def dist_eval(args):
iterator = MINDIterator
model = NRMSModel(hparams, iterator, seed=seed)
model.model.load_weights(
os.path.join(model_dir, "ckpt_ep{}".format(args.ep)))
test_news_file = os.path.join(data_path, "valid", 'news.tsv')
test_behaviors_file = os.path.join(data_path, "valid",
'behaviors.{}.tsv'.format(args.fsplit))
group_impr_indexes, group_labels, group_preds = model.run_slow_eval(
test_news_file, test_behaviors_file)
with open(
os.path.join(
data_path,
'results/nrms-valid-prediction.{}.txt'.format(args.fsplit)),
'w') as f:
for labels, preds in tqdm(zip(group_labels, group_preds)):
label_str = ",".join([str(x) for x in labels])
pred_str = ",".join([str(x) for x in preds])
f.write("{}\t{}\n".format(label_str, pred_str))
def test(args):
iterator = MINDIterator
model = NRMSModel(hparams, iterator, seed=seed, test_mode=True)
model.model.load_weights(
os.path.join(model_dir, "ckpt_ep{}".format(args.ep)))
test_news_file = os.path.join(data_path, "test", 'news.tsv')
test_behaviors_file = os.path.join(data_path, "test",
'behaviors.{}.tsv'.format(args.fsplit))
group_impr_indexes, group_labels, group_preds = model.run_slow_eval(
test_news_file, test_behaviors_file)
with open(
os.path.join(
data_path,
'results/nrms-test-prediction.{}.txt'.format(args.fsplit)),
'w') as f:
for impr_index, preds in tqdm(zip(group_impr_indexes, group_preds)):
impr_index += 1
pred_rank = (np.argsort(np.argsort(preds)[::-1]) + 1).tolist()
pred_rank = '[' + ','.join([str(i) for i in pred_rank]) + ']'
f.write(' '.join([str(impr_index), pred_rank]) + '\n')
iterator = MINDIterator
model = NRMSModel(hparams, iterator, seed=seed)
# print(model.run_eval(valid_news_file, valid_behaviors_file))
model.fit(train_news_file, train_behaviors_file, valid_news_file, valid_behaviors_file)
# res_syn = model.run_eval(valid_news_file, valid_behaviors_file)
# print(res_syn)
# pm.record("res_syn", res_syn)
model_path = os.path.join(model_path, "model")
os.makedirs(model_path, exist_ok=True)
model.model.save_weights(os.path.join(model_path, "nrms_ckpt"))
group_impr_indexes, group_labels, group_preds = model.run_slow_eval(test_news_file, test_behaviors_file)
# res = cal_metric(group_labels, group_preds, hparams.metrics)
with open(os.path.join(data_path, 'nrms_prediction.txt'), 'w') as f:
for impr_index, preds in tqdm(zip(group_impr_indexes, group_preds)):
impr_index += 1
pred_rank = (np.argsort(np.argsort(preds)[::-1]) + 1).tolist()
pred_rank = '[' + ','.join([str(i) for i in pred_rank]) + ']'
f.write(' '.join([str(impr_index), pred_rank])+ '\n')
f = zipfile.ZipFile(os.path.join(data_path, 'nrms_prediction.zip'), 'w', zipfile.ZIP_DEFLATED)
f.write(os.path.join(data_path, 'nrms_prediction.txt'), arcname='nrms_prediction.txt')
f.close()