def main(args):
model = RCNN(vocab_size=args.vocab_size,
embedding_dim=args.embedding_dim,
hidden_size=args.hidden_size,
hidden_size_linear=args.hidden_size_linear,
class_num=args.class_num,
dropout=args.dropout).to(args.device)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=0)
train_texts, train_labels = read_file(args.train_file_path)
word2idx = build_dictionary(train_texts, vocab_size=args.vocab_size)
logger.info('Dictionary Finished!')
full_dataset = CustomTextDataset(train_texts, train_labels, word2idx)
num_train_data = len(full_dataset) - args.num_val_data
train_dataset, val_dataset = random_split(
full_dataset, [num_train_data, args.num_val_data])
train_dataloader = DataLoader(dataset=train_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
valid_dataloader = DataLoader(dataset=val_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
train(model, optimizer, train_dataloader, valid_dataloader, args)
logger.info('******************** Train Finished ********************')
# Test
if args.test_set:
test_texts, test_labels = read_file(args.test_file_path)
test_dataset = CustomTextDataset(test_texts, test_labels, word2idx)
test_dataloader = DataLoader(dataset=test_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
model.load_state_dict(
torch.load(os.path.join(args.model_save_path, "best.pt")))
_, accuracy, precision, recall, f1, cm = evaluate(
model, test_dataloader, args)
logger.info('-' * 50)
logger.info(
f'|* TEST SET *| |ACC| {accuracy:>.4f} |PRECISION| {precision:>.4f} |RECALL| {recall:>.4f} |F1| {f1:>.4f}'
)
logger.info('-' * 50)
logger.info('---------------- CONFUSION MATRIX ----------------')
for i in range(len(cm)):
logger.info(cm[i])
logger.info('--------------------------------------------------')
def load_model(test_arguments):
rcnn = RCNN(test_arguments.pos_loss_method, test_arguments.loss_weight_lambda).cuda()
rcnn.load_state_dict(t.load(test_arguments.model_path))
rcnn.eval() # dropout rate = 0
return rcnn
def main(args):
acc_list = []
f1_score_list = []
prec_list = []
recall_list = []
for i in range(10):
setup_data()
model = RCNN(vocab_size=args.vocab_size,
embedding_dim=args.embedding_dim,
hidden_size=args.hidden_size,
hidden_size_linear=args.hidden_size_linear,
class_num=args.class_num,
dropout=args.dropout).to(args.device)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=0)
train_texts, train_labels = read_file(args.train_file_path)
word2idx, embedding = build_dictionary(train_texts, args.vocab_size,
args.lexical, args.syntactic,
args.semantic)
logger.info('Dictionary Finished!')
full_dataset = CustomTextDataset(train_texts, train_labels, word2idx,
args)
num_train_data = len(full_dataset) - args.num_val_data
train_dataset, val_dataset = random_split(
full_dataset, [num_train_data, args.num_val_data])
train_dataloader = DataLoader(dataset=train_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
valid_dataloader = DataLoader(dataset=val_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
train(model, optimizer, train_dataloader, valid_dataloader, embedding,
args)
logger.info('******************** Train Finished ********************')
# Test
if args.test_set:
test_texts, test_labels = read_file(args.test_file_path)
test_dataset = CustomTextDataset(test_texts, test_labels, word2idx,
args)
test_dataloader = DataLoader(
dataset=test_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
model.load_state_dict(
torch.load(os.path.join(args.model_save_path, "best.pt")))
_, accuracy, precision, recall, f1, cm = evaluate(
model, test_dataloader, embedding, args)
logger.info('-' * 50)
logger.info(
f'|* TEST SET *| |ACC| {accuracy:>.4f} |PRECISION| {precision:>.4f} |RECALL| {recall:>.4f} |F1| {f1:>.4f}'
)
logger.info('-' * 50)
logger.info('---------------- CONFUSION MATRIX ----------------')
for i in range(len(cm)):
logger.info(cm[i])
logger.info('--------------------------------------------------')
