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Copy pathtime_proj.py
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executable file
·601 lines (510 loc) · 23.5 KB
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from models import *
from helper import *
from random import *
from pprint import pprint
import pandas as pd
import scipy.sparse as sp
import uuid, sys, os, time, argparse
import pickle, pdb, operator, random, sys
import tensorflow as tf
from collections import defaultdict as ddict
from sklearn.metrics import precision_recall_fscore_support
YEARMIN = -50
YEARMAX = 3000
class HyTE(Model):
def read_valid(self,filename):
valid_triples = []
with open(filename,'r') as filein:
temp = []
for line in filein:
temp = [int(x.strip()) for x in line.split()[0:3]]
temp.append([line.split()[3],line.split()[4]])
valid_triples.append(temp)
return valid_triples
def getOneHot(self, start_data, end_data, num_class):
temp = np.zeros((len(start_data), num_class), np.float32)
for i, ele in enumerate(start_data):
if end_data[i] >= start_data[i]:
temp[i,start_data[i]:end_data[i]+1] = 1/(end_data[i]+1-start_data[i])
else:
pdb.set_trace()
return temp
def getBatches(self, data, shuffle = True):
if shuffle: random.shuffle(data)
num_batches = len(data) // self.p.batch_size
for i in range(num_batches):
start_idx = i * self.p.batch_size
yield data[start_idx : start_idx + self.p.batch_size]
def create_year2id(self,triple_time):
year2id = dict()
freq = ddict(int)
count = 0
year_list = []
for k,v in triple_time.items():
try:
start = v[0].split('-')[0]
end = v[1].split('-')[0]
except:
pdb.set_trace()
if start.find('#') == -1 and len(start) == 4: year_list.append(int(start))
if end.find('#') == -1 and len(end) ==4: year_list.append(int(end))
# for k,v in entity_time.items():
# start = v[0].split('-')[0]
# end = v[1].split('-')[0]
# if start.find('#') == -1 and len(start) == 4: year_list.append(int(start))
# if end.find('#') == -1 and len(end) ==4: year_list.append(int(end))
# # if int(start) > int(end):
# # pdb.set_trace()
year_list.sort()
for year in year_list:
freq[year] = freq[year] + 1
year_class =[]
count = 0
for key in sorted(freq.keys()):
count += freq[key]
if count > 300:
year_class.append(key)
count = 0
prev_year = 0
i=0
for i,yr in enumerate(year_class):
year2id[(prev_year,yr)] = i
prev_year = yr+1
year2id[(prev_year, max(year_list))] = i + 1
self.year_list =year_list
# for k,v in entity_time.items():
# if v[0] == '####-##-##' or v[1] == '####-##-##':
# continue
# if len(v[0].split('-')[0])!=4 or len(v[1].split('-')[0])!=4:
# continue
# start = v[0].split('-')[0]
# end = v[1].split('-')[0]
# for start in start_list:
# if start not in start_year2id:
# start_year2id[start] = count_start
# count_start+=1
# for end in end_list:
# if end not in end_year2id:
# end_year2id[end] = count_end
# count_end+=1
return year2id
def get_span_ids(self, start, end):
start =int(start)
end=int(end)
if start > end:
end = YEARMAX
if start == YEARMIN:
start_lbl = 0
else:
for key,lbl in sorted(self.year2id.items(), key=lambda x:x[1]):
if start >= key[0] and start <= key[1]:
start_lbl = lbl
if end == YEARMAX:
end_lbl = len(self.year2id.keys())-1
else:
for key,lbl in sorted(self.year2id.items(), key=lambda x:x[1]):
if end >= key[0] and end <= key[1]:
end_lbl = lbl
return start_lbl, end_lbl
def create_id_labels(self,triple_time,dtype):
YEARMAX = 3000
YEARMIN = -50
inp_idx, start_idx, end_idx =[], [], []
for k,v in triple_time.items():
start = v[0].split('-')[0]
end = v[1].split('-')[0]
if start == '####':
start = YEARMIN
elif start.find('#') != -1 or len(start)!=4:
continue
if end == '####':
end = YEARMAX
elif end.find('#')!= -1 or len(end)!=4:
continue
