def validate_pubtator(content, document):
""" Returns bool if the provided str is a valid
pubtator (BioC) response for the Document instance
"""
try:
r = BioCReader(source=content)
r.read()
# Check general Collection + Document attributes
assert (len(r.collection.documents) == 1), 'The response included more than the provided Document'
assert (document.document_id == int(r.collection.documents[0].id)), 'The response does not include the requested PMID'
assert (len(r.collection.documents[0].passages) == 2), 'The response document does not include the correct number of sections'
# Check the Title
assert (int(r.collection.documents[0].passages[0].offset) == 0), 'The title does not start at 0'
section = document.section_set.first()
assert (section.text == r.collection.documents[0].passages[0].text), 'The response title does not equal the provided text'
assert (section.id == int(r.collection.documents[0].passages[0].infons.get('id'))), 'The response title is not correctly identified'
# Check the Abstract
assert (int(r.collection.documents[0].passages[1].offset) >= 1), 'The abstract does not start after 0'
section = document.section_set.last()
assert (section.text == r.collection.documents[0].passages[1].text), 'The response abstract does not equal the provided text'
assert (section.id == int(r.collection.documents[0].passages[1].infons.get('id'))), 'The response abstract is not correctly identified'
return True
except Exception:
client.captureException()
return False
def test_document_as_bioc_with_pubtator(self):
pub_query_set = Pubtator.objects.filter(
document=self.doc,
session_id='',
content__isnull=False)
response = self.client.get('/document/pubtator/{pmid}.json'.format(pmid=self.doc.document_id))
json_string = response.content
self.assertNotEqual(json_string, '', msg='API returned empty response for document BioC Pubtator Representation.')
data = json.loads(json_string)
# Make sure it's the same document in BioC as DB
self.assertEqual(int(data.get('collection').get('document').get('id')), self.doc.document_id)
self.assertEqual(len(data.get('collection').get('document').get('passage')), 2)
self.assertEqual(data.get('collection').get('document').get('passage')[0].get('text'), self.doc.section_set.first().text)
self.assertEqual(data.get('collection').get('document').get('passage')[1].get('text'), self.doc.section_set.last().text)
# Make sure it contains any annotations
self.assertNotEqual(len(data.get('collection').get('document').get('passage')[0].get('annotation')), 0)
self.assertNotEqual(len(data.get('collection').get('document').get('passage')[1].get('annotation')), 0)
# We already validated everything in JSON b/c it's easier. Let's just
# make sure the XML document passes too without specific checks
response = self.client.get('/document/pubtator/{pmid}.xml'.format(pmid=self.doc.document_id))
r = BioCReader(source=response.content)
r.read()
self.assertEqual(len(r.collection.documents), 1)
self.assertEqual(int(r.collection.documents[0].id), self.doc.document_id)
self.assertEqual(len(r.collection.documents[0].passages), 2)
self.assertNotEqual(len(r.collection.documents[0].passages[0].annotations), 0)
self.assertNotEqual(len(r.collection.documents[0].passages[1].annotations), 0)
def handle(self, *args, **options):
types_arr = []
errors = 0
if options['keys']:
for pubtator in Pubtator.objects.filter(content__isnull=False).all():
try:
r = BioCReader(source=pubtator.content)
r.read()
for d_idx, document in enumerate(r.collection.documents):
for p_idx, passage in enumerate(document.passages):
for annotation in r.collection.documents[d_idx].passages[p_idx].annotations:
types_arr.append( annotation.infons['type'] )
except Exception as e:
'''
print '%' in pubtator.content
for sec in pubtator.document.available_sections():
print '%' in sec.text
print ' - - - '
'''
