class CAT(object):
""" Annotate a dataset
"""
SEPARATOR = ""
NESTED_ENTITIES = os.getenv("NESTED_ENTITIES", 'false').lower() == 'true'
def __init__(self, cdb, vocab=None, skip_stopwords=True):
self.cdb = cdb
self.vocab = vocab
# Build the required spacy pipeline
self.nlp = SpacyPipe(spacy_split_all)
#self.nlp.add_punct_tagger(tagger=spacy_tag_punct)
self.nlp.add_punct_tagger(
tagger=partial(spacy_tag_punct, skip_stopwords=skip_stopwords))
# Add spell checker pipe
self.spell_checker = CustomSpellChecker(cdb_vocab=self.cdb.vocab,
data_vocab=self.vocab)
self.nlp.add_spell_checker(spell_checker=self.spell_checker)
# Add cat
self.spacy_cat = SpacyCat(cdb=self.cdb, vocab=self.vocab)
self.nlp.add_cat(spacy_cat=self.spacy_cat)
def __call__(self, text):
return self.nlp(text)
def add_concept_cntx(self, cui, text, tkn_inds, negative=False):
doc = self(text)
tkns = [doc[ind] for ind in range(tkn_inds[0], tkn_inds[-1] + 1)]
self.spacy_cat._add_cntx_vec(cui=cui,
doc=doc,
tkns=tkns,
negative=negative,
lr=0.1,
anneal=False)
def unlink_concept_name(self, cui, name):
# Unlink a concept from a name
p_name, _, _, _ = get_all_from_name(name=name,
source_value=name,
nlp=self.nlp)
# To be sure unlink the orignal and the processed name
names = [name, p_name]
for name in names:
if name in self.cdb.cui2names[cui]:
self.cdb.cui2names[cui].remove(name)
if len(self.cdb.cui2names[cui]) == 0:
del self.cdb.cui2names[cui]
if name in self.cdb.name2cui:
if cui in self.cdb.name2cui[name]:
self.cdb.name2cui[name].remove(cui)
if len(self.cdb.name2cui[name]) == 0:
del self.cdb.name2cui[name]
def add_name(self, cui, name):
onto = 'def'
if cui in self.cdb.cui2onto:
onto = self.cdb.cui2onto[cui][0]
p_name, tokens, snames, tokens_vocab = get_all_from_name(
name=name, source_value=name, nlp=self.nlp)
# This will add a new concept if the cui doesn't exist
#or link the name to an existing concept if it exists.
self.cdb.add_concept(cui,
p_name,
onto,
tokens,
snames,
tokens_vocab=tokens_vocab,
original_name=name)
def add_concept(self, concept, text=None, tkn_inds=None):
cui = concept['cui']
onto = concept.get('onto', 'user')
pretty_name = concept['name']
source_value = concept['source_value']
name, tokens, snames, tokens_vocab = get_all_from_name(
name=pretty_name, source_value=source_value, nlp=self.nlp)
tui = concept.get('tui', 'None')
unique = True
# Add the new concept
self.cdb.add_concept(cui,
name,
onto,
tokens,
snames,
tui=tui,
pretty_name=pretty_name,
is_pref_name=True,
tokens_vocab=tokens_vocab,
unique=unique,
original_name=source_value)
if tkn_inds and text:
# Add the context
self.add_concept_cntx(cui, text, tkn_inds)
def train_supervised(self, data):
""" Given data learns vector embeddings for concepts
in a suppervised way.
data: json data in format <>
"""
pass
@property
def train(self):
return self.spacy_cat.train
@train.setter
def train(self, val):
self.spacy_cat.train = val
def run_training(self, data_iterator, fine_tune=False):
""" Runs training on the data
data_iterator: Simple iterator over sentences/documents, e.g. a open file
or an array or anything else that we can use in a for loop.
fine_tune: If False old training will be removed
"""
self.train = True
cnt = 0
if not fine_tune:
print("Removing old training data!\n")
self.cdb.reset_training()
self.cdb.coo_dict = {}
self.spacy_cat._train_skip_names = {}
for line in data_iterator:
if line is not None:
try:
_ = self(line)
except Exception as e:
print("LINE: '{}' \t WAS SKIPPED".format(line))
print("BECAUSE OF: " + str(e))
if cnt % 1000 == 0:
print("DONE: " + str(cnt))
cnt += 1
self.train = False
def get_entities(self, text):
""" Get entities
text: text to be annotated
return: entities
"""
doc = self(text)
out = []
out_ent = {}
if self.NESTED_ENTITIES:
_ents = doc._.ents
else:
_ents = doc.ents
for ind, ent in enumerate(_ents):
out_ent['cui'] = str(ent._.cui)
out_ent['tui'] = str(ent._.tui)
out_ent['type'] = str(self.cdb.tui2name.get(out_ent['tui'], ''))
out_ent['source_value'] = str(ent.text)
out_ent['acc'] = str(ent._.acc)
out_ent['start_tkn'] = ent[0].i
out_ent['end_tkn'] = ent[-1].i
out_ent['start_ind'] = ent.start_char
out_ent['end_ind'] = ent.end_char
out_ent['label'] = str(ent.label_)
out_ent['id'] = str(ent._.id)
out_ent['pretty_name'] = self.cdb.cui2pretty_name.get(
ent._.cui, '')
out.append(dict(out_ent))
return out
def get_json(self, text):
""" Get output in json format
text: text to be annotated
return: json with fields {'entities': <>, 'text': text}
"""
ents = self.get_entities(text)
out = {'entities': ents, 'text': text}
return json.dumps(out)
def multi_processing(self, in_data, nproc=8, batch_size=100):
""" 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), ...]
"""
# 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, args=(in_q, out_dict, i))
p.start()
procs.append(p)
data = []
for id, text in in_data:
data.append((id, 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]
print("Merging training data for proc: " + str(key))
out.extend(data[2])
print("Done processing {} documents\n".format(len(out)))
return out
def multi_processing_coo(self,
in_data,
nproc=8,
batch_size=100,
coo=False):
""" Run multiprocessing NOT FOR TRAINING
in_data: an iterator or array with format: [(id, text), (id, text), ...]
nproc: number of processors
return: an list of tuples: [(id, doc_json), (id, doc_json), ...]
