本文主要是介绍实验四 决策树算法及应用，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

实验一 决策树算法及应用

目录

- 一、实验目的

- 二、实验内容

- 三、实验报告要求

- 四、实验过程及步骤

- 五、实验小结

作业信息

一、实验目的

1.理解决策树算法原理，掌握决策树算法框架；
2.理解决策树学习算法的特征选择、树的生成和树的剪枝；
3.能根据不同的数据类型，选择不同的决策树算法；
4.针对特定应用场景及数据，能应用决策树算法解决实际问题

二、实验内容

1.设计算法实现熵、经验条件熵、信息增益等方法。
2.实现ID3算法。
3.熟悉sklearn库中的决策树算法；
4.针对iris数据集，应用sklearn的决策树算法进行类别预测。
5.针对iris数据集，利用自编决策树算法进行类别预测。

三、实验报告要求

1.对照实验内容，撰写实验过程、算法及测试结果；
2.代码规范化：命名规则、注释；
3.分析核心算法的复杂度；
4.查阅文献，讨论ID3、5算法的应用场景；
查询文献，分析决策树剪枝策略。

四、实验过程及其步骤

实验代码

1.设计算法实现熵、经验条件熵、信息增益等方法。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

from collections import Counter
import math
from math import log

import pprint

def create_data():
    datasets = [['青年', '否', '否', '一般', '否'],
               ['青年', '否', '否', '好', '否'],
               ['青年', '是', '否', '好', '是'],
               ['青年', '是', '是', '一般', '是'],
               ['青年', '否', '否', '一般', '否'],
               ['中年', '否', '否', '一般', '否'],
               ['中年', '否', '否', '好', '否'],
               ['中年', '是', '是', '好', '是'],
               ['中年', '否', '是', '非常好', '是'],
               ['中年', '否', '是', '非常好', '是'],
               ['老年', '否', '是', '非常好', '是'],
               ['老年', '否', '是', '好', '是'],
               ['老年', '是', '否', '好', '是'],
               ['老年', '是', '否', '非常好', '是'],
               ['老年', '否', '否', '一般', '否'],
               ]
    labels = [u'年龄', u'有工作', u'有自己的房子', u'信贷情况', u'类别']
    # 返回数据集和每个维度的名称
    return datasets, labels

datasets, labels = create_data()

train_data = pd.DataFrame(datasets, columns=labels)

train_data

X, y = data[:,:-1], data[:,-1]  # 数据类型转换，为了后面的数学计算

# 熵
def calc_ent(datasets):
    data_length = len(datasets)
    label_count = {}
    for i in range(data_length):
        label = datasets[i][-1]
        if label not in label_count:
            label_count[label] = 0
        label_count[label] += 1
    ent = -sum([(p/data_length)*log(p/data_length, 2) for p in label_count.values()])
    return ent

# 经验条件熵
def cond_ent(datasets, axis=0):
    data_length = len(datasets)
    feature_sets = {}
    for i in range(data_length):
        feature = datasets[i][axis]
        if feature not in feature_sets:
            feature_sets[feature] = []
        feature_sets[feature].append(datasets[i])
    cond_ent = sum([(len(p)/data_length)*calc_ent(p) for p in feature_sets.values()])
    return cond_ent

# 信息增益
def info_gain(ent, cond_ent):
    return ent - cond_ent

def info_gain_train(datasets):
    count = len(datasets[0]) - 1
    ent = calc_ent(datasets)
    best_feature = []
    for c in range(count):
        c_info_gain = info_gain(ent, cond_ent(datasets, axis=c))
        best_feature.append((c, c_info_gain))
        print('特征({}) - info_gain - {:.3f}'.format(labels[c], c_info_gain))
    # 比较大小
    best_ = max(best_feature, key=lambda x: x[-1])
    return '特征({})的信息增益最大，选择为根节点特征'.format(labels[best_[0]])

info_gain_train(np.array(datasets))

2.利用ID3算法生成决策树

# 定义节点类 二叉树
class Node:
    def __init__(self, root=True, label=None, feature_name=None, feature=None):
        self.root = root
        self.label = label
        self.feature_name = feature_name
        self.feature = feature
        self.tree = {}
        self.result = {'label:': self.label, 'feature': self.feature, 'tree': self.tree}

    def __repr__(self):
        return '{}'.format(self.result)

    def add_node(self, val, node):
        self.tree[val] = node

    def predict(self, features):
        if self.root is True:
            return self.label
        return self.tree[features[self.feature]].predict(features)
    
class DTree:
    def __init__(self, epsilon=0.1):
        self.epsilon = epsilon
        self._tree = {}

