图神经网络（一）—GraphSAGE-pytorch版本代码详解

GraphSAGE代码详解-pytorch版本1. GraphSAGE导入2. 代码解析2.1 加载数据2.2 Unsupervised Loss2.3 Models2.4 评估与模型使用2.5 Main参考资料1. GraphSAGE导入论文标题：Inductive Representation Learning on Large Graphs作者：William L. Hamilton, Re

Arvin Ou

13384人浏览 · 2021-05-16 15:10:26

Arvin Ou · 2021-05-16 15:10:26 发布

GraphSAGE代码详解-pytorch版本

写在前面…
在研究生的工作中使用到了图神经网络，所以平时会看一些与图神经网络相关的论文和代码。写这个系列的目的是为了帮助自己再理一遍算法的基本思想和流程，如果同时也能对其他人提供帮助是极好的~博主也是在学习过程中，有些地方有误还请大家批评指正！

github: https://github.com/OuYangg/GNNs

1. GraphSAGE基本介绍

论文标题：Inductive Representation Learning on Large Graphs
作者：William L. Hamilton, Rex Ying and Jure Leskovec

在GraphSAGE之前提出的图神经网络方法，如GCN，都属于transductive模型，此类模型在当网络的结构稍微出现一点改变，就需要重新训练，无法满足实时快速产生网络节点嵌入的需求。为了解决这一问题，Jure大佬等人提出了一个infuctive模型，那就是GraphSAGE。GraphSAGE的目标是训练多个aggregator以聚合目标节点不同阶的邻居节点信息，从而可以快速生成未知节点的低维向量表示。
GraphSAGE的基本流程见下图：

1）首先通过随机游走获得固定大小的邻域网络 2）然后通过aggregator把有限阶邻居节点的特征聚合给目标节点，伪代码如下

由上面的伪代码可见，GraphSAGE的输入为：目标网络 $G$ 、节点的特征向量 $x_v$ 、权重矩阵 $W^k$ 、非线性激活函数 $\sigma$ 、aggregator函数以及邻居函数 $N$ . 1）首先 $h_0$ 为节点的特征向量，循环 $K$ 步 2）遍历每个节点，对于每个节点首先聚合邻居节点 $k - 1$ 时刻的特征，然后将聚合的结果与当前节点 $k - 1$ 时刻的特征进行concat并经过一个激活函数 3）当循环完K步之后进行一个令 $h_k$ 除以 $h_k||_2$ 得到节点的低维表示。
Aggregators： 从上面的流程图和伪代码可见，GraphSAGE需要用到aggregators，那这个aggregator是什么呢？起到什么作用呢？其实aggregator的作用就是将目标节点的邻居信息进行一个聚合，作者在文中给出了3种不同的aggregators分别是：
1）Mean aggregator： 该策略是将邻居节点与目标节点特征向量的值取平均

2）LSTM aggregator： 利用LSTM来聚合邻居节点的信息。
3）Pooling aggregator： 在使用pooling聚合器的时候，每个邻居节点的特征逐一的经过一个全连接层，从而进行池化操作

这里的max是一个element-wise的max.

2. 代码解析

代码参考地址：graphSAGE-pytorch
导入所需的库

import numpy as np
import pandas as pd
import os,sys
import argparse
import torch 
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random
import math
from sklearn.utils import shuffle
from sklearn.metrics import f1_score
from collections import defaultdict

2.1 加载数据

本代码使用的数据cora共包含两个文件，分别是cora_content和cora_cite. 数据详细描述可参考链接：Cora数据集描述

class DataCenter(object):
    """加载数据集
	Parameter:
		file_paths:{数据文件存放地址1,数据文件存放地址2}
	"""
    def __init__(self,file_paths):
        """file_paths:{name:root,...,}"""
        super(DataCenter,self).__init__()
        self.file_paths = file_paths
    
    def load_Dataset(self,dataset='cora'):
        """读取存放在指定路径的数据集"""
        feat_list = [] # 用于存放每个节点特征向量的列表
        label_list = [] # 用于存放每个节点对应类别的列表
        node_map = {} # 将节点进行重新编码
        label_map = {} # 将label映射为数字
        
