def test_save(model, config, tf_transition_params_test, dataset):
step = 1
ans = []
t = 0
if dataset == "test":
iters = model.testing_iters
else:
iters = model.deving_iters
tp = 0
fn = 0
fp = 0
model.qa.batch_index_dev = 0
model.qa.batch_index_test = 0
while (step - 1) * config.batch_size < iters:
x_lens, test_x, y = model.qa.load_data(config.batch_size, dataset)
t += len(x_lens)
tf_unary_scores = model.sess.run(model.unary_scores,
feed_dict={model.question_ids: test_x,
model.x_lens: x_lens})
for tf_unary_scores_, y_, sequence_lengths_t_ in zip(tf_unary_scores, y,
x_lens):
tf_unary_scores_ = tf_unary_scores_[:sequence_lengths_t_]
viterbi_sequence, _ = tf.contrib.crf.viterbi_decode(
tf_unary_scores_, tf_transition_params_test)
ans.append(viterbi_sequence)
for i, p in enumerate(viterbi_sequence):
if p == 1 and y_[i] == 1:
tp += 1
elif p == 0 and y_[i] == 1:
fn += 1
elif p == 1 and y_[i] == 0:
fp += 1
step += 1
pred = tp * 1.0 / (tp + fp)
recall = tp * 1.0 / (tp + fn)
f1 = 2 * pred * recall / (pred + recall)
print("{} pred: {} recall: {} f1: {}".format(dataset, pred, recall, f1))
questions = model.qa.get_questions(dataset)
print("ans len:\t", t)
print("question:\t", len(questions))
output = []
for i in range(len(questions)):
output.append("qid: {}".format(i))
output.append(FileUtil.list2str(label2entity(questions[i], ans[i]), split="\t"))
FileUtil.writeFile(output, "{}/sq.{}.label".format(config.answer_path, dataset))