File size: 11,876 Bytes
2d06dcc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 |
import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
import pandas as pd
from sklearn.cluster import KMeans
from utils.metrics import clustering_score
from sklearn.metrics import accuracy_score, confusion_matrix
from tqdm import trange, tqdm
from torch.utils.data import (DataLoader, SequentialSampler, RandomSampler, TensorDataset)
from losses import loss_map
from utils.functions import save_model, restore_model, set_seed
from utils.faster_mix_k_means_pytorch import K_Means as SemiSupKMeans
from scipy.optimize import minimize_scalar
from functools import partial
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from sklearn.metrics import adjusted_rand_score as ari_score
from scipy.optimize import linear_sum_assignment as linear_assignment
class GCDManager:
def __init__(self, args, data, model, logger_name = 'Discovery'):
self.logger = logging.getLogger(logger_name)
set_seed(args.seed)
loader = data.dataloader
self.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_input_ids, self.train_input_mask, self.train_segment_ids, self.train_label_ids= \
loader.train_outputs['input_ids'], loader.train_outputs['input_mask'], loader.train_outputs['segment_ids'], loader.train_outputs['label_ids']
self.aug_train_dataloader = self.get_augment_dataloader(args, self.train_label_ids, data_aug = True)
self.set_model_optimizer(args, data, model)
self.num_labels = data.num_labels
self.temperature=0.07
self.sup_con_weight = 0.5
self.loss_fct = loss_map[args.loss_fct]
if not args.train:
self.model = restore_model(self.model, args.model_output_dir)
def set_model_optimizer(self, args, data, model):
self.model = model.set_model(args, data, 'bert', args.freeze_train_bert_parameters)
self.optimizer , self.scheduler = model.set_optimizer(self.model, len(data.dataloader.train_examples), args.train_batch_size, \
args.num_train_epochs, args.lr, args.warmup_proportion)
self.device = model.device
def batch_chunk(self, x):
x1, x2 = torch.chunk(input=x, chunks=2, dim=1)
x1, x2 = x1.squeeze(1), x2.squeeze(1)
return x1, x2
def semisupvised_kmeans(self, args):
# Semi-Kmeans
feats, all_labels = self.get_outputs(args, mode = 'train')
l_index = [k for k,i in enumerate(all_labels) if i !=-1]
u_index = [k for k,i in enumerate(all_labels) if i ==-1]
print('Fitting Semi-Supervised K-Means...')
kmeans = SemiSupKMeans(k=self.num_labels, tolerance=1e-4, max_iterations=200, init='k-means++',
n_init=100, random_state=args.seed, n_jobs=None, pairwise_batch_size=1024, mode=None)
u_feats = feats[u_index]
l_feats = feats[l_index]
l_targets = all_labels[l_index]
u_targets = all_labels[u_index]
l_feats, u_feats, l_targets, u_targets = (torch.from_numpy(x).to(self.device) for
x in (l_feats, u_feats, l_targets, u_targets))
kmeans.fit_mix(u_feats, l_feats, l_targets)
self.semisupvised_kmeans_cluster = kmeans.cluster_centers_
def train(self, args, data):
wait = 0
best_model = None
best_eval_score = 0
criterion = loss_map['SupConLoss']
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
train_acc = 0
for step, batch in enumerate(tqdm(self.aug_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):
input_ids_a, input_ids_b = self.batch_chunk(input_ids)
input_mask_a, input_mask_b = self.batch_chunk(input_mask)
segment_ids_a, segment_ids_b = self.batch_chunk(segment_ids)
label_ids = torch.chunk(input=label_ids, chunks=2, dim=1)[0][:, 0]
x_a = self.model(input_ids_a, segment_ids_a, input_mask_a, mode = 'train')
x_b = self.model(input_ids_b, segment_ids_b, input_mask_b, mode = 'train')
aug_mlp_outputs_a = self.model.mlp_head(x_a)
aug_mlp_outputs_b = self.model.mlp_head(x_b)
norm_logits = F.normalize(aug_mlp_outputs_a)
norm_aug_logits = F.normalize(aug_mlp_outputs_b)
contrastive_feats = torch.cat((norm_logits, norm_aug_logits))
contrastive_logits, contrastive_labels = self.info_nce_logits(features=contrastive_feats)
contrastive_loss = self.loss_fct(contrastive_logits, contrastive_labels)
mask_lab = torch.from_numpy(np.array([0 if i ==-1 else 1 for i in label_ids])).bool()
f1, f2 = [f[mask_lab] for f in contrastive_feats.chunk(2)]
sup_con_feats = torch.cat([f1.unsqueeze(1), f2.unsqueeze(1)], dim=1)
sup_con_labels = label_ids[mask_lab]
sup_loss = criterion(features = sup_con_feats, labels = sup_con_labels, device = self.device)
loss = self.sup_con_weight * sup_loss + (1 - self.sup_con_weight) * contrastive_loss
self.optimizer.zero_grad()
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
self.optimizer.step()
self.scheduler.step()
train_loss = tr_loss / nb_tr_steps
features, y_true = self.get_outputs(args, mode = 'eval')
km = KMeans(n_clusters = int(data.n_known_cls), random_state=args.seed).fit(features)
y_pred = km.labels_
eval_score = clustering_score(y_true, y_pred)
eval_results = {
'train_loss': train_loss,
'eval_score': eval_score,
'best_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['ACC'] > best_eval_score:
best_model = copy.deepcopy(self.model)
wait = 0
best_eval_score = eval_score['ACC']
else:
wait += 1
if wait >= args.wait_patient:
break
self.logger.info('GCD training finished...')