        if dataset == 'cora':
            content = self.file_paths['cora_content'] # 获取cora_content的地址
            cite = self.file_paths['cora_cite'] # 获取cora_cite的地址
            with open(content) as f1:
                for i,each_sample in enumerate(f1.readlines()): # 遍历每个样本的特征
                    sample_clean = each_sample.strip().split()
                    # 提取每个样本的特征，其中第一个元素和最后一个元素是样本名称和对应的标签
                    feat_list.append(sample_clean[1:-1])
                    # 把节点名称映射为节点编号 
                    node_map[sample_clean[0]]=i
                    label = sample_clean[-1]
                    if label not in label_map.keys():
                    	# 把label转化为数字
                        label_map[label] = len(label_map)
                    label_list.append(label_map[label])
                feat_list = np.asarray(feat_list,dtype=np.float64)
                label_list = np.asarray(label_list,dtype=np.int64)
            
            # 获得每个节点的邻居{v0:[v0的邻居集合],v1:[v1的邻居集合]}
            adj_lists = defaultdict(set)
            with open(cite) as f2:
                for j,each_pair in enumerate(f2.readlines()):
                    pair = each_pair.strip().split()
                    assert len(pair) == 2
                    adj_lists[node_map[pair[0]]].add(node_map[pair[1]])
                    adj_lists[node_map[pair[1]]].add(node_map[pair[0]])
            
            assert len(feat_list) == len(label_list) == len(adj_lists)
            train_index,test_index,val_index = self._split_data(feat_list.shape[0])
            # 使用getattr()可以获得数据
            setattr(self,dataset+'_test',test_index)
            setattr(self,dataset+'_val',val_index)
            setattr(self,dataset+'_train',train_index)
            setattr(self,dataset+'_feats',feat_list)
            setattr(self,dataset+'_labels',label_list)
            setattr(self,dataset+'_adj_lists',adj_lists)
        
    def _split_data(self,number_of_nodes,test_split=3,val_split=6):
    	"""获得训练集、验证集和测试集"""
    	# 打乱顺序
        rand_indices = np.random.permutation(number_of_nodes)
        test_size = number_of_nodes // test_split
        val_size = number_of_nodes // val_split
        test_index = rand_indices[:test_size]
        val_index = rand_indices[test_size:test_size+val_size]
        train_index = rand_indices[test_size+val_size:]
        return train_index,test_index,val_index

2.2 Unsupervised Loss

GraphSAGE定义的Loss函数如下所示， $J_G(z_u)=-log(\sigma(z_u^Tz_v))-QE_{v_n ~ P_n(v)}log(\sigma(-z_u^Tz_{v_n}))$
其中， $Q$ 为负样本数量，前一项是根据正样本计算的Loss，后一项是根据负样本计算的Loss.

class UnsupervisedLoss(object):
	"""docstring for UnsupervisedLoss"""
	def __init__(self, adj_lists, train_nodes, device):
        """初始化参数"""
		super(UnsupervisedLoss, self).__init__()
		self.Q = 10 # 负样本的数量
		self.N_WALKS = 6 # 每个节点随机游走的次数
		self.WALK_LEN = 1 # 每次随机游走的步长
		self.N_WALK_LEN = 5 # 每次负样本随机游走几个节点
		self.MARGIN = 3 
		self.adj_lists = adj_lists #{v0:[v0的邻居集合],v1:[v1的邻居集合],...,vn:[vn的邻居集合]}
		self.train_nodes = train_nodes # 训练节点
		self.device = device # cpu or gpu

		self.target_nodes = None
		self.positive_pairs = [] # 存放正例样本 [(v0,v0邻居中采样到的正例节点),....,]
		self.negtive_pairs = [] # 存放负例样本 [(v0,v0邻居中采样到的负例节点),....,]
		self.node_positive_pairs = {} # {v0:[(v0,从v0开始随机游走采样到的正例节点),(v0,从v0开始随机游走采样到的正例节点)],...,vn:[(vn,从vn开始随机游走采样到的正例节点)]}
		self.node_negtive_pairs = {} # {v0:[(v0,从v0开始随机游走采样到的负例节点),(v0,从v0开始随机游走采样到的负例节点)],...,vn:[(vn,从vn开始随机游走采样到的负例节点)]}
		self.unique_nodes_batch = [] # 一个batch所有会用到的节点及其邻居节点

