def run_cv(cdb_path,
data_path,
vocab_path,
cv=100,
nepochs=16,
reset_cui_count=True,
test_size=0.1):
from medcat.cat import CAT
from medcat.utils.vocab import Vocab
from medcat.cdb import CDB
import json
f1s = {}
ps = {}
rs = {}
tps = {}
fns = {}
fps = {}
cui_counts = {}
for i in range(cv):
cdb = CDB()
cdb.load_dict(cdb_path)
vocab = Vocab()
vocab.load_dict(path=vocab_path)
cat = CAT(cdb, vocab=vocab)
cat.train = False
cat.spacy_cat.MIN_ACC = 0.30
cat.spacy_cat.MIN_ACC_TH = 0.30
fp, fn, tp, p, r, f1, cui_counts = cat.train_supervised(
data_path=data_path,
lr=1,
nepochs=nepochs,
anneal=True,
print_stats=True,
use_filters=True,
reset_cui_count=reset_cui_count,
terminate_last=True,
test_size=test_size)
for key in f1.keys():
if key in f1s:
f1s[key].append(f1[key])
else:
f1s[key] = [f1[key]]
if key in ps:
ps[key].append(p[key])
else:
ps[key] = [p[key]]
if key in rs:
rs[key].append(r[key])
else:
rs[key] = [r[key]]
if key in tps:
tps[key].append(tp.get(key, 0))
else:
tps[key] = [tp.get(key, 0)]
if key in fps:
fps[key].append(fp.get(key, 0))
else:
fps[key] = [fp.get(key, 0)]
if key in fns:
fns[key].append(fn.get(key, 0))
else:
fns[key] = [fn.get(key, 0)]
return fps, fns, tps, ps, rs, f1s, cui_counts
def run_cv(cdb_path, data_path, vocab_path, cv=100, nepochs=16, test_size=0.1, lr=1, groups=None, **kwargs):
from medcat.cat import CAT
from medcat.utils.vocab import Vocab
from medcat.cdb import CDB
import json
use_groups = False
if groups is not None:
use_groups = True
f1s = {}
ps = {}
rs = {}
tps = {}
fns = {}
fps = {}
cui_counts = {}
examples = {}
for i in range(cv):
cdb = CDB()
cdb.load_dict(cdb_path)
vocab = Vocab()
vocab.load_dict(path=vocab_path)
cat = CAT(cdb, vocab=vocab)
cat.train = False
cat.spacy_cat.MIN_ACC = 0.30
cat.spacy_cat.MIN_ACC_TH = 0.30
# Add groups if they exist
if groups is not None:
for cui in cdb.cui2info.keys():
if "group" in cdb.cui2info[cui]:
del cdb.cui2info[cui]['group']
groups = json.load(open("./groups.json"))
for k,v in groups.items():
for val in v:
cat.add_cui_to_group(val, k)
fp, fn, tp, p, r, f1, cui_counts, examples = cat.train_supervised(data_path=data_path,
lr=1, test_size=test_size, use_groups=use_groups, nepochs=nepochs, **kwargs)
for key in f1.keys():
if key in f1s:
f1s[key].append(f1[key])
else:
f1s[key] = [f1[key]]
if key in ps:
ps[key].append(p[key])
else:
ps[key] = [p[key]]
if key in rs:
rs[key].append(r[key])
else:
rs[key] = [r[key]]
if key in tps:
tps[key].append(tp.get(key, 0))
else:
tps[key] = [tp.get(key, 0)]
if key in fps:
fps[key].append(fp.get(key, 0))
else:
fps[key] = [fp.get(key, 0)]
if key in fns:
fns[key].append(fn.get(key, 0))
else:
fns[key] = [fn.get(key, 0)]
return fps, fns, tps, ps, rs, f1s, cui_counts, examples
cat.config.linking['calculate_dynamic_threshold'] = True
cat.train(df.text.values, fine_tune=True)
cdb.config.general['spacy_disabled_components'] = ['ner', 'parser', 'vectors', 'textcat',
'entity_linker', 'sentencizer', 'entity_ruler', 'merge_noun_chunks',
'merge_entities', 'merge_subtokens']
%load_ext autoreload
%autoreload 2
# Train
_ = cat.train(open("./tmp_medmentions_text_only.txt", 'r'), fine_tune=False)
_ = cat.train_supervised("/home/ubuntu/data/medmentions/medmentions.json", reset_cui_count=True, nepochs=13, train_from_false_positives=True, print_stats=3, test_size=0.1)
cdb.save("/home/ubuntu/data/umls/2020ab/cdb_trained_medmen.dat")
_ = cat.train_supervised("/home/ubuntu/data/medmentions/medmentions.json", reset_cui_count=False, nepochs=13, train_from_false_positives=True, print_stats=3, test_size=0)
cat = CAT(cdb=cdb, config=cdb.config, vocab=vocab)
cat.config.linking['similarity_threshold'] = 0.1
cat.config.ner['min_name_len'] = 2
cat.config.ner['upper_case_limit_len'] = 1
cat.config.linking['train_count_threshold'] = -2
cat.config.linking['filters']['cuis'] = set()
cat.config.linking['context_vector_sizes'] = {'xlong': 27, 'long': 18, 'medium': 9, 'short': 3}
cat.config.linking['context_vector_weights'] = {'xlong': 0.1, 'long': 0.4, 'medium': 0.4, 'short': 0.1}
cat.config.linking['similarity_threshold_type'] = 'static'