一、问题说明
1、爬取中国、美国、巴西、印度、俄罗斯、法国、英国、土耳其、阿根廷、哥伦比亚、日本等11个国家以及中国31个省（自治区、直辖市）在2022.0101-2022.06.19的新冠疫情数据。如果对数据爬虫技术不熟悉，可使用data文件中提供的数据，其中中国各省数据为confirmedCount、curedCount、deadCount；world_confirmedCount、world_curedCount、world_deadCount数据为11个国家的爬取数据。2、根据爬取或提供的疫情数据，将最近日期（2022.06.19）确诊病例数、死亡人数、康复人数在上述11个国家、国内各地区两个维度进行可视化展示（如柱状图或者饼状图）。3、采用机器学习模型对2022.6.20-2022.6.30每一天的全国确诊人数、死亡人数、治愈人数进行预测。4、2022.6.20-2022.6.30的确诊人数、死亡人数、治愈人数结果将在2022.7.1公布，请根据真实结果，计算决定系数R2，最终以该系数作为本项目的最终得分
二、模型与算法
在模型算法方面，这次我们选择的是LSTM算法，LSTM是RNN的一个优秀的变种模型，继承了大部分RNN模型的特性，同时很利于解决本题大量数据的问题。Long ShortTerm 网络是一种RNN特殊的类型，可以学习长期依赖信息。LSTM和基线RNN并没有特别大的结构不同，但是它们用了不同的函数来计算隐状态。LSTM的“记忆”叫做细胞，可以直接把它们想做黑盒，这个黑盒的输入为前状态h和当前输入x。这些“细胞”会决定哪些之前的信息和状态需要保留/记住，而哪些要被抹去。实际的应用中发现，这种方式可以有效地保存很长时间之前的关联信息。在LSTM模型算法方面，我们使用LSTM中的重复模块则包含四个交互的层，三个Sigmoid 和一个tanh层，以一种非常特殊的方式进行交互，同时使用LSTM有通过精心设计的称作为“门”的结构来去除和增加信息到细胞状态。利用一个sigmoid神经网络层和一个pointwise乘法的非线性操作（0代表“不许任何量通过”，1就指“允许任意量通过”），从而使得网络就能了解哪些数据是需要我们去遗忘，哪些数据是需要我们去保存的，得到我们真正需要去训练的数据，即训练集，这点在死亡人数数据处理上很重要，对数据集进行反复的训练，得到我们最终的预测图以及预测结果。
三、实验设置过程
1.爬取各国各省数据
利用json将我们需要的中国、美国、巴西、印度、俄罗斯、法国、英国、土耳其、阿根廷、哥伦比亚、日本等11个国家以及中国31个省（自治区、直辖市）在2022.0101-2022.06.19的新冠疫情数据爬取下来，并将其导入我们的平台中。
1.1国内数据：
代码部分：
国内部分：
@Time : 2022/6/30 19:20
@Author : 徐以鹏
@File : 国内数据.py
import requests
from lxml import etree
import json
import pandas as pd
import numpy as np
找出请求头以及url，xpath解析
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'}
url = 'https://ncov.dxy.cn/ncovh5/view/pneumonia'
req = requests.get(url, headers=headers)
req.encoding = "utf-8"
html = etree.HTML(req.text)
req_data=html.xpath("//*[@id='getAreaStat']/text()")
print(req_data)
提取字符串
req_str = req_data[0]+''
data_str = req_str[req_str.find('[{'):req_str.find('}catch')]
data_json = json.loads(data_str) # 转化为json
存储每个省份的Json，provinceShortName存储省份简称
countryJson=[] # 以列表形式存储国家和国家对应的Json
for i in data_json:
countryJson.append([i['provinceShortName'],i['statisticsData']])此时的countryJson存储的便是要求的十一个国家以及其所对应的json
下载json
for i in countryJson:
countryName = i[0]
jsonAddress = i[1]
print(countryName,jsonAddress)
try:
r = requests.get(jsonAddress,headers=headers)
r.raise_for_status()
r.encoding = "utf-8" # 防止乱码
CountryDataJson = json.loads(r.text)
toWriteFilePath = countryName + '.json'
with open(toWriteFilePath, 'w',encoding='UTF-8') as file:
json.dump(CountryDataJson, file, ensure_ascii=False)
print(countryName + "已经下载完毕")
except:
print(countryName+" 数据下载失败！")
爬取山东的疫情数据
file = '山东.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
confirmed = pd.DataFrame([])
data_d = pd.DataFrame(data_list)
data_d.set_index('dateId',inplace=True)
data_u = data_d[data_d.index >= 20220101]
data_u = data_u[data_u.index <= 20220619]
data_u = data_u.T
data_u.drop(data_u.index, inplace=True)
confirmed = data_u.drop(index=data_u.index)
cured = data_u.drop(index=data_u.index)
dead = data_u.drop(index=data_u.index)
整合各省疫情数据
nameList=[]
for i in countryJson:
nameList.append(i[0])for i in nameList:
file = i + '.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
data_df = pd.DataFrame(data_list)
data_df.set_index('dateId',inplace = True)
data_df = data_df[['confirmedCount', 'curedCount', 'deadCount']]
data_ult = data_df[data_df.index >= 20220101]
