NBClassify.py

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)