def test_saveStandoffFile_noArgNames():
text = "The colorectal cancer was caused by mutations in APC"
e1 = kindred.Entity(entityType="disease",text="colorectal cancer",position=[(4, 21)],sourceEntityID="T1")
e2 = kindred.Entity(entityType="gene",text="APC",position=[(49, 52)],sourceEntityID="T2")
rel = kindred.Relation(relationType="causes",entityIDs=[e1.entityID,e2.entityID])
doc = kindred.Document(text,[e1,e2],[rel],relationsUseSourceIDs=False)
corpus = kindred.Corpus()
corpus.addDocument(doc)
tempDir = tempfile.mkdtemp()
kindred.save(corpus,'standoff',tempDir)
loadedCorpus = kindred.loadDir('standoff',tempDir)
assert isinstance(loadedCorpus,kindred.Corpus)
assert len(loadedCorpus.documents) == 1
loadedDoc = loadedCorpus.documents[0]
assert isinstance(loadedDoc,kindred.Document)
entities = loadedDoc.getEntities()
relations = loadedDoc.getRelations()
sourceEntityIDsToEntityIDs = loadedDoc.getSourceEntityIDsToEntityIDs()
assertEntity(entities[0],expectedType='disease',expectedText='colorectal cancer',expectedPos=[(4,21)],expectedSourceEntityID="T1")
assertEntity(entities[1],expectedType='gene',expectedText='APC',expectedPos=[(49,52)],expectedSourceEntityID="T2")
assert relations == [kindred.Relation('causes',[sourceEntityIDsToEntityIDs["T1"],sourceEntityIDsToEntityIDs["T2"]],['arg1','arg2'])], "(%s) not as expected" % relations
shutil.rmtree(tempDir)
def test_entity_equals():
e1 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID=None)
e2 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID="T16")
e3 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID=None)
rel1 = kindred.Relation(relationType="causes",
entityIDs=[1, 2],
argNames=None)
assert e1 == e1
assert e1 != e2
assert e1 != e3
assert e2 == e2
assert e2 != e3
assert e1 != rel1
assert e2 != rel1
assert e3 != rel1
def test_document_init():
text = "Cancer is caused by mutations in ABCDE1."
e1 = kindred.Entity('disease', 'Cancer', [(0, 6)], 'T1')
e2 = kindred.Entity('gene', 'ABCDE1', [(33, 39)], 'T2')
doc = kindred.Document(text, [e1, e2])
expected = "<Document Cancer is caused by mutations in ABCDE1. [<Entity disease:'Cancer' sourceid=T1 [(0, 6)]>, <Entity gene:'ABCDE1' sourceid=T2 [(33, 39)]>] []>"
assert str(doc) == expected
def test_saveStandoffFile():
text = "The colorectal cancer was caused by mutations in APC"
e1 = kindred.Entity(entityType="disease",
text="colorectal cancer",
position=[(4, 21)],
sourceEntityID="T1")
e2 = kindred.Entity(entityType="gene",
text="APC",
position=[(49, 52)],
sourceEntityID="T2")
rel = kindred.Relation(relationType="causes",
entities=[e1, e2],
argNames=['obj', 'subj'])
doc = kindred.Document(text, [e1, e2], [rel])
corpus = kindred.Corpus()
corpus.addDocument(doc)
with TempDir() as tempDir:
kindred.save(corpus, 'standoff', tempDir)
for filename in os.listdir(tempDir):
if filename.endswith('.a2'):
checkRelationAnnotations(os.path.join(tempDir, filename))
loadedCorpus = kindred.load('standoff', tempDir)
assert isinstance(loadedCorpus, kindred.Corpus)
assert len(loadedCorpus.documents) == 1
loadedDoc = loadedCorpus.documents[0]
assert isinstance(loadedDoc, kindred.Document)
entities = loadedDoc.entities
relations = loadedDoc.relations
sourceEntityIDToEntity = {
entity.sourceEntityID: entity
for entity in entities
}
assertEntity(entities[0],
expectedType='disease',
expectedText='colorectal cancer',
expectedPos=[(4, 21)],
expectedSourceEntityID="T1")
assertEntity(entities[1],
expectedType='gene',
expectedText='APC',
expectedPos=[(49, 52)],
expectedSourceEntityID="T2")
assert relations == [
kindred.Relation(
'causes',
[sourceEntityIDToEntity["T1"], sourceEntityIDToEntity["T2"]],
['obj', 'subj'],
sourceRelationID='R1')
], "(%s) not as expected" % relations
def test_document_init_withRel():
text = "Cancer is caused by mutations in ABCDE1."
