def train(train_data, dev_data, test_data, d, dictionary, dictionary_reverse,
opt, fold_idx, isMeddra_dict):
logging.info("train the ensemble normalization model ...")
external_train_data = []
if d.config.get('norm_ext_corpus') is not None:
for k, v in d.config['norm_ext_corpus'].items():
if k == 'tac':
external_train_data.extend(
load_data_fda(v['path'], True, v.get('types'),
v.get('types'), False, True))
else:
raise RuntimeError("not support external corpus")
if len(external_train_data) != 0:
train_data.extend(external_train_data)
logging.info("build alphabet ...")
word_alphabet = Alphabet('word')
norm_utils.build_alphabet_from_dict(word_alphabet, dictionary,
isMeddra_dict)
norm_utils.build_alphabet(word_alphabet, train_data)
if opt.dev_file:
norm_utils.build_alphabet(word_alphabet, dev_data)
if opt.test_file:
norm_utils.build_alphabet(word_alphabet, test_data)
norm_utils.fix_alphabet(word_alphabet)
if d.config.get('norm_emb') is not None:
logging.info("load pretrained word embedding ...")
pretrain_word_embedding, word_emb_dim = build_pretrain_embedding(
d.config.get('norm_emb'), word_alphabet, opt.word_emb_dim, False)
word_embedding = nn.Embedding(word_alphabet.size(),
word_emb_dim,
padding_idx=0)
word_embedding.weight.data.copy_(
torch.from_numpy(pretrain_word_embedding))
embedding_dim = word_emb_dim
else:
logging.info("randomly initialize word embedding ...")
word_embedding = nn.Embedding(word_alphabet.size(),
d.word_emb_dim,
padding_idx=0)
word_embedding.weight.data.copy_(
torch.from_numpy(
random_embedding(word_alphabet.size(), d.word_emb_dim)))
embedding_dim = d.word_emb_dim
dict_alphabet = Alphabet('dict')
norm_utils.init_dict_alphabet(dict_alphabet, dictionary)
norm_utils.fix_alphabet(dict_alphabet)
# rule
logging.info("init rule-based normer")
multi_sieve.init(opt, train_data, d, dictionary, dictionary_reverse,
isMeddra_dict)
if opt.ensemble == 'learn':
logging.info("init ensemble normer")
poses = vsm.init_vector_for_dict(word_alphabet, dict_alphabet,
dictionary, isMeddra_dict)
ensemble_model = Ensemble(word_alphabet, word_embedding, embedding_dim,
dict_alphabet, poses)
if pretrain_neural_model is not None:
ensemble_model.neural_linear.weight.data.copy_(
pretrain_neural_model.linear.weight.data)
if pretrain_vsm_model is not None:
ensemble_model.vsm_linear.weight.data.copy_(
pretrain_vsm_model.linear.weight.data)
ensemble_train_X = []
ensemble_train_Y = []
for doc in train_data:
temp_X, temp_Y = generate_instances(doc, word_alphabet,
dict_alphabet, dictionary,
dictionary_reverse,
isMeddra_dict)
ensemble_train_X.extend(temp_X)
ensemble_train_Y.extend(temp_Y)
ensemble_train_loader = DataLoader(MyDataset(ensemble_train_X,
ensemble_train_Y),
opt.batch_size,
shuffle=True,
collate_fn=my_collate)
ensemble_optimizer = optim.Adam(ensemble_model.parameters(),
lr=opt.lr,
weight_decay=opt.l2)
if opt.tune_wordemb == False:
freeze_net(ensemble_model.word_embedding)
else:
# vsm
logging.info("init vsm-based normer")
poses = vsm.init_vector_for_dict(word_alphabet, dict_alphabet,
dictionary, isMeddra_dict)
# alphabet can share between vsm and neural since they don't change
# but word_embedding cannot
vsm_model = vsm.VsmNormer(word_alphabet, copy.deepcopy(word_embedding),
