def __init__(self, archPath, model_weight=[0.25, 0.25, 0.25, 0.25]):
"""
初始化模型, 初始化一个:
lmirBM25Model
Input:
archPath: archtectureDataset的数据位置
modelWeight: list of model weight [BM25, JM, DIR, ABS]
"""
self.archDataset = Arch(annotationFile=archPath)
self.archDataset.reverseCharForAllContext()
# generate annotation and corpora list
self.annIdList = []
self.corporaList = []
self.notCutCorporaList = []
for annotation, content in self.archDataset.anns.items():
self.annIdList.append(annotation)
self.corporaList.append(content["cutConcateText"])
self.notCutCorporaList.append(content["concateText"])
# model weight [staticModel, featureModel]
self.model_weight = model_weight
self.model = lmirBm25Model(self.corporaList,
modelWeight=self.model_weight)
def __init__(self, archPath, modelPath):
"""
初始化模型, 初始化一个:
lmirBM25Model
Input:
archPath: archtectureDataset的数据位置
modelPath: doc2vec训练好的模型位置
"""
self.archDataset = Arch(annotationFile=archPath)
self.archDataset.reverseCharForAllContext()
# generate annotation and corpora list
self.annIdList = []
self.corporaList = []
self.notCutCorporaList = []
for annotation, content in self.archDataset.anns.items():
self.annIdList.append(annotation)
self.corporaList.append(content["cutConcateText"])
self.notCutCorporaList.append(content["concateText"])
# modelPath
self.modelPath = modelPath
self.model = doc2vecModel(self.corporaList, model_path=self.modelPath)
def __init__(self, archPath, modelPath, model_weight=[0.5, 0.5]):
"""
初始化模型, 分别初始化两个:
1. lmirBM25
2. Doc2Vec
Input:
archPath: archtectureDataset的数据位置
modelPath: doc2vec训练好的模型位置
modelWeight: list of model weight[staticModel, featureModel]
"""
self.archDataset = Arch(annotationFile=archPath)
self.archDataset.reverseCharForAllContext()
# generate annotation and corpora list
self.annIdList = []
self.corporaList = []
self.notCutCorporaList = []
for annotation, content in self.archDataset.anns.items():
self.annIdList.append(annotation)
self.corporaList.append(content["cutConcateText"])
self.notCutCorporaList.append(content["concateText"])
# model weight [staticModel, featureModel]
self.model_weight = model_weight
self.staticModel = lmirBm25Model(self.corporaList)
self.featureModel = doc2vecModel(self.corporaList, modelPath)
class archDoc2vecModel():
def __init__(self, archPath, modelPath):
"""
初始化模型, 初始化一个:
lmirBM25Model
Input:
archPath: archtectureDataset的数据位置
modelPath: doc2vec训练好的模型位置
"""
self.archDataset = Arch(annotationFile=archPath)
self.archDataset.reverseCharForAllContext()
# generate annotation and corpora list
self.annIdList = []
self.corporaList = []
self.notCutCorporaList = []
for annotation, content in self.archDataset.anns.items():
self.annIdList.append(annotation)
self.corporaList.append(content["cutConcateText"])
self.notCutCorporaList.append(content["concateText"])
# modelPath
self.modelPath = modelPath
self.model = doc2vecModel(self.corporaList, model_path=self.modelPath)
def standardization(self, data):
minValue = np.min(data)
maxValue = np.max(data)
return (data - minValue) / (maxValue - minValue) if (maxValue - minValue) != 0 else data
def searchSentence(self, listWords):
"""
Search a sentence contains keyword
Input:
listWords: 未分好词的查询部分, 是一个或几个字符串的数组
"""
# cut words
to_search = []
for i in listWords:
to_search += list(jieba.cut(i, True))
# search
result = self.model.forward(to_search)
# get index
index = np.argsort(result["ALL"])[::-1]
sortedResult = self.standardization(np.sort(result["ALL"])[::-1])
# result
imageId = []
annoId = []
copora = []
for i in index:
annoId.append(self.annIdList[i])
copora.append(self.notCutCorporaList[i])
imageId.append(self.archDataset.anns[self.annIdList[i]]["imageId"])
return sortedResult, index, copora, annoId, imageId
