class NerArchiveTests(unittest.TestCase):
def setUp(self) -> None:
self.config = Config()
self.config.general['log_level'] = logging.INFO
cdb = CDB(config=self.config)
self.nlp = Pipe(tokenizer=spacy_split_all, config=self.config)
self.nlp.add_tagger(tagger=tag_skip_and_punct,
name='skip_and_punct',
additional_fields=['is_punct'])
# Add a couple of names
cdb.add_names(cui='S-229004',
names=prepare_name('Movar', self.nlp, {}, self.config))
cdb.add_names(cui='S-229004',
names=prepare_name('Movar viruses', self.nlp, {},
self.config))
cdb.add_names(cui='S-229005',
names=prepare_name('CDB', self.nlp, {}, self.config))
# Check
#assert cdb.cui2names == {'S-229004': {'movar', 'movarvirus', 'movarviruses'}, 'S-229005': {'cdb'}}
self.vocab_path = "./tmp_vocab.dat"
if not os.path.exists(self.vocab_path):
import requests
tmp = requests.get(
"https://medcat.rosalind.kcl.ac.uk/media/vocab.dat")
with open(self.vocab_path, 'wb') as f:
f.write(tmp.content)
vocab = Vocab.load(self.vocab_path)
# Make the pipeline
self.nlp = Pipe(tokenizer=spacy_split_all, config=self.config)
self.nlp.add_tagger(tagger=tag_skip_and_punct,
name='skip_and_punct',
additional_fields=['is_punct'])
spell_checker = BasicSpellChecker(cdb_vocab=cdb.vocab,
config=self.config,
data_vocab=vocab)
self.nlp.add_token_normalizer(spell_checker=spell_checker,
config=self.config)
ner = NER(cdb, self.config)
self.nlp.add_ner(ner)
# Add Linker
link = Linker(cdb, vocab, self.config)
self.nlp.add_linker(link)
self.text = "CDB - I was running and then Movar Virus attacked and CDb"
def tearDown(self) -> None:
self.nlp.destroy()
def test_limits_for_tokens_and_uppercase(self):
self.config.ner['max_skip_tokens'] = 1
self.config.ner['upper_case_limit_len'] = 4
self.config.linking['disamb_length_limit'] = 2
d = self.nlp(self.text)
assert len(d._.ents) == 2
assert d._.ents[0]._.link_candidates[0] == 'S-229004'
def test_change_limit_for_skip(self):
self.config.ner['max_skip_tokens'] = 3
d = self.nlp(self.text)
assert len(d._.ents) == 3
def test_change_limit_for_upper_case(self):
self.config.ner['upper_case_limit_len'] = 3
d = self.nlp(self.text)
assert len(d._.ents) == 4
def test_check_name_length_limit(self):
self.config.ner['min_name_len'] = 4
d = self.nlp(self.text)
assert len(d._.ents) == 2
def test_speed(self):
text = "CDB - I was running and then Movar Virus attacked and CDb"
text = text * 300
self.config.general['spell_check'] = True
start = timer()
for i in range(50):
d = self.nlp(text)
end = timer()
print("Time: ", end - start)
def test_without_spell_check(self):
# Now without spell check
self.config.general['spell_check'] = False
start = timer()
for i in range(50):
d = self.nlp(self.text)
end = timer()
print("Time: ", end - start)
def test_for_linker(self):
self.config = Config()
self.config.general['log_level'] = logging.DEBUG
cdb = CDB(config=self.config)
# Add a couple of names
cdb.add_names(cui='S-229004',
names=prepare_name('Movar', self.nlp, {}, self.config))
cdb.add_names(cui='S-229004',
names=prepare_name('Movar viruses', self.nlp, {},
self.config))
cdb.add_names(cui='S-229005',
names=prepare_name('CDB', self.nlp, {}, self.config))
cdb.add_names(cui='S-2290045',
names=prepare_name('Movar', self.nlp, {}, self.config))
# Check
#assert cdb.cui2names == {'S-229004': {'movar', 'movarvirus', 'movarviruses'}, 'S-229005': {'cdb'}, 'S-2290045': {'movar'}}
cuis = list(cdb.cui2names.keys())
for cui in cuis[0:50]:
vectors = {
'short': np.random.rand(300),
'long': np.random.rand(300),
'medium': np.random.rand(300)
}
cdb.update_context_vector(cui, vectors, negative=False)
d = self.nlp(self.text)
vocab = Vocab.load(self.vocab_path)
cm = ContextModel(cdb, vocab, self.config)
cm.train_using_negative_sampling('S-229004')
self.config.linking['train_count_threshold'] = 0
cm.train('S-229004', d._.ents[1], d)
cm.similarity('S-229004', d._.ents[1], d)
cm.disambiguate(['S-2290045', 'S-229004'], d._.ents[1], 'movar', d)
class CAT(object):
r'''
The main MedCAT class used to annotate documents, it is built on top of spaCy
and works as a spaCy pipline. Creates an instance of a spaCy pipline that can
be used as a spacy nlp model.
