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test_pcnn.py
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import sys
import os
import json
import nrekit
import numpy as np
import tensorflow as tf
dataset_name = "FB15K-237+NYT/text"
if len(sys.argv) > 1:
dataset_name = sys.argv[1]
# dataset_name = 'pcnn_small'
os.environ['CUDA_VISIBLE_DEVICES'] = '7'
dataset_dir = os.path.join('/data/base/Joint-Datasets/', dataset_name)
if not os.path.isdir(dataset_dir):
raise Exception("[ERROR] Dataset dir %s doesn't exist!" % (dataset_dir))
# The first 3 parameters are train / test data file name, word embedding file name and relation-id mapping file name respectively.
train_loader = nrekit.data_loader.json_file_data_loader(os.path.join(dataset_dir, 'train.json'),
os.path.join(dataset_dir, 'word_vec.json'),
os.path.join(dataset_dir, 'rel2id.json'),
mode=nrekit.data_loader.json_file_data_loader.MODE_RELFACT_BAG,
shuffle=True, batch_size=60, case_sensitive=False,
reprocess=False)
test_loader = nrekit.data_loader.json_file_data_loader(os.path.join(dataset_dir, 'test.json'),
os.path.join(dataset_dir, 'word_vec.json'),
os.path.join(dataset_dir, 'rel2id.json'),
mode=nrekit.data_loader.json_file_data_loader.MODE_ENTPAIR_BAG,
shuffle=False, batch_size=60, case_sensitive=False,
reprocess=False)
framework = nrekit.framework.re_framework(train_loader, test_loader)
class model(nrekit.framework.re_model):
encoder = "pcnn"
selector = "att"
def __init__(self, train_data_loader, batch_size, max_length=120):
nrekit.framework.re_model.__init__(self, train_data_loader, batch_size, max_length=max_length)
self.mask = tf.placeholder(dtype=tf.int32, shape=[None, max_length], name="mask")
# Embedding
x = nrekit.network.embedding.word_position_embedding(self.word, self.word_vec_mat, self.pos1, self.pos2)
# Encoder
if model.encoder == "pcnn":
x_train = nrekit.network.encoder.pcnn(x, self.mask, keep_prob=0.5)
x_test = nrekit.network.encoder.pcnn(x, self.mask, keep_prob=1.0)
elif model.encoder == "cnn":
x_train = nrekit.network.encoder.cnn(x, keep_prob=0.5)
x_test = nrekit.network.encoder.cnn(x, keep_prob=1.0)
elif model.encoder == "rnn":
x_train = nrekit.network.encoder.rnn(x, self.length, keep_prob=0.5)
x_test = nrekit.network.encoder.rnn(x, self.length, keep_prob=1.0)
elif model.encoder == "birnn":
x_train = nrekit.network.encoder.birnn(x, self.length, keep_prob=0.5)
x_test = nrekit.network.encoder.birnn(x, self.length, keep_prob=1.0)
else:
raise NotImplementedError
# Selector
if model.selector == "att":
self._train_logit, train_repre = nrekit.network.selector.bag_attention(x_train, self.scope, self.ins_label,
self.rel_tot, True, keep_prob=0.5)
self._test_logit, test_repre = nrekit.network.selector.bag_attention(x_test, self.scope, self.ins_label,
self.rel_tot, False, keep_prob=1.0)
elif model.selector == "ave":
self._train_logit, train_repre = nrekit.network.selector.bag_average(x_train, self.scope, self.rel_tot,
keep_prob=0.5)
self._test_logit, test_repre = nrekit.network.selector.bag_average(x_test, self.scope, self.rel_tot,
keep_prob=1.0)
self._test_logit = tf.nn.softmax(self._test_logit)
elif model.selector == "max":
self._train_logit, train_repre = nrekit.network.selector.bag_maximum(x_train, self.scope, self.ins_label,
self.rel_tot, True, keep_prob=0.5)
self._test_logit, test_repre = nrekit.network.selector.bag_maximum(x_test, self.scope, self.ins_label,
self.rel_tot, False, keep_prob=1.0)
self._test_logit = tf.nn.softmax(self._test_logit)
else:
raise NotImplementedError
# Classifier
self._loss = nrekit.network.classifier.softmax_cross_entropy(self._train_logit, self.label, self.rel_tot,
weights_table=self.get_weights())
def loss(self):
return self._loss
def train_logit(self):
return self._train_logit
def test_logit(self):
return self._test_logit
def get_weights(self):
with tf.variable_scope("weights_table", reuse=tf.AUTO_REUSE):
print("Calculating weights_table...")
_weights_table = np.zeros((self.rel_tot), dtype=np.float32)
for i in range(len(self.train_data_loader.data_rel)):
_weights_table[self.train_data_loader.data_rel[i]] += 1.0
_weights_table = 1 / (_weights_table ** 0.05)
weights_table = tf.get_variable(name='weights_table', dtype=tf.float32, trainable=False,
initializer=_weights_table)
print("Finish calculating")
return weights_table
# # if len(sys.argv) > 2:
# model.encoder = sys.argv[2]
# if len(sys.argv) > 3:
# model.selector = sys.argv[3]
model.encoder = 'pcnn'
model.selector = 'att'
pred_result = framework.test(model, ckpt="/data/base/Joint-Checkpoint/FB15K-237+NYT/text_pcnn_att", return_result=True)
with open("/data/base/Joint-Datasets/FB15K-237+NYT/text/text_pcnn_att_pred.json", 'w') as outfile:
json.dump(pred_result, outfile)
# sorted_pred_result = sorted(pred_result, key=lambda x: x['score'], reverse=True)
# with open('./test_result/' + dataset_name + "_" + model.encoder + "_" + model.selector + "_pred.txt", 'w') as outfile:
# for score_dict in sorted_pred_result:
# entpair = score_dict['entpair'].decode("utf-8").split('#')
# entpair.append(str(score_dict['relation']))
# entpair.append(str(score_dict['score']))
# outfile.write('\t'.join(entpair))
# outfile.write('\n')
for etypair in pred_result.keys():
top1 = pred_result[etypair][:1]
# top5 = pred_result[etypair][:5]
# top10 = pred_result[etypair][:10]
with open('/data/base/Joint-Datasets/FB15K-237+NYT/text/top1.txt', 'a') as outfile:
for score_dict in top1:
entpair = etypair.split('#')
entpair.append(str(score_dict['relation']))
entpair.append(str(score_dict['score']))
outfile.write('\t'.join(entpair))
outfile.write('\n')
# with open('./test_result/top5.txt', 'a') as outfile:
# for score_dict in top5:
# entpair = etypair.split('#')
# entpair.append(str(score_dict['relation']))
# entpair.append(str(score_dict['score']))
# outfile.write('\t'.join(entpair))
# outfile.write('\n')
# with open('./test_result/top10.txt', 'a') as outfile:
# for score_dict in top10:
# entpair = etypair.split('#')
# entpair.append(str(score_dict['relation']))
# entpair.append(str(score_dict['score']))
# outfile.write('\t'.join(entpair))
# outfile.write('\n')