embedding_dim, dict_alphabet, poses)
vsm_train_X = []
vsm_train_Y = []
for doc in train_data:
if isMeddra_dict:
temp_X, temp_Y = vsm.generate_instances(
doc.entities, word_alphabet, dict_alphabet)
else:
temp_X, temp_Y = vsm.generate_instances_ehr(
doc.entities, word_alphabet, dict_alphabet,
dictionary_reverse)
vsm_train_X.extend(temp_X)
vsm_train_Y.extend(temp_Y)
vsm_train_loader = DataLoader(vsm.MyDataset(vsm_train_X, vsm_train_Y),
opt.batch_size,
shuffle=True,
collate_fn=vsm.my_collate)
vsm_optimizer = optim.Adam(vsm_model.parameters(),
lr=opt.lr,
weight_decay=opt.l2)
if opt.tune_wordemb == False:
freeze_net(vsm_model.word_embedding)
if d.config['norm_vsm_pretrain'] == '1':
vsm.dict_pretrain(dictionary, dictionary_reverse, d, True,
vsm_optimizer, vsm_model)
# neural
logging.info("init neural-based normer")
neural_model = norm_neural.NeuralNormer(word_alphabet,
copy.deepcopy(word_embedding),
embedding_dim, dict_alphabet)
neural_train_X = []
neural_train_Y = []
for doc in train_data:
if isMeddra_dict:
temp_X, temp_Y = norm_neural.generate_instances(
doc.entities, word_alphabet, dict_alphabet)
else:
temp_X, temp_Y = norm_neural.generate_instances_ehr(
doc.entities, word_alphabet, dict_alphabet,
dictionary_reverse)
neural_train_X.extend(temp_X)
neural_train_Y.extend(temp_Y)
neural_train_loader = DataLoader(norm_neural.MyDataset(
neural_train_X, neural_train_Y),
opt.batch_size,
shuffle=True,
collate_fn=norm_neural.my_collate)
neural_optimizer = optim.Adam(neural_model.parameters(),
lr=opt.lr,
weight_decay=opt.l2)
if opt.tune_wordemb == False:
freeze_net(neural_model.word_embedding)
if d.config['norm_neural_pretrain'] == '1':
neural_model.dict_pretrain(dictionary, dictionary_reverse, d, True,
neural_optimizer, neural_model)
best_dev_f = -10
best_dev_p = -10
best_dev_r = -10
bad_counter = 0
logging.info("start training ...")
for idx in range(opt.iter):
epoch_start = time.time()
if opt.ensemble == 'learn':
ensemble_model.train()
ensemble_train_iter = iter(ensemble_train_loader)
ensemble_num_iter = len(ensemble_train_loader)
for i in range(ensemble_num_iter):
x, rules, lengths, y = next(ensemble_train_iter)
y_pred = ensemble_model.forward(x, rules, lengths)
l = ensemble_model.loss(y_pred, y)
l.backward()
if opt.gradient_clip > 0:
torch.nn.utils.clip_grad_norm_(ensemble_model.parameters(),
opt.gradient_clip)
ensemble_optimizer.step()
ensemble_model.zero_grad()
else:
vsm_model.train()
vsm_train_iter = iter(vsm_train_loader)
vsm_num_iter = len(vsm_train_loader)
for i in range(vsm_num_iter):
x, lengths, y = next(vsm_train_iter)
l, _ = vsm_model.forward_train(x, lengths, y)
l.backward()
if opt.gradient_clip > 0:
torch.nn.utils.clip_grad_norm_(vsm_model.parameters(),
opt.gradient_clip)
vsm_optimizer.step()
vsm_model.zero_grad()
neural_model.train()
neural_train_iter = iter(neural_train_loader)
neural_num_iter = len(neural_train_loader)
for i in range(neural_num_iter):
x, lengths, y = next(neural_train_iter)
y_pred = neural_model.forward(x, lengths)
l = neural_model.loss(y_pred, y)
l.backward()
if opt.gradient_clip > 0:
torch.nn.utils.clip_grad_norm_(neural_model.parameters(),
opt.gradient_clip)
neural_optimizer.step()
neural_model.zero_grad()
epoch_finish = time.time()
logging.info("epoch: %s training finished. Time: %.2fs" %
(idx, epoch_finish - epoch_start))
if opt.dev_file:
if opt.ensemble == 'learn':
# logging.info("weight w1: %.4f, w2: %.4f, w3: %.4f" % (ensemble_model.w1.data.item(), ensemble_model.w2.data.item(), ensemble_model.w3.data.item()))
p, r, f = norm_utils.evaluate(dev_data, dictionary,
dictionary_reverse, None, None,
ensemble_model, d, isMeddra_dict)
else:
p, r, f = norm_utils.evaluate(dev_data, dictionary,
dictionary_reverse, vsm_model,
neural_model, None, d,
isMeddra_dict)
logging.info("Dev: p: %.4f, r: %.4f, f: %.4f" % (p, r, f))
else:
f = best_dev_f
if f > best_dev_f:
logging.info("Exceed previous best f score on dev: %.4f" %
(best_dev_f))
if opt.ensemble == 'learn':
if fold_idx is None:
torch.save(ensemble_model,
os.path.join(opt.output, "ensemble.pkl"))
else:
torch.save(
ensemble_model,
os.path.join(opt.output,
"ensemble_{}.pkl".format(fold_idx + 1)))
else:
if fold_idx is None:
torch.save(vsm_model, os.path.join(opt.output, "vsm.pkl"))
torch.save(neural_model,
os.path.join(opt.output, "norm_neural.pkl"))
else:
torch.save(
vsm_model,
os.path.join(opt.output,
"vsm_{}.pkl".format(fold_idx + 1)))
torch.save(
neural_model,
os.path.join(opt.output,
"norm_neural_{}.pkl".format(fold_idx +
1)))
best_dev_f = f
best_dev_p = p
best_dev_r = r
bad_counter = 0
else:
bad_counter += 1
if len(opt.dev_file) != 0 and bad_counter >= opt.patience:
logging.info('Early Stop!')
break
logging.info("train finished")
if fold_idx is None:
multi_sieve.finalize(True)
else:
if fold_idx == opt.cross_validation - 1:
multi_sieve.finalize(True)
else:
multi_sieve.finalize(False)
if len(opt.dev_file) == 0:
if opt.ensemble == 'learn':
torch.save(ensemble_model, os.path.join(opt.output,
"ensemble.pkl"))
else:
torch.save(vsm_model, os.path.join(opt.output, "vsm.pkl"))
torch.save(neural_model, os.path.join(opt.output,
"norm_neural.pkl"))
return best_dev_p, best_dev_r, best_dev_f
def train(train_data, dev_data, test_data, d, dictionary, dictionary_reverse,
opt, fold_idx, isMeddra_dict):
logging.info("train the vsm-based normalization model ...")
external_train_data = []
if d.config.get('norm_ext_corpus') is not None:
for k, v in d.config['norm_ext_corpus'].items():
if k == 'tac':
external_train_data.extend(
load_data_fda(v['path'], True, v.get('types'),
v.get('types'), False, True))
else:
raise RuntimeError("not support external corpus")
if len(external_train_data) != 0:
train_data.extend(external_train_data)
logging.info("build alphabet ...")
word_alphabet = Alphabet('word')
norm_utils.build_alphabet_from_dict(word_alphabet, dictionary,
isMeddra_dict)
norm_utils.build_alphabet(word_alphabet, train_data)
if opt.dev_file:
norm_utils.build_alphabet(word_alphabet, dev_data)
if opt.test_file:
norm_utils.build_alphabet(word_alphabet, test_data)
norm_utils.fix_alphabet(word_alphabet)
logging.info("alphabet size {}".format(word_alphabet.size()))
if d.config.get('norm_emb') is not None:
logging.info("load pretrained word embedding ...")
