基于Pytorch全连接神经网络优化器Adam实现天气温度的回归预测仿真源码+数据（课设）.zip

#处理数据 import numpy as np #天气样本数据读取 import pandas as pd #处理时间数据 import datetime #可视化工具 import matplotlib.pyplot as plt #数据预处理工具 from sklearn import preprocessing #神经网络搭建 import torch import torch.optim as optim #忽略警告消息 import warnings warnings.filterwarnings("ignore") #-----------------------------------1、导入天气温度数据样本---------------------------------- features = pd.read_csv('temps.csv') features.head() print('数据维度before：',features.shape) years = features['year'] months = features['month'] days = features['day'] #把年月日转换为str类型，再转换为datetime类格式 zip（）函数用于并行遍历 dates = [str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month,day in zip(years,months,days)] dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in dates] #----------------------------------2、数据可视化绘图------------------------------------ #指定绘图风格 plt.style.use('fivethirtyeight') #绘制4个图（最大值、前天、昨天、朋友预测） ax1 ax2为第一行两个子图，ax3 ax4为第二行两个子图 底部日期标签旋转45° fig,((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2,ncols=2,figsize=(10,10)) fig.autofmt_xdate(rotation = 45) #标签值 当天真实温度最高温度 横轴日期 竖轴气温 ax1.plot(dates,features['actual']) ax1.set_xlabel('');ax1.set_ylabel('Temperature');ax1.set_title('Max Temp') #昨天的最高温度值 ax2.plot(dates,features['temp_1']) ax2.set_xlabel('');ax2.set_ylabel('Temparature');ax2.set_title('Previous Max Temp') #前天的最高温度 ax3.plot(dates,features['temp_2']) ax3.set_xlabel('Date');ax3.set_ylabel('Temparature');ax3.set_title('Two Days Prior Max Temp') #朋友预测 ax4.plot(dates,features['friend']) ax4.set_xlabel('Date');ax4.set_ylabel('Temparature');ax4.set_title('Friend Esitimate') #调整子图内边距 plt.tight_layout(pad=2) plt.show() #--------------------------------------3、数据预处理--------------------------------- #独热编码 features = pd.get_dummies(features) features.head(5) #将特征值和标签分开 labels = np.array(features['actual']) features = features.drop('actual',axis = 1) feature_list = list(features.columns) features = np.array(features) print('数据维度new：',features.shape) # 特征值的Z-score归一化 使其均值为0 方差为1 input_features = preprocessing.StandardScaler().fit_transform(features) #--------------------------------------4、搭建网络模型--------------------------------- #构建网络模型 input_size = input_features.shape[1] hidden_size = 128 output_size = 1 batch_size = 16 my_nn = torch.nn.Sequential( torch.nn.Linear(input_size,hidden_size), torch.nn.Sigmoid(), torch.nn.Linear(hidden_size,output_size), ) cost = torch.nn.MSELoss(reduction = 'mean') optimizer = torch.optim.Adam(my_nn.parameters(),lr=0.001) #训练网络 losses = [] for i in range(1000): batch_loss = [] #使用MINI-Batch方法来训练 for start in range(0,len(input_features),batch_size): end = start + batch_size if start + batch_size < len(input_features) else len(input_features) xx = torch.tensor(input_features[start:end],dtype = torch.float,requires_grad = True) yy = torch.tensor(labels[start:end],dtype = torch.float,requires_grad = True) prediction = my_nn(xx) loss = cost(prediction,yy) optimizer.zero_grad() loss.backward(retain_graph=True) optimizer.step() batch_loss.append(loss.data.numpy()) if i % 100 == 0: losses.append(np.mean(batch_loss)) print(i,np.mean(batch_loss)) #--------------------------------------5、预测天气并可视化--------------------------------- #预测训练结果 x = torch.tensor(input_features,dtype = torch.float) predict = my_nn(x).data.numpy() months = features[:,feature_list.index('month')] days = features[:,feature_list.index('day')] years = features[:,feature_list.index('year')] test_dates = [str(int(year))+'-'+str(int(month))+'-'+str(int(day)) for year,month,day in zip(years,months,days)] test_dates = [datetime.datetime.strptime(date,'%Y-%m-%d') for date in test_dates] #预测天气温度表 predictions_data = pd.DataFrame(data = {'date':test_dates,'prediction':predict.reshape(-1)}) #实际天气温度表 true_data = pd.DataFrame(data={'date':dates,'actual':labels}) #绘制‘预测’和‘实际’的温度变化折线图 plt.plot(predictions_data['date'],predictions_data['prediction'],'ro',label = 'prediction') plt.plot(true_data['date'],true_data['actual'],'b-',label = 'actual') plt.xticks(rotation = '60') plt.legend() plt.xlabel('Date');plt.ylabel('Maximum Temparature(F)' );plt.title('Actual and Predicted Values'); plt.show()