errors = errors + 1
print 'Errors:', errors
print Counter(types_arr)
def as_pubtator_annotation_df(self):
# If the document has 3 solid annotations
# "GNormPlus"
# "DNorm"
# "tmChem"
df_columns = ('uid', 'source', 'ann_type', 'text', 'offset', 'location')
pubtator_dfs = []
if self.valid_pubtator():
pubtators = Pubtator.objects.filter(
document=self,
session_id='',
content__isnull=False).all()
for pubtator in pubtators:
r = BioCReader(source=pubtator.content)
r.read()
pubtator_arr = []
bioc_document = r.collection.documents[0]
for passage in bioc_document.passages:
for annotation in passage.annotations:
infons = annotation.infons
annotation_type = None
uid_type = None
uid = None
for key in infons.keys():
if key == 'type':
annotation_type = infons.get(key, None)
else:
uid_type = key
uid = infons.get(uid_type, None)
#print infons.keys()
#print infons
#print uid_type, uid, '('+str(annotation_type)+')'
#print ' - '*40
pubtator_arr.append({
'uid': uid,
'source': uid_type,
'ann_type': annotation_type,
'text': str(annotation.text),
'offset': int(passage.offset),
'location': str(annotation.locations[0])
})
pubtator_dfs.append( pd.DataFrame(pubtator_arr, columns=df_columns) )
if len(pubtator_dfs):
return pd.concat(pubtator_dfs)
else:
return pd.DataFrame([], columns=df_columns)
def as_writer(self, request=None):
r = BioCReader(source=self.content)
r.read()
bioc_writer = BioCWriter()
bioc_writer.collection = r.collection
return bioc_writer
def get_instance(self):
"""
Returns the pubtator BioC instance if valid or None
"""
try:
r = BioCReader(source=self.content)
r.read()
return r
except Exception:
# If one of them doesn't validate leave
return False
def count_annotations(self):
if self.valid():
count = 0
reader = BioCReader(source=self.content)
reader.read()
for doc in reader.collection.documents:
for passage in doc.passages:
count += len(passage.annotations)
return count
else:
return 0
def count_annotations(self):
""" Returns an int count of all types of ER annotations in the Pubtator instance
If none are found or the document is invalid, return 0
"""
try:
r = BioCReader(source=self.content)
r.read()
return sum([
len(passage.annotations)
for passage in r.collection.documents[0].passages
])
except Exception:
return 0
def test_document_as_bioc_with_m2c(self):
# Submit Annotations (As User 1) so they show up when inspecting the M2C submissions
self.assertEqual(Annotation.objects.count(), 0)
self.client.login(username='******', password='******')
response = self.client.get(reverse('common:quest-home', kwargs={'quest_pk': self.task.pk}), follow=True)
doc = response.context['document']
abstract = doc.available_sections().last()
# Annotation submit URL
url = reverse('document:create', kwargs={'task_pk': self.task.pk, 'section_pk': abstract.pk})
self.assertEqual(self.client.post(url, {'type': 0, 'text': 'text annotation 0', 'start': 0}).status_code, 200)
self.assertEqual(self.client.post(url, {'type': 1, 'text': 'text annotation 1', 'start': 10}).status_code, 200)
self.assertEqual(self.client.post(url, {'type': 2, 'text': 'text annotation 2', 'start': 20}).status_code, 200)
self.assertEqual(Annotation.objects.count(), 3)
# Then submit the document for the Quest
response = self.client.post(reverse('common:doc-quest-submit', kwargs={'quest_pk': self.task.pk, 'document_pk': doc.pk}), follow=True)
self.client.logout()
# Submit Annotations (As User 1) so they show up when inspecting the M2C submissions
self.assertEqual(Annotation.objects.count(), 3)
self.client.login(username='******', password='******')
response = self.client.get(reverse('common:quest-home', kwargs={'quest_pk': self.task.pk}), follow=True)
# Annotation submit URL
url = reverse('document:create', kwargs={'task_pk': self.task.pk, 'section_pk': abstract.pk})
self.assertEqual(self.client.post(url, {'type': 0, 'text': 'text annotation 3', 'start': 30}).status_code, 200)
