Python财政收入影响因素分析及预测

分析

 

import numpy as np
import pandas as pd

inputfile = r'C:\Users\86184\Desktop\文件集\data/data.csv' # 输入的数据文件
data = pd.read_csv(inputfile) # 读取数据

# 描述性统计分析
description = [data.min(), data.max(), data.mean(), data.std()]  # 依次计算最小值、最大值、均值、标准差
description = pd.DataFrame(description, index = ['Min', 'Max', 'Mean', 'STD']).T  # 将结果存入数据框
print('描述性统计结果：\n',np.round(description, 2))  # 保留两位小数



# 代码6-2

# 相关性分析
corr = data.corr(method = 'pearson')  # 计算相关系数矩阵
print('相关系数矩阵为：\n',np.round(corr, 2))  # 保留两位小数



# 绘制热力图
import matplotlib.pyplot as plt
import seaborn as sns
inputfile = r'C:\Users\86184\Desktop\文件集\data/data.csv' # 输入的数据文件
data = pd.read_csv(inputfile) # 读取数据
plt.subplots(figsize=(10, 10)) # 设置画面大小 
sns.heatmap(corr, annot=True, vmax=1, square=True, cmap="Blues") 
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.title('相关性热力图 （20信计1班邓红琼3013）',fontsize=20)
plt.show()

 

预测

 

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

 

灰色预测

import sys
sys.path.append(r'C:\Users\86184\Desktop\文件集\data\code')  # 设置路径
import numpy as np
import pandas as pd
from GM11 import GM11  # 引入自编的灰色预测函数

inputfile1 = r'C:\Users\86184\Desktop\文件集\data\new_reg_data.csv'  # 输入的数据文件
inputfile2 = r'C:\Users\86184\Desktop\文件集\data\data.csv'  # 输入的数据文件
new_reg_data = pd.read_csv(inputfile1)  # 读取经过特征选择后的数据
data = pd.read_csv(inputfile2)  # 读取总的数据
new_reg_data.index = range(1994, 2014)
new_reg_data.loc[2014] = None
new_reg_data.loc[2015] = None
l = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']
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
outputfile = r'C:\Users\86184\Desktop\文件集\data\new_reg_data_GM11.csv'  # 灰色预测后保存的路径
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])
new_reg_data['y'] = y
new_reg_data.to_csv(outputfile)  # 结果输出
print('预测结果为：\n',new_reg_data.loc[2014:2015,:])  # 预测结果展示

import sys
sys.path.append(r'C:\Users\86184\Desktop\文件集\data\code')  # 设置路径
import numpy as np
import pandas as pd
from GM11 import GM11  # 引入自编的灰色预测函数

import matplotlib.pyplot as plt
from sklearn.svm import LinearSVR

inputfile = r'C:\Users\86184\Desktop\文件集\data\new_reg_data_GM11.xls'  # 灰色预测后保存的路径
data = pd.read_excel(inputfile,index_col = 0,header =0)  # 读取数据
feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13'] 
#print(data) # 属性所在列
data_train = data.loc[range(1994,2014)].copy() 
print(data_train)
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean)/data_std  # 数据标准化
x_train = data_train[feature].values  # 属性数据
y_train = data_train['y'].values # 标签数据
linearsvr = LinearSVR()  # 调用LinearSVR()函数
linearsvr.fit(x_train,y_train)
x = ((data[feature] - data_mean[feature])/data_std[feature]).values  # 预测，并还原结果。
data['y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
outputfile = r'C:\Users\86184\Desktop\文件集\data\new_reg_data_GM11_revenue.xls'  # SVR预测后保存的结果


print('真实值与预测值分别为：\n',data[['y','y_pred']])


fig = data[['y','y_pred']].plot(subplots = True, style=['b-o','r-*'])  # 画出预测结果图
plt.title('利用GM11对财政收入进行预测 （20信计1班邓红琼3013）',fontsize=20)
plt.show()