start = int(start)
end = int(end)
if start > end:
end = YEARMAX
inp_idx.append(k)
if start == YEARMIN:
start_idx.append(0)
else:
for key,lbl in sorted(self.year2id.items(), key=lambda x:x[1]):
if start >= key[0] and start <= key[1]:
start_idx.append(lbl)
if end == YEARMAX:
end_idx.append(len(self.year2id.keys())-1)
else:
for key,lbl in sorted(self.year2id.items(), key=lambda x:x[1]):
if end >= key[0] and end <= key[1]:
end_idx.append(lbl)
return inp_idx, start_idx, end_idx
def load_data(self):
triple_set = []
with open(self.p.triple2id,'r') as filein:
for line in filein:
tup = (int(line.split()[0].strip()) , int(line.split()[1].strip()), int(line.split()[2].strip()))
triple_set.append(tup)
triple_set=set(triple_set)
train_triples = []
self.start_time , self.end_time, self.num_class = ddict(dict), ddict(dict), ddict(dict)
triple_time, entity_time = dict(), dict()
self.inp_idx, self.start_idx, self.end_idx ,self.labels = ddict(list), ddict(list), ddict(list), ddict(list)
max_ent, max_rel, count = 0, 0, 0
with open(self.p.dataset,'r') as filein:
for line in filein:
train_triples.append([int(x.strip()) for x in line.split()[0:3]])
triple_time[count] = [x.split('-')[0] for x in line.split()[3:5]]
count+=1
# self.start_time['triple'], self.end_time['triple'] = self.create_year2id(triple_time,'triple')
with open(self.p.entity2id,'r', encoding="utf-8") as filein2:
for line in filein2:
# entity_time[int(line.split('\t')[1])]=[x.split()[0] for x in line.split()[2:4]]
max_ent = max_ent+1
self.year2id = self.create_year2id(triple_time)
# self.start_time['entity'], self.end_time['entity'] = self.create_year2id(entity_time,'entiy')
# self.inp_idx['entity'],self.start_idx['entity'], self.end_idx['entity'] = self.create_id_labels(entity_time,'entity')
self.inp_idx['triple'], self.start_idx['triple'], self.end_idx['triple'] = self.create_id_labels(triple_time,'triple')
#pdb.set_trace()
for i,ele in enumerate(self.inp_idx['entity']):
if self.start_idx['entity'][i] > self.end_idx['entity'][i]:
print(self.inp_idx['entity'][i],self.start_idx['entity'][i],self.end_idx['entity'][i])
self.num_class = len(self.year2id.keys())
# for dtype in ['entity','triple']:
# self.labels[dtype] = self.getOneHot(self.start_idx[dtype],self.end_idx[dtype], self.num_class)# Representing labels by one hot notation
keep_idx = set(self.inp_idx['triple'])
for i in range (len(train_triples)-1,-1,-1):
if i not in keep_idx:
del train_triples[i]
with open(self.p.relation2id, 'r') as filein3:
for line in filein3:
max_rel = max_rel +1
index = randint(1,len(train_triples))-1
posh, rela, post = zip(*train_triples)
head, rel, tail = zip(*train_triples)
posh = list(posh)
post = list(post)
rela = list(rela)
head = list(head)
tail = list(tail)
rel = list(rel)
for i in range(len(posh)):
if self.start_idx['triple'][i] < self.end_idx['triple'][i]:
for j in range(self.start_idx['triple'][i] + 1,self.end_idx['triple'][i] + 1):
head.append(posh[i])
rel.append(rela[i])
tail.append(post[i])
self.start_idx['triple'].append(j)
self.ph, self.pt, self.r,self.nh, self.nt , self.triple_time = [], [], [], [], [], []
for triple in range(len(head)):
neg_set = set()
for k in range(self.p.M):
possible_head = randint(0,max_ent-1)
while (possible_head, rel[triple], tail[triple]) in triple_set or (possible_head, rel[triple],tail[triple]) in neg_set:
possible_head = randint(0,max_ent-1)
self.nh.append(possible_head)
self.nt.append(tail[triple])
self.r.append(rel[triple])
self.ph.append(head[triple])
self.pt.append(tail[triple])
self.triple_time.append(self.start_idx['triple'][triple])
neg_set.add((possible_head, rel[triple],tail[triple]))
for triple in range(len(tail)):
neg_set = set()
for k in range(self.p.M):
