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from importlib import import_module
import torch
import numpy as np
import os
import copy
import logging
from torch import nn
from datetime import datetime
from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from scipy.stats import norm as dist_model
from losses import loss_map
from utils.functions import restore_model, save_model
from utils.metrics import F_measure
class DOCManager:
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.loss_fct = loss_map[args.loss_fct]
if args.train:
self.best_mu_stds = None
else:
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
wait = 0
best_eval_score = 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
with torch.set_grad_enabled(True):
loss = self.model(input_ids, segment_ids, input_mask, label_ids, mode='train', loss_fct=self.loss_fct)
self.optimizer.zero_grad()
loss.backward()
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(accuracy_score(y_true, y_pred) * 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
elif eval_score > 0:
wait += 1
if wait >= args.wait_patient:
break
self.model = best_model
if args.save_model:
save_model(self.model, args.model_output_dir)
np.save(os.path.join(args.method_output_dir, 'mu_stds.npy'), self.best_mu_stds)
def test(self, args, data, show=False):
mu_stds = self.get_outputs(args, data, mode = 'train', get_mu_stds = True)
y_true, y_pred = self.get_outputs(args, data, mode = 'test', mu_stds = mu_stds)
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
def get_outputs(self, args, data, mode = 'eval', get_feats = False, get_mu_stds = False, mu_stds = 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_logits = 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
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_logits = torch.cat((total_logits, logits))
total_features = torch.cat((total_features, pooled_output))
if get_feats:
feats = total_features.cpu().numpy()
return feats
else:
total_probs, y_pred = total_logits.max(dim = 1)
y_true = total_labels.cpu().numpy()
y_logit = total_logits.cpu().numpy()
y_pred = y_pred.cpu().numpy()
if mode == 'eval':
return y_true, y_pred
else:
if get_mu_stds == True:
mu_stds = self.cal_mu_std(y_logit, y_true, data.num_labels)
return mu_stds
else:
y_pred = self.classify_doc(data, args, y_logit, mu_stds)
return y_true, y_pred
def classify_doc(self, data, args, y_prob, mu_stds):
thresholds = {}
for col in range(data.num_labels):
threshold = max(0.5, 1 - args.scale * mu_stds[col][1])
label = data.known_label_list[col]
thresholds[label] = threshold
thresholds = np.array(thresholds)
self.logger.info('Probability thresholds of each class: %s', thresholds)
y_pred = []
for p in y_prob:
max_class = np.argmax(p)
max_value = np.max(p)
threshold = max(0.5, 1 - args.scale * mu_stds[max_class][1])
if max_value > threshold:
y_pred.append(max_class)
else:
y_pred.append(data.unseen_label_id)
return np.array(y_pred)
def fit(self, prob_pos_X):
prob_pos = [p for p in prob_pos_X] + [2 - p for p in prob_pos_X]
pos_mu, pos_std = dist_model.fit(prob_pos)
return pos_mu, pos_std
def cal_mu_std(self, y_prob, trues, num_labels):
mu_stds = []
for i in range(num_labels):
pos_mu, pos_std = self.fit(y_prob[trues == i, i])
mu_stds.append([pos_mu, pos_std])
return mu_stds
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