data_ult = data_ult[data_ult.index <= 20220619]
data_ult = data_ult.replace(0, np.nan)
data_ult.bfill(inplace = True)
data_ult.to_csv('各省数据/'+i + '.csv')
data_confirmed = data_ult[['confirmedCount']].T
data_cured = data_ult[['curedCount']].T
data_dead = data_ult[['deadCount']].T
confirmed = pd.concat([confirmed,data_confirmed])
cured = pd.concat([cured,data_cured])
dead = pd.concat([dead,data_dead])confirmed.index = nameList
cured.index = nameList
dead.index = nameList
confirmed.to_csv('各省数据/'+'China_confirmeCount'+'.csv')
cured.to_csv('各省数据/'+'China_curedCount'+'.csv')
dead.to_csv('各省数据/'+'China_deadCount'+'.csv')
结果部分：
1.2国外数据：
代码部分：
@Time : 2022/6/30 9:59
@Author : 徐以鹏
@File : 国外数据.py
import requests
from lxml import etree
import json
import pandas as pd
import numpy as np
找出请求头以及url，xpath解析
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'}
url = 'https://ncov.dxy.cn/ncovh5/view/pneumonia'
req = requests.get(url, headers=headers)
req.encoding = "utf-8"
html = etree.HTML(req.text)
req_data=html.xpath("//*[@id='getListByCountryTypeService2true']/text()")
提取字符串
req_str = req_data[0]+''
data_str = req_str[req_str.find('[{'):req_str.find('}catch')]
data_json = json.loads(data_str) # 转化为json
存储每个国家的json
countryJson=[] # 以列表形式存储国家和国家对应的Json
for i in data_json:
#中国、美国、巴西、印度、俄罗斯、法国、英国、土耳其、阿根廷、哥伦比亚
if i["provinceName"] in ['中国','美国','巴西','印度','俄罗斯','英国','法国','土耳其','阿根廷','哥伦比亚','日本']:
countryJson.append([i['provinceName'],i['statisticsData']])此时的countryJson存储的便是要求的十一个国家以及其所对应的json
下载json
for i in countryJson:
countryName = i[0]
jsonAddress = i[1]
print(countryName,jsonAddress)
try:
r=requests.get(jsonAddress,headers=headers)
r.raise_for_status()
r.encoding = "utf-8" # 防止乱码
CountryDataJson = json.loads(r.text)
toWriteFilePath = ''+countryName + '.json'
with open(toWriteFilePath, 'w',encoding='UTF-8') as file:
json.dump(CountryDataJson, file, ensure_ascii=False)
print(countryName + "已经下载完毕")
except:
print(countryName+" 数据下载失败！")
爬取中国的疫情数据
file = '中国.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
confirmed = pd.DataFrame([])
data_d = pd.DataFrame(data_list)
data_d.set_index('dateId',inplace=True)
data_u = data_d[data_d.index >= 20220101]
data_u = data_u[data_u.index <= 20220619]
data_u = data_u.T
data_u.drop(data_u.index, inplace=True)
confirmed = data_u.drop(index=data_u.index)
cured = data_u.drop(index=data_u.index)
dead = data_u.drop(index=data_u.index)
整合世界疫情数据
nameList = ['中国','美国','巴西','印度','俄罗斯','英国','法国','土耳其','阿根廷','哥伦比亚','日本']
for i in nameList:
file = i + '.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
data_df = pd.DataFrame(data_list)
data_df.set_index('dateId',inplace = True)
data_df = data_df[['confirmedCount', 'curedCount', 'deadCount']]
data_ult = data_df[data_df.index >= 20220101]
data_ult = data_ult[data_ult.index <= 20220619]
data_ult = data_ult.replace(0, np.nan)
data_ult.bfill(inplace = True)
data_ult.to_csv('各国数据/'+i + '.csv')
data_confirmed = data_ult[['confirmedCount']].T
data_cured = data_ult[['curedCount']].T
data_dead = data_ult[['deadCount']].T
confirmed = pd.concat([confirmed,data_confirmed])
cured = pd.concat([cured,data_cured])