possible_tail = randint(0,max_ent-1)
while (head[triple], rel[triple],possible_tail) in triple_set or (head[triple], rel[triple],possible_tail) in neg_set:
possible_tail = randint(0,max_ent-1)
self.nh.append(head[triple])
self.nt.append(possible_tail)
self.r.append(rel[triple])
self.ph.append(head[triple])
self.pt.append(tail[triple])
self.triple_time.append(self.start_idx['triple'][triple])
neg_set.add((head[triple], rel[triple],possible_tail))
# self.triple_time = triple_time
# self.entity_time = entity_time
self.max_rel = max_rel
self.max_ent = max_ent
self.max_time = len(self.year2id.keys())
self.data = list(zip(self.ph, self.pt, self.r , self.nh, self.nt, self.triple_time))
self.data = self.data + self.data[0:self.p.batch_size]
def calculated_score_for_positive_elements(self, t, epoch, f_valid, eval_mode='valid'):
loss =np.zeros(self.max_ent)
start_trip = t[3][0].split('-')[0]
end_trip = t[3][1].split('-')[0]
if start_trip == '####':
start_trip = YEARMIN
elif start_trip.find('#') != -1 or len(start_trip)!=4:
return
if end_trip == '####':
end_trip = YEARMAX
elif end_trip.find('#')!= -1 or len(end_trip)!=4:
return
start_lbl, end_lbl = self.get_span_ids(start_trip, end_trip)
if eval_mode == 'test':
f_valid.write(str(t[0])+'\t'+str(t[1])+'\t'+str(t[2])+'\n')
elif eval_mode == 'valid' and epoch == self.p.test_freq:
f_valid.write(str(t[0])+'\t'+str(t[1])+'\t'+str(t[2])+'\n')
pos_head = sess.run(self.pos ,feed_dict = { self.pos_head: np.array([t[0]]).reshape(-1,1),
self.rel: np.array([t[1]]).reshape(-1,1),
self.pos_tail: np.array([t[2]]).reshape(-1,1),
self.start_year :np.array([start_lbl]*self.max_ent),
self.end_year : np.array([end_lbl]*self.max_ent),
self.mode: -1,
self.pred_mode: 1,
self.query_mode: 1})
pos_head = np.squeeze(pos_head)
pos_tail = sess.run(self.pos ,feed_dict = { self.pos_head: np.array([t[0]]).reshape(-1,1),
self.rel: np.array([t[1]]).reshape(-1,1),
self.pos_tail: np.array([t[2]]).reshape(-1,1),
self.start_year :np.array([start_lbl]*self.max_ent),
self.end_year : np.array([end_lbl]*self.max_ent),
self.mode: -1,
self.pred_mode: -1,
self.query_mode: 1})
pos_tail = np.squeeze(pos_tail)
pos_rel = sess.run(self.pos ,feed_dict = { self.pos_head: np.array([t[0]]).reshape(-1,1),
self.rel: np.array([t[1]]).reshape(-1,1),
self.pos_tail: np.array([t[2]]).reshape(-1,1),
self.start_year :np.array([start_lbl]*self.max_rel),
self.end_year : np.array([end_lbl]*self.max_rel),
self.mode: -1,
self.pred_mode: -1,
self.query_mode: -1})
pos_rel = np.squeeze(pos_rel)
return pos_head, pos_tail, pos_rel
def add_placeholders(self):
self.start_year = tf.placeholder(tf.int32, shape=[None], name = 'start_time')
self.end_year = tf.placeholder(tf.int32, shape=[None],name = 'end_time')
self.pos_head = tf.placeholder(tf.int32, [None,1])
self.pos_tail = tf.placeholder(tf.int32, [None,1])
self.rel = tf.placeholder(tf.int32, [None,1])
self.neg_head = tf.placeholder(tf.int32, [None,1])
self.neg_tail = tf.placeholder(tf.int32, [None,1])
self.mode = tf.placeholder(tf.int32, shape = ())
self.pred_mode = tf.placeholder(tf.int32, shape = ())
self.query_mode = tf.placeholder(tf.int32, shape = ())
def create_feed_dict(self, batch, wLabels=True,dtype='train'):
ph, pt, r, nh, nt, start_idx = zip(*batch)
feed_dict = {}
feed_dict[self.pos_head] = np.array(ph).reshape(-1,1)
feed_dict[self.pos_tail] = np.array(pt).reshape(-1,1)
feed_dict[self.rel] = np.array(r).reshape(-1,1)
feed_dict[self.start_year] = np.array(start_idx)
# feed_dict[self.end_year] = np.array(end_idx)
if dtype == 'train':
feed_dict[self.neg_head] = np.array(nh).reshape(-1,1)
feed_dict[self.neg_tail] = np.array(nt).reshape(-1,1)
feed_dict[self.mode] = 1
feed_dict[self.pred_mode] = 0
