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import torch
import torch.nn.functional as F
import numpy as np
import copy
import logging
from sklearn.metrics import confusion_matrix
from sklearn.cluster import KMeans
from tqdm import trange, tqdm
from utils.functions import set_seed
from utils.metrics import clustering_score
from utils.functions import restore_model, save_model
def target_distribution(q):
weight = q ** 2 / q.sum(0)
return (weight.T / weight.sum(1)).T
class CDACPlusManager:
def __init__(self, args, data, model, logger_name = 'Discovery'):
self.logger = logging.getLogger(logger_name)
set_seed(args.seed)
loader = data.dataloader
self.train_dataloader, self.eval_dataloader, self.test_dataloader = \
loader.train_outputs['loader'], loader.eval_outputs['loader'], loader.test_outputs['loader']
self.train_labeled_dataloader = loader.train_labeled_outputs['loader']
self.train_unlabeled_dataloader = loader.train_unlabeled_outputs['loader']
self.model = model.set_model(args, data, 'bert')
self.optimizer1 , self.scheduler1 = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \
args.num_train_epochs, args.lr, args.warmup_proportion)
self.optimizer2 , self.scheduler2 = model.set_optimizer(self.model, data.dataloader.num_train_examples, args.train_batch_size, \
args.num_refine_epochs, args.lr, args.warmup_proportion)
self.device = model.device
if not args.train:
self.model = restore_model(self.model, args.model_output_dir)
def initialize_centroids(self, args, data):
self.logger.info("Initialize centroids...")
feats = self.get_outputs(args, mode = 'train_unlabeled', get_feats = True)
km = KMeans(n_clusters=data.num_labels, n_jobs=-1, random_state=args.seed)
km.fit(feats)
self.logger.info("Initialization finished...")
self.model.cluster_layer.data = torch.tensor(km.cluster_centers_).to(self.device)
def train(self, args, data):
self.logger.info('Pairwise-similarity Learning begin...')
u = args.u
l = args.l
eta = 0
eval_pred_last = np.zeros_like(data.dataloader.eval_examples)
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0
self.model.train()
for step, batch in enumerate(tqdm(self.train_labeled_dataloader, desc="Iteration (labeled)")):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = self.model(input_ids, segment_ids, input_mask, label_ids, u_threshold = u, l_threshold = l, mode = 'train')
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
self.optimizer1.step()
self.scheduler1.step()
self.optimizer1.zero_grad()
train_labeled_loss = tr_loss / nb_tr_steps
tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0
for step, batch in enumerate(tqdm(self.train_dataloader, desc="Iteration (all train)")):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = self.model(input_ids, segment_ids, input_mask, label_ids, u_threshold = u, l_threshold = l, mode = 'train', semi = True)
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
self.optimizer1.step()
self.scheduler1.step()
self.optimizer1.zero_grad()
train_loss = tr_loss / nb_tr_steps
eval_true, eval_pred = self.get_outputs(args, mode = 'eval')
eval_score = clustering_score(eval_true, eval_pred)['NMI']
delta_label = np.sum(eval_pred != eval_pred_last).astype(np.float32) / eval_pred.shape[0]
eval_pred_last = np.copy(eval_pred)
train_results = {
'u_threshold': round(u, 4),
'l_threshold': round(l, 4),
'train_labeled_loss': train_labeled_loss,
'train_loss': train_loss,
'delta_label': delta_label,
'eval_score': eval_score
}
self.logger.info("***** Epoch: %s: Eval results *****", str(epoch))
for key in sorted(train_results.keys()):
self.logger.info(" %s = %s", key, str(train_results[key]))
eta += 1.1 * 0.009
u = 0.95 - eta
l = 0.455 + eta * 0.1
if u < l:
break
self.logger.info('Pairwise-similarity Learning finished...')
self.refine(args, data)
def refine(self, args, data):
self.logger.info('Cluster refining begin...')
self.initialize_centroids(args, data)
best_model = None
wait = 0
train_preds_last = None
best_eval_score = 0
for epoch in range(args.num_refine_epochs):
eval_true, eval_pred = self.get_outputs(args, mode = 'eval')
eval_score = clustering_score(eval_true, eval_pred)['NMI']
if eval_score > best_eval_score:
best_model = copy.deepcopy(self.model)
wait = 0
best_eval_score = eval_score
self.model = best_model
else:
wait += 1
if wait > args.wait_patient:
break
train_pred_logits = self.get_outputs(args, mode = 'train', get_logits = True)
p_target = target_distribution(train_pred_logits)
train_preds = train_pred_logits.argmax(1)
delta_label = np.sum(train_preds != train_preds_last).astype(np.float32) / train_preds.shape[0]
train_preds_last = np.copy(train_preds)
if epoch > 0 and delta_label < 0.001:
self.logger.info('Break at epoch: %s and delta_label: %f.', str(epoch + 1), round(delta_label, 2))
break
# Fine-tuning with auxiliary distribution
self.model.train()
tr_loss, nb_tr_examples, nb_tr_steps = 0, 0, 0
for step, batch in enumerate(self.train_dataloader):
batch = tuple(t.to(self.device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
feats, logits = self.model(input_ids, segment_ids, input_mask, mode='finetune')
kl_loss = F.kl_div(logits.log(), torch.Tensor(p_target[step * args.train_batch_size: (step + 1) * args.train_batch_size]).to(self.device))
kl_loss.backward()
tr_loss += kl_loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
self.optimizer2.step()
self.scheduler2.step()
self.optimizer2.zero_grad()
train_loss = tr_loss / nb_tr_steps
eval_results = {
'kl_loss': round(train_loss, 4),
'delta_label': delta_label.round(4),
'eval_score': round(eval_score, 2),
'best_eval_score': round(best_eval_score, 2)
}
self.logger.info("***** Epoch: %s: Eval results *****", str(epoch))
for key in sorted(eval_results.keys()):
self.logger.info(" %s = %s", key, str(eval_results[key]))
self.logger.info('Cluster refining finished...')
if args.save_model:
save_model(self.model, args.model_output_dir)
def get_outputs(self, args, mode = 'eval', get_feats = False, get_logits = False):
if mode == 'eval':
dataloader = self.eval_dataloader
elif mode == 'test':
dataloader = self.test_dataloader
elif mode == 'train_unlabeled':
dataloader = self.train_unlabeled_dataloader
elif mode == 'train':
dataloader = self.train_dataloader
self.model.eval()
total_labels = torch.empty(0,dtype=torch.long).to(self.device)
total_preds = torch.empty(0,dtype=torch.long).to(self.device)
total_features = torch.empty((0, args.num_labels)).to(self.device)
total_logits = torch.empty((0, args.num_labels)).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
with torch.set_grad_enabled(False):
pooled_output, logits = self.model(input_ids, segment_ids, input_mask)
total_labels = torch.cat((total_labels, label_ids))
total_features = torch.cat((total_features, pooled_output))
total_logits = torch.cat((total_logits, logits))
if get_feats:
feats = total_features.cpu().numpy()
return feats
elif get_logits:
logits = total_logits.cpu().numpy()
return logits
else:
total_preds = total_logits.argmax(1)
y_pred = total_preds.cpu().numpy()
y_true = total_labels.cpu().numpy()
return y_true, y_pred
def test(self, args, data):
y_true, y_pred = self.get_outputs(args, mode = 'test')
test_results = clustering_score(y_true, y_pred)
cm = confusion_matrix(y_true,y_pred)
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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