def searchWords(self, listWords, weights):
"""
Search a list of keyword
Input:
listWords: 未分好词的查询部分, 是一个或几个字符串的数组
"""
# cut words
to_search = []
for i in listWords:
to_search += list(jieba.cut(i, True))
# search
result = self.model.forwardWords(to_search, weights)
# get index
index = np.argsort(result["ALL"]["ALL"])[::-1]
sortedResult = self.standardization(np.sort(result["ALL"])[::-1])
# result
imageId = []
annoId = []
copora = []
for i in index:
annoId.append(self.annIdList[i])
copora.append(self.notCutCorporaList[i])
imageId.append(self.archDataset.anns[self.annIdList[i]]["imageId"])
# result
return sortedResult, index, copora, annoId, imageId
def retrieve(self, test_text, model_dm, corpus):
test_vec = np.expand_dims(model_dm.infer_vector(test_text), axis=0)
sim_array = np.zeros(len(corpus))
for idx, sample in enumerate(corpus):
sample_vec = np.expand_dims(model_dm.infer_vector(sample), axis=0)
sim_array[idx] = cosine_similarity(test_vec, sample_vec)
return sim_array
if __name__ == '__main__':
# load archdataset
ArchDataset = Arch(
annotationFile="../../../Dataset/Arch/DemoData_20201228.json",
imageFolder=None)
ArchDataset.reverseCharForAllContext()
# generate annotation and corpora list
annIdList = []
corporaList = []
corporaList_d2v = []
TaggededDocument = gensim.models.doc2vec.TaggedDocument # 方便gensim用的文档对象
for i, (annotation, content) in enumerate(ArchDataset.anns.items()):
annIdList.append(annotation)
corporaList.append(content["cutConcateText"])
document = TaggededDocument(content["cutConcateText"], tags=[i])
corporaList_d2v.append(document)
class archLmirBm25Model():
def __init__(self, archPath, model_weight=[0.25, 0.25, 0.25, 0.25]):
"""
初始化模型, 初始化一个:
lmirBM25Model
Input:
archPath: archtectureDataset的数据位置
modelWeight: list of model weight [BM25, JM, DIR, ABS]
"""
self.archDataset = Arch(annotationFile=archPath)
self.archDataset.reverseCharForAllContext()
# generate annotation and corpora list
self.annIdList = []
self.corporaList = []
self.notCutCorporaList = []
for annotation, content in self.archDataset.anns.items():
self.annIdList.append(annotation)
self.corporaList.append(content["cutConcateText"])
self.notCutCorporaList.append(content["concateText"])
# model weight [staticModel, featureModel]
self.model_weight = model_weight
self.model = lmirBm25Model(self.corporaList,
modelWeight=self.model_weight)
def standardization(self, data):
minValue = np.min(data)
maxValue = np.max(data)
return (data - minValue) / (maxValue - minValue) if (
maxValue - minValue) != 0 else data
def searchSentence(self, listWords):
"""
Search a sentence contains keyword
Input:
listWords: 未分好词的查询部分, 是一个或几个字符串的数组
"""
# cut words
to_search = []
for i in listWords:
to_search += list(jieba.cut(i, True))
# search
result = self.model.forward(to_search)
# get index
index = np.argsort(result["ALL"])[::-1]
sortedResult = self.standardization(np.sort(result["ALL"])[::-1])
# result
imageId = []
annoId = []
copora = []
for i in index:
annoId.append(self.annIdList[i])
copora.append(self.notCutCorporaList[i])
imageId.append(self.archDataset.anns[self.annIdList[i]]["imageId"])
return sortedResult, index, copora, annoId, imageId
def cut_words(self, listWords, weights):
"""
预处理: 分词分weight
Input:
listWords: ["中国红花"]
weights: ["1"]
Return:
cutWords:["中国", "红花"]
weights: [0.5, 0.5]
"""
cutWords = []
newWeight = []
for i in range(len(listWords)):
# cut word into ["中国", "红花"]
toCut = listWords[i]
cutWord = list(jieba.cut(toCut, True))
cutWords += cutWord
# cal weight
weight = weights[i]
weight = float(weight) / len(cutWords)
newWeight += [weight for i in range(len(cutWords))]
return cutWords, newWeight
def searchWords(self, listWords, weights):
"""
Search a list of keyword
Input:
listWords: 未分好词的查询部分, 是一个或几个字符串的数组
"""
# cut words
listWords, weights = self.cut_words(listWords, weights)
# search
result = self.model.forwardWords(listWords, weights)
# get index
index = np.argsort(result["ALL"])[::-1]
sortedResult = self.standardization(np.sort(result["ALL"])[::-1])
# result
imageId = []
annoId = []
copora = []
for i in index:
annoId.append(self.annIdList[i])
copora.append(self.notCutCorporaList[i])
imageId.append(self.archDataset.anns[self.annIdList[i]]["imageId"])
# result
return sortedResult, index, copora, annoId, imageId
class archMixModel():
def __init__(self, archPath, modelPath, model_weight=[0.5, 0.5]):
"""
初始化模型, 分别初始化两个:
1. lmirBM25
2. Doc2Vec
Input:
archPath: archtectureDataset的数据位置
modelPath: doc2vec训练好的模型位置
modelWeight: list of model weight[staticModel, featureModel]
"""
self.archDataset = Arch(annotationFile=archPath)
self.archDataset.reverseCharForAllContext()
# generate annotation and corpora list
self.annIdList = []
self.corporaList = []
self.notCutCorporaList = []
for annotation, content in self.archDataset.anns.items():
self.annIdList.append(annotation)
self.corporaList.append(content["cutConcateText"])
self.notCutCorporaList.append(content["concateText"])
# model weight [staticModel, featureModel]
self.model_weight = model_weight
self.staticModel = lmirBm25Model(self.corporaList)
self.featureModel = doc2vecModel(self.corporaList, modelPath)
def standardization(self, data):
minValue = np.min(data)
maxValue = np.max(data)
return (data - minValue) / (maxValue - minValue) if (maxValue - minValue) != 0 else data
def forward(self, X):
"""
查询部分, 由于可以整个句子输入, 所以不加weight.
Input:
X: list 分好词的查询部分, 一个元素是一个词, 多个词组合在一起查询.
Return:
dict{
“模型名”: [结果list]
“ALL”: [加权结果list]
}
"""
resultStatic = self.staticModel.forward(X)["ALL"]
resultFeature = self.featureModel.forward(X)["ALL"]
resultWeighted = resultStatic * self.model_weight[0] + \
resultFeature * self.model_weight[1]
resultWeighted = self.standardization(resultWeighted)
return {
"staticModel": resultStatic,
"featureModel": resultFeature,
"ALL": resultWeighted,
}
# TODO
def forwardWords(self, X, weights):
"""
查询部分, 输入为一系列的关键词, 无多余字符.
Input:
X: list 分好词的查询部分, 每一个元素是一个词.
wordWeight: (list) 对于每一个词占算法重要性的比例, 在算法计算完成后最终融合时, 将会按照权重进行加权求和.
Return:
dict{
"1": {
“模型名”: [结果nparray]
“ALL”: [加权结果nparray]
},
"2": {
“模型名”: [结果nparray]
“ALL”: [加权结果nparray]
},
"ALL": {
“模型名”: [结果nparray]
“ALL”: [加权结果nparray]
},
}
"""
raise NotImplementedError
def searchSentence(self, listWords):
"""
Search a sentence contains keyword
Input:
listWords: 未分好词的查询部分, 是一个或几个字符串的数组
"""
# cut words
to_search = []
for i in listWords:
to_search += list(jieba.cut(i, True))
# search
result = self.forward(to_search)
# get index
index = np.argsort(result["ALL"])[::-1]
sortedResult = self.standardization(np.sort(result["ALL"])[::-1])
# result
imageId = []
annoId = []
copora = []
for i in index:
annoId.append(self.annIdList[i])
copora.append(self.notCutCorporaList[i])
imageId.append(self.archDataset.anns[self.annIdList[i]]["imageId"])
return sortedResult, index, copora, annoId, imageId
def searchWords(self, listWords, weights):
"""
Search a list of keyword
Input:
listWords: 未分好词的查询部分, 是一个或几个字符串的数组
"""
# cut words
to_search = []
for i in listWords:
to_search += list(jieba.cut(i, True))
# search
result = self.forwardWords(to_search, weights)
# get index
index = np.argsort(result["ALL"])[::-1]
sortedResult = self.standardization(np.sort(result["ALL"])[::-1])
# result
imageId = []
annoId = []
copora = []
for i in index:
annoId.append(self.annIdList[i])
copora.append(self.notCutCorporaList[i])
imageId.append(self.archDataset.anns[self.annIdList[i]]["imageId"])
# result
return sortedResult, index, copora, annoId, imageId