dead = pd.concat([dead,data_dead])confirmed.index = nameList
cured.index = nameList
dead.index = nameList
confirmed.to_csv('各国数据/'+'World_confirmeCount'+'.csv')
cured.to_csv('各国数据/'+'World_curedCount'+'.csv')
dead.to_csv('各国数据/'+'World_deadCount'+'.csv')
结果部分：
2.进行可视化处理
根据我们爬取下来的数据，利用pandas、numpy、matplotlib等库，将数据做一个可视化处理。
3.进行预测处理
因为这三个维度本质上都是一样的，所以说我们只需要对一个维度的数据进行处理，然后将其应用到其他的两个数据维度方面就可以，其中我们要注意一点，那就是我们得到的死亡数据中一部分出现了断层，所以说我们需要经过简单的插值处理，得到真实的需要处理的数据。最后通过我们的LSTM，对三个维度的数据进行训练以及预测，画出疫情变化趋势图，得到每一项的决定系数R2，再将这三项数据取平均值，得到我们最后的结果。
4.LSTM模型代码：
@Time : 2022/6/30 12:01
@Author : 徐以鹏
@File : 预测.py
import numpy as np
import matplotlib.pyplot as plt
import paddle
import pandas as pd
path = "/home/aistudio/work/"
Data = pd.read_csv(path + '中国.csv', index_col='dateId', parse_dates=['dateId']) # 读取文件
Data.head()
predict_name = 'confirmedCount' # 取文件中我们需要的数据
training = Datapredict_name.values # 训练的数据我们取到6月19号
test = Datapredict_name.values # 测试的数据取到6月20号
trainlist, testlist = [0], [0] # 将训练和测试的数据都存储在我们创建好的新列表中
for i in range(1, len(training)):
trainlist.append(training[i] - training[i - 1])for j in range(1, len(test)):
testlist.append(test[j] - test[j - 1])用np.array()把我们的训练和测试的数据由列表转化为数组
training = np.array(trainlist)
test = np.array(testlist)
取训练集中的最小值和最大值,分别为mintrain和maxtrain
mintrain = training.min()
maxtrain = training.max()
train_set_range = maxtrain - mintrain
def my_MinMaxScaler(data):
return (data - mintrain) / (train_set_range)
def reverse_min_max_scaler(a_num):
return a_num * train_set_range + mintrain
normalized_train_set = my_MinMaxScaler(training)
normalized_test_set = my_MinMaxScaler(test)
normalized_train_set = normalized_train_set.astype('float32')
定义MyDataset()类,定义出需要的transform函数
class MyDataset(paddle.io.Dataset):
def __init__(self, normalized_train_set):
super(MyDataset, self).__init__()
self.train_set_data_X = []
self.train_set_data_Y = []
self.transform(normalized_train_set)
def transform(self, data):
for i in range(60, len(data)):
self.train_set_data_X.append(np.array(data[i - 60:i].reshape(-1, 1)))
self.train_set_data_Y.append(np.array(data[i]))
def __getitem__(self, index):
data = self.train_set_data_X[index]
label = self.train_set_data_Y[index]
return data, label
def __len__(self):
return len(self.train_set_data_X)
dataSet = MyDataset(normalized_train_set)
trainLoader = paddle.io.DataLoader(dataSet, batch_size=200, shuffle=False)
class StockNet(paddle.nn.Layer):
def __init__(self):
super(StockNet, self).__init__()
self.lstm = paddle.nn.LSTM(input_size=1,
hidden_size=50,
num_layers=4,
dropout=0.2,
time_major=False)
self.fc = paddle.nn.Linear(in_features=50, out_features=1)
def forward(self, inputs):
outputs, final_states = self.lstm(inputs)
y = self.fc(final_states[0][3])
return y由于在训练过程中会存在的梯度消失问题，所以我们采用LSTM模型来处理我们的数据，以下为模型：
model = StockNet()
optimstic = paddle.optimizer.RMSProp(parameters=model.parameters(), learning_rate=0.01)
lossFN= paddle.nn.MSELoss()
epochs = 1000
for epoch in range(epochs):
for batch_id, data in enumerate(trainLoader()):
x_data = data[0]
y_data = data[1]
predicts = model(x_data)
loss = lossFN(predicts, y_data.reshape((-1, 1)))
loss.backward()
optimstic.step()