e1 = kindred.Entity('disease', 'Cancer', [(0, 6)], 'T1')
e2 = kindred.Entity('gene', 'ABCDE1', [(33, 39)], 'T2')
rel = kindred.Relation('causes', [e1, e2], ['subj', 'obj'])
doc = kindred.Document(text, [e1, e2], [rel])
expected = "<Document Cancer is caused by mutations in ABCDE1. [<Entity disease:'Cancer' sourceid=T1 [(0, 6)]>, <Entity gene:'ABCDE1' sourceid=T2 [(33, 39)]>] [<Relation causes [<Entity disease:'Cancer' sourceid=T1 [(0, 6)]>, <Entity gene:'ABCDE1' sourceid=T2 [(33, 39)]>] ['subj', 'obj']>]>"
assert str(doc) == expected
def test_relation_repr():
e1 = kindred.Entity('mutation','BRAF V600E mutation',[])
e2 = kindred.Entity('event','vemurafenib resistance',[])
rel1 = kindred.Relation(relationType="causes",entities=[e1,e2],argNames=None)
rel2 = kindred.Relation(relationType="causes",entities=[e1,e2],argNames=["drug","disease"])
expected1 = "<Relation causes [<Entity mutation:'BRAF V600E mutation' sourceid=None []>, <Entity event:'vemurafenib resistance' sourceid=None []>] None>"
expected2 = "<Relation causes [<Entity mutation:'BRAF V600E mutation' sourceid=None []>, <Entity event:'vemurafenib resistance' sourceid=None []>] ['drug', 'disease']>"
assert rel1.__repr__() == expected1
assert rel2.__repr__() == expected2
def test_relation_repr():
e1 = kindred.Entity('mutation','BRAF V600E mutation',[])
e2 = kindred.Entity('event','vemurafenib resistance',[])
rel1 = kindred.CandidateRelation(entities=[e1,e2])
rel2 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[("causes",["drug","disease"])])
expected1 = "<CandidateRelation [<Entity mutation:'BRAF V600E mutation' sourceid=None []>, <Entity event:'vemurafenib resistance' sourceid=None []>] []>"
expected2 = "<CandidateRelation [<Entity mutation:'BRAF V600E mutation' sourceid=None []>, <Entity event:'vemurafenib resistance' sourceid=None []>] [('causes', ['drug', 'disease'])]>"
assert rel1.__repr__() == expected1
assert rel2.__repr__() == expected2
def test_relation_hash():
e1 = kindred.Entity('mutation','BRAF V600E mutation',[])
e2 = kindred.Entity('event','vemurafenib resistance',[])
rel1 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[])
rel2 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[])
rel3 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[("causes",["drug","disease"])])
rel4 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[("causes",["drug","disease"])])
assert hash(rel1) == hash(rel2)
assert hash(rel3) == hash(rel4)
assert hash(rel1) != hash(rel3)
def test_entity_str():
e1 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID=None)
e2 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID="T16")
expected1 = "<Entity drug:'Erlotinib' sourceid=None [(0, 9)]>"
expected2 = "<Entity drug:'Erlotinib' sourceid=T16 [(0, 9)]>"
assert str(e1) == expected1
assert str(e2) == expected2
def test_entity_repr():
e1 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID=None)
e2 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID="T16")
expected1 = "<Entity drug:'Erlotinib' id=%d sourceid=None [(0, 9)]>" % e1.entityID
expected2 = "<Entity drug:'Erlotinib' id=%d sourceid=T16 [(0, 9)]>" % e2.entityID
assert e1.__repr__() == expected1
assert e2.__repr__() == expected2
def test_sentence_addEntityWithLocations(capfd):
text = 'lots of mutations cause dangerous cancer'
tokens = [kindred.Token(w, None, None, 0, 0) for w in text.split()]
s = kindred.Sentence(text,
tokens,
dependencies=[(2, 3, 'a'), (3, 5, 'b'), (4, 5, 'c')])
e1 = kindred.Entity('thingA', 'mutations', [(0, 1)])
e2 = kindred.Entity('thingB', 'cancer', [(0, 1)])
s.addEntityAnnotation(e1, [2])
s.addEntityAnnotation(e2, [5])
assert s.entityAnnotations == [(e1, [2]), (e2, [5])]
