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main_PMAES_many2many.py
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main_PMAES_many2many.py
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import os
import time
import argparse
import random
import numpy as np
from torch.utils.data import DataLoader
from models.PMAES import EssayEncoder, Scorer, PromptMappingCL
from configs import Configs
from utils.read_data import read_pos_vocab, read_essays_single_score
from utils.general_utils import pad_hierarchical_text_sequences, get_single_scaled_down_score
import torch
from data_set import PMAESDataSet
from train.train_PMAES import TrainSingleOverallScoring
def seed_all(seed_value):
"""
Setting the random seed across the pipeline
"""
torch.manual_seed(seed_value) # cpu vars
random.seed(seed_value)
np.random.seed(seed_value) # cpu vars
os.environ['PYTHONHASHSEED'] = str(seed_value)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value) # gpu vars
torch.backends.cudnn.deterministic = True # needed
torch.backends.cudnn.benchmark = False
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="PAES_attributes models")
parser.add_argument('--seed', type=int, default=12, help='set random seed')
parser.add_argument('--attribute_name', type=str, default='score', help='set random seed')
parser.add_argument('--source2target', type=str, default='many2many', help='Setting of source-target pair')
parser.add_argument('--source_ids', type=list, default=[1, 2], help='source prompt ids')
parser.add_argument('--target_ids', type=list, default=[3, 4], help='set random seed')
parser.add_argument('--embedding_dim', type=int, default=50, help='Only useful when embedding is randomly initialised')
parser.add_argument('--num_epochs', type=int, default=50, help='number of epochs for training')
parser.add_argument('--filter_num', type=int, default=100, help='Num of filters in conv layer')
parser.add_argument('--kernel_size', type=int, default=3, help='filter length in 1st conv layer')
parser.add_argument('--rnn_type', type=str, default='LSTM', help='Recurrent type')
parser.add_argument('--lstm_units', type=int, default=50, help='Num of hidden units in recurrent layer')
parser.add_argument('--learning_rate', type=float, default=1e-4, help='Initial learning rate')
parser.add_argument('--dropout', type=float, default=0.2, help='Dropout rate for layers')
parser.add_argument('--device', type=str, help='cpu or gpu', default='cuda')
args = parser.parse_args()
source2target = 's' + ''.join([str(ids) for ids in args.source_ids]) + '-' + 't' + ''.join([str(ids) for ids in args.target_ids])
seed = args.seed
seed_all(seed)
print("Test prompt id is {} of type {}".format(source2target, type(source2target)))
print("Seed: {}".format(seed))
configs = Configs()
data_path = 'data/many2many/'
train_path = data_path + str(source2target) + '/train.pk'
dev_path = data_path + str(source2target) + '/dev.pk'
test_path = data_path + str(source2target) + '/test.pk'
features_path = configs.FEATURES_PATH
readability_path = configs.READABILITY_PATH
read_configs = {
'train_path': train_path,
'dev_path': dev_path,
'test_path': test_path,
'features_path': features_path,
'readability_path': readability_path,
}
pos_vocab = read_pos_vocab(read_configs)
train_data, valid_data, test_data = read_essays_single_score(read_configs, pos_vocab, args.attribute_name)
max_sent_len = max(train_data['max_sent_len'], valid_data['max_sent_len'], test_data['max_sent_len'])
max_sent_num = max(train_data['max_sent_num'], valid_data['max_sent_num'], test_data['max_sent_num'])