feed_dict[self.query_mode] = 0
else:
feed_dict[self.mode] = -1
return feed_dict
def time_projection(self,data,t):
inner_prod = tf.tile(tf.expand_dims(tf.reduce_sum(data*t,axis=1),axis=1),[1,self.p.inp_dim])
prod = t*inner_prod
data = data - prod
return data
def add_model(self):
#nn_in = self.input_x
with tf.name_scope("embedding"):
self.ent_embeddings = tf.get_variable(name = "ent_embedding", shape = [self.max_ent, self.p.inp_dim], initializer = tf.contrib.layers.xavier_initializer(uniform = False), regularizer=self.regularizer)
self.rel_embeddings = tf.get_variable(name = "rel_embedding", shape = [self.max_rel, self.p.inp_dim], initializer = tf.contrib.layers.xavier_initializer(uniform = False), regularizer=self.regularizer)
self.time_embeddings = tf.get_variable(name = "time_embedding",shape = [self.max_time, self.p.inp_dim], initializer = tf.contrib.layers.xavier_initializer(uniform =False))
transE_in_dim = self.p.inp_dim
transE_in = self.ent_embeddings
####################------------------------ time aware GCN MODEL ---------------------------##############
## Some transE style model ####
neutral = tf.constant(0) ## mode = 1 for train mode = -1 test
test_type = tf.constant(0) ## pred_mode = 1 for head -1 for tail
query_type = tf.constant(0) ## query mode =1 for head tail , -1 for rel
def f_train():
pos_h_e = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.pos_head))
pos_t_e = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.pos_tail))
pos_r_e = tf.squeeze(tf.nn.embedding_lookup(self.rel_embeddings, self.rel))
return pos_h_e, pos_t_e, pos_r_e
def f_test():
def head_tail_query():
def f_head():
e2 = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.pos_tail))
pos_h_e = transE_in
pos_t_e = tf.reshape(tf.tile(e2,[self.max_ent]),(self.max_ent, transE_in_dim))
return pos_h_e, pos_t_e
def f_tail():
e1 = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.pos_head))
pos_h_e = tf.reshape(tf.tile(e1,[self.max_ent]),(self.max_ent, transE_in_dim))
pos_t_e = transE_in
return pos_h_e, pos_t_e
pos_h_e, pos_t_e = tf.cond(self.pred_mode > test_type, f_head, f_tail)
r = tf.squeeze(tf.nn.embedding_lookup(self.rel_embeddings,self.rel))
pos_r_e = tf.reshape(tf.tile(r,[self.max_ent]),(self.max_ent,transE_in_dim))
return pos_h_e, pos_t_e, pos_r_e
def rel_query():
e1 = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.pos_head))
e2 = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.pos_tail))
pos_h_e = tf.reshape(tf.tile(e1,[self.max_rel]),(self.max_rel, transE_in_dim))
pos_t_e = tf.reshape(tf.tile(e2,[self.max_rel]),(self.max_rel, transE_in_dim))
pos_r_e = self.rel_embeddings
return pos_h_e, pos_t_e, pos_r_e
pos_h_e, pos_t_e, pos_r_e = tf.cond(self.query_mode > query_type, head_tail_query, rel_query)
return pos_h_e, pos_t_e, pos_r_e
pos_h_e, pos_t_e, pos_r_e = tf.cond(self.mode > neutral, f_train, f_test)
neg_h_e = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.neg_head))
neg_t_e = tf.squeeze(tf.nn.embedding_lookup(transE_in, self.neg_tail))
#### ----- time -----###
t_1 = tf.squeeze(tf.nn.embedding_lookup(self.time_embeddings, self.start_year))
pos_h_e_t_1 = self.time_projection(pos_h_e,t_1)
neg_h_e_t_1 = self.time_projection(neg_h_e,t_1)
pos_t_e_t_1 = self.time_projection(pos_t_e,t_1)
neg_t_e_t_1 = self.time_projection(neg_t_e,t_1)
pos_r_e_t_1 = self.time_projection(pos_r_e,t_1)
# pos_r_e_t_1 = pos_r_e
if self.p.L1_flag:
pos = tf.reduce_sum(abs(pos_h_e_t_1 + pos_r_e_t_1 - pos_t_e_t_1), 1, keep_dims = True)
neg = tf.reduce_sum(abs(neg_h_e_t_1 + pos_r_e_t_1 - neg_t_e_t_1), 1, keep_dims = True)
#self.predict = pos
else:
pos = tf.reduce_sum((pos_h_e_t_1 + pos_r_e_t_1 - pos_t_e_t_1) ** 2, 1, keep_dims = True)