def test_relation_hash():
e1 = kindred.Entity('mutation','BRAF V600E mutation',[])
e2 = kindred.Entity('event','vemurafenib resistance',[])
rel1 = kindred.Relation(relationType="causes",entities=[e1,e2],argNames=None)
rel2 = kindred.Relation(relationType="causes",entities=[e1,e2],argNames=None)
rel3 = kindred.Relation(relationType="causes",entities=[e1,e2],argNames=["drug","disease"])
rel4 = kindred.Relation(relationType="causes",entities=[e1,e2],argNames=["drug","disease"])
assert hash(rel1) == hash(rel2)
assert hash(rel3) == hash(rel4)
assert hash(rel1) != hash(rel3)
assert hash(rel1) == hash((rel1.relationType,tuple(rel1.entities),rel1.probability))
assert hash(rel3) == hash((rel3.relationType,tuple(rel3.entities),tuple(rel3.argNames),rel3.probability))
def test_sentence_str(capfd):
text = 'lots of mutations cause dangerous cancer'
tokens = [kindred.Token(w, None, None, 0, 0) for w in text.split()]
e1 = kindred.Entity('thingA', 'mutations', [(0, 1)])
e2 = kindred.Entity('thingB', 'cancer', [(0, 1)])
entitiesWithLocations = [(e1, [2]), (e2, [5])]
s = kindred.Sentence(text,
tokens,
dependencies=[(2, 3, 'a'), (3, 5, 'b'), (4, 5, 'c')],
entitiesWithLocations=entitiesWithLocations)
assert s.__repr__() == "lots of mutations cause dangerous cancer"
def annotateStarAlleles(corpus):
for doc in corpus.documents:
genes = [e for e in doc.entities if e.entityType == 'Gene']
for gene in genes:
geneEnd = gene.position[0][1]
geneID = gene.metadata['conceptid']
offset = geneEnd
regex = '^(,|and|or|/|\s|\+)*(?P<main>\*\s*[0-9]([\w:]*\w+)?)'
star = re.search(regex, doc.text[offset:])
while star:
_, length = star.span()
startPos, endPos = star.span('main')
text = star.group('main')
sourceEntityID = getNextSourceEntityID(doc)
alleleName = text.strip()[1:].strip()
conceptid = '*%s' % alleleName
starAllele = kindred.Entity(
'Mutation',
text, [(offset + startPos, offset + endPos)],
sourceEntityID=sourceEntityID,
metadata={
'conceptid': conceptid,
'associated_gene': geneID
})
doc.addEntity(starAllele)
offset += length
star = re.search(regex, doc.text[offset:])
def test_relation_equals():
rel1 = kindred.Relation(relationType="causes",
entityIDs=[1, 2],
argNames=None)
rel2 = kindred.Relation(relationType="causes",
entityIDs=[1, 2],
argNames=None)
rel3 = kindred.Relation(relationType="causes",
entityIDs=[1, 2],
argNames=["drug", "disease"])
rel4 = kindred.Relation(relationType="causes",
entityIDs=[1, 2],
argNames=["drug", "disease"])
e1 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID=None)
assert rel1 == rel2
assert rel1 != rel3
assert rel1 != rel4
assert rel2 != rel3
assert rel3 == rel4
assert rel1 != e1
assert rel2 != e1
assert rel3 != e1
assert rel4 != e1
def test_entity_str_withExternalID():
e1 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID=None,
externalID="id:1234")
e2 = kindred.Entity(entityType="drug",
text="Erlotinib",
position=[(0, 9)],
sourceEntityID="T16",
externalID="id:9876")
expected1 = "<Entity drug:'Erlotinib' id=%d sourceid=None externalid=id:1234 [(0, 9)]>" % e1.entityID
expected2 = "<Entity drug:'Erlotinib' id=%d sourceid=T16 externalid=id:9876 [(0, 9)]>" % e2.entityID
assert str(e1) == expected1
assert str(e2) == expected2
def convertBiocDocToKindredDocs(document):
assert isinstance(document,bioc.BioCDocument)
kindredDocs = []
for passage in document.passages:
assert isinstance(passage,bioc.BioCPassage)
text = passage.text
offset = int(native(passage.offset))
entities = []
relations = []
for a in passage.annotations:
assert isinstance(a,bioc.BioCAnnotation)
entityType = a.infons['type']
sourceEntityID = a.id
position = []
segments = []
for l in a.locations:
assert isinstance(l,bioc.BioCLocation)
startPos = int(native(l.offset)) - offset
endPos = startPos + int(native(l.length))
position.append((startPos,endPos))
segments.append(text[startPos:endPos])
entityText = " ".join(segments)
e = kindred.Entity(entityType,entityText,position,sourceEntityID)
entities.append(e)
sourceEntityIDToEntity = { entity.sourceEntityID:entity for entity in entities }
for r in passage.relations:
assert isinstance(r,bioc.BioCRelation)
relationType = r.infons['type']
arguments = []
for n in r.nodes:
assert isinstance(n,bioc.BioCNode)
arguments.append((n.role,n.refid))
arguments = sorted(arguments)
argNames = [ argName for argName,sourceEntityID in arguments]
sourceEntityIDs = [ sourceEntityID for argName,sourceEntityID in arguments]
for sourceEntityID in sourceEntityIDs:
assert sourceEntityID in sourceEntityIDToEntity, "Relation references entity %s which does not exist in BioC document id=%s" % (sourceEntityID,str(document.id))
entities = [ sourceEntityIDToEntity[sourceEntityID] for sourceEntityID in sourceEntityIDs ]
r = kindred.Relation(relationType,entities,argNames)
relations.append(r)
metadata = dict(document.infons)
metadata.update(passage.infons)
metadata['id'] = document.id
relData = kindred.Document(text,entities=entities,relations=relations,metadata=metadata)
kindredDocs.append(relData)
return kindredDocs
def annotate(self, corpus):
"""
Annotate a parsed corpus with the wordlist lookup and other entity types
:param corpus: Corpus to annotate
:type corpus: kindred.Corpus
"""
assert corpus.parsed == True, "Corpus must already be parsed before entity recognition"
for doc in corpus.documents:
for sentence in doc.sentences:
words = [t.word for t in sentence.tokens]
extractedTermData = self._processWords(words)
for locs, terms, termtypesAndids in extractedTermData:
#text = " ".join(terms)
startToken = locs[0]
endToken = locs[1]
startPos = sentence.tokens[startToken].startPos
endPos = sentence.tokens[endToken - 1].endPos
text = doc.text[startPos:endPos]
loc = list(range(startToken, endToken))
for entityType, externalID in termtypesAndids:
e = kindred.Entity(entityType,
text, [(startPos, endPos)],
externalID=externalID)
doc.addEntity(e)
sentence.addEntityWithLocation(e, loc)
def annotate(self, corpus):
"""
Annotate a parsed corpus with the wordlist lookup and other entity types
:param corpus: Corpus to annotate
:type corpus: kindred.Corpus
"""
assert corpus.parsed == True, "Corpus must already be parsed before entity recognition"
for doc in corpus.documents:
entityCount = len(doc.entities)
for sentence in doc.sentences:
extractedTermData = self._processWords(sentence)
for locs, terms, termtypesAndids in extractedTermData:
startToken = locs[0]
endToken = locs[1]
startPos = sentence.tokens[startToken].startPos
endPos = sentence.tokens[endToken - 1].endPos
text = doc.text[startPos:endPos]
loc = list(range(startToken, endToken))
for entityType, externalID in termtypesAndids:
sourceEntityID = "T%d" % (entityCount + 1)
e = kindred.Entity(entityType,
text, [(startPos, endPos)],
externalID=externalID,
sourceEntityID=sourceEntityID)
#doc.addEntity(e)
doc.entities.append(e)
sentence.addEntityAnnotation(e, loc)
entityCount += 1
def annotate(self, corpus):
"""
Annotate a corpus for numerical values
:param corpus: Corpus to annotate
:type corpus: kindred.Corpus
"""
assert corpus.parsed == True, "Corpus must already be parsed before entity recognition"
for doc in corpus.documents:
entityCount = len(doc.entities)
for sentence in doc.sentences:
words = [t.word for t in sentence.tokens]
for i, t in enumerate(sentence.tokens):
if not isNumber(t.word):
continue
sourceEntityID = "T%d" % (entityCount + 1)