optimstic.clear_grad()tmpInput = np.hstack((normalized_train_set[-60:], normalized_test_set))
tmpInput = tmpInput.astype('float32')
testData = MyDataset(tmpInput)
testLoader = paddle.io.DataLoader(testData, batch_size=len(testData), drop_last=False)
model.train()
testResult = None
for batch_id, data in enumerate(testLoader()):
x_data = data[0]
predicts = model(x_data)
testResult = predicts.reshape((-1,))trainResult = None
for batch_id, data in enumerate(trainLoader()):
trainData = data[0]
trainPredicts = model(trainData)
trainResult = trainPredicts.reshape((-1,))testPredicts = reverse_min_max_scaler(testResult.detach().numpy())
trainPredicts = reverse_min_max_scaler(trainResult.detach().numpy())
realtrain_predict, realtest_predict = [], []
temptrain, temptest = Datapredict_name, Datapredict_name
for i in trainPredicts:
temptrain += i
realtrain_predict.append(temptrain)for j in testPredicts:
temptest += j
realtest_predict.append(temptest)
画出预测图
def plot_predictions(test, predicted):
plt.plot(test, color='red', label='Realvalue')
plt.plot(predicted, color='black', label='Predictedvalue')
plt.title('confirmedCount_Prediction')
plt.xlabel('Days')
plt.ylabel('People')
plt.legend()
plt.show()plot_predictions(Datapredict_name.values, realtest_predict)
计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Datapredict_name.values, realtest_predict[:11])
print(r2_score(Datapredict_name.values, realtest_predict[:11]))
四、新冠疫情可视化
1.各国家确诊人数
plt.xlabel("confirmedCount")
plt.barh(Country,last_confirmedCount2)
2.各国家治愈人数
plt.xlabel("curedCount")
plt.barh(Country,last_curedCount2)
3.各国家死亡人数
plt.xlabel("deadCount")
plt.barh(Country,last_deadCount2)
4.全国各省总确诊人数分布饼状图
plt.figure(figsize=(10,10))
plt.pie(last_confirmedCount1,radius=1.5,shadow=True,autopct='%1.1f%%')
plt.legend(Province, loc='best')
5.全国各省治愈人数
plt.figure(figsize=(10,10))
plt.pie(last_curedCount1,radius=1.5,shadow=True,autopct='%1.1f%%')
plt.legend(Province, loc='best')
五、疫情数据预测
1.2022.6.20-2022.6.30的全国确诊人数：
画出预测图
def plot_predictions(test, predicted):
plt.plot(test, color='red', label='Realvalue')
plt.plot(predicted, color='black', label='Predictedvalue')
plt.title('confirmedCount_Prediction')
plt.xlabel('Days')
plt.ylabel('People')
plt.legend()
plt.show()plot_predictions(Datapredict_name.values, realtest_predict)
计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Datapredict_name.values, realtest_predict[:11])
print(r2_score(Datapredict_name.values, realtest_predict[:11]))
r2=0.9425994713615403
2.2022.6.20-2022.6.30的全国死亡人数：
画出预测图
def plot_predictions(test, predicted):
plt.plot(test, color='red', label='Realvalue')
plt.plot(predicted, color='black', label='Predictedvalue')
plt.title('deadCount_Count_Prediction')
plt.xlabel('Days')
plt.ylabel('People')
plt.legend()
plt.show()plot_predictions(Datapredict_name.values, realtest_predict)
计算出预测结果的r2值
from sklearn.metrics import r2_score
deadCount = r2_score(Datapredict_name.values, realtest_predict[:11])
print(r2_score(Datapredict_name.values, realtest_predict[:11]))
r2=0.9741672899742679
3.2022.6.20-2022.6.30的全国治愈人数：
画出预测图
def plot_predictions(test, predicted):
plt.plot(test, color='red', label='Realvalue')
