Initial commit

代码/.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

代码/.idea/.name

@@ -0,0 +1 @@
+任务8.2 分析财政收入数据特征的相关性.py

代码/.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,12 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="denglu.*" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

代码/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

代码/.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12" project-jdk-type="Python SDK" />
+</project>

代码/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/代码.iml" filepath="$PROJECT_DIR$/.idea/代码.iml" />
+    </modules>
+  </component>
+</project>

代码/.idea/other.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PySciProjectComponent">
+    <option name="PY_INTERACTIVE_PLOTS_SUGGESTED" value="true" />
+  </component>
+</project>

代码/.idea/代码.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

代码/GM11.py

@@ -0,0 +1,15 @@
+#-*- coding: utf-8 -*-
+
+def GM11(x0): #自定义灰色预测函数
+  import numpy as np
+  x1 = x0.cumsum() #1-AGO序列
+  z1 = (x1[:len(x1)-1] + x1[1:])/2.0 #紧邻均值（MEAN）生成序列
+  z1 = z1.reshape((len(z1),1))
+  B = np.append(-z1, np.ones_like(z1), axis = 1)
+  Yn = x0[1:].reshape((len(x0)-1, 1))
+  [[a],[b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn) #计算参数
+  f = lambda k: (x0[0]-b/a)*np.exp(-a*(k-1))-(x0[0]-b/a)*np.exp(-a*(k-2)) #还原值
+  delta = np.abs(x0 - np.array([f(i) for i in range(1,len(x0)+1)]))
+  C = delta.std()/x0.std()
+  P = 1.0*(np.abs(delta - delta.mean()) < 0.6745*x0.std()).sum()/len(x0)
+  return f, a, b, x0[0], C, P #返回灰色预测函数、a、b、首项、方差比、小残差概率

代码/data.csv

@@ -0,0 +1,21 @@
+x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,y
+3831732,181.54,448.19,7571,6212.7,6370241,525.71,985.31,60.62,65.66,120,1.029,5321,64.87
+3913824,214.63,549.97,9038.16,7601.73,6467115,618.25,1259.2,73.46,95.46,113.5,1.051,6529,99.75
+3928907,239.56,686.44,9905.31,8092.82,6560508,638.94,1468.06,81.16,81.16,108.2,1.064,7008,88.11
+4282130,261.58,802.59,10444.6,8767.98,6664862,656.58,1678.12,85.72,91.7,102.2,1.092,7694,106.07
+4453911,283.14,904.57,11255.7,9422.33,6741400,758.83,1893.52,88.88,114.61,97.7,1.2,8027,137.32
+4548852,308.58,1000.69,12018.52,9751.44,6850024,878.26,2139.18,92.85,152.78,98.5,1.198,8549,188.14
+4962579,348.09,1121.13,13966.53,11349.47,7006896,923.67,2492.74,94.37,170.62,102.8,1.348,9566,219.91
+5029338,387.81,1248.29,14694,11467.35,7125979,978.21,2841.65,97.28,214.53,98.9,1.467,10473,271.91
+5070216,453.49,1370.68,13380.47,10671.78,7206229,1009.24,3203.96,103.07,202.18,97.6,1.56,11469,269.1
+5210706,533.55,1494.27,15002.59,11570.58,7251888,1175.17,3758.62,109.91,222.51,100.1,1.456,12360,300.55
+5407087,598.33,1677.77,16884.16,13120.83,7376720,1348.93,4450.55,117.15,249.01,101.7,1.424,14174,338.45
+5744550,665.32,1905.84,18287.24,14468.24,7505322,1519.16,5154.23,130.22,303.41,101.5,1.456,16394,408.86
+5994973,738.97,2199.14,19850.66,15444.93,7607220,1696.38,6081.86,128.51,356.99,102.3,1.438,17881,476.72
+6236312,877.07,2624.24,22469.22,18951.32,7734787,1863.34,7140.32,149.87,429.36,103.4,1.474,20058,838.99
+6529045,1005.37,3187.39,25316.72,20835.95,7841695,2105.54,8287.38,169.19,508.84,105.9,1.515,22114,843.14
+6791495,1118.03,3615.77,27609.59,22820.89,7946154,2659.85,9138.21,172.28,557.74,97.5,1.633,24190,1107.67
+7110695,1304.48,4476.38,30658.49,25011.61,8061370,3263.57,10748.28,188.57,664.06,103.2,1.638,29549,1399.16
+7431755,1700.87,5243.03,34438.08,28209.74,8145797,3412.21,12423.44,204.54,710.66,105.5,1.67,34214,1535.14
+7512997,1969.51,5977.27,38053.52,30490.44,8222969,3758.39,13551.21,213.76,760.49,103,1.825,37934,1579.68
+7599295,2110.78,6882.85,42049.14,33156.83,8323096,4454.55,15420.14,228.46,852.56,102.6,1.906,41972,2088.14

