import numpy as npimport pandas as pdimport matplotlib.pyplot as plt%matplotlib inline
from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom collections import Counterimport mathfrom math import logimport pprint

# 书上题目5.1def 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

# 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# def entropy(y):#     """#     Entropy of a label sequence#     """#     hist = np.bincount(y)#     ps = hist / np.sum(hist)#     return -np.sum([p * np.log2(p) for p in ps if p > 0])

# 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)#     ent = entropy(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))

# 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'])

# datadef 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 DecisionTreeClassifierfrom sklearn.tree import export_graphvizimport 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)