import matplotlib.pyplot as plt

import numpy as np

from sklearn import datasets, linear_model

from sklearn.metrics import mean_squared_error, r2_score

# 加载糖尿病数据集

diabetes = datasets.load_diabetes()

# 只使用一个特征

diabetes_X = diabetes.data[:, np.newaxis, 2]

# 将数据分为训练集和测试集

diabetes_X_train = diabetes_X[:-20]

diabetes_X_test = diabetes_X[-20:]

diabetes_y_train = diabetes.target[:-20]

diabetes_y_test = diabetes.target[-20:]

# 创建线性回归对象

regr = linear_model.LinearRegression()

# 训练模型

regr.fit(diabetes_X_train, diabetes_y_train)

# 在测试集上进行预测

diabetes_y_pred = regr.predict(diabetes_X_test)

# 系数

print('Coefficients: \n', regr.coef_)

# 均方误差

print("Mean squared error: %.2f"

% mean_squared_error(diabetes_y_test, diabetes_y_pred))

# Explained variance score: 1 is perfect prediction

print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))

# plot绘制

plt.scatter(diabetes_X_test, diabetes_y_test, color='black')

plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)

plt.xticks(())

plt.yticks(())

plt.show()