Args:
cdb (medcat.cdb.CDB):
The concept database that will be used for NER+L
config (medcat.config.Config):
Global configuration for medcat
vocab (medcat.vocab.Vocab, optional):
Vocabulary used for vector embeddings and spelling. Default: None
meta_cats (list of medcat.meta_cat.MetaCAT, optional):
A list of models that will be applied sequentially on each
detected annotation.
Attributes (limited):
cdb (medcat.cdb.CDB):
Concept database used with this CAT instance, please do not assign
this value directly.
config (medcat.config.Config):
The global configuration for medcat. Usuall cdb.config can be used for this
field.
vocab (medcat.utils.vocab.Vocab):
The vocabulary object used with this instance, please do not assign
this value directly.
config - WILL BE REMOVED - TEMPORARY PLACEHOLDER
Examples:
>>>cat = CAT(cdb, vocab)
>>>spacy_doc = cat("Put some text here")
>>>print(spacy_doc.ents) # Detected entites
'''
log = logging.getLogger(__package__)
# Add file and console handlers
log = add_handlers(log)
def __init__(self, cdb, config, vocab, meta_cats=[]):
self.cdb = cdb
self.vocab = vocab
# Take config from the cdb
self.config = config
# Set log level
self.log.setLevel(self.config.general['log_level'])
# Build the pipeline
self.nlp = Pipe(tokenizer=spacy_split_all, config=self.config)
self.nlp.add_tagger(tagger=partial(tag_skip_and_punct,
config=self.config),
name='skip_and_punct',
additional_fields=['is_punct'])
spell_checker = BasicSpellChecker(cdb_vocab=self.cdb.vocab,
config=self.config,
data_vocab=vocab)
self.nlp.add_token_normalizer(spell_checker=spell_checker,
config=self.config)
# Add NER
self.ner = NER(self.cdb, self.config)
self.nlp.add_ner(self.ner)
# Add LINKER
self.linker = Linker(self.cdb, vocab, self.config)
self.nlp.add_linker(self.linker)
# Add meta_annotaiton classes if they exist
self._meta_annotations = False
for meta_cat in meta_cats:
self.nlp.add_meta_cat(meta_cat, meta_cat.category_name)
self._meta_annotations = True
# Set max document length
self.nlp.nlp.max_length = self.config.preprocessing.get(
'max_document_length', 1000000)
def get_spacy_nlp(self):
''' Returns the spacy pipeline with MedCAT
'''
return self.nlp.nlp
def __call__(self, text, do_train=False):
r'''
Push the text through the pipeline.
Args:
text (string):
The text to be annotated, if it is longer than self.config.preprocessing['max_document_length'] it will be trimmed
to that length.
do_train (bool, defaults to `False`):
This causes so many screwups when not there, so I'll force training
to False. To run training it is much better to use the self.train() function
but for some special cases I'm leaving it here also.
Returns:
A spacy document with the extracted entities
'''
# Should we train - do not use this for training, unles you know what you are doing. Use the
#self.train() function
self.config.linking['train'] = do_train
if text and len(text) > 0:
return self.nlp(text[0:self.config.preprocessing.
get('max_document_length', 1000000)])
else:
return None
def _print_stats(self,
data,
epoch=0,
use_filters=False,
use_overlaps=False,
use_cui_doc_limit=False,
use_groups=False):
r''' TODO: Refactor and make nice
Print metrics on a dataset (F1, P, R), it will also print the concepts that have the most FP,FN,TP.
