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一、背景
极端天气情况一直困扰着人们的工作和生活。部分企业或者工种对极端天气的要求不同，但是目前主流的天气推荐系统是直接将天气信息推送给全部用户。这意味着重要的天气信息在用户手上得不到筛选，降低用户的满意度，甚至导致用户的经济损失。我们计划开发一个基于图神经网络的天气靶向模型，根据用户的历史交互行为，判断不同天气对他的利害程度。如果有必要，则将该极端天气情况推送给该用户，让其有时间做好应对准备。该模型能够减少不必要的信息传递，提高用户的体验感。
二、模型介绍
四、模型介绍
（一）数据集共有三个txt文件，分别是user.txt，weather.txt，rating.txt。这些文件一共包含900名用户，1600个天气状况，95964条用户的历史交互记录。
- user.txt
用户的信息记录在user.txt中。格式如下：
用户ID\t年龄\t性别\t职业\t地理位置
- weather.txt
天气的信息记录在weather.txt中。格式如下：
天气ID\t天气类型\t温度\t湿度\t风速
- rating.txt
用户的历史交互记录在rating.txt中。格式如下：
用户ID\t天气ID\t评分
三、项目结构
如下图 data里面存放了数据集
四、运行结果
开始训练 可以看到第一行显示了一些训练的基本配置内容 包括用的设备cpu 训练批次 学习率等等
可以看出随着训练次数的增加 损失率在不断降低
最后会自动选出一个最佳的测试和训练集的损失值
结果可视化如下
五、代码
部分源码如下
train类
import pandas as pd import time from utils import fix_seed_torch, draw_loss_pic import argparse from model import GCN from Logger import Logger from mydataset import MyDataset import torch from torch.nn import MSELoss from torch.optim import Adam from torch.utils.data import DataLoader, random_split import sys import os os.environ['KMP_DUPLICATE_LIB_OK']='TRUE' # 固定随机数种子 fix_seed_torch(seed=2021) # 设置训练的超参数 parser = argparse.ArgumentParser() parser.add_argument('--gcn_layers', type=int, default=2, help='the number of gcn layers') parser.add_argument('--n_epochs', type=int, default=20, help='the number of epochs') parser.add_argument('--embedSize', type=int, default=64, help='dimension of user and entity embeddings') parser.add_argument('--batch_size', type=int, default=1024, help='batch size') parser.add_argument('--lr', type=float, default=0.001, help='learning rate') parser.add_argument('--ratio', type=float, default=0.8, help='size of training dataset') args = parser.parse_args() # 设备是否支持cuda device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") args.device = device # 读取用户特征、天气特征、评分 user_feature = pd.read_csv('./data/user.txt', encoding='utf-8', sep='\t') item_feature = pd.read_csv('./data/weather.txt', encoding='utf-8', sep='\t') rating = pd.read_csv('./data/rating.txt', encoding='utf-8', sep='\t') # 构建数据集 dataset = MyDataset(rating) trainLen = int(args.ratio * len(dataset)) train, test = random_split(dataset, [trainLen, len(dataset) - trainLen]) train_loader = DataLoader(train, batch_size=args.batch_size, shuffle=True, pin_memory=True) test_loader = DataLoader(test, batch_size=len(test)) # 记录训练的超参数 start_time = '{}'.format(time.strftime("%m-%d-%H-%M", time.localtime())) logger = Logger('./log/log-{}.txt'.format(start_time)) logger.info(' '.join('%s: %s' % (k, str(v)) for k, v in sorted(dict(vars(args)).items()))) # 定义模型 model = GCN(args, user_feature, item_feature, rating) model.to(device) # 定义优化器 optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=0.001) # 定义损失函数 loss_function = MSELoss() train_result = [] test_result = [] # 最好的epoch best_loss = sys.float_info.max # 训练 for i in range(args.n_epochs): model.train() for batch in train_loader: optimizer.zero_grad() prediction=model(batch[0].to(device),batch[1].to(device)) train_loss=torch.sqrt(loss_function(batch[2].float().to(device),prediction)) train_loss.backward() optimizer.step() train_result.append(train_loss.item()) model.eval() for data in test_loader: prediction=model(data[0].to(device),data[1].to(device)) test_loss=torch.sqrt(loss_function(data[2].float().to(device),prediction)) test_loss=test_loss.item() if best_loss>test_loss: best_loss=test_loss torch.save(model.state_dict(),'./model/bestModeParms-{}.pth'.format(start_time)) test_result.append(test_loss) logger.info("Epoch{:d}:trainLoss{:.4f},testLoss{:.4f}".format(i,train_loss,test_loss)) else: model.load_state_dict(torch.load("./model/bestModeParms-11-18-19-47.pth")) user_id=input("请输入用户id") item_num=rating['itemId'].max()+1 u=torch.tensor([int(user_id)for i in range(item_num)],dtype=float) 气ID".format(user_id)) print(i[0]for i in result) # 画图 draw_loss_pic(train_result, test_result)