"""
# 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, args=(in_q, out_dict, i))
p.start()
procs.append(p)
data = []
for id, text in in_data:
data.append((id, 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)
in_q.close()
for p in procs:
p.join()
# Merge all the new CDB versions and get the output
out = []
for key in out_dict.keys():
if 'pid' in key:
data = out_dict[key]
print("Merging training data for proc: " + str(key))
out.extend(data[2])
return out
def _mp_cons(self, in_q, out_dict, pid=0):
cnt = 0
out = []
while True:
if not in_q.empty():
data = in_q.get()
if data is None:
print("DONE " + str(pid))
out_dict['pid: {}'.format(pid)] = (self.cdb.coo_dict,
self.cdb.cui_count_ext,
out)
break
for id, text in data:
try:
doc = json.loads(self.get_json(text))
out.append((id, doc))
except Exception as e:
print(e)
sleep(1)
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
vocab (medcat.utils.vocab.Vocab, optional):
Vocabulary used for vector embeddings and spelling. Default: None
skip_stopwords (bool):
If True the stopwords will be ignored and not detected in the pipeline.
Default: True
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.
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
'''
def __init__(self,
cdb,
vocab=None,
skip_stopwords=True,
meta_cats=[],
config={},
tokenizer=None):
self.cdb = cdb
self.vocab = vocab
self.config = config
# Build the spacy pipeline
self.nlp = SpacyPipe(spacy_split_all)
#self.nlp.add_punct_tagger(tagger=spacy_tag_punct)
self.nlp.add_punct_tagger(tagger=partial(
spacy_tag_punct,
skip_stopwords=skip_stopwords,
keep_punct=self.config.get("keep_punct", [':', '.'])))
# Add spell checker
self.spell_checker = CustomSpellChecker(cdb_vocab=self.cdb.vocab,
data_vocab=self.vocab)
self.nlp.add_spell_checker(spell_checker=self.spell_checker)
# Add them cat class that does entity detection
self.spacy_cat = SpacyCat(cdb=self.cdb,
vocab=self.vocab,
tokenizer=tokenizer)
self.nlp.add_cat(spacy_cat=self.spacy_cat)
# 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
def __call__(self, text):
r'''
Push the text through the pipeline.
Args:
text (string):
The text to be annotated
Returns:
A spacy document with the extracted entities
'''
return self.nlp(text)
def add_concept_cntx(self,
cui,
text,
tkn_inds,
negative=False,
lr=None,
anneal=None,
spacy_doc=None):
if spacy_doc is None:
spacy_doc = self(text)
tkns = [spacy_doc[ind] for ind in range(tkn_inds[0], tkn_inds[-1] + 1)]
self.spacy_cat._add_cntx_vec(cui=cui,
doc=spacy_doc,
tkns=tkns,
negative=negative,
lr=lr,
anneal=anneal)
def unlink_concept_name(self, cui, name, full_unlink=True):
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
full_unlink (boolean):
If True, the `name` will not only be removed from the given `cui` but from
each concept in the database that is associated with this name.
Examples:
>>> # To never again link C0020538 to HTN
>>> cat.unlink_concept_name('C0020538', 'htn', False)
'''
names = [name, name.lower()]
# Unlink a concept from a name
p_name, tokens, _, _ = get_all_from_name(name=name,
source_value=name,
nlp=self.nlp,
version='clean')
# Add the clean version of the name
names.append(p_name)
# Get the raw version
p_name, tokens, _, _ = get_all_from_name(name=name,
source_value=name,
nlp=self.nlp,
version='raw')
# Append the raw evrsion
names.append(p_name)
if tokens[-1].lower() == "s":
# Remove last 's' - a stupid bug
names.append(p_name[0:-1])
for name in names:
cuis = [cui]
if full_unlink and name in self.cdb.name2cui:
cuis = list(self.cdb.name2cui[name])
for cui in cuis:
if cui in self.cdb.cui2names and name in self.cdb.cui2names[
cui]:
self.cdb.cui2names[cui].remove(name)
if len(self.cdb.cui2names[cui]) == 0:
del self.cdb.cui2names[cui]
if name in self.cdb.name2cui:
if cui in self.cdb.name2cui[name]:
self.cdb.name2cui[name].remove(cui)
if len(self.cdb.name2cui[name]) == 0:
del self.cdb.name2cui[name]
def _add_name(self,
cui,
source_val,
is_pref_name,
only_new=False,
desc=None,
tui=None):
r'''
Please do not use directly. This function will add a name to a CUI (existing or new).
Args:
cui (str):
The CUI to which to add the name
source_val (str):
The `name` or span or source_value that will be linked to the cui
is_pref_name (boolean):
Is this source_val the prefered `name` for this CUI (concept)
only_new (bool):
Only add the name if it does not exist in the current CDB and is not linked
to any concept (CUI) in the current CDB.
desc (str):
Description for this concept
tui (str):
Semenantic Type identifer for this concept, should be a TUI that exisit in the
current CDB. Have a look at cdb.tui2names - for a list of all existing TUIs
in the current CDB.
Examples:
Do not use.
'''
onto = 'def'
all_cuis = []
if cui in self.cdb.cui2ontos and self.cdb.cui2ontos[cui]:
onto = list(self.cdb.cui2ontos[cui])[0]
# Add the original version of the name just lowercased
p_name, tokens, snames, tokens_vocab = get_all_from_name(
name=source_val,
source_value=source_val,
nlp=self.nlp,
version='none')
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[
cui]:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui,
p_name,
onto,
tokens,
snames,
tokens_vocab=tokens_vocab,
original_name=source_val,
is_pref_name=False,
desc=desc,
tui=tui)
all_cuis.extend(self.cdb.name2cui[p_name])
p_name, tokens, snames, tokens_vocab = get_all_from_name(
name=source_val,
source_value=source_val,
nlp=self.nlp,
version='clean')
# This will add a new concept if the cui doesn't exist
# or link the name to an existing concept if it exists.
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[
cui]:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui,
p_name,
onto,
tokens,
snames,
tokens_vocab=tokens_vocab,
original_name=source_val,
is_pref_name=False,
desc=desc,
tui=tui)
all_cuis.extend(self.cdb.name2cui[p_name])
# Add the raw also if needed
p_name, tokens, snames, tokens_vocab = get_all_from_name(
name=source_val,
source_value=source_val,
nlp=self.nlp,
version='raw')
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[
cui] or is_pref_name:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui,
p_name,
onto,
tokens,
snames,
tokens_vocab=tokens_vocab,
original_name=source_val,
is_pref_name=is_pref_name,
desc=desc,
tui=tui)
all_cuis.extend(self.cdb.name2cui[p_name])
# Fix for ntkns in cdb
if p_name in self.cdb.name2ntkns:
if len(tokens) not in self.cdb.name2ntkns[p_name]:
self.cdb.name2ntkns[p_name].add(len(tokens))
return list(set(all_cuis))
def add_name(self,
cui,
source_val,
text=None,
is_pref_name=False,
tkn_inds=None,
text_inds=None,
spacy_doc=None,
lr=None,
anneal=None,
negative=False,
only_new=False,
desc=None,
tui=None,
manually_created=False):
r'''
This function will add a `name` (source_val) to a CUI (existing or new). It will teach medcat
that this source_val is linked to this CUI.