Args:
data (list of dict):
The json object that we get from MedCATtrainer on export.
epoch (int):
Used during training, so we know what epoch is it.
use_filters (boolean):
Each project in medcattrainer can have filters, do we want to respect those filters
when calculating metrics.
use_overlaps (boolean):
Allow overlapping entites, nearly always False as it is very difficult to annotate overlapping entites.
use_cui_doc_limit (boolean):
If True the metrics for a CUI will be only calculated if that CUI appears in a document, in other words
if the document was annotated for that CUI. Useful in very specific situations when during the annotation
process the set of CUIs changed.
use_groups (boolean):
If True concepts that have groups will be combined and stats will be reported on groups.
Returns:
fps (dict):
False positives for each CUI
fns (dict):
False negatives for each CUI
tps (dict):
True positives for each CUI
cui_prec (dict):
Precision for each CUI
cui_rec (dict):
Recall for each CUI
cui_f1 (dict):
F1 for each CUI
cui_counts (dict):
Number of occurrence for each CUI
examples (dict):
Examples for each of the fp, fn, tp. Foramt will be examples['fp']['cui'][<list_of_examples>]
'''
tp = 0
fp = 0
fn = 0
fps = {}
fns = {}
tps = {}
cui_prec = {}
cui_rec = {}
cui_f1 = {}
cui_counts = {}
examples = {'fp': {}, 'fn': {}, 'tp': {}}
fp_docs = set()
fn_docs = set()
# Backup for filters
_filters = deepcopy(self.config.linking['filters'])
# Shortcut for filters
filters = self.config.linking['filters']
for pind, project in tqdm(enumerate(data['projects']),
desc="Stats project",
total=len(data['projects']),
leave=False):
if use_filters:
if type(project.get('cuis', None)) == str:
# Old filters
filters['cuis'] = process_old_project_filters(
cuis=project.get('cuis', None),
type_ids=project.get('tuis', None),
cdb=self.cdb)
elif type(project.get('cuis', None)) == list:
# New filters
filters['cuis'] = project.get('cuis')
start_time = time.time()
for dind, doc in tqdm(enumerate(project['documents']),
desc='Stats document',
total=len(project['documents']),
leave=False):
if type(doc['annotations']) == list:
anns = doc['annotations']
elif type(doc['annotations']) == dict:
anns = doc['annotations'].values()
# Apply document level filtering if
if use_cui_doc_limit:
_cuis = set([ann['cui'] for ann in anns])
if _cuis:
filters['cuis'] = _cuis
spacy_doc = self(doc['text'])
if use_overlaps:
p_anns = spacy_doc._.ents
else:
p_anns = spacy_doc.ents
anns_norm = []
anns_norm_neg = []
anns_examples = []
anns_norm_cui = []
for ann in anns:
cui = ann['cui']
if not use_filters or check_filters(cui, filters):
if use_groups:
cui = self.cdb.addl_info['cui2group'].get(cui, cui)
if ann.get('validated',
True) and (not ann.get('killed', False) and
not ann.get('deleted', False)):
anns_norm.append((ann['start'], cui))
anns_examples.append({
"text":
doc['text'][max(0, ann['start'] -
60):ann['end'] + 60],
"cui":
cui,
"source value":
ann['value'],
"acc":
1,
"project index":
pind,
"document inedex":
dind
})
elif ann.get('validated', True) and (ann.get(
'killed', False) or ann.get('deleted', False)):
anns_norm_neg.append((ann['start'], cui))
if ann.get("validated", True):
# This is used to test was someone annotating for this CUI in this document
anns_norm_cui.append(cui)
