GCN（一）数据集介绍

骑猪看日落 2022-09-09 09:47 214阅读 0赞

# 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:

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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 torch
    
    
    def encode_onehot(labels):
        classes = set(labels)
        classes_dict = { c: np.identity(len(classes))[i, :] for i, c in
                        enumerate(classes)}
        labels_onehot = np.array(list(map(classes_dict.get, labels)),
                                 dtype=np.int32)
        return labels_onehot
    
    
    def 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feature
        labels = encode_onehot(idx_features_labels[:, -1])  # one-hot label
    
        # build graph
        idx = 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_test
    
    
    def 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0
        r_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 mx
    
    
    def 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 × 1433 2708 \\times 1433 2708×1433，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 × 2708 2708 \\times 2708 2708×2708
 *  idx\_train：0-139
 *  idx\_val：200-499
 *  idx\_test：500-1499