def run_evaluation():
print(params.output_path)
print(tf.train.latest_checkpoint(params.output_path, 'checkpoint'))
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_relations(params.data_path)
test_data = ntn_input.load_test_data(params.data_path)
test_data = data_to_indexed(test_data, entities_list, relations_list)
batch_size= len(test_data)
num_entities = len(entities_list)
num_relations = len(relations_list)
slice_size = params.slice_size
(init_word_embeds, entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
batches, labels = data_to_relation_sets(test_data, num_relations)
with tf.Graph().as_default():
sess = tf.Session()
batch_placeholders = [tf.placeholder(tf.float32, shape=(None, 3)) for i in range(num_relations)]
label_placeholders = [tf.placeholder(tf.float32, shape=(None, 1)) for i in range(num_relations)]
corrupt_placeholder = tf.placeholder(tf.bool, shape=(1))
inference = ntn.inference(batch_placeholders, corrupt_placeholder, init_word_embeds, entity_to_wordvec, \
num_entities, num_relations, slice_size, batch_size, True, label_placeholders)
eval_correct = ntn.eval(inference)
saver = tf.train.Saver()
saver.restore(sess, params.output_path+'around100/Wordnet70.sess')
#init = tf.initialize_all_variables()
#sess.run(init)
print do_eval(sess, eval_correct, batch_placeholders, label_placeholders, corrupt_placeholder, batches, labels, batch_size)
def getPredictions():
best_thresholds = getThresholds()
test_data = ntn_input.load_test_data()
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_entities(params.data_path)
num_entities = len(entities_list)
num_relations = len(relations_list)
slice_size = params.slice_size
(init_word_embeds, entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
batch_placeholder = tf.placeholder(tf.float32, shape=(4, batch_size))
corrupt_placeholder = tf.placeholder(tf.bool, shape=(1)) #Which of e1 or e2 to corrupt?
predictions_list = ntn.inference(batch_placeholder, corrupt_placeholder, init_word_embeds,entity_to_wordvec, num_entities, num_relations, slice_size, batch_size)
predictions = tf.zeros((test_data.shape[0], 1))
for i in range(test_data.shape[0]):
# get relation
rel = test_data[i, 1]
# get labels based on predictions
if(preictions_list[i, 0] <= self.best_thresholds[rel, 0]):
predictions[i, 0] = 1
else:
predictions[i, 0] = -1
return predictions
def run_training():
print("Begin!")
#python list of (e1, R, e2) for entire training set in string form
print("Load training data...")
raw_training_data = ntn_input.load_training_data(params.data_path)
print("Load entities and relations...")
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_relations(params.data_path)
#python list of (e1, R, e2) for entire training set in index form
indexed_training_data = data_to_indexed(raw_training_data, entities_list, relations_list)
print("Load embeddings...")
(init_word_embeds, entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
# init_word_embeds size (67447,100), entity_to_wordvec size (38696)
# entity_to_wordvec -> [[45792], [50003], [19154, 50004], [11403], [7456, 6932], [47896, 50004], [24589], [50005, 50006, 50004], [6551], [12288]]
num_entities = len(entities_list)
num_relations = len(relations_list)
num_iters = params.num_iter
batch_size = params.batch_size
corrupt_size = params.corrupt_size
slice_size = params.slice_size
with tf.Graph().as_default():
print("Starting to build graph "+str(datetime.datetime.now()))
batch_placeholders = [tf.placeholder(tf.int32, shape=(None, 3), name='batch_'+str(i)) for i in range(num_relations)]
label_placeholders = [tf.placeholder(tf.float32, shape=(None, 1), name='label_'+str(i)) for i in range(num_relations)]
corrupt_placeholder = tf.placeholder(tf.bool, shape=(1)) #Which of e1 or e2 to corrupt?
