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import torch
import copy
import pandas as pd
import logging
from tqdm import trange, tqdm
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
from sklearn.neighbors import LocalOutlierFactor
from utils.functions import restore_model, save_model
from utils.metrics import F_measure
from .KNNCL_utils import create_negative_dataset, generate_positive_sample, _prepare_inputs
class KNNCLManager:
def __init__(self, args, data, model, logger_name = 'Detection'):
self.logger = logging.getLogger(logger_name)
self.set_model_optimizer(args, data, model)
self.data = data
self.train_dataloader = data.dataloader.train_labeled_loader
self.eval_dataloader= data.dataloader.eval_loader
self.test_dataloader = data.dataloader.test_loader
self.negative_data = create_negative_dataset(self.train_dataloader)
if not args.train:
restore_model(self.model, args.model_output_dir)
def set_model_optimizer(self, args, data, model):
self.model = model.set_model(args, 'bert')
self.optimizer, self.scheduler = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \
args.num_train_epochs, args.lr, args.warmup_proportion)
self.device = model.device
def train(self, args, data):
best_model = None
best_eval_score = 0
wait = 0
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
self.model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(self.train_dataloader, desc="Iteration")):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
positive_sample = None
positive_sample = generate_positive_sample(self.negative_data,args, label_ids)
positive_sample = _prepare_inputs(self.device, positive_sample)
batch_dict = {"labels":label_ids,"input_ids":input_ids,"token_type_ids":segment_ids,"attention_mask":input_mask}
outputs = self.model(batch_dict, mode='train', positive_sample=positive_sample)
loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), args.clip)
self.optimizer.step()
self.scheduler.step()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
loss = tr_loss / nb_tr_steps
y_true, y_pred = self.get_outputs(args, data, mode='eval')
eval_score = round(f1_score(y_true, y_pred, average='macro') * 100, 2)
eval_results = {
'train_loss': loss,
'eval_score': eval_score,
'best_eval_score': best_eval_score
}
self.logger.info("***** Epoch: %s: Eval results *****", str(epoch + 1))
for key in sorted(eval_results.keys()):
self.logger.info(" %s = %s", key, str(eval_results[key]))
if eval_score >= best_eval_score:
best_model = copy.deepcopy(self.model)
wait = 0
best_eval_score = eval_score
else:
wait += 1
if wait >= args.wait_patient:
break
self.model = best_model
if args.save_model:
save_model(self.model, args.model_output_dir)
def classify_lof(self, args, data, preds, train_feats, pred_feats):
lof = LocalOutlierFactor(n_neighbors=args.n_neighbors, contamination = args.contamination, novelty=True, n_jobs=-1)
lof.fit(train_feats)
y_pred_lof = pd.Series(lof.predict(pred_feats))
preds[y_pred_lof[y_pred_lof == -1].index] = data.unseen_label_id
return preds
def get_outputs(self, args, data, mode, get_feats = False, train_feats = None):
if mode == 'train':
dataloader = self.train_dataloader
elif mode == 'eval':
dataloader = self.eval_dataloader
elif mode == 'test':
dataloader = self.test_dataloader
self.model.eval()
total_labels = torch.empty(0,dtype=torch.long).to(self.device)
total_probs = torch.empty((0, data.num_labels)).to(self.device)
total_features = torch.empty((0,args.feat_dim)).to(self.device)
for batch in tqdm(dataloader, desc="Iteration"):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
batch_dict = {"labels":label_ids,"input_ids":input_ids,"token_type_ids":segment_ids,"attention_mask":input_mask}
with torch.set_grad_enabled(False):
output = self.model(batch_dict, mode='test')
total_labels = torch.cat((total_labels,label_ids))
total_probs = torch.cat((total_probs, output[0]))
total_features = torch.cat((total_features, output[1]))
if get_feats:
feats = total_features.cpu().numpy()
return feats
else:
total_probs, y_pred = total_probs.max(dim = 1)
y_pred = y_pred.cpu().numpy()
y_true = total_labels.cpu().numpy()
if train_feats is not None:
feats = total_features.cpu().numpy()
y_pred = self.classify_lof(args, data, y_pred, train_feats, feats)
return y_true, y_pred
def test(self, args, data, show=False):
train_feats = self.get_outputs(args, data, mode = 'train', get_feats = True)
y_true, y_pred = self.get_outputs(args, data, mode = 'test', train_feats = train_feats)
cm = confusion_matrix(y_true, y_pred)
test_results = F_measure(cm)
acc = round(accuracy_score(y_true, y_pred) * 100, 2)
test_results['Acc'] = acc
self.logger.info
self.logger.info("***** Test: Confusion Matrix *****")
self.logger.info("%s", str(cm))
self.logger.info("***** Test results *****")
for key in sorted(test_results.keys()):
self.logger.info(" %s = %s", key, str(test_results[key]))
test_results['y_true'] = y_true
test_results['y_pred'] = y_pred
return test_results
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