text = doc.text[t.startPos:t.endPos]
loc = [i]
e = kindred.Entity('quantity',
text, [(t.startPos, t.endPos)],
sourceEntityID=sourceEntityID)
doc.addEntity(e)
sentence.addEntityAnnotation(e, loc)
entityCount += 1
def parseSimpleTag_helper(node,currentPosition=0,ignoreEntities=[]):
text,entities,relationTuples = '',[],[]
for s in node.childNodes:
if s.nodeType == s.ELEMENT_NODE:
insideText,insideEntities,insideRelationTuples = parseSimpleTag_helper(s,currentPosition+len(text))
if s.tagName == 'relation':
relationType = s.getAttribute('type')
arguments = [ (argName,entityID) for argName,entityID in s.attributes.items() if argName != 'type' ]
arguments = sorted(arguments)
sourceEntityIDs = [ sourceEntityID for argName,sourceEntityID in arguments]
argNames = [ argName for argName,sourceEntityID in arguments]
relationTuple = (relationType,sourceEntityIDs,argNames)
relationTuples.append(relationTuple)
else: # Entity
entityType = s.tagName
sourceEntityID = s.getAttribute('id')
position = [(currentPosition+len(text),currentPosition+len(text)+len(insideText))]
assert len(insideText) > 0, "Name (text inside tags) is empty for entity of type %s" % entityType
if not entityType in ignoreEntities:
e = kindred.Entity(entityType,insideText,position,sourceEntityID=sourceEntityID)
entities.append(e)
text += insideText
entities += insideEntities
relationTuples += insideRelationTuples
elif s.nodeType == s.TEXT_NODE:
text += s.nodeValue
return text,entities,relationTuples
def loadEntity(filename,line,text):
assert line[0] == 'T', "ERROR in %s. Entity input should start with a T" % filename
split = line.strip().split('\t')
assert len(split) == 3, "ERROR in %s" % filename
entityID = split[0]
typeInfo = split[1]
tokens = split[2]
textChunks = []
typeSpacePos = typeInfo.index(' ')
typeName = typeInfo[:typeSpacePos]
positionText = typeInfo[typeSpacePos:]
positions = []
for coordinates in positionText.strip().split(';'):
a,b = coordinates.strip().split(' ')
a,b = int(a.strip()),int(b.strip())
textChunk = text[a:b].replace('\n',' ').strip()
textChunks.append(textChunk)
positions.append((a,b))
# Check that the tokens match up to the text
chunkTest = " ".join(textChunks)
tokensTest = tokens
chunkTest = re.sub(r'\s\s+', ' ', chunkTest)
tokensTest = re.sub(r'\s\s+', ' ', tokensTest)
chunkTest = chunkTest.strip()
tokensTest = tokensTest.strip()
assert chunkTest == tokensTest , u"ERROR in " + filename + u"For id=" + entityID + ", tokens '" + tokens.encode('ascii', 'ignore') + "' don't match up with positions: " + str(positions)
entity = kindred.Entity(typeName, tokensTest, positions, entityID)
return entity
def test_document_addEntity():
text = "Cancer is caused by mutations in ABCDE1."
doc = kindred.Document(text, [])
e1 = kindred.Entity('disease', 'Cancer', [(0, 6)], 'T1')
doc.addEntity(e1)
expected = "<Document Cancer is caused by mutations in ABCDE1. [<Entity disease:'Cancer' sourceid=T1 [(0, 6)]>] []>"
assert str(doc) == expected
def test_relation_triple_equals():
e1 = kindred.Entity('mutation','BRAF V600E mutation',[])
e2 = kindred.Entity('event','vemurafenib resistance',[])
e3 = kindred.Entity('citation','28028924',[])
rel1 = kindred.CandidateRelation(entities=[e1,e2,e3])
rel2 = kindred.CandidateRelation(entities=[e1,e2,e3])
rel3 = kindred.CandidateRelation(entities=[e1,e2,e3],knownTypesAndArgNames=[("causes",["whathappened","whatdiditcause","citation"])])
rel4 = kindred.CandidateRelation(entities=[e1,e2,e3],knownTypesAndArgNames=[("causes",["whathappened","whatdiditcause","citation"])])
assert rel1 == rel2
assert rel1 != rel3
assert rel1 != rel4
assert rel2 != rel3
assert rel3 == rel4
assert rel1 != e1
assert rel2 != e1
assert rel3 != e1
assert rel4 != e1
def test_relation_equals():
e1 = kindred.Entity('mutation','BRAF V600E mutation',[])
e2 = kindred.Entity('event','vemurafenib resistance',[])
rel1 = kindred.CandidateRelation(entities=[e1,e2])
rel2 = kindred.CandidateRelation(entities=[e1,e2])
rel3 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[("causes",["drug","disease"])])
rel4 = kindred.CandidateRelation(entities=[e1,e2],knownTypesAndArgNames=[("causes",["drug","disease"])])
e1 = kindred.Entity(entityType="drug",text="Erlotinib",position=[(0,9)],sourceEntityID=None)
assert rel1 == rel2
assert rel1 != rel3
assert rel1 != rel4
assert rel2 != rel3
assert rel3 == rel4
assert rel1 != e1
assert rel2 != e1
assert rel3 != e1
assert rel4 != e1
def parseJSON(data, ignoreEntities=[]):
entities = []
relations = []
text = data['text']
if 'denotations' in data:
for d in data['denotations']:
sourceEntityID = None
if 'id' in d:
sourceEntityID = d['id']
entityType = d['obj']
span = d['span']
startPos, endPos = span['begin'], span['end']
position = [(startPos, endPos)]
entityText = text[startPos:endPos]
if not entityType in ignoreEntities:
entity = kindred.Entity(entityType,
entityText,
position,
sourceEntityID=sourceEntityID)
entities.append(entity)
if 'relations' in data:
for r in data['relations']:
obj = r['obj']
relationType = r['pred']
subj = r['subj']
entityIDs = [obj, subj]
argNames = ['obj', 'subj']
relation = kindred.Relation(relationType=relationType,
entityIDs=entityIDs,
argNames=argNames)
relations.append(relation)
expected = [
'denotations', 'divid', 'modifications', 'namespaces', 'project',
'relations', 'sourcedb', 'sourceid', 'target', 'text', 'tracks'
]
extraFields = [k for k in data.keys() if not k in expected]
assert len(extraFields
) == 0, "Found additional unexpected fields (%s) in JSON" % (
",".join(extraFields))
combinedData = kindred.Document(text,
entities=entities,
relations=relations)
return combinedData
def parseJSON(data,ignoreEntities=[]):
entities = []
relations = []
if isinstance(data,list):
assert len(data) == 1 and isinstance(data[0],dict), "JSON loading expects a dictionary or a list with one dictionary in it"
data = data[0]
assert isinstance(data,dict), "JSON loading expects a dictionary or a list with one dictionary in it"
text = data['text']
if 'denotations' in data:
for d in data['denotations']:
sourceEntityID = None
if 'id' in d:
sourceEntityID = d['id']
entityType = d['obj']
span = d['span']
startPos,endPos = span['begin'],span['end']
position = [(startPos,endPos)]
entityText = text[startPos:endPos]
if not entityType in ignoreEntities:
entity = kindred.Entity(entityType,entityText,position,sourceEntityID=sourceEntityID)
entities.append(entity)
sourceEntityIDToEntity = { entity.sourceEntityID:entity for entity in entities }
if 'relations' in data:
for r in data['relations']:
obj = r['obj']
relationType = r['pred']
subj = r['subj']
sourceEntityIDs = [obj,subj]
argNames = ['obj','subj']
entitiesInRelation = [ sourceEntityIDToEntity[sourceEntityID] for sourceEntityID in sourceEntityIDs ]
relation = kindred.Relation(relationType,entitiesInRelation,argNames)
relations.append(relation)
expected = ['denotations','divid','modifications','namespaces','project','relations','sourcedb','sourceid','target','text','tracks']
extraFields = [ k for k in data.keys() if not k in expected]
assert len(extraFields) == 0, "Found additional unexpected fields (%s) in JSON" % (",".join(extraFields))
combinedData = kindred.Document(text,entities=entities,relations=relations)
return combinedData
def splitIntoSentences(self):
"""
Create a new corpus with one document for each sentence in this document.
