def iter_test(self, test_iter, step, sum_sent_count=3):
"""
select sentences in each iteration
given selected sentences, predict the next one
"""
self.model.eval()
stats = Statistics()
#dir_name = os.path.dirname(self.args.result_path)
base_name = os.path.basename(self.args.result_path)
#base_dir = os.path.join(dir_name, 'iter_eval')
base_dir = os.path.dirname(self.args.result_path)
if (not os.path.exists(base_dir)):
os.makedirs(base_dir)
can_path = '%s/%s_step%d_itereval.candidate'%(base_dir, base_name, step)
gold_path = '%s/%s_step%d_itereval.gold' % (base_dir, base_name, step)
all_pred_ids, all_gold_ids, all_doc_ids = [], [], []
all_gold_texts, all_pred_texts = [], []
with torch.no_grad():
for batch in test_iter:
doc_ids = batch.doc_id
oracle_ids = [set([j for j in seq if j > -1]) for seq in batch.label_seq.tolist()]
sel_sent_idxs, sel_sent_masks = self.model.infer_sentences(batch, sum_sent_count, stats=stats)
sel_sent_idxs = sel_sent_idxs.tolist()
all_pred_ids.extend(sel_sent_idxs)
for i in range(batch.batch_size):
_pred = '<q>'.join([batch.src_str[i][idx].strip() for j, idx in enumerate(sel_sent_idxs[i]) if sel_sent_masks[i][j]])
all_pred_texts.append(_pred)
all_gold_texts.append(batch.tgt_str[i])
all_gold_ids.append(oracle_ids[i])
all_doc_ids.append(doc_ids[i])
macro_precision, micro_precision = self._output_predicted_summaries(
all_doc_ids, all_pred_ids, all_gold_ids,
all_pred_texts, all_gold_texts, can_path, gold_path)
rouge1_arr, rouge2_arr = du.cal_rouge_score(all_pred_texts, all_gold_texts)
rouge_1, rouge_2 = du.aggregate_rouge(rouge1_arr, rouge2_arr)
logger.info('[PERF]At step %d: rouge1:%.2f rouge2:%.2f' % (
step, rouge_1 * 100, rouge_2 * 100))
if(step!=-1 and self.args.report_precision):
macro_arr = ["P@%s:%.2f%%" % (i+1, macro_precision[i] * 100) for i in range(3)]
micro_arr = ["P@%s:%.2f%%" % (i+1, micro_precision[i] * 100) for i in range(3)]
logger.info('[PERF]MacroPrecision at step %d: %s' % (step, '\t'.join(macro_arr)))
logger.info('[PERF]MicroPrecision at step %d: %s' % (step, '\t'.join(micro_arr)))
if(step!=-1 and self.args.report_rouge):
rouge_str, detail_rouge = test_rouge(self.args.temp_dir, can_path, gold_path, all_doc_ids, show_all=True)
logger.info('[PERF]Rouges at step %d: %s \n' % (step, rouge_str))
result_path = '%s_step%d_itereval.rouge' % (self.args.result_path, step)
if detail_rouge is not None:
du.output_rouge_file(result_path, rouge1_arr, rouge2_arr, detail_rouge, all_doc_ids)
self._report_step(0, step, valid_stats=stats)
return stats
def test(self, test_iter, step, cal_lead=False, cal_oracle=False):
""" Validate model.
valid_iter: validate data iterator
Returns:
:obj:`nmt.Statistics`: validation loss statistics
"""