cui_counts[cui] = cui_counts.get(cui, 0) + 1
p_anns_norm = []
p_anns_examples = []
for ann in p_anns:
cui = ann._.cui
if use_groups:
cui = self.cdb.addl_info['cui2group'].get(cui, cui)
p_anns_norm.append((ann.start_char, cui))
p_anns_examples.append({
"text":
doc['text'][max(0, ann.start_char - 60):ann.end_char +
60],
"cui":
cui,
"source value":
ann.text,
"acc":
float(ann._.context_similarity),
"project index":
pind,
"document inedex":
dind
})
for iann, ann in enumerate(p_anns_norm):
cui = ann[1]
if ann in anns_norm:
tp += 1
tps[cui] = tps.get(cui, 0) + 1
example = p_anns_examples[iann]
examples['tp'][cui] = examples['tp'].get(
cui, []) + [example]
else:
fp += 1
fps[cui] = fps.get(cui, 0) + 1
fp_docs.add(doc.get('name', 'unk'))
# Add example for this FP prediction
example = p_anns_examples[iann]
if ann in anns_norm_neg:
# Means that it really was annotated as negative
example['real_fp'] = True
examples['fp'][cui] = examples['fp'].get(
cui, []) + [example]
for iann, ann in enumerate(anns_norm):
if ann not in p_anns_norm:
cui = ann[1]
fn += 1
fn_docs.add(doc.get('name', 'unk'))
fns[cui] = fns.get(cui, 0) + 1
examples['fn'][cui] = examples['fn'].get(
cui, []) + [anns_examples[iann]]
try:
prec = tp / (tp + fp)
rec = tp / (tp + fn)
f1 = 2 * (prec * rec) / (prec + rec)
print("Epoch: {}, Prec: {}, Rec: {}, F1: {}\n".format(
epoch, prec, rec, f1))
print("Docs with false positives: {}\n".format("; ".join(
[str(x) for x in list(fp_docs)[0:10]])))
print("Docs with false negatives: {}\n".format("; ".join(
[str(x) for x in list(fn_docs)[0:10]])))
# Sort fns & prec
fps = {
k: v
for k, v in sorted(
fps.items(), key=lambda item: item[1], reverse=True)
}
fns = {
k: v
for k, v in sorted(
fns.items(), key=lambda item: item[1], reverse=True)
}
tps = {
k: v
for k, v in sorted(
tps.items(), key=lambda item: item[1], reverse=True)
}
# F1 per concept
for cui in tps.keys():
prec = tps[cui] / (tps.get(cui, 0) + fps.get(cui, 0))
rec = tps[cui] / (tps.get(cui, 0) + fns.get(cui, 0))
f1 = 2 * (prec * rec) / (prec + rec)
cui_prec[cui] = prec
cui_rec[cui] = rec
cui_f1[cui] = f1
# Get top 10
pr_fps = [(self.cdb.cui2preferred_name.get(
cui,
list(self.cdb.cui2names.get(cui, [cui]))[0]), cui, fps[cui])
for cui in list(fps.keys())[0:10]]
pr_fns = [(self.cdb.cui2preferred_name.get(
cui,
list(self.cdb.cui2names.get(cui, [cui]))[0]), cui, fns[cui])
for cui in list(fns.keys())[0:10]]
pr_tps = [(self.cdb.cui2preferred_name.get(
cui,
list(self.cdb.cui2names.get(cui, [cui]))[0]), cui, tps[cui])
for cui in list(tps.keys())[0:10]]
print("\n\nFalse Positives\n")
for one in pr_fps:
print("{:70} - {:20} - {:10}".format(
str(one[0])[0:69],
str(one[1])[0:19], one[2]))
print("\n\nFalse Negatives\n")
for one in pr_fns:
print("{:70} - {:20} - {:10}".format(
str(one[0])[0:69],
str(one[1])[0:19], one[2]))
print("\n\nTrue Positives\n")
for one in pr_tps:
print("{:70} - {:20} - {:10}".format(
str(one[0])[0:69],
str(one[1])[0:19], one[2]))
print("*" * 110 + "\n")
except Exception as e:
traceback.print_exc()
self.config.linking['filters'] = _filters
return fps, fns, tps, cui_prec, cui_rec, cui_f1, cui_counts, examples
def train(self, data_iterator, fine_tune=True, progress_print=1000):
""" Runs training on the data, note that the maximum lenght of a line
or document is 1M characters. Anything longer will be trimmed.