Args:
cui (str):
The CUI to which to add the name
source_val (str):
The `name` or span or source_value that will be linked to the cui
text (str, optional):
Text in which an example of this source_val can be found. Used for supervised/online
training. This is basically one sample in a dataset for supervised training.
is_pref_name (boolean):
Is this source_val the prefered `name` for this CUI (concept)
tkn_inds (list of ints, optional):
Should be in the form: [3, 4, 5, ...]. This should be used only if you are providing a spacy_doc also.
It gives the indicies of the tokens in a spacy document where the source_val can be found.
text_inds (list, optional):
A list that has only two values the start index for this `source_val` in the `text` and the end index.
Used if you are not providing a spacy_doc. But are providing a `text` - it is optional and if not provided
medcat will try to automatically find the start and end index.
spacy_doc ()
TODO:
lr (float):
The learning rate that will be used if you are providing the `text` that will be used for supervised/active
learning.
only_new (bool):
Only add the name if it does not exist in the current CDB and is not linked
to any concept (CUI) in the current CDB.
desc (str):
Description for this concept
tui (str):
Semenantic Type identifer for this concept, should be a TUI that exisit in the
current CDB. Have a look at cdb.tui2names - for a list of all existing TUIs
in the current CDB.
Examples:
Do not use.
'''
# First add the name, get bac all cuis that link to this name
all_cuis = self._add_name(cui,
source_val,
is_pref_name,
only_new=only_new,
desc=desc,
tui=tui)
# Now add context if text is present
if (text is not None and (source_val in text or text_inds)) or \
(spacy_doc is not None and (text_inds or tkn_inds)):
if spacy_doc is None:
spacy_doc = self(text)
if tkn_inds is None:
tkn_inds = tkn_inds_from_doc(spacy_doc=spacy_doc,
text_inds=text_inds,
source_val=source_val)
if tkn_inds is not None and len(tkn_inds) > 0:
self.add_concept_cntx(cui,
text,
tkn_inds,
spacy_doc=spacy_doc,
lr=lr,
anneal=anneal,
negative=negative)
if manually_created:
all_cuis.remove(cui)
for _cui in all_cuis:
self.add_concept_cntx(_cui,
text,
tkn_inds,
spacy_doc=spacy_doc,
lr=lr,
anneal=anneal,
negative=True)
def _print_stats(self,
data,
epoch=0,
use_filters=False,
use_overlaps=False,
use_cui_doc_limit=False,
use_groups=False):
r'''
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
'''
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()
if self.spacy_cat.TUI_FILTER is None:
_tui_filter = None
else:
_tui_filter = list(self.spacy_cat.TUI_FILTER)
if self.spacy_cat.CUI_FILTER is None:
_cui_filter = None
else:
_cui_filter = list(self.spacy_cat.CUI_FILTER)
for pind, project in tqdm(enumerate(data['projects']),
desc="Stats project",
total=len(data['projects']),
leave=False):
cui_filter = None
tui_filter = None
if use_filters:
if 'cuis' in project and len(project['cuis'].strip()) > 0:
cui_filter = set(
[x.strip() for x in project['cuis'].split(",")])
if 'tuis' in project and len(project['tuis'].strip()) > 0:
tui_filter = set([
x.strip().upper() for x in project['tuis'].split(",")
])
self.spacy_cat.TUI_FILTER = tui_filter
self.spacy_cat.CUI_FILTER = cui_filter
start_time = time.time()
for dind, doc in tqdm(enumerate(project['documents']),
desc='Stats document',
total=len(project['documents']),
leave=False):
spacy_doc = self(doc['text'])
anns = doc['annotations']
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:
if (cui_filter is None and tui_filter is None) or (cui_filter is not None and ann['cui'] in cui_filter) or \
(tui_filter is not None and self.cdb.cui2tui.get(ann['cui'], 'unk') in tui_filter):
cui = ann['cui']
if use_groups:
cui = self.cdb.cui2info.get(cui,
{}).get("group", 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.cui2info.get(cui, {}).get("group", 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._.acc),
"project index":
pind,
"document inedex":
dind
})
for iann, ann in enumerate(p_anns_norm):
if not use_cui_doc_limit or ann[1] in anns_norm_cui:
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['name'])
# 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['name'])
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 = (prec + rec) / 2
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 = (prec + rec) / 2
cui_prec[cui] = prec
cui_rec[cui] = rec
cui_f1[cui] = f1
# Get top 10
pr_fps = [(self.cdb.cui2pretty_name.get(
cui,
list(self.cdb.cui2original_names.get(cui, [cui]))[0]), cui,
fps[cui]) for cui in list(fps.keys())[0:10]]
pr_fns = [(self.cdb.cui2pretty_name.get(
cui,
list(self.cdb.cui2original_names.get(cui, [cui]))[0]), cui,
fns[cui]) for cui in list(fns.keys())[0:10]]
pr_tps = [(self.cdb.cui2pretty_name.get(
cui,
list(self.cdb.cui2original_names.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.spacy_cat.TUI_FILTER = _tui_filter
self.spacy_cat.CUI_FILTER = _cui_filter
return fps, fns, tps, cui_prec, cui_rec, cui_f1, cui_counts, examples
def train_supervised(self,
data_path,
reset_cdb=False,
reset_cui_count=False,
nepochs=30,
lr=None,
anneal=None,
print_stats=True,
use_filters=False,
terminate_last=False,
use_overlaps=False,
use_cui_doc_limit=False,
test_size=0,
force_manually_created=False,
use_groups=False,
never_terminate=False):
r'''
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_cdb (boolean):
This will remove all concepts from the existing CDB and build a new CDB based on the
concepts that appear in the training data. It will be impossible to get back the removed
concepts.
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.
lr (int):
If set it will overwrite the global LR from config.
anneal (boolean):
If true annealing will be used when training.
print_stats (boolean):
If true stats will be printed during training (prints stats every 5 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.
force_manually_created (float):
Check add_name for more details, if true all concepts in the dataset will be treated as manually
created.
