def evaluate_simple(eval_file, answer_dict):
reference_corpus = []
translation_corpus = []
rouges = []
meteor = Meteor()
res, gts = [], []
for key, answers in answer_dict.items():
answers = sorted(answers, key=lambda x: x[0], reverse=True)
ground_truths = [
list(map(lambda x: x.lower(), eval_file[key]["question"][1:-1]))
]
prediction = answers[0][1].lower().split()
translation_corpus.append(prediction)
reference_corpus.append(ground_truths)
rouge = compute_rouge_L(prediction, ground_truths)
rouges.append(rouge)
res.append(' '.join(prediction))
gts.append([' '.join(ground_truth) for ground_truth in ground_truths])
bleu = compute_bleu(reference_corpus, translation_corpus)
mete = meteor.compute_score(gts, res)
return {
"bleu": bleu[0] * 100,
"meteor": mete[0] * 100,
"rougeL": np.mean(rouges) * 100
}
def evaluate_full(gts, gen_res, gen_dict, gts_arc, emo_feat, method=None):
assert (len(gts) == len(gen_res))
reward = 0
if method == "comet":
assert (len(gen_dict) == len(gts_arc))
reward = compute_edit_distance_v2(gen_dict, gts_arc)
elif method == "clf":
pred_dist = np.zeros_like(emo_feat)
for k, v in gen_dict.items():
pred_dist[k, :] = v
reward = compute_emotion_distance(pred_dist,
emo_feat,
batch_normalize=True,
method=method)
elif method == 'clf_prob':
pred_dist = np.zeros_like(emo_feat)
for k, v in gen_dict.items():
pred_dist[k, :] = v
reward = get_emotion_prob(pred_dist, gts_arc, batch_normalize=True)
translation_corpus = gen_res
reference_corpus = gts
rouges = []
meteor = Meteor()
bleu = compute_bleu([[j.split()] for j in reference_corpus],
[i.split() for i in translation_corpus])
mete = meteor.compute_score([[r] for r in reference_corpus],
translation_corpus)
return {
"bleu": bleu[0] * 100,
"meteor": mete[0] * 100,
"best_reward": reward
}
def evaluate(eval_file, answer_dict):
reference_corpus = []
translation_corpus = []
translation_corpus_rouge_oracle = []
translation_corpus_bleu_oracle = []
rouges = []
div_bleus = []
rouges_oracle = []
meteor = Meteor()
res, res_oracle, gts = [], [], []
for key, answers in answer_dict.items():
answers = sorted(answers, key=lambda x: x[0], reverse=True)
ground_truths = [
list(map(lambda x: x.lower(), eval_file[key]["question"][1:-1]))
]
prediction = answers[0][1].lower().split()
answers_tmp = []
for i, answer in enumerate(answers):
rouge = compute_rouge_L(answer[1].lower().split(), ground_truths)
mete = meteor.compute_score(
[[' '.join(ground_truth) for ground_truth in ground_truths]],
[' '.join(answer[1].lower().split())])
bleu = compute_bleu([ground_truths], [answer[1].lower().split()],
smooth=True)
answers_tmp.append((rouge, mete[0], bleu[0], answer[0], answer[1]))
answers_rouge = sorted(answers_tmp, key=lambda x: x[0], reverse=True)
answers_mete = sorted(answers_tmp, key=lambda x: x[1], reverse=True)
answers_bleu = sorted(answers_tmp, key=lambda x: x[2], reverse=True)
prediction_rouge_oracle = answers_rouge[0][4].lower().split()
prediction_mete_oracle = answers_mete[0][4].lower().split()
prediction_bleu_oracle = answers_bleu[0][4].lower().split()
translation_corpus.append(prediction)
translation_corpus_rouge_oracle.append(prediction_rouge_oracle)
translation_corpus_bleu_oracle.append(prediction_bleu_oracle)
reference_corpus.append(ground_truths)
rouge = compute_rouge_L(prediction, ground_truths)
rouge_oracle = compute_rouge_L(prediction_rouge_oracle, ground_truths)
rouges.append(rouge)
rouges_oracle.append(rouge_oracle)
res.append(' '.join(prediction))
res_oracle.append(' '.join(prediction_mete_oracle))
gts.append([' '.join(ground_truth) for ground_truth in ground_truths])
div_bleus.append(diverse_bleu(answers))
bleu = compute_bleu(reference_corpus, translation_corpus)
bleu_oracle = compute_bleu(reference_corpus,
translation_corpus_bleu_oracle)
mete = meteor.compute_score(gts, res)
mete_oracle = meteor.compute_score(gts, res_oracle)
return {
"bleu": bleu[0] * 100,
"meteor": mete[0] * 100,
"rougeL": np.mean(rouges) * 100,
"bleu_oracle": bleu_oracle[0] * 100,
"meteor_oracle": mete_oracle[0] * 100,
"rougeL_oracle": np.mean(rouges_oracle) * 100,
"diverse_bleu": np.mean(div_bleus) * 100
}
def evaluate_rl(eval_file,
qa_id,
symbols,
symbols_rl,
id2word,
metric="rouge"):
meteor = Meteor()
ques_limit = len(symbols)
batch_size, _ = symbols[0].shape
rewards = np.zeros([batch_size], dtype=np.float32)
rewards_rl = np.zeros([batch_size], dtype=np.float32)
rewards_base = np.zeros([batch_size], dtype=np.float32)
ques_rl = np.zeros([batch_size, ques_limit], dtype=np.int32)
for i, (qid, syms,
syms_rl) in enumerate(zip(qa_id, zip(*symbols), zip(*symbols_rl))):
syms = list(np.reshape(syms, [-1]))
syms_rl = list(syms_rl)
ground_truths = [
list(
map(lambda x: x.lower(),
eval_file[str(qid)]["question"][1:-1]))
]
context_tokens = eval_file[str(qid)]["paragraph"]
if 3 in syms:
syms = syms[:syms.index(3)]
if 3 in syms_rl:
syms_rl = syms_rl[:syms_rl.index(3)]
prediction = [
id2word[sym] if sym in id2word else context_tokens[sym -
len(id2word)]
for sym in syms
]
prediction_rl = [
id2word[sym] if sym in id2word else context_tokens[sym -
len(id2word)]
for sym in syms_rl
]
if metric == "rouge":
reward_base = compute_rouge_L(prediction, ground_truths)
reward_rl = compute_rouge_L(prediction_rl, ground_truths)
elif metric == "bleu":
reward_base, _ = compute_bleu([ground_truths], [prediction],
smooth=True)
reward_rl, _ = compute_bleu([ground_truths], [prediction_rl],
smooth=True)
elif metric == "meteor":
reward_base = meteor.compute_score(
[[' '.join(ground_truth) for ground_truth in ground_truths]],
[' '.join(prediction)])[0]
reward_rl = meteor.compute_score(
[[' '.join(ground_truth) for ground_truth in ground_truths]],
[' '.join(prediction_rl)])[0]
else:
print("Wrong Metric!")
exit()
rewards[i] = reward_rl - reward_base
rewards_rl[i] = reward_rl
rewards_base[i] = reward_base
# add GO and EOS
syms_rl = [2] + syms_rl[:ques_limit - 2] + [3]
for j, sym_rl in enumerate(syms_rl):
ques_rl[i, j] = sym_rl
return rewards, rewards_rl, rewards_base, ques_rl