inference = ntn.inference(batch_placeholders, corrupt_placeholder, init_word_embeds, entity_to_wordvec, \
num_entities, num_relations, slice_size, batch_size, False, label_placeholders)
# [2,r*batch_predictions]
loss = ntn.loss(inference, params.regularization)
training = ntn.training(loss, params.learning_rate)
# Create a session for running Ops on the Graph.
print('1 Here------->>>>>>>> run init <<<<<<<<------------')
sess = tf.Session()
# Run the Op to initialize the variables.
init = tf.global_variables_initializer()
print('2 Here------->>>>>>>> run init <<<<<<<<------------')
sess.run(init)
print('run init')
saver = tf.train.Saver(tf.trainable_variables())
for i in range(1, num_iters):
print("Starting iter "+str(i)+" "+str(datetime.datetime.now()))
data_batch = get_batch(batch_size, indexed_training_data, num_entities, corrupt_size) # [batch*10,4]
relation_batches = split_batch(data_batch, num_relations) # [num_relations,batch*10,3]
if i % params.save_per_iter == 0:
saver.save(sess, params.output_path+"/"+params.data_name+str(i)+'.sess')
feed_dict = fill_feed_dict(relation_batches, params.train_both, batch_placeholders, label_placeholders, corrupt_placeholder)
_, loss_value = sess.run([training, loss], feed_dict=feed_dict)
print('loss_'+str(i)+': ',loss_value)
def getThresholds():
dev_data = ntn_input.load_dev_data()
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_relations(params.data_path)
num_entities = len(entities_list)
num_relations = len(relations_list)
slice_size = params.slice_size
(init_word_embeds,
entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
batch_placeholder = tf.placeholder(tf.float32, shape=(4, batch_size))
corrupt_placeholder = tf.placeholder(
tf.bool, shape=(1)) # Which of e1 or e2 to corrupt?
predictions_list = ntn.inference(batch_placeholder, corrupt_placeholder,
init_word_embeds, entity_to_wordvec,
num_entities, num_relations, slice_size,
batch_size)
min_score = tf.reduce_min(predictions_list)
max_score = tf.reduce_max(predictions_list)
# initialize thresholds and accuracies
best_thresholds = tf.zeros([params.num_relations, 1])
best_accuracies = tf.zeros([params.num_relations, 1])
for i in range(params.num_relations):
best_thresholds[i, :] = min_score
best_accuracies[i, :] = -1
score = min_score
increment = 0.01
while (score <= max_score):
# iterate through relations list to find
for i in range(params.num_relations):
current_relation_list = (dev_data[:, 1] == i)
predictions = (predictions_list[current_relation_list, 0] <=
score) * 2 - 1
accuracy = tf.reduce_mean(
(predictions == dev_labels[current_relations_list, 0]))
# update threshold and accuracy
if (accuracy > best_accuracies[i, 0]):
best_accuracies[i, 0] = accuracy
best_thresholds[i, 0] = score
score += increment
# store threshold values
return best_thresholds
def run_training():
print("Begin!")
#python list of (e1, R, e2) for entire training set in string form
print("Load training data...")
raw_training_data = ntn_input.load_training_data(params.data_path)
print("Load entities and relations...")
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_relations(params.data_path)
#python list of (e1, R, e2) for entire training set in index form
indexed_training_data = data_to_indexed(raw_training_data, entities_list, relations_list)
print("Load embeddings...")
(init_word_embeds, entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
num_entities = len(entities_list)
num_relations = len(relations_list)
num_iters = params.num_iter
batch_size = params.batch_size
corrupt_size = params.corrupt_size
slice_size = params.slice_size
with tf.Graph().as_default():
print("Starting to build graph "+str(datetime.datetime.now()))
batch_placeholders = [tf.placeholder(tf.int32, shape=(None, 3), name='batch_'+str(i)) for i in range(num_relations)]
label_placeholders = [tf.placeholder(tf.float32, shape=(None, 1), name='label_'+str(i)) for i in range(num_relations)]
corrupt_placeholder = tf.placeholder(tf.bool, shape=(1)) #Which of e1 or e2 to corrupt?