代码/new_reg_data.csv

@@ -0,0 +1,21 @@
+,x1,x3,x4,x5,x6,x7,x8,x13
+0,3831732,448.19,7571.0,6212.7,6370241,525.71,985.31,5321
+1,3913824,549.97,9038.16,7601.73,6467115,618.25,1259.2,6529
+2,3928907,686.44,9905.31,8092.82,6560508,638.94,1468.06,7008
+3,4282130,802.59,10444.6,8767.98,6664862,656.58,1678.12,7694
+4,4453911,904.57,11255.7,9422.33,6741400,758.83,1893.52,8027
+5,4548852,1000.69,12018.52,9751.44,6850024,878.26,2139.18,8549
+6,4962579,1121.13,13966.53,11349.47,7006896,923.67,2492.74,9566
+7,5029338,1248.29,14694.0,11467.35,7125979,978.21,2841.65,10473
+8,5070216,1370.68,13380.47,10671.78,7206229,1009.24,3203.96,11469
+9,5210706,1494.27,15002.59,11570.58,7251888,1175.17,3758.62,12360
+10,5407087,1677.77,16884.16,13120.83,7376720,1348.93,4450.55,14174
+11,5744550,1905.84,18287.24,14468.24,7505322,1519.16,5154.23,16394
+12,5994973,2199.14,19850.66,15444.93,7607220,1696.38,6081.86,17881
+13,6236312,2624.24,22469.22,18951.32,7734787,1863.34,7140.32,20058
+14,6529045,3187.39,25316.72,20835.95,7841695,2105.54,8287.38,22114
+15,6791495,3615.77,27609.59,22820.89,7946154,2659.85,9138.21,24190
+16,7110695,4476.38,30658.49,25011.61,8061370,3263.57,10748.28,29549
+17,7431755,5243.03,34438.08,28209.74,8145797,3412.21,12423.44,34214
+18,7512997,5977.27,38053.52,30490.44,8222969,3758.39,13551.21,37934
+19,7599295,6882.85,42049.14,33156.83,8323096,4454.55,15420.14,41972

代码/tset.py

@@ -0,0 +1,10 @@
+import time
+import keyboard
+
+time.sleep(3)  # 等待3秒
+with open(r'C:\Users\唐志超\Desktop\pp.txt', encoding='utf-8') as file:
+    for line in file:
+        keyboard.write(line)
+
+
+

代码/任务8.2 分析财政收入数据特征的相关性.py

@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+
+###############################################################################
+#######################           任务实现             #######################
+###############################################################################
+
+# 代码 8-1
+import numpy as np
+import pandas as pd
+# inputfile = '../data/data.csv' ## 输入的数据文件
+inputfile = './data.csv' ## 输入的数据文件
+data = pd.read_csv(inputfile) ## 读取数据
+## 保留两位小数
+print('相关系数矩阵为：',np.round(data.corr(method = 'pearson'), 2))
+

代码/任务8.3 使用Lasso回归选取财政收入预测的关键特征.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+
+###############################################################################
+#######################           任务实现             #######################
+###############################################################################
+
+# 代码 8-2
+# 使用了 Lasso 回归模型来分析数据集的特征，
+# 从中筛选出与目标变量 y 相关的重要特征，并将结果保存到一个新的 CSV 文件中。以下是对代码的逐步分析：
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import Lasso
+# inputfile = '../data/data.csv' #输入的数据文件
+inputfile = './data.csv' #输入的数据文件
+data = pd.read_csv(inputfile) #读取数据
+# lasso = Lasso(1000)  #调用Lasso()函数，设置λ的值为1000
+lasso = Lasso(alpha=1000, max_iter=5000)  # 增加迭代次数
+lasso.fit(data.iloc[:,0:13],data['y'])
+print('相关系数为：',np.round(lasso.coef_,5))  #输出结果，保留五位小数
+
+## 计算相关系数非零的个数
+print('相关系数非零个数为：',np.sum(lasso.coef_ != 0))
+
+
+mask = lasso.coef_ != 0  #返回一个相关系数是否为零的布尔数组
+print('相关系数是否为零：',mask)
+
+# outputfile = '../tmp/new_reg_data.csv'  #输出的数据文件
+outputfile = './new_reg_data.csv'  #输出的数据文件
+# new_reg_data = data.iloc[:, mask]  #返回相关系数非零的数据
+# 返回相关系数非零的数据
+new_reg_data = data.iloc[:, :13].loc[:, mask]  # 只选择0到12列中非零的列
+new_reg_data.to_csv(outputfile)  #存储数据
+print('输出数据的维度为：',new_reg_data.shape)  #查看输出数据的维度

