缺失值算是决策树里处理起来比较麻烦的了，其他简单的我就不发布了。
# encoding:utf-8 from __future__ import division __author__ = 'HP' import copy import math import numpy as np import pandas as pd from collections import Counter from sklearn.preprocessing import LabelEncoder ################################ # id3 # 离散属性 # 多分类 # 多重字典记录学习规则 # 非递归 # 深度优先 # 预剪枝 ### 缺失值处理 # 解决两个问题 # 如何进行划分属性选择，缺失值如何处理 # 如何进行样本划分，缺失值对应的样本如何划分 ################################ ''' 缺失值处理 1. 如何进行属性选择 a. 第一次选择划分属性时，样本等权重，均为1，找出未缺失的样本集，计算该样本集的信息增益 和 该样本集的占比，两者相乘即为真正的信息增益 . 注意这时计算占比，就是数个数，因为权重都是1 . 计算信息增益时，P也是数个数 b. 后面选择划分属性时，样本不等权重，找出未缺失的样本集，计算该样本集的信息增益 和 该样本集的占比，两者相乘即为真正的信息增益 . 此时样本权重不全为1 . 计算占比时不是数个数，而是求权重和 . 计算信息增益的P时，也是求权重和 2. 如何划分节点 a. 未缺失按照正常方法划分，权重都为1 b. 缺失值划到所有子集当中，权重不为1， 而是该属性值占未缺失的样本集的比例 ''' def mydata(): data = pd.read_csv('xg3.txt',index_col=[0], encoding='gbk') data[[-1]] = data.apply(lambda x:x[-1].strip(), axis=1) # print(data) # print(pd.get_dummies(data[[0]])) data.columns = range(9) # print(data) encode_str = LabelEncoder() str_cols = [0, 1, 2, 3, 4, 5, 8] for i in str_cols: data[[i]] = encode_str.fit_transform(data[[i]]) return data.values def get_label(labels): count_label = Counter(labels) key = None sum = 0 for label, count in count_label.items(): if count > sum: sum = count key = label return key def entropy(attr): # 信息熵 attr_values_count = Counter(attr) attr_len = len(attr) sum = 0 for i in attr_values_count.values(): sum += -1 * i / attr_len * math.log(i / attr_len, 2) return sum def gain_queshi_equal_weight(attr, label): # 缺失属性的信息增益，用于初次划分，初次划分样本权重都为1 index_nan = np.isnan(attr) index_nonan = np.where(attr>=0) # 未缺失属性及标签 attr_new = attr[index_nonan] label_new = label[index_nonan] # 未缺失样本数 count_nonan = label_new.shape[0] # 未缺失占比 zhanbi = attr_new.shape[0]/attr.shape[0] # 未缺失的原始熵 ori_entropy = entropy(label_new) # 未缺失的新熵 new_entropy = 0 for key, count in Counter(attr_new).items(): # 未缺失中属性值为key的占比 * key对应的样本集的熵 new_entropy += count/count_nonan * entropy(label_new[np.where(attr_new == key)]) # 信息增益 gain = zhanbi * (ori_entropy - new_entropy) return gain def split_node_queshi(node, attr_split): # 属性有缺失值的样本划分 index_nan = np.isnan(node[:,attr_split]) index_nonan = np.where(node[:,attr_split]>=0) # 未缺失属性值对应的样本集 node_new = node[index_nonan] # 缺失属性值对应的样本集 sample_queshi = node[index_nan] # 未缺失样本大小 count_nonan = node_new.shape[0] ### 对该样本集进行划分 # 未缺失的划分 [属性值，样本集，样本占比] split = [] for key, node_child in pd.DataFrame(node_new).groupby(attr_split): # 属性值为key的样本在未缺失样本中占比 zhanbi_key = round(len(node_child) / count_nonan, 3) # 未缺失样本权重为1 weight = [1] * len(node_child) # 添加缺失样本 node_child = np.vstack((node_child.values, sample_queshi)) # 缺失样本权重 weight.extend([zhanbi_key] * len(sample_queshi)) split.append([key, node_child, np.array(weight)]) return split def entropy_no_equal_weight(attr, weight): # 样本不等权重的信息熵 sum = 0 sum_weight = np.sum(weight) for key in Counter(attr).keys(): index = np.where(attr==key) zhanbi = np.sum(weight[index]) / sum_weight sum += -1 * zhanbi * math.log(zhanbi, 2) return sum def gain_queshi_no_equal_weight(attr, weight, label): # 缺失属性的信息增益，样本权重不相等，用于第一次之后的属性选择 index_nan = np.isnan(attr) index_nonan = np.where(attr>=0) # 未缺失的属性/标签/权重 attr_new = attr[index_nonan] label_new = label[index_nonan] weight_new = weight[index_nonan] # 未缺失对应的样本占比 zhanbi = np.sum(weight_new) / np.sum(weight) ### 未缺失对应的信息增益 # 未缺失对应的原始熵 ori_entropy = entropy_no_equal_weight(label_new, weight_new) # 未缺失的新熵 new_entropy = 0 for key in Counter(attr_new).keys(): index_key = np.where(attr_new==key) label_key = label_new[index_key] weight_key = weight_new[index_key] new_entropy += len(label_key) / len(label_new) * entropy_no_equal_weight(label_key, weight_key) # 信息增益 gain = zhanbi * (ori_entropy - new_entropy) return gain if __name__ == '__main__': data = mydata() # 离散型样本 data = data[:,[0,1,2,3,4,5,8]] data[0, 0] = None data[4, 0] = None data[12, 0] = None data[7, 3] = None data[9, 3] = None print(data) # 缺失属性的信息增益 样本等权重 for i in range(data.shape[1]): print gain_queshi_equal_weight(data[:,i], data[:,-1]) # 缺失值属性的样本划分 split = split_node_queshi(data, 3) print(split) # 缺失属性的信息增益 样本不等权重 # weight = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1/3, 1/3]) # gain_queshi_no_equal_weight(data[:,0], weight, data[:,-1]) # 以色泽为例 gain = gain_queshi_no_equal_weight(split[2][1][:,0], split[2][2],split[2][1][:,-1]) print(gain)