def get_metrics(self, ref_dir, dec_dir):
reference = []
decoded = []
for i, j in zip(sorted(glob.glob(dec_dir + '/' + '*.txt')),
sorted(glob.glob(ref_dir + '/' + '*.txt'))):
ref_tex = ''
dec_tex = ''
for k in open(i).readlines():
dec_tex = dec_text + k.strip()
if len(dec_tex) == 0:
dec_tex = ' '
for l in open(j).readlines():
ref_tex = ref_tex + l
reference.append(ref_tex)
decoded.append(dec_tex)
if len(reference) != len(decoded):
raise ValueError(
"Hypotheses and References don't have equal lengths")
rouge_dict = rouge.rouge(decoded, reference)
file_path = os.path.join(self._decode_dir, 'results.txt')
f = open(file_path, 'w')
for key in rouge_dict:
print("%s\t%f" % (key, rouge_dict[key]), file=f)
bleu_score = bleu.moses_multi_bleu(decoded, reference)
print("%s\t%f" % ('bleu', bleu_score), file=f)
tf.logging.info("BLEU, ROUGE values saved to results.txt")
def cal_bleu(infer, ref):
while True:
try:
bleu_score = bleu.moses_multi_bleu(infer, ref)
return bleu_score
except FileNotFoundError:
print("Failed to test bleu_score. Sleeping for %i secs...", 0.01)
time.sleep(3)
def moses_bl_rouge(p, l):
bl = bleu.moses_multi_bleu(p, l)
x = rouge.rouge(p, l)
print(
'Moses BLEU: %f\nROUGE1-F: %f\nROUGE1-P: %f\nROUGE1-R: %f\nROUGE2-F: %f\nROUGE2-P: %f\nROUGE2-R: %f\nROUGEL-F: %f\nROUGEL-P: %f\nROUGEL-R: %f'
%
(bl, x['rouge_1/f_score'], x['rouge_1/p_score'], x['rouge_1/r_score'],
x['rouge_2/f_score'], x['rouge_2/p_score'], x['rouge_2/r_score'],
x['rouge_l/f_score'], x['rouge_l/p_score'], x['rouge_l/r_score']))
def predict(self,sess,data_name):
pred_losses = []
preds_all=np.zeros(self.max_decoder_len)
for step, batch in enumerate(self.getBatches(self.data_list[data_name])):
feed = self.create_feed_dict(batch,drop = 1.0,forward_only=True)
loss, preds,= sess.run([self.loss_op, self.preds], feed_dict=feed)
pred_losses.append(loss)
preds_all = np.row_stack((preds_all,preds))
preds_ids=np.delete(preds_all,0,0)
preds_list=self.get_words_from_ids(preds_ids)[0:len(self.data_list[data_name])]
labels_list=self.get_targs_list(self.data[data_name]['dec_targ'])
bl=bleu.moses_multi_bleu(preds_list,labels_list)
return np.mean(pred_losses),bl,preds_list,labels_list
def evaluate(dataset_f, predictions_f, all_metrics=False, save_dir=""):
with open(dataset_f) as dataset_file:
dataset_json = json.load(dataset_file)
dataset = dataset_json['data']
with open(predictions_f) as prediction_file:
predictions = json.load(prediction_file)
gt = []
pred = []
f1 = exact_match = total = count = 0
for article in dataset:
for paragraph in article['paragraphs']:
if str(
article['title']
) not in predictions: #needs a lookup in case of dev-v1.1.json
continue
for qa in paragraph['qas']:
total += 1
ground_truths = list(map(lambda x: x['text'], qa['answers']))
if str(qa['id']) not in predictions:
prediction = ""
else:
prediction = predictions[str(qa['id'])]
if prediction == "":
prediction = 'n_a'
gt.append(ground_truths[0])
pred.append(prediction)
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(f1_score, prediction,
ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
if all_metrics:
rouge_dict = rouge(pred, gt)
file_path = os.path.join(save_dir, 'results.txt')
f = open(file_path, 'w')
for key in rouge_dict:
print("%s\t%f" % (key, rouge_dict[key]), file=f)
bleu_score = moses_multi_bleu(pred, gt)
print("%s\t%f" % ('bleu', bleu_score), file=f)
print("%s\t%f" % ('f1', f1), file=f)
print("%s\t%f" % ('exact_match', exact_match), file=f)
return exact_match, f1
def run_epoch(self,sess,epoch):
t0 = time.time()
losses = []
preds_all=np.zeros(self.max_decoder_len)
for step, batch in enumerate(self.getBatches(self.data_list['train'])):
feed = self.create_feed_dict(batch,drop = self.params.dropout,forward_only=False)
loss, preds, _= sess.run([self.loss_op, self.preds, self.train_op], feed_dict=feed)
losses.append(loss)
preds_all = np.row_stack((preds_all,preds))
self.logger.info('Epoch:{} \t Step:{} \t Batch Loss: {} \t Avg loss: {}'.format(epoch+1, step,loss, np.mean(losses)))
preds_ids=np.delete(preds_all,0,0)
preds_list=self.get_words_from_ids(preds_ids)[0:len(self.data_list['train'])]
labels_list=self.get_targs_list(self.data['train']['dec_targ'])
bl=bleu.moses_multi_bleu(preds_list,labels_list)
self.logger.info('Train Loss:{}, Train BLEU: {}'.format(np.mean(losses), bl))
t1 = time.time()
self.logger.info('Time to run an epoch: %i seconds', t1 - t0)
return np.mean(losses), bl,preds_list,labels_list