代码/任务8.4 使用灰色预测和SVR构建财政收入预测模型.py

@@ -0,0 +1,92 @@
+# -*- coding: utf-8 -*-
+
+###############################################################################
+#######################           任务实现             ########################
+###############################################################################
+# 代码 8-3这段代码的主要功能是使用灰色预测模型对时间序列数据进行预测，并将结果保存到 Excel 文件中。以下是代码的逐步解析：
+import numpy as np
+import pandas as pd
+from GM11 import GM11  # 引入自编的灰色预测函数
+
+# 输入的数据文件
+inputfile = './new_reg_data.csv'
+inputfile1 = './data.csv'
+new_reg_data = pd.read_csv(inputfile)  # 读取经过特征选择后的数据
+data = pd.read_csv(inputfile1)  # 读取总的数据
+
+# 设置索引
+new_reg_data.index = range(1994, 2014)
+new_reg_data.loc[2014] = None
+new_reg_data.loc[2015] = None
+
+# 需要预测的列
+l = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
+outputfile = './new_reg_data_GM11.xlsx'
+y = list(data['y'].values)  # 提取财政收入列，合并至新数据框中
+y.extend([np.nan, np.nan])  # 添加2014和2015年对应的NaN值
+
+for i in l:
+    f = GM11(new_reg_data.loc[range(1994, 2014), i].values)[0]
+    new_reg_data.loc[2014, i] = f(len(new_reg_data) - 1)  # 2014年预测结果
+    new_reg_data.loc[2015, i] = f(len(new_reg_data))      # 2015年预测结果
+    new_reg_data[i] = new_reg_data[i].round(2)  # 保留两位小数
+
+# 使用 .xls 扩展名
+new_reg_data['y'] = y  # 添加财政收入列到new_reg_data
+new_reg_data.to_excel(outputfile, index=True)  # 结果输出，索引列也写入
+# 显示预测结果
+print('预测结果为：', new_reg_data.loc[2014:2015, :])  # 预测结果展示
+
+
+
+# 代码 8-4
+# 利用灰色预测的输出，进一步应用 SVR 模型进行建模与预测，并将结果进行保存
+import pandas as pd
+import numpy as np
+from sklearn.svm import LinearSVR
+import matplotlib.pyplot as plt
+from sklearn.metrics import explained_variance_score,\
+mean_absolute_error,mean_squared_error,\
+median_absolute_error,r2_score
+# inputfile = '../tmp/new_reg_data_GM11.xls' #灰色预测后保存的路径
+inputfile = './new_reg_data_GM11.xlsx' #灰色预测后保存的路径
+data = pd.read_excel(inputfile,index_col=0) #读取数据
+feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
+data_train = data.loc[range(1994,2014)].copy()#取2014年前的数据建模
+data_mean = data_train.mean()
+data_std = data_train.std()
+data_train = (data_train - data_mean)/data_std #数据标准化
+# 计算数据的均值和标准差，然后对数据进行标准化处理，使其均值为0，标准差为1。
+# 特征数据和标签数据
+x_train = data_train[feature].values  # 使用 .values 替代 .as_matrix()
+y_train = data_train['y'].values  # 使用 .values 替代 .as_matrix()
+# 创建 SVR 模型实例，并使用训练数据进行拟合。
+linearsvr = LinearSVR()   #调用LinearSVR()函数
+linearsvr.fit(x_train,y_train)
+# 将数据再次进行标准化，然后用模型进行预测，并将预测值还原到原始尺度上（解标准化）。
+x = ((data[feature] - data_mean[feature])/ \
+# data_std[feature]).as_matrix()  #预测，并还原结果。
+data_std[feature]).values  #预测，并还原结果。
+data[u'y_pred'] = linearsvr.predict(x) * \
+data_std['y'] + data_mean['y']
+## SVR预测后保存的结果
+# outputfile = '../tmp/new_reg_data_GM11_revenue.xls'
+outputfile = './new_reg_data_GM11_revenue.xlsx'
+data.to_excel(outputfile)
+print('真实值与预测值分别为：',data[['y','y_pred']])
+
+print('预测图为：',data[['y','y_pred']].plot(subplots = True,
+      style=['b-o','r-*']))
+# 绘制真实值与预测值在同一个图形中
+# 为确保中文标题能正常显示
+plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 替换为可以显示中文的字体
+plt.rcParams['axes.unicode_minus'] = False  # 确保负号显示正常
+ax = data[['y', 'y_pred']].plot(style=['r-o', 'b-*'], figsize=(10, 5))
+plt.title('真实值与预测值对比')  # 添加标题
+plt.xlabel('时间')  # 添加x轴标签
+plt.ylabel('值')    # 添加y轴标签
+
+# 添加图例，明确哪条线是哪个
+plt.legend(['真实值', '预测值'])  # 添加图例
+plt.show()  # 显示图形
+

