# Embedding similarities
avg_emb_sims, ext_emb_sims, greedy_emb_sims = metrics.batch_sim_bow(
hyp_texts, ref_texts)
avg_emb_sim = np.mean(avg_emb_sims)
ext_emb_sim = np.mean(ext_emb_sims)
greedy_emb_sim = np.mean(greedy_emb_sims)
# SIF embedding similarity
sif_emb_sims = metrics.batch_sif_emb_sim(hyp_texts, ref_texts)
sif_emb_sim = np.mean(sif_emb_sims)
# Distinct n-grams
intra_dist1, intra_dist2, inter_dist1, inter_dist2, \
intra_types1, intra_types2, inter_types1, inter_types2 \
= metrics.batch_div_distinct(hyp_texts)
# Average sentence length
hyp_tokens_lst = [
eval_tokenizer.convert_string_to_tokens(sent) for sent in hyp_texts
]
hyp_lens = [len(tokens) for tokens in hyp_tokens_lst]
avg_len = np.mean(hyp_lens)
# Output
log_s = \
f"\n<Tst> - {time.time()-start_time:.3f}s - \n"\
f"\tbleu: {bleu:.5g}\n"\
f"\tbow extrema: {ext_emb_sim:.5g}\n"\
f"\tbow avg: {avg_emb_sim:.5g}\n"\
f"\tbow greedy: {greedy_emb_sim:.5g}\n"\
f"\tSIF emb sim: {sif_emb_sim:.5g}\n"\
f"\tintra dist 1: {intra_dist1:.5g}\n"\
f"\tintra dist 2: {intra_dist2:.5g}\n"\
f"\tinter dist 1: {inter_dist1:.5g}\n"\
f"\tinter dist 2: {inter_dist2:.5g}\n"\
bleu_scores = metrics.batch_bleu(hyp_texts, ref_texts)
bleu = np.mean(bleu_scores)
# Embedding similarities
avg_emb_sims, ext_emb_sims, greedy_emb_sims = metrics.batch_sim_bow(hyp_texts, ref_texts)
avg_emb_sim = np.mean(avg_emb_sims)
ext_emb_sim = np.mean(ext_emb_sims)
greedy_emb_sim = np.mean(greedy_emb_sims)
# SIF embedding similarity
sif_emb_sims = metrics.batch_sif_emb_sim(hyp_texts, ref_texts)
sif_emb_sim = np.mean(sif_emb_sims)
# Distinct n-grams
intra_dist1, intra_dist2, inter_dist1, inter_dist2, \
intra_types1, intra_types2, inter_types1, inter_types2 \
= metrics.batch_div_distinct(hyp_texts)
# Average sentence length
hyp_tokens_lst = [eval_tokenizer.convert_string_to_tokens(sent) for sent in hyp_texts]
hyp_lens = [len(tokens) for tokens in hyp_tokens_lst]
avg_len = np.mean(hyp_lens)
# Output
log_s = \
f"\n<Tst> - {time.time()-start_time:.3f}s - \n"\
f"\tbleu: {bleu:.5g}\n"\
f"\tbow extrema: {ext_emb_sim:.5g}\n"\
f"\tbow avg: {avg_emb_sim:.5g}\n"\
f"\tbow greedy: {greedy_emb_sim:.5g}\n"\
f"\tSIF emb sim: {sif_emb_sim:.5g}\n"\
f"\tintra dist 1: {intra_dist1:.5g}\n"\
f"\tintra dist 2: {intra_dist2:.5g}\n"\
f"\tinter dist 1: {inter_dist1:.5g}\n"\
f"\tinter dist 2: {inter_dist2:.5g}\n"\
f"\tintra types 1: {intra_types1:.5g}\n"\