data_iterator:
Simple iterator over sentences/documents, e.g. a open file
or an array or anything that we can use in a for loop.
fine_tune:
If False old training will be removed
progress_print:
Print progress after N lines
"""
if not fine_tune:
self.log.info("Removing old training data!")
self.cdb.reset_training()
cnt = 0
for line in data_iterator:
if line is not None and line:
# Convert to string
line = str(line).strip()
try:
_ = self(line, do_train=True)
except Exception as e:
self.log.warning("LINE: '{}...' \t WAS SKIPPED".format(
line[0:100]))
self.log.warning("BECAUSE OF: " + str(e))
if cnt % progress_print == 0:
self.log.info("DONE: " + str(cnt))
cnt += 1
self.config.linking['train'] = False
def add_cui_to_group(self, cui, group_name, reset_all_groups=False):
r'''
Ads a CUI to a group, will appear in cdb.addl_info['cui2group']
Args:
cui (str):
The concept to be added
group_name (str):
The group to whcih the concept will be added
reset_all_groups (boolean):
If True it will reset all existing groups and remove them.
Examples:
>>> cat.add_cui_to_group("S-17", 'pain')
'''
# Reset if needed
if reset_all_groups:
self.cdb.addl_info['cui2group'] = {}
# Add group_name
self.cdb.addl_info['cui2group'][cui] = group_name
def unlink_concept_name(self, cui, name, preprocessed_name=False):
r'''
Unlink a concept name from the CUI (or all CUIs if full_unlink), removes the link from
the Concept Database (CDB). As a consequence medcat will never again link the `name`
to this CUI - meaning the name will not be detected as a concept in the future.
Args:
cui (str):
The CUI from which the `name` will be removed
name (str):
The span of text to be removed from the linking dictionary
Examples:
>>> # To never again link C0020538 to HTN
>>> cat.unlink_concept_name('C0020538', 'htn', False)
'''
cuis = [cui]
if preprocessed_name:
names = {name: 'nothing'}
else:
names = prepare_name(name, self, {}, self.config)
# If full unlink find all CUIs
if self.config.general.get('full_unlink', False):
for name in names:
cuis.extend(self.cdb.name2cuis.get(name, []))
# Remove name from all CUIs
for cui in cuis:
self.cdb.remove_names(cui=cui, names=names)
def add_and_train_concept(self,
cui,
name,
spacy_doc=None,
spacy_entity=None,
ontologies=set(),
name_status='A',
type_ids=set(),
description='',
full_build=True,
negative=False,
devalue_others=False,
do_add_concept=True):
r''' Add a name to an existing concept, or add a new concept, or do not do anything if the name and concept alraedy exist. Perform
training if spacy_entity and spacy_doc are set.
Args:
cui (str):
CUI of the concept
name (str):
Name to be linked to the concept (in the case of MedCATtrainer this is simply the
selected value in text, no preprocessing or anything needed).
spacy_doc (spacy.tokens.Doc):
Spacy represenation of the document that was manually annotated.
spacy_entity (List[spacy.tokens.Token]):
Given the spacy document, this is the annotated span of text - list of annotated tokens that are marked with this CUI.
negative (bool):
Is this a negative or positive example.
devalue_others:
If set, cuis to which this name is assigned and are not `cui` will receive negative training given
that negative=False.
**other:
Refer to CDB.add_concept
'''
names = prepare_name(name, self, {}, self.config)
if do_add_concept:
self.cdb.add_concept(cui=cui,
names=names,
ontologies=ontologies,
name_status=name_status,
type_ids=type_ids,
description=description,
full_build=full_build)
if spacy_entity is not None and spacy_doc is not None:
# Train Linking
self.linker.context_model.train(cui=cui,
entity=spacy_entity,
doc=spacy_doc,
negative=negative,
names=names)
if not negative and devalue_others:
# Find all cuis
cuis = set()
for name in names:
cuis.update(self.cdb.name2cuis.get(name, []))
# Remove the cui for which we just added positive training
cuis.remove(cui)
# Add negative training for all other CUIs that link to these names
for _cui in cuis:
self.linker.context_model.train(cui=_cui,
entity=spacy_entity,
doc=spacy_doc,
negative=True)
def train_supervised(self,
data_path,
reset_cui_count=False,
nepochs=1,
print_stats=0,
use_filters=False,
terminate_last=False,
use_overlaps=False,
use_cui_doc_limit=False,
test_size=0,
devalue_others=False,
use_groups=False,
never_terminate=False,
train_from_false_positives=False):
r''' TODO: Refactor, left from old
Run supervised training on a dataset from MedCATtrainer. Please take care that this is more a simiulated
online training then supervised.
