机器学习笔记 ——决策树和随机森林

DecisionTree

import numpy as np
import pandas as pd
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.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
import pydotplus

if __name__ == "__main__":
    mpl.rcParams['font.sans-serif'] = ['simHei']
    mpl.rcParams['axes.unicode_minus'] = False

    iris_feature_E = 'sepal length', 'sepal width', 'petal length', 'petal width'
    iris_feature = '花萼长度', '花萼宽度', '花瓣长度', '花瓣宽度'
    iris_class = 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'

    path = 'iris.data'
    data = pd.read_csv(path, header=None)
    x = data[list(range(4))]
    y = LabelEncoder().fit_transform(data[4])
    # 为了可视化，只显示前两列特征
    x = x[[0, 1]]
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)

    # 决策树参数估计
    model = DecisionTreeClassifier(criterion='entropy')  # criterion选择特征的标准，默认gini
    model.fit(x_train, y_train)
    y_train_pred = model.predict(x_train)
    print('训练集正确率：', accuracy_score(y_train, y_train_pred))
    y_test_hat = model.predict(x_test)
    print('测试集正确率', accuracy_score(y_test, y_test_hat))

    # 保存
    # 1.输出
    tree.export_graphviz(model, out_file='iris.dot', feature_names=iris_feature_E[0:2], class_names=iris_class,
                         filled=True, rounded=True, special_characters=True)
    # 2.给定文件名
    # tree.export_graphviz(model, out_file='iris.dot')
    # 3.输出为pdf格式
    dot_data = tree.export_graphviz(model, out_file=None, feature_names=iris_feature_E[0:2], class_names=iris_class,
                                    filled=True, rounded=True, special_characters=True)

    # graph = pydotplus.graph_from_dot_data(dot_data)
    # graph.write_pdf('iris.pdf')
    # f = open('iris.png', 'wb')
    # f.write(graph.create_png())
    # f.close()

    # 画图
    N, M = 60, 60  # 横纵采样各多少值
    x1_min, x2_min = x.min()
    x1_max, x2_max = x.max()
    t1 = np.linspace(x1_min, x2_min, N)
    t2 = np.linspace(x1_max, 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
    print(x_show.shape)
    print(('x_show = \n', x_show))

    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
    print(y_show_hat.shape)
    print(y_show_hat)
    y_show_hat = y_show_hat.reshape(x1.shape)  # 使之与输入的形状相同
    print(y_show_hat)

    plt.figure(facecolor='w')
    plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light)  # 预测值显示
    plt.scatter(x_test[0], x_test[1], c=y_test.ravel(), edgecolors='k', s=100,
                   zorder=10, cmap=cm_dark, marker="*")
    plt.scatter(x[0], x[1], c=y.ravel(), edgecolors='k', s=20, cmap=cm_dark)
    plt.xlabel(iris_feature[0], fontsize=13)
    plt.ylabel(iris_feature[1], fontsize=13)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)
    plt.grid(b=True, ls=':', color='#606060')
    plt.title('鸢尾花数据的决策树分类', fontsize=15)
    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)
    acc = np.mean(result)
    print('准确度：%.2f%%' % (100 * acc))

    # 过拟合：错误率
    depth = np.arange(1, 15)
    err_train_list = []
    err_test_list = []
    clf = DecisionTreeClassifier(criterion='entropy')
    for d in depth:
        clf.set_params(max_depth=d)
        clf.fit(x_train, y_train)
        y_train_pred = clf.predict(x_train)
        err_train = 1 - accuracy_score(y_train, y_train_pred)
        err_train_list.append(err_train)
        y_test_pred = clf.predict(x_test)
        err_test = 1 - accuracy_score(y_test, y_test_pred)
        err_test_list.append(err_test)
        print(d, ' 测试集错误率: %.2f%%' % (100 * err_test))
    plt.figure(facecolor='w')
    plt.plot(depth, err_test_list, 'ro-', markeredgecolor='k', lw=2, label='测试集错误率')
    plt.plot(depth, err_train_list, 'go-', markeredgecolor='k', lw=2, label='训练集错误率')
    plt.xlabel('决策树深度', fontsize=13)
    plt.ylabel('错误率', fontsize=13)
    plt.legend(loc='lower left', fontsize=13)
    plt.title('决策树深度与过拟合', fontsize=15)
    plt.grid(b=True, ls=':', color='#606060')
    plt.show()