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from modules import tqdm
from sklearn_crfsuite.metrics import flat_classification_report
import logging
import torch
from .optimization import BertAdam
from modules.analyze_utils.plot_metrics import get_mean_max_metric
from modules.data.bert_data import get_data_loader_for_predict
def train_step(dl, model, optimizer, num_epoch=1):
model.train()
epoch_loss = 0
idx = 0
pr = tqdm(dl, total=len(dl), leave=False)
for batch in pr:
idx += 1
model.zero_grad()
loss = model.score(batch)
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss = loss.data.cpu().tolist()
epoch_loss += loss
pr.set_description("train loss: {}".format(epoch_loss / idx))
torch.cuda.empty_cache()
logging.info("\nepoch {}, average train epoch loss={:.5}\n".format(
num_epoch, epoch_loss / idx))
def transformed_result(preds, mask, id2label, target_all=None, pad_idx=0):
preds_cpu = []
targets_cpu = []
lc = len(id2label)
if target_all is not None:
for batch_p, batch_t, batch_m in zip(preds, target_all, mask):
for pred, true_, bm in zip(batch_p, batch_t, batch_m):
sent = []
sent_t = []
bm = bm.sum().cpu().data.tolist()
for p, t in zip(pred[:bm], true_[:bm]):
p = p.cpu().data.tolist()
p = p if p < lc else pad_idx
sent.append(p)
sent_t.append(t.cpu().data.tolist())
preds_cpu.append([id2label[w] for w in sent])
targets_cpu.append([id2label[w] for w in sent_t])
else:
for batch_p, batch_m in zip(preds, mask):
for pred, bm in zip(batch_p, batch_m):
assert len(pred) == len(bm)
bm = bm.sum().cpu().data.tolist()
sent = pred[:bm].cpu().data.tolist()
preds_cpu.append([id2label[w] for w in sent])
if target_all is not None:
return preds_cpu, targets_cpu
else:
return preds_cpu
def transformed_result_cls(preds, target_all, cls2label, return_target=True):
preds_cpu = []
targets_cpu = []
for batch_p, batch_t in zip(preds, target_all):
for pred, true_ in zip(batch_p, batch_t):
preds_cpu.append(cls2label[pred.cpu().data.tolist()])
if return_target:
targets_cpu.append(cls2label[true_.cpu().data.tolist()])
if return_target:
return preds_cpu, targets_cpu
return preds_cpu
def validate_step(dl, model, id2label, sup_labels, id2cls=None):
model.eval()
idx = 0
preds_cpu, targets_cpu = [], []
preds_cpu_cls, targets_cpu_cls = [], []
for batch in tqdm(dl, total=len(dl), leave=False):
idx += 1
labels_mask, labels_ids = batch[1], batch[3]
preds = model.forward(batch)
if id2cls is not None:
preds, preds_cls = preds
preds_cpu_, targets_cpu_ = transformed_result_cls([preds_cls], [batch[-1]], id2cls)
preds_cpu_cls.extend(preds_cpu_)
targets_cpu_cls.extend(targets_cpu_)
preds_cpu_, targets_cpu_ = transformed_result([preds], [labels_mask], id2label, [labels_ids])
preds_cpu.extend(preds_cpu_)
targets_cpu.extend(targets_cpu_)
clf_report = flat_classification_report(targets_cpu, preds_cpu, labels=sup_labels, digits=3)
if id2cls is not None:
clf_report_cls = flat_classification_report([targets_cpu_cls], [preds_cpu_cls], digits=3)
return clf_report, clf_report_cls
return clf_report
def predict(dl, model, id2label, id2cls=None):
model.eval()
idx = 0
preds_cpu = []
preds_cpu_cls = []
for batch in tqdm(dl, total=len(dl), leave=False, desc="Predicting"):
idx += 1
labels_mask, labels_ids = batch[1], batch[3]
preds = model.forward(batch)
if id2cls is not None:
preds, preds_cls = preds