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
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 = {}
self.train = False
data = json.load(open(data_path))
cui_counts = {}
if test_size == 0:
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:
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_cdb:
self.cdb = CDB()
self.spacy_cat.cdb = self.cdb
self.spacy_cat.cat_ann.cdb = self.cdb
if reset_cui_count:
# Get all CUIs
cuis = []
for project in train_set['projects']:
for doc in project['documents']:
for ann in doc['annotations']:
cuis.append(ann['cui'])
for cui in set(cuis):
if cui in self.cdb.cui_count:
self.cdb.cui_count[cui] = 10
# Remove entites that were terminated
if not never_terminate:
for project in train_set['projects']:
for doc in project['documents']:
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'])
for ann in doc['annotations']:
if not ann.get('killed', False):
cui = ann['cui']
start = ann['start']
end = ann['end']
deleted = ann.get('deleted', False)
manually_created = False
if force_manually_created or ann.get(
'manually_created', False) or ann.get(
'alternative', False):
manually_created = True
self.add_name(cui=cui,
source_val=ann['value'],
spacy_doc=spacy_doc,
text_inds=[start, end],
negative=deleted,
lr=lr,
anneal=anneal,
manually_created=manually_created)
if terminate_last and not never_terminate:
# Remove entites that were terminated, but after all training is done
for project in train_set['projects']:
for doc in project['documents']:
for ann in doc['annotations']:
if ann.get('killed', False):
self.unlink_concept_name(
ann['cui'], ann['value'])
if epoch % 5 == 0:
if print_stats:
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
@property
def train(self):
return self.spacy_cat.train
@train.setter
def train(self, val):
self.spacy_cat.train = val
def run_training(self, data_iterator, fine_tune=False):
""" Runs training on the data
data_iterator: Simple iterator over sentences/documents, e.g. a open file
or an array or anything else that we can use in a for loop.
fine_tune: If False old training will be removed
"""
self.train = True
cnt = 0
if not fine_tune:
print("Removing old training data!\n")
self.cdb.reset_training()
self.cdb.coo_dict = {}
self.spacy_cat._train_skip_names = {}
for line in data_iterator:
if line is not None:
try:
_ = self(line)
except Exception as e:
print("LINE: '{}' \t WAS SKIPPED".format(line))
print("BECAUSE OF: " + str(e))
if cnt % 1000 == 0:
print("DONE: " + str(cnt))
cnt += 1
self.train = False
def get_entities(self,
text,
cat_filter=None,
only_cui=False,
skip_info=False):
""" Get entities
text: text to be annotated
return: entities
"""
doc = self(text)
out = []
if cat_filter:
cat_filter(doc, self)
out_ent = {}
if self.config.get('nested_entities', False):
_ents = doc._.ents
else:
_ents = doc.ents
for ind, ent in enumerate(_ents):
cui = str(ent._.cui)
if not only_cui:
out_ent['pretty_name'] = self.cdb.cui2pretty_name.get(cui, '')
out_ent['cui'] = cui
out_ent['tui'] = str(ent._.tui)
out_ent['type'] = str(self.cdb.tui2name.get(
out_ent['tui'], ''))
out_ent['source_value'] = str(ent.text)
out_ent['acc'] = str(ent._.acc)
out_ent['start'] = ent.start_char
out_ent['end'] = ent.end_char
if not skip_info:
out_ent['info'] = self.cdb.cui2info.get(cui, {})
out_ent['id'] = str(ent._.id)
out_ent['meta_anns'] = {}
if hasattr(ent._, 'meta_anns') and ent._.meta_anns:
for key in ent._.meta_anns.keys():
one = {'name': key, 'value': ent._.meta_anns[key]}
out_ent['meta_anns'][key] = one
out.append(dict(out_ent))
else:
out.append(cui)
return out
def get_json(self, text, cat_filter=None, only_cui=False, skip_info=False):
""" Get output in json format
text: text to be annotated
return: json with fields {'entities': <>, 'text': text}
"""
ents = self.get_entities(text,
cat_filter,
only_cui,
skip_info=skip_info)
out = {'entities': ents, 'text': text}
return json.dumps(out)
def multi_processing(self,
in_data,
nproc=8,
batch_size=100,
cat_filter=None,
only_cui=False,
skip_info=False):
""" 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,
args=(in_q, out_dict, i, cat_filter, only_cui,
skip_info))
p.start()
procs.append(p)
data = []
for id, text in in_data:
data.append((id, 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,
cat_filter=None,
only_cui=False,
skip_info=False):
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 = json.loads(
self.get_json(text, cat_filter, only_cui,
skip_info))
out.append((id, doc))
except Exception as e:
print("Exception in _mp_cons")
print(e)
sleep(1)
def add_cui_to_group(self, cui, group_name, reset_all_groups=False):
r'''
Ads a CUI to a group, will appear in cdb.cui2info['group']
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:
for _cui in self.cdb.cui2info.keys():
_ = self.cdb.cui2info[_cui].pop('group', None)
# Add
if cui in self.cdb.cui2info:
self.cdb.cui2info[cui]['group'] = group_name
else:
self.cdb.cui2info[cui] = {'group': group_name}
class MakeVocab(object):
SKIP_STOPWORDS = False
def __init__(self, cdb, vocab=None, word_tokenizer=None):
self.cdb = cdb
self.w2v = None
if vocab is not None:
self.vocab = vocab
else:
self.vocab = Vocab()
# Build the required spacy pipeline
self.nlp = SpacyPipe(spacy_split_all, disable=['ner', 'parser', 'vectors', 'textcat'])
self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct, skip_stopwords=self.SKIP_STOPWORDS))
self.spell_checker = CustomSpellChecker(cdb_vocab=cdb.vocab, data_vocab=vocab)
self.nlp.add_spell_checker(spell_checker=self.spell_checker)
# Get the tokenizer
if word_tokenizer is not None:
self.tokenizer = word_tokenizer
else:
self.tokenizer = self._tok
def _tok(self, text):
return [text]
def make(self, iter_data, out_folder, join_cdb=True):
