def __init__(self,
conf_path: Union[str, Path],
output_directory: Union[str, Path],
*,
events_address: Optional[str] = None,
events_client: EventsClient = None,
serializer: Optional[str] = None,
include_label_text: bool = False):
if events_address == 'None' or events_address == 'none' or events_address == 'null' or events_address == '':
events_address = None
if events_client is not None:
self.close_client = False
self.events_client = events_client
else:
self.close_client = True
self.events_client = EventsClient(address=events_address)
self.pipeline = Pipeline.from_yaml_file(conf_path)
if serializer == 'None':
serializer = None
if serializer is not None:
serialization_proc = SerializationProcessor(
get_serializer(serializer),
output_directory,
include_label_text=include_label_text)
ser_comp = LocalProcessor(serialization_proc,
component_id='serializer',
client=self.events_client)
self.pipeline.append(ser_comp)
class DefaultPipeline:
"""The biomedicus default pipeline for processing clinical documents.
Attributes
events_client (mtap.EventsClient): An MTAP events client used by the pipeline.
pipeline (mtap.Pipeline): An MTAP pipeline to use to process documents.
"""
def __init__(self, conf: PipelineConf, *, events_client: EventsClient = None):
conf.populate_addresses()
if events_client is not None:
self.close_client = False
self.events_client = events_client
elif conf.events_address is not None:
self.close_client = True
self.events_client = EventsClient(address=conf.events_address)
else:
raise ValueError("Events client or address not specified.")
pipeline = [
(conf.sentences_id, conf.sentences_address),
(conf.section_headers_id, conf.section_headers_address),
(conf.tagger_id, conf.tagger_address),
(conf.acronyms_id, conf.acronyms_address),
(conf.concepts_id, conf.concepts_address),
(conf.negation_id, conf.negation_address),
(conf.selective_dependencies_id, conf.selective_dependencies_address),
(conf.deepen_id, conf.deepen_address)
]
if conf.use_discovery:
self.pipeline = Pipeline(
*[RemoteProcessor(identifier) for identifier, _ in pipeline]
)
else:
self.pipeline = Pipeline(
*[RemoteProcessor(identifier, address=addr) for identifier, addr in pipeline]
)
if conf.serializer is not None:
serialization_proc = SerializationProcessor(get_serializer(conf.serializer),
conf.output_directory,
include_label_text=conf.include_label_text)
ser_comp = LocalProcessor(serialization_proc, component_id='serializer',
client=self.events_client)
self.pipeline.append(ser_comp)
def process_text(self, text: str, *, event_id: str = None) -> ProcessingResult:
with Event(event_id=event_id, client=self.events_client) as event:
document = event.create_document('plaintext', text=text)
f = self.pipeline.run(document)
return f
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.pipeline.close()
if self.close_client:
self.events_client.close()
class DefaultPipeline:
def __init__(self,
conf: DefaultPipelineConf,
*,
events_client: EventsClient = None):
if events_client is not None:
self.close_client = False
self.events_client = events_client
elif conf.events_address is not None:
self.close_client = True
self.events_client = EventsClient(address=conf.events_address)
else:
raise ValueError("Events client or address not specified.")
pipeline = [(conf.sentences_id, conf.sentences_address),
(conf.tagger_id, conf.tagger_address),
(conf.acronyms_id, conf.acronyms_address),
(conf.concepts_id, conf.concepts_address),
(conf.negation_id, conf.negation_address)]
if conf.use_discovery:
self.pipeline = Pipeline(
*[RemoteProcessor(identifier) for identifier, _ in pipeline],
n_threads=conf.threads)
else:
self.pipeline = Pipeline(*[
RemoteProcessor(identifier, address=addr)
for identifier, addr in pipeline
],
n_threads=conf.threads)
if conf.serializer is not None:
serialization_proc = SerializationProcessor(
get_serializer(conf.serializer),
conf.output_directory,
include_label_text=conf.include_label_text)
ser_comp = LocalProcessor(serialization_proc,
component_id='serializer',
client=self.events_client)
self.pipeline.append(ser_comp)
def process_text(self,
text: str,
*,
event_id: str = None) -> ProcessingResult:
with Event(event_id=event_id, client=self.events_client) as event:
document = event.create_document('plaintext', text=text)
f = self.pipeline.run(document)
return f
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.pipeline.close()
if self.close_client:
self.events_client.close()
class DefaultPipeline:
"""The biomedicus default pipeline for processing clinical documents.
Attributes
events_client (mtap.EventsClient): An MTAP events client used by the pipeline.
pipeline (mtap.Pipeline): An MTAP pipeline to use to process documents.
"""
def __init__(self,
conf_path: Union[str, Path],
output_directory: Union[str, Path],
*,
events_address: Optional[str] = None,
events_client: EventsClient = None,
serializer: Optional[str] = None,
include_label_text: bool = False):
if events_address == 'None' or events_address == 'none' or events_address == 'null' or events_address == '':
events_address = None
if events_client is not None:
self.close_client = False
self.events_client = events_client
else:
self.close_client = True
self.events_client = EventsClient(address=events_address)
self.pipeline = Pipeline.from_yaml_file(conf_path)
if serializer == 'None':
serializer = None
if serializer is not None:
serialization_proc = SerializationProcessor(
get_serializer(serializer),
output_directory,
include_label_text=include_label_text)
ser_comp = LocalProcessor(serialization_proc,
component_id='serializer',
client=self.events_client)
self.pipeline.append(ser_comp)
def process_text(self,
text: str,
*,
event_id: str = None) -> ProcessingResult:
with Event(event_id=event_id, client=self.events_client) as event:
document = event.create_document('plaintext', text=text)
f = self.pipeline.run(document)
return f
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.pipeline.close()
if self.close_client:
self.events_client.close()
class DefaultPipeline:
"""The biomedicus default pipeline for processing clinical documents.
