基于python的疫情分析.zip

import operator import matplotlib.pyplot as plt import numpy as np from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression,LogisticRegression import matplotlib.pyplot as plt def init_data(): # 原始数据 countrydatahistorys=[{'date': '2020-02-10', 'confirmedNum': 42708, 'suspectedNum': 21675, 'curesNum': 3998, 'deathsNum': 1017, 'suspectedIncr': 3536}, {'date': '2020-02-09', 'confirmedNum': 40224, 'suspectedNum': 23589, 'curesNum': 3283, 'deathsNum': 909, 'suspectedIncr': 4008}, {'date': '2020-02-08', 'confirmedNum': 37162, 'suspectedNum': 28942, 'curesNum': 2651, 'deathsNum': 812, 'suspectedIncr': 3916}, {'date': '2020-02-07', 'confirmedNum': 34594, 'suspectedNum': 27657, 'curesNum': 2052, 'deathsNum': 723, 'suspectedIncr': 4214}, {'date': '2020-02-06', 'confirmedNum': 31197, 'suspectedNum': 26359, 'curesNum': 1542, 'deathsNum': 637, 'suspectedIncr': 4833}, {'date': '2020-02-05', 'confirmedNum': 28060, 'suspectedNum': 24702, 'curesNum': 1153, 'deathsNum': 564, 'suspectedIncr': 5328}, {'date': '2020-02-04', 'confirmedNum': 24363, 'suspectedNum': 23260, 'curesNum': 892, 'deathsNum': 491, 'suspectedIncr': 3971}, {'date': '2020-02-03', 'confirmedNum': 20471, 'suspectedNum': 23214, 'curesNum': 630, 'deathsNum': 425, 'suspectedIncr': 5072}, {'date': '2020-02-02', 'confirmedNum': 17238, 'suspectedNum': 21558, 'curesNum': 475, 'deathsNum': 361, 'suspectedIncr': 5173}, {'date': '2020-02-01', 'confirmedNum': 14411, 'suspectedNum': 19544, 'curesNum': 328, 'deathsNum': 304, 'suspectedIncr': 4562}, {'date': '2020-01-31', 'confirmedNum': 11821, 'suspectedNum': 17988, 'curesNum': 243, 'deathsNum': 259, 'suspectedIncr': 5019}, {'date': '2020-01-30', 'confirmedNum': 9720, 'suspectedNum': 15238, 'curesNum': 171, 'deathsNum': 213, 'suspectedIncr': 4812}, {'date': '2020-01-29', 'confirmedNum': 7736, 'suspectedNum': 12167, 'curesNum': 124, 'deathsNum': 170, 'suspectedIncr': 4148}, {'date': '2020-01-28', 'confirmedNum': 5997, 'suspectedNum': 9239, 'curesNum': 103, 'deathsNum': 132, 'suspectedIncr': 3248}, {'date': '2020-01-27', 'confirmedNum': 4535, 'suspectedNum': 6973, 'curesNum': 51, 'deathsNum': 106, 'suspectedIncr': 2077}, {'date': '2020-01-26', 'confirmedNum': 2761, 'suspectedNum': 5794, 'curesNum': 49, 'deathsNum': 80, 'suspectedIncr': 3806}, {'date': '2020-01-25', 'confirmedNum': 1985, 'suspectedNum': 2684, 'curesNum': 38, 'deathsNum': 56, 'suspectedIncr': 1309}, {'date': '2020-01-24', 'confirmedNum': 1297, 'suspectedNum': 1965, 'curesNum': 38, 'deathsNum': 41, 'suspectedIncr': 1118}, {'date': '2020-01-23', 'confirmedNum': 830, 'suspectedNum': 1072, 'curesNum': 34, 'deathsNum': 25, 'suspectedIncr': 680}] # 按照时间排序全国趋势数据 countrydatahistorys=sorted(countrydatahistorys, key=operator.itemgetter('date')) # 结构化全国各省图表所需数据 xdata=list(range(len(countrydatahistorys))) xlabel=list(row['date'] for row in countrydatahistorys) # 