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原力计划机器学习如何做到疫情可视化——疫情数据分析与预测实战

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Hello,大家好我叫是Dream呀,一个有趣的Python博主,多多关照

 

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:sparkles::sparkles::sparkles:

 

前言:
本文将带领大家爬取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。并且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,做出疫情可视化图形并且求出最终的相关系数R2!

 

本文目录:

 

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 = Data[predict_name][:'20220619'].values  # 训练的数据我们取到6月19号
test = Data[predict_name]['20220620':].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 = Data[predict_name]['20220101'], Data[predict_name]['20220620']
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(Data[predict_name]['20220620':].values, realtest_predict)
# 计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Data[predict_name]['20220620':].values, realtest_predict[:11])
print(r2_score(Data[predict_name]['20220620':].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(Data[predict_name]['20220620':].values, realtest_predict)
# 计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Data[predict_name]['20220620':].values, realtest_predict[:11])
print(r2_score(Data[predict_name]['20220620':].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(Data[predict_name]['20220620':].values, realtest_predict)
# 计算出预测结果的r2值
from sklearn.metrics import r2_score
deadCount = r2_score(Data[predict_name]['20220620':].values, realtest_predict[:11])
print(r2_score(Data[predict_name]['20220620':].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(Data[predict_name]['20220620':].values, realtest_predict)
# 计算出预测结果的r2值
from sklearn.metrics import r2_score
curedCount = r2_score(Data[predict_name]['20220620':].values, realtest_predict[:11])
print(r2_score(Data[predict_name]['20220620':].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(Data[predict_name]['20220620':].values, realtest_predict[:11])
print(r2_score(Data[predict_name]['20220620':].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 = Data[predict_name][:'20220619'].values  # 训练的数据我们取到6月19号
test = Data[predict_name]['20220620':].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, label
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 = Data[predict_name]['20220101'], Data[predict_name]['20220620']
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(Data[predict_name]['20220620':].values, realtest_predict)
# 计算出预测结果的r2值
from sklearn.metrics import r2_score
confirmedCount = r2_score(Data[predict_name]['20220620':].values, realtest_predict[:11])
print(r2_score(Data[predict_name]['20220620':].values, realtest_predict[:11]))
print((confirmedCount+curedCount+deadCount)/3)

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