neg = tf.reduce_sum((neg_h_e_t_1 + pos_r_e_t_1 - neg_t_e_t_1) ** 2, 1, keep_dims = True)
#self.predict = pos
'''
debug_nn([self.pred_mode,self.mode], feed_dict = self.create_feed_dict(self.data[0:self.p.batch_size],dtype='test'))
'''
return pos, neg
def add_loss(self, pos, neg):
with tf.name_scope('Loss_op'):
loss = tf.reduce_sum(tf.maximum(pos - neg + self.p.margin, 0))
if self.regularizer != None: loss += tf.contrib.layers.apply_regularization(self.regularizer, tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
return loss
def add_optimizer(self, loss):
with tf.name_scope('Optimizer'):
optimizer = tf.train.AdamOptimizer(self.p.lr)
train_op = optimizer.minimize(loss)
time_normalizer = tf.assign(self.time_embeddings, tf.nn.l2_normalize(self.time_embeddings,dim = 1))
return train_op
def __init__(self, params):
self.p = params
self.p.batch_size = self.p.batch_size
if self.p.l2 == 0.0: self.regularizer = None
else: self.regularizer = tf.contrib.layers.l2_regularizer(scale=self.p.l2)
self.load_data()
self.nbatches = len(self.data) // self.p.batch_size
self.add_placeholders()
self.pos, neg = self.add_model()
self.loss = self.add_loss(self.pos, neg)
self.train_op = self.add_optimizer(self.loss)
self.merged_summ = tf.summary.merge_all()
self.summ_writer = None
print('model done')
def run_epoch(self, sess,data,epoch):
drop_rate = self.p.dropout
losses = []
# total_correct, total_cnt = 0, 0
for step, batch in enumerate(self.getBatches(data, shuffle)):
feed = self.create_feed_dict(batch)
l, a = sess.run([self.loss, self.train_op],feed_dict = feed)
losses.append(l)
return np.mean(losses)
def fit(self, sess):
saver = tf.train.Saver(max_to_keep=None)
save_dir = 'checkpoints/' + self.p.name + '/'
if not os.path.exists(save_dir): os.makedirs(save_dir)
save_dir_results = './results/'+ self.p.name + '/'
if not os.path.exists(save_dir_results): os.makedirs(save_dir_results)
if self.p.restore:
save_path = os.path.join(save_dir, 'epoch_{}'.format(self.p.restore_epoch))
saver.restore(sess, save_path)
if not self.p.onlyTest:
print('start fitting')
validation_data = self.read_valid(self.p.valid_data)
for epoch in range(self.p.max_epochs):
l = self.run_epoch(sess,self.data,epoch)
if epoch%50 == 0:
print('Epoch {}\tLoss {}\t model {}'.format(epoch,l,self.p.name))
if epoch % self.p.test_freq == 0 and epoch != 0:
save_path = os.path.join(save_dir, 'epoch_{}'.format(epoch)) ## -- check pointing -- ##
saver.save(sess=sess, save_path=save_path)
if epoch == self.p.test_freq:
f_valid = open(save_dir_results +'/valid.txt','w')
fileout_head = open(save_dir_results +'/valid_head_pred_{}.txt'.format(epoch),'w')
fileout_tail = open(save_dir_results +'/valid_tail_pred_{}.txt'.format(epoch),'w')
fileout_rel = open(save_dir_results +'/valid_rel_pred_{}.txt'.format(epoch), 'w')
for i,t in enumerate(validation_data):
score = self.calculated_score_for_positive_elements(t, epoch, f_valid, 'valid')
if score:
fileout_head.write(' '.join([str(x) for x in score[0]]) + '\n')
fileout_tail.write(' '.join([str(x) for x in score[1]]) + '\n')
fileout_rel.write (' '.join([str(x) for x in score[2]] ) + '\n')
if i%500 == 0:
print('{}. no of valid_triples complete'.format(i))
fileout_head.close()
fileout_tail.close()
fileout_rel.close()
if epoch ==self.p.test_freq:
f_valid.close()
print("Validation Ended")
else:
print('start Testing')
test_data = self.read_valid(self.p.test_data)
f_test = open(save_dir_results +'/test.txt','w')
fileout_head = open(save_dir_results +'/test_head_pred_{}.txt'.format(self.p.restore_epoch),'w')
fileout_tail = open(save_dir_results +'/test_tail_pred_{}.txt'.format(self.p.restore_epoch),'w')
fileout_rel = open(save_dir_results +'/test_rel_pred_{}.txt'.format(self.p.restore_epoch), 'w')