Args:
data_path (str):
The path to the json file that we get from MedCATtrainer on export.
reset_cui_count (boolean):
Used for training with weight_decay (annealing). Each concept has a count that is there
from the beginning of the CDB, that count is used for annealing. Resetting the count will
significantly incrase the training impact. This will reset the count only for concepts
that exist in the the training data.
nepochs (int):
Number of epochs for which to run the training.
print_stats (int):
If > 0 it will print stats every print_stats epochs.
use_filters (boolean):
Each project in medcattrainer can have filters, do we want to respect those filters
when calculating metrics.
terminate_last (boolean):
If true, concept termination will be done after all training.
use_overlaps (boolean):
Allow overlapping entites, nearly always False as it is very difficult to annotate overlapping entites.
use_cui_doc_limit (boolean):
If True the metrics for a CUI will be only calculated if that CUI appears in a document, in other words
if the document was annotated for that CUI. Useful in very specific situations when during the annotation
process the set of CUIs changed.
test_size (float):
If > 0 the data set will be split into train test based on this ration. Should be between 0 and 1.
Usually 0.1 is fine.
devalue_others(bool):
Check add_name for more details.
use_groups (boolean):
If True concepts that have groups will be combined and stats will be reported on groups.
never_terminate (boolean):
If True no termination will be applied
train_from_false_positives (boolean):
If True it will use false positive examples detected by medcat and train from them as negative examples.
Returns:
fp (dict):
False positives for each CUI
fn (dict):
False negatives for each CUI
tp (dict):
True positives for each CUI
p (dict):
Precision for each CUI
r (dict):
Recall for each CUI
f1 (dict):
F1 for each CUI
cui_counts (dict):
Number of occurrence for each CUI
examples (dict):
FP/FN examples of sentences for each CUI
'''
fp = fn = tp = p = r = f1 = cui_counts = examples = {}
data = json.load(open(data_path))
cui_counts = {}
if test_size == 0:
self.log.info("Running without a test set, or train=test")
test_set = data
train_set = data
else:
train_set, test_set, _, _ = make_mc_train_test(data,
self.cdb,
test_size=test_size)
if print_stats > 0:
self._print_stats(test_set,
use_filters=use_filters,
use_cui_doc_limit=use_cui_doc_limit,
use_overlaps=use_overlaps,
use_groups=use_groups)
if reset_cui_count:
# Get all CUIs
cuis = []
for project in train_set['projects']:
for doc in project['documents']:
if type(doc['annotations']) == list:
doc_annotations = doc['annotations']
elif type(doc['annotations']) == dict:
doc_annotations = doc['annotations'].values()
for ann in doc_annotations:
cuis.append(ann['cui'])
for cui in set(cuis):
if cui in self.cdb.cui2count_train:
self.cdb.cui2count_train[cui] = 10
# Remove entities that were terminated
if not never_terminate:
for project in train_set['projects']:
for doc in project['documents']:
if type(doc['annotations']) == list:
doc_annotations = doc['annotations']
elif type(doc['annotations']) == dict:
doc_annotations = doc['annotations'].values()
for ann in doc_annotations:
if ann.get('killed', False):
self.unlink_concept_name(ann['cui'], ann['value'])
for epoch in tqdm(range(nepochs), desc='Epoch', leave=False):
# Print acc before training
for project in tqdm(train_set['projects'],
desc='Project',
leave=False,
total=len(train_set['projects'])):
for i_doc, doc in tqdm(enumerate(project['documents']),
desc='Document',
leave=False,
total=len(project['documents'])):