# Save the preprocessed data, used for emb training
out_path = out_folder + "data.txt"
vocab_path = out_folder + "vocab.dat"
out = open(out_path, 'w')
for ind, doc in enumerate(iter_data):
if ind % 10000 == 0:
print(ind)
doc = self.nlp(doc)
line = ""
for token in doc:
if token._.to_skip:
continue
if len(token._.norm) > 1:
self.vocab.inc_or_add(token._.norm)
# Add also the unnormalized version if it is different
if token._.norm != token.lower_:
self.vocab.inc_or_add(token.lower_)
line = line + " " + "_".join(token._.norm.split(" "))
out.write(line.strip())
out.write("\n")
out.close()
if join_cdb and self.cdb:
for word in self.cdb.vocab.keys():
if word not in self.vocab:
self.vocab.add_word(word)
else:
# Update the count with the counts from the new dataset
self.cdb.vocab[word] += self.vocab[word]
# Save the vocab also
self.vocab.save_dict(path=vocab_path)
def add_vectors(self, in_path, overwrite=False):
data = SimpleIter(in_path)
w2v = Word2Vec(data, window=10, min_count=10, workers=8, size=300, iter=2)
for word in w2v.wv.vocab.keys():
if word in self.vocab:
if overwrite:
self.vocab.add_vec(word, w2v.wv.get_vector(word))
else:
if self.vocab.vec(word) is None:
self.vocab.add_vec(word, w2v.wv.get_vector(word))
return w2v
class CAT(object):
""" Annotate a dataset
"""
SEPARATOR = ""
NESTED_ENTITIES = os.getenv("NESTED_ENTITIES", 'false').lower() == 'true'
KEEP_PUNCT = os.getenv("KEEP_PUNCT", ":|.").split("|")
def __init__(self, cdb, vocab=None, skip_stopwords=True, meta_cats=[]):
self.cdb = cdb
self.vocab = vocab
# Build the required spacy pipeline
self.nlp = SpacyPipe(spacy_split_all)
#self.nlp.add_punct_tagger(tagger=spacy_tag_punct)
self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct,
skip_stopwords=skip_stopwords,
keep_punct=self.KEEP_PUNCT))
# Add spell checker pipe
self.spell_checker = CustomSpellChecker(cdb_vocab=self.cdb.vocab, data_vocab=self.vocab)
self.nlp.add_spell_checker(spell_checker=self.spell_checker)
# Add cat
self.spacy_cat = SpacyCat(cdb=self.cdb, vocab=self.vocab)
self.nlp.add_cat(spacy_cat=self.spacy_cat)
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
def __call__(self, text):
return self.nlp(text)
def add_concept_cntx(self, cui, text, tkn_inds, negative=False, lr=None, anneal=None, spacy_doc=None):
if spacy_doc is None:
spacy_doc = self(text)
tkns = [spacy_doc[ind] for ind in range(tkn_inds[0], tkn_inds[-1] + 1)]
self.spacy_cat._add_cntx_vec(cui=cui, doc=spacy_doc, tkns=tkns,
negative=negative, lr=lr, anneal=anneal)
def unlink_concept_name(self, cui, name, full_unlink=True):
names = [name, name.lower()]
# Unlink a concept from a name
p_name, tokens, _, _ = get_all_from_name(name=name, source_value=name, nlp=self.nlp, version='clean')
# Add the clean version of the name
names.append(p_name)
# Get the raw version
p_name, tokens, _, _ = get_all_from_name(name=name, source_value=name, nlp=self.nlp, version='raw')
# Append the raw evrsion
names.append(p_name)
if tokens[-1].lower() == "s":
# Remove last 's' - a stupid bug
names.append(p_name[0:-1])
for name in names:
cuis = [cui]
if full_unlink and name in self.cdb.name2cui:
cuis = list(self.cdb.name2cui[name])
for cui in cuis:
if cui in self.cdb.cui2names and name in self.cdb.cui2names[cui]:
self.cdb.cui2names[cui].remove(name)
if len(self.cdb.cui2names[cui]) == 0:
del self.cdb.cui2names[cui]
if name in self.cdb.name2cui:
if cui in self.cdb.name2cui[name]:
self.cdb.name2cui[name].remove(cui)
if len(self.cdb.name2cui[name]) == 0:
del self.cdb.name2cui[name]
def _add_name(self, cui, source_val, is_pref_name, only_new=False, desc=None, tui=None):
onto = 'def'
all_cuis = []
if cui in self.cdb.cui2ontos and self.cdb.cui2ontos[cui]:
onto = list(self.cdb.cui2ontos[cui])[0]
# Add the original version of the name just lowercased
p_name, tokens, snames, tokens_vocab = get_all_from_name(name=source_val,
source_value=source_val,
nlp=self.nlp, version='none')
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[cui]:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui, p_name, onto, tokens, snames, tokens_vocab=tokens_vocab,
original_name=source_val, is_pref_name=False, desc=desc, tui=tui)
all_cuis.extend(self.cdb.name2cui[p_name])
p_name, tokens, snames, tokens_vocab = get_all_from_name(name=source_val,
source_value=source_val,
nlp=self.nlp, version='clean')
# This will add a new concept if the cui doesn't exist
# or link the name to an existing concept if it exists.
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[cui]:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui, p_name, onto, tokens, snames, tokens_vocab=tokens_vocab,
original_name=source_val, is_pref_name=False, desc=desc, tui=tui)
all_cuis.extend(self.cdb.name2cui[p_name])
# Add the raw also if needed
p_name, tokens, snames, tokens_vocab = get_all_from_name(name=source_val,
source_value=source_val,
nlp=self.nlp, version='raw')
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[cui] or is_pref_name:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui, p_name, onto, tokens, snames, tokens_vocab=tokens_vocab,
original_name=source_val, is_pref_name=is_pref_name, desc=desc, tui=tui)
all_cuis.extend(self.cdb.name2cui[p_name])
# Fix for ntkns in cdb
if p_name in self.cdb.name2ntkns:
if len(tokens) not in self.cdb.name2ntkns[p_name]:
self.cdb.name2ntkns[p_name].add(len(tokens))
return list(set(all_cuis))
def add_name(self, cui, source_val, text=None, is_pref_name=False, tkn_inds=None, text_inds=None,
spacy_doc=None, lr=None, anneal=None, negative=False, only_new=False, desc=None, tui=None,
manually_created=False):
""" Adds a new concept or appends the name to an existing concept
if the cui already exists in the DB.
