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一、数据集简介
我们将使用Cora数据集。
该数据集共2708个样本点，每个样本点都是一篇科学论文，所有样本点被分为7个类别，类别分别是1）基于案例；2）遗传算法；3）神经网络；4）概率方法；5）强化学习；6）规则学习；7）理论
每篇论文都由一个1433维的词向量表示，所以，每个样本点具有1433个特征。词向量的每个元素都对应一个词，且该元素只有0或1两种取值。取0表示该元素对应的词不在论文中，取1表示在论文中。所有的词来源于一个具有1433个词的字典。
每篇论文都至少引用了一篇其他论文，或者被其他论文引用，也就是样本点之间存在联系，没有任何一个样本点与其他样本点完全没联系。如果将样本点看作图中的点，则这是一个连通的图，不存在孤立点。
数据集主要文件有两个：cora.cites, cora.content。其中，cora.content包含了2708个样本的具体信息，每行代表一个论文样本，格式为
<论文id> <由01组成的1433维特征> <论文类别（label）>
总的来说，如果将论文当作“图”的节点，则引用关系则为“图”的边，论文节点信息和引用关系共同构成了图数据。本次实验，我们将利用这些信息，对论文所属的类别进行预测，完成关于论文类别的分类任务。
二、图神经网络与图卷积神经网络简介
图神经网络（Graph Neural Networks, GNN）作为新的人工智能学习模型，可以将实际问题看作图数据中节点之间的连接和消息传播问题，对节点之间的依赖关系进行建模，挖掘传统神经网络无法分析的非欧几里得空间数据的潜在信息。在自然语言处理、计算机视觉、生物化学等领域中，图神经网络得到广泛的应用，并发挥着重要作用。
图卷积神经网络(Graph Convolutional Networks, GCN)是目前主流的图神经网络分支，分类任务则是机器学习中的常见任务。我们将利用GCN算法完成分类任务，进一步体会理解图神经网络工作的原理、GCN的构建实现过程，以及如何将GCN应用于分类任务。
三、运行效果
如下图 可见随着训练次数的增加，损失率在下降，精确度在上升，大概在200次左右收敛。
四、部分源码
主测试类代码如下
from __future__ import division from __future__ import print_function import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" import time import argparse import numpy as np from torch.utils.data import DataLoader import torch import torch.nn.functional as F import torch.optim as optim from utils import load_data, accuracy from models import GCN import matplotlib.pyplot as plt # Training settings parser = argparse.ArgumentParser() parser.add_argument('--no-cuda', action='store_true', default=False, help='Disables CUDA training.') parser.add_argument('--fastmode', action='store_true', default=False, help='Validate during training pass.') parser.add_argument('--seed', type=int, default=42, help='Random seed.') parser.add_argument('--epochs', type=int, default=300, help='Number of epochs to train.') parser.add_argument('--lr', type=float, default=0.01, help='Initial learning rate.') parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).') parser.add_argument('--hidden', type=int, default=16, help='Number of hidden units.') parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() .manual_seed(args.seed) # Load data adj, features, labels, idx_train, idx_val, idx_test = load_data() # Model and optimizer model = GCN(nfeat=features.shape[1], nhid=args.hidden, nclass=labels.max().item() + 1, dropout=args.dropout) optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) if args.cuda: model.cuda() features = features.cuda() adj = adj.cuda() labels = labels.cuda() idx_train = idx_train.cuda() idx_val = idx_val.cuda() idx_test = idx_test.cuda() Loss_list = [] accval=[] def train(epoch): t=time.time() model.train() optimizer.zero_grad() output=model(features,adj) loss_train=F.nll_loss(output[idx_train],labels[idx_train]) acc_train=accuracy(output[idx_train],labels[idx_train]) loss_train.backward() optimizer.step() if not args.fastmode: model.eval() output=model(features,adj) loss_val=F.nll_loss(output[idx_val],labels[idx_val]) acc_val=accuracy(output[idx_val],labels[idx_val]) print('Epoch:{:04d}'.format(epoch+1), 'loss_train:{:.4f}'.format(loss_train.item()), 'acc_train:{:.4f}'.format(acc_train.item()), 'loss_val:{:.4f}'.format(loss_val.item()), 'acc_val:{:.4f}'.format(acc_val.item()), 'time:{:.4f}s'.format(time.time()-t)) Loss_list.append(loss_train.item()) Accuracy_list.append(acc_train.item()) lossval.append(loss_val.item()) accval.append(acc_val.item()) def test(): model.eval() output = model(features, adj) loss_test = F.nll_loss(output[idx_test], labels[idx_test]) acc_test = accuracy(output[idx_test], labels[idx_test]) print("Test set results:", "loss= {:.4f}".format(loss_test.item()), "accuracy= {:.4f}".format(acc_test.item())) acc=acc_test.detach().numpy() loss=loss_test.detach().numpy() print(type(loss_test)) print(type(acc_test)) # 定义两个数组 # Train model t_total = time.time() for epoch in range(args.epochs): train(epoch) print("Optimization Finished!") printal time elapsed: {:.4f}s".format(time.time() - t_total)) ''' plt.plot([i for i in range(len(Loss_list))],Loss_list) pplot([i for i in range(len(Accuracy_list))],Accuracy_list) ''' plt.plot([i for i in range(len(lossval))],lossval) plot([i for i in range(len(accval))],accval) print(type(Loss_list)) print(type(Accuracy_list)) #plt.plot([i for i in range(len(Accuracy_list),Accuracy_list)]) plt.show() # Testing test()
模型类如下
import torch.nn as nn import torch.nn.functional as F from layers import GraphConvolution class GCN(nn.Module): def __init__(self, nfeat, nhid, nclass, dropout): super(GCN, self).__init__() self.gc1 = GraphConvolution(nfeat, nhid) on(nhid, nclass) self.dropout = dropout def forward(self, x, adj): x=F.relu(self.gc1(x,adj)) x=F.dropout(x,self.dropout,training=self.training) x=self.gc2(x,adj) return F.log_softmax(x,dim=1)
layer类如下
import math import torch from torch.nn.parameter import Parameter from torch.nn.modules.module import Module class GraphConvolution(Module): """ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 """ def __init__(self, in_features, out_features, bias=True): super(GraphConvolution, self).__init__() self.in_features=in_features self.out_features=out_features self.weight=Parameter(torch.FloatTensor(in_features,out_features)) if bias: self.bias=Parameter(torch.FloatTensor(out_features)) else: self.register_parameter('bias',None) self.reset_parameters() def reset_parameters(self): stdv = 1. / math.sqrt(self.weight.size(1)) self.weight.data.uniform_(-stdv, stdv) if self.bias is not None: self.bias.data.uniform_(-stdv, stdv) def forward(self, input, adj): support=torch.mm(input,self.weight) output=torch.spmm(adj,support) if self.bias is not None: return output+self.bias else: return output def __repr__(self): return self.__class__.__name__ + ' (' \ + str(self.in_features) + ' -> ' \ + str(self.out_features) + ')'
util类如下
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) labels = encode_onehot(idx_features_labels[:, -1]) # build graph idx = np.array(idx_features_labels[:, 0], dtype=np.int32) idx_map = {j: i for i, j in enumerate(idx)} edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.int32) edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape) adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32) # build symmetric adjacency matrix adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj) features = normalize(features) adj = normalize(adj + sp.eye(adj.shape[0])) idx_train = range(140) idx_val = range(200, 500) idx_test = range(500, 1500) features = torch.FloatTensor(np.array(features.todense())) labels = torch.LongTensor(np.where(labels)[1]) 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)) r_inv = np.power(rowsum, -1).flatten() r_inv[np.isinf(r_inv)] = 0. r_mat_inv = sp.diags(r_inv) mx = r_mat_inv.dot(mx) return mx de_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)
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