    # 熵
    @staticmethod
    def calc_ent(datasets):
        data_length = len(datasets)
        label_count = {}
        for i in range(data_length):
            label = datasets[i][-1]
            if label not in label_count:
                label_count[label] = 0
            label_count[label] += 1
        ent = -sum([(p/data_length)*log(p/data_length, 2) for p in label_count.values()])
        return ent

    # 经验条件熵
    def cond_ent(self, datasets, axis=0):
        data_length = len(datasets)
        feature_sets = {}
        for i in range(data_length):
            feature = datasets[i][axis]
            if feature not in feature_sets:
                feature_sets[feature] = []
            feature_sets[feature].append(datasets[i])
        cond_ent = sum([(len(p)/data_length)*self.calc_ent(p) for p in feature_sets.values()])
        return cond_ent

    # 信息增益
    @staticmethod
    def info_gain(ent, cond_ent):
        return ent - cond_ent

    def info_gain_train(self, datasets):
        count = len(datasets[0]) - 1
        ent = self.calc_ent(datasets)
        best_feature = []
        for c in range(count):
            c_info_gain = self.info_gain(ent, self.cond_ent(datasets, axis=c))
            best_feature.append((c, c_info_gain))
        # 比较大小
        best_ = max(best_feature, key=lambda x: x[-1])
        return best_

    def train(self, train_data):
        """
        input:数据集D(DataFrame格式)，特征集A，阈值eta
        output:决策树T
        """
        _, y_train, features = train_data.iloc[:, :-1], train_data.iloc[:, -1], train_data.columns[:-1]
        # 1,若D中实例属于同一类Ck，则T为单节点树，并将类Ck作为结点的类标记，返回T
        if len(y_train.value_counts()) == 1:
            return Node(root=True,
                        label=y_train.iloc[0])

        # 2, 若A为空，则T为单节点树，将D中实例树最大的类Ck作为该节点的类标记，返回T
        if len(features) == 0:
            return Node(root=True, label=y_train.value_counts().sort_values(ascending=False).index[0])

        # 3,计算最大信息增益 同5.1,Ag为信息增益最大的特征
        max_feature, max_info_gain = self.info_gain_train(np.array(train_data))
        max_feature_name = features[max_feature]

        # 4,Ag的信息增益小于阈值eta,则置T为单节点树，并将D中是实例数最大的类Ck作为该节点的类标记，返回T
        if max_info_gain < self.epsilon:
            return Node(root=True, label=y_train.value_counts().sort_values(ascending=False).index[0])

        # 5,构建Ag子集
        node_tree = Node(root=False, feature_name=max_feature_name, feature=max_feature)

        feature_list = train_data[max_feature_name].value_counts().index
        for f in feature_list:
            sub_train_df = train_data.loc[train_data[max_feature_name] == f].drop([max_feature_name], axis=1)

            # 6, 递归生成树
            sub_tree = self.train(sub_train_df)
            node_tree.add_node(f, sub_tree)

        # pprint.pprint(node_tree.tree)
        return node_tree

    def fit(self, train_data):
        self._tree = self.train(train_data)
        return self._tree

    def predict(self, X_test):
        return self._tree.predict(X_test)

datasets, labels = create_data()
data_df = pd.DataFrame(datasets, columns=labels)
dt = DTree()
tree = dt.fit(data_df)

tree

dt.predict(['老年', '否', '否', '一般'])

# data
def create_data():
    iris = load_iris()
    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    df['label'] = iris.target
    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']
    data = np.array(df.iloc[:100, [0, 1, -1]])
    # print(data)
    return data[:,:2], data[:,-1]

X, y = create_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

from sklearn.tree import DecisionTreeClassifier

from sklearn.tree import export_graphviz
import graphviz

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train,)

clf.score(X_test, y_test)

tree_pic = export_graphviz(clf, out_file="mytree.pdf")
with open('mytree.pdf') as f:
    dot_graph = f.read()

graphviz.Source(dot_graph)