inference = ntn.inference(batch_placeholders, corrupt_placeholder, init_word_embeds, entity_to_wordvec, \
num_entities, num_relations, slice_size, batch_size, False, label_placeholders)
loss = ntn.loss(inference, params.regularization)
training = ntn.training(loss, params.learning_rate)
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Run the Op to initialize the variables.
init = tf.initialize_all_variables()
sess.run(init)
saver = tf.train.Saver(tf.trainable_variables())
for i in range(1, num_iters):
print("Starting iter "+str(i)+" "+str(datetime.datetime.now()))
data_batch = get_batch(batch_size, indexed_training_data, num_entities, corrupt_size)
relation_batches = split_batch(data_batch, num_relations)
if i % params.save_per_iter == 0:
saver.save(sess, params.output_path+"/"+params.data_name+str(i)+'.sess')
feed_dict = fill_feed_dict(relation_batches, params.train_both, batch_placeholders, label_placeholders, corrupt_placeholder)
_, loss_value = sess.run([training, loss], feed_dict=feed_dict)
def run_training():
print("Begin!")
#python list of (e1, R, e2) for entire training set in string form
print("Load training data...")
raw_training_data = ntn_input.load_training_data(params.data_path)
print("Load entities and relations...")
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_relations(params.data_path)
#python list of (e1, R, e2) for entire training set in index form
indexed_training_data = data_to_indexed(raw_training_data, entities_list, relations_list)
print("Load embeddings...")
(init_word_embeds, entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
num_entities = len(entities_list)
num_relations = len(relations_list)
num_iters = params.num_iter
batch_size = params.batch_size
corrupt_size = params.corrupt_size
slice_size = params.slice_size
with tf.Graph().as_default():
print("Starting to build graph "+str(datetime.datetime.now()))
batch_placeholders = [tf.placeholder(tf.int32, shape=(None, 3), name='batch_'+str(i)) for i in range(num_relations)]
label_placeholders = [tf.placeholder(tf.float32, shape=(None, 1), name='label_'+str(i)) for i in range(num_relations)]
corrupt_placeholder = tf.placeholder(tf.bool, shape=(1)) #Which of e1 or e2 to corrupt?
inference = ntn.inference(batch_placeholders, corrupt_placeholder, init_word_embeds, entity_to_wordvec, \
num_entities, num_relations, slice_size, batch_size, False, label_placeholders)
loss = ntn.loss(inference, params.regularization)
training = ntn.training(loss, params.learning_rate)
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Run the Op to initialize the variables.
init = tf.initialize_all_variables()
sess.run(init)
saver = tf.train.Saver(tf.trainable_variables())
for i in range(1, num_iters):
print("Starting iter "+str(i)+" "+str(datetime.datetime.now()))
data_batch = get_batch(batch_size, indexed_training_data, num_entities, corrupt_size)
relation_batches = split_batch(data_batch, num_relations)
if i % params.save_per_iter == 0:
saver.save(sess, params.output_path+"/"+params.data_name+str(i)+'.sess')
feed_dict = fill_feed_dict(relation_batches, params.train_both, batch_placeholders, label_placeholders, corrupt_placeholder)
_, loss_value = sess.run([training, loss], feed_dict=feed_dict)
def getThresholds():
dev_data = ntn_input.load_dev_data()
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_entities(params.data_path)
num_entities = len(entities_list)
num_relations = len(relations_list)
slice_size = params.slice_size
(init_word_embeds, entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
batch_placeholder = tf.placeholder(tf.float32, shape=(4, batch_size))
corrupt_placeholder = tf.placeholder(tf.bool, shape=(1)) #Which of e1 or e2 to corrupt?