pretrain_word_embedding, word_emb_dim = build_pretrain_embedding(
d.config.get('norm_emb'), word_alphabet, opt.word_emb_dim, False)
word_embedding = nn.Embedding(word_alphabet.size(),
word_emb_dim,
padding_idx=0)
word_embedding.weight.data.copy_(
torch.from_numpy(pretrain_word_embedding))
embedding_dim = word_emb_dim
else:
logging.info("randomly initialize word embedding ...")
word_embedding = nn.Embedding(word_alphabet.size(),
d.word_emb_dim,
padding_idx=0)
word_embedding.weight.data.copy_(
torch.from_numpy(
random_embedding(word_alphabet.size(), d.word_emb_dim)))
embedding_dim = d.word_emb_dim
dict_alphabet = Alphabet('dict')
norm_utils.init_dict_alphabet(dict_alphabet, dictionary)
norm_utils.fix_alphabet(dict_alphabet)
logging.info("init_vector_for_dict")
poses, poses_lengths = init_vector_for_dict(word_alphabet, dict_alphabet,
dictionary, isMeddra_dict)
vsm_model = VsmNormer(word_alphabet, word_embedding, embedding_dim,
dict_alphabet, poses, poses_lengths)
logging.info("generate instances for training ...")
train_X = []
train_Y = []
for doc in train_data:
if isMeddra_dict:
temp_X, temp_Y = generate_instances(doc.entities, word_alphabet,
dict_alphabet)
else:
temp_X, temp_Y = generate_instances_ehr(doc.entities,
word_alphabet,
dict_alphabet,
dictionary_reverse)
train_X.extend(temp_X)
train_Y.extend(temp_Y)
train_loader = DataLoader(MyDataset(train_X, train_Y),
opt.batch_size,
shuffle=True,
collate_fn=my_collate)
optimizer = optim.Adam(vsm_model.parameters(),
lr=opt.lr,
weight_decay=opt.l2)
if opt.tune_wordemb == False:
freeze_net(vsm_model.word_embedding)
if d.config['norm_vsm_pretrain'] == '1':
dict_pretrain(dictionary, dictionary_reverse, d, isMeddra_dict,
optimizer, vsm_model)
best_dev_f = -10
best_dev_p = -10
best_dev_r = -10
bad_counter = 0
logging.info("start training ...")
for idx in range(opt.iter):
epoch_start = time.time()
vsm_model.train()
train_iter = iter(train_loader)
num_iter = len(train_loader)
sum_loss = 0
correct, total = 0, 0
for i in range(num_iter):
x, lengths, y = next(train_iter)
l, y_pred = vsm_model.forward_train(x, lengths, y)
sum_loss += l.item()
l.backward()
if opt.gradient_clip > 0:
torch.nn.utils.clip_grad_norm_(vsm_model.parameters(),
opt.gradient_clip)
optimizer.step()
vsm_model.zero_grad()
total += y.size(0)
_, pred = torch.max(y_pred, 1)
correct += (pred == y).sum().item()
epoch_finish = time.time()
accuracy = 100.0 * correct / total
logging.info(
"epoch: %s training finished. Time: %.2fs. loss: %.4f Accuracy %.2f"
% (idx, epoch_finish - epoch_start, sum_loss / num_iter, accuracy))
if opt.dev_file:
p, r, f = norm_utils.evaluate(dev_data, dictionary,
dictionary_reverse, vsm_model, None,
None, d, isMeddra_dict)
logging.info("Dev: p: %.4f, r: %.4f, f: %.4f" % (p, r, f))
else:
f = best_dev_f
if f > best_dev_f:
logging.info("Exceed previous best f score on dev: %.4f" %
(best_dev_f))
if fold_idx is None:
torch.save(vsm_model, os.path.join(opt.output, "vsm.pkl"))
else:
torch.save(
vsm_model,
os.path.join(opt.output,
"vsm_{}.pkl".format(fold_idx + 1)))
best_dev_f = f
best_dev_p = p
best_dev_r = r
bad_counter = 0
else:
bad_counter += 1
if len(opt.dev_file) != 0 and bad_counter >= opt.patience:
logging.info('Early Stop!')