Attributes
events_client (mtap.EventsClient): An MTAP events client used by the pipeline.
pipeline (mtap.Pipeline): An MTAP pipeline to use to process documents.
"""
def __init__(self,
conf: PipelineConf,
*,
events_client: EventsClient = None):
conf.populate_addresses()
if events_client is not None:
self.close_client = False
self.events_client = events_client
elif conf.events_address is not None:
self.close_client = True
self.events_client = EventsClient(address=conf.events_address)
else:
raise ValueError("Events client or address not specified.")
pipeline = [(conf.sentences_id, conf.sentences_address),
(conf.tagger_id, conf.tagger_address)]
if conf.use_discovery:
self.pipeline = Pipeline(
*[RemoteProcessor(identifier) for identifier, _ in pipeline])
else:
self.pipeline = Pipeline(*[
RemoteProcessor(identifier, address=addr)
for identifier, addr in pipeline
])
def process_text(self,
text: str,
*,
event_id: str = None) -> ProcessingResult:
with Event(event_id=event_id, client=self.events_client) as event:
document = event.create_document('plaintext', text=text)
f = self.pipeline.run(document)
return f
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.pipeline.close()
if self.close_client:
self.events_client.close()
def main(args=None):
parser = ArgumentParser()
parser.add_argument('input',
metavar='INPUT_FILE',
help='The input GENIA XML file.')
parser.add_argument('--events',
metavar='EVENTS',
default=None,
help='The address of the events service.')
parser.add_argument('--tnt-trainer',
metavar='TRAINER',
default=None,
help='The address of the TnT trainer.')
args = parser.parse_args(args)
etree = ElementTree.parse(args.input)
set = etree.getroot()
with EventsClient(args.events) as client, Pipeline(
RemoteProcessor('biomedicus-tnt-trainer',
address=args.tnt_trainer)) as pipeline:
for article in set.findall('article'):
id = list(article.find('articleinfo'))[0].text
with Event(id, client) as event:
db = DocumentBuilder()
for sentence in article.find('title').findall(
'sentence') + article.find('abstract').findall(
'sentence'):
db.add_sentence(sentence)
d = db.build_doc(event)
pipeline.run(d)
def main(args=None):
parser = ArgumentParser()
parser.add_argument("input_directory", metavar="INPUT_DIR")
parser.add_argument("output_directory", metavar="OUTPUT_DIR")
parser.add_argument("--events")
parser.add_argument("--tagger")
parser.add_argument("--sentences")
parser.add_argument("--acronyms")
parser.add_argument("--norms")
parser.add_argument("--concepts")
args = parser.parse_args(args)
input_dir = Path(args.input_directory)
with EventsClient(address=args.events) as client, Pipeline(
RemoteProcessor('biomedicus-sentences', address=args.sentences),
RemoteProcessor('biomedicus-tnt-tagger', address=args.tagger),
RemoteProcessor('biomedicus-acronyms', address=args.acronyms),
RemoteProcessor('biomedicus-concepts', address=args.concepts),
LocalProcessor(SerializationProcessor(
JsonSerializer, output_dir=args.output_directory),
component_id='serialize',
client=client)) as pipeline:
for path in input_dir.glob("**/*.txt"):
print("READING FILE:", str(path))
with path.open('r') as f:
contents = f.read()
with Event(event_id=path.stem, client=client) as event:
document = event.create_document("plaintext", text=contents)
pipeline.run(document)
pipeline.print_times()
def test_dependencies(events_service, dependencies_service, test_results):
test_dir = Path(os.environ['BIOMEDICUS_TEST_DATA']) / 'dependencies'
uas = Accuracy('UAS', equivalence_test=uas_equal)
las = Accuracy('LAS', equivalence_test=las_equal)
with EventsClient(address=events_service) as client, \
Pipeline(
RemoteProcessor(processor_id='biomedicus-dependencies',
address=dependencies_service),
LocalProcessor(Metrics(uas, las, tested='dependencies', target='gold_dependencies'),
component_id='accuracy', client=client)
) as pipeline:
for test_file in test_dir.glob('**/*.pickle'):
with PickleSerializer.file_to_event(test_file,
client=client) as event:
document = event.documents['plaintext']
results = pipeline.run(document)
accuracy_dict = results.component_result(
'accuracy').result_dict
print('Results for document: UAS: {}. LAS: {}.'.format(
accuracy_dict['UAS'], accuracy_dict['LAS']))
print('UAS:', uas.value)
print('LAS:', las.value)
timing_info = pipeline.processor_timer_stats(
'biomedicus-dependencies').timing_info
test_results['biomedicus-dependencies'] = {
'UAS': uas.value,
'LAS': las.value,
'Corpus': "MiPACQ converted to UD from PTB test set",
'Remote Call Duration': str(timing_info['remote_call'].mean),
'Process Method Duration': str(timing_info['process_method'].mean)
}
def test_tnt_performance(events_service, pos_tags_service, test_results):
input_dir = Path(os.environ['BIOMEDICUS_TEST_DATA']) / 'pos_tags'
accuracy = Accuracy()
with EventsClient(address=events_service) as client, Pipeline(
RemoteProcessor(processor_id='biomedicus-tnt-tagger',
address=pos_tags_service,
params={'token_index': 'gold_tags'}),
LocalProcessor(Metrics(accuracy,
tested='pos_tags',
target='gold_tags'),
component_id='metrics'),
events_client=client) as pipeline:
for test_file in input_dir.glob('**/*.pickle'):
event = PickleSerializer.file_to_event(test_file, client=client)
with event:
document = event.documents['gold']
results = pipeline.run(document)
print(
'Accuracy for event - ', event.event_id, ':',
results.component_result(
'metrics').result_dict['accuracy'])
print('Accuracy:', accuracy.value)
pipeline.print_times()
timing_info = pipeline.processor_timer_stats(
'biomedicus-tnt-tagger').timing_info
test_results['TnT Pos Tagger'] = {
'Accuracy': accuracy.value,
'Remote Call Duration': str(timing_info['remote_call'].mean),
'Process Method Duration': str(timing_info['process_method'].mean)
}
