def biomarkerMediumRelations(filename):
# Processing the input data and converting to sentences
# If the file has already been parsed, there is no point in reparsing.
# Just open the already parsed sentences
pckl_f = "cache/" + filename + "/" + 'sentences.pkl'
# try:
# #Try to see if the sentences have been parsed
# with open(pckl_f, 'rb') as f:
# #Load em in if they have
# sentences = cPickle.load(f)
# except:
sentences = doc_parser.parseDoc(filename)
# Serialize so that you dont have to deal with parsing in the future
# if not os.path.exists("cache/" + _filename +"/" ):
# os.makedirs("cache/" + _filename + "/")
# with open(pckl_f, 'w+') as f:
# cPickle.dump(sentences, f)
biomarker_ngrams = Ngrams(n_max=1)
medium_ngrams = Ngrams(n_max=3)
# Create the two matchers who have been defined in separate classes
BM = matchers.getBiomarkerMatcher()
MM = matchers.getMediumMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_MM = CandidateExtractor(medium_ngrams, MM)
# Create the relations using the two matchers
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_MM)
return possiblePairs
def biomarkerDrugRelations(filename):
# Processing the input data and converting to sentences
sentences = doc_parser.parseDoc(filename)
# If the file has already been parsed, there is no point in reparsing.
# Just open the already parsed sentences
# print sentences
# Serialize so that you dont have to deal with parsing in the future
# if not os.path.exists("cache/" + filename +"/" ):
# os.makedirs("cache/" + filename + "/")
# with open(pckl_f, 'w+') as f:
# cPickle.dump(sentences, f)
biomarker_ngrams = Ngrams(n_max=1)
drug_association_ngrams = Ngrams(n_max=5)
BM = matchers.getBiomarkerMatcher()
DAM = matchers.getDrugAssociationMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_DAM = CandidateExtractor(drug_association_ngrams, DAM)
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_DAM)
return possiblePairs
def generateRelations(sentences):
biomarker_ngrams = Ngrams(n_max=1)
unit_ngrams = Ngrams(n_max=10)
# Retrieve Matchers:
BM = BiomarkerMatcher.getBiomarkerMatcher()
U = UnitsMatcher.getUnitsMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_U = CandidateExtractor(unit_ngrams, U)
# Create Relations Object
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_U)
return possiblePairs
def biomarkerTestsetRelations(sentences):
biomarker_ngrams = Ngrams(n_max=1)
test_set_ngrams = Ngrams(n_max=10)
# Retrieve Matchers:
BM = matchers.getBiomarkerMatcher()
TS = matchers.getTestSetMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_TS = CandidateExtractor(test_set_ngrams, TS)
# Create Relations Object
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_TS)
return possiblePairs
def biomarkerMeasurementRelations(sentences):
biomarker_ngrams = Ngrams(n_max=1)
measurement_type_ngrams = Ngrams(n_max=5)
# Retrieve Matchers:
BM = matchers.getBiomarkerMatcher()
MT = matchers.getMeasurementTypeMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_MT = CandidateExtractor(measurement_type_ngrams, MT)
# Create Relations Object
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_MT)
return possiblePairs
def biomarkerLevelsRelations(sentences):
biomarker_ngrams = Ngrams(n_max=1)
levels_ngrams = Ngrams(n_max=15)
# Retrieve Matchers:
BM = matchers.getBiomarkerMatcher()
L = matchers.getLevelsMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_L = CandidateExtractor(levels_ngrams, L)
# Create Relations Object
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_L)
return possiblePairs
def biomarkerTypeRelations(filename):
"""
Processing the input data and converting to sentences
"""
# If the file has already been parsed, there is no point in reparsing.
# Just open the already parsed sentences
# pckl_f = "cache/" + filename + "/" + 'sentences.pkl'
# try:
# #Try to see if the sentences have been parsed
# with open(pckl_f, 'rb') as f:
# #Load em in if they have
# sentences = cPickle.load(f)
# except:
sentences = doc_parser.parseDoc(filename)
# Serialize so that you dont have to deal with parsing in the future
# if not os.path.exists("cache/" + filename +"/" ):
# os.makedirs("cache/" + filename + "/")
# with open(pckl_f, 'w+') as f:
# cPickle.dump(sentences, f)
biomarker_ngrams = Ngrams(n_max=1)
biomarker_type_ngrams = Ngrams(n_max=2)
BM = matchers.getBiomarkerMatcher()
TM = matchers.getBiomarkerTypeMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
CandidateExtractor_TM = CandidateExtractor(biomarker_type_ngrams, TM)
possiblePairs = Relations(sentences, CandidateExtractor_BM,
CandidateExtractor_TM)
return possiblePairs
def generateRelations(_filename):
# Processing the input data and converting to sentences
# If the file has already been parsed, there is no point in reparsing.
# Just open the already parsed sentences
#pckl_f = _filename
# try:
# #Try to see if the sentences have been parsed
# with open(pckl_f, 'rb') as f:
# #Load em in if they have
# sentences = pickle.load(f)
# except:
#doc_parser = TextDocParser(_filename)
#sent_parser = SentenceParser()
#corpus = Corpus(doc_parser, sent_parser)
# print corpus
# Sentences havent been parsed, so parse them now
#sentences = corpus.get_contexts()
sentences = doc_parser.parseDoc(_filename)
print sentences
# Serialize so that you dont have to deal with parsing in the future
# if not os.path.exists("cache/" + _filename +"/" ):
# os.makedirs("cache/" + _filename + "/")
# with open(pckl_f, 'w+') as f:
# cPickle.dump(sentences, f)
biomarker_ngrams = Ngrams(n_max=1)
disease_ngrams = Ngrams(n_max=5)
# Create the two matchers who have been defined in separate classes
BM = BiomarkerMatcher.getBiomarkerMatcher()
CandidateExtractor_BM = CandidateExtractor(biomarker_ngrams, BM)
DM = DiseaseMatcher.getDiseaseMatcher()
CandidateExtractor_DM = CandidateExtractor(disease_ngrams, DM)
# #fix disease candidate generator error- "ovarian and prostate cancer"
# with open('diseaseDatabase.pickle', 'rb') as f:
# diseaseDictionary = pickle.load(f)
# DiseaseMatch = DictionaryMatch(label = "Diseases", dictionary = diseaseDictionary, ignore_case= True)
# E = Entities(sentences, DiseaseMatch)
# filename = "AGR2_blood_biomarker.txt"
# text = open(filename, "r").read()
# editedText = DiseaseCandidateGenerator.addDiseaseBases(E, diseaseDictionary ,text)
# editedSentences = []
# sentence_parser = SentenceParser()
# list = sentence_parser.parse(editedText, 1)
# for editsentence in list:
# editedSentences.append(editsentence)
# Create the relations using the two matchers
possiblePairs = Relations(
sentences, CandidateExtractor_BM, CandidateExtractor_DM)
# badCount = 0
# goodCount = 0
# allGoodPairs = []
# for goodPair in possiblePairs:
# allGoodPairs.append(goodPair.sent_id)
# #Return these pairs
# for sentence in sentences:
# if sentence.sent_id in allGoodPairs:
# print "GOOD PAIR: "
# print sentence.words
# print "\n"
# goodCount += 1
# else:
# print "BAD PAIR:"
# print sentence.words
# print "\n"
# badCount += 1
# print goodCount
# print badCount
# print possiblePairs
return possiblePairs