for i,t in enumerate(test_data):
score = self.calculated_score_for_positive_elements(t, self.p.restore_epoch, f_test, 'test')
fileout_head.write(' '.join([str(x) for x in score[0]]) + '\n')
fileout_tail.write(' '.join([str(x) for x in score[1]]) + '\n')
fileout_rel.write (' '.join([str(x) for x in score[2]] ) + '\n')
if i%500 == 0:
print('{}. no of test_triples complete'.format(i))
fileout_head.close()
fileout_tail.close()
fileout_rel.close()
print("Test ended")
if __name__== "__main__":
print('here in main')
parser = argparse.ArgumentParser(description='HyTE')
parser.add_argument('-data_type', dest= "data_type", default ='yago', choices = ['yago','wiki_data'], help ='dataset to choose')
parser.add_argument('-version',dest = 'version', default = 'large', choices = ['large','small'], help = 'data version to choose')
parser.add_argument('-test_freq', dest="test_freq", default = 25, type=int, help='Batch size')
parser.add_argument('-neg_sample', dest="M", default = 5, type=int, help='Batch size')
parser.add_argument('-gpu', dest="gpu", default='1', help='GPU to use')
parser.add_argument('-name', dest="name", default='test_'+str(uuid.uuid4()),help='Name of the run')
parser.add_argument('-drop', dest="dropout", default=1.0, type=float, help='Dropout for full connected layer')
parser.add_argument('-rdrop', dest="rec_dropout", default=1.0, type=float, help='Recurrent dropout for LSTM')
parser.add_argument('-lr', dest="lr", default=0.0001, type=float, help='Learning rate')
parser.add_argument('-lam_1', dest="lambda_1", default=0.5, type=float, help='transE weight')
parser.add_argument('-lam_2', dest="lambda_2", default=0.25, type=float, help='entitty loss weight')
parser.add_argument('-margin', dest="margin", default=1, type=float, help='margin')
parser.add_argument('-batch', dest="batch_size", default= 50000, type=int, help='Batch size')
parser.add_argument('-epoch', dest="max_epochs", default= 5000, type=int, help='Max epochs')
parser.add_argument('-l2', dest="l2", default=0.0, type=float, help='L2 regularization')
parser.add_argument('-seed', dest="seed", default=1234, type=int, help='Seed for randomization')
parser.add_argument('-inp_dim', dest="inp_dim", default = 128, type=int, help='Hidden state dimension of Bi-LSTM')
parser.add_argument('-L1_flag', dest="L1_flag", action='store_false', help='Hidden state dimension of FC layer')
parser.add_argument('-onlytransE', dest="onlytransE", action='store_true', help='Evaluate model on only transE loss')
parser.add_argument('-onlyTest', dest="onlyTest", action='store_true', help='Evaluate model for test data')
parser.add_argument('-restore', dest="restore", action='store_true', help='Restore from the previous best saved model')
parser.add_argument('-res_epoch', dest="restore_epoch", default=200, type =int, help='Restore from the previous best saved model')
args = parser.parse_args()
args.dataset = 'data/'+ args.data_type +'/'+ args.version+'/train.txt'
args.entity2id = 'data/'+ args.data_type +'/'+ args.version+'/entity2id.txt'
args.relation2id = 'data/'+ args.data_type +'/'+ args.version+'/relation2id.txt'
args.valid_data = 'data/'+ args.data_type +'/'+ args.version+'/valid.txt'
args.test_data = 'data/'+ args.data_type +'/'+ args.version+'/test.txt'
args.triple2id = 'data/'+ args.data_type +'/'+ args.version+'/triple2id.txt'
# if not args.restore: args.name = args.name + '_' + time.strftime("%d_%m_%Y") + '_' + time.strftime("%H:%M:%S")
tf.set_random_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
set_gpu(args.gpu)
model = HyTE(args)
print('model object created')
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('enter fitting')
model.fit(sess)