GCN(一)数据集介绍
1.数据集介绍
1.1 数据集概述
Cora数据集由机器学习论文组成,是近年来图深度学习很喜欢使用的数据集。在数据集中,论文分为以下七类之一:
- 基于案例
- 遗传算法
- 神经网络
- 概率方法
- 强化学习
- 规则学习
- 理论
论文的选择方式是,在最终语料库中,每篇论文引用或被至少一篇其他论文引用。整个语料库中有2708篇论文。
在词干堵塞和去除词尾后,只剩下1433个独特的单词。文档频率小于10的所有单词都被删除。
1.2 数据集组成
content文件:
content文件包含以下格式的论文描述:<paper_id> <word_attributes>+ <class_label>
每行的第一个条目包含纸张的唯一字符串标识,后跟二进制值,指示词汇中的每个单词在文章中是存在(由1表示)还是不存在(由0表示)。
最后,该行的最后一个条目包含论文的类别标签。因此数据集的feature应该为2708 × 1433 维度。第一列为idx,最后一列为label。
部分数据:
31336 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Neural_Networks
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Cites文件
那个.cites文件包含语料库的引用’图’。每行以以下格式描述一个链接:<被引论文编号> <引论文编号>
每行包含两个论文id。第一个条目是被引用论文的标识,第二个标识代表包含引用的论文。链接的方向是从右向左。
如果一行由“论文1 论文2”表示,则链接是“论文2 - >论文1”。可以通过论文之间的索引关系建立邻接矩阵adj
部分数据:
35 1033
35 103482
35 103515
35 1050679
35 1103960
35 1103985
35 1109199
35 1112911
......
1.2如何读取
import numpy as np
import scipy.sparse as sp
import torchdef encode_onehot(labels):classes = set(labels)classes_dict = {c: np.identity(len(classes))[i, :] for i, c inenumerate(classes)}labels_onehot = np.array(list(map(classes_dict.get, labels)),dtype=np.int32)return labels_onehotdef load_data(path="../data/cora/", dataset="cora"):"""Load citation network dataset (cora only for now)"""print('Loading {} dataset...'.format(dataset))idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),dtype=np.dtype(str))features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32) # 座标格式,取特征featurelabels = encode_onehot(idx_features_labels[:, -1]) # one-hot label# build graphidx = np.array(idx_features_labels[:, 0], dtype=np.int32) # 节点idx_map = {j: i for i, j in enumerate(idx)} # 构建节点的索引字典edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), # 导入edge的数据dtype=np.int32)edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),dtype=np.int32).reshape(edges_unordered.shape) # 将之前的转换成字典编号后的边adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), # 构建边的邻接矩阵shape=(labels.shape[0], labels.shape[0]),dtype=np.float32)# build symmetric adjacency matrix,计算转置矩阵。将有向图转成无向图adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)features = normalize(features) # 对特征做了归一化的操作adj = normalize(adj + sp.eye(adj.shape[0])) # 对A+I归一化# 训练,验证,测试的样本idx_train = range(140)idx_val = range(200, 500)idx_test = range(500, 1500)# 将numpy的数据转换成torch格式features = torch.FloatTensor(np.array(features.todense()))labels = torch.LongTensor(np.where(labels)[1]) # 标签, np.where(labels)返回元组,第一个元组表示横坐标,第二个元组表示纵坐标adj = sparse_mx_to_torch_sparse_tensor(adj)idx_train = torch.LongTensor(idx_train)idx_val = torch.LongTensor(idx_val)idx_test = torch.LongTensor(idx_test)return adj, features, labels, idx_train, idx_val, idx_testdef normalize(mx):"""Row-normalize sparse matrix"""rowsum = np.array(mx.sum(1)) # 矩阵行求和r_inv = np.power(rowsum, -1).flatten() # 求和的-1次方r_inv[np.isinf(r_inv)] = 0. # 如果是inf,转换成0r_mat_inv = sp.diags(r_inv) # 构造对角戏矩阵mx = r_mat_inv.dot(mx) # 构造D-1*A,非对称方式,简化方式return mxdef accuracy(output, labels):preds = output.max(1)[1].type_as(labels)correct = preds.eq(labels).double()correct = correct.sum()return correct / len(labels)def sparse_mx_to_torch_sparse_tensor(sparse_mx):"""Convert a scipy sparse matrix to a torch sparse tensor."""sparse_mx = sparse_mx.tocoo().astype(np.float32)indices = torch.from_numpy(np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))values = torch.from_numpy(sparse_mx.data)shape = torch.Size(sparse_mx.shape)return torch.sparse.FloatTensor(indices, values, shape)
1.3 运行调试结果
- features:论文的属性特征,维度2708×14332708 \times 14332708×1433,并且做了归一化,即每一篇论文属性值的和为1.
- labels:每一篇论文对应的分类编号:0-6
- adj:邻接矩阵,维度2708×27082708 \times 27082708×2708
- idx_train:0-139
- idx_val:200-499
- idx_test:500-1499
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