl.load(f)) |
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x, y, tx, ty, allx, ally, graph = tuple(objects) |
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test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset)) |
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test_idx_range = np.sort(test_idx_reorder) |
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if dataset == 'citeseer': |
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# Fix citeseer dataset (there are some isolated nodes in the graph) |
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# Find isolated nodes, add them as zero-vecs into the right position、 |
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test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) |
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tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) |
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tx_extended[test_idx_range-min(test_idx_range), :] = tx |
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tx = tx_extended |
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ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) |
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ty_extended[test_idx_range - min(test_idx_range), :] = ty |
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ty = ty_extended |
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features = sp.vstack((allx, tx)).tolil() |
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features[test_idx_reorder, :] = features[test_idx_range, :] |
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adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) |
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labels = np.vstack((ally, ty)) |
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labels[test_idx_reorder, :] = labels[test_idx_range, :] |
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idx_test = test_idx_range.tolist() |
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idx_train = range(len(y)) |
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idx_val = range(len(y), len(y) + 500) |
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train_mask = sample_mask(idx_train, labels.shape[0]) |
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val_mask = sample_mask(idx_val, labels.shape[0]) |
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test_mask = sample_mask(idx_test, labels.shape[0]) |
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y_train = np.zeros(labels.shape) |
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y_val = np.zeros(labels.shape) |
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y_test = np.zeros(labels.shape) |
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y_train[train_mask, :] = labels[train_mask, :] |
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y_val[val_mask, :] = labels[val_mask, :] |
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y_test[test_mask, :] = labels[test_mask, :] |
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return adj, features, y_test, tx, ty, test_mask, np.argmax(labels,1) |
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def load_alldata(dataset_str): |
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"""Load data.""" |
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names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] |
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objects = [] |
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for i in range(len(names)): |
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objects.append(pkl.load(open("data/ind.{}.{}".format(dataset_str, names[i])))) |
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x, y, tx, ty, allx, ally, graph = tuple(objects) |
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test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str)) |
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test_idx_range = np.sort(test_idx_reorder) |
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if dataset_str == 'citeseer': |
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# Fix citeseer dataset (there are some isolated nodes in the graph) |
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# Find isolated nodes, add them as zero-vecs into the right position |
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test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) |
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tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) |
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tx_extended[test_idx_range-min(test_idx_range), :] = tx |
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tx = tx_extended |
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ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) |
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ty_extended[test_idx_range-min(test_idx_range), :] = ty |
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ty = ty_extended |
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features = sp.vstack((allx, tx)).tolil() |
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features[test_idx_reorder, :] = features[test_idx_range, :] |
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adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) |
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labels = np.vstack((ally, ty)) |
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labels[test_idx_reorder, :] = labels[test_idx_range, :] |
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idx_test = test_idx_range.tolist() |
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idx_train = range(len(y)) |
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idx_val = range(len(y), len(y)+500) |
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train_mask = sample_mask(idx_train, labels.shape[0]) |
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val_mask = sample_mask(idx_val, labels.shape[0]) |
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test_mask = sample_mask(idx_test, labels.shape[0]) |
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y_train = np.zeros(labels.shape) |
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y_val = np.zeros(labels.shape) |
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y_test = np.zeros(labels.shape) |
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y_train[train_mask, :] = labels[train_mask, :] |
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y_val[val_mask, :] = labels[val_mask, :] |
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y_test[test_mask, :] = labels[test_mask, :] |
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return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, np.argmax(labels, 1)
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