import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
inputfile = r"C:\Users\asus\Desktop\data.csv"
data = pd.read_csv(inputfile)
description = [data.min(),data.max(),data.mean(),data.std()]
description = pd.DataFrame(description,index = ['Min','Max','Mean','STD']).T
print('描述性统计结果：\n',np.round(description,2))
corr = data.corr(method = 'pearson')
print('相关系数矩阵为：\n',np.round(corr,2))
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.subplots(figsize = (10,10))
sns.heatmap(corr,annot = True,vmax = 1,square = True,cmap = 'Greens')
plt.title('学号2020310143036')
plt.show()
plt.close
import numpy as np
import pandas as pd
from sklearn.linear_model import Lasso
inputfile =r"C:\Users\asus\Desktop\data.csv"
data = pd.read_csv(inputfile)
lasso = Lasso(1000)
lasso.fit(data.iloc[:,0:13],data['y'])
print('相关系数为：',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"C:\Users\asus\Desktop\1.csv"
new_reg_data = data.iloc[:,mask]
new_reg_data.to_csv(outputfile)
print('输出数据的维度为：',new_reg_data.shape)
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
inputfile1 = r"C:\Users\asus\Desktop\data.csv" # 输入的数据文件
inputfile2 = r"C:\Users\asus\Desktop\1.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\asus\Desktop\2.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_excel(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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVR
inputfile = r"C:\Users\asus\Desktop\2.csv" # 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'] # 属性所在列
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].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\asus\Desktop\3.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']])
plt.rcParams['font.sans-serif'] = 'SimHei'
fig = data[['y','y_pred']].plot(subplots = True, style=['b-o','r-*']) # 画出预测结果图
plt.title('学号2020310143036')
plt.show()