决策树决策森林2

1 Bagging策略

用同一组数据做好几次试验

2.随机森林

3.Bagging解决回归问题 （取均值降噪声）

4.样本不均衡的解决方案

5.一个最基本的决策树算法

#!/usr/bin/python
# -*- coding:utf-8 -*-

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.pipeline import Pipeline
import pydotplus
#直接判断精度
# from metrics import accu


# 花萼长度、花萼宽度，花瓣长度，花瓣宽度
iris_feature_E = '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'
iris_class = 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'


if __name__ == "__main__":
    #设置图片格式
    mpl.rcParams['font.sans-serif'] = [u'SimHei']
    mpl.rcParams['axes.unicode_minus'] = False

    path = 'iris.data'  # 数据文件路径
    data = pd.read_csv(path, header=None)
    x = data[[0, 1, 2 ,3]]
    #将y进行one-hot编码
    y = pd.Categorical(data[4]).codes
    # 为了可视化，仅使用前两列特征
    x = x.iloc[:, :2]
    x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=1)
    print(y_test.shape)

    # 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
    #criterion='entropy'设置熵，也可以设置成基尼系数，同时在决策树时候参数可以设置为深度
    model = DecisionTreeClassifier(criterion='entropy')
    model.fit(x_train, y_train)
    y_test_hat = model.predict(x_test)      # 测试数据
    # accuracy_score(y_test, y_test_hat)

    # 保存
    # dot -Tpng my.dot -o my.png
    # 1、输出
    with open('iris.dot', 'w') as f:
        #
        tree.export_graphviz(model, out_file=f)
    # 2、给定文件名
    tree.export_graphviz(model, out_file='iris1.dot')
    # 3、输出为pdf格式
    # 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
    N, M = 50, 50  # 横纵各采样多少个值
    x1_min, x2_min = x.min()
    x1_max, x2_max = x.max()
    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
    print("x_show", x_show)
    print("x_show", x_show)

    # # 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
    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)  # 预测值的显示
    plt.scatter(x_test[0], x_test[1], c=y_test.ravel(), edgecolors='k', s=150, zorder=10, cmap=cm_dark, marker='*')  # 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.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
        if d == 1:
            print(result)
        err = 1 - np.mean(result)
        err_list.append(err)
        # print d, ' 准确度: %.2f%%' % (100 * err)
        print(d, ' 错误率: %.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()