机器学习笔记 —— 回归

import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso, Ridge
from sklearn.model_selection import GridSearchCV

if __name__ == "__main__":
    show = True
    path = './Advertising.csv'

    # pandas读取数据
    data = pd.read_csv(path)  # TV Radio Newspaper Sales
    x = data[['TV', 'Radio', 'Newspaper']]
    y = data['Sales']
    print(x)
    print(y)

    # 将数据集分为训练集和测试集 并设置随机种子
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)
    # model = Lasso()
    model = Ridge()
    alpha_can = np.logspace(-3, 2, 10)
    print('alpha_can = ', alpha_can)
    np.set_printoptions(suppress=True)
    lasso_model = GridSearchCV(model, param_grid={'alpha' : alpha_can}, cv=5)
    lasso_model.fit(x_train, y_train)
    print('超参数：', lasso_model.best_params_)

    order = y_test.argsort(axis=0)
    y_test = y_test.values[order]
    x_test = x_test.values[order, :]
    y_hat = lasso_model.predict(x_test)
    print(lasso_model.score(x_test, y_test))
    mse = np.average((y_hat - np.array(y_test)) ** 2)  # MSE
    rmse = np.sqrt(mse)  # RMSE
    print('mse=', mse)
    print('rmse=', rmse)

    t = np.arange(len(x_test))
    mpl.rcParams['font.sans-serif'] = ['simHei']
    mpl.rcParams['axes.unicode_minus'] = False
    plt.figure(facecolor='w')
    plt.plot(t, y_test, 'r-', linewidth=2, label='真实数据')
    plt.plot(t, y_hat, 'g-', linewidth=2, label='预测数据')
    plt.legend(loc='upper left')
    plt.title('线性回归预测销量', fontsize=18)
    plt.grid(b=True, ls=':')
    plt.show()