博客班级 AHPU机器学习
作业要求 作业要求
作业目标 决策树算法及应用
学号 3180701131 莫申勇

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;

四、实验过程及结果

实验代码

1.
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


2.

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


3.

datasets, labels = create_data()


4.

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


5.

train_data


6.

# 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]])



7.

info_gain_train(np.array(datasets))


8.

# 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)




9.

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


10.

tree


11.

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)

五、实验小结

本次实验后,能熟练应用决策树算法解决实际问题,本次实验后理解了决策树的原理,能根据不同的数据类型,选择不同的决策树算法;
能针对特定应用场景及数据,应用决策树算法解决实际问题。决策树计算复杂度不高,输出结果易于理解,对中间值的缺失不敏感,可以处理不相关特征数据;但也有缺点:可能会产生过度匹配问题。

实验四 任务内容 计划完成需要的时间(min) 实际完成需要的时间(min)
Planning 计划 120 8
Development 开发 100 150
Analysis 需求分析(包括学习新技术) 10 10
Design Spec 生成设计文档 30 40
Design Review 设计复审 5 10
Coding Standard 代码规范 3 2
Design 具体设计 10 12
Coding 具体编码 36 21
Code Review 代码复审 5 7
Test 测试(自我测试,修改代码,提交修改) 10 15
Reporting 报告 9 6
Test Report 测试报告 3 2
Size Measurement 计算工作量 2 1
Postmortem & Process Improvement Plan 事后总结,并提出过程改进计划 3 3