plt.plot(predicted, color='black', label='Predictedvalue')
plt.title('curedCount_Count_Prediction')
plt.xlabel('Days')
plt.ylabel('People')
plt.legend()
plt.show()plot_predictions(Datapredict_name.values, realtest_predict)
计算出预测结果的r2值
from sklearn.metrics import r2_score
curedCount = r2_score(Datapredict_name.values, realtest_predict[:11])
print(r2_score(Datapredict_name.values, realtest_predict[:11]))
r2=0.9819537632078106
4.求出三者的平均值
计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Datapredict_name.values, realtest_predict[:11])
print(r2_score(Datapredict_name.values, realtest_predict[:11]))
print((confirmedCount+curedCount+deadCount)/3)
r2=(confirmedCount+curedCount+deadCount)/3=0.96624
六、结果分析与总结
我们得到的最终的预测结果的r2值达到了0.96624，说明我们的模型拟合程度非常不错，可以准确的预测以后的确诊人数、死亡人数和治愈人数。这种结果的达成，离不开我们优秀的LSTM模型，LSTM与RNNs一样比CNN能更好地处理时间序列的任务；同时LSTM解决了RNN的长期依赖问题，并且缓解了RNN在训练时反向传播带来的“梯度消失”问题。LSTM是RNN的一个优秀的变种模型，继承了大部分RNN模型的特性，同时解决了梯度反传过程由于逐步缩减而产生的Vanishing Gradient问题。但是LSTM本身的模型结构就相对复杂，训练比起CNN来说更加耗时，对于本问题而言，LSTM模型预测准确，可以帮助我们很好的知道疫情趋势的变化。
七、代码分享
1.爬虫部分：
国内部分：
@Time : 2022/6/30 19:20
@Author : 徐以鹏
@File : 国内数据.py
import requests
from lxml import etree
import json
import pandas as pd
import numpy as np
找出请求头以及url，xpath解析
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'}
url = 'https://ncov.dxy.cn/ncovh5/view/pneumonia'
req = requests.get(url, headers=headers)
req.encoding = "utf-8"
html = etree.HTML(req.text)
req_data=html.xpath("//*[@id='getAreaStat']/text()")
print(req_data)
提取字符串
req_str = req_data[0]+''
data_str = req_str[req_str.find('[{'):req_str.find('}catch')]
data_json = json.loads(data_str) # 转化为json
存储每个省份的Json，provinceShortName存储省份简称
countryJson=[] # 以列表形式存储国家和国家对应的Json
for i in data_json:
countryJson.append([i['provinceShortName'],i['statisticsData']])此时的countryJson存储的便是要求的十一个国家以及其所对应的json
下载json
for i in countryJson:
countryName = i[0]
jsonAddress = i[1]
print(countryName,jsonAddress)
try:
r = requests.get(jsonAddress,headers=headers)
r.raise_for_status()
r.encoding = "utf-8" # 防止乱码
CountryDataJson = json.loads(r.text)
toWriteFilePath = countryName + '.json'
with open(toWriteFilePath, 'w',encoding='UTF-8') as file:
json.dump(CountryDataJson, file, ensure_ascii=False)
print(countryName + "已经下载完毕")
except:
print(countryName+" 数据下载失败！")
爬取山东的疫情数据
file = '山东.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
confirmed = pd.DataFrame([])
data_d = pd.DataFrame(data_list)
data_d.set_index('dateId',inplace=True)
data_u = data_d[data_d.index >= 20220101]
data_u = data_u[data_u.index <= 20220619]
data_u = data_u.T
data_u.drop(data_u.index, inplace=True)
confirmed = data_u.drop(index=data_u.index)
cured = data_u.drop(index=data_u.index)
dead = data_u.drop(index=data_u.index)
整合各省疫情数据
nameList=[]
for i in countryJson:
nameList.append(i[0])for i in nameList:
file = i + '.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
data_df = pd.DataFrame(data_list)
data_df.set_index('dateId',inplace = True)
data_df = data_df[['confirmedCount', 'curedCount', 'deadCount']]
data_ult = data_df[data_df.index >= 20220101]
data_ult = data_ult[data_ult.index <= 20220619]
data_ult = data_ult.replace(0, np.nan)
data_ult.bfill(inplace = True)
