python实现决策树ID3算法的示例代码

Python  /  管理员 发布于 7年前   248

在周志华的西瓜书和李航的统计机器学习中对决策树ID3算法都有很详细的解释，如何实现呢？核心点有如下几个步骤

step1:计算香农熵

from math import logimport operator# 计算香农熵def calculate_entropy(data):  label_counts = {}  for feature_data in data:    laber = feature_data[-1] # 最后一行是laber    if laber not in label_counts.keys():      label_counts[laber] = 0    label_counts[laber] += 1  count = len(data)  entropy = 0.0  for key in label_counts:    prob = float(label_counts[key]) / count    entropy -= prob * log(prob, 2)  return entropy

step2.计算某个feature的信息增益的方法

# 计算某个feature的信息增益# index:要计算信息增益的feature 对应的在data 的第几列# data 的香农熵def calculate_relative_entropy(data, index, entropy):  feat_list = [number[index] for number in data] # 得到某个特征下所有值（某列）  uniqual_vals = set(feat_list)  new_entropy = 0  for value in uniqual_vals:    sub_data = split_data(data, index, value)    prob = len(sub_data) / float(len(data))     new_entropy += prob * calculate_entropy(sub_data) # 对各子集香农熵求和  relative_entropy = entropy - new_entropy # 计算信息增益  return relative_entropy

step3.选择最大信息增益的feature

# 选择最大信息增益的featuredef choose_max_relative_entropy(data):  num_feature = len(data[0]) - 1  base_entropy = calculate_entropy(data)#香农熵  best_infor_gain = 0  best_feature = -1  for i in range(num_feature):    info_gain=calculate_relative_entropy(data, i, base_entropy)    #最大信息增益    if (info_gain > best_infor_gain):      best_infor_gain = info_gain      best_feature = i  return best_feature

step4.构建决策树

def create_decision_tree(data, labels):  class_list=[example[-1] for example in data]  # 类别相同，停止划分  if class_list.count(class_list[-1]) == len(class_list):    return class_list[-1]  # 判断是否遍历完所有的特征时返回个数最多的类别  if len(data[0]) == 1:    return most_class(class_list)  # 按照信息增益最高选取分类特征属性  best_feat = choose_max_relative_entropy(data)  best_feat_lable = labels[best_feat] # 该特征的label  decision_tree = {best_feat_lable: {}} # 构建树的字典  del(labels[best_feat]) # 从labels的list中删除该label  feat_values = [example[best_feat] for example in data]  unique_values = set(feat_values)  for value in unique_values:    sub_lables=labels[:]    # 构建数据的子集合，并进行递归    decision_tree[best_feat_lable][value] = create_decision_tree(split_data(data, best_feat, value), sub_lables)  return decision_tree

在构建决策树的过程中会用到两个工具方法：

# 当遍历完所有的特征时返回个数最多的类别def most_class(classList):  class_count={}  for vote in classList:    if vote not in class_count.keys():class_count[vote]=0    class_count[vote]+=1  sorted_class_count=sorted(class_count.items,key=operator.itemgetter(1),reversed=True)  return sorted_class_count[0][0]  # 工具函数输入三个变量（待划分的数据集，特征，分类值）返回不含划分特征的子集def split_data(data, axis, value):  ret_data=[]  for feat_vec in data:    if feat_vec[axis]==value :      reduce_feat_vec=feat_vec[:axis]      reduce_feat_vec.extend(feat_vec[axis+1:])      ret_data.append(reduce_feat_vec)  return ret_data

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