数据/data.csv

@@ -0,0 +1,21 @@
+x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,y
+3831732,181.54,448.19,7571,6212.7,6370241,525.71,985.31,60.62,65.66,120,1.029,5321,64.87
+3913824,214.63,549.97,9038.16,7601.73,6467115,618.25,1259.2,73.46,95.46,113.5,1.051,6529,99.75
+3928907,239.56,686.44,9905.31,8092.82,6560508,638.94,1468.06,81.16,81.16,108.2,1.064,7008,88.11
+4282130,261.58,802.59,10444.6,8767.98,6664862,656.58,1678.12,85.72,91.7,102.2,1.092,7694,106.07
+4453911,283.14,904.57,11255.7,9422.33,6741400,758.83,1893.52,88.88,114.61,97.7,1.2,8027,137.32
+4548852,308.58,1000.69,12018.52,9751.44,6850024,878.26,2139.18,92.85,152.78,98.5,1.198,8549,188.14
+4962579,348.09,1121.13,13966.53,11349.47,7006896,923.67,2492.74,94.37,170.62,102.8,1.348,9566,219.91
+5029338,387.81,1248.29,14694,11467.35,7125979,978.21,2841.65,97.28,214.53,98.9,1.467,10473,271.91
+5070216,453.49,1370.68,13380.47,10671.78,7206229,1009.24,3203.96,103.07,202.18,97.6,1.56,11469,269.1
+5210706,533.55,1494.27,15002.59,11570.58,7251888,1175.17,3758.62,109.91,222.51,100.1,1.456,12360,300.55
+5407087,598.33,1677.77,16884.16,13120.83,7376720,1348.93,4450.55,117.15,249.01,101.7,1.424,14174,338.45
+5744550,665.32,1905.84,18287.24,14468.24,7505322,1519.16,5154.23,130.22,303.41,101.5,1.456,16394,408.86
+5994973,738.97,2199.14,19850.66,15444.93,7607220,1696.38,6081.86,128.51,356.99,102.3,1.438,17881,476.72
+6236312,877.07,2624.24,22469.22,18951.32,7734787,1863.34,7140.32,149.87,429.36,103.4,1.474,20058,838.99
+6529045,1005.37,3187.39,25316.72,20835.95,7841695,2105.54,8287.38,169.19,508.84,105.9,1.515,22114,843.14
+6791495,1118.03,3615.77,27609.59,22820.89,7946154,2659.85,9138.21,172.28,557.74,97.5,1.633,24190,1107.67
+7110695,1304.48,4476.38,30658.49,25011.61,8061370,3263.57,10748.28,188.57,664.06,103.2,1.638,29549,1399.16
+7431755,1700.87,5243.03,34438.08,28209.74,8145797,3412.21,12423.44,204.54,710.66,105.5,1.67,34214,1535.14
+7512997,1969.51,5977.27,38053.52,30490.44,8222969,3758.39,13551.21,213.76,760.49,103,1.825,37934,1579.68
+7599295,2110.78,6882.85,42049.14,33156.83,8323096,4454.55,15420.14,228.46,852.56,102.6,1.906,41972,2088.14