def train_ner(output_dir: str,
train_data_path: str,
dev_data_path: str,
test_data_path: str,
run_test: bool = None,
model: str = None,
n_iter: int = 10,
meta_overrides: str = None):
util.fix_random_seed(util.env_opt("seed", 0))
train_data = read_ner_from_tsv(train_data_path)
dev_data = read_ner_from_tsv(dev_data_path)
test_data = read_ner_from_tsv(test_data_path)
os.makedirs(output_dir, exist_ok=True)
if run_test:
nlp = spacy.load(model)
print("Loaded model '%s'" % model)
evaluate_ner(nlp,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
else:
train(model, train_data, dev_data, test_data, output_dir, n_iter,
meta_overrides)
def train_ner(output_dir: str,
data_path: str,
run_test: bool = None,
model: str = None,
n_iter: int = 100,
label_granularity: int = None):
if label_granularity is not None:
umls_tree = construct_umls_tree_from_tsv(
"data/umls_semantic_type_tree.tsv")
label_mapping = umls_tree.get_collapsed_type_id_map_at_level(
label_granularity)
if label_granularity == 0:
span_only = True
else:
label_mapping = None
span_only = False
train_data, dev_data, test_data = read_full_med_mentions(
data_path, label_mapping, span_only)
os.makedirs(output_dir, exist_ok=True)
if run_test:
nlp = spacy.load(model)
print("Loaded model '%s'" % model)
evaluate_ner(nlp,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
else:
train(model, train_data, dev_data, output_dir, n_iter)
def main(model_path: str, dataset: str, output_path: str, code: Optional[str],
med_mentions_folder_path: Optional[str]):
if code is not None:
# need to import code before loading a spacy model
spec = importlib.util.spec_from_file_location(name, str(loc))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
nlp = spacy.load(model_path)
if dataset.startswith("medmentions"):
train_data, dev_data, test_data = read_full_med_mentions(
med_mentions_folder_path, None, False)
data_split = dataset.split("-")[1]
if data_split == "train":
data = train_data
elif data_split == "dev":
data = dev_data
elif data_split == "test":
data = test_data
else:
raise Exception(f"Unrecognized split {data_split}")
else:
data = read_ner_from_tsv(dataset)
evaluate_ner(nlp, data, dump_path=output_path)
def train_ner(output_dir: str,
train_data_path: str,
dev_data_path: str,
test_data_path: str,
run_test: bool = None,
model: str = None,
n_iter: int = 10,
meta_overrides: str = None):
model = "en_core_sci_sm"
util.fix_random_seed(util.env_opt("seed", 0))
with Path(train_data_path).open('rb') as file:
train_data = pickle.load(file)
with Path(dev_data_path).open('rb') as file:
dev_data = pickle.load(file)
with Path(test_data_path).open('rb') as file:
test_data = pickle.load(file)
# train_data = read_ner_from_tsv(train_data_path)
# dev_data = read_ner_from_tsv(dev_data_path)
# test_data = read_ner_from_tsv(test_data_path)
os.makedirs(output_dir, exist_ok=True)
if run_test:
nlp = spacy.load(model)
print("Loaded model '%s'" % model)
evaluate_ner(nlp,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
else:
train(model, train_data, dev_data, test_data, output_dir, n_iter,
meta_overrides)
def train(model, train_data, dev_data, test_data, output_dir, n_iter,
meta_overrides):
"""Load the model, set up the pipeline and train the entity recognizer."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank('en') # create blank Language class
print("Created blank 'en' model")
if meta_overrides is not None:
metadata = json.load(open(meta_overrides))
nlp.meta.update(metadata)
original_tokenizer = nlp.tokenizer
nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if 'ner' not in nlp.pipe_names and "parser" in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, after="parser")
elif 'ner' not in nlp.pipe_names and "tagger" in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, after="tagger")
elif 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, last=True)
# otherwise, get it so we can add labels
else:
ner = nlp.get_pipe('ner')
# add labels
for _, annotations in train_data:
for ent in annotations.get('entities'):
ner.add_label(ent[2])
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
dropout_rates = util.decaying(util.env_opt('dropout_from', 0.2),
util.env_opt('dropout_to', 0.2),
util.env_opt('dropout_decay', 0.005))
batch_sizes = util.compounding(util.env_opt('batch_from', 1),
util.env_opt('batch_to', 32),
util.env_opt('batch_compound', 1.001))
with nlp.disable_pipes(*other_pipes):
optimizer = nlp.begin_training()
best_epoch = 0
best_f1 = 0
for i in range(n_iter):
random.shuffle(train_data)
count = 0
losses = {}
total = len(train_data)
with nlp.disable_pipes(*other_pipes): # only train NER
with tqdm.tqdm(total=total, leave=True) as pbar:
for batch in minibatch(train_data, size=batch_sizes):
docs, golds = zip(*batch)
nlp.update(docs,
golds,
sgd=optimizer,
losses=losses,
drop=next(dropout_rates))
pbar.update(len(batch))
if count % 100 == 0 and count > 0:
print('sum loss: %s' % losses['ner'])
count += 1
# save model to output directory
output_dir_path = Path(output_dir + "/" + str(i))
if not output_dir_path.exists():
output_dir_path.mkdir()
with nlp.use_params(optimizer.averages):
nlp.tokenizer = original_tokenizer
nlp.to_disk(output_dir_path)
print("Saved model to", output_dir_path)
# test the saved model
print("Loading from", output_dir_path)
nlp2 = util.load_model_from_path(output_dir_path)
nlp2.tokenizer = WhitespaceTokenizer(nlp.vocab)
metrics = evaluate_ner(nlp2, dev_data)
if metrics["f1-measure-overall"] > best_f1:
best_f1 = metrics["f1-measure-overall"]
best_epoch = i
# save model to output directory
best_model_path = Path(output_dir + "/" + "best")
print(f"Best Epoch: {best_epoch} of {n_iter}")
if os.path.exists(best_model_path):
shutil.rmtree(best_model_path)
shutil.copytree(os.path.join(output_dir, str(best_epoch)), best_model_path)
# test the saved model
print("Loading from", best_model_path)
nlp2 = util.load_model_from_path(best_model_path)
nlp2.tokenizer = WhitespaceTokenizer(nlp.vocab)
evaluate_ner(nlp2,
dev_data,
dump_path=os.path.join(output_dir, "dev_metrics.json"))
evaluate_ner(nlp2,
test_data,
dump_path=os.path.join(output_dir, "test_metrics.json"))