assert accuracy.value > 0.9
def test_concepts_performance(events_service, concepts_service, test_results):
input_dir = Path(os.environ['BIOMEDICUS_TEST_DATA']) / 'concepts'
recall = Accuracy(name='recall', mode='any', fields=['cui'])
precision = Accuracy(name='precision', mode='any', fields=['cui'])
with EventsClient(address=events_service) as client, \
Pipeline(
RemoteProcessor(processor_id='biomedicus-concepts', address=concepts_service),
LocalProcessor(Metrics(recall, tested='umls_concepts', target='gold_concepts'),
component_id='metrics'),
LocalProcessor(Metrics(precision, tested='gold_concepts', target='umls_concepts'),
component_id='metrics_reverse'),
events_client=client
) as pipeline:
for test_file in input_dir.glob('**/*.pickle'):
with PickleSerializer.file_to_event(test_file, client=client) as event:
document = event.documents['plaintext']
pipeline.run(document)
print('Precision:', precision.value)
print('Recall:', recall.value)
timing_info = pipeline.processor_timer_stats('biomedicus-concepts').timing_info
test_results['Concepts'] = {
'Precision': precision.value,
'Recall': recall.value,
'Remote Call Duration': str(timing_info['remote_call'].mean),
'Process Method Duration': str(timing_info['process_method'].mean)
}
assert recall.value > 0.6
def test_java_references(python_events, java_references_processor):
with EventsClient(address=python_events) as client, Pipeline(
RemoteProcessor('mtap-java-reference-labels-example-processor',
address=java_references_processor)
) as pipeline:
with Event(event_id='1', client=client) as event:
document = event.create_document('plaintext', 'abcd')
pipeline.run(document)
references = document.labels['references']
assert references[0].a == GenericLabel(0, 1)
assert references[0].b == GenericLabel(1, 2)
assert references[1].a == GenericLabel(2, 3)
assert references[1].b == GenericLabel(3, 4)
map_references = document.labels['map_references']
assert map_references[0].ref == {
'a': GenericLabel(0, 1),
'b': GenericLabel(1, 2),
'c': GenericLabel(2, 3),
'd': GenericLabel(3, 4)
}
list_references = document.labels['list_references']
assert list_references[0].ref == [GenericLabel(0, 1), GenericLabel(1, 2)]
assert list_references[1].ref == [GenericLabel(2, 3), GenericLabel(3, 4)]
def test_modification_detector_performance(events_service, modification_detector_service,
test_results):
input_dir = Path(os.environ['BIOMEDICUS_TEST_DATA']) / 'negation' / 'i2b2_2010'
confusion = metrics.FirstTokenConfusion()
metrics_processor = metrics.Metrics(confusion, tested='negated', target='i2b2concepts',
target_filter=is_negated)
with EventsClient(address=events_service) as client, Pipeline(
RemoteProcessor('biomedicus-negation', address=modification_detector_service,
params={'terms_index': 'i2b2concepts'}),
LocalProcessor(metrics_processor, component_id='metrics', client=client)
) as pipeline:
for test_file in input_dir.glob('**/*.pickle'):
with PickleSerializer.file_to_event(test_file, client=client) as event:
document = event.documents['plaintext']
results = pipeline.run(document)
print('F1 for event - "{}": {:0.3f} - elapsed: {}'.format(
event.event_id,
results.component_result('metrics').result_dict['first_token_confusion']['f1'],
results.component_result('biomedicus-negation').timing_info['process_method']
))
print('Overall Precision:', confusion.precision)
print('Overall Recall:', confusion.recall)
print('Overall F1:', confusion.f1)
pipeline.print_times()
timing_info = pipeline.processor_timer_stats('biomedicus-negation').timing_info
test_results['biomedicus-modification'] = {
'Gold Standard': "2010 i2b2-VA",
'Precision': confusion.precision,
'Recall': confusion.recall,
'F1': confusion.f1,
'Per-Document Mean Remote Call Duration': str(timing_info['remote_call'].mean),
'Per-Document Mean Process Method Duration': str(timing_info['process_method'].mean)
}
def test_sentence_performance(events_service, sentences_service, test_results):
input_dir = Path(os.environ['BIOMEDICUS_TEST_DATA']) / 'sentences'
confusion = metrics.FirstTokenConfusion()
with EventsClient(address=events_service) as client, Pipeline(
RemoteProcessor(processor_id='biomedicus-sentences',
address=sentences_service),
LocalProcessor(metrics.Metrics(confusion,
tested='sentences',
target='Sentence'),
component_id='metrics',
client=client)) as pipeline:
for test_file in input_dir.glob('**/*.json'):
with JsonSerializer.file_to_event(test_file,
client=client) as event:
document = event.documents['plaintext']
results = pipeline.run(document)
print('F1 for event - "{}": {:0.3f} - elapsed: {}'.format(
event.event_id,
results[1].results['first_token_confusion']['f1'],
results[0].timing_info['process_method']))
print('Overall Precision:', confusion.precision)
print('Overall Recall:', confusion.recall)
print('Overall F1:', confusion.f1)
pipeline.print_times()
timing_info = pipeline.processor_timer_stats()[0].timing_info
test_results['Sentences'] = {
'Precision': confusion.precision,
'Recall': confusion.recall,
'F1': confusion.f1,
'Remote Call Duration': str(timing_info['remote_call'].mean),
'Process Method Duration': str(timing_info['process_method'].mean)
}
assert confusion.f1 > 0.85
def main(args=None):
parser = ArgumentParser()
parser.add_argument('--events-service')
parser.add_argument('--sentences-service')
parser.add_argument('--dependencies-service')
parser.add_argument('input_file')
conf = parser.parse_args(args)
with EventsClient(address=conf.events_service) as client, \
Pipeline(
RemoteProcessor('biomedicus-sentences', address=conf.sentences_service),
RemoteProcessor('biomedicus-dependencies', address=conf.dependencies_service)
) as pipeline:
with open(conf.input_file, 'r') as in_f:
txt = in_f.read()
with Event(event_id=Path(conf.input_file).name,
