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train.py
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from model import GIKT
import tensorflow as tf
import numpy as np
import os
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, precision_recall_fscore_support, accuracy_score
from data_process import DataGenerator
def train(args,train_dkt):
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
print(args.model)
model = GIKT(args)
saver = tf.train.Saver()
index = 0
if train_dkt:
# lr = 0.4
# lr_decay = 0.92
sess.run(tf.global_variables_initializer())
model_dir = save_model_dir(args)
best_valid_auc = 0
for epoch in tqdm(range(args.num_epochs)):
train_generator = DataGenerator(args.train_seqs, args.max_step, batch_size=args.batch_size,
feature_size=args.feature_answer_size - 2,
hist_num=args.hist_neighbor_num)
valid_generator = DataGenerator(args.valid_seqs, args.max_step, batch_size=args.batch_size,
feature_size=args.feature_answer_size - 2,
hist_num=args.hist_neighbor_num)
# assign_lr()
print("epoch:", epoch)
# self.assign_lr(self.sess,self.args.lr * self.args.lr_decay ** epoch)
overall_loss = 0
train_generator.shuffle()
preds, binary_preds, targets = list(), list(), list()
train_step = 0
while not train_generator.end:
train_step += 1
[features_answer_index,target_answers,seq_lens,hist_neighbor_index] = train_generator.next_batch()
binary_pred, pred, loss = model.train(sess,features_answer_index,target_answers,seq_lens,hist_neighbor_index)
overall_loss += loss
for seq_idx, seq_len in enumerate(seq_lens):
preds.append(pred[seq_idx, 0:seq_len])
binary_preds.append(binary_pred[seq_idx, 0:seq_len])
targets.append(target_answers[seq_idx, 0:seq_len])
# print("\r idx:{0}, overall_loss:{1}".format(train_generator.pos, overall_loss)),
train_loss = overall_loss / train_step
preds = np.concatenate(preds)
binary_preds = np.concatenate(binary_preds)
targets = np.concatenate(targets)
auc_value = roc_auc_score(targets, preds)
accuracy = accuracy_score(targets, binary_preds)
precision, recall, f_score, _ = precision_recall_fscore_support(targets, binary_preds)
print("\ntrain loss = {0},auc={1}, accuracy={2}".format(train_loss, auc_value, accuracy))
write_log(args,model_dir,auc_value, accuracy, epoch, name='train_')
# if epoch == self.args.num_epochs-1:
# self.save(epoch)
# valid
valid_generator.reset()
preds, binary_preds, targets = list(), list(), list()
valid_step = 0
#overall_loss = 0
while not valid_generator.end:
valid_step += 1
[features_answer_index,target_answers,seq_lens,hist_neighbor_index] = valid_generator.next_batch()
binary_pred, pred = model.evaluate(sess,features_answer_index,target_answers,seq_lens,hist_neighbor_index,valid_step)
#overall_loss += loss
for seq_idx, seq_len in enumerate(seq_lens):
preds.append(pred[seq_idx, 0:seq_len])
binary_preds.append(binary_pred[seq_idx, 0:seq_len])
targets.append(target_answers[seq_idx, 0:seq_len])
# compute metrics
#valid_loss = overall_loss / valid_step
preds = np.concatenate(preds)
binary_preds = np.concatenate(binary_preds)
targets = np.concatenate(targets)
auc_value = roc_auc_score(targets, preds)
accuracy = accuracy_score(targets, binary_preds)
precision, recall, f_score, _ = precision_recall_fscore_support(targets, binary_preds)
print("\nvalid auc={0}, accuracy={1}, precision={2}, recall={3}".format(auc_value, accuracy, precision,
recall))
write_log(args,model_dir,auc_value, accuracy, epoch, name='valid_')
if auc_value > best_valid_auc:
print('%3.4f to %3.4f' % (best_valid_auc, auc_value))
best_valid_auc = auc_value
best_epoch = epoch
#np.save('feature_embedding.npy', feature_embedding)
checkpoint_dir = os.path.join(args.checkpoint_dir, model_dir)
save(best_epoch,sess,checkpoint_dir,saver)
# print(model_dir)
print(model_dir+"\t"+str(best_valid_auc))
else:
if self.load():
print('CKPT loaded')
else:
raise Exception('CKPT need')
test_data_generator = DataGenerator(args.test_seqs, args.max_step, batch_size=args.batch_size,
feature_size=args.feature_answer_size - 2,
hist_num=args.hist_neighbor_num)
data_generator.reset()
correct_times = np.zeros(self.num_skills + 1)
preds, binary_preds, targets = list(), list(), list()
while not test_data_generator.end:
[features_answer_index, target_answers, seq_lens, hist_neighbor_index] = valid_generator.next_batch()
binary_pred, pred = model.evaluate(sess, features_answer_index, target_answers, seq_lens,
hist_neighbor_index)
# overall_loss += loss
for seq_idx, seq_len in enumerate(seq_lens):
preds.append(pred[seq_idx, 0:seq_len])
binary_preds.append(binary_pred[seq_idx, 0:seq_len])
targets.append(target_answers[seq_idx, 0:seq_len])
preds = np.concatenate(preds)
binary_preds = np.concatenate(binary_preds)
targets = np.concatenate(targets)
auc_value = roc_auc_score(targets, preds)
accuracy = accuracy_score(targets, binary_preds)
precision, recall, f_score, _ = precision_recall_fscore_support(targets, binary_preds)
print("\ntest auc={0}, accuracy={1}, precision={2}, recall={3}".format(auc_value, accuracy, precision,
recall))
print(model_dir)
write_log(args, model_dir, auc_value, accuracy, epoch, name='test_')
def save(global_step,sess,checkpoint_dir,saver):
model_name = 'GIKT'
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir)
saver.save(sess, os.path.join(checkpoint_dir, model_name), global_step=global_step)
print('Save checkpoint at %d' % (global_step))
def save_model_dir(args):
return '{}_{}_{}lr_{}hop_{}sn_{}qn_{}hn_{}nn_{}_{}bound_{}keep_{}'.format(args.dataset,
args.model,args.lr,args.n_hop,args.skill_neighbor_num,args.question_neighbor_num,args.hist_neighbor_num,\
args.next_neighbor_num,args.sim_emb,args.att_bound,args.dropout_keep_probs,args.tag)
def write_log(args,model_dir,auc, accuracy, epoch, name='train_'):
log_path = os.path.join(args.log_dir, name+model_dir+'.csv')
if not os.path.exists(log_path):
log_file = open(log_path, 'w')
log_file.write('Epoch\tAuc\tAccuracy\n')
else:
log_file = open(log_path, 'a')
log_file.write(str(epoch) + '\t' + str(auc) + '\t' + str(accuracy) + '\n')
log_file.flush()