机器学习基础算法之决策树和随机森林比较（实现鸢尾花数据集分析）

code:

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.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pydotplus
 
if __name__ == "__main__":
   
    iris_feature_E = "sepal lenght", "sepal width", "petal length", "petal width"
    iris_feature = "the length of sepal", "the width of sepal", "the length of petal", "the width of petal"
    iris_class = "Iris-setosa", "Iris-versicolor", "Iris-virginica"
    
    data = pd.read_csv("iris.data", header=None)
    iris_types = data[4].unique()
    for i, type in enumerate(iris_types):
        data.set_value(data[4] == type, 4, i)
    x, y = np.split(data.values, (4,), axis=1)
    x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=1)
    print(y_test)
 
    model = DecisionTreeClassifier(criterion='entropy', max_depth=6)
    model = model.fit(x_train, y_train)
    y_test_hat = model.predict(x_test)
    with open('iris.dot', 'w') as f:
        tree.export_graphviz(model, out_file=f)
    dot_data = tree.export_graphviz(model, out_file=None, feature_names=iris_feature_E, 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
    # 横纵各采样多少个值
    N, M = 50, 50
    # 第0列的范围
    x1_min, x1_max = x[:, 0].min(), x[:, 0].max()
    # 第1列的范围
    x2_min, x2_max = x[:, 1].min(), x[:, 1].max()
    t1 = np.linspace(x1_min, x1_max, N)
    t2 = np.linspace(x2_min, x2_max, M)
    # 生成网格采样点
    x1, x2 = np.meshgrid(t1, t2)
    # # 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])
    # 测试点
    x_show = np.stack((x1.flat, x2.flat, x3, x4), axis=1)
    print("x_show_shape:\n", x_show.shape)
 
    cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
    cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
    # 预测值
    y_show_hat = model.predict(x_show)
    print(y_show_hat.shape)
    print(y_show_hat)
    # 使之与输入的形状相同
    y_show_hat = y_show_hat.reshape(x1.shape)
    print(y_show_hat)
    plt.figure(figsize=(15, 15), facecolor='w')
    # 预测值的显示
    plt.pcolormesh(x1, x2, y_show_hat, cmap=cm_light)
    print(y_test)
    print(y_test.ravel())
    # 测试数据
    plt.scatter(x_test[:, 0], x_test[:, 1], c=np.squeeze(y_test), edgecolors='k', s=120, cmap=cm_dark, marker='*')
    # 全部数据
    plt.scatter(x[:, 0], x[:, 1], c=np.squeeze(y), edgecolors='k', s=40, cmap=cm_dark)
    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('yuanwei flowers regressiong with DecisionTree', 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)
    # True则预测正确，False则预测错误
    result = (y_test_hat == y_test)
    acc = np.mean(result)
    print('accuracy: %.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)
        # 测试数据
        y_test_hat = clf.predict(x_test)
        # True则预测正确，False则预测错误
        result = (y_test_hat == y_test)
        err = 1 - np.mean(result)
        err_list.append(err)
        print(d, 'error ratio: %.2f%%' % (100 * err))
    plt.figure(figsize=(15, 15), facecolor='w')
    plt.plot(depth, err_list, 'ro-', lw=2)
    plt.xlabel('DecisionTree Depth', fontsize=15)
    plt.ylabel('error ratio', fontsize=15)
    plt.title('DecisionTree Depth and Overfit', fontsize=17)
    plt.grid(True)
    plt.show()

image.png
image.png
image.png

生成的图文件：

image.png

鸢尾花的数据特征一共有四种：花萼长度、花萼宽度，花瓣长度，花瓣宽度。然后再使用决策树两两特征进行分类：

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.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pydotplus
 
if __name__ == "__main__":
   
    iris_feature_E = "sepal lenght", "sepal width", "petal length", "petal width"
    iris_feature = "the length of sepal", "the width of sepal", "the length of petal", "the width of petal"
    iris_class = "Iris-setosa", "Iris-versicolor", "Iris-virginica"
    
    data = pd.read_csv("iris.data", header=None)
    iris_types = data[4].unique()
    for i, type in enumerate(iris_types):
        data.set_value(data[4] == type, 4, i)
    x_train, y = np.split(data.values, (4,), axis=1)
 
    feature_pairs = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]
    plt.figure(figsize=(15, 15), facecolor='w')
    for i, pair in enumerate(feature_pairs):
        # 准备数据
        x = x_train[:, pair]
        # 决策树进行学习
        clf = DecisionTreeClassifier(criterion='entropy', min_samples_leaf=3)
        dt_clf = clf.fit(x, y)
        # 开始画图
        N, M = 500, 500
        # 第0列的范围
        x1_min, x1_max = x[:, 0].min(), x[:, 0].max()   
        # 第1列的范围
        x2_min, x2_max = x[:, 1].min(), x[:, 1].max()   
        t1 = np.linspace(x1_min, x1_max, N)
        t2 = np.linspace(x2_min, x2_max, M)
        # 生成网格采样点
        x1, x2 = np.meshgrid(t1, t2)           
        # 测试点         
        x_test = np.stack((x1.flat, x2.flat), axis=1)
        # 在训练集上预测结果
        y_hat = dt_clf.predict(x)
        y = y.reshape(-1)
        # 统计预测正确的个数
        c = np.count_nonzero(y_hat == y)
        print("y_hat:\n", y_hat)
        print("y:\n", y)
        '''
        set1 = set(y_hat)
        set2 = set(y)
        print(list(set1 & set2))
        if y_hat.any() != y.any():
            print('predict:%.3f   real:%.3f' %(y_hat.all(), y.all()))
        '''
        # 打印相关信息
        print('features:\t', iris_feature[pair[0]], ' + ', iris_feature[pair[1]])
        print('the number of true prediction:', c)
        print('acc:%.2f%%' %(100 * float(c) / float(len(y))))
 
        # 画图显示
        cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
        cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
        # 预测值
        y_test_hat = dt_clf.predict(x_test)
        # reshape到和输入的x1相同格式
        y_test_hat = y_test_hat.reshape(x1.shape)
        plt.subplot(2, 3, i+1)
        plt.pcolormesh(x1, x2, y_test_hat, cmap=cm_light)
        plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', cmap=cm_dark)
        plt.xlabel(iris_feature[pair[0]], fontsize=14)
        plt.ylabel(iris_feature[pair[1]], fontsize=14)
        plt.xlim(x1_min, x1_max)
        plt.ylim(x2_min, x2_max)
        plt.grid()
    plt.suptitle('the result of yuanwei flowers in each two features with dcisiontree', fontsize=20)
    plt.tight_layout(2)
    plt.subplots_adjust(top=0.92)
    plt.show()

image.png
image.png

显然第二种组合效果还可以的。
接着我们使用随机森林算法来分类看看效果：

只需要在上面的代码中修改：

# 决策树进行学习
clf = DecisionTreeRegressor(n_estimators=200, criterion='entropy', max_depth=6)

为：

# 决策树进行学习
clf = RandomForestClassifier(n_estimators=200, criterion='entropy', max_depth=6)

效果：

image.png
image.png

看得出来随机森林的分类要比决策树好，随机森林因为是根据多个决策树弱分类器联合成一个强分类器，所以其边界出呈现很多的锯齿，分类的准确度也提高很多,150个数据，最后只有一个分错。