追加预测未来一周的自变量X xdata.extend(list(range(19, 26))) from datetime import date, datetime, timedelta start_date = date(2020, 2, 11) xlabel.extend(list(str(start_date + timedelta(i)) for i in range(7))) # 生成累计确诊数据，即y值 confirmedNum=list(row['confirmedNum'] for row in countrydatahistorys) suspectedNum=list(row['suspectedNum'] for row in countrydatahistorys) #进行数据格式转换，生成训练集、测试集和预测集 Xlabel=np.array(xlabel).reshape(-1, 1) X=np.array(xdata).reshape(-1, 1) y=np.array(confirmedNum).reshape(-1, 1) X_train=X[:15] X_test=X[15:19] X_predict=X[19:] y_train=y[:15] y_test=y[15:19] return X_train,X_test,y_train,y_test,X_predict,X,y,xlabel X_train,X_test,y_train,y_test,X_predict,X,y,Xlabel=init_data() lr=LinearRegression().fit(X_train,y_train) coef=lr.coef_ intercept=lr.intercept_ score_train=lr.score(X_train,y_train) score_test=lr.score(X_test,y_test) y_predict=lr.predict(X_test) #-------------------------------- lg=LogisticRegression(C=0.2) lg.fit(X_train,y_train) y_lg_predict=lg.predict(X) print('y_lg_predict=',y_lg_predict) #-------------------------------- poly1 =PolynomialFeatures(degree=1) X_ploy =poly1.fit_transform(X_train) l1=LinearRegression() l1.fit(X_ploy,y_train) #-------------------------------- poly2 =PolynomialFeatures(degree=2) X_ploy =poly2.fit_transform(X_train) l2=LinearRegression() l2.fit(X_ploy,y_train) #-------------------------------- poly3 =PolynomialFeatures(degree=3) X_ploy =poly3.fit_transform(X_train) l3=LinearRegression() l3.fit(X_ploy,y_train) #-------------------------------- poly4 =PolynomialFeatures(degree=4) X_ploy =poly4.fit_transform(X_train) l4=LinearRegression() l4.fit(X_ploy,y_train) #-------------------------------- poly5 =PolynomialFeatures(degree=5) X_ploy =poly5.fit_transform(X_train) l5=LinearRegression() l5.fit(X_ploy,y_train) #-------------------------------- poly6 =PolynomialFeatures(degree=6) X_ploy =poly6.fit_transform(X_train) l6=LinearRegression() l6.fit(X_ploy,y_train) fig=plt.figure(figsize=(10,5.5)) plt.rcParams['font.sans-serif']=['SimHei'] # 画出实际值，X和y不等，X训练集加测试集和实际y值相等 plt.plot(np.vstack((X_train,X_test)),y,color='black',marker='o',linestyle='-',label='累计确诊病例（官方统计）') plt.plot(X,y_lg_predict,color='cyan',marker='*',linestyle=':',label='逻辑回归') plt.plot(X,intercept+X*coef,color='red',marker='*',linestyle=':',label='线性预测') plt.plot(X,l2.predict(poly2.fit_transform(X)),color='blue',marker='*',linestyle=':',label='2次多项式') plt.plot(X,l3.predict(poly3.fit_transform(X)),color='yellow',marker='*',linestyle=':',label='3次多项式') plt.plot(X,l4.predict(poly4.fit_transform(X)),color='green',marker='*',linestyle=':',label='4次多项式') # 设置x轴标签及其字号 plt.xlabel('日期',fontsize=14) # 设置y轴标签及其字号 plt.ylabel('累计确诊病例数量',fontsize=14) # 设置X轴序列标签值 plt.xticks(X-1,Xlabel,rotation=30,fontsize=10) # 添加训练集、测试集、预测集分割垂直直线 plt.axvline(x=14.5,linestyle='--',c="green") plt.axvline(x=18.5,linestyle='--',c="green") # 显示图例 plt.legend() plt.show()