data_ult.to_csv('各省数据/'+i + '.csv')
data_confirmed = data_ult[['confirmedCount']].T
data_cured = data_ult[['curedCount']].T
data_dead = data_ult[['deadCount']].T
confirmed = pd.concat([confirmed,data_confirmed])
cured = pd.concat([cured,data_cured])
dead = pd.concat([dead,data_dead])confirmed.index = nameList
cured.index = nameList
dead.index = nameList
confirmed.to_csv('各省数据/'+'China_confirmeCount'+'.csv')
cured.to_csv('各省数据/'+'China_curedCount'+'.csv')
dead.to_csv('各省数据/'+'China_deadCount'+'.csv')
国外部分：
@Time : 2022/6/30 9:59
@Author : 徐以鹏
@File : 国外数据.py
import requests
from lxml import etree
import json
import pandas as pd
import numpy as np
找出请求头以及url，xpath解析
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'}
url = 'https://ncov.dxy.cn/ncovh5/view/pneumonia'
req = requests.get(url, headers=headers)
req.encoding = "utf-8"
html = etree.HTML(req.text)
req_data=html.xpath("//*[@id='getListByCountryTypeService2true']/text()")
提取字符串
req_str = req_data[0]+''
data_str = req_str[req_str.find('[{'):req_str.find('}catch')]
data_json = json.loads(data_str) # 转化为json
存储每个国家的json
countryJson=[] # 以列表形式存储国家和国家对应的Json
for i in data_json:
#中国、美国、巴西、印度、俄罗斯、法国、英国、土耳其、阿根廷、哥伦比亚
if i["provinceName"] in ['中国','美国','巴西','印度','俄罗斯','英国','法国','土耳其','阿根廷','哥伦比亚','日本']:
countryJson.append([i['provinceName'],i['statisticsData']])此时的countryJson存储的便是要求的十一个国家以及其所对应的json
下载json
for i in countryJson:
countryName = i[0]
jsonAddress = i[1]
print(countryName,jsonAddress)
try:
r=requests.get(jsonAddress,headers=headers)
r.raise_for_status()
r.encoding = "utf-8" # 防止乱码
CountryDataJson = json.loads(r.text)
toWriteFilePath = ''+countryName + '.json'
with open(toWriteFilePath, 'w',encoding='UTF-8') as file:
json.dump(CountryDataJson, file, ensure_ascii=False)
print(countryName + "已经下载完毕")
except:
print(countryName+" 数据下载失败！")
爬取中国的疫情数据
file = '中国.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
confirmed = pd.DataFrame([])
data_d = pd.DataFrame(data_list)
data_d.set_index('dateId',inplace=True)
data_u = data_d[data_d.index >= 20220101]
data_u = data_u[data_u.index <= 20220619]
data_u = data_u.T
data_u.drop(data_u.index, inplace=True)
confirmed = data_u.drop(index=data_u.index)
cured = data_u.drop(index=data_u.index)
dead = data_u.drop(index=data_u.index)
整合世界疫情数据
nameList = ['中国','美国','巴西','印度','俄罗斯','英国','法国','土耳其','阿根廷','哥伦比亚','日本']
for i in nameList:
file = i + '.json'
with open(file,'r') as f:
data = json.load(f)
data_list = data['data']
data_df = pd.DataFrame(data_list)
data_df.set_index('dateId',inplace = True)
data_df = data_df[['confirmedCount', 'curedCount', 'deadCount']]
data_ult = data_df[data_df.index >= 20220101]
data_ult = data_ult[data_ult.index <= 20220619]
data_ult = data_ult.replace(0, np.nan)
data_ult.bfill(inplace = True)
data_ult.to_csv('各国数据/'+i + '.csv')
data_confirmed = data_ult[['confirmedCount']].T
data_cured = data_ult[['curedCount']].T
data_dead = data_ult[['deadCount']].T
confirmed = pd.concat([confirmed,data_confirmed])
cured = pd.concat([cured,data_cured])
dead = pd.concat([dead,data_dead])confirmed.index = nameList
cured.index = nameList
dead.index = nameList
confirmed.to_csv('各国数据/'+'World_confirmeCount'+'.csv')