client=client) as event:
document = event.create_document('plaintext', txt)
pipeline.run(document)
for sentence in document.labels['sentences']:
print(sentence.text)
print('\n')
for dependency in document.labels['dependencies'].inside(
sentence):
print((dependency.text, dependency.deprel,
dependency.head.text
if dependency.head is not None else 'ROOT'))
print('\n')
def test_acronyms_performance(events_service, acronyms_service, test_results):
input_dir = Path(os.environ['BIOMEDICUS_PHI_TEST_DATA']) / 'acronyms'
top_score_accuracy = Accuracy(name='top_score_accuracy',
fields=['expansion'])
any_accuracy = Accuracy(name='any_accuracy',
mode='any',
fields=['expansion'])
detection_recall = Accuracy(name='detection_recall',
mode='location',
fields=['expansion'])
detection_precision = Accuracy(name='detection_precision',
mode='location',
fields=['expansion'])
with EventsClient(address=events_service) as client, Pipeline(
RemoteProcessor(processor_id='biomedicus-acronyms',
address=acronyms_service),
LocalProcessor(Metrics(top_score_accuracy,
detection_recall,
tested='acronyms',
target='gold_acronyms'),
component_id='top_score_metrics',
client=client),
LocalProcessor(Metrics(detection_precision,
tested='gold_acronyms',
target='acronyms'),
component_id='top_score_reverse',
client=client),
LocalProcessor(Metrics(any_accuracy,
tested='all_acronym_senses',
target='gold_acronyms'),
component_id='all_senses_metrics',
client=client)) as pipeline:
for test_file in input_dir.glob('**/*.json'):
with JsonSerializer.file_to_event(test_file,
client=client) as event:
document = event.documents['plaintext']
pipeline.run(document)
print('Top Sense Accuracy:', top_score_accuracy.value)
print('Any Sense Accuracy:', any_accuracy.value)
print('Detection Recall:', detection_recall.value)
print('Detection Precision:', detection_precision.value)
pipeline.print_times()
timing_info = pipeline.processor_timer_stats(
'biomedicus-acronyms').timing_info
test_results['acronyms'] = {
'Top sense accuracy': top_score_accuracy.value,
'Any sense accuracy': any_accuracy.value,
'Detection Recall': detection_recall.value,
'Detection Precision': detection_precision.value,
'Remote Call Duration': str(timing_info['remote_call'].mean),
'Process Method Duration': str(timing_info['process_method'].mean)
}
assert top_score_accuracy.value > 0.4
assert any_accuracy.value > 0.4
assert detection_recall.value > 0.65
def main(args=None):
parser = ArgumentParser()
parser.add_argument("input_directory", metavar="INPUT_DIR")
parser.add_argument("concepts_csv", metavar="PATH_TO_CONCEPTS_CSV")
parser.add_argument("output_directory", metavar="OUTPUT_DIR")
parser.add_argument("--sentences")
parser.add_argument("--tagger")
parser.add_argument("--acronyms")
parser.add_argument("--events")
ns = parser.parse_args(args)
print('Reading concepts csv...')
concepts = {}
with open(ns.concepts_csv, 'r') as f:
for line in f.readlines():
splits = line.split(',')
end = splits[0]
start = splits[1]
cui = splits[5]
identifier = splits[6]
try:
v = concepts[identifier]
except KeyError:
v = []
concepts[identifier] = v
v.append((start, end, cui))
print('Reading mipacq source files...')
with EventsClient(address=ns.events) as client, \
Pipeline(
RemoteProcessor('biomedicus-sentences', address=ns.sentences),
RemoteProcessor('biomedicus-tnt-tagger', address=ns.tagger),
RemoteProcessor('biomedicus-acronyms', address=ns.acronyms),
LocalProcessor(SerializationProcessor(PickleSerializer,
output_dir=ns.output_directory),
component_id='serialize',
client=client)
) as pipeline:
for path in Path(ns.input_directory).glob('**/*.source'):
identifier = path.stem.split('-')[0]
try:
doc_concepts = concepts[identifier]
except KeyError:
continue
with Event(event_id=identifier, client=client) as event:
with path.open('r') as f:
text = f.read()
document = event.create_document('plaintext', text)
with document.get_labeler('gold_concepts') as label_concept:
for start, end, cui in doc_concepts:
label_concept(start, end, cui=cui)
pipeline.run(document)
def __init__(self,
conf: PipelineConf,
*,
events_client: EventsClient = None):
conf.populate_addresses()
if events_client is not None:
self.close_client = False
self.events_client = events_client
elif conf.events_address is not None:
self.close_client = True
self.events_client = EventsClient(address=conf.events_address)
else:
raise ValueError("Events client or address not specified.")
pipeline = [(conf.sentences_id, conf.sentences_address),
(conf.section_headers_id, conf.section_headers_address),
(conf.tagger_id, conf.tagger_address),
(conf.acronyms_id, conf.acronyms_address),
(conf.concepts_id, conf.concepts_address),
(conf.negation_id, conf.negation_address),
(conf.selective_dependencies_id,
conf.selective_dependencies_address),
(conf.deepen_id, conf.deepen_address)]
if conf.use_discovery:
self.pipeline = Pipeline(
*[RemoteProcessor(identifier) for identifier, _ in pipeline])
else:
self.pipeline = Pipeline(*[
RemoteProcessor(identifier, address=addr)
for identifier, addr in pipeline
])
if conf.serializer is not None:
serialization_proc = SerializationProcessor(
get_serializer(conf.serializer),
conf.output_directory,
include_label_text=conf.include_label_text)
ser_comp = LocalProcessor(serialization_proc,
component_id='serializer',
client=self.events_client)
self.pipeline.append(ser_comp)
def __init__(self,
conf: PipelineConf,
*,
events_client: EventsClient = None):
conf.populate_addresses()
if events_client is not None:
self.close_client = False
self.events_client = events_client
elif conf.events_address is not None:
self.close_client = True
self.events_client = EventsClient(address=conf.events_address)
else:
raise ValueError("Events client or address not specified.")