	def get_loss_sage(self, embeddings, nodes):
        """根据论文里的公式计算损失函数"""
		assert len(embeddings) == len(self.unique_nodes_batch) #判断是不是每个节点都有了embeddings
		assert False not in [nodes[i]==self.unique_nodes_batch[i] for i in range(len(nodes))] # 判断目标节点集和unique集里的节点是否1一一对应
		node2index = {n:i for i,n in enumerate(self.unique_nodes_batch)} # 把节点重新编码

		nodes_score = []
		assert len(self.node_positive_pairs) == len(self.node_negtive_pairs) # 确定正例节点对和负例节点对的数量是否相同
		for node in self.node_positive_pairs: # 遍历所有节点
			pps = self.node_positive_pairs[node] # 获得对应的正例 [(v0,v0正例样本1),(v0,v0正例样本2),...,(v0,v0正例样本n)]
			nps = self.node_negtive_pairs[node] # 获得每个节点对应的负例 [(v0,v0负例样本1),(v0,v0负例样本2),...,(v0,v0负例样本n)]
			if len(pps) == 0 or len(nps) == 0: # 判断是否都有正例和负例
				continue

			# Q * Exception(negative score)计算负例样本的Loss，即Loss函数的后一项
			indexs = [list(x) for x in zip(*nps)] # [[源节点,...,源节点],[采样得到的负节点1,...,采样得到的负节点n]]
			node_indexs = [node2index[x] for x in indexs[0]] # 获得源节点的编号
			neighb_indexs = [node2index[x] for x in indexs[1]] # 负样本节点的编号
			neg_score = F.cosine_similarity(embeddings[node_indexs], embeddings[neighb_indexs]) # 计算余弦相似性
			neg_score = self.Q*torch.mean(torch.log(torch.sigmoid(-neg_score)), 0) # 计算损失的后一项
			#print(neg_score)

			# multiple positive score 计算正列样本的Loss，即Loss函数的前一项
			indexs = [list(x) for x in zip(*pps)]
			node_indexs = [node2index[x] for x in indexs[0]]
			neighb_indexs = [node2index[x] for x in indexs[1]]
			pos_score = F.cosine_similarity(embeddings[node_indexs], embeddings[neighb_indexs])
			pos_score = torch.log(torch.sigmoid(pos_score)) # 计算损失的前一项
			#print(pos_score)

			nodes_score.append(torch.mean(- pos_score - neg_score).view(1,-1)) # 把每个节点的损失加入到列表中
				
		loss = torch.mean(torch.cat(nodes_score, 0)) # 求平均
		
		return loss

	def get_loss_margin(self, embeddings, nodes):
		assert len(embeddings) == len(self.unique_nodes_batch)
		assert False not in [nodes[i]==self.unique_nodes_batch[i] for i in range(len(nodes))]
		node2index = {n:i for i,n in enumerate(self.unique_nodes_batch)}

		nodes_score = []
		assert len(self.node_positive_pairs) == len(self.node_negtive_pairs)
		for node in self.node_positive_pairs:
			pps = self.node_positive_pairs[node]
			nps = self.node_negtive_pairs[node]
			if len(pps) == 0 or len(nps) == 0:
				continue

			indexs = [list(x) for x in zip(*pps)]
			node_indexs = [node2index[x] for x in indexs[0]]
			neighb_indexs = [node2index[x] for x in indexs[1]]
			pos_score = F.cosine_similarity(embeddings[node_indexs], embeddings[neighb_indexs])
			pos_score, _ = torch.min(torch.log(torch.sigmoid(pos_score)), 0)

			indexs = [list(x) for x in zip(*nps)]
			node_indexs = [node2index[x] for x in indexs[0]]
			neighb_indexs = [node2index[x] for x in indexs[1]]
			neg_score = F.cosine_similarity(embeddings[node_indexs], embeddings[neighb_indexs])
			neg_score, _ = torch.max(torch.log(torch.sigmoid(neg_score)), 0)

			nodes_score.append(torch.max(torch.tensor(0.0).to(self.device), neg_score-pos_score+self.MARGIN).view(1,-1))
			# nodes_score.append((-pos_score - neg_score).view(1,-1))

		loss = torch.mean(torch.cat(nodes_score, 0),0)

