机器学习二（python sklearn逻辑回归）

深藏阁楼爱情的钟 2022-05-22 03:54 392阅读 0赞

### 1.前言 ###

看了Andrew ng的课程，用python sklearn总结一下逻辑回归。这就不说理论了，直接上py代码

### 2.环境配置 ###

不多说，用的python3.x，numpy包，环境下载pycharm，然后file->setting->Project Interpreter->右侧绿色+号->搜索输入需要的包进行安装，然后可能有报错日志，根据日志循环上述过程安装缺少的包。

### 3.python代码 ###

（1）ex2data1数据资源：[https://download.csdn.net/download/bjjoy2009/10478703][https_download.csdn.net_download_bjjoy2009_10478703]  
（2）回归函数（LogisticRegression）详细说明请看：  
[https://blog.csdn.net/loveliuzz/article/details/78708359][https_blog.csdn.net_loveliuzz_article_details_78708359]

import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from matplotlib.colors import ListedColormap
    
    # 绘制原始数据图
    def plot_data():
        data = np.loadtxt("ex2data1.txt", delimiter=",")
        data0 = data[data[:, 2] == 0]
        data1 = data[data[:, 2] == 1]
        plt.plot(data0[:, 0], data0[:, 1], 'o')
        plt.plot(data1[:, 0], data1[:, 1], '+')
        plt.xlabel("score1")
        plt.ylabel("score2")
        plt.title("two exam score")
        plt.show()
    
    
    def sigmoid(z):
        return 1/(1 + np.exp(-z))
    
    # 绘制决策边界，
    def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
        # setup marker generator and color map
        markers = ('s', 'x', 'o', '^', 'v')
        colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
        cmap = ListedColormap(colors[:len(np.unique(y))])
        # plot the decision surface
        x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))
        Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
        Z = Z.reshape(xx1.shape)
        plt.contour(xx1, xx2, Z, alpha=0.4, cmap=cmap)
        plt.xlim(xx1.min(), xx1.max())
        plt.ylim(xx2.min(), xx2.max())
        # plot class samples
        for idx, cl in enumerate(np.unique(y)):
            plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],alpha=0.8, c=cmap(idx),marker=markers[idx], label=cl)
        # highlight test samples
        if test_idx:
            X_test, y_test = X[test_idx, :], y[test_idx]
            plt.scatter(X_test[:, 0], X_test[:, 1], c='', alpha=1.0, linewidth=1, marker='o', s=55, label='test set')
        plt.show()
    
    # 绘制决策边界,数据标准化后,适用于0=theta1*x1+theta2*x2+theta0
    def plot_decision_line(x_regular, Y, classifier):
        # 绘制数据图，o是分类1，+分类2
        data_regular = np.hstack((x_regular, Y.reshape(100, 1)))
        data0 = data_regular[data_regular[:, 2] == 0]
        data1 = data_regular[data_regular[:, 2] == 1]
        plt.plot(data0[:, 0], data0[:, 1], 'o')
        plt.plot(data1[:, 0], data1[:, 1], '+')
        # 绘制决策边界
        xx1 = np.arange(np.min(x_regular), np.max(x_regular), 0.01)
        exam2 = -(classifier.coef_[0][0] * xx1 + classifier.intercept_[0]) / classifier.coef_[0][1]
        plt.plot(xx1, exam2)
        plt.xlabel("exam1")
        plt.ylabel("exam2")
        plt.title("regular exam score")
        plt.show()
    
    
    # 测试集，画图对预测值和实际值进行比较
    def test_validate(x_test, y_test, y_predict, classifier):
        x = range(len(y_test))
        plt.plot(x, y_test, "ro", markersize=8, zorder=3, label=u"true_v")
        plt.plot(x, y_predict, "go", markersize=14, zorder=2, label=u"predict_v,$R^2$=%.3f" % classifier.score(x_test, y_test))
        plt.legend(loc="center left")
        plt.xlabel("number")
        plt.ylabel("true?")
        plt.show()
    
    
    def logistic_regression():
        data = np.loadtxt("ex2data1.txt", delimiter=",")
        # 提取数据
        x = data[:, 0:2]
        y = data[:, 2]
    
        # 对数据的训练集进行标准化
        ss = StandardScaler()
        x_regular = ss.fit_transform(x)
        # 划分训练集与测试集
        x_train, x_test, y_train, y_test = train_test_split(x_regular, y, test_size=0.1)
    
        lr = LogisticRegression()
        lr.fit(x_train, y_train)
    
        # 模型效果获取
        r = lr.score(x_train, y_train)
        print("R值(准确率):", r)
        print("theta: ", lr.coef_)
        print("截距(theta0):", lr.intercept_)
        # 预测
        y_predict = lr.predict(x_test)  # 预测
    
        # 绘制原始数据
        plot_data()
        # 绘制决策边界，自己写的
        plot_decision_line(x_regular, y, classifier=lr)
        # 绘制决策边界，博客找的方法
        plot_decision_regions(x_regular, y, classifier=lr)
        # 绘制测试集结果验证
        test_validate(x_test=x_test, y_test=y_test, y_predict=y_predict, classifier=lr)
    
    
    logistic_regression()

### 4.运行结果 ###

（1）plot\_data()方法实现原始数据散点图。蓝点是未通过0，黄加号是通过1，横坐标score1是课程1得分，纵坐标score2是课程2得分。  
![这里写图片描述][70]  
（2）plot\_decision\_line()自定义绘制决策边界方法，这里分数数据进行标准化，边界直线图公式0=theta1\*x1+theta2\*x2+theta0 –>x2=-(theta1\*x1+theta0)/theta2，x1和x2对应exam1和exam2得分。theta通过sklearn的逻辑回归方法算出，具体看代码。  
![这里写图片描述][70 1]  
（3）plot\_decision\_regions其它博客给出绘制决策边界方法，挺好看的，大致和上边图一样  
![这里写图片描述][70 2]  
（4）test\_validate计算测试数据集准确度  
红点是真实值，绿点是根据training set得到回归方程计算出的预测值，这里测试集只有10个数据。  
![这里写图片描述][70 3]

### 5总结 ###

主要逻辑都在logistic\_regression()方法中，sklearn方法参数很多，需要稍微看看博客文档，理论还是看看课程比较好。  
sklearn文档：[http://scikit-learn.org/stable/index.html][http_scikit-learn.org_stable_index.html]  
matplotlib文档：[https://matplotlib.org/gallery/index.html][https_matplotlib.org_gallery_index.html]

[https_download.csdn.net_download_bjjoy2009_10478703]: https://download.csdn.net/download/bjjoy2009/10478703
[https_blog.csdn.net_loveliuzz_article_details_78708359]: https://blog.csdn.net/loveliuzz/article/details/78708359
[70]: /images/20220522/27dd96b0a86148bcb62737b85dfa7c9b.png
[70 1]: /images/20220522/4d60bde953f34f61829fd248b7d7b2c4.png
[70 2]: /images/20220522/1411a1dcc32d46e1aced636781f04ca4.png
[70 3]: /images/20220522/9b31f74bf6594117b33a5884d509cdca.png
[http_scikit-learn.org_stable_index.html]: http://scikit-learn.org/stable/index.html
[https_matplotlib.org_gallery_index.html]: https://matplotlib.org/gallery/index.html