self.assertEqual(self.client.post(url, {'type': 1, 'text': 'text annotation 4', 'start': 40}).status_code, 200)
self.assertEqual(self.client.post(url, {'type': 2, 'text': 'text annotation 5', 'start': 50}).status_code, 200)
self.assertEqual(Annotation.objects.count(), 6)
# Then submit the document for the Quest
response = self.client.post(reverse('common:doc-quest-submit', kwargs={'quest_pk': self.task.pk, 'document_pk': doc.pk}), follow=True)
self.client.logout()
# As Anon user, export the documents submissions
res = self.client.get(reverse('document:read-users-bioc', kwargs={'pubmed_id': doc.document_id, 'format_type': 'xml'}), follow=True)
self.assertEqual(res.status_code, 200)
bioc = BioCReader(source=res.content)
bioc.read()
# Make sure the BioC document has the opponent's infp
self.assertEqual(len(bioc.collection.documents), 1)
self.assertEqual(int(bioc.collection.documents[0].id), doc.document_id)
self.assertEqual(len(bioc.collection.documents[0].passages), 2)
self.assertEqual(len(bioc.collection.documents[0].passages[0].annotations), 0)
self.assertEqual(len(bioc.collection.documents[0].passages[1].annotations), 6)
def valid(self):
# (TODO) This may return 2 different "types" check on
# implications of this discrepancy
if self.validate_cache:
return True
if self.session_id != '':
return False
if self.content is None:
return False
try:
r = BioCReader(source=self.content)
r.read()
return r
except Exception:
# If one of them doesn't validate leave
return False
def test_group_for_all_user_annotations(self):
self.load_fake_annotations()
# Fetch the Group BioC as JSON to ensure is online
response = self.client.get(reverse('api:group-users-bioc', kwargs={'group_pk': self.group.pk, 'format_type': 'json'}))
self.assertEqual(response.status_code, 200)
# Fetch the Group BioC for all user annotations
response = self.client.get(reverse('api:group-users-bioc', kwargs={'group_pk': self.group.pk, 'format_type': 'xml'}))
self.assertEqual(response.status_code, 200)
r = BioCReader(source=response.content)
r.read()
# Does BioC have correct number of Group Documents
self.assertEqual(len(r.collection.documents), self.group.get_documents().count())
# Does BioC have correct number of Group Annotations
total_bioc_annotation_int = 0
for bioc_doc in r.collection.documents:
for bioc_passage in bioc_doc.passages:
total_bioc_annotation_int += len(bioc_passage.annotations)
self.assertEqual(Annotation.objects.count(), total_bioc_annotation_int)
def entity_recognition_df(self,
documents=[],
users=[],
include_pubtator=True,
writer=None):
if len(documents):
from .models import Document
doc_arr = []
for d in documents:
if type(d) == Document:
doc_arr.append(str(d.pk))
elif type(d) is str or type(d) is unicode and d.isdigit():
doc_arr.append(d)
elif type(d) is int or type(d) is long:
doc_arr.append(str(d))
filter_doc_level = 'WHERE `document_section`.`document_id` IN ({0})'.format(
','.join(doc_arr))
else:
filter_doc_level = ''
if len(users):
from django.contrib.auth.models import User
user_arr = []
for u in users:
if type(u) == User:
user_arr.append(str(u.pk))
elif type(u) is str or type(u) is unicode and d.isdigit():
user_arr.append(u)
elif type(u) is int:
user_arr.append(str(u))
filter_user_level = '{0} `document_view`.`user_id` IN ({1})'.format(
'WHERE' if filter_doc_level == '' else 'AND',
','.join(user_arr))
else:
filter_user_level = ''
content_type_id = str(
ContentType.objects.get_for_model(
EntityRecognitionAnnotation.objects.first()).id)
df_arr = []
cmd_str = ""
with open('mark2cure/document/commands/get-er-results.sql', 'r') as f:
cmd_str = f.read()
cmd_str = cmd_str.format(content_type_pk=content_type_id,
filter_doc_level=filter_doc_level,
filter_user_level=filter_user_level)
c = connection.cursor()
try:
c.execute(cmd_str)