pipeline = [(conf.sentences_id, conf.sentences_address),
(conf.tagger_id, conf.tagger_address)]
if conf.use_discovery:
self.pipeline = Pipeline(
*[RemoteProcessor(identifier) for identifier, _ in pipeline])
else:
self.pipeline = Pipeline(*[
RemoteProcessor(identifier, address=addr)
for identifier, addr in pipeline
])
def main(args=None):
parser = ArgumentParser()
parser.add_argument('input',
metavar='INPUT_FOLDER',
help='A folder containing PTB formatted documents.')
parser.add_argument('--glob', metavar='GLOB', default='*.mrg')
parser.add_argument('--source-name',
metavar='DOCUMENT_NAME',
default='source',
help='What document to dump the PTB text into.')
parser.add_argument(
'--target-name',
metavar='DOCUMENT_NAME',
default='plaintext',
help='What document to the plaintext and annotations into.')
parser.add_argument('--events',
metavar='EVENTS',
default=None,
help='The address of the events service.')
parser.add_argument('--ptb-reader',
metavar='READER',
default=None,
help='The address of the PTB Reader.')
parser.add_argument('--tnt-trainer',
metavar='TRAINER',
default=None,
help='The address of the TnT trainer.')
args = parser.parse_args(args)
with EventsClient(address=args.events) as client, Pipeline(
RemoteProcessor('ptb-reader',
address=args.ptb_reader,
params={
'source_document_name': args.source_name,
'target_document_name': args.target_name
}),
RemoteProcessor('biomedicus-tnt-trainer',
address=args.tnt_trainer,
params={'document_name':
args.target_name})) as pipeline:
for f in Path(args.input).rglob(args.glob):
print('Reading:', f)
with f.open('r') as r:
text = r.read()
with Event(event_id=f.name, client=client) as event:
d = Document(args.source_name, text=text)
event.add_document(d)
pipeline.run(event)
def test_normalization(events_service, normalization_processor):
with EventsClient(address=events_service) as client, \
Pipeline(RemoteProcessor(processor_id='biomedicus_normalizer',
address=normalization_processor)) as pipeline, \
PickleSerializer.file_to_event(Path(__file__).parent / '97_95.pickle',
client=client) as event:
document = event.documents['plaintext']
pipeline.run(document)
for norm_form in document.get_label_index('norm_forms'):
if norm_form.text == "according":
assert norm_form.norm == "accord"
if norm_form.text == "expressing":
assert norm_form.norm == "express"
if norm_form.text == "receiving":
assert norm_form.norm == "receive"
if norm_form.text == "days":
assert norm_form.norm == "day"
def main(args=None):
parser = ArgumentParser()
parser.add_argument('--events-service', default='localhost:10100')
parser.add_argument('--sentences-service', default='localhost:10102')
conf = parser.parse_args(args)
with Pipeline(
RemoteProcessor(
'biomedicus-sentences',
address=conf.sentences_service)) as pipeline, EventsClient(
address=conf.events_service) as events_client:
text = sys.stdin.read()
with Event(client=events_client) as event:
doc = event.create_document('plaintext', text)
result = pipeline.run(doc)
for sentence in doc.get_label_index('sentences'):
print('S: "', sentence.text, '"')
for k, v in result[0].timing_info.items():
print('{}: {}'.format(k, v))
def test_disc_pipeline(disc_python_events, disc_python_processor, disc_java_processor):
with EventsClient(address=disc_python_events) as client, mtap.Pipeline(
RemoteProcessor('mtap-example-processor-python', address='localhost:50501',
params={'do_work': True}),
RemoteProcessor('mtap-example-processor-java', address='localhost:50502',
params={'do_work': True})
) as pipeline:
with Event(event_id='1', client=client) as event:
event.metadata['a'] = 'b'
document = event.create_document('plaintext', PHASERS)
pipeline.run(document)
letter_counts = document.get_label_index('mtap.examples.letter_counts')
a_counts = letter_counts[0]
assert a_counts.count == 23
b_counts = letter_counts[1]
assert b_counts.count == 6
pipeline.print_times()
thes = document.get_label_index("mtap.examples.word_occurrences")
assert thes[0].start_index == 121
assert thes[0].end_index == 124
def main(args=None):
parser = ArgumentParser(
description=
'Converts files from the i2b2/VA 2010 format to serialized MTAP events '
'containing the ')
parser.add_argument(
'input_directory',
type=Path,
help=
'An input directory containing a "txt" folder containing text files '
'and an "ast" folder containing the assertions in the i2b2/VA '
'pipe-delimited format.')
parser.add_argument(
'output_directory',
type=Path,
help='An output directory to write the serialized mtap events to.')
parser.add_argument('--target-document', default='plaintext')
parser.add_argument('--serializer',
default='pickle',
choices=standard_serializers.keys(),
help='The serializer to use.')
parser.add_argument('--events', help="Address of the events client.")
parser.add_argument('--tagger', help="Address of the pos tagger to use.")