		# loss = -torch.log(torch.sigmoid(pos_score))-4*torch.log(torch.sigmoid(-neg_score))
		
		return loss


	def extend_nodes(self, nodes, num_neg=6):
		"""获得目标节点集的正样本和负样本，输出这些节点的集合"""
		self.positive_pairs = []
		self.node_positive_pairs = {}
		self.negtive_pairs = []
		self.node_negtive_pairs = {}

		self.target_nodes = nodes
		self.get_positive_nodes(nodes)
		# print(self.positive_pairs)
		self.get_negtive_nodes(nodes, num_neg) 
		# print(self.negtive_pairs)
		self.unique_nodes_batch = list(set([i for x in self.positive_pairs for i in x]) | set([i for x in self.negtive_pairs for i in x]))
		assert set(self.target_nodes) < set(self.unique_nodes_batch)
		return self.unique_nodes_batch

	def get_positive_nodes(self, nodes):
		return self._run_random_walks(nodes) # 通过随机游走获得正列样本

	def get_negtive_nodes(self, nodes, num_neg):
        """
        生成负样本，也就是让目标节点与目标节点相隔很远的节点组成一个负例
        """
		for node in nodes: # 遍历每个节点
			neighbors = set([node])
			frontier = set([node])
			for i in range(self.N_WALK_LEN):
				current = set() 
				for outer in frontier:
					current |= self.adj_lists[int(outer)] #获取frontier中所有的邻居节点
				frontier = current - neighbors #去除源节点
				neighbors |= current # 源节点+邻居节点
			far_nodes = set(self.train_nodes) - neighbors # 减去train_nodes里源节点及其一阶邻居
			neg_samples = random.sample(far_nodes, num_neg) if num_neg < len(far_nodes) else far_nodes # 从二阶邻居开始采样
			self.negtive_pairs.extend([(node, neg_node) for neg_node in neg_samples])
			self.node_negtive_pairs[node] = [(node, neg_node) for neg_node in neg_samples]
		return self.negtive_pairs

	def _run_random_walks(self, nodes):
		for node in nodes: # 遍历每个节点
			if len(self.adj_lists[int(node)]) == 0: # 若该节点没有邻居节点则跳过
				continue
			cur_pairs = [] # 创建一个
			for i in range(self.N_WALKS): # 每个节点会有N_WALKS次的随机游走
				curr_node = node # 
				for j in range(self.WALK_LEN): # 每次随机游走走WALK_LEN的长度
					neighs = self.adj_lists[int(curr_node)]
					next_node = random.choice(list(neighs))
					# self co-occurrences are useless
					if next_node != node and next_node in self.train_nodes:
						self.positive_pairs.append((node,next_node))
						cur_pairs.append((node,next_node))
					curr_node = next_node

			self.node_positive_pairs[node] = cur_pairs
		return self.positive_pairs

2.3 Models

Classification model

class Classification(nn.Module):
    """一个最简单的一层分类模型
    Parameters:
        input_size:输入维度
        num_classes:类别数量
    return:
        logists:最大概率对应的标签
    """
    def __init__(self,input_size,num_classes):
        super(Classification,self).__init__()
        self.fc1 = nn.Linear(input_size,num_classes) # 定义一个input_size*num_classes的线性层
        self.init_params() # 初始化权重参数
        
    def init_params(self):
        for param in self.parameters():
            if len(param.size()) == 2: # 如果参数是矩阵的话就重新初始化
                nn.init.xavier_uniform_(param) 
    
    def forward(self,x):
        logists = torch.log_softmax(self.fc1(x),1) # 利用log_softmax来获得最终输出的类别
        return logists

