from __future__ import division
from __future__ import print_function
# internal
from load_data import Data
if __name__ == '__main__':
dataset = []
# Load data
data = Data(dataset='WN18', reverse=True)
# Prepare train input and targets
train_data_idxs = data.get_data_idxs(
data.train_data, data.entity_idxs, data.relation_idxs)
print('Number of training data points: {}'.format(len(train_data_idxs)))
dataset.append({'name': 'train', 'data': train_data_idxs})
valid_data_idxs = data.get_data_idxs(
data.valid_data, data.entity_idxs, data.relation_idxs)
print('Number of validation data points: {}'.format(len(valid_data_idxs)))
dataset.append({'name': 'val', 'data': valid_data_idxs})
test_data_idxs = data.get_data_idxs(
data.test_data, data.entity_idxs, data.relation_idxs)
def metric_fn(features, labels, logits):
hits = []
ranks = []
for i in range(10):
hits.append([])
samples = []
data = Data(dataset='WN18', reverse=True)
valid_data_idxs = data.get_data_idxs(data.valid_data, data.entity_idxs,
data.relation_idxs)
# test_data_idxs = data.get_data_idxs(data)
er_vocab = data.get_er_vocab(
data.get_data_idxs(data.valid_data, data.entity_idxs,
data.relation_idxs))
for i in range(0, len(valid_data_idxs), 128):
data_batch, _ = data.get_batch(er_vocab, valid_data_idxs, i)
e2_idx = data_batch[:, 2]
for j in range(data_batch.shape[0]):
logits_list = []
filt = er_vocab[(data_batch[j][0], data_batch[j][1])]
target_value = logits[j][e2_idx[j]]
logits_unstacked = tf.unstack(logits[j])
for it in range(len(logits_unstacked)):
logits_list.append(logits_unstacked[it])
for k in range(len(filt)):
result = tf.cond(tf.equal(e2_idx[j], filt[k]),
lambda: target_value, lambda: 0.0)
logits_list[filt[k]] = result
samples.append(logits_list)
e2_idx = labels
logits = tf.stack(samples)
sort_idxs = tf.argsort(logits, axis=1, direction='DESCENDING')
for j in range(logits.shape[0]):
rank = tf.where(tf.equal(sort_idxs[j], e2_idx[j]))
ranks.append(rank + 1)
for hits_level in range(10):
result = tf.cond(
tf.squeeze(rank) <= hits_level, lambda: 1.0, lambda: 0.0)
hits[hits_level].append(result)
accuracy = tf.metrics.mean(hits[9])
return {"accuracy": accuracy}