acc_list.append(accuracy / 100)
prec_list.append(precision)
recall_list.append(recall)
f1_score_list.append(f1)
avg_acc = sum(acc_list) / len(acc_list)
avg_prec = sum(prec_list) / len(prec_list)
avg_recall = sum(recall_list) / len(recall_list)
avg_f1_score = sum(f1_score_list) / len(f1_score_list)
logger.info('--------------------------------------------------')
logger.info(
f'|* TEST SET *| |Avg ACC| {avg_acc:>.4f} |Avg PRECISION| {avg_prec:>.4f} |Avg RECALL| {avg_recall:>.4f} |Avg F1| {avg_f1_score:>.4f}'
)
logger.info('--------------------------------------------------')
plot_df = pd.DataFrame({
'x_values': range(10),
'avg_acc': acc_list,
'avg_prec': prec_list,
'avg_recall': recall_list,
'avg_f1_score': f1_score_list
})
plt.plot('x_values',
'avg_acc',
data=plot_df,
marker='o',
markerfacecolor='blue',
markersize=12,
color='skyblue',
linewidth=4)
plt.plot('x_values',
'avg_prec',
data=plot_df,
marker='',
color='olive',
linewidth=2)
plt.plot('x_values',
'avg_recall',
data=plot_df,
marker='',
color='olive',
linewidth=2,
linestyle='dashed')
plt.plot('x_values',
'avg_f1_score',
data=plot_df,
marker='',
color='olive',
linewidth=2,
linestyle='dashed')
plt.legend()
fname = 'lexical-semantic-syntactic.png' if args.lexical and args.semantic and args.syntactic \
else 'semantic-syntactic.png' if args.semantic and args.syntactic \
else 'lexical-semantic.png' if args.lexical and args.semantic \
else 'lexical-syntactic.png'if args.lexical and args.syntactic \
else 'lexical.png' if args.lexical \
else 'syntactic.png' if args.syntactic \
else 'semantic.png' if args.semantic \
else 'plain.png'
if not (path.exists('./images')):
mkdir('./images')
plt.savefig(path.join('./images', fname))
def load_models(config):
# train_data = pickle.load(open(os.path.join(config.data_path, config.train_name), "rb"))
# debug
# if config.debug:
# train_data = train_data[0:30]
# dev_data = pickle.load(open(os.path.join(config.data_path, config.dev_name), "rb"))
# test_data = pickle.load(open(os.path.join(config.data_path, config.test_name), "rb"))
vocabulary = pickle.load(
open(os.path.join(config.data_path, config.vocabulary_name), "rb"))
# load w2v data
# weight = pickle.load(open(os.path.join(config.data_path, config.weight_name), "rb"))
cnn = CNN(vocab_size=len(vocabulary),
embed_dim=config.embed_dim,
class_num=config.class_num,
kernel_num=config.kernel_num,
kernel_sizes=config.kernel_sizes,
dropout=config.dropout,
static=config.static,
in_channels=config.in_channels)
state_dict = torch.load(
os.path.join(config.save_path,
config.ensemble_models[0])).state_dict()
cnn.load_state_dict(state_dict)
lstm = LSTM(vocab_size=len(vocabulary),
embed_dim=config.embed_dim,
output_dim=config.class_num,
hidden_dim=config.hidden_dim,
num_layers=config.num_layers,
dropout=config.dropout)
state_dict = torch.load(
os.path.join(config.save_path,
config.ensemble_models[1])).state_dict()
lstm.load_state_dict(state_dict)
lstm_mxp = LSTM_maxpool(vocab_size=len(vocabulary),
embed_dim=config.embed_dim,
output_dim=config.class_num,
hidden_dim=config.hidden_dim,
num_layers=config.num_layers,
dropout=config.dropout)
state_dict = torch.load(
os.path.join(config.save_path,
config.ensemble_models[2])).state_dict()
lstm_mxp.load_state_dict(state_dict)
rcnn = RCNN(vocab_size=len(vocabulary),
embed_dim=config.embed_dim,
output_dim=config.class_num,
hidden_dim=config.hidden_dim,
num_layers=config.num_layers,
dropout=config.dropout)
state_dict = torch.load(
os.path.join(config.save_path,
config.ensemble_models[3])).state_dict()
rcnn.load_state_dict(state_dict)
schemas = get_schemas(config.source_path)
state_dict = torch.load(
os.path.join(config.save_path,
config.ensemble_models[4])).state_dict()
bert = BertForMultiLabelSequenceClassification.from_pretrained(
config.bert_folder, state_dict=state_dict, num_labels=len(schemas))
bert.load_state_dict(state_dict)
return cnn, lstm, lstm_mxp, rcnn, bert