def test(model, test_dataset, dictionary, sess):
batches_idx = helper.get_batches_idx(len(test_dataset), args.batch_size)
print('number of test batches = ', len(batches_idx))
num_batches = len(batches_idx)
predicts, targets = [], []
map, mrr, ndcg_1, ndcg_3, ndcg_5, ndcg_10 = 0, 0, 0, 0, 0, 0
for batch_no in range(1, num_batches + 1): #1,...,num_batches
batch_idx = batches_idx[batch_no - 1]
batch_data = [test_dataset.dataset[i] for i in batch_idx]
#将一批数据转换为模型输入的格式
(hist_query_input, hist_doc_input, session_num, hist_query_num,
hist_query_len, hist_click_num, hist_doc_len, cur_query_input,
cur_doc_input, cur_query_num, cur_query_len, cur_click_num,
cur_doc_len, query, q_len, doc, d_len, y, next_q, next_q_len,
maximum_iterations) = helper.batch_to_tensor(batch_data,
args.max_query_len,
args.max_doc_len)
indices, slots_num = model.get_memory_input(session_num)
feed_dict = {
model.hist_query_input: hist_query_input,
model.hist_doc_input: hist_doc_input,
model.session_num: session_num,
model.hist_query_num: hist_query_num,
model.hist_query_len: hist_query_len,
model.hist_click_num: hist_click_num,
model.hist_doc_len: hist_doc_len,
model.cur_query_input: cur_query_input,
model.cur_doc_input: cur_doc_input,
model.cur_query_num: cur_query_num,
model.cur_query_len: cur_query_len,
model.cur_click_num: cur_click_num,
model.cur_doc_len: cur_doc_len,
model.q: query,
model.q_len: q_len,
model.d: doc,
model.d_len: d_len,
model.indices: indices,
model.slots_num: slots_num,
model.maximum_iterations: maximum_iterations
}
click_prob_, predicting_ids_, predicting_len_ = sess.run(
[model.click_prob, model.predicting_ids, model.predicting_len],
feed_dict=feed_dict)
map += mean_average_precision(click_prob_, y)
mrr += MRR(click_prob_, y)
ndcg_1 += NDCG(click_prob_, y, 1)
ndcg_3 += NDCG(click_prob_, y, 3)
ndcg_5 += NDCG(click_prob_, y, 5)
ndcg_10 += NDCG(click_prob_, y, 10)
batch_predicting_text = helper.generate_predicting_text(
predicting_ids_, predicting_len_, dictionary)
batch_target_text, batch_query_text = helper.generate_target_text(
batch_data, dictionary, args.max_query_len)
predicts += batch_predicting_text
targets += batch_target_text
map = map / num_batches
mrr = mrr / num_batches
ndcg_1 = ndcg_1 / num_batches
ndcg_3 = ndcg_3 / num_batches
ndcg_5 = ndcg_5 / num_batches
ndcg_10 = ndcg_10 / num_batches
print('MAP - ', map)
print('MRR - ', mrr)
print('NDCG@1 - ', ndcg_1)
print('NDCG@3 - ', ndcg_3)
print('NDCG@5 - ', ndcg_5)
print('NDCG@10 - ', ndcg_10)
print("targets size = ", len(targets))
print("predicts size = ", len(predicts))
multi_bleu.print_multi_bleu(predicts, targets)
args.max_example,
whole_session=True)
print('test set size = ', len(test_corpus.data))
targets, candidates = [], []
if args.attn_type:
fw = open(args.save_path + 'seq2seq_attn_predictions.txt', 'w')
else:
fw = open(args.save_path + 'seq2seq_predictions.txt', 'w')
for prev_q, current_q in test_corpus.data:
q1_var, q1_len, q2_var, q2_len = helper.batch_to_tensor(
[(prev_q, current_q)],
dictionary,
reverse=args.reverse,
iseval=True)
if args.cuda:
q1_var = q1_var.cuda() # batch_size x max_len
q2_var = q2_var.cuda() # batch_size x max_len
q2_len = q2_len.cuda() # batch_size
target = generate_next_query(model, q1_var, q1_len, dictionary)
candidate = " ".join(current_q.query_terms[1:-1])
targets.append(target)
candidates.append(candidate)
fw.write(candidate + '\t' + target + '\n')
fw.close()
print("target size = ", len(targets))
print("candidate size = ", len(candidates))
multi_bleu.print_multi_bleu(targets, candidates)