cured.to_csv('各国数据/'+'World_curedCount'+'.csv')
dead.to_csv('各国数据/'+'World_deadCount'+'.csv')
2.可视化部分
import requests
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error
import datetime
from sklearn.metrics import mean_squared_error , r2_score
from sklearn import datasets , linear_model
%matplotlib inline
Country =['China','USA','Brazil','India','Russia','Britain','France','Turkey','Argentina','Columbia','Japan']
Province =['Hubei','Zhejiang','Guangdong','Henan','Hunan','Anhui','Chongqing','Jiangxi','Shangdong','Sichuan','Jiangsu','Beijing','Shanghai','Fujian','Guangxi','Yunnan','Shanxi3','Hebei','Henan','Heilongjiang','Liaoning','Shanxi1','Tiajin','Gansu','InnerMongolia','Ningxia','Xinjiang','Jilin','Guizhou','Hongkong','Taiwan','Qinghai','Macao','Xizang']
Province = ["Shanghai","Yunnan","Neimeng","Beijing","Taiwan","Jilin","Sichuan","Tianjin","Ningxia","Anhui","Shandong","Shanxi","Guangdong","Guangxi","Xinjiang","Jiangsu","Jiangxi","Hebei","Henan","Zhejiang","Hainan","Hubei","Hunan","Macao","Gansu","Fujian","Xizang","Guizhou","Liaoning","Chongqing","Shaanxi","Qinghai","Hong Kong","Heilongjiang"]
path = "/home/aistudio/work/"
FileName = ["confirmedCount","curedCount","deadCount","world_confirmedCount","world_curedCount","world_deadCount"]
Data = []
for i in FileName:
data = pd.read_csv(path+i+".csv").loc[:,"20220101":"20220619"]
#while data.isnull().values.any():
# data = data.fillna(method='ffill',axis=1)
Data.append(np.array(data))last_confirmedCount1 = [data[-1] for data in Data[0]]
last_curedCount1 = [data[-1] for data in Data[1]]
last_deadCount1 = [data[-1] for data in Data[2]]
last_confirmedCount2 = [data[-1] for data in Data[3]]
last_curedCount2 = [data[-1] for data in Data[4]]
last_deadCount2 = [data[-1] for data in Data[5]]
plt.xlabel("confirmedCount")
plt.barh(Country,last_confirmedCount2)
plt.xlabel("curedCount")
plt.barh(Country,last_curedCount2)
plt.xlabel("deadCount")
plt.barh(Country,last_deadCount2)
plt.figure(figsize=(10,10))
plt.pie(last_confirmedCount1,radius=1.5,shadow=True,autopct='%1.1f%%')
plt.legend(Province, loc='best')
plt.figure(figsize=(10,10))
plt.pie(last_curedCount1,radius=1.5,shadow=True,autopct='%1.1f%%')
plt.legend(Province, loc='best')
3.预测部分
@Time : 2022/7/3 12:01
@Author : 是Dream呀
@File : 预测.py
import numpy as np
import matplotlib.pyplot as plt
import paddle
import pandas as pd
path = "/home/aistudio/work/"
Data = pd.read_csv(path + '中国.csv', index_col='dateId', parse_dates=['dateId']) # 读取文件
Data.head()
predict_name = 'confirmedCount' # 取文件中我们需要的数据
training = Datapredict_name.values # 训练的数据我们取到6月19号
test = Datapredict_name.values # 测试的数据取到6月20号
trainlist, testlist = [0], [0] # 将训练和测试的数据都存储在我们创建好的新列表中
for i in range(1, len(training)):
trainlist.append(training[i] - training[i - 1])for j in range(1, len(test)):
testlist.append(test[j] - test[j - 1])用np.array()把我们的训练和测试的数据由列表转化为数组
training = np.array(trainlist)
test = np.array(testlist)
取训练集中的最小值和最大值,分别为mintrain和maxtrain
mintrain = training.min()
maxtrain = training.max()
train_set_range = maxtrain - mintrain
def my_MinMaxScaler(data):
return (data - mintrain) / (train_set_range)