Logger类
import sys import os import logging class Logger(object): def __init__(self, filename): self.logger = logging.getLogger(filename) self.logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s: %(message)s', datefmt='%Y-%m-%d %H-%M-%S') # write into file fh = logging.FileHandler(filename) fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) # show on console ch = logging.StreamHandler(sys.stdout) ch.setLevel(logging.DEBUG) ch.setFormatter(formatter) # add to Handler self.logger.addHandler(fh) self.logger.addHandler(ch) def _flush(self): for handler in self.logger.handlers: handler.flush() def info(self, message): self.logger.info(message) self._flush()
model类
import numpy as np import torch.nn import torch.nn as nn from utils import * from torch.nn import Module import scipy.sparse as sp class GCN_Layer(Module): def __init__(self,inF,outF): super(GCN_Layer,self).__init__() self.W1=torch.nn.Linear(in_features=inF,out_features=outF) self.W2=torch.nn.Linear(in_features=inF,out_features=outF) def forward(self,graph,selfLoop,features): part1=self.W1(torch.sparse.mm(graph+selfLoop,features)) part2 = self.W2(torch.mul(torch.sparse.mm(graph,features),features)) return nn.LeakyReLU()(part1+part2) ###################### # 请你补充代码 # ###################### class GCN(Module): def __init__(self, args, user_feature, item_feature, rating): super(GCN, self).__init__() self.args = args self.device = args.device self.user_feature = user_feature self.item_feature = item_feature self.rating = rating self.num_user = rating['user_id'].max() + 1 self.num_item = rating['item_id'].max() + 1 # user embedding self.user_id_embedding = nn.Embedding(user_feature['id'].max() + 1, 32) self.user_age_embedding = nn.Embedding(user_feature['age'].max() + 1, 4) self.user_gender_embedding = nn.Embedding(user_feature['gender'].max() + 1, 2) self.user_occupation_embedding = nn.Embedding(user_feature['occupation'].max() + 1, 8) self.user_location_embedding = nn.Embedding(user_feature['location'].max() + 1, 18) # item embedding self.item_id_embedding = nn.Embedding(item_feature['id'].max() + 1, 32) self.item_type_embedding = nn.Embedding(item_feature['type'].max() + 1, 8) self.item_temperature_embedding = nn.Embedding(item_feature['temperature'].max() + 1, 8) self.item_humidity_embedding = nn.Embedding(item_feature['humidity'].max() + 1, 8) self.item_windSpeed_embedding = nn.Embedding(item_feature['windSpeed'].max() + 1, 8) # 自循环 self.selfLoop = self.getSelfLoop(self.num_user + self.num_item) # 堆叠GCN层 self.GCN_Layers = torch.nn.ModuleList() for _ in range(self.args.gcn_layers): self.GCN_Layers.append(GCN_Layer(self.args.embedSize, self.args.embedSize)) self.graph = self.buildGraph() self.transForm = nn.Linear(in_features=self.args.embedSize * (self.args.gcn_layers + 1), out_features=self.args.embedSize) def getSelfLoop(self, num): i = torch.LongTensor( [[k for k in range(0, num)], [j for j in range(0, num)]]) val = torch.FloatTensor([1] * num) return