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# coding: utf-8
# In[1]:
import json
import os
import parser
from src import dataio
import glob
from sklearn.metrics import accuracy_score
from seqeval.metrics import f1_score, precision_score, recall_score
# In[2]:
try:
dir_path = os.path.dirname(os.path.abspath( __file__ ))
except:
dir_path = '.'
# In[3]:
# 실행시간 측정 함수
import time
_start_time = time.time()
def tic():
global _start_time
_start_time = time.time()
def tac():
t_sec = round(time.time() - _start_time)
(t_min, t_sec) = divmod(t_sec,60)
(t_hour,t_min) = divmod(t_min,60)
result = '{}hour:{}min:{}sec'.format(t_hour,t_min,t_sec)
return result
# In[4]:
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=2).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
with open('./data/frame_coreFE_list.json','r') as f:
frame_coreFE = json.load(f)
def weighting(frame, args):
weighted_args = []
for arg in args:
weighted_args.append(arg)
if arg in frame_coreFE[frame]:
weighted_args.append(arg)
else:
pass
return weighted_args
# In[5]:
def test(srl=False, masking=False, viterbi=False, language=False, model_path=False,
result_dir=False, train_lang=False, tgt=False,
pretrained="bert-base-multilingual-cased"):
if not result_dir:
result_dir = '/disk/data/models/'+model_dir.split('/')[-2]+'-result/'
else:
pass
if