from typing import Dict, Any
import stanza
from mtap import Document, processor, run_processor, GenericLabel
from mtap.processing import DocumentProcessor
from mtap.processing.descriptions import label_index, label_property
MAX_ITER = 5000
@processor('biomedicus-dependencies',
human_name="BioMedICUS Stanza Dependency Parser",
entry_point=__name__,
description="Calls out to the Stanford Stanza framework for dependency parsing.",
inputs=[
label_index(name='sentences', reference='biomedicus-sentences/sentences')
],
outputs=[
label_index(name='dependencies',
description="The dependent words.",
properties=[
label_property(
'deprel',
description="The dependency relation",
data_type='str'
),
label_property(
'head',
description="The head of this label or null if its the root.",
nullable=True,
data_type='ref:dependencies'
@processor(
name='biomedicus-negex-triggers',
human_name='Negex Triggers Tagger',
description='Labels phrases which are negation triggers.',
entry_point=__name__,
parameters=[
parameter(
name='terms_index',
data_type='str',
description=
'The label index containing terms that should be checked for negation'
)
],
inputs=[
label_index(name='sentences',
reference='biomedicus-sentences/sentences'),
label_index(name='umls_terms',
reference='biomedicus-concepts/umls_terms',
name_from_parameter='terms_index')
],
outputs=[
label_index("negation_trigger",
description="Spans of phrases that trigger negation.",
properties=[
label_property(
"tags",
data_type='List[str]',
description='The tags that apply to the trigger, '
'for example: POST PREN')
])
])
for match in _split.finditer(text):
split_timer.stop()
start = match.start()
local_text = text[prev:start]
for ss, se in predict_segment(model, input_mapper, local_text):
yield prev + ss, prev + se
prev = match.end()
split_timer.start()
@processor('biomedicus-sentences',
human_name="Sentence Detector",
description="Labels sentences given document text.",
entry_point=__name__,
outputs=[
label_index('sentences')
])
class SentenceProcessor(DocumentProcessor):
def __init__(self, input_mapper: InputMapping, model: BiLSTM):
self.input_mapper = input_mapper
self.model = model
def process_document(self, document: Document, params: Dict[str, Any]):
with document.get_labeler('sentences', distinct=True) as add_sentence:
for start, end in predict_text(self.model, self.input_mapper, document.text):
add_sentence(start, end)
def bi_lstm_hparams_parser():
parser = ArgumentParser(add_help=False)
parser.add_argument('--embeddings', type=Path)
@processor(
name='biomedicus-deepen',
human_name='DEEPEN Negation Detector',
description='Detects which UMLS terms are negated.',
entry_point=__name__,
parameters=[
parameter(
name='terms_index',
data_type='str',
description=
'The label index containing terms that should be checked for negation'
)
],
inputs=[
label_index(name='sentences',
reference='biomedicus-sentences/sentences'),
label_index(
name='dependencies',
reference='biomedicus-selective-dependencies/dependencies'),
label_index(name='umls_terms',
reference='biomedicus-concepts/umls_terms',
name_from_parameter='terms_index')
],
outputs=[
label_index("negated", description="Spans of negated terms."),
label_index("negation_trigger",
description="Spans of phrases that trigger negation.")
])
class DeepenProcessor(mtap.processing.DocumentProcessor):
def __init__(self):
self.negex = DeepenTagger()
from mtap import Document, processor, run_processor, GenericLabel
from mtap.processing import DocumentProcessor
from mtap.processing.descriptions import label_index, label_property
MAX_ITER = 5000
@processor(
'biomedicus-selective-dependencies',
human_name="BioMedICUS Stanza Selective Dependency Parser",
entry_point=__name__,
description=
"Calls out to the Stanford Stanza framework for dependency parsing"
"on a appropriate subset of sentences.",
inputs=[
label_index(name='sentences',
reference='biomedicus-sentences/sentences'),
label_index(name='umls_terms',
reference='biomedicus-concepts/umls_terms',
name_from_parameter='terms_index'),
label_index("negation_triggers", reference='biomedicus-negex-triggers')
],
outputs=[
label_index(name='dependencies',
description="The dependent words.",
properties=[
label_property('deprel',
description="The dependency relation",
data_type='str'),
label_property(
'head',
description=
with Processor.started_stopwatch('segment_splitting') as split_timer:
for match in _split.finditer(text):
split_timer.stop()
start = match.start()
local_text = text[prev:start]
for ss, se in predict_segment(model, input_mapper, local_text):
yield prev + ss, prev + se
prev = match.end()
split_timer.start()
@processor('biomedicus-sentences',
human_name="Sentence Detector",
description="Labels sentences given document text.",
entry_point=__name__,
outputs=[label_index('sentences')])
class SentenceProcessor(DocumentProcessor):
def __init__(self, input_mapper: InputMapping, model: BiLSTM):
self.input_mapper = input_mapper
self.model = model
def process_document(self, document: Document, params: Dict[str, Any]):
with document.get_labeler('sentences', distinct=True) as add_sentence:
for start, end in predict_text(self.model, self.input_mapper,
document.text):
add_sentence(start, end)
def bi_lstm_hparams_parser():
parser = ArgumentParser(add_help=False)
parser.add_argument('--embeddings', type=Path)
_pattern = re.compile(r'^[\s]*(.*?)[\s]*$', re.MULTILINE)
def get_sentences(text: str) -> List[Location]:
for match in _pattern.finditer(text):
yield match.start(1), match.end(1)
@processor(
name='biomedicus-sentences-one-per-line',
human_name='One per Line Sentences',
description=
'Labels sentences where each line in the input document is a sentence.',
entry_point=__name__,
outputs=[label_index(name='sentences')])
class OnePerLineSentencesProcessor(mtap.processing.DocumentProcessor):
def process_document(self, document: Document, params: Dict[str, Any]):
with document.get_labeler('sentences') as sentence_labeler:
for start, end in get_sentences(document.text):
sentence_labeler(start, end)
def main(args=None):
proc = OnePerLineSentencesProcessor()
mtap.run_processor(proc, args=args)
if __name__ == '__main__':
main()
@mtap.processor(
'mtap-example-processor-python',
human_name="Python Example Processor",
description=
"counts the number of times the letters a and b occur in a document",
parameters=[
parameter('do_work',
required=True,
data_type='bool',
description="Whether the processor should do anything.")
],
outputs=[
label_index('mtap.examples.letter_counts',
properties=[
label_property('letter', data_type='str'),
label_property('count', data_type='int')
])
])
class ExampleProcessor(DocumentProcessor):
"""Does some labeling of the counts of the letter 'a' and 'b' in a document.
"""
def process_document(self, document: Document,
params: Dict[str, Any]) -> Optional[Dict[str, Any]]:
if params['do_work']:
with self.started_stopwatch('fetch_time'):
text = document.text
a_count = text.count('a')
b_count = text.count('b')
from mtap.processing import DocumentProcessor
from mtap.processing.descriptions import parameter, label_index, label_property
from mtap.processing.service import _ProcessorServicer
@processor('mtap-test-processor',
description='Processor desc.',
parameters=[
parameter('a_param',
required=True,
data_type='bool',
description="desc.")
],
inputs=[
label_index(
'input_index',
properties=[label_property('bar', data_type='bool')])
],
outputs=[
label_index('output_index',
description='desc.',
properties=[
label_property('foo',
data_type='str',
nullable=True,
description='A label property.')
])
])
class ExampleTestProcessor(DocumentProcessor):
def process_document(self, document: Document, params: Dict[str, Any]):
pass
