def __init__(
self,
class_id=MUT_CLASS_ID,
st_model=pkg_resources.resource_filename('nala.data', 'st_model'),
all3_model=pkg_resources.resource_filename('nala.data',
'all3_model'),
features_pipeline=None,
execute_pp=True,
keep_silent=True,
keep_genetic_markers=True,
keep_unnumbered=True,
keep_rs_ids=True):
super().__init__([class_id])
tagger1 = NalaSingleModelTagger(class_id, st_model, features_pipeline)
tagger2 = NalaSingleModelTagger(class_id,
all3_model,
tagger1.features_pipeline,
execute_pipeline=False)
self.tagger = MultipleModelTagger(tagger1, tagger2, [class_id])
# ---
self.post = None
if execute_pp:
self.post = PostProcessing(
keep_silent=keep_silent,
keep_genetic_markers=keep_genetic_markers,
keep_unnumbered=keep_unnumbered,
keep_rs_ids=keep_rs_ids)
def __init__(self,
class_id=MUT_CLASS_ID,
bin_model=pkg_resources.resource_filename(
'nala.data', 'all3_model'),
features_pipeline=None,
execute_pipeline=True,
execute_pp=True,
keep_silent=True,
keep_genetic_markers=True,
keep_unnumbered=True,
keep_rs_ids=True):
super().__init__([class_id])
self.class_id = class_id
self.bin_model = bin_model
self.features_pipeline = features_pipeline if features_pipeline else get_prepare_pipeline_for_best_model(
)
self.execute_pipeline = execute_pipeline
# ---
self.crf = PyCRFSuite(model_file=self.bin_model)
self.post = None
if execute_pp:
self.post = PostProcessing(
keep_silent=keep_silent,
keep_genetic_markers=keep_genetic_markers,
keep_unnumbered=keep_unnumbered,
keep_rs_ids=keep_rs_ids)
def filter(self, documents):
pycrf = PyCRFSuite(self.binary_model)
for pmid, doc in documents:
dataset = Dataset()
dataset.documents[pmid] = doc
self.pipeline.execute(dataset)
self.labeler.label(dataset)
pycrf.tag(dataset, MUT_CLASS_ID)
PostProcessing().process(dataset)
ExclusiveNLDefiner().define(dataset)
total_nl_mentions = []
for part in doc:
# print(part.annotations)
print_verbose('predicted_annotations:',
part.predicted_annotations)
nl_mentions = [
(ann.text, ann.subclass, ann.confidence)
for ann in part.predicted_annotations
if ann.subclass != 0 and ann.confidence <= self.threshold
]
total_nl_mentions += nl_mentions
if any(total_nl_mentions):
print('nl mentions', json.dumps(total_nl_mentions, indent=4))
yield pmid, doc
print_verbose('nothing found')
class NalaSingleModelTagger(Tagger):
def __init__(self,
class_id=MUT_CLASS_ID,
bin_model=pkg_resources.resource_filename(
'nala.data', 'all3_model'),
features_pipeline=None,
execute_pipeline=True,
execute_pp=True,
keep_silent=True,
keep_genetic_markers=True,
keep_unnumbered=True,
keep_rs_ids=True):
super().__init__([class_id])
self.class_id = class_id
self.bin_model = bin_model
self.features_pipeline = features_pipeline if features_pipeline else get_prepare_pipeline_for_best_model(
)
self.execute_pipeline = execute_pipeline
# ---
self.crf = PyCRFSuite(model_file=self.bin_model)
self.post = None
if execute_pp:
self.post = PostProcessing(
keep_silent=keep_silent,
keep_genetic_markers=keep_genetic_markers,
keep_unnumbered=keep_unnumbered,
keep_rs_ids=keep_rs_ids)
def tag(self, dataset, class_id=None):
class_id = self.class_id if class_id is None else class_id
if self.execute_pipeline:
self.features_pipeline.execute(dataset)
self.crf.annotate(dataset, class_id)
if self.post:
self.post.process(dataset, class_id=class_id)