GraphSAGE

class SageLayer(nn.Module):
	"""
	一层SageLayer
	"""
	def __init__(self, input_size, out_size, gcn=False): 
		super(SageLayer, self).__init__()
		self.input_size = input_size
		self.out_size = out_size
		self.gcn = gcn
		self.weight = nn.Parameter(torch.FloatTensor(out_size, self.input_size if self.gcn else 2 * self.input_size)) #初始化权重参数w*input.T
		self.init_params() # 调整权重参数分布

	def init_params(self):
		for param in self.parameters():
			nn.init.xavier_uniform_(param)

	def forward(self, self_feats, aggregate_feats, neighs=None):
		"""
		Parameters:
			self_feats:源节点的特征向量
			aggregate_feats:聚合后的邻居节点特征
		"""
		if not self.gcn: # 如果不是gcn的话就要进行concatenate
			combined = torch.cat([self_feats, aggregate_feats], dim=1)
		else:
			combined = aggregate_feats
		combined = F.relu(self.weight.mm(combined.t())).t()
		return combined

class GraphSage(nn.Module):
	"""定义一个GraphSage模型"""
	def __init__(self, num_layers, input_size, out_size, raw_features, adj_lists, device, gcn=False, agg_func='MEAN'):
		super(GraphSage, self).__init__()
		self.input_size = input_size 
		self.out_size = out_size
		self.num_layers = num_layers # Graphsage的层数
		self.gcn = gcn
		self.device = device
		self.agg_func = agg_func
		self.raw_features = raw_features
		self.adj_lists = adj_lists
		# 定义每一层的输入和输出
		for index in range(1, num_layers+1):
			layer_size = out_size if index != 1 else input_size
			setattr(self, 'sage_layer'+str(index), SageLayer(layer_size, out_size, gcn=self.gcn))#除了第1层的输入为input_size,其余层的输入和输出均为outsize

	def forward(self, nodes_batch):
		"""
		为一批节点生成嵌入表示
		Parameters:
			nodes_batch:目标批次的节点
		"""
		lower_layer_nodes = list(nodes_batch) # 初始化第一层节点
		nodes_batch_layers = [(lower_layer_nodes,)] # 存放每一层的节点信息
		for i in range(self.num_layers):
			lower_samp_neighs, lower_layer_nodes_dict, lower_layer_nodes= self._get_unique_neighs_list(lower_layer_nodes) # 根据当前层节点获得下一层节点
			nodes_batch_layers.insert(0, (lower_layer_nodes, lower_samp_neighs, lower_layer_nodes_dict))

		assert len(nodes_batch_layers) == self.num_layers + 1

		pre_hidden_embs = self.raw_features # 初始化h0
		for index in range(1, self.num_layers+1):
			nb = nodes_batch_layers[index][0]  #所有邻居节点
			pre_neighs = nodes_batch_layers[index-1] # 上一层的邻居节点
			aggregate_feats = self.aggregate(nb, pre_hidden_embs, pre_neighs)
			sage_layer = getattr(self, 'sage_layer'+str(index))
			if index > 1:
				nb = self._nodes_map(nb, pre_hidden_embs, pre_neighs)
			# self.dc.logger.info('sage_layer.')
			cur_hidden_embs = sage_layer(self_feats=pre_hidden_embs[nb],
										aggregate_feats=aggregate_feats)
			pre_hidden_embs = cur_hidden_embs

		return pre_hidden_embs

	def _nodes_map(self, nodes, hidden_embs, neighs):
		layer_nodes, samp_neighs, layer_nodes_dict = neighs
		assert len(samp_neighs) == len(nodes)
		index = [layer_nodes_dict[x] for x in nodes]
		return index

	def _get_unique_neighs_list(self, nodes, num_sample=10):
		_set = set 
		to_neighs = [self.adj_lists[int(node)] for node in nodes] # 获取目标节点集的所有邻居节点[[v0的邻居],[v1的邻居],[v2的邻居]]
		if not num_sample is None: # 如果num_sample为实数的话
			_sample = random.sample  
			samp_neighs = [_set(_sample(to_neigh, num_sample)) if len(to_neigh) >= num_sample else to_neigh for to_neigh in to_neighs] # [set(随机采样的邻居集合),set(),set()]
            # 遍历所有邻居集合如果邻居节点数>=num_sample，就从邻居节点集中随机采样num_sample个邻居节点，否则直接把邻居节点集放进去
		else:
			samp_neighs = to_neighs 
		samp_neighs = [samp_neigh | set([nodes[i]]) for i, samp_neigh in enumerate(samp_neighs)] # 把源节点也放进去
		_unique_nodes_list = list(set.union(*samp_neighs)) #展平
		i = list(range(len(_unique_nodes_list))) # 重新编号
		unique_nodes = dict(list(zip(_unique_nodes_list, i)))
		return samp_neighs, unique_nodes, _unique_nodes_list