predictions_list = ntn.inference(batch_placeholder, corrupt_placeholder, init_word_embeds,entity_to_wordvec, num_entities, num_relations, slice_size, batch_size)
min_score = tf.reduce_min(predictions_list)
max_score = tf.reduce_max(predictions_list)
# initialize thresholds and accuracies
best_thresholds = tf.zeros([params.num_relations, 1])
best_accuracies = tf.zeros([params.num_relations, 1])
for i in range(params.num_relations):
best_thresholds[i, :] = score_min
best_accuracies[i, :] = -1
score = min_score
increment = 0.01
while(score <= max_score):
# iterate through relations list to find
for i in range(params.num_relations):
current_relation_list = (dev_data[:, 1] == i)
predictions = (predictions_list[current_relation_list, 0] <= score) * 2 - 1
accuracy = tf.reduce_mean((predictions == dev_labels[current_relations_list, 0]))
# update threshold and accuracy
if(accuracy > best_accuracies[i, 0]):
best_accuracies[i, 0] = accuracy
best_thresholds[i, 0] = score
score += increment
# store threshold values
return best_thresholds
def run_training():
print("Begin!")
# python list of (e1, R, e2) for entire training set in string form
print("Load training data...")
# shape of raw training data: (112581, 3)
raw_training_data = ntn_input.load_training_data(params.data_path)
raw_dev_data = ntn_input.load_dev_data(params.data_path)
raw_test_data = ntn_input.load_test_data(params.data_path)
print("Load entities and relations...")
entities_list = ntn_input.load_entities(params.data_path)
relations_list = ntn_input.load_relations(params.data_path)
num_entities = len(entities_list) # entity: 38696
num_relations = len(relations_list) # relations: 11
# python list of (e1, R, e2) for entire training set in index form
indexed_training_data = data_to_indexed(raw_training_data, entities_list,
relations_list)
indexed_dev_data = data_to_indexed(raw_dev_data, entities_list,
relations_list)
indexed_test_data = data_to_indexed(raw_test_data, entities_list,
relations_list)
print("Load embeddings...")
# shape of word embeds: 67447, 100; number of entities: 38696
(init_word_embeds,
entity_to_wordvec) = ntn_input.load_init_embeds(params.data_path)
num_epoches = params.epoches
batch_size = params.batch_size
corrupt_size = params.corrupt_size
slice_size = params.slice_size
n_iterations_per_epoch = len(indexed_training_data) // batch_size
n_iterations_validation = len(indexed_dev_data) // batch_size
n_iterations_evaluation = len(indexed_test_data) // batch_size
print("# of iteration/epoch", n_iterations_per_epoch)
print("# of iteration/validation", n_iterations_validation)
print("# of iteration/evaluation", n_iterations_evaluation)
with tf.Graph().as_default():
print("Starting to build graph " + str(datetime.datetime.now()))
batch_placeholders = [
tf.placeholder(tf.int32, shape=(None, 3), name='batch_' + str(i))
for i in range(num_relations)
]
label_placeholders = [
tf.placeholder(tf.float32, shape=(None, 1), name='label_' + str(i))
for i in range(num_relations)
]
corrupt_placeholder = tf.placeholder(tf.bool, shape=1)
train_inference = ntn.inference(batch_placeholders,
corrupt_placeholder, init_word_embeds,
entity_to_wordvec, num_entities,
num_relations, slice_size, batch_size,
False, label_placeholders)
test_inference = ntn.inference(batch_placeholders, corrupt_placeholder,
init_word_embeds, entity_to_wordvec,
num_entities, num_relations, slice_size,
batch_size, True, label_placeholders)
train_loss = ntn.loss(train_inference, params.regularization)
training = ntn.training(train_loss, params.learning_rate)
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Run the Op to initialize the variables.
