import numpy as np
import random
# import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
from pandas.plotting import scatter_matrix
from sklearn.linear_model import Ridge,RidgeCV
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from matplotlib import pyplot as plt #as就是重新命名的意思
from matplotlib.font_manager import FontProperties #导入中文字体
from sklearn.linear_model import Lasso,LassoCV
from sklearn.metrics import mean_squared_error
import numpy as np
from sklearn.model_selection import StratifiedKFold

#font = FontProperties(fname=r"/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc")
matplotlib.rcParams['font.family']='SimHei'
matplotlib.rcParams['font.sans-serif']=['SimHei']

pd_data=pd.read_csv(r"./BostonHousing2.csv",header=1)
#print(pd_data)

df1=pd_data[['cmedv']]
df2=pd_data[['crim', 'zn', 'indus',
       'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'b',
       'lstat']]

# #任务一:
print("***************************************************************")
print("任务一")

dff=pd_data[['crim', 'zn', 'indus',
        'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'b',
      'lstat','cmedv']]
plt.figure() #自变量
scatter_matrix(dff, alpha = 0.3, figsize = (14,8));
plt.grid(True)
plt.savefig('cmedy')
plt.show()

##任务二:
print("***************************************************************")
print("任务二")

li=[]
for epoch in range(10):
    X_train,X_test,Y_train,Y_test = train_test_split(df2,df1,train_size=0.8) 

    model = Ridge(alpha=0.5,fit_intercept=True)
    model = RidgeCV(alphas=[0.1, 1.0, 10.0]) # 通过 RidgeCV 使用交叉验证获取最佳参数值  
    model.fit(X_train,Y_train) 

    a=model.intercept_
    b=model.coef_
    y_train_pred =model.predict(X_train)
    score=model.score(X_test, Y_test)
    li.append(score)
x = list(range(1, 11, 1))
y = [round(i,2) for i in li]

plt.plot(x, y,  linewidth=2, color='g', marker='o',
          markerfacecolor='blue', markersize=3)

#绘制图片
plt.plot(x,y)
plt.ylim(0, 1) # 限定纵轴的范围

for a, b in zip(x, y):
    plt.text(a, b, b, ha='center', va='bottom', fontsize=20)
plt.title("房价预测")
plt.xlabel("训练轮数")
plt.ylabel("可决系数")
plt.show()

#任务三  (包含任务5)

#利用岭回归模型随机选取变量进行十折交叉，计算可决系数
print("任务三  (包含任务5)")
print("下面对变量进行随机抽取：")
X_train,X_test,Y_train,Y_test = train_test_split(df2,df1,train_size=0.8)
for p in range(10):
    ans=random.randint(1, 12)
    
    df3 = X_train.sample(n=ans,axis=1)
    l=list(df3.columns)
    df4=pd.DataFrame(X_test,columns=l)
    
    model = Ridge(alpha=0.5,fit_intercept=True)
    model = RidgeCV(alphas=[0.01,0.1,0.2, 0.5, 1],normalize = True,cv=10)
    model.fit(df3,Y_train)
    ridge_best_alpha = model.alpha_ #得到最佳lambda值
    
    a=model.intercept_
    b=model.coef_
    yy_train_pred =model.predict(df3)
    
    score = model.score(df4,Y_test)
    print(f"第{p+1}轮,随机抽取{ans}个变量\n岭回归关键正则参数={ridge_best_alpha},可决系数{round(score,2)}")

#任务四   (包含任务5)
print("*************************************************")
print("任务四  (包含任务5)")
print(f"岭回归回归模型中关键正则参数𝛌的选择:")
X_train,X_test,Y_train,Y_test = train_test_split(df2,df1,train_size=0.8)
model = Ridge(alpha=0.5,fit_intercept=True)
model = RidgeCV(alphas=[0.01,0.1,0.2, 0.5, 1],normalize = True,cv=10)
model.fit(X_train,Y_train)
ridge_best_alpha = model.alpha_ #得到最佳lambda值
print(f"岭回归关键正则参数𝛌={ridge_best_alpha}")

ridge = Ridge(alpha = ridge_best_alpha,normalize = True)
ridge.fit(X_train,Y_train)
ridge_predict = ridge.predict(X_test)
mse = np.sqrt(mean_squared_error(Y_test,ridge_predict))
score=model.score(X_test, Y_test)
print(f"在最优参数𝛌的条件下,损失值{round(mse,2)},可决系数:{round(score,2)}")


print("*************************************************")
print(f"LASSO回归模型中关键正则参数𝛌的选择:")
X_train,X_test,Y_train,Y_test = train_test_split(df2,df1,train_size=0.8)
lasso_cv = LassoCV(alphas = [0.01,0.1,0.2, 0.5, 1], normalize=True, cv = 10)
lasso_cv.fit(X_train,pd.DataFrame(Y_train).values.ravel())  #本来需要一维向量，但是输入为列向量，所以找到出错的位置，应用ravel()函数即可。
lasso_best_alpha = lasso_cv.alpha_  # 取出最佳的lambda值
print(f"LASSO回归关键正则参数𝛌={lasso_best_alpha}")
lasso = Lasso(alpha = lasso_best_alpha, normalize=True)
lasso.fit(X_train,pd.DataFrame(Y_train).values.ravel())
lasso_predict = lasso.predict(X_test) #预测
MSE = np.sqrt(mean_squared_error(Y_test,lasso_predict))
ss=model.score(X_test, Y_test)
print(f"在最优参数𝛌的条件下,损失值{round(MSE,2)},可决系数:{round(ss,2)}")

print("***************************************************************")
print("任务五")

ll=lasso.coef_
print("各变量回归系数如下:")
print(ll)
ans=0
for i in ll:
    if abs(i)<=0.05:
        ans+=1
tmp=round(ans/12,2)
tmp=int(tmp*100)
print(f"回归系数为零的个数与全部自变量个数比例:{tmp}%.")