1.數(shù)據(jù)集介紹

1.1 數(shù)據(jù)集概述

Cora數(shù)據(jù)集由機(jī)器學(xué)習(xí)論文組成，是近年來(lái)圖深度學(xué)習(xí)很喜歡使用的數(shù)據(jù)集。在數(shù)據(jù)集中，論文分為以下七類之一:

• 基于案例
• 遺傳算法
• 神經(jīng)網(wǎng)絡(luò)
• 概率方法
• 強(qiáng)化學(xué)習(xí)
• 規(guī)則學(xué)習(xí)
• 理論

論文的選擇方式是，在最終語(yǔ)料庫(kù)中，每篇論文引用或被至少一篇其他論文引用。整個(gè)語(yǔ)料庫(kù)中有2708篇論文。

在詞干堵塞和去除詞尾后，只剩下1433個(gè)獨(dú)特的單詞。文檔頻率小于10的所有單詞都被刪除。

1.2 數(shù)據(jù)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(gè)條目包含紙張的唯一字符串標(biāo)識(shí)，后跟二進(jìn)制值，指示詞匯中的每個(gè)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(gè)條目包含論文的類別標(biāo)簽。因此數(shù)據(jù)集的feature應(yīng)該為2708 × 1433 維度。第一列為idx，最后一列為label。

部分?jǐn)?shù)據(jù):

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Cites文件
那個(gè).cites文件包含語(yǔ)料庫(kù)的引用’圖’。每行以以下格式描述一個(gè)鏈接:<被引論文編號(hào)> <引論文編號(hào)>

每行包含兩個(gè)論文id。第一個(gè)條目是被引用論文的標(biāo)識(shí)，第二個(gè)標(biāo)識(shí)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(guò)論文之間的索引關(guān)系建立鄰接矩陣adj

部分?jǐn)?shù)據(jù)：

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(biāo)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(jié)點(diǎn)idx_map = {j: i for i, j in enumerate(idx)} # 構(gòu)建節(jié)點(diǎn)的索引字典edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), # 導(dǎo)入edge的數(shù)據(jù)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(zhuǎn)換成字典編號(hào)后的邊adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), # 構(gòu)建邊的鄰接矩陣shape=(labels.shape[0], labels.shape[0]),dtype=np.float32)# build symmetric adjacency matrix，計(jì)算轉(zhuǎn)置矩陣。將有向圖轉(zhuǎn)成無(wú)向圖adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)features = normalize(features) # 對(duì)特征做了歸一化的操作adj = normalize(adj + sp.eye(adj.shape[0])) # 對(duì)A+I歸一化# 訓(xùn)練，驗(yàn)證，測(cè)試的樣本idx_train = range(140)idx_val = range(200, 500)idx_test = range(500, 1500)# 將numpy的數(shù)據(jù)轉(zhuǎn)換成torch格式features = torch.FloatTensor(np.array(features.todense()))labels = torch.LongTensor(np.where(labels)[1]) # 標(biāo)簽， np.where(labels)返回元組，第一個(gè)元組表示橫坐標(biāo)，第二個(gè)元組表示縱坐標(biāo)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(zhuǎn)換成0r_mat_inv = sp.diags(r_inv) # 構(gòu)造對(duì)角戲矩陣mx = r_mat_inv.dot(mx) # 構(gòu)造D-1*A，非對(duì)稱方式，簡(jiǎn)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(yùn)行調(diào)試結(jié)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(duì)應(yīng)的分類編號(hào)：0-6
• adj：鄰接矩陣，維度$2708\times 2708$
• idx_train：0-139
• idx_val：200-499
• idx_test：500-1499

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