def reverse_min_max_scaler(a_num):
return a_num * train_set_range + mintrain
normalized_train_set = my_MinMaxScaler(training)
normalized_test_set = my_MinMaxScaler(test)
normalized_train_set = normalized_train_set.astype('float32')
定义MyDataset()类,定义出需要的transform函数
class MyDataset(paddle.io.Dataset):
def __init__(self, normalized_train_set):
super(MyDataset, self).__init__()
self.train_set_data_X = []
self.train_set_data_Y = []
self.transform(normalized_train_set)
def __len__(self):
return len(self.train_set_data_X)
def transform(self, data):
for i in range(60, len(data)):
self.train_set_data_X.append(np.array(data[i - 60:i].reshape(-1, 1)))
self.train_set_data_Y.append(np.array(data[i]))
def __getitem__(self, index):
data = self.train_set_data_X[index]
label = self.train_set_data_Y[index]
return data, labeldataSet = MyDataset(normalized_train_set)
trainLoader = paddle.io.DataLoader(dataSet, batch_size=200, shuffle=False)
class StockNet(paddle.nn.Layer):
def __init__(self):
super(StockNet, self).__init__()
self.lstm = paddle.nn.LSTM(input_size=1,hidden_size=50,num_layers=4,dropout=0.2,time_major=False)
self.fc = paddle.nn.Linear(in_features=50, out_features=1)
def forward(self, inputs):
outputs, final_states = self.lstm(inputs)
y = self.fc(final_states[0][3])
return y由于在训练过程中会存在的梯度消失问题，所以我们采用LSTM模型来处理我们的数据，以下为模型：
model = StockNet()
optimstic = paddle.optimizer.RMSProp(parameters=model.parameters(), learning_rate=0.01)
lossFN= paddle.nn.MSELoss()
epochs = 1000
for epoch in range(epochs):
for batch_id, data in enumerate(trainLoader()):
x_data = data[0]
y_data = data[1]
predicts = model(x_data)
loss = lossFN(predicts, y_data.reshape((-1, 1)))
loss.backward()
optimstic.step()
optimstic.clear_grad()tmpInput = np.hstack((normalized_train_set[-60:], normalized_test_set))
tmpInput = tmpInput.astype('float32')
testData = MyDataset(tmpInput)
testLoader = paddle.io.DataLoader(testData, batch_size=len(testData), drop_last=False)
model.train()
testResult = None
for batch_id, data in enumerate(testLoader()):
x_data = data[0]
predicts = model(x_data)
testResult = predicts.reshape((-1,))trainResult = None
for batch_id, data in enumerate(trainLoader()):
trainData = data[0]
trainPredicts = model(trainData)
trainResult = trainPredicts.reshape((-1,))testPredicts = reverse_min_max_scaler(testResult.detach().numpy())
trainPredicts = reverse_min_max_scaler(trainResult.detach().numpy())
realtrain_predict, realtest_predict = [], []
temptrain, temptest = Datapredict_name, Datapredict_name
for i in trainPredicts:
temptrain += i
realtrain_predict.append(temptrain)for j in testPredicts:
temptest += j
realtest_predict.append(temptest)
画出预测图
def plot_predictions(test, predicted):
plt.plot(test, color='red', label='Real')
plt.plot(predicted, color='red', label='Predicted')
plt.title('confirmedCount_Prediction')
plt.xlabel('Days')
plt.ylabel('People')
plt.legend()
plt.show()plot_predictions(Datapredict_name.values, realtest_predict)
计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Datapredict_name.values, realtest_predict[:11])
print(r2_score(Datapredict_name.values, realtest_predict[:11]))
print((confirmedCount+curedCount+deadCount)/3)
