From f9b98b62354d2005b66fba34a95f4de0b137a1a7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=94=90=E5=BF=97=E8=B6=85?= <2219099463@qq.com> Date: Thu, 14 Nov 2024 20:31:47 +0800 Subject: [PATCH] Initial commit --- 代码/.idea/.gitignore | 8 ++ 代码/.idea/.name | 1 + .../inspectionProfiles/Project_Default.xml | 12 +++ .../inspectionProfiles/profiles_settings.xml | 6 ++ 代码/.idea/misc.xml | 7 ++ 代码/.idea/modules.xml | 8 ++ 代码/.idea/other.xml | 6 ++ 代码/.idea/代码.iml | 8 ++ 代码/GM11.py | 15 +++ 代码/__pycache__/GM11.cpython-312.pyc | Bin 0 -> 2127 bytes 代码/data.csv | 21 ++++ 代码/new_reg_data.csv | 21 ++++ 代码/new_reg_data_GM11.xlsx | Bin 0 -> 6849 bytes 代码/new_reg_data_GM11_revenue.xlsx | Bin 0 -> 7155 bytes 代码/tset.py | 10 ++ ...析财政收入数据特征的相关性.py | 15 +++ ...选取财政收入预测的关键特征.py | 34 +++++++ ...测和SVR构建财政收入预测模型.py | 92 ++++++++++++++++++ 数据/data.csv | 21 ++++ 财政收入预测分析报告.docx | Bin 0 -> 1266570 bytes 20 files changed, 285 insertions(+) create mode 100644 代码/.idea/.gitignore create mode 100644 代码/.idea/.name create mode 100644 代码/.idea/inspectionProfiles/Project_Default.xml create mode 100644 代码/.idea/inspectionProfiles/profiles_settings.xml create mode 100644 代码/.idea/misc.xml create mode 100644 代码/.idea/modules.xml create mode 100644 代码/.idea/other.xml create mode 100644 代码/.idea/代码.iml create mode 100644 代码/GM11.py create mode 100644 代码/__pycache__/GM11.cpython-312.pyc create mode 100644 代码/data.csv create mode 100644 代码/new_reg_data.csv create mode 100644 代码/new_reg_data_GM11.xlsx create mode 100644 代码/new_reg_data_GM11_revenue.xlsx create mode 100644 代码/tset.py create mode 100644 代码/任务8.2 分析财政收入数据特征的相关性.py create mode 100644 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 create mode 100644 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 create mode 100644 数据/data.csv create mode 100644 财政收入预测分析报告.docx diff --git a/代码/.idea/.gitignore b/代码/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/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 diff --git a/代码/.idea/.name b/代码/.idea/.name new file mode 100644 index 0000000..9d8508e --- /dev/null +++ b/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 \ No newline at end of file diff --git a/代码/.idea/inspectionProfiles/Project_Default.xml b/代码/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..f3c7f10 --- /dev/null +++ b/代码/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,12 @@ + + + + \ No newline at end of file diff --git a/代码/.idea/inspectionProfiles/profiles_settings.xml b/代码/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/代码/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/代码/.idea/misc.xml b/代码/.idea/misc.xml new file mode 100644 index 0000000..db8786c --- /dev/null +++ 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b/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)) + diff --git a/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 b/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 new file mode 100644 index 0000000..9490dfe --- /dev/null +++ b/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 diff --git a/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 b/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 new file mode 100644 index 0000000..919e3fb --- /dev/null +++ b/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 + diff --git a/数据/data.csv b/数据/data.csv new file mode 100644 index 0000000..6a7d20d --- /dev/null +++ b/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 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