break
logging.info("train finished")
if len(opt.dev_file) == 0:
torch.save(vsm_model, os.path.join(opt.output, "vsm.pkl"))
return best_dev_p, best_dev_r, best_dev_f
def train(train_data, dev_data, d, meddra_dict, opt, fold_idx):
logging.info("train the vsm-based normalization model ...")
external_train_data = []
if d.config.get('norm_ext_corpus') is not None:
for k, v in d.config['norm_ext_corpus'].items():
if k == 'tac':
external_train_data.extend(
load_data_fda(v['path'], True, v.get('types'),
v.get('types'), False, True))
else:
raise RuntimeError("not support external corpus")
if len(external_train_data) != 0:
train_data.extend(external_train_data)
vsm_model = VsmNormer()
logging.info("build alphabet ...")
norm_utils.build_alphabet(vsm_model.word_alphabet, train_data)
if opt.dev_file:
norm_utils.build_alphabet(vsm_model.word_alphabet, dev_data)
norm_utils.build_alphabet_from_dict(vsm_model.word_alphabet, meddra_dict)
norm_utils.fix_alphabet(vsm_model.word_alphabet)
if d.config.get('norm_emb') is not None:
logging.info("load pretrained word embedding ...")
pretrain_word_embedding, word_emb_dim = build_pretrain_embedding(
d.config.get('norm_emb'), vsm_model.word_alphabet,
opt.word_emb_dim, False)
vsm_model.word_embedding = nn.Embedding(vsm_model.word_alphabet.size(),
word_emb_dim)
vsm_model.word_embedding.weight.data.copy_(
torch.from_numpy(pretrain_word_embedding))
vsm_model.embedding_dim = word_emb_dim
else:
logging.info("randomly initialize word embedding ...")
vsm_model.word_embedding = nn.Embedding(vsm_model.word_alphabet.size(),
d.word_emb_dim)
vsm_model.word_embedding.weight.data.copy_(
torch.from_numpy(
random_embedding(vsm_model.word_alphabet.size(),
d.word_emb_dim)))
vsm_model.embedding_dim = d.word_emb_dim
if torch.cuda.is_available():
vsm_model.word_embedding = vsm_model.word_embedding.cuda(vsm_model.gpu)
logging.info("init_vector_for_dict")
vsm_model.init_vector_for_dict(meddra_dict)
norm_utils.fix_alphabet(vsm_model.dict_alphabet)
vsm_model.train()
best_dev_f = -10
best_dev_p = -10
best_dev_r = -10
if opt.dev_file:
p, r, f = norm_utils.evaluate(dev_data, meddra_dict, vsm_model)
logging.info("Dev: p: %.4f, r: %.4f, f: %.4f" % (p, r, f))
else:
f = best_dev_f
if f > best_dev_f:
logging.info("Exceed previous best f score on dev: %.4f" %
(best_dev_f))
if fold_idx is None:
logging.info("save model to {}".format(
os.path.join(opt.output, "vsm.pkl")))
torch.save(vsm_model, os.path.join(opt.output, "vsm.pkl"))
else:
logging.info("save model to {}".format(
os.path.join(opt.output, "vsm_{}.pkl".format(fold_idx + 1))))
torch.save(
vsm_model,
os.path.join(opt.output, "vsm_{}.pkl".format(fold_idx + 1)))
best_dev_f = f
best_dev_p = p
best_dev_r = r
logging.info("train finished")
if len(opt.dev_file) == 0:
logging.info("save model to {}".format(
os.path.join(opt.output, "vsm.pkl")))
torch.save(vsm_model, os.path.join(opt.output, "vsm.pkl"))
return best_dev_p, best_dev_r, best_dev_f