conf = parser.parse_args(args)
serializer = standard_serializers[conf.serializer]
with EventsClient(address=conf.events) as client, Pipeline(
LocalProcessor(OnePerLineSentencesProcessor(),
component_id='sentences',
client=client),
RemoteProcessor('biomedicus-tnt-tagger', address=conf.tagger),
LocalProcessor(SerializationProcessor(
serializer, output_dir=conf.output_directory),
component_id='serializer',
client=client)) as pipeline:
results = pipeline.run_multithread(
events(conf.input_directory, conf.target_document, client=client))
pipeline.print_times()
def main(args=None):
parser = ArgumentParser()
parser.add_argument('input',
metavar='INPUT_DIR',
help='A folder containing PTB formatted documents.')
parser.add_argument('output',
metavar='OUTPUT_DIR',
help='A folder to write the json files to.')
parser.add_argument('--glob', metavar='GLOB', default='*.mrg')
parser.add_argument('--events',
metavar='EVENTS',
default=None,
help='The address of the events service.')
parser.add_argument('--ptb-reader',
metavar='READER',
default=None,
help='The address of the PTB Reader.')
args = parser.parse_args(args)
with EventsClient(address=args.events) as client, Pipeline(
RemoteProcessor('ptb-reader',
address=args.ptb_reader,
params={
'source_document_name': 'source',
'target_document_name': 'gold',
'pos_tags_index': 'gold_tags'
}),
LocalProcessor(SerializationProcessor(JsonSerializer,
output_dir=args.output),
component_id='serializer',
client=client)) as pipeline:
for f in Path(args.input).rglob(args.glob):
print('Reading:', f)
with f.open('r') as r:
text = r.read()
with Event(event_id=f.name, client=client) as event:
d = Document('source', text=text)
event.add_document(d)
pipeline.run(event)
def main(args=None):
parser = ArgumentParser()
parser.add_argument("input_directory", metavar="INPUT_DIR")
parser.add_argument("output_directory", metavar="OUTPUT_DIR")
parser.add_argument("--events")
parser.add_argument("--rtf")
parser.add_argument("--tagger")
parser.add_argument("--acronyms")
parser.add_argument("--sentences")
args = parser.parse_args(args)
input_dir = Path(args.input_directory)
with EventsClient(address=args.events) as client, Pipeline(
RemoteProcessor('rtf-processor',
address=args.rtf,
params={
'binary_data_name': 'rtf',
'output_document_name': 'plaintext'
}),
RemoteProcessor('sentences',
address=args.sentences,
params={'document_name': 'plaintext'}),
RemoteProcessor('tnt-tagger',
address=args.tagger,
params={'document_name': 'plaintext'}),
RemoteProcessor('acronyms', address=args.acronyms),
LocalProcessor(SerializationProcessor(
JsonSerializer, output_dir=args.output_directory),
component_id='serialize',
client=client)) as pipeline:
for path in input_dir.glob("**/*.rtf"):
with path.open('rb') as f:
contents = f.read()
with Event(event_id=path.stem, client=client) as event:
event.binaries['rtf'] = contents
pipeline.run(event)
pipeline.print_times()
def call():
client = EventsClient(address='a', _pool=object(), _channel=events_channel)
result = client.get_label_index_info(event_id='1', document_name='plaintext')
return result
def call():
client = EventsClient(address='a', _pool=object(), _channel=events_channel)
result = client.get_all_binary_data_names(event_id='1')
return result
class Pipeline(MutableSequence['processing.ComponentDescriptor']):
"""An object which can be used to build and run a pipeline of remote and local processors.
Pipelines are a :obj:`~typing.MutableSequence` containing
one or more :obj:`~mtap.processing.pipeline.ComponentDescriptor`,
a pipeline can be modified after creation using this functionality.
Args:
*components (ComponentDescriptor):
A list of component descriptors created using :class:`RemoteProcessor` or
:class:`LocalProcessor`.
Keyword Args:
name (~typing.Optional[str]): An optional name for the pipeline, defaults to 'pipeline'.
config (~typing.Optional[Config]): An optional config override.
Examples:
Remote pipeline with name discovery:
>>> with mtap.Events() as events, mtap.Pipeline(
>>> RemoteProcessor('processor-1-id'),
>>> RemoteProcessor('processor-2-id'),
>>> RemoteProcessor('processor-3-id')
>>> ) as pipeline:
>>> for txt in txts:
>>> with events.open_event() as event:
>>> document = event.add_document('plaintext', txt)
>>> results = pipeline.run(document)
Remote pipeline using addresses:
>>> with mtap.Events(address='localhost:50051') as events, mtap.Pipeline(
>>> RemoteProcessor('processor-1-name', address='localhost:50052'),
>>> RemoteProcessor('processor-2-id', address='localhost:50053'),
>>> RemoteProcessor('processor-3-id', address='localhost:50054')
>>> ) as pipeline:
>>> for txt in txts:
>>> event = events.open_event()
>>> document = event.add_document('plaintext', txt)
>>> results = pipeline.run(document)
Modifying pipeline
>>> pipeline = Pipeline(RemoteProcessor('foo', address='localhost:50000'),
RemoteProcessor('bar', address='localhost:50000'))
>>> pipeline
Pipeline(RemoteProcessor(processor_id='foo', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='bar', address='localhost:50000', component_id=None, params=None))
>>> pipeline.append(RemoteProcessor('baz', address='localhost:50001'))
>>> pipeline
Pipeline(RemoteProcessor(processor_id='foo', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='bar', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='baz', address='localhost:50001', component_id=None, params=None))
>>> del pipeline[1]
>>> pipeline
Pipeline(RemoteProcessor(processor_id='foo', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='baz', address='localhost:50001', component_id=None, params=None))
>>> pipeline[1] = RemoteProcessor(processor_id='bar', address='localhost:50003')
>>> pipeline
Pipeline(RemoteProcessor(processor_id='foo', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='bar', address='localhost:50003', component_id=None, params=None))
>>> pipeline += list(pipeline) # Putting in a new list to prevent an infinite recursion
>>> pipeline
Pipeline(RemoteProcessor(processor_id='foo', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='bar', address='localhost:50003', component_id=None, params=None),
RemoteProcessor(processor_id='foo', address='localhost:50000', component_id=None, params=None),
RemoteProcessor(processor_id='bar', address='localhost:50003', component_id=None, params=None))
Attributes:
name (str): The pipeline's name.