cui: Concept uniqe ID
source_val: Source value in the text
text: the text of a document where source_val was found
"""
# First add the name, get bac all cuis that link to this name
all_cuis = self._add_name(cui, source_val, is_pref_name, only_new=only_new, desc=desc, tui=tui)
# Now add context if text is present
if (text is not None and (source_val in text or text_inds)) or \
(spacy_doc is not None and (text_inds or tkn_inds)):
if spacy_doc is None:
spacy_doc = self(text)
if tkn_inds is None:
tkn_inds = tkn_inds_from_doc(spacy_doc=spacy_doc, text_inds=text_inds,
source_val=source_val)
if tkn_inds is not None and len(tkn_inds) > 0:
self.add_concept_cntx(cui, text, tkn_inds, spacy_doc=spacy_doc, lr=lr, anneal=anneal,
negative=negative)
if manually_created:
all_cuis.remove(cui)
for _cui in all_cuis:
self.add_concept_cntx(_cui, text, tkn_inds, spacy_doc=spacy_doc, lr=lr, anneal=anneal,
negative=True)
def _print_stats(self, data, epoch=0, use_filters=False, use_overlaps=False):
tp = 0
fp = 0
fn = 0
fps = {}
fns = {}
tps = {}
cui_prec = {}
cui_rec = {}
cui_f1 = {}
docs_with_problems = set()
if self.spacy_cat.TUI_FILTER is None:
_tui_filter = None
else:
_tui_filter = list(self.spacy_cat.TUI_FILTER)
if self.spacy_cat.CUI_FILTER is None:
_cui_filter = None
else:
_cui_filter = list(self.spacy_cat.CUI_FILTER)
# Stupid
for project in data['projects']:
cui_filter = None
tui_filter = None
if use_filters:
if 'cuis' in project and len(project['cuis'].strip()) > 0:
cui_filter = [x.strip().upper() for x in project['cuis'].split(",")]
if 'tuis' in project and len(project['tuis'].strip()) > 0:
tui_filter = [x.strip().upper() for x in project['tuis'].split(",")]
self.spacy_cat.TUI_FILTER = tui_filter
self.spacy_cat.CUI_FILTER = cui_filter
for doc in project['documents']:
spacy_doc = self(doc['text'])
anns = doc['annotations']
if use_overlaps:
p_anns = spacy_doc._.ents
else:
p_anns = spacy_doc.ents
anns_norm = []
for ann in anns:
if ann.get('validated', True) and (not ann.get('killed', False) and not ann.get('deleted', False)):
anns_norm.append((ann['start'], ann['cui']))
p_anns_norm = []
for ann in p_anns:
p_anns_norm.append((ann.start_char, ann._.cui))
for ann in p_anns_norm:
if ann in anns_norm:
tp += 1
if ann[1] in tps:
tps[ann[1]] += 1
else:
tps[ann[1]] = 1
else:
if ann[1] in fps:
fps[ann[1]] += 1
else:
fps[ann[1]] = 1
fp += 1
docs_with_problems.add(doc['name'])
for ann in anns_norm:
if ann not in p_anns_norm:
fn += 1
docs_with_problems.add(doc['name'])
if ann[1] in fns:
fns[ann[1]] += 1
else:
fns[ann[1]] = 1
try:
prec = tp / (tp + fp)
rec = tp / (tp + fn)
f1 = (prec + rec) / 2
print("Epoch: {}, Prec: {}, Rec: {}, F1: {}".format(epoch, prec, rec, f1))
print("First 10 out of {} docs with problems: {}".format(len(docs_with_problems),
"; ".join([str(x) for x in list(docs_with_problems)[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 = (prec + rec) / 2
cui_prec[cui] = prec
cui_rec[cui] = rec
cui_f1[cui] = f1
# Get top 10
pr_fps = [(self.cdb.cui2pretty_name.get(cui,
list(self.cdb.cui2original_names.get(cui, ["UNK"]))[0]), cui, fps[cui]) for cui in list(fps.keys())[0:10]]
pr_fns = [(self.cdb.cui2pretty_name.get(cui,
list(self.cdb.cui2original_names.get(cui, ["UNK"]))[0]), cui, fns[cui]) for cui in list(fns.keys())[0:10]]
pr_tps = [(self.cdb.cui2pretty_name.get(cui,
list(self.cdb.cui2original_names.get(cui, ["UNK"]))[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(one[0], one[1], one[2]))
print("\n\nFalse Negatives\n")
for one in pr_fns:
print("{:70} - {:20} - {:10}".format(one[0], one[1], one[2]))
print("\n\nTrue Positives\n")
for one in pr_tps:
print("{:70} - {:20} - {:10}".format(one[0], one[1], one[2]))
print("*"*110 + "\n")
except Exception as e:
traceback.print_exc()
self.spacy_cat.TUI_FILTER = _tui_filter
self.spacy_cat.CUI_FILTER = _cui_filter
return fps, fns, tps, cui_prec, cui_rec, cui_f1
def train_supervised(self, data_path, reset_cdb=False, reset_cui_count=False, nepochs=30, lr=None,
anneal=None, print_stats=False, test_set=None, use_filters=False):
""" Given data learns vector embeddings for concepts
in a suppervised way.
data_path: path to data in json format
"""
self.train = False
data = json.load(open(data_path))
if print_stats:
if test_set:
self._print_stats(test_set, use_filters=use_filters)
else:
self._print_stats(data, use_filters=use_filters)
if reset_cdb:
self.cdb = CDB()
self.spacy_cat.cdb = self.cdb
self.spacy_cat.cat_ann.cdb = self.cdb
if reset_cui_count:
# Get all CUIs
cuis = []
for project in data['projects']:
for doc in project['documents']:
for ann in doc['annotations']:
cuis.append(ann['cui'])
for cui in set(cuis):
if cui in self.cdb.cui_count:
self.cdb.cui_count[cui] = 10
# Remove entites that were terminated
for project in data['projects']:
for doc in project['documents']:
for ann in doc['annotations']:
if ann.get('killed', False):
self.unlink_concept_name(ann['cui'], ann['value'])
for epoch in range(nepochs):
print("Starting epoch: {}".format(epoch))
log.info("Starting epoch: {}".format(epoch))
# Print acc before training
for project in data['projects']:
for i_doc, doc in enumerate(project['documents']):
spacy_doc = self(doc['text'])
for ann in doc['annotations']:
if not ann.get('killed', False):
cui = ann['cui']
start = ann['start']
end = ann['end']
deleted = ann.get('deleted', False)
manually_created = ann.get('manually_created', False)
self.add_name(cui=cui,
source_val=ann['value'],
spacy_doc=spacy_doc,
text_inds=[start, end],
negative=deleted,
lr=lr,
anneal=anneal,
manually_created=manually_created)
if epoch % 5 == 0:
if print_stats:
if test_set:
self._print_stats(test_set, epoch=epoch+1, use_filters=use_filters)
else:
self._print_stats(data, epoch=epoch+1, use_filters=use_filters)
@property
def train(self):
return self.spacy_cat.train
@train.setter
def train(self, val):
self.spacy_cat.train = val
def run_training(self, data_iterator, fine_tune=False):
""" Runs training on the data
data_iterator: Simple iterator over sentences/documents, e.g. a open file
or an array or anything else that we can use in a for loop.
