# -*- coding: utf-8 -*- import sys import os import numpy as np import pickle from sklearn import metrics #导入数据集 def loadDataSet(): postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'], ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him', 'my'], ['stop', 'posting', 'stupid', 'worthless', 'garbage'], ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'], ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']] classVec = [0, 1, 0, 1, 0, 1] # 1 is abusive, 0 not,分类 return postingList, classVec # 读取文件 def readfile(path): fp = open(path, "rb") content = fp.read() fp.close() return content """ #计算分类精度： def metrics_result(actual,predict): print('精度:{0:.3f}'.format(metrics.precision_score(actual,predict))) print ('召回:{0:0.3f}'.format(metrics.recall_score(actual,predict))) print ('f1-score:{0:.3f}'.format(metrics.f1_score(actual,predict))) """ # 读取bunch对象 def readbunchobj(path): file_obj = open(path, "rb") bunch = pickle.load(file_obj) file_obj.close() return bunch # 写入bunch对象 def writebunchobj(path, bunchobj): file_obj = open(path, "wb") pickle.dump(bunchobj, file_obj) file_obj.close() class NBayes(object): def __init__(self): self.vocabulary = [] # 词典 self.idf = 0 # 词典的idf权值向量 self.tf = 0 # 训练集的权值矩阵 self.tdm = 0 # P(x|yi) self.Pcates = {} # P(yi)--是个类别字典，这个集合就是p(yi)的值的集合 self.labels = [] # 对应每个文本的分类，是个外部导入的列表 self.doclength = 0 # 训练集文本数 self.vocablen = 0 # 词典词长 self.testset = 0 # 测试集 # 加载训练集并生成词典，以及tf, idf值 def train_set(self, trainset, classVec): self.cate_prob(classVec) # 计算每个分类在数据集中的概率：P(yi) self.doclength = len(trainset) tempset = set() [tempset.add(word) for doc in trainset for word in doc] # 生成词典 self.vocabulary = list(tempset) self.vocablen = len(self.vocabulary) self.calc_wordfreq(trainset) # self.calc_tfidf(trainset) # 生成tf-idf权值 self.build_tdm() # 按分类累计向量空间的每维值：P(x|yi) # 生成 tf-idf def calc_tfidf(self, trainset): self.idf = np.zeros([1, self.vocablen]) self.tf = np.zeros([self.doclength, self.vocablen]) for indx in range(self.doclength): for word in trainset[indx]: self.tf[indx, self.vocabulary.index(word)] += 1 # 消除不同句长导致的偏差 self.tf[indx] = self.tf[indx] / float(len(trainset[indx])) for signleword in set(trainset[indx]): self.idf[0, self.vocabulary.index(signleword)] += 1 self.idf = np.log(float(self.doclength) / self.idf) self.tf = np.multiply(self.tf, self.idf) # 矩阵与向量的点乘 # 生成普通的词频向量 def calc_wordfreq(self, trainset): self.idf = np.zeros([1, self.vocablen]) # 1*词典数 self.tf = np.zeros([self.doclength, self.vocablen]) # 训练集文件数*词典数 for indx in range(self.doclength): # 遍历所有的文本 for word in trainset[indx]: # 遍历文本中的每个词 self.tf[indx, self.vocabulary.index(word)] += 1 # 找到文本的词在字典中的位置+1 for signleword in set(trainset[indx]): self.idf[0, self.vocabulary.index(signleword)] += 1 # 计算每个分类在数据集中的概率：P(yi) def cate_prob(self, classVec): self.labels = classVec#让分类作为相对应的标签 labeltemps = set(self.labels) # 获取全部分类,返回的是一个集合，其值为{0,1} #print('分类的结果：',labeltemps) for labeltemp in labeltemps: # 统计列表中重复的值：self.labels.count(labeltemp) self.Pcates[labeltemp] = float(self.labels.count(labeltemp)) / float(len(self.labels))#求分类列表中重复的值，就是0和1在所有当中所占的比例 # 按分类累计向量空间的每维值：P(x|yi) def build_tdm(self): self.tdm = np.zeros([len(self.Pcates), self.vocablen]) # 类别行*词典列 sumlist = np.zeros([len(self.Pcates), 1]) # 统计每个分类的总值 for indx in range(self.doclength): self.tdm[self.labels[indx]] += self.tf[indx] # 将同一类别的词向量空间值加总 sumlist[self.labels[indx]] = np.sum(self.tdm[self.labels[indx]]) # 统计每个分类的总值--是个标量 self.tdm = self.tdm / sumlist # P(x|yi) # 测试集映射到当前词典 def map2vocab(self, testdata): self.testset = np.zeros([1, self.vocablen]) for word in testdata: self.testset[0, self.vocabulary.index(word)] += 1 # 输出分类类别 def predict(self, testset): if np.shape(testset)[1] != self.vocablen: print("输入错误") exit(0) predvalue = 0 predclass = "" for tdm_vect, keyclass in zip(self.tdm, self.Pcates): # P(x|yi)P(yi) temp = np.sum(testset * tdm_vect * self.Pcates[keyclass]) if temp > predvalue: predvalue = temp predclass = keyclass return predclass