from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing
import numpy as np
import pandas as pd
from sklearn import tree
import graphviz
features = ["年龄", "有工作", "有自己的房子", "信贷情况"]
X_train = pd.DataFrame([
    ["青年", "否", "否", "一般"],
    ["青年", "否", "否", "好"],
    ["青年", "是", "否", "好"],
    ["青年", "是", "是", "一般"],
    ["青年", "否", "否", "一般"],
    ["中年", "否", "否", "一般"],
    ["中年", "否", "否", "好"],
    ["中年", "是", "是", "好"],
    ["中年", "否", "是", "非常好"],
    ["中年", "否", "是", "非常好"],
    ["老年", "否", "是", "非常好"],
    ["老年", "否", "是", "好"],
    ["老年", "是", "否", "好"],
    ["老年", "是", "否", "非常好"],
    ["老年", "否", "否", "一般"]
])
y_train = pd.DataFrame(["否", "否", "是", "是", "否",
                        "否", "否", "是", "是", "是",
                        "是", "是", "是", "是", "否"])
# 数据预处理
le_x = preprocessing.LabelEncoder()
le_x.fit(np.unique(X_train))
X_train = X_train.apply(le_x.transform)
le_y = preprocessing.LabelEncoder()
le_y.fit(np.unique(y_train))
y_train = y_train.apply(le_y.transform)
# 调用sklearn.DT建立训练模型
model_tree = DecisionTreeClassifier()
model_tree.fit(X_train, y_train)
# 可视化
dot_data = tree.export_graphviz(model_tree, out_file=None,
                                    feature_names=features,
                                    class_names=[str(k) for k in np.unique(y_train)],
                                    filled=True, rounded=True,
                                    special_characters=True)
graph = graphviz.Source(dot_data)
graph

import numpy as np
class LeastSqRTree:
    def __init__(self, train_X, y, epsilon):
        # 训练集特征值
        self.x = train_X
        # 类别
        self.y = y
        # 特征总数
        self.feature_count = train_X.shape[1]
        # 损失阈值
        self.epsilon = epsilon
        # 回归树
        self.tree = None
    def _fit(self, x, y, feature_count, epsilon):
        # 选择最优切分点变量j与切分点s
        (j, s, minval, c1, c2) = self._divide(x, y, feature_count)
        # 初始化树
        tree = {"feature": j, "value": x[s, j], "left": None, "right": None}
        if minval < self.epsilon or len(y[np.where(x[:, j] <= x[s, j])]) <= 1:
            tree["left"] = c1
        else:
            tree["left"] = self._fit(x[np.where(x[:, j] <= x[s, j])],
                                     y[np.where(x[:, j] <= x[s, j])],
                                     self.feature_count, self.epsilon)
        if minval < self.epsilon or len(y[np.where(x[:, j] > s)]) <= 1:
            tree["right"] = c2
        else:
            tree["right"] = self._fit(x[np.where(x[:, j] > x[s, j])],
                                      y[np.where(x[:, j] > x[s, j])],
                                      self.feature_count, self.epsilon)
        return tree
    def fit(self):
        self.tree = self._fit(self.x, self.y, self.feature_count, self.epsilon)
    @staticmethod
    def _divide(x, y, feature_count):
        # 初始化损失误差
        cost = np.zeros((feature_count, len(x)))
        # 公式5.21
        for i in range(feature_count):
            for k in range(len(x)):
                # k行i列的特征值
                value = x[k, i]
                y1 = y[np.where(x[:, i] <= value)]
                c1 = np.mean(y1)
                y2 = y[np.where(x[:, i] > value)]
                c2 = np.mean(y2)
                y1[:] = y1[:] - c1
                y2[:] = y2[:] - c2
                cost[i, k] = np.sum(y1 * y1) + np.sum(y2 * y2)
        # 选取最优损失误差点
        cost_index = np.where(cost == np.min(cost))
        # 选取第几个特征值
        j = cost_index[0][0]
        # 选取特征值的切分点
        s = cost_index[1][0]
        # 求两个区域的均值c1,c2
        c1 = np.mean(y[np.where(x[:, j] <= x[s, j])])
        c2 = np.mean(y[np.where(x[:, j] > x[s, j])])
        return j, s, cost[cost_index], c1, c2

train_X = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).T
y = np.array([4.50, 4.75, 4.91, 5.34, 5.80, 7.05, 7.90, 8.23, 8.70, 9.00])
model_tree = LeastSqRTree(train_X, y, .2)
model_tree.fit()
model_tree.tree

五、实验小结

本次实验学习了决策树决策树算法原理，并且实现了简单的掌握决策树算法，以及决策树学习算法的特征选择、树的生成和树的剪枝。决策树只需要一次构建，反复使用，效率较高，每一次预测的最大计算次数不超过决策树的深度，可以处理不相关特征数据，能够处理多输出的问题，并且对缺失值不敏感；但是对连续性的字段比较难预测，容易出现过拟合，当类别太多时，错误可能就会增加的比较快，而且对于各类别样本数量不一致的数据，在决策树当中，信息增益的结果偏向于那些具有更多数值的特征。

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