spacy_doc = self(doc['text'])
# Compatibility with old output where annotations are a list
if type(doc['annotations']) == list:
doc_annotations = doc['annotations']
elif type(doc['annotations']) == dict:
doc_annotations = doc['annotations'].values()
for ann in doc_annotations:
if not ann.get('killed', False):
cui = ann['cui']
start = ann['start']
end = ann['end']
spacy_entity = tkns_from_doc(spacy_doc=spacy_doc,
start=start,
end=end)
deleted = ann.get('deleted', False)
self.add_and_train_concept(
cui=cui,
name=ann['value'],
spacy_doc=spacy_doc,
spacy_entity=spacy_entity,
negative=deleted,
devalue_others=devalue_others)
if train_from_false_positives:
fps = get_false_positives(doc, spacy_doc)
for fp in fps:
self.add_and_train_concept(cui=fp._.cui,
name=fp.text,
spacy_doc=spacy_doc,
spacy_entity=fp,
negative=True,
do_add_concept=False)
if terminate_last and not never_terminate:
# Remove entities that were terminated, but after all training is done
for project in train_set['projects']:
for doc in project['documents']:
if type(doc['annotations']) == list:
doc_annotations = doc['annotations']
elif type(doc['annotations']) == dict:
doc_annotations = doc['annotations'].values()
for ann in doc_annotations:
if ann.get('killed', False):
self.unlink_concept_name(
ann['cui'], ann['value'])
if print_stats > 0 and (epoch + 1) % print_stats == 0:
fp, fn, tp, p, r, f1, cui_counts, examples = self._print_stats(
test_set,
epoch=epoch + 1,
use_filters=use_filters,
use_cui_doc_limit=use_cui_doc_limit,
use_overlaps=use_overlaps,
use_groups=use_groups)
return fp, fn, tp, p, r, f1, cui_counts, examples
def get_entities(self,
text,
only_cui=False,
addl_info=['cui2icd10', 'cui2ontologies']):
r''' Get entities
text: text to be annotated
return: entities
'''
cnf_annotation_output = getattr(self.config, 'annotation_output', {})
doc = self(text)
out = {'entities': {}, 'tokens': []}
if doc is not None:
out_ent = {}
if self.config.general.get('show_nested_entities', False):
_ents = doc._.ents
else:
_ents = doc.ents
if cnf_annotation_output.get("lowercase_context", True):
doc_tokens = [tkn.text_with_ws.lower() for tkn in list(doc)]
else:
doc_tokens = [tkn.text_with_ws for tkn in list(doc)]
if cnf_annotation_output.get('doc_extended_info', False):
# Add tokens if extended info
out['tokens'] = doc_tokens
context_left = cnf_annotation_output.get('context_left', -1)
context_right = cnf_annotation_output.get('context_right', -1)
doc_extended_info = cnf_annotation_output.get(
'doc_extended_info', False)
for ind, ent in enumerate(_ents):
cui = str(ent._.cui)
if not only_cui:
out_ent['pretty_name'] = self.cdb.cui2preferred_name.get(
cui, '')
out_ent['cui'] = cui
out_ent['tuis'] = list(self.cdb.cui2type_ids.get(cui, ''))
out_ent['types'] = [
self.cdb.addl_info['type_id2name'].get(tui, '')
for tui in out_ent['tuis']
]
out_ent['source_value'] = ent.text
out_ent['detected_name'] = str(ent._.detected_name)
out_ent['acc'] = float(ent._.context_similarity)
out_ent['context_similarity'] = float(
ent._.context_similarity)
out_ent['start'] = ent.start_char
out_ent['end'] = ent.end_char
for addl in addl_info:
tmp = self.cdb.addl_info[addl].get(cui, [])
out_ent[addl.split("2")[-1]] = list(tmp) if type(
tmp) == set else tmp
out_ent['id'] = ent._.id
out_ent['meta_anns'] = {}
if doc_extended_info:
out_ent['start_tkn'] = ent.start
out_ent['end_tkn'] = ent.end
if context_left > 0 and context_right > 0:
out_ent['context_left'] = doc_tokens[
max(ent.start - context_left, 0):ent.start]
out_ent['context_right'] = doc_tokens[
ent.end:min(ent.end +
context_right, len(doc_tokens))]
out_ent['context_center'] = doc_tokens[ent.start:ent.
