Python实现的朴素贝叶斯分类器示例

发布日期:2018-01-07 06:33:04来源:初学者
[导读]这篇文章主要介绍了Python实现的朴素贝叶斯分类器,结合具体实例形式分析了基于Python实现的朴素贝叶斯分类器相关定义与使用技巧,需要的朋友可以参考下

本文实例讲述了Python实现的朴素贝叶斯分类器。分享给大家供大家参考,具体如下:

因工作中需要,自己写了一个朴素贝叶斯分类器。

对于未出现的属性,采取了拉普拉斯平滑,避免未出现的属性的概率为零导致整个条件概率都为零的情况出现。

朴素贝叶斯的基本原理网上很容易查到,这里不再叙述,直接附上代码

因工作中需要,自己写了一个朴素贝叶斯分类器。对于未出现的属性,采取了拉普拉斯平滑,避免未出现的属性的概率为零导致整个条件概率都为零的情况出现。

class NBClassify(object):
  def __init__(self, fillNa = 1):
    self.fillNa = 1
    pass
  def train(self, trainSet):
    # 计算每种类别的概率
    # 保存所有tag的所有种类,及它们出现的频次
    dictTag = {}
    for subTuple in trainSet:
      dictTag[str(subTuple[1])] = 1 if str(subTuple[1]) not in dictTag.keys() else dictTag[str(subTuple[1])] + 1
    # 保存每个tag本身的概率
    tagProbablity = {}
    totalFreq = sum([value for value in dictTag.values()])
    for key, value in dictTag.items():
      tagProbablity[key] = value / totalFreq
    # print(tagProbablity)
    self.tagProbablity = tagProbablity
    ##############################################################################
    # 计算特征的条件概率
    # 保存特征属性基本信息{特征1:{值1:出现5次, 值2:出现1次}, 特征2:{值1:出现1次, 值2:出现5次}}
    dictFeaturesBase = {}
    for subTuple in trainSet:
      for key, value in subTuple[0].items():
        if key not in dictFeaturesBase.keys():
          dictFeaturesBase[key] = {value:1}
        else:
          if value not in dictFeaturesBase[key].keys():
            dictFeaturesBase[key][value] = 1
          else:
            dictFeaturesBase[key][value] += 1
    # dictFeaturesBase = {
      # '职业': {'农夫': 1, '教师': 2, '建筑工人': 2, '护士': 1},
      # '症状': {'打喷嚏': 3, '头痛': 3}
      # }
    dictFeatures = {}.fromkeys([key for key in dictTag])
    for key in dictFeatures.keys():
      dictFeatures[key] = {}.fromkeys([key for key in dictFeaturesBase])
    for key, value in dictFeatures.items():
      for subkey in value.keys():
        value[subkey] = {}.fromkeys([x for x in dictFeaturesBase[subkey].keys()])
    # dictFeatures = {
    #  '感冒 ': {'症状': {'打喷嚏': None, '头痛': None}, '职业': {'护士': None, '农夫': None, '建筑工人': None, '教师': None}},
    #  '脑震荡': {'症状': {'打喷嚏': None, '头痛': None}, '职业': {'护士': None, '农夫': None, '建筑工人': None, '教师': None}},
    #  '过敏 ': {'症状': {'打喷嚏': None, '头痛': None}, '职业': {'护士': None, '农夫': None, '建筑工人': None, '教师': None}}
    #  }
    # initialise dictFeatures
    for subTuple in trainSet:
      for key, value in subTuple[0].items():
        dictFeatures[subTuple[1]][key][value] = 1 if dictFeatures[subTuple[1]][key][value] == None else dictFeatures[subTuple[1]][key][value] + 1
    # print(dictFeatures)
    # 将驯良样本中没有的项目,由None改为一个非常小的数值,表示其概率极小而并非是零
    for tag, featuresDict in dictFeatures.items():
      for featureName, fetureValueDict in featuresDict.items():
        for featureKey, featureValues in fetureValueDict.items():
          if featureValues == None:
            fetureValueDict[featureKey] = 1
    # 由特征频率计算特征的条件概率P(feature|tag)
    for tag, featuresDict in dictFeatures.items():
      for featureName, fetureValueDict in featuresDict.items():
        totalCount = sum([x for x in fetureValueDict.values() if x != None])
        for featureKey, featureValues in fetureValueDict.items():
          fetureValueDict[featureKey] = featureValues/totalCount if featureValues != None else None
    self.featuresProbablity = dictFeatures
    ##############################################################################
  def classify(self, featureDict):
    resultDict = {}
    # 计算每个tag的条件概率
    for key, value in self.tagProbablity.items():
      iNumList = []
      for f, v in featureDict.items():
        if self.featuresProbablity[key][f][v]:
          iNumList.append(self.featuresProbablity[key][f][v])
      conditionPr = 1
      for iNum in iNumList:
        conditionPr *= iNum
      resultDict[key] = value * conditionPr
    # 对比每个tag的条件概率的大小
    resultList = sorted(resultDict.items(), key=lambda x:x[1], reverse=True)
    return resultList[0][0]
if __name__ == '__main__':
  trainSet = [
    ({"症状":"打喷嚏", "职业":"护士"}, "感冒 "),
    ({"症状":"打喷嚏", "职业":"农夫"}, "过敏 "),
    ({"症状":"头痛", "职业":"建筑工人"}, "脑震荡"),
    ({"症状":"头痛", "职业":"建筑工人"}, "感冒 "),
    ({"症状":"打喷嚏", "职业":"教师"}, "感冒 "),
    ({"症状":"头痛", "职业":"教师"}, "脑震荡"),
  ]
  monitor = NBClassify()
  # trainSet is something like that [(featureDict, tag), ]
  monitor.train(trainSet)
  # 打喷嚏的建筑工人
  # 请问他患上感冒的概率有多大?
  result = monitor.classify({"症状":"打喷嚏", "职业":"建筑工人"})
  print(result)

另:关于朴素贝叶斯算法详细说明还可参看本站前面一篇http://www.jb51.net/article/129903.htm。

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