# Set model in validating mode.
def _get_ngrams(n, text):
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(text[i:i + n]))
return ngram_set
def _block_tri(c, p):
tri_c = _get_ngrams(3, c.split())
for s in p:
tri_s = _get_ngrams(3, s.split())
if len(tri_c.intersection(tri_s))>0:
return True
return False
if (not cal_lead and not cal_oracle):
self.model.eval()
stats = Statistics()
base_dir = os.path.dirname(self.args.result_path)
if (not os.path.exists(base_dir)):
os.makedirs(base_dir)
can_path = '%s_step%d_initial.candidate'%(self.args.result_path,step)
gold_path = '%s_step%d_initial.gold' % (self.args.result_path, step)
all_pred_ids, all_gold_ids, all_doc_ids = [], [], []
all_gold_texts, all_pred_texts = [], []
with torch.no_grad():
for batch in test_iter:
src = batch.src
labels = batch.labels
segs = batch.segs
clss = batch.clss
mask = batch.mask
mask_cls = batch.mask_cls
doc_ids = batch.doc_id
group_idxs = batch.groups
oracle_ids = [set([j for j in seq if j > -1]) for seq in batch.label_seq.tolist()]
if (cal_lead):
selected_ids = [list(range(batch.clss.size(1)))] * batch.batch_size
elif (cal_oracle):
selected_ids = [[j for j in range(batch.clss.size(1)) if labels[i][j] == 1] for i in
range(batch.batch_size)]
else:
sent_scores, mask = self.model(src, mask, segs, clss, mask_cls, group_idxs, candi_sent_masks=mask_cls, is_test=True)
#selected sentences in candi_masks can be set to 0
loss = -self.logsoftmax(sent_scores) * labels.float() #batch_size, max_sent_count
loss = (loss*mask.float()).sum()
batch_stats = Statistics(float(loss.cpu().data.numpy()), len(labels))
stats.update(batch_stats)
sent_scores[mask==False] = float('-inf')
# give a cap 1 to sentscores, so no need to add 1000
sent_scores = sent_scores.cpu().data.numpy()
selected_ids = np.argsort(-sent_scores, 1)
for i, idx in enumerate(selected_ids):
_pred = []
_pred_ids = []
if(len(batch.src_str[i])==0):
continue
for j in selected_ids[i][:len(batch.src_str[i])]:
if(j>=len( batch.src_str[i])):
continue
candidate = batch.src_str[i][j].strip()
if(self.args.block_trigram):
if(not _block_tri(candidate,_pred)):
_pred.append(candidate)
_pred_ids.append(j)
else:
_pred.append(candidate)
_pred_ids.append(j)
if ((not cal_oracle) and (not self.args.recall_eval) and len(_pred) == 3):
break
_pred = '<q>'.join(_pred)
if(self.args.recall_eval):
_pred = ' '.join(_pred.split()[:len(batch.tgt_str[i].split())])
all_pred_texts.append(_pred)
all_pred_ids.append(_pred_ids)
all_gold_texts.append(batch.tgt_str[i])
all_gold_ids.append(oracle_ids[i])
all_doc_ids.append(doc_ids[i])
macro_precision, micro_precision = self._output_predicted_summaries(
all_doc_ids, all_pred_ids, all_gold_ids,
all_pred_texts, all_gold_texts, can_path, gold_path)
rouge1_arr, rouge2_arr = du.cal_rouge_score(all_pred_texts, all_gold_texts)
rouge_1, rouge_2 = du.aggregate_rouge(rouge1_arr, rouge2_arr)
logger.info('[PERF]At step %d: rouge1:%.2f rouge2:%.2f' % (
step, rouge_1 * 100, rouge_2 * 100))
if(step!=-1 and self.args.report_precision):
macro_arr = ["P@%s:%.2f%%" % (i+1, macro_precision[i] * 100) for i in range(3)]
micro_arr = ["P@%s:%.2f%%" % (i+1, micro_precision[i] * 100) for i in range(3)]
logger.info('[PERF]MacroPrecision at step %d: %s' % (step, '\t'.join(macro_arr)))
logger.info('[PERF]MicroPrecision at step %d: %s' % (step, '\t'.join(micro_arr)))
if(step!=-1 and self.args.report_rouge):
rouge_str, detail_rouge = test_rouge(self.args.temp_dir, can_path, gold_path, all_doc_ids, show_all=True)
logger.info('[PERF]Rouges at step %d: %s \n' % (step, rouge_str))
result_path = '%s_step%d_initial.rouge' % (self.args.result_path, step)
if detail_rouge is not None:
du.output_rouge_file(result_path, rouge1_arr, rouge2_arr, detail_rouge, all_doc_ids)
self._report_step(0, step, valid_stats=stats)
return stats
print(model_dir)
cp_files = sorted(glob.glob(os.path.join(args.model_path, 'model_step_*.pt')))
for cp in cp_files:
step = int(cp.split('.')[-2].split('_')[-1])
test(args, device_id, cp, step)
else:
try:
step = int(cp.split('.')[-2].split('_')[-1])
except:
step = 0
test(args, device_id, cp, step)
elif (args.mode == 'getrouge'):
if args.model_name == 'base':
pattern = '*step*initial.candidate'
else:
pattern = '*step*.candidate' #evaluate all
candi_files = sorted(glob.glob("%s_%s" % (args.result_path, pattern)))
#print(args.result_path)
#print(candi_files)
for can_path in candi_files:
gold_path = can_path.replace('candidate', 'gold')
rouge1_arr, rouge2_arr = du.compute_metrics(can_path, gold_path)
step = os.path.basename(gold_path)
precs_path = can_path.replace('candidate', 'precs')
all_doc_ids = du.read_prec_file(precs_path)
rouge_str, detail_rouge = test_rouge(args.temp_dir, can_path, gold_path, all_doc_ids, show_all=True)
logger.info('Rouges at step %s \n%s' % (step, rouge_str))
result_path = can_path.replace('candidate', 'rouge')
if detail_rouge is not None:
du.output_rouge_file(result_path, rouge1_arr, rouge2_arr, detail_rouge, all_doc_ids)