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver(tf.trainable_variables())
# training
for i in range(1, num_epoches):
print("Starting iter " + str(i) + " " +
str(datetime.datetime.now()))
for j in range(1, n_iterations_per_epoch + 1):
data_train_batch = get_train_batch(batch_size,
indexed_training_data,
num_entities, corrupt_size)
relation_train_batches = split_train_batch(
data_train_batch, num_relations)
feed_dict_training = fill_feed_dict(relation_train_batches,
params.train_both,
batch_placeholders,
label_placeholders,
corrupt_placeholder)
_, train_loss_value, train_eval_value = sess.run(
[training, train_loss,
ntn.eval(train_inference)],
feed_dict=feed_dict_training)
print("Iter {}, batch {}, Training data loss = {}".format(
i, j, train_eval_value))
if i % params.save_per_iter == 0:
saver.save(
sess, params.output_path + "/" + params.data_name +
str(i) + '.sess')
print("Model saved at iter {}".format(i))
# At the end of each epoch, test the dev data
for j in range(1, n_iterations_validation + 1):
data_dev_batch = get_test_batch(batch_size, indexed_dev_data)
relation_dev_batches = split_test_batch(
data_dev_batch, num_relations)
feed_dict_dev = fill_feed_dict(relation_dev_batches,
params.train_both,
batch_placeholders,
label_placeholders,
corrupt_placeholder)
dev_eval_value = sess.run(ntn.eval(test_inference),
feed_dict=feed_dict_dev)
print("Iter {}, batch {}, Dev data loss = {}".format(
i, j, dev_eval_value))
# testing
for j in range(1, n_iterations_evaluation):
data_test_batch = get_test_batch(batch_size, indexed_test_data)
relation_test_batches = split_test_batch(data_test_batch,
num_relations)
feed_dict_testing = fill_feed_dict(relation_test_batches,
params.train_both,
batch_placeholders,
label_placeholders,
corrupt_placeholder)
test_eval_value = sess.run(ntn.eval(test_inference),
feed_dict=feed_dict_testing)
print("Final Test Accuracy = {}".format(test_eval_value))
def prepare_data(corrupt_samples):
raw_training_data = ntn_input.load_training_data(ntn_input.data_path)
raw_dev_data = ntn_input.load_dev_data(ntn_input.data_path)
print("Load entities and relations...")
entities_list = ntn_input.load_entities(ntn_input.data_path)
relations_list = ntn_input.load_relations(ntn_input.data_path)
#python list of (e1, R, e2) for entire training set in index form
indexed_training_data = data_to_indexed(raw_training_data, entities_list,
relations_list)
indexed_dev_data = data_to_indexed(raw_dev_data, entities_list,
relations_list)
print("Load embeddings...")
(init_word_embeds,
entity_to_wordvec) = ntn_input.load_init_embeds(ntn_input.data_path)
num_entities = len(entities_list)
num_relations = len(relations_list)
e1, e2, labels_train, labels_dev, t1, t2 = {}, {}, [], [], {}, {}
for i in indexed_training_data:
try:
e1[i[1]].append(init_word_embeds[i[0]])
e2[i[1]].append(init_word_embeds[i[2]])
except:
e1[i[1]] = []
e2[i[1]] = []
max_len_e1 = max([len(e1[i]) for i in e1])
labels_train = [1] * max_len_e1
e1, e2 = fill_entity(e1, e2, max_len_e1)
#bre
for i in range(max_len_e1):
for j in range(corrupt_samples):
for k in range(11):
e1[k].append(init_word_embeds[indexed_training_data[i][0]])
e2[k].append(init_word_embeds[random.randrange(
0, len(init_word_embeds))])
labels_train.append(0)
for i in indexed_dev_data:
try:
t1[i[1]].append(init_word_embeds[i[0]])
t2[i[1]].append(init_word_embeds[i[2]])
except:
t1[i[1]] = []
t2[i[1]] = []
max_len_t1 = max([len(t1[i]) for i in t1])
labels_dev = [1] * max_len_t1
t1, t2 = fill_entity(t1, t2, max_len_t1)
for i in range(max_len_t1):
for j in range(corrupt_samples):
for k in range(11):
t1[k].append(init_word_embeds[indexed_dev_data[i][0]])
t2[k].append(init_word_embeds[random.randrange(
0, len(init_word_embeds))])
labels_dev.append(0)
labels_train, labels_dev = np.array(labels_train), np.array(labels_dev)
new_lab_train, new_lab_dev = [], []
for i in labels_train:
new_lab_train.append([i] * 11)
for j in labels_train:
new_lab_dev.append([j] * 11)
return e1, e2, np.array(new_lab_train), t1, t2, np.array(
new_lab_dev), num_relations