def load(self):
"""Load and clean the reports."""
collection = bioc.BioCCollection()
reports = pd.read_csv(self.reports_path,
header=None,
names=[REPORTS])[REPORTS].tolist()
for i, report in enumerate(reports):
clean_report = self.clean(report)
document = text2bioc.text2document(str(i), clean_report)
if self.extract_impression:
document = section_split.split_document(document)
self.extract_impression_from_passages(document)
split_document = self.splitter.split_doc(document)
assert len(split_document.passages) == 1,\
('Each document must have a single passage, ' +
'the Impression section.')
collection.add_document(split_document)
self.reports = reports
self.collection = collection
def get_bioc_collection(df):
collection = bioc.BioCCollection()
splitter = NegBioSSplitter()
for i, report in enumerate(df["Report Impression"]):
document = text2bioc.text2document(str(i), report)
document = splitter.split_doc(document)
collection.add_document(document)
return collection
def load(self):
collection = bioc.BioCCollection()
for i, sentence in enumerate(self.report):
clean_report = self.clean(sentence)
document = text2bioc.text2document(str(i), clean_report)
split_document = self.splitter.split_doc(document)
assert len(split_document.passages) == 1,\
('Each document must have a single passage, ' +
'the Impression section.')
collection.add_document(split_document)
self.collection = collection
def prep_collection(self):
"""Apply splitter and create bioc collection"""
collection = bioc.BioCCollection()
for i, report in enumerate(self.reports):
clean_report = self.clean(report)
document = text2bioc.text2document(str(i), clean_report)
if self.extract_impression:
document = section_split.split_document(document)
self.extract_impression_from_passages(document)
split_document = self.splitter.split_doc(document)
assert len(split_document.passages) == 1,\
('Each document must have a single passage, ' +
'the Impression section.')
collection.add_document(split_document)
self.collection = collection
