zuoye

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['font.sans-serif'] = ['SimHei']   #解决中文显示问题
plt.rcParams['axes.unicode_minus'] = False    # 解决中文显示问题
shuju='E:\桌面\data.csv'
data = pd.read_csv(shuju)
print(data)
print(data.describe())

#相关性分析
corr=data.corr(method='pearson')
print('相关系数矩阵为：\n',np.round(corr,2))



plt.subplots(figsize=(10,10))
sns.heatmap(corr,annot=True,vmax=1,square=True,cmap="Blues")
plt.title("相关性热力图")
plt.show()
plt.close

import numpy as np
import pandas as pd
from sklearn.linear_model import Lasso
inputfile='E:\桌面\data.csv'
data=pd.read_csv(inputfile)
lasso=Lasso(1000)
lasso.fit(data.iloc[:,0:13],data['y'])
print('相关系数：',np.round(lasso.coef_,5))
print('相关系数非零个数为：',np.sum(lasso.coef_ !=0))
mask=lasso.coef_ !=0
mask = np.append(mask,True)
print('相关系数是否为零:',mask)
outputfile=r'E:\桌面\new_reg_data.csv'
new_reg_data=data.iloc[:,mask]
new_reg_data.to_csv(outputfile)
print('输出的数据维度为:',new_reg_data.shape)

import sys
# sys.path.append('../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
plt.rcParams['font.sans-serif'] = ['SimHei']   #解决中文显示问题
plt.rcParams['axes.unicode_minus'] = False    # 解决中文显示问题

inputfile1 = r'D:\桌面\new_reg_data.csv'  # 输入的数据文件
inputfile2 = r'D:\桌面\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
new_reg_data.loc[2016] = 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)-2)  # 2014年预测结果
  new_reg_data.loc[2015,i] = f(len(new_reg_data)-1)# 2015年预测结果
  new_reg_data.loc[2016,i] = f(len(new_reg_data))
  new_reg_data[i] = new_reg_data[i].round(2)  # 保留两位小数
outputfile = r'E:\桌面\new_reg_data_GM11_2.xls'  # 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,np.nan])
new_reg_data['y'] = y
new_reg_data.to_excel(outputfile)  # 结果输出
print('预测结果为：\n',new_reg_data.loc[2014:2016,:])  # 预测结果展示

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

inputfile = r'E:\桌面\new_reg_data_GM11_2.xls'  # 灰色预测后保存的路径
data = pd.read_excel(inputfile)  # 读取数据
feature = ['x1', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x13']  # 属性所在列
data_train=data.iloc[0:20,:]
data_train.head()
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
data_train[feature]
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'D:\桌面\new_reg_data_GM11_2.xls'  # SVR预测后保存的结果
data.to_excel(outputfile)

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.xlabel('学号3030')
plt.show()