torch.sparse.FloatTensor(i, val).to(self.device) def buildGraph(self): rating=self.rating.values graph=sp.coo_matrix( (rating[:,2],(rating[:,0],rating[:,1])),shape=(self.num_user,self.num_item)).tocsr() graph=sp.bmat([[sp.csr_matrix((graph.shape[0],graph.shape[0])),graph], [graph.T,sp.csr_matrix((graph.shape[1],graph.shape[1]))]]) row_sum_sqrt=sp.diags(1/(np.sqrt(graph.sum(axis=1).A.ravel())+1e-8)) col_sum_sqrt = sp.diags(1 / (np.sqrt(graph.sum(axis=0).A.ravel()) + 1e-8)) graph=row_sum_sqrt@graph@col_sum_sqrt graph=graph.tocoo() values=graph.data indices=np.vstack((graph.row,graph.col)) graph=torch.sparse.FloatTensor(torch.LongTensor(indices),torch.FloatTensor(values),torch.Size(graph.shape)) return graph.to(self.device) ###################### # 请你补充代码 # ###################### def getFeature(self): # 根据用户特征获取对应的embedding user_id = self.user_id_embedding(torch.tensor(self.user_feature['id']).to(self.device)) age = self.user_age_embedding(torch.tensor(self.user_feature['age']).to(self.device)) gender = self.user_gender_embedding(torch.tensor(self.user_feature['gender']).to(self.device)) occupation = self.user_occupation_embedding(torch.tensor(self.user_feature['occupation']).to(self.device)) location = self.user_location_embedding(torch.tensor(self.user_feature['location']).to(self.device)) user_emb = torch.cat((user_id, age, gender, occupation, location), dim=1) # 根据天气特征获取对应的embedding item_id = self.item_id_embedding(torch.tensor(self.item_feature['id']).to(self.device)) item_type = self.item_type_embedding(torch.tensor(self.item_feature['type']).to(self.device)) temperature = self.item_temperature_embedding(torch.tensor(self.item_feature['temperature']).to(self.device)) humidity = self.item_humidity_embedding(torch.tensor(self.item_feature['humidity']).to(self.device)) windSpeed = self.item_windSpeed_embedding(torch.tensor(self.item_feature['windSpeed']).to(self.device)) item_emb = torch.cat((item_id, item_type, temperature, humidity, windSpeed), dim=1) # 拼接到一起 concat_emb = torch.cat([user_emb, item_emb], dim=0) return concat_emb.to(self.device) def forward(self, users, items): features=self.getFeature() final_emb=features.clone() for GCN_Layer in self.GCN_Layers: features=GCN_Layer(self.graph,self.selfLoop,features) final_emb=torch.cat((final_emb,features.clone()),dim=1) user_emb,item_emb=torch.split(final_emb,[self.num_user,self.num_item]) user_emb=user_emb[users] item_emb=item_emb[items] user_emb=self.transForm(user_emb) item_emb=self.transForm(item_emb) prediction=torch.mul(user_emb,item_emb).sum(1) return prediction ###################### # 请你补充代码 # ######################
mydataset类
from torch.utils.data import Dataset import pandas as pd class MyDataset(Dataset): def __init__(self, rating): super(Dataset, self).__init__() self.user = rating['user_id'] self.weather = rating['item_id'] self.rating = rating['rating'] def __len__(self): return len(self.rating) def __getitem__(self, item): return self.user[item], self.weather[item], self.rating[item]
utils类
from torch.utils.data import Dataset import pandas as pd class MyDataset(Dataset): def __init__(self, rating): super(Dataset, self).__init__() self.user = rating['user_id'] self.weather = rating['item_id'] self.rating = rating['rating'] def __len__(self): return len(self.rating) def __getitem__(self, item): return self.user[item], self.weather[item], self.rating[item]
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