def evaluation(args, data, split, model, domain, epoch, str_res='results', ner_model=True, predictor=None):
# evaluate performance on data
model.eval()
eval_dict = initialize_eval_dict()
eval_dict['epoch'] = epoch
#for batch in prepare_data.iterate_batch(data, args.batch_size, args.device):
for batch in prepare_data.iterate_batch_rand_bucket_choosing(data, args.batch_size, args.device, ner_model=ner_model, predictor=predictor):
if ner_model is not None:
word, char, pos, ner, heads, arc_tags, auto_label, masks, lengths, f_f, f_p, b_f, b_p, w_f, mask_v, file_no = batch
out_arc, out_arc_tag, masks, lengths = model.forward(word, char, pos, mask=masks, length=lengths, f_f=f_f, f_p=f_p, b_f=b_f, b_p=b_p, w_f=w_f, file_no=file_no, mask_v=mask_v)
else:
word, char, pos, ner, heads, arc_tags, auto_label, masks, lengths = batch
out_arc, out_arc_tag, masks, lengths = model.forward(word, char, pos, mask=masks, length=lengths)
heads_pred, arc_tags_pred, _ = model.decode(out_arc, out_arc_tag, mask=masks, length=lengths,
leading_symbolic=prepare_data.NUM_SYMBOLIC_TAGS)
lengths = lengths.cpu().numpy()
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
ner = ner.data.cpu().numpy()
heads = heads.data.cpu().numpy()
arc_tags = arc_tags.data.cpu().numpy()
heads_pred = heads_pred.data.cpu().numpy()
arc_tags_pred = arc_tags_pred.data.cpu().numpy()
stats, stats_nopunc, stats_root, num_inst = parse.eval_(word, pos, heads_pred, arc_tags_pred, heads,
arc_tags, args.alphabets['word_alphabet'], args.alphabets['pos_alphabet'],
lengths, punct_set=args.punct_set, symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
eval_dict['dp_ucorrect'] += ucorr
eval_dict['dp_lcorrect'] += lcorr
eval_dict['dp_total'] += total
eval_dict['dp_ucomplete_match'] += ucm
eval_dict['dp_lcomplete_match'] += lcm
eval_dict['dp_ucorrect_nopunc'] += ucorr_nopunc
eval_dict['dp_lcorrect_nopunc'] += lcorr_nopunc
eval_dict['dp_total_nopunc'] += total_nopunc
eval_dict['dp_ucomplete_match_nopunc'] += ucm_nopunc
eval_dict['dp_lcomplete_match_nopunc'] += lcm_nopunc
eval_dict['dp_root_correct'] += corr_root
eval_dict['dp_total_root'] += total_root
eval_dict['dp_total_inst'] += num_inst
eval_dict['dp_uas'] = eval_dict['dp_ucorrect'] * 100 / eval_dict['dp_total'] # considering w. punctuation
eval_dict['dp_las'] = eval_dict['dp_lcorrect'] * 100 / eval_dict['dp_total'] # considering w. punctuation
print_results(eval_dict, split, domain, str_res)
return eval_dict
def evaluation(args,
data,
split,
model,
domain,
epoch,
flag,
str_res='results'):
# evaluate performance on data
model.eval()
alpha_dir = args.model_path
eval_dict = initialize_eval_dict()
eval_dict['epoch'] = epoch
for batch in prepare_data.iterate_batch(data, args.batch_size,
args.device):
word, char, pos, ner, heads, arc_tags, auto_label, masks, lengths = batch
# pdb.set_trace()
out_arc, out_arc_tag, masks, lengths = model.forward(word,
char,
pos,
mask=masks,
length=lengths)
heads_pred, arc_tags_pred, _ = model.decode(
out_arc,
out_arc_tag,
ner,
flag,
mask=masks,
length=lengths,
leading_symbolic=prepare_data.NUM_SYMBOLIC_TAGS)
lengths = lengths.cpu().numpy()
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
ner = ner.data.cpu().numpy()
heads = heads.data.cpu().numpy()
arc_tags = arc_tags.data.cpu().numpy()
heads_pred = heads_pred.data.cpu().numpy()
arc_tags_pred = arc_tags_pred.data.cpu().numpy()
stats, stats_nopunc, stats_root, num_inst = parse.eval_(
word,
pos,
heads_pred,
arc_tags_pred,
heads,
arc_tags,
args.alphabets['word_alphabet'],
args.alphabets['pos_alphabet'],
lengths,
punct_set=args.punct_set,
symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
eval_dict['dp_ucorrect'] += ucorr
eval_dict['dp_lcorrect'] += lcorr
eval_dict['dp_total'] += total
eval_dict['dp_ucomplete_match'] += ucm
eval_dict['dp_lcomplete_match'] += lcm
eval_dict['dp_ucorrect_nopunc'] += ucorr_nopunc
eval_dict['dp_lcorrect_nopunc'] += lcorr_nopunc
eval_dict['dp_total_nopunc'] += total_nopunc
eval_dict['dp_ucomplete_match_nopunc'] += ucm_nopunc
eval_dict['dp_lcomplete_match_nopunc'] += lcm_nopunc
eval_dict['dp_root_correct'] += corr_root
eval_dict['dp_total_root'] += total_root
eval_dict['dp_total_inst'] += num_inst
eval_dict['dp_uas'] = eval_dict['dp_ucorrect'] * 100 / eval_dict[
'dp_total'] # considering w. punctuation
eval_dict['dp_las'] = eval_dict['dp_lcorrect'] * 100 / eval_dict[
'dp_total'] # considering w. punctuation
print_results(eval_dict, split, domain, str_res)
return eval_dict