	def aggregate(self, nodes, pre_hidden_embs, pre_neighs, num_sample=10):
        """聚合邻居节点信息
        Parameters:
            nodes:从最外层开始的节点集合
            pre_hidden_embs:上一层的节点嵌入
            pre_neighs:上一层的节点
        """
		unique_nodes_list, samp_neighs, unique_nodes = pre_neighs # 上一层的源节点，...,....,
		assert len(nodes) == len(samp_neighs) 
		indicator = [(nodes[i] in samp_neighs[i]) for i in range(len(samp_neighs))] # 判断每个节点是否出现在邻居节点中
		assert (False not in indicator)
		if not self.gcn:
			# 如果不适用gcn就要把源节点去除
			samp_neighs = [(samp_neighs[i]-set([nodes[i]])) for i in range(len(samp_neighs))]
		if len(pre_hidden_embs) == len(unique_nodes):
			embed_matrix = pre_hidden_embs
		else:
			embed_matrix = pre_hidden_embs[torch.LongTensor(unique_nodes_list)]
		# self.dc.logger.info('3')
		mask = torch.zeros(len(samp_neighs), len(unique_nodes))
		column_indices = [unique_nodes[n] for samp_neigh in samp_neighs for n in samp_neigh]
		row_indices = [i for i in range(len(samp_neighs)) for j in range(len(samp_neighs[i]))]
		mask[row_indices, column_indices] = 1 # 每个源节点为一行，一行元素中1对应的就是邻居节点的位置

		if self.agg_func == 'MEAN':
			num_neigh = mask.sum(1, keepdim=True) # 计算每个源节点有多少个邻居节点
			mask = mask.div(num_neigh).to(embed_matrix.device) # 
			aggregate_feats = mask.mm(embed_matrix)

		elif self.agg_func == 'MAX':
			# print(mask)
			indexs = [x.nonzero() for x in mask==1]
			aggregate_feats = []
			for feat in [embed_matrix[x.squeeze()] for x in indexs]:
				if len(feat.size()) == 1:
					aggregate_feats.append(feat.view(1, -1))
				else:
					aggregate_feats.append(torch.max(feat,0)[0].view(1, -1))
			aggregate_feats = torch.cat(aggregate_feats, 0)
		return aggregate_feats

2.4 评估与模型使用

def evaluate(dataCenter, ds, graphSage, classification, device, max_vali_f1, name, cur_epoch):
    """
    测试模型的性能
    Parameters:
    	datacenter:创建好的datacenter对像
    	ds:数据集的名称
    	graphSage：训练好的graphSage对像
    	classification:训练好的classificator
    	
    """
	test_nodes = getattr(dataCenter, ds+'_test') # 获得测试集
	val_nodes = getattr(dataCenter, ds+'_val') # 获得验证集
	labels = getattr(dataCenter, ds+'_labels') # 获得标签

	models = [graphSage, classification] 

	params = [] # 将两个模型的参数存入一个列表中
	for model in models:
		for param in model.parameters():
			if param.requires_grad:
				param.requires_grad = False
				params.append(param)

	embs = graphSage(val_nodes)
	logists = classification(embs)
	_, predicts = torch.max(logists, 1)
	labels_val = labels[val_nodes]
	assert len(labels_val) == len(predicts)
	comps = zip(labels_val, predicts.data)

	vali_f1 = f1_score(labels_val, predicts.cpu().data, average="micro")
	print("Validation F1:", vali_f1)

	if vali_f1 > max_vali_f1:
		max_vali_f1 = vali_f1
		embs = graphSage(test_nodes)
		logists = classification(embs)
		_, predicts = torch.max(logists, 1)
		labels_test = labels[test_nodes]
		assert len(labels_test) == len(predicts)
		comps = zip(labels_test, predicts.data)

