1.數據集介紹

1.1 數據集概述

Cora數據集由機器學習論文組成，是近年來圖深度學習很喜歡使用的數據集。在數據集中，論文分為以下七類之一:

• 基于案例
• 遺傳算法
• 神經網絡
• 概率方法
• 強化學習
• 規則學習
• 理論

論文的選擇方式是，在最終語料庫中，每篇論文引用或被至少一篇其他論文引用。整個語料庫中有2708篇論文。

在詞干堵塞和去除詞尾后，只剩下1433個獨特的單詞。文檔頻率小于10的所有單詞都被刪除。

1.2 數據集組成

content文件：
content文件包含以下格式的論文描述:<paper_id> <word_attributes>+ <class_label>

每行的第一個條目包含紙張的唯一字符串標識，后跟二進制值，指示詞匯中的每個單詞在文章中是存在(由1表示)還是不存在(由0表示)。

最后，該行的最后一個條目包含論文的類別標簽。因此數據集的feature應該為2708 × 1433 維度。第一列為idx，最后一列為label。

部分數據:

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Cites文件
那個.cites文件包含語料庫的引用’圖’。每行以以下格式描述一個鏈接:<被引論文編號> <引論文編號>

每行包含兩個論文id。第一個條目是被引用論文的標識，第二個標識代表包含引用的論文。鏈接的方向是從右向左。

如果一行由“論文1 論文2”表示，則鏈接是“論文2 - >論文1”。可以通過論文之間的索引關系建立鄰接矩陣adj

部分數據：

35 1033 35 103482 35 103515 35 1050679 35 1103960 35 1103985 35 1109199 35 1112911 ......

1.2如何讀取

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) # 座標格式,取特征featurelabels = encode_onehot(idx_features_labels[:, -1]) # one-hot label# build graphidx = np.array(idx_features_labels[:, 0], dtype=np.int32) # 節點idx_map = {j: i for i, j in enumerate(idx)} # 構建節點的索引字典edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), # 導入edge的數據dtype=np.int32)edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),dtype=np.int32).reshape(edges_unordered.shape) # 將之前的轉換成字典編號后的邊adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), # 構建邊的鄰接矩陣shape=(labels.shape[0], labels.shape[0]),dtype=np.float32)# build symmetric adjacency matrix，計算轉置矩陣。將有向圖轉成無向圖adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)features = normalize(features) # 對特征做了歸一化的操作adj = normalize(adj + sp.eye(adj.shape[0])) # 對A+I歸一化# 訓練，驗證，測試的樣本idx_train = range(140)idx_val = range(200, 500)idx_test = range(500, 1500)# 將numpy的數據轉換成torch格式features = torch.FloatTensor(np.array(features.todense()))labels = torch.LongTensor(np.where(labels)[1]) # 標簽， np.where(labels)返回元組，第一個元組表示橫坐標，第二個元組表示縱坐標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)) # 矩陣行求和r_inv = np.power(rowsum, -1).flatten() # 求和的-1次方r_inv[np.isinf(r_inv)] = 0. # 如果是inf，轉換成0r_mat_inv = sp.diags(r_inv) # 構造對角戲矩陣mx = r_mat_inv.dot(mx) # 構造D-1*A，非對稱方式，簡化方式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 運行調試結果

• features：論文的屬性特征，維度$2708\times 1433$，并且做了歸一化，即每一篇論文屬性值的和為1.
• labels：每一篇論文對應的分類編號：0-6
• adj：鄰接矩陣，維度$2708\times 2708$
• idx_train：0-139
• idx_val：200-499
• idx_test：500-1499

總結

以上是生活随笔為你收集整理的GCN（一）数据集介绍的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。