fine_tune: If False old training will be removed
"""
self.train = True
cnt = 0
if not fine_tune:
print("Removing old training data!\n")
self.cdb.reset_training()
self.cdb.coo_dict = {}
self.spacy_cat._train_skip_names = {}
for line in data_iterator:
if line is not None:
try:
_ = self(line)
except Exception as e:
print("LINE: '{}' \t WAS SKIPPED".format(line))
print("BECAUSE OF: " + str(e))
if cnt % 1000 == 0:
print("DONE: " + str(cnt))
cnt += 1
self.train = False
def get_entities(self, text, cat_filter=None, only_cui=False):
""" Get entities
text: text to be annotated
return: entities
"""
doc = self(text)
out = []
if cat_filter:
cat_filter(doc, self)
out_ent = {}
if self.NESTED_ENTITIES:
_ents = doc._.ents
else:
_ents = doc.ents
for ind, ent in enumerate(_ents):
cui = str(ent._.cui)
if not only_cui:
out_ent['cui'] = cui
out_ent['tui'] = str(ent._.tui)
out_ent['type'] = str(self.cdb.tui2name.get(out_ent['tui'], ''))
out_ent['source_value'] = str(ent.text)
out_ent['acc'] = str(ent._.acc)
out_ent['start'] = ent.start_char
out_ent['end'] = ent.end_char
out_ent['id'] = str(ent._.id)
out_ent['pretty_name'] = self.cdb.cui2pretty_name.get(cui, '')
if cui in self.cdb.cui2info and 'icd10' in self.cdb.cui2info[cui]:
icds = []
for icd10 in self.cdb.cui2info[cui]['icd10']:
icds.append(str(icd10['chapter']))
out_ent['icd10'] = ",".join(icds)
else:
out_ent['icd10'] = ""
if cui in self.cdb.cui2info and 'umls' in self.cdb.cui2info[cui]:
umls = [str(u) for u in self.cdb.cui2info[cui]['umls']]
out_ent['umls'] = ",".join(umls)
else:
out_ent['umls'] = ''
if cui in self.cdb.cui2info and 'snomed' in self.cdb.cui2info[cui]:
snomed = [str(u) for u in self.cdb.cui2info[cui]['snomed']]
out_ent['snomed'] = ",".join(snomed)
else:
out_ent['snomed'] = ''
if hasattr(ent._, 'meta_anns') and ent._.meta_anns:
out_ent['meta_anns'] = []
for key in ent._.meta_anns.keys():
one = {'name': key, 'value': ent._.meta_anns[key]}
out_ent['meta_anns'].append(one)
out.append(dict(out_ent))
else:
out.append(cui)
return out
def get_json(self, text, cat_filter=None, only_cui=False):
""" Get output in json format
text: text to be annotated
return: json with fields {'entities': <>, 'text': text}
"""
ents = self.get_entities(text, cat_filter, only_cui)
out = {'entities': ents, 'text': text}
return json.dumps(out)
def multi_processing(self, in_data, nproc=8, batch_size=100, cat_filter=None, only_cui=False):
""" 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, args=(in_q, out_dict, i, cat_filter, only_cui))
p.start()
procs.append(p)
data = []
for id, text in in_data:
data.append((id, 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, cat_filter=None, only_cui=False):
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 = json.loads(self.get_json(text, cat_filter, only_cui))
out.append((id, doc))
except Exception as e:
print("Exception in _mp_cons")
print(e)
sleep(1)
class CAT(object):
""" Annotate a dataset
"""
SEPARATOR = ""
NESTED_ENTITIES = os.getenv("NESTED_ENTITIES", 'false').lower() == 'true'
KEEP_PUNCT = os.getenv("KEEP_PUNCT", ":|.").split("|")
def __init__(self, cdb, vocab=None, skip_stopwords=True):
self.cdb = cdb
self.vocab = vocab
# Build the required spacy pipeline
self.nlp = SpacyPipe(spacy_split_all)
#self.nlp.add_punct_tagger(tagger=spacy_tag_punct)
self.nlp.add_punct_tagger(tagger=partial(spacy_tag_punct,
skip_stopwords=skip_stopwords,
keep_punct=self.KEEP_PUNCT))
# Add spell checker pipe
self.spell_checker = CustomSpellChecker(cdb_vocab=self.cdb.vocab, data_vocab=self.vocab)
self.nlp.add_spell_checker(spell_checker=self.spell_checker)
# Add cat
self.spacy_cat = SpacyCat(cdb=self.cdb, vocab=self.vocab)
self.nlp.add_cat(spacy_cat=self.spacy_cat)
def __call__(self, text):
return self.nlp(text)
def add_concept_cntx(self, cui, text, tkn_inds, negative=False, lr=None, anneal=None, spacy_doc=None):
if spacy_doc is None:
spacy_doc = self(text)
tkns = [spacy_doc[ind] for ind in range(tkn_inds[0], tkn_inds[-1] + 1)]
self.spacy_cat._add_cntx_vec(cui=cui, doc=spacy_doc, tkns=tkns,
negative=negative, lr=lr, anneal=anneal)
def unlink_concept_name(self, cui, name):
# Unlink a concept from a name
p_name, _, _, _ = get_all_from_name(name=name, source_value=name, nlp=self.nlp)
# To be sure unlink the orignal and the processed name
names = [name, p_name]
for name in names:
if name in self.cdb.cui2names[cui]:
self.cdb.cui2names[cui].remove(name)
if len(self.cdb.cui2names[cui]) == 0:
del self.cdb.cui2names[cui]
if name in self.cdb.name2cui:
if cui in self.cdb.name2cui[name]:
self.cdb.name2cui[name].remove(cui)
if len(self.cdb.name2cui[name]) == 0:
del self.cdb.name2cui[name]
def _add_name(self, cui, source_val, is_pref_name, only_new=False):
onto = 'def'
if cui in self.cdb.cui2ontos and self.cdb.cui2ontos[cui]:
onto = list(self.cdb.cui2ontos[cui])[0]
p_name, tokens, snames, tokens_vocab = get_all_from_name(name=source_val,
source_value=source_val,
nlp=self.nlp, version='clean')
# This will add a new concept if the cui doesn't exist
#or link the name to an existing concept if it exists.
