线性回归，岭回归，套索回归代码实践

代码操练起来就好~~

一，代码

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_diabetes
from sklearn.datasets import load_boston
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.linear_model import SGDClassifier

'''

X = [[1], [4], [3]]
y = [3, 5, 3]
lr = LinearRegression().fit(X, y)
z = np.linspace(0, 5, 20)
plt.scatter(X, y, s=80)
plt.plot(z, lr.predict(z.reshape(-1, 1)), c='k')
plt.title("Straight Line")
plt.show()
print('y = {:.3f}'.format(lr.coef_[0]), 'x', '+ {:.3f}'.format(lr.intercept_))

X, y = make_regression(n_samples=50, n_features=1, n_informative=1,
                       noise=50, random_state=1)
reg = LinearRegression()
reg.fit(X, y)
z = np.linspace(-3, 3, 200).reshape(-1, 1)
plt.scatter(X, y, c='b', s=60)
plt.plot(z, reg.predict(z), c='k')
plt.title("Linear Regression")
plt.show()
print('直线的系数是： {:.2f}'.format(reg.coef_[0]))
print('直线的截距是： {:.2f}'.format(reg.intercept_))

X, y = make_regression(
    n_samples=100, n_features=2, n_informative=2, random_state=38
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=8)
lr = LinearRegression().fit(X_train, y_train)
print('lr.coef_: {}'.format(lr.coef_[:]))
print('or.interceft: {}'.format(lr.intercept_))
print('训练数据得分：{:.2f}'.format(lr.score(X_train, y_train)))
print('测试数据得分：{:.2f}'.format(lr.score(X_test, y_test)))
X, y = load_diabetes().data, load_diabetes().target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=8)
lr = LinearRegression().fit(X_train, y_train)
print('lr.coef_: {}'.format(lr.coef_[:]))
print('or.interceft: {}'.format(lr.intercept_))
print('训练数据得分：{:.2f}'.format(lr.score(X_train, y_train)))
print('测试数据得分：{:.2f}'.format(lr.score(X_test, y_test)))
X, y = load_boston().data, load_boston().target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=8)
ridge = Ridge(alpha=0.1).fit(X_train, y_train)
print('岭回归训练数据得分：{:.2f}'.format(ridge.score(X_train, y_train)))
print('岭回归测试数据得分：{:.2f}'.format(ridge.score(X_test, y_test)))
'''
X, y = load_diabetes().data, load_diabetes().target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=8)
lasso = Lasso(alpha=0.0001, max_iter=100000).fit(X_train, y_train)
print('套索回归训练数据得分：{:.2f}'.format(lasso.score(X_train, y_train)))
print('套索回归测试数据得分：{:.2f}'.format(lasso.score(X_test, y_test)))
print('套索回归使用的特征数：{}'.format(np.sum(lasso.coef_ != 0)))

二，输出

C:\Users\ccc\AppData\Local\Programs\Python\Python38\python.exe D:/Code/Metis-Org/app/service/time_series_detector/algorithm/ai_test.py
套索回归训练数据得分：0.53
套索回归测试数据得分：0.46
套索回归使用的特征数：10

Process finished with exit code 0