print('max sent length: {}'.format(max_sent_len))
print('max sent num: {}'.format(max_sent_num))
train_data['score_scaled'] = get_single_scaled_down_score(train_data['score'], train_data['prompt_ids'], args.attribute_name)
valid_data['score_scaled'] = get_single_scaled_down_score(valid_data['score'], valid_data['prompt_ids'], args.attribute_name)
test_data['score_scaled'] = get_single_scaled_down_score(test_data['score'], test_data['prompt_ids'], args.attribute_name)
train_prompt_ids = train_data['prompt_ids']
dev_prompt_ids = valid_data['prompt_ids']
test_prompt_ids = test_data['prompt_ids']
train_essay_pos = pad_hierarchical_text_sequences(train_data['essay_pos'], max_sent_num, max_sent_len)
valid_essay_pos = pad_hierarchical_text_sequences(valid_data['essay_pos'], max_sent_num, max_sent_len)
test_essay_pos = pad_hierarchical_text_sequences(test_data['essay_pos'], max_sent_num, max_sent_len)
train_essay_pos = train_essay_pos.reshape((train_essay_pos.shape[0], train_essay_pos.shape[1] * train_essay_pos.shape[2]))
valid_essay_pos = valid_essay_pos.reshape((valid_essay_pos.shape[0], valid_essay_pos.shape[1] * valid_essay_pos.shape[2]))
test_essay_pos = test_essay_pos.reshape((test_essay_pos.shape[0], test_essay_pos.shape[1] * test_essay_pos.shape[2]))
train_score = np.array(train_data['score_scaled'])
valid_score = np.array(valid_data['score_scaled'])
test_score = np.array(test_data['score_scaled'])
train_linguistic = np.array(train_data['linguistic'])
valid_linguistic = np.array(valid_data['linguistic'])
test_linguistic = np.array(test_data['linguistic'])
train_readability = np.array(train_data['readability'])
valid_readability = np.array(valid_data['readability'])
test_readability = np.array(test_data['readability'])
tr_s_num, tr_t_num = len(train_essay_pos), len(test_essay_pos)
batch_num = 2
s_batch_size = int(tr_s_num / batch_num)
t_batch_size = int(tr_t_num / batch_num)
s_batch_num = int(tr_s_num / s_batch_size)
t_batch_num = int(tr_t_num / t_batch_size)
while t_batch_num > s_batch_num:
s_batch_size -= 1
s_batch_num = int(tr_s_num / s_batch_size)
tr_s_loader = DataLoader(PMAESDataSet(train_prompt_ids, train_essay_pos, train_linguistic, train_readability, train_score), batch_size=s_batch_size)
va_s_loader = DataLoader(PMAESDataSet(dev_prompt_ids, valid_essay_pos, valid_linguistic, valid_readability, valid_score), batch_size=s_batch_size)
te_t_loader = DataLoader(PMAESDataSet(test_prompt_ids, test_essay_pos, test_linguistic, test_readability, test_score), batch_size=t_batch_size)
essay_encoder = EssayEncoder(args, max_num=max_sent_num, max_len=max_sent_len, embed_dim=args.embedding_dim, pos_vocab=pos_vocab).to(args.device)
scorer = Scorer(args).to(args.device)
pm_cl = PromptMappingCL(args, tr_s_num, tr_t_num).to(args.device)
optims = torch.optim.Adam([{'params': essay_encoder.parameters()}, {'params': scorer.parameters()}, {'params': pm_cl.parameters()}], lr=args.learning_rate)
tr_log = {
'Epoch_best_dev_qwk': [0, 0, 0],
'Best_dev_qwk': [0, 0],
}
epochs = 50
for e_index in range(1, epochs+1):
TrainSingleOverallScoring(args,
essay_encoder, scorer, pm_cl, optims,
tr_s_loader, va_s_loader, te_t_loader,
args.target_ids, e_index,
tr_log, args.attribute_name)
file_time = time.strftime("%Y-%m-%d", time.localtime())
result_file = 'result/many2many/PMAES-{}.txt'.format(seed)
with open(result_file, 'a', encoding='utf-8') as f:
f.write(file_time + 'TargetPrompt: {} Trait: {}'.format(args.target_ids, args.attribute_name) + '\n')
for key, value in tr_log.items():
f.write(key + ':' + str(value) + '\n')