def find_number_of_documents():
data = read_data(39, read_base=False)
train, test = data.stratified_split()
del data
del train
pipeline = get_prepare_pipeline_for_best_model()
pipeline.execute(test)
BIEOLabeler().label(test)
PyCRFSuite().tag(test, 'idp4_model')
PostProcessing().process(test)
ExclusiveNLDefiner().define(test)
keys = test.documents.keys()
for test_size in range(30, 101, 10):
sample = Dataset()
random_keys = random.sample(keys, test_size)
sample.documents = {key: test.documents[key] for key in random_keys}
print('============== {} =============='.format(test_size))
calculate_standard_error(sample)
def filter(self, documents, min_found=1, use_nala=False):
"""
:type documents: collections.Iterable[(str, nalaf.structures.data.Document)]
"""
_progress = 1
_start_time = time.time()
_total_time = 0
_time_avg_per_pattern = 0
_pattern_calls = 0
_time_reg_pattern_total = 0
_time_max_pattern = 0
_low_performant_pattern = ""
# NLDefiners init
exclusive_definer = ExclusiveNLDefiner()
_e_array = [0, 0, 0]
inclusive_definer = InclusiveNLDefiner()
_i_array = [0, 0]
last_found = 0
crf = PyCRFSuite(self.location_binary_model)
# counter_to_stop_for_caching = 0
for pmid, doc in documents:
# if any part of the document contains any of the keywords
# yield that document
# if counter_to_stop_for_caching > 400:
# break
# counter_to_stop_for_caching += 1
# print(counter_to_stop_for_caching)
part_offset = 0
data_tmp = Dataset()
data_tmp.documents[pmid] = doc
data_nala = deepcopy(data_tmp)
NLTKSplitter().split(data_tmp)
# data_tmvar = TmVarTagger().generate_abstracts([pmid])
if use_nala:
self.pipeline.execute(data_nala)
self.labeler.label(data_nala)
crf.tag(data_nala, MUT_CLASS_ID)
PostProcessing().process(data_nala)
ExclusiveNLDefiner().define(data_nala)
used_regexs = {}
positive_sentences = 0
for i, x in enumerate(doc.parts):
# print("Part", i)
sent_offset = 0
cur_part = doc.parts.get(x)
sentences = cur_part.sentences_
for sent in sentences:
sent_length = len(sent)
new_text = sent.lower()
new_text = re.sub('[\./\\-(){}\[\],%]', ' ', new_text)
# new_text = re.sub('\W+', ' ', new_text)
found_in_sentence = False
for i, reg in enumerate(self.patterns):
_lasttime = time.time() # time start var
match = reg.search(new_text)
# debug bottleneck patterns
_time_current_reg = time.time(
) - _lasttime # time end var
_pattern_calls += 1 # pattern calls already occured
_time_reg_pattern_total += _time_current_reg # total time spent on searching with patterns
if _time_reg_pattern_total > 0:
_time_avg_per_pattern = _time_reg_pattern_total / _pattern_calls # avg spent time per pattern call
# todo create pattern performance eval for descending amount of recognized patterns
# if _pattern_calls > len(patterns) * 20 and _time_avg_per_pattern * 10000 < _time_current_reg:
# print("BAD_PATTERN_PERFORMANCE:", _time_avg_per_pattern, _time_current_reg, reg.pattern)
# if _time_max_pattern < _time_current_reg:
# _time_max_pattern = _time_current_reg
# _low_performant_pattern = reg.pattern
# print(_time_avg_per_pattern, _low_performant_pattern, _time_max_pattern)
# if reg.pattern == r'(\b\w*\d+\w*\b\s?){1,3} (\b\w+\b\s?){1,4} (\b\w*\d+\w*\b\s?){1,3} (\b\w+\b\s?){1,4} (deletion|deleting|deleted)':
# if _time_current_reg > _time_avg_per_pattern * 10:
# # print(_time_avg_per_pattern, _time_current_reg)
# f.write("BAD_PATTERN\n")
# f.write(sent + "\n")
# f.write(new_text + "\n")
if match:
# if pmid in data_tmvar.documents:
# anti_doc = data_tmvar.documents.get(pmid)
nala_doc = data_nala.documents.get(pmid)
start = part_offset + sent_offset + match.span()[0]
end = part_offset + sent_offset + match.span()[1]
# print("TmVar is not overlapping?:", not anti_doc.overlaps_with_mention(start, end))
# print(not nala_doc.overlaps_with_mention(start, end, annotated=False))
if reg.pattern in used_regexs:
used_regexs[reg.pattern] += 1
else:
used_regexs[reg.pattern] = 1
print(color.PURPLE + new_text.replace(
match.group(), color.BOLD + color.DARKCYAN +
color.UNDERLINE + match.group() + color.END +
color.PURPLE) + color.END)
if not found_in_sentence:
positive_sentences += 1
found_in_sentence = True
# if not anti_doc.overlaps_with_mention(start,
# end) \
# and not nala_doc.overlaps_with_mention(start, end, annotated=False):
# _e_result = exclusive_definer.define_string(
# new_text[match.span()[0]:match.span()[1]])
# _e_array[_e_result] += 1
# _i_result = inclusive_definer.define_string(
# new_text[match.span()[0]:match.span()[1]])
# _i_array[_i_result] += 1
# todo write to file param + saving to manually annotate and find tp + fp for performance eval on each pattern
# print("e{}\ti{}\t{}\t{}\t{}\n".format(_e_result, _i_result, sent, match, reg.pattern))
# last_found += 1
# found_in_sentence = True
# else:
# # if nala not used only tmvar considered
# if not anti_doc.overlaps_with_mention(start, end):
# _e_result = exclusive_definer.define_string(
# new_text[match.span()[0]:match.span()[1]])
# _e_array[_e_result] += 1
# _i_result = inclusive_definer.define_string(
# new_text[match.span()[0]:match.span()[1]])
# _i_array[_i_result] += 1
# # todo write to file param + saving to manually annotate and find tp + fp for performance eval on each pattern
# # print("e{}\ti{}\t{}\t{}\t{}\n".format(_e_result, _i_result, sent, match, reg.pattern))
# last_found += 1
# found_in_sentence = True
if use_nala:
nala_found_mention = nala_doc.overlaps_with_mention(
start, end, annotated=False)
if nala_found_mention:
print_verbose(nala_found_mention)
if nala_found_mention.subclass > 0 and nala_found_mention.confidence <= self.threshold:
yield pmid, doc
if _lasttime - time.time() > 1:
print_verbose('time intensive regex', i)
sent_offset += 2 + sent_length
# for per sentence positives
if found_in_sentence:
positive_sentences += 1
part_offset += sent_offset
if use_nala:
for part in nala_doc:
for ann in part.predicted_annotations:
if ann.subclass > 0:
print_verbose(part.text[:ann.offset] + color.BOLD +
ann.text + color.END +
part.text[ann.offset +
len(ann.text):])
positive_sentences += min_found
_old_time = _start_time
_start_time = time.time()
_one_time = _start_time - _old_time
if _one_time > 0.3 and positive_sentences > min_found:
_progress += 1
_total_time += _one_time
_time_per_doc = _total_time / _progress
print_verbose(
"PROGRESS: {:.2f} secs ETA per one positive document:"
" {:.2f} secs".format(_total_time, _time_per_doc))
print_debug('used regular expressions:',
json.dumps(used_regexs, indent=4))
if positive_sentences >= min_found:
last_found = 0
print_verbose('YEP', pmid)
yield pmid, doc
else:
print_verbose('NOPE', pmid)