day10决策树和随机森林实践

落日映苍穹つ 2021-12-22 03:31 235阅读 0赞

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10.1

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注：左半边为欠拟合，右半边过拟合，，此图的主要目的是告诉我们如何调参。

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#!/usr/bin/python
    # -*- coding:utf-8 -*-
    
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    from sklearn import tree
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.pipeline import Pipeline
    
    def iris_type(s):
        it = {b'Iris-setosa': 0, b'Iris-versicolor': 1, b'Iris-virginica': 2}
        return it[s]
    
    
    # 花萼长度、花萼宽度，花瓣长度，花瓣宽度
    # iris_feature = 'sepal length', 'sepal width', 'petal length', 'petal width'
    iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
    
    if __name__ == "__main__":
        mpl.rcParams['font.sans-serif'] = [u'SimHei']
        mpl.rcParams['axes.unicode_minus'] = False
    
        # path = '..\\8.Regression\\8.iris.data'  # 数据文件路径
        path = '8.iris.data'  # 数据文件路径
        data = np.loadtxt(path, dtype=float, delimiter=',', converters={4: iris_type})
        x, y = np.split(data, (4,), axis=1)
        # 为了可视化，仅使用前两列特征
        x = x[:, :2]
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)
        #ss = StandardScaler()
        #ss = ss.fit(x_train)
    
        # 决策树参数估计
        # min_samples_split = 10：如果该结点包含的样本数目大于10，则(有可能)对其分支
        # min_samples_leaf = 10：若将某结点分支后，得到的每个子结点样本数目都大于10，则完成分支；否则，不进行分支
        model = Pipeline([
            ('ss', StandardScaler()),
            ('DTC', DecisionTreeClassifier(criterion='entropy', max_depth=3))])
        # clf = DecisionTreeClassifier(criterion='entropy', max_depth=3)
        model = model.fit(x_train, y_train)
        y_test_hat = model.predict(x_test)      # 测试数据
    
        # 保存
        # dot -Tpng -o 1.png 1.dot
        f = open('.\\iris_tree.dot', 'w')
        tree.export_graphviz(model.get_params('DTC')['DTC'], out_file=f)
    
        # 画图
        N, M = 100, 100  # 横纵各采样多少个值
        x1_min, x1_max = x[:, 0].min(), x[:, 0].max()  # 第0列的范围
        x2_min, x2_max = x[:, 1].min(), x[:, 1].max()  # 第1列的范围
        t1 = np.linspace(x1_min, x1_max, N)
        t2 = np.linspace(x2_min, x2_max, M)
        x1, x2 = np.meshgrid(t1, t2)  # 生成网格采样点
        x_show = np.stack((x1.flat, x2.flat), axis=1)  # 测试点
    
        # # 无意义，只是为了凑另外两个维度
        # # 打开该注释前，确保注释掉x = x[:, :2]
        # x3 = np.ones(x1.size) * np.average(x[:, 2])
        # x4 = np.ones(x1.size) * np.average(x[:, 3])
        # x_test = np.stack((x1.flat, x2.flat, x3, x4), axis=1)  # 测试点
    
        cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
        cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
        y_show_hat = model.predict(x_show)  # 预测值
        y_show_hat = y_show_hat.reshape(x1.shape)  # 使之与输入的形状相同
        plt.figure(facecolor='w')
        plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light)  # 预测值的显示
        plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test.ravel(), edgecolors='k', s=100, cmap=cm_dark, marker='o')  # 测试数据
        plt.scatter(x[:, 0], x[:, 1], c=y.ravel(), edgecolors='k', s=40, cmap=cm_dark)  # 全部数据
        plt.xlabel(iris_feature[0], fontsize=15)
        plt.ylabel(iris_feature[1], fontsize=15)
        plt.xlim(x1_min, x1_max)
        plt.ylim(x2_min, x2_max)
        plt.grid(True)
        plt.title(u'鸢尾花数据的决策树分类', fontsize=17)
        plt.show()
    
        # 训练集上的预测结果
        y_test = y_test.reshape(-1)
        print(y_test_hat)
        print(y_test)
        result = (y_test_hat == y_test)   # True则预测正确，False则预测错误
        acc = np.mean(result)
        print('准确度: %.2f%%' % (100 * acc))
    
        # 过拟合：错误率
        depth = np.arange(1, 15)
        err_list = []
        for d in depth:
            clf = DecisionTreeClassifier(criterion='entropy', max_depth=d)
            clf = clf.fit(x_train, y_train)
            y_test_hat = clf.predict(x_test)  # 测试数据
            result = (y_test_hat == y_test)  # True则预测正确，False则预测错误
            err = 1 - np.mean(result)
            err_list.append(err)
            print(d, ' 准确度: %.2f%%' % (100 * err))
        plt.figure(facecolor='w')
        plt.plot(depth, err_list, 'ro-', lw=2)
        plt.xlabel(u'决策树深度', fontsize=15)
        plt.ylabel(u'错误率', fontsize=15)
        plt.title(u'决策树深度与过拟合', fontsize=17)
        plt.grid(True)
        plt.show()

代码

10.2

转载于:https://www.cnblogs.com/chengxiaofeng/p/10811281.html

[1570918-20190505094100481-1238213640.png]: /images/20211220/7c9d0bc279984ccbbb53b5e760d0b156.png
[1570918-20190505154322481-1654443088.png]: /images/20211220/89a8d57220fd440ebe2f5e5960d4422e.png
[1570918-20190505153848909-443659312.png]: /images/20211220/190e02acb15549baba051ec778e9ae08.png
[1570918-20190505154003959-744612788.png]: /images/20211220/c6b1a15a826f47de883c7868ad84cb57.png
[ContractedBlock.gif]: https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif
[ExpandedBlockStart.gif]: /images/20211220/05b795166d194e0f8aa419a9fb25cbd5.png