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[cui]:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui, p_name, onto, tokens, snames, tokens_vocab=tokens_vocab,
original_name=source_val, is_pref_name=False)
# Add the raw also if needed
p_name, tokens, snames, tokens_vocab = get_all_from_name(name=source_val,
source_value=source_val,
nlp=self.nlp, version='raw')
if cui not in self.cdb.cui2names or p_name not in self.cdb.cui2names[cui] or is_pref_name:
if not only_new or p_name not in self.cdb.name2cui:
self.cdb.add_concept(cui, p_name, onto, tokens, snames, tokens_vocab=tokens_vocab,
original_name=source_val, is_pref_name=is_pref_name)
def add_name(self, cui, source_val, text=None, is_pref_name=False, tkn_inds=None, text_inds=None, spacy_doc=None, lr=None, anneal=None, negative=False, only_new=False):
""" Adds a new concept or appends the name to an existing concept
if the cui already exists in the DB.
cui: Concept uniqe ID
source_val: Source value in the text
text: the text of a document where source_val was found
"""
# First add the name
self._add_name(cui, source_val, is_pref_name, only_new=only_new)
# Now add context if text is present
if text is not None and (source_val in text or text_inds):
if spacy_doc is None:
spacy_doc = self(text)
if tkn_inds is None:
tkn_inds = tkn_inds_from_doc(spacy_doc=spacy_doc, text_inds=text_inds,
source_val=source_val)
if tkn_inds is not None and len(tkn_inds) > 0:
self.add_concept_cntx(cui, text, tkn_inds, spacy_doc=spacy_doc, lr=lr, anneal=anneal,
negative=negative)
def train_supervised(self, data_path, reset_cdb=False, reset_cui_count=False, epochs=2, lr=None,
anneal=None):
""" Given data learns vector embeddings for concepts
in a suppervised way.
data_path: path to data in json format
"""
self.train = False
data = json.load(open(data_path))
if reset_cdb:
self.cdb = CDB()
if reset_cui_count:
# Get all CUIs
cuis = []
for doc in data['documents']:
for ann in doc['annotations']:
cuis.append(ann['cui'])
for cui in set(cuis):
if cui in self.cdb.cui_count:
self.cdb.cui_count[cui] = 1
for epoch in epochs:
log.info("Starting epoch: {}".format(epoch))
for doc in data['documents']:
spacy_doc = self(doc['text'])
for ann in doc['annotations']:
cui = ann['cui']
start = ann['start']
end = ann['end']
deleted = ann['deleted']
if deleted:
# Add negatives only if they exist in the CDB
if cui in self.cdb.cui2names:
self.add_name(cui=cui,
source_val=ann['value'],
spacy_doc=spacy_doc,
text_inds=[start, end],
negative=deleted,
lr=lr,
anneal=anneal)
else:
self.add_name(cui=cui,
source_val=ann['value'],
spacy_doc=spacy_doc,
text_inds=[start, end],
lr=lr,
anneal=anneal)
@property
def train(self):
return self.spacy_cat.train
@train.setter
def train(self, val):
self.spacy_cat.train = val
def run_training(self, data_iterator, fine_tune=False):
""" Runs training on the data
data_iterator: Simple iterator over sentences/documents, e.g. a open file
or an array or anything else that we can use in a for loop.
fine_tune: If False old training will be removed
"""
self.train = True
cnt = 0
if not fine_tune:
print("Removing old training data!\n")
self.cdb.reset_training()
self.cdb.coo_dict = {}
self.spacy_cat._train_skip_names = {}
for line in data_iterator:
if line is not None:
try:
_ = self(line)
except Exception as e:
print("LINE: '{}' \t WAS SKIPPED".format(line))
print("BECAUSE OF: " + str(e))
if cnt % 1000 == 0:
print("DONE: " + str(cnt))
cnt += 1
self.train = False
def get_entities(self, text, cat_filter=None, only_cui=False):
""" Get entities
text: text to be annotated
return: entities
"""
doc = self(text)
out = []
if cat_filter:
cat_filter(doc, self)
out_ent = {}
if self.NESTED_ENTITIES:
_ents = doc._.ents
else:
_ents = doc.ents
for ind, ent in enumerate(_ents):
cui = str(ent._.cui)
if not only_cui:
out_ent['cui'] = cui
out_ent['tui'] = str(ent._.tui)
out_ent['type'] = str(self.cdb.tui2name.get(out_ent['tui'], ''))
out_ent['source_value'] = str(ent.text)
out_ent['acc'] = str(ent._.acc)
out_ent['start'] = ent.start_char
out_ent['end'] = ent.end_char
out_ent['id'] = str(ent._.id)
out_ent['pretty_name'] = self.cdb.cui2pretty_name.get(cui, '')
if cui in self.cdb.cui2info and 'icd10' in self.cdb.cui2info[cui]:
icds = []
for icd10 in self.cdb.cui2info[cui]['icd10']:
icds.append(str(icd10['chapter']))
out_ent['icd10'] = ",".join(icds)
else:
out_ent['icd10'] = ""
if cui in self.cdb.cui2info and 'umls' in self.cdb.cui2info[cui]:
umls = [str(u) for u in self.cdb.cui2info[cui]['umls']]
out_ent['umls'] = ",".join(umls)
else:
out_ent['umls'] = ''
if cui in self.cdb.cui2info and 'snomed' in self.cdb.cui2info[cui]:
snomed = [str(u) for u in self.cdb.cui2info[cui]['snomed']]
out_ent['snomed'] = ",".join(snomed)
else:
out_ent['snomed'] = ''
out.append(dict(out_ent))
else:
out.append(cui)
return out
def get_json(self, text, cat_filter=None, only_cui=False):
""" Get output in json format
text: text to be annotated
return: json with fields {'entities': <>, 'text': text}
"""
ents = self.get_entities(text, cat_filter, only_cui)
out = {'entities': ents, 'text': text}
return json.dumps(out)
def multi_processing(self, in_data, nproc=8, batch_size=100, cat_filter=None, only_cui=False):
""" 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), ...]
"""
# 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, args=(in_q, out_dict, i, cat_filter, only_cui))
p.start()
procs.append(p)
data = []
for id, text in in_data:
data.append((id, 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]
print("Merging training data for proc: " + str(key))
out.extend(data)
print("Done processing {} documents\n".format(len(out)))
# Sometimes necessary to free memory
out_dict.clear()
del out_dict
return out
def _mp_cons(self, in_q, out_dict, pid=0, cat_filter=None, only_cui=False):
cnt = 0
out = []
while True:
if not in_q.empty():
data = in_q.get()
if data is None:
print("DONE " + str(pid))
out_dict['pid: {}'.format(pid)] = out
break
for id, text in data:
try:
doc = json.loads(self.get_json(text, cat_filter, only_cui))
out.append((id, doc))
except Exception as e:
print(e)
sleep(1)