# Get the full writer in advnaced!!
if not writer:
writer = Document.objects.as_writer(documents=documents)
res = [x for x in c.fetchall()]
# We group the response to reduce BioCDocument offset dict lookups
for key, doc_group in groupby(res, lambda x: x[5]):
bioc_documents = filter(
lambda d: d.infons.get('document_pk') == str(key),
writer.collection.documents)
# If a pubtator doesn't exist for the document, we can't include any annotations as the passage offsets need to come from Pubtator
if len(bioc_documents) == 1:
# Use the BioC pubtator file for the offset values
offset_dict = {}
for passage in bioc_documents[0].passages:
offset_dict[int(
passage.infons.get('id'))] = passage.offset
for x in doc_group:
df_arr.append(
self._create_er_df_row(
uid=x[0],
source='db',
user_id=x[8],
text=x[2],
ann_type_idx=x[1],
document_pk=x[5],
section_id=x[7],
section_offset=offset_dict[x[7]],
offset_relative=True,
start_position=x[3],
length=len(x[2])))
finally:
c.close()
if include_pubtator:
'''
This is the component that merges the 3 different pubtator
reponses into 1 main file. It performances selective
ordering and precedence for some annotations types / instances
'''
cmd_str = ""
with open(
'mark2cure/document/commands/get-er-pubtator-results.sql',
'r') as f:
cmd_str = f.read()
cmd_str = cmd_str.format(','.join(doc_arr))
c = connection.cursor()
try:
c.execute(cmd_str)
res = [x for x in c.fetchall()]
finally:
c.close()
# Counter({'Disease': 3676, 'Chemical': 2928, 'Species': 1553, 'Gene': 1544, 'FamilyName': 536, 'DomainMotif': 20}) (Sampleing from DB 11/30/2016)
pubtator_types = ['Disease', 'Gene', 'Chemical']
for pubtator_content in res:
r = BioCReader(source=pubtator_content[2])
r.read()
bioc_document = r.collection.documents[0]
section_ids = pubtator_content[3].split(',')
# Iterate over all the annotations in both passages
for p_idx, passage in enumerate(bioc_document.passages):
for annotation in passage.annotations:
# Determine some meta-data (UID info) about the BioCAnnotation
annotation_type = None
uid_type = None
uid = None
for key in annotation.infons.keys():
if key == 'type':
annotation_type = annotation.infons.get(
key, None)
else:
uid_type = key
uid = annotation.infons.get(uid_type, None)
# We're only interested in Pubtator Annotations that are the same concepts users highlight
if annotation_type in pubtator_types:
start, length = str(
annotation.locations[0]).split(':')
df_arr.append(
self._create_er_df_row(
uid=uid,
source=uid_type if uid_type else None,
user_id=None,
text=annotation.text,
ann_type_idx=pubtator_types.index(
annotation_type),
document_pk=pubtator_content[1],
section_id=section_ids[p_idx],
section_offset=passage.offset,
offset_relative=False,
start_position=start,
length=length))
return pd.DataFrame(df_arr, columns=DF_COLUMNS)
def entity_recognition_df(self, documents=[], users=[], include_pubtator=True, writer=None):
if len(documents):
from .models import Document
doc_arr = []
for d in documents:
if type(d) == Document:
doc_arr.append(str(d.pk))
elif type(d) is str or type(d) is unicode and d.isdigit():
doc_arr.append(d)
elif type(d) is int or type(d) is long:
doc_arr.append(str(d))
filter_doc_level = 'WHERE `document_section`.`document_id` IN ({0})'.format(','.join(doc_arr))
else:
filter_doc_level = ''
if len(users):
from django.contrib.auth.models import User
user_arr = []
for u in users:
if type(u) == User:
user_arr.append(str(u.pk))
elif type(u) is str or type(u) is unicode and d.isdigit():
user_arr.append(u)
elif type(u) is int:
user_arr.append(str(u))
filter_user_level = '{0} `document_view`.`user_id` IN ({1})'.format(
'WHERE' if filter_doc_level == '' else 'AND',
','.join(user_arr))
else:
filter_user_level = ''
content_type_id = str(ContentType.objects.get_for_model(
