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"""
决策树算法
"""
from __future__ import division
import math
import operator
from collections import Counter
__author__ = 'xyz'
def (data_set):
"""
计算香农熵
:param data_set:
:return:
"""
data_length = len(data_set)
label_counts = Counter([val[-1] for val in data_set])
pilog2pi = [val / data_length * math.log(val / data_length, 2) for val in label_counts.itervalues()]
return - reduce(
operator.add,
pilog2pi
) if pilog2pi else 0
def split_data_set(data_set, axis, value):
"""
分割数据集，筛选指定特征下的数据值的集合
:param data_set: 数据集合
:param axis: 第几列
:param value: 筛选的值
:return: 除去axis列的，并且axis列的值为value的的数据集合
"""
return [[v for i, v in enumerate(val) if i != axis] for val in data_set if val[axis] == value]
def choose_best_feature_to_split(data_set):
"""
选择最好的数据集划分方式
:param data_set: 数据集
:return: 划分方式最好是第几项
"""
base_ent = calc_ 大专栏 决策树shannon_ent(data_set)
best_info_gain, best_feature = 0.0, -1
for i in range(len(data_set[0]) - 1):
unique_value = set(data_set[i])
child_ent = 0.0
for val in unique_value:
child_data_set = split_data_set(data_set, i, val)
child_ent += (len(data_set) - 1) / len(data_set) * calc_shannon_ent(child_data_set)
info_gain = base_ent - child_ent
if info_gain > best_info_gain:
best_info_gain = info_gain
best_feature = i
return best_feature
def majority_ent(class_list):
"""
取出出现次数最多的标签
:param class_list:
:return:
"""
class_count = Counter(class_list)
sorted_class_count = sorted(class_count.items(), key=lambda x, y: cmp(x[1], y[1]), reverse=True)
return sorted_class_count[0][0]
def create_tree(data_set, labels):
"""
创建树
:param data_set: 数据集
:param labels: 标签集合
:return: 决策树
"""
class_list = [val[-1] for val in data_set]
if class_list.count(class_list[0]) == len(class_list):
return class_list[0]
if len(data_set[0]) == 1:
return majority_ent(class_list)
best_feat = choose_best_feature_to_split(data_set)
best_feat_label = labels[best_feat]
my_tree = {best_feat_label: {}}
del labels[best_feat]
feat_values = [val[best_feat] for val in data_set]
unique_vals = set(feat_values)
for value in unique_vals:
sub_labels = labels[:]
my_tree[best_feat_label][value] = create_tree(split_data_set(data_set, best_feat, value), sub_labels)
return my_tree
if __name__ == "__main__":
data_set = [[1, 1, 'yes'], [1, 1, 'yes'], [1, 0, 'no'], [0, 1, 'no'], [0, 1, 'no']]
print calc_shannon_ent(data_set)
print split_data_set(data_set, 0, 1)
print choose_best_feature_to_split(data_set)
print create_tree(data_set, ['no surfacing', 'flippers'])
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