:return: Corpus with one document per sentence
:rtype: kindred.Corpus
"""
sentenceCorpus = kindred.Corpus()
for sentence in self.sentences:
sentenceStart = sentence.tokens[0].startPos
entitiesInSentence = [
entity for entity, tokenIndices in sentence.entityAnnotations
]
entityMap = OrderedDict()
for e in entitiesInSentence:
startPos, endPos = e.position[0]
newPosition = [(startPos - sentenceStart,
endPos - sentenceStart)]
newE = kindred.Entity(e.entityType, e.text, newPosition,
e.sourceEntityID, e.externalID)
entityMap[e] = newE
relationsInSentence = [
r for r in self.relations
if all(e in entitiesInSentence for e in r.entities)
]
newRelationsInSentence = []
for r in relationsInSentence:
newEntitiesInRelation = [entityMap[e] for e in r.entities]
newRelation = kindred.Relation(r.relationType,
newEntitiesInRelation,
r.argNames, r.probability)
newRelationsInSentence.append(newRelation)
newEntitiesInSentence = list(entityMap.values())
doc = kindred.Document(sentence.text, newEntitiesInSentence,
newRelationsInSentence)
newTokens = [
kindred.Token(t.word, t.lemma, t.partofspeech,
t.startPos - sentenceStart,
t.endPos - sentenceStart)
for t in sentence.tokens
]
newSentence = kindred.Sentence(sentence.text, newTokens,
sentence.dependencies,
sentence.sourceFilename)
newEntityAnnotations = [
(entityMap[e], tokenIndices)
for e, tokenIndices in sentence.entityAnnotations
]
newSentence.entityAnnotations = newEntityAnnotations
doc.sentences = [newSentence]
sentenceCorpus.addDocument(doc)
return sentenceCorpus
def convertBiocDocToKindredDocs(document):
assert isinstance(document, bioc.BioCDocument)
kindredDocs = []
for passage in document.passages:
assert isinstance(passage, bioc.BioCPassage)
text = passage.text
offset = int(native(passage.offset))
entities = []
relations = []
for a in passage.annotations:
assert isinstance(a, bioc.BioCAnnotation)
entityType = a.infons['type']
sourceEntityID = a.id
metadata = a.infons
del metadata['type']
position = []
segments = []
for l in a.locations:
assert isinstance(l, bioc.BioCLocation)
startPos = int(native(l.offset)) - offset
endPos = startPos + int(native(l.length))
assert startPos >= 0 and startPos <= len(
text
) and endPos >= 0 and endPos <= len(
text
), "Entity offsets (offset=%s,length=%s) are outside the span of the text (%s)" % (
str(l.offset), str(l.length), passage.text)
position.append((startPos, endPos))
segments.append(text[startPos:endPos])
entityText = " ".join(segments)
assert entityText == a.text, "Mismatch in entity annotation between expected text (%s) and extracted text (%s) using offset info for passage with text: %s" % (
a.text, entityText, text)
e = kindred.Entity(entityType,
entityText,
position,
sourceEntityID,
metadata=metadata)
entities.append(e)
sourceEntityIDToEntity = {
entity.sourceEntityID: entity
for entity in entities
}
for r in passage.relations:
assert isinstance(r, bioc.BioCRelation)
relationType = r.infons['type']
arguments = []
for n in r.nodes:
assert isinstance(n, bioc.BioCNode)
arguments.append((n.role, n.refid))
arguments = sorted(arguments)
argNames = [argName for argName, sourceEntityID in arguments]
sourceEntityIDs = [
sourceEntityID for argName, sourceEntityID in arguments
]
for sourceEntityID in sourceEntityIDs:
assert sourceEntityID in sourceEntityIDToEntity, "Relation references entity %s which does not exist in BioC document id=%s" % (
sourceEntityID, str(document.id))
entities = [
sourceEntityIDToEntity[sourceEntityID]
for sourceEntityID in sourceEntityIDs
]
r = kindred.Relation(relationType, entities, argNames)
relations.append(r)
metadata = dict(document.infons)
metadata.update(passage.infons)
metadata['id'] = document.id
relData = kindred.Document(text,
entities=entities,
relations=relations,
metadata=metadata)
kindredDocs.append(relData)
return kindredDocs
entityTypes = set([
entity.entityType
for entity, tokenIndices in sentence.entityAnnotations
])
entityInfo = [(e.entityType, e.text)
for e, tokenIndices in sentence.entityAnnotations
]
hasMutation = "Mutation" in entityTypes
hasChemical = "Chemical" in entityTypes
if hasMutation and hasChemical:
sentenceStart = sentence.tokens[0].startPos
sentenceEntities = [
kindred.Entity(e.entityType,
e.text,
[(e.position[0][0] - sentenceStart,
e.position[0][1] - sentenceStart)],
e.sourceEntityID,
e.externalID,
metadata=e.metadata)
for e, _ in sentence.entityAnnotations
]
newDoc = kindred.Document(sentence.text,
sentenceEntities,
metadata=doc.metadata)
sentenceCorpus.addDocument(newDoc)
kindred.save(sentenceCorpus, 'biocxml', args.outBioc)