"""
__slots__ = [
'_component_ids', 'name', '_component_descriptors', 'events_address',
'mp_config', '_created_events_client', '_events_client', 'times_map',
'__components'
]
def __init__(self,
*components: 'processing.ComponentDescriptor',
name: Optional[str] = None,
events_address: Optional[str] = None,
events_client: Optional[EventsClient] = None,
mp_config: Optional[MpConfig] = None):
self._component_ids = {}
self.name = name or 'pipeline'
self._component_descriptors = list(components)
self.events_address = events_address
self._created_events_client = False
self._events_client = None
if events_client is not None:
self.events_client = events_client
self.mp_config = mp_config or MpConfig()
self.times_map = {}
def __reduce__(self):
return _create_pipeline, (self.name, self.events_address,
self._events_client, self.mp_config) + tuple(
self._component_descriptors)
@staticmethod
def from_yaml_file(conf_path: Union[pathlib.Path, str]) -> 'Pipeline':
"""Creates a pipeline from a yaml pipeline configuration file.
Args:
conf_path (str or pathlib.Path): The path to the configuration file.
Returns:
Pipeline object from the configuration.
"""
conf_path = pathlib.Path(conf_path)
from yaml import load
try:
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader
with conf_path.open('rb') as f:
conf = load(f, Loader=Loader)
return Pipeline.load_configuration(conf)
@staticmethod
def load_configuration(conf: Dict) -> 'Pipeline':
"""Creates a pipeline from a pipeline configuration dictionary.
Args:
conf (Dict): The pipeline configuration dictionary.
Returns:
Pipeline created from the configuration.
"""
name = conf.get('name', None)
events_address = conf.get('events_address', None) or conf.get(
'events_addresses', None)
components = []
conf_components = conf.get('components', [])
for conf_component in conf_components:
components.append(
RemoteProcessor(processor_id=conf_component['processor_id'],
address=conf_component['address'],
component_id=conf_component.get(
'component_id', None),
params=dict(conf_component.get('params', {}))))
mp_config = MpConfig.from_configuration(conf.get('mp_config', {}))
return Pipeline(*components,
name=name,
events_address=events_address,
mp_config=mp_config)
@property
def events_client(self) -> EventsClient:
if self._events_client is not None:
return self._events_client
self._created_events_client = True
self._events_client = EventsClient(address=self.events_address)
return self._events_client
@events_client.setter
def events_client(self, value: EventsClient):
self._events_client = value
@property
def _components(self) -> 'List[processing.ProcessingComponent]':
try:
return self.__components
except AttributeError:
self.__components = [
desc.create_pipeline_component(self._component_ids,
lambda: self.events_client)
for desc in self._component_descriptors
]
return self.__components
@_components.deleter
def _components(self):
for component in self.__components:
component.close()
del self.__components
def run_multithread(self,
source: Union[Iterable[Union['mtap.Document',
'mtap.Event']],
'processing.ProcessingSource'],
*,
params: Optional[Dict[str, Any]] = None,
show_progress: Optional[bool] = None,
total: Optional[int] = None,
close_events: Optional[bool] = None,
max_failures: Optional[int] = None,
workers: Optional[int] = None,
read_ahead: Optional[int] = None,
mp_context=None):
"""Runs this pipeline on a source which provides multiple documents / events.
Concurrency is per-event, with each event being provided a thread which runs it through the
pipeline.
Args:
source (~typing.Union[~typing.Iterable[~typing.Union[Event, Document]], ProcessingSource])
A generator of events or documents to process. This should be an
:obj:`~typing.Iterable` of either :obj:`Event` or :obj:`Document` objects or a
:obj:`~mtap.processing.ProcessingSource`.
params (~typing.Optional[dict[str, ~typing.Any]])
Json object containing params specific to processing this event, the existing params
dictionary defined in :func:`~PipelineBuilder.add_processor` will be updated with
the contents of this dict.
show_progress (~typing.Optional[bool])
Whether to print a progress bar using tqdm.
total (~typing.Optional[int])
An optional argument indicating the total number of events / documents that will be
provided by the iterable, for the progress bar.
close_events (~typing.Optional[bool])
Whether the pipeline should close events after they have been fully processed
through all components.
max_failures (~typing.Optional[int])
The number of acceptable failures. Once this amount is exceeded processing will
halt. Note that because of the nature of conccurrency processing may continue for a
short amount of time before termination.
workers (~typing.Optional[int])
The number of threads to process documents on.
read_ahead (~typing.Optional[int])
The number of source documents to read ahead into memory before processing.
mp_context (multiprocessing context, optional)
An optional override for the multiprocessing context.
Examples:
>>> docs = list(Path('abc/').glob('*.txt'))
>>> def document_source():
>>> for path in docs:
>>> with path.open('r') as f:
>>> txt = f.read()
>>> with Event(event_id=path.name, client=client) as event:
>>> doc = event.create_document('plaintext', txt)
>>> yield doc
>>>
>>> pipeline.run_multithread(document_source(), total=len(docs))
"""
show_progress = show_progress if show_progress is not None else self.mp_config.show_progress
close_events = close_events if close_events is not None else self.mp_config.close_events
max_failures = max_failures if max_failures is not None else self.mp_config.max_failures
workers = workers if workers is not None else self.mp_config.workers
mp_context = (multiprocessing.get_context(
self.mp_config.mp_start_method)
if mp_context is None else mp_context)
read_ahead = read_ahead if read_ahead is not None else self.mp_config.read_ahead
with _PipelineMultiRunner(self, source, params, show_progress, total,
close_events, max_failures, workers,
read_ahead, mp_context) as runner:
runner.run()
def run(
self,
target: Union['mtap.Event', 'mtap.Document'],
*,
params: Optional[Dict[str,
Any]] = None) -> 'processing.PipelineResult':
"""Processes the event/document using all of the processors in the pipeline.