preds_cpu_ = transformed_result_cls([preds_cls], [preds_cls], id2cls, False)
preds_cpu_cls.extend(preds_cpu_)
preds_cpu_ = transformed_result([preds], [labels_mask], id2label)
preds_cpu.extend(preds_cpu_)
if id2cls is not None:
return preds_cpu, preds_cpu_cls
return preds_cpu
class NerLearner(object):
def __init__(self, model, data, best_model_path, lr=0.001, betas=[0.8, 0.9], clip=1.0,
verbose=True, sup_labels=None, t_total=-1, warmup=0.1, weight_decay=0.01,
validate_every=1, schedule="warmup_linear", e=1e-6):
logging.basicConfig(level=logging.INFO)
self.model = model
self.optimizer = BertAdam(model, lr, t_total=t_total, b1=betas[0], b2=betas[1], max_grad_norm=clip)
self.optimizer_defaults = dict(
model=model, lr=lr, warmup=warmup, t_total=t_total, schedule=schedule,
b1=betas[0], b2=betas[1], e=e, weight_decay=weight_decay,
max_grad_norm=clip)
self.lr = lr
self.betas = betas
self.clip = clip
self.sup_labels = sup_labels
self.t_total = t_total
self.warmup = warmup
self.weight_decay = weight_decay
self.validate_every = validate_every
self.schedule = schedule
self.data = data
self.e = e
if sup_labels is None:
sup_labels = data.train_ds.idx2label[4:]
self.sup_labels = sup_labels
self.best_model_path = best_model_path
self.verbose = verbose
self.history = []
self.cls_history = []
self.epoch = 0
self.best_target_metric = 0.
def fit(self, epochs=100, resume_history=True, target_metric="f1"):
if not resume_history:
self.optimizer_defaults["t_total"] = epochs * len(self.data.train_dl)
self.optimizer = BertAdam(**self.optimizer_defaults)
self.history = []
self.cls_history = []
self.epoch = 0
self.best_target_metric = 0.
elif self.verbose:
logging.info("Resuming train... Current epoch {}.".format(self.epoch))
try:
for _ in range(epochs):
self.epoch += 1
self.fit_one_cycle(self.epoch, target_metric)
except KeyboardInterrupt:
pass
def fit_one_cycle(self, epoch, target_metric="f1"):
train_step(self.data.train_dl, self.model, self.optimizer, epoch)
if epoch % self.validate_every == 0:
if self.data.train_ds.is_cls:
rep, rep_cls = validate_step(
self.data.valid_dl, self.model, self.data.train_ds.idx2label, self.sup_labels,
self.data.train_ds.idx2cls)
self.cls_history.append(rep_cls)
else:
rep = validate_step(
self.data.valid_dl, self.model, self.data.train_ds.idx2label, self.sup_labels)
self.history.append(rep)
idx, metric = get_mean_max_metric(self.history, target_metric, True)
if self.verbose:
logging.info("on epoch {} by max_{}: {}".format(idx, target_metric, metric))
print(self.history[-1])
if self.data.train_ds.is_cls:
logging.info("on epoch {} classification report:")
print(self.cls_history[-1])
# Store best model
if self.best_target_metric < metric:
self.best_target_metric = metric
if self.verbose:
logging.info("Saving new best model...")
self.save_model()
def predict(self, dl=None, df_path=None, df=None):
if dl is None:
dl = get_data_loader_for_predict(self.data, df_path, df)
if self.data.train_ds.is_cls:
return predict(dl, self.model, self.data.train_ds.idx2label, self.data.train_ds.idx2cls)
return predict(dl, self.model, self.data.train_ds.idx2label)
def save_model(self, path=None):
path = path if path else self.best_model_path
torch.save(self.model.state_dict(), path)
def load_model(self, path=None):
path = path if path else self.best_model_path
self.model.load_state_dict(torch.load(path))