import numpy as np
import pandas as pd

inputfile = "D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\data(1).csv"
data = pd.read_csv(inputfile)

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

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

import matplotlib.pyplot as plt
import seaborn as sns
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'] # 添加这条可以让图形显示中文
plt.rcParams['axes.unicode_minus'] = False # 添加这条可以让图形显示负号
plt.title('相关性热力图(学号3110)')
plt.show()
plt.close

import numpy as np
import pandas as pd
from sklearn.linear_model import Lasso

inputfile = "D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\data(1).csv"
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.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

print('相关系数非零个数为：',np.sum(lasso.coef_ !=0)) #计算相关系数非零的个数
mask = lasso.coef_ !=0 #返回一个相关系数是否为零的布尔数组
mask = np.append(mask,True) #将mask的元素补齐到14个
print('相关系数是否为零：',mask)

outputfile = "D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\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
new_reg_data.to_csv(outputfile) #存储数据
print('输出数据的维度为：',new_reg_data.shape) #查出输出数据的维度

import sys
sys.path.append("C:\\Users\\86130\\GM11.py")
import numpy as np
import pandas as pd
from GM11 import GM11 #引入自编的灰色预测函数

inputfile1 = "D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\new_reg_data.csv"
inputfile2 = "D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\data(1).csv"
new_reg_data = pd.read_csv(inputfile1) #读取经过属性选择后的数据
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
cols = ['x1','x3','x4','x5','x6','x7','x8','x13']
for i in cols:
    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 = ("D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\new_reg_data_GM11.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])
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[2014:2015,:]) #预测展示

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVR

inputfile = "D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\new_reg_data_GM11.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.index = range(1994, 2016)
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
x_train = data_train[feature].to_numpy()  # 属性数据
y_train = data_train['y'].to_numpy()  # 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]).to_numpy()  # 预测，并还原结果。
data['y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y']
outputfile ="D:\\360MoveData\\Users\\86130\\Documents\\Tencent Files\\2268756693\\FileRecv\\new_reg_data_GM11_revenue.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.rcParams['font.sans-serif'] = ['SimHei'] # 添加这条可以让图形显示中文
plt.title('学号3110')
plt.show()