		test_f1 = f1_score(labels_test, predicts.cpu().data, average="micro")
		print("Test F1:", test_f1)

		for param in params:
			param.requires_grad = True

		torch.save(models, './model_best_{}_ep{}_{:.4f}.torch'.format(name, cur_epoch, test_f1))

	for param in params:
		param.requires_grad = True

	return max_vali_f1

def get_gnn_embeddings(gnn_model, dataCenter, ds):
	"""使用GraphSage获得节点的嵌入表示"""
    print('Loading embeddings from trained GraphSAGE model.')
    features = np.zeros((len(getattr(dataCenter, ds+'_labels')), gnn_model.out_size))
    nodes = np.arange(len(getattr(dataCenter, ds+'_labels'))).tolist()
    b_sz = 500
    batches = math.ceil(len(nodes) / b_sz)
    embs = []
    for index in range(batches):
        nodes_batch = nodes[index*b_sz:(index+1)*b_sz]
        embs_batch = gnn_model(nodes_batch)
        assert len(embs_batch) == len(nodes_batch)
        embs.append(embs_batch)
        # if ((index+1)*b_sz) % 10000 == 0:
        #     print(f'Dealed Nodes [{(index+1)*b_sz}/{len(nodes)}]')

    assert len(embs) == batches
    embs = torch.cat(embs, 0)
    assert len(embs) == len(nodes)
    print('Embeddings loaded.')
    return embs.detach()

def train_classification(dataCenter, graphSage, classification, ds, device, max_vali_f1, name, epochs=800):
	"""训练分类器"""
	print('Training Classification ...')
	c_optimizer = torch.optim.SGD(classification.parameters(), lr=0.5)
	# train classification, detached from the current graph
	#classification.init_params()
	b_sz = 50
	train_nodes = getattr(dataCenter, ds+'_train')
	labels = getattr(dataCenter, ds+'_labels')
	features = get_gnn_embeddings(graphSage, dataCenter, ds)
	for epoch in range(epochs):
		train_nodes = shuffle(train_nodes)
		batches = math.ceil(len(train_nodes) / b_sz)
		visited_nodes = set()
		for index in range(batches):
			nodes_batch = train_nodes[index*b_sz:(index+1)*b_sz]
			visited_nodes |= set(nodes_batch)
			labels_batch = labels[nodes_batch]
			embs_batch = features[nodes_batch]

			logists = classification(embs_batch)
			loss = -torch.sum(logists[range(logists.size(0)), labels_batch], 0)
			loss /= len(nodes_batch)
			# print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Dealed Nodes [{}/{}] '.format(epoch+1, epochs, index, batches, loss.item(), len(visited_nodes), len(train_nodes)))

			loss.backward()
			
			nn.utils.clip_grad_norm_(classification.parameters(), 5)
			c_optimizer.step()
			c_optimizer.zero_grad()

		max_vali_f1 = evaluate(dataCenter, ds, graphSage, classification, device, max_vali_f1, name, epoch)
	return classification, max_vali_f1

def apply_model(dataCenter, ds, graphSage, classification, unsupervised_loss, b_sz, unsup_loss, device, learn_method):
	test_nodes = getattr(dataCenter, ds+'_test')
	val_nodes = getattr(dataCenter, ds+'_val')
	train_nodes = getattr(dataCenter, ds+'_train')
	labels = getattr(dataCenter, ds+'_labels')

	if unsup_loss == 'margin':
		num_neg = 6
	elif unsup_loss == 'normal':
		num_neg = 100
	else:
		print("unsup_loss can be only 'margin' or 'normal'.")
		sys.exit(1)

	train_nodes = shuffle(train_nodes)

	models = [graphSage, classification]
	params = []
	for model in models:
		for param in model.parameters():
			if param.requires_grad:
				params.append(param)

	optimizer = torch.optim.SGD(params, lr=0.7)
	optimizer.zero_grad()
	for model in models:
		model.zero_grad()

	batches = math.ceil(len(train_nodes) / b_sz)

	visited_nodes = set()
	for index in range(batches):
		nodes_batch = train_nodes[index*b_sz:(index+1)*b_sz]

