Python实现的朴素贝叶斯分类器示例

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

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//www.jb51.net/article/129903.htm。