因果关系的骚操作

import numpy as np
from sklearn.datasets import load_boston
boston = load_boston()
# print(boston.target.shape)
# print(boston.data.shape)
# print(boston.feature_names)
import pandas as pd
from numpy import genfromtxt
bos1= genfromtxt('D:\\迅雷下载\\boston.csv', delimiter=',')#这个是自己改的
#bos = pd.DataFrame(boston.data)
X = bos1[1:,0:-1]#不断的修改这个东西就可以0换成1等等
print(X)
#Y = pd.DataFrame(boston.target)
Y=bos1[1:,-1]
print(Y)
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.33)
print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape)
from sklearn.preprocessing import StandardScaler
X_test= StandardScaler().fit_transform(X_test)
from sklearn.preprocessing import StandardScaler
X_train= StandardScaler().fit_transform(X_train)
#这快是主干--------------------------------------------------
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import mean_squared_error
from math import sqrt
lm = SGDRegressor(eta0=0.01,learning_rate='constant',shuffle=False,max_iter=5000,tol=0.001)
lm.partial_fit(X_train, Y_train)
Y_pred = lm.predict(X_test)
print("Actual Y's: \n {}".format(np.array(Y_test[0])))
print("Predicted Y's: \n {}".format(np.array(Y_pred)))
import numpy as np
delta_y = np.array(Y_test[0]) - np.array(Y_pred)#这里是误差
mse=0
for i in delta_y:
    mse+=i*i
print("测评标准MSE from SGDRegressor: {}".format(mse/delta_y.shape[0]))#均方误差（这里考虑均方误差越小越好）
# import seaborn as sns
# sns.set_style('whitegrid')
# sns.kdeplot(np.array(Y_test[0]), bw=0.5)
# plt.show()
# sns.set_style('whitegrid')
# sns.kdeplot(Y_pred, bw=0.5)
# plt.show()
print("权重大小为SGDRegressor weights: \n{}".format(lm.coef_))
print("b为",lm.intercept_)