Args:
target (~typing.Union[Event, Document]): Either an event or a document to process.
params (dict[str, ~typing.Any]):
Json object containing params specific to processing this event, the existing params
dictionary defined in :func:`~PipelineBuilder.add_processor` will be updated with
the contents of this dict.
Returns:
list[ProcessingResult]: The results of all the processors in the pipeline.
Examples:
>>> e = mtap.Event()
>>> document = mtap.Document('plaintext', text="...", event=e)
>>> with Pipeline(...) as pipeline:
>>> pipeline.run(document)
>>> # is equivalent to pipeline.run(document.event, params={'document_name': document.document_name})
The 'document_name' param is used to indicate to :obj:`~mtap.DocumentProcessor`
which document on the event to process.
"""
event, params = _event_and_params(target, params)
event_id = event.event_id
result = self._run_by_event_id(event_id,
event.event_service_instance_id, params)
self._add_result_times(result)
for component_result in result.component_results:
try:
event.add_created_indices(component_result.created_indices)
except AttributeError:
pass
return result
def _run_by_event_id(self, event_id, event_service_instance_id, params):
start = datetime.now()
results = [
component.call_process(event_id, event_service_instance_id, params)
for component in self._components
]
total = datetime.now() - start
results = [
_base.ProcessingResult(identifier=component.component_id,
result_dict=result[0],
timing_info=result[1],
created_indices=result[2])
for component, result in zip(self._components, results)
]
logger.debug('Finished processing event_id: %s', event_id)
return _base.PipelineResult(results, total)
def _add_result_times(self, result):
times = {}
for component_id, _, component_times, _ in result.component_results:
times.update({
component_id + ':' + k: v
for k, v in component_times.items()
})
times[self.name + 'total'] = result.elapsed_time
_timing.add_times(self.times_map, times)
@overload
def processor_timer_stats(self) -> 'List[processing.AggregateTimingInfo]':
"""Returns the timing information for all processors.
Returns:
List[AggregateTimingInfo]:
A list of timing info objects, one for each processor, in the same order
that the processors were added to the pipeline.
"""
...
@overload
def processor_timer_stats(
self, identifier: str) -> 'processing.AggregateTimingInfo':
"""Returns the timing info for one processor.
Args:
identifier (Optional[str]): The pipeline component_id for the processor to return
timing info.
Returns:
AggregateTimingInfo: The timing info for the specified processor.
"""
...
def processor_timer_stats(self, identifier=None):
if identifier is not None:
aggregates = _timing.create_timer_stats(self.times_map,
identifier + ':')
aggregates = {
k[(len(identifier) + 1):]: v
for k, v in aggregates.items()
}
return _base.AggregateTimingInfo(identifier=identifier,
timing_info=aggregates)
timing_infos = []
for component in self._components:
component_id = component.component_id
aggregates = _timing.create_timer_stats(self.times_map,
component_id + ':')
aggregates = {
k[(len(component_id) + 1):]: v
for k, v in aggregates.items()
}
timing_infos.append(
_base.AggregateTimingInfo(identifier=component_id,
timing_info=aggregates))
return timing_infos
def pipeline_timer_stats(self) -> 'processing.AggregateTimingInfo':
"""The aggregated statistics for the global runtime of the pipeline.
Returns:
AggregateTimingInfo: The timing stats for the global runtime of the pipeline.
"""
pipeline_id = self.name
aggregates = _timing.create_timer_stats(self.times_map, pipeline_id)
aggregates = {k[len(pipeline_id):]: v for k, v in aggregates.items()}
return _base.AggregateTimingInfo(identifier=self.name,
timing_info=aggregates)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def close(self):
"""Closes any open connections to remote processors.
"""
for component in self._components:
try:
component.close()
except AttributeError:
pass
if self._created_events_client:
self._events_client.close()
def as_processor(self) -> 'processing.EventProcessor':
"""Returns the pipeline as a processor.
Returns:
EventProcessor: An event processor that can be added to other pipelines or hosted.
"""
return _PipelineProcessor(self._components)
def print_times(self):
"""Prints all of the times collected during this pipeline using :func:`print`.
"""
self.pipeline_timer_stats().print_times()
for pipeline_timer in self.processor_timer_stats():
pipeline_timer.print_times()
def __getitem__(self, item):
return self._component_descriptors[item]
def __setitem__(self, key, value):
self._clear_components()
self._component_descriptors[key] = value
def __delitem__(self, key):
self._clear_components()
del self._component_descriptors[key]
def __len__(self):
return len(self._component_descriptors)
def _clear_components(self):
try:
del self._components
except AttributeError:
pass
def insert(self, index, o) -> None:
self._clear_components()
self._component_descriptors.insert(index, o)
def __repr__(self):
return "Pipeline(" + ', '.join(
[repr(component)
for component in self._component_descriptors]) + ')'
def events_client(self) -> EventsClient:
if self._events_client is not None:
return self._events_client
self._created_events_client = True
self._events_client = EventsClient(address=self.events_address)
return self._events_client
# Copyright 2019 Regents of the University of Minnesota.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Hello world tutorial pipeline."""
import sys
if __name__ == '__main__':
from mtap import Document, Event, EventsClient, Pipeline, RemoteProcessor
with EventsClient(address=sys.argv[1]) as client, \
Pipeline(
RemoteProcessor(processor_id='hello', address=sys.argv[2])
) as pipeline:
with Event(event_id='1', client=client) as event:
document = Document(document_name='name', text='YOUR NAME')
event.add_document(document)
pipeline.run(document)
index = document.get_label_index('hello')
for label in index:
print(label.response)