		# extend nodes batch for unspervised learning
		# no conflicts with supervised learning
		nodes_batch = np.asarray(list(unsupervised_loss.extend_nodes(nodes_batch, num_neg=num_neg)))
		visited_nodes |= set(nodes_batch)

		# get ground-truth for the nodes batch
		labels_batch = labels[nodes_batch]

		# feed nodes batch to the graphSAGE
		# returning the nodes embeddings
		embs_batch = graphSage(nodes_batch)

		if learn_method == 'sup':
			# superivsed learning
			logists = classification(embs_batch)
			loss_sup = -torch.sum(logists[range(logists.size(0)), labels_batch], 0)
			loss_sup /= len(nodes_batch)
			loss = loss_sup
		elif learn_method == 'plus_unsup':
			# superivsed learning
			logists = classification(embs_batch)
			loss_sup = -torch.sum(logists[range(logists.size(0)), labels_batch], 0)
			loss_sup /= len(nodes_batch)
			# unsuperivsed learning
			if unsup_loss == 'margin':
				loss_net = unsupervised_loss.get_loss_margin(embs_batch, nodes_batch)
			elif unsup_loss == 'normal':
				loss_net = unsupervised_loss.get_loss_sage(embs_batch, nodes_batch)
			loss = loss_sup + loss_net
		else:
			if unsup_loss == 'margin':
				loss_net = unsupervised_loss.get_loss_margin(embs_batch, nodes_batch)
			elif unsup_loss == 'normal':
				loss_net = unsupervised_loss.get_loss_sage(embs_batch, nodes_batch)
			loss = loss_net

		print('Step [{}/{}], Loss: {:.4f}, Dealed Nodes [{}/{}] '.format(index+1, batches, loss.item(), len(visited_nodes), len(train_nodes)))
		loss.backward()
		for model in models:
			nn.utils.clip_grad_norm_(model.parameters(), 5)
		optimizer.step()

		optimizer.zero_grad()
		for model in models:
			model.zero_grad()

	return graphSage, classification

2.5 Main

file_paths = {'cora_content':'./cora.content','cora_cite':'./cora.cites'}
datacenter  = DataCenter(file_paths)
datacenter.load_Dataset()
feature_data = torch.FloatTensor(getattr(datacenter, 'cora'+'_feats'))
label_data = torch.from_numpy(getattr(datacenter,'cora'+'_labels')).long()
adj_lists = getattr(datacenter,'cora'+'_adj_lists')
random.seed(824)
np.random.seed(824)
torch.manual_seed(824)
torch.cuda.manual_seed_all(824)
learn_method = 'sup'
ds = 'cora'
epochs = 50
max_vali_f1=0
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
graphSage = GraphSage(2, feature_data.size(1), 128, feature_data, getattr(datacenter, ds+'_adj_lists'), device, gcn='store_true', agg_func='MEAN')
num_labels = len(set(getattr(datacenter, ds+'_labels')))
classification = Classification(128, num_labels)
unsupervised_loss = UnsupervisedLoss(getattr(datacenter, ds+'_adj_lists'), getattr(datacenter, ds+'_train'), device)
if learn_method == 'sup':
    print('GraphSage with Supervised Learning')
elif learn_method == 'plus_unsup':
    print('GraphSage with Supervised Learning plus Net Unsupervised Learning')
else:
    print('GraphSage with Net Unsupervised Learning')

for epoch in range(epochs):
    print('----------------------EPOCH %d-----------------------' % epoch)
    graphSage, classification = apply_model(datacenter, ds, graphSage, classification, unsupervised_loss, 20, 'normal', device, learn_method)
	if (epoch+1) % 2 == 0 and learn_method == 'unsup':
    	classification, max_vali_f1 = train_classification(datacenter, graphSage, classification, ds, device,max_vali_f1, 'debug')
if learn_method != 'unsup':
		max_vali_f1 = evaluate(datacenter, ds, graphSage, classification, device, max_vali_f1 , 'debug', epoch)

输出结果如下：

参考资料

[1] Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs[J]. arXiv preprint arXiv:1706.02216, 2017.
[2] https://github.com/twjiang/graphSAGE-pytorch