end]
if hasattr(ent._, 'meta_anns') and ent._.meta_anns:
out_ent['meta_anns'] = ent._.meta_anns
out['entities'][out_ent['id']] = dict(out_ent)
else:
out['entities'][ent._.id] = cui
return out
def get_json(self,
text,
only_cui=False,
addl_info=['cui2icd10', 'cui2ontologies']):
""" Get output in json format
text: text to be annotated
return: json with fields {'entities': <>, 'text': text}
"""
ents = self.get_entities(text, only_cui,
addl_info=addl_info)['entities']
out = {'annotations': ents, 'text': text}
return json.dumps(out)
def multiprocessing(self,
in_data,
nproc=8,
batch_size=100,
only_cui=False,
addl_info=[]):
r''' Run multiprocessing NOT FOR TRAINING
in_data: an iterator or array with format: [(id, text), (id, text), ...]
nproc: number of processors
batch_size: obvious
return: an list of tuples: [(id, doc_json), (id, doc_json), ...]
'''
if self._meta_annotations:
# Hack for torch using multithreading, which is not good here
import torch
torch.set_num_threads(1)
# Create the input output for MP
in_q = Queue(maxsize=4 * nproc)
manager = Manager()
out_dict = manager.dict()
out_dict['processed'] = []
# Create processes
procs = []
for i in range(nproc):
p = Process(target=self._mp_cons,
kwargs={
'in_q': in_q,
'out_dict': out_dict,
'pid': i,
'only_cui': only_cui,
'addl_info': addl_info
})
p.start()
procs.append(p)
data = []
for id, text in in_data:
data.append((id, str(text)))
if len(data) == batch_size:
in_q.put(data)
data = []
# Put the last batch if it exists
if len(data) > 0:
in_q.put(data)
for _ in range(nproc): # tell workers we're done
in_q.put(None)
for p in procs:
p.join()
# Close the queue as it can cause memory leaks
in_q.close()
out = []
for key in out_dict.keys():
if 'pid' in key:
data = out_dict[key]
out.extend(data)
# Sometimes necessary to free memory
out_dict.clear()
del out_dict
return out
def _mp_cons(self, in_q, out_dict, pid=0, only_cui=False, addl_info=[]):
cnt = 0
out = []
while True:
if not in_q.empty():
data = in_q.get()
if data is None:
out_dict['pid: {}'.format(pid)] = out
break
for id, text in data:
try:
doc = self.get_entities(text=text,
only_cui=only_cui,
addl_info=addl_info)
doc['text'] = text
out.append((id, doc))
except Exception as e:
self.log.warning("Exception in _mp_cons")
self.log.warning(e, stack_info=True)
sleep(1)
if not os.path.exists(vocab_path):
import requests
tmp = requests.get("https://s3-eu-west-1.amazonaws.com/zkcl/vocab.dat")
with open(vocab_path, 'wb') as f:
f.write(tmp.content)
vocab = Vocab.load(vocab_path)
# Make the pipeline
nlp = Pipe(tokenizer=spacy_split_all, config=config)
nlp.add_tagger(tagger=partial(tag_skip_and_punct, config=config),
name='skip_and_punct',
additional_fields=['is_punct'])
spell_checker = BasicSpellChecker(cdb_vocab=cdb.vocab,
config=config,
data_vocab=vocab)
nlp.add_token_normalizer(spell_checker=spell_checker, config=config)
ner = NER(cdb, config)
nlp.add_ner(ner)
# Add Linker
link = Linker(cdb, vocab, config)
nlp.add_linker(link)
# Test limits for tokens and uppercase
config.ner['max_skip_tokens'] = 1
config.ner['upper_case_limit_len'] = 4
config.linking['disamb_length_limit'] = 2
text = "CDB - I was running and then Movar Virus attacked and CDb"
d = nlp(text)
assert len(d._.ents) == 2