self.model = best_model
if args.save_model:
save_model(self.model, args.model_output_dir)
self.semisupvised_kmeans(args)
def get_outputs(self, args, mode):
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_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 = self.model(input_ids, segment_ids, input_mask)
total_labels = torch.cat((total_labels,label_ids))
total_features = torch.cat((total_features, pooled_output))
feats = total_features.cpu().numpy()
y_true = total_labels.cpu().numpy()
return feats, y_true
def info_nce_logits(self, features):
b_ = 0.5 * int(features.size(0))
labels = torch.cat([torch.arange(b_) for i in range(2)], dim=0)
labels = (labels.unsqueeze(0) == labels.unsqueeze(1)).float()
labels = labels.to(self.device)
features = F.normalize(features, dim=1)
similarity_matrix = torch.matmul(features, features.T)
mask = torch.eye(labels.shape[0], dtype=torch.bool).to(self.device)
labels = labels[~mask].view(labels.shape[0], -1)
similarity_matrix = similarity_matrix[~mask].view(similarity_matrix.shape[0], -1)
positives = similarity_matrix[labels.bool()].view(labels.shape[0], -1)
negatives = similarity_matrix[~labels.bool()].view(similarity_matrix.shape[0], -1)
logits = torch.cat([positives, negatives], dim=1)
labels = torch.zeros(logits.shape[0], dtype=torch.long).to(self.device)
logits = logits / self.temperature
return logits, labels
def get_augment_dataloader(self, args, pseudo_labels, data_aug = False):
train_input_ids = self.train_input_ids.unsqueeze(1)
train_input_mask = self.train_input_mask.unsqueeze(1)
train_segment_ids = self.train_segment_ids.unsqueeze(1)
train_label_ids = torch.tensor(pseudo_labels).unsqueeze(1)
train_input_ids = torch.cat(([train_input_ids, train_input_ids]), dim = 1)
train_input_mask = torch.cat(([train_input_mask, train_input_mask]), dim = 1)
train_segment_ids = torch.cat(([train_segment_ids, train_segment_ids]), dim = 1)
train_label_ids = torch.cat(([train_label_ids, train_label_ids]), dim = 1)
train_data = TensorDataset(train_input_ids, train_input_mask, train_segment_ids, train_label_ids)
label_len = len(self.loader.train_labeled_examples)
unlabelled_len = len(self.loader.train_unlabeled_examples)
sample_weights = [1 if i < label_len else label_len / unlabelled_len for i in range(len(self.loader.train_examples))]
sample_weights = torch.DoubleTensor(sample_weights)
sampler = torch.utils.data.WeightedRandomSampler(sample_weights, num_samples=len(self.loader.train_examples))
train_dataloader = DataLoader(train_data, sampler = sampler, batch_size = args.train_batch_size)
return train_dataloader
def test(self, args, data):
feats, y_true = self.get_outputs(args, mode = 'test')
centers = self.semisupvised_kmeans_cluster
print("self.semisupvised_kmeans_cluster", self.semisupvised_kmeans_cluster)
dis = (torch.from_numpy(feats).to(self.device).unsqueeze(dim=1)-centers.unsqueeze(dim=0))**2
dis = dis.sum(dim = -1)
u_mindist, y_pred = torch.min(dis, dim=1)
y_pred = y_pred.cpu().numpy()
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
|