def testCollectCrossDomainLinks():
shicai_dict_path = ROOT_PATH.seg_dictwin64 + u'jieba_shicai_cta.txt'
yixuebaike_dict_path = ROOT_PATH.seg_dictwin64 + u'jieba_yixuebaike.txt'
shicai_Entities = medGraphSupOpt.loadEntitiesFromDict(shicai_dict_path)
yixuebaike_Entities = medGraphSupOpt.loadEntitiesFromDict(yixuebaike_dict_path)
modelStoragePath = ROOT_PATH.root_win64 + u'model\\word2vec\\zongheword2vecModel.vector'
linkWriteFileInfo = ROOT_PATH.root_win64 + u'model_cache\\shicai2bingzheng_links.txt=a'
crossDomainLinkDic = medGraphSupOpt.collectCrossDomainLinks(modelStoragePath, shicai_Entities, yixuebaike_Entities, [5, 20, 300], 20, linkWriteFileInfo)
print('dic size: ' + str(len(crossDomainLinkDic.keys())))
def testFindCrossLinkedWords():
shicai_dict_path = ROOT_PATH.seg_dictwin64 + u'jieba_shicai.txt'
yixuebaike_dict_path = ROOT_PATH.seg_dictwin64 + u'jieba_yixuebaike.txt'
shicai_Entities = medGraphSupOpt.loadEntitiesFromDict(shicai_dict_path)
yixuebaike_Entities = medGraphSupOpt.loadEntitiesFromDict(yixuebaike_dict_path)
modelStoragePath = ROOT_PATH.root_win64 + u'model\\word2vec\\zongheword2vecModel.vector'
shicaiEntityStr = u'莲子/n'
tagTuples = medGraphSupOpt.findCrossLinkedWords(modelStoragePath, shicaiEntityStr, shicai_Entities, yixuebaike_Entities, [10, 50, 500])
print('source: ' + shicaiEntityStr)
print('linked to: ')
for e in tagTuples:
print(e[0] + ': ' + str(e[1]))
def testFindLinkedWords():
dict_path = ROOT_PATH.seg_dictwin64 + u'jieba_yixuebaike.txt'
domainEntities = medGraphSupOpt.loadEntitiesFromDict(dict_path)
modelStoragePath = ROOT_PATH.root_win64 + u'model\\word2vec\\zongheword2vecModel.vector'
entityStr = u'肺炎/n'
simTuples = medGraphSupOpt.findLinkedWords(modelStoragePath, entityStr, domainEntities, scanRange=10)
for e in simTuples:
print(e[0] + ': ' + str(e[1]))
print('--------------------------------------')
def testCrossLinkedWordsPrecess():
dict_path = ROOT_PATH.seg_dictwin64 + u'jieba_shicai.txt'
sourceEntities = medGraphSupOpt.loadEntitiesFromDict(dict_path)
modelStoragePath = ROOT_PATH.root_win64 + u'model\\word2vec\\zongheword2vecModel.vector'
entityStr = u'党参/n'
# get the source scan tuples result, to pure the mid scan result
# pureTuples = findLinkedWords(modelStoragePath, entityStr, sourceEntities, pureScanRange)
sourceTuples = medGraphSupOpt.seekSimWords(modelStoragePath, entities=[entityStr], pureCleanWords=[], scanRange=10)
supTuples = WordTypeFilter().ditInOutWordFilter(sourceTuples, sourceEntities, 'out')
cleanTuples = WordTypeFilter().ditInOutWordFilter(sourceTuples, sourceEntities, 'in')
pureSupWords = [entityStr]
pureSupWords.extend(e[0] for e in supTuples)
pureCleanWords = []
pureCleanWords.extend(e[0] for e in cleanTuples)
# get the mid scan tuples result, to link the tag scan result
# need to test use sup tuples into positive word list
midTuples = medGraphSupOpt.seekSimWords(modelStoragePath, entities=pureSupWords, pureCleanWords=pureCleanWords, scanRange=20)
# midTuples = seekSimWords(modelStoragePath, [entityStr], pureCleanWords=pureCleanWords, scanRange=midScanRange)
for e in midTuples:
print(e[0] + ': ' + str(e[1]))
print('--------------------------------------')