EntityRecognitionAnnotation.objects.first()).id)
df_arr = []
cmd_str = '''
SELECT
`entity_recognition_entityrecognitionannotation`.`id`,
`entity_recognition_entityrecognitionannotation`.`type`,
`entity_recognition_entityrecognitionannotation`.`text`,
`entity_recognition_entityrecognitionannotation`.`start`,
`document_annotation`.`created`,
`document_document`.`id` as `document_pk`,
`document_document`.`document_id` as `pmid`,
`document_view`.`section_id`,
`document_view`.`user_id`
FROM `entity_recognition_entityrecognitionannotation`
INNER JOIN `document_annotation`
ON `document_annotation`.`object_id` = `entity_recognition_entityrecognitionannotation`.`id` AND `document_annotation`.`content_type_id` = {content_type_pk}
INNER JOIN `document_view`
ON `document_annotation`.`view_id` = `document_view`.`id`
INNER JOIN `document_section`
ON `document_view`.`section_id` = `document_section`.`id`
INNER JOIN `document_document`
ON `document_document`.`id` = `document_section`.`document_id`
{filter_doc_level}
{filter_user_level}
'''.format(content_type_pk=content_type_id, filter_doc_level=filter_doc_level, filter_user_level=filter_user_level)
c = connection.cursor()
try:
c.execute(cmd_str)
# Get the full writer in advnaced!!
if not writer:
writer = Document.objects.as_writer(documents=documents)
res = [x for x in c.fetchall()]
# We group the response to reduce BioCDocument offset dict lookups
for key, doc_group in groupby(res, lambda x: x[5]):
bioc_documents = filter(lambda d: d.infons.get('document_pk') == str(key), writer.collection.documents)
# If a pubtator doesn't exist for the document, we can't include any annotations as the passage offsets need to come from Pubtator
if len(bioc_documents) == 1:
# Use the BioC pubtator file for the offset values
offset_dict = {}
for passage in bioc_documents[0].passages:
offset_dict[int(passage.infons.get('id'))] = passage.offset
for x in doc_group:
df_arr.append(self._create_er_df_row(
uid=x[0], source='db', user_id=x[8],
text=x[2], ann_type=x[1],
document_pk=x[5], section_id=x[7], section_offset=offset_dict[x[7]], offset_relative=True,
start_position=x[3], length=len(x[2])))
finally:
c.close()
if include_pubtator:
'''
This is the component that merges the 3 different pubtator
reponses into 1 main file. It performances selective
ordering and precedence for some annotations types / instances
'''
cmd_str = '''
SELECT
`document_pubtator`.`id`,
`document_pubtator`.`document_id`,
`document_pubtator`.`content`,
GROUP_CONCAT(DISTINCT `document_section`.`id`) as `section_ids`
FROM `document_pubtator`
JOIN `document_section`
ON `document_section`.`document_id` = `document_pubtator`.`document_id`
WHERE `document_pubtator`.`content` != ''
AND `document_pubtator`.`document_id` IN ({0})
GROUP BY `document_pubtator`.`id`
'''.format(','.join(doc_arr))
c = connection.cursor()
try:
c.execute(cmd_str)
res = [x for x in c.fetchall()]
finally:
c.close()
for pubtator_content in res:
r = BioCReader(source=pubtator_content[2])
r.read()
bioc_document = r.collection.documents[0]
section_ids = pubtator_content[3].split(',')
# Iterate over all the annotations in both passages
for p_idx, passage in enumerate(bioc_document.passages):
for annotation in passage.annotations:
# Determine some meta-data (UID info) about the BioCAnnotation
annotation_type = None
uid_type = None
uid = None
for key in annotation.infons.keys():
if key == 'type':
annotation_type = annotation.infons.get(key, None)
else:
uid_type = key
uid = annotation.infons.get(uid_type, None)
start, length = str(annotation.locations[0]).split(':')
df_arr.append(self._create_er_df_row(
uid=uid, source=uid_type if uid_type else None, user_id=None,
text=annotation.text, ann_type=annotation_type if annotation_type else None,
document_pk=pubtator_content[1], section_id=section_ids[p_idx], section_offset=passage.offset, offset_relative=False,
start_position=start, length=length))
return pd.DataFrame(df_arr, columns=DF_COLUMNS)
def test_document_as_bioc_for_pairing(self):
# Ensure the player views the Q but can't match b/c no Anns exist
self.client.login(username='******', password='******')
# Ensure the User info is showing up in the header
response = self.client.get('/dashboard/')
self.assertInHTML('<p>Level: Expert</p>', response.content)
# Ensure no User >> Quest views until after viewed once
self.assertEqual(UserQuestRelationship.objects.count(), 0)
response = self.client.get(reverse('common:quest-home', kwargs={'quest_pk': self.task.pk}), follow=True)
doc = response.context['document']
self.assertEqual(UserQuestRelationship.objects.count(), 1)
# Ensure this returns a 500 for the player b/c there are no submissions yet
response = self.client.get(reverse('document:results-bioc', kwargs={'task_pk': self.task.pk, 'doc_pk': doc.pk, 'format_type': 'xml'}))
self.assertEqual(response.status_code, 500)
self.assertEqual(response.content, 'no_points_awarded')
self.client.logout()
#
# Submit bogus Annotations as opponent to try match again for player
#
self.client.login(username='******', password='******')
self.assertEqual(Annotation.objects.count(), 0)
response = self.client.get(reverse('common:quest-home', kwargs={'quest_pk': self.task.pk}), follow=True)
# Annotation submit URL
abstract = doc.available_sections().last()
url = reverse('document:create', kwargs={'task_pk': self.task.pk, 'section_pk': abstract.pk})
self.assertEqual(self.client.post(url, {'type': 0, 'text': 'text annotation 0', 'start': 0}).status_code, 200)
self.assertEqual(self.client.post(url, {'type': 1, 'text': 'text annotation 1', 'start': 10}).status_code, 200)
self.assertEqual(self.client.post(url, {'type': 2, 'text': 'text annotation 2', 'start': 20}).status_code, 200)
self.assertEqual(Annotation.objects.count(), 3)
# Then submit the document for the Quest
response = self.client.post(reverse('common:doc-quest-submit', kwargs={'quest_pk': self.task.pk, 'document_pk': doc.pk}), follow=True)
self.client.logout()
#
# Try again as the player to see if comparison uses opponents
#
self.client.login(username='******', password='******')
# Submit this Document without contributing any Annotations
response = self.client.post(reverse('common:doc-quest-submit', kwargs={'quest_pk': self.task.pk, 'document_pk': doc.pk}), follow=True)
# Fetch the BioC Document again
response = self.client.get(reverse('document:results-bioc', kwargs={'task_pk': self.task.pk, 'doc_pk': doc.pk, 'format_type': 'xml'}))
self.assertEqual(response.status_code, 200)
r = BioCReader(source=response.content)
r.read()
# Make sure the BioC document has the opponent's infp
self.assertEqual(len(r.collection.documents), 1)
self.assertEqual(int(r.collection.documents[0].id), doc.document_id)
self.assertEqual(len(r.collection.documents[0].passages), 2)
self.assertEqual(len(r.collection.documents[0].passages[0].annotations), 0)
self.assertEqual(len(r.collection.documents[0].passages[1].annotations), 3)
self.assertEqual(r.collection.documents[0].passages[1].annotations[0].infons['user_name'], 'opponent')
self.assertEqual(int(r.collection.documents[0].passages[1].annotations[0].infons['type']), 0)
self.assertEqual(r.collection.documents[0].passages[1].annotations[0].text, 'text annotation 0')
self.assertEqual(int(r.collection.infons['points']), 0)
self.assertEqual(r.collection.infons['partner'], 'opponent')
self.client.logout()
def as_writer(self, documents=[]):
'''
Return a blank BioC Writer that is based off the pubtator content.
Problems: This requires every document to have at least 1 pubtator model
Pros: This prevents us from generating our own BioC file which may
have inconsistencies
'''
if len(documents):
from .models import Document
doc_arr = []
for d in documents:
if type(d) == Document:
doc_arr.append(str(d.pk))
elif type(d) is str or type(d) is unicode and d.isdigit():
doc_arr.append(d)
elif type(d) is int or type(d) is long:
doc_arr.append(str(d))
str_doc_arr = list(set(doc_arr))
else:
raise ValueError('No documents supplied to generator writer')
cmd_str = ""
with open('mark2cure/document/commands/get-pubtators.sql', 'r') as f:
cmd_str = f.read()
cmd_str = cmd_str.format(','.join(str_doc_arr))
c = connection.cursor()
try:
c.execute(cmd_str)
res = [(x[0], x[1], x[2]) for x in c.fetchall()]
finally:
c.close()
writer = bioc_writer(None)
for pubtator_content in res:
section_ids = pubtator_content[2].split(',')
r = BioCReader(source=pubtator_content[1])
r.read()
doc = r.collection.documents[0]
doc.put_infon('document_pk', str(pubtator_content[0]))
for idx, passage in enumerate(doc.passages):
passage.clear_annotations()
passage.put_infon('section', ['title', 'paragraph'][idx])
passage.put_infon('id', str(section_ids[idx]))
writer.collection.add_document(doc)
str_doc_arr.remove(str(pubtator_content[0]))
# Capture all the documents not available via pubtators
for document_pk_str in str_doc_arr:
# Can optimize this model retrieval but should rarely occur
document_model = Document.objects.get(pk=document_pk_str)
bioc_document = BioCDocument()
bioc_document.id = str(document_model.document_id)
bioc_document.put_infon('document_pk', document_pk_str)
passage_offset = 0
for idx, section in enumerate(document_model.available_sections()):
passage = BioCPassage()
passage.put_infon('section', ['title', 'paragraph'][idx])
passage.put_infon('id', str(section.pk))
# (TODO) Missing a "type" infon?
passage.text = section.text
passage.offset = str(passage_offset)
passage_offset += len(passage.text) + 1
bioc_document.add_passage(passage)
writer.collection.add_document(bioc_document)
return writer
def as_writer(self, documents=[]):
'''
Return a blank BioC Writer that is based off the pubtator content.
Problems: This requires every document to have at least 1 pubtator model
Pros: This prevents us from generating our own BioC file which may
have inconsistencies
'''
if len(documents):
from .models import Document
doc_arr = []
for d in documents:
if type(d) == Document:
doc_arr.append(str(d.pk))
elif type(d) is str or type(d) is unicode and d.isdigit():
doc_arr.append(d)
elif type(d) is int or type(d) is long:
doc_arr.append(str(d))
str_doc_arr = list(set(doc_arr))
else:
raise ValueError('No documents supplied to generator writer')
cmd_str = '''
SELECT
`document_pubtator`.`document_id`,
ANY_VALUE(`document_pubtator`.`content`),
GROUP_CONCAT(DISTINCT `document_section`.`id`) as `section_ids`
FROM `document_pubtator`
JOIN `document_section`
ON `document_section`.`document_id` = `document_pubtator`.`document_id`
WHERE `document_pubtator`.`content` != '' AND `document_pubtator`.`document_id` IN ({0})
GROUP BY `document_pubtator`.`document_id`;
'''.format(','.join(str_doc_arr))
c = connection.cursor()
try:
c.execute(cmd_str)
res = [(x[0], x[1], x[2]) for x in c.fetchall()]
finally:
c.close()
writer = bioc_writer(None)
for pubtator_content in res:
section_ids = pubtator_content[2].split(',')
r = BioCReader(source=pubtator_content[1])
r.read()
doc = r.collection.documents[0]
doc.put_infon('document_pk', str(pubtator_content[0]))
for idx, passage in enumerate(doc.passages):
passage.clear_annotations()
passage.put_infon('section', ['title', 'paragraph'][idx])
passage.put_infon('id', str(section_ids[idx]))
writer.collection.add_document(doc)
str_doc_arr.remove(str(pubtator_content[0]))
# Capture all the documents not available via pubtators
for document_pk_str in str_doc_arr:
# Can optimize this model retrieval but should rarely occur
document_model = Document.objects.get(pk=document_pk_str)
bioc_document = BioCDocument()
bioc_document.id = str(document_model.document_id)
bioc_document.put_infon('document_pk', document_pk_str)
passage_offset = 0
for idx, section in enumerate(document_model.available_sections()):
passage = BioCPassage()
passage.put_infon('section', ['title', 'paragraph'][idx])
passage.put_infon('id', str(section.pk))
# (TODO) Missing a "type" infon?
passage.text = section.text
passage.offset = str(passage_offset)
passage_offset += len(passage.text) + 1
bioc_document.add_passage(passage)
writer.collection.add_document(bioc_document)
return writer
def as_bioc_with_pubtator_annotations(self, request=None):
'''
This is a function that merges the 3 different pubtator
reponses into 1 main file. It performances selective
ordering and precedence for some annotations types / instances
'''
approved_types = ['Disease', 'Gene', 'Chemical']
self.init_pubtator()
reader = self.as_writer(request)
pub_query_set = Pubtator.objects.filter(
document=self,
session_id='',
content__isnull=False)
# Load up our various pubtator responses
pub_readers = []
for pubtator in pub_query_set.all():
r = BioCReader(source=pubtator.content)
r.read()
pub_readers.append(r)
for d_idx, document in enumerate(reader.collection.documents):
for p_idx, passage in enumerate(document.passages):
# For each passage in each document in the collection
# add the appropriate annotation
for p in pub_readers:
for annotation in p.collection.documents[d_idx].passages[p_idx].annotations:
ann_type = annotation.infons['type']
infons = annotation.infons
if ann_type in approved_types:
uid_type = None
uid = None
for key in infons.keys():
if key != 'type':
uid_type = key
uid = infons.get(uid_type, None)
annotation.clear_infons()
annotation.put_infon('type', str(approved_types.index(ann_type)))
annotation.put_infon('user', 'pubtator')
annotation.put_infon('uid', str(uid))
reader.collection.documents[d_idx].passages[p_idx].add_annotation(annotation)
# Remove the shorter annotation if they're multiple
# at the same start position
anns = reader.collection.documents[d_idx].passages[p_idx].annotations
ann_offsets = [x.locations[0].offset for x in anns]
import collections
# For each of the offset positions where there are multiple annotations
for offset in [x for x, y in collections.Counter(ann_offsets).items() if y > 1]:
conflicting_anns = [x for x in anns if x.locations[0].offset == offset]
longest_ann = max(conflicting_anns, key=lambda a: int(a.locations[0].length))
for ann in conflicting_anns:
if ann is not longest_ann:
reader.collection.documents[d_idx].passages[p_idx].remove_annotation(ann)
# Remove any annoations that overlap, prefer selection for longest
anns = reader.collection.documents[d_idx].passages[p_idx].annotations
for needle_ann in anns:
needle_ann_offset = int(needle_ann.locations[0].offset)
needle_ann_length = int(needle_ann.locations[0].length)
for stack_ann in anns:
stack_ann_offset = int(stack_ann.locations[0].offset)
stack_ann_length = int(stack_ann.locations[0].length)
if needle_ann_offset >= stack_ann_offset and needle_ann_length < stack_ann_length:
try:
reader.collection.documents[d_idx].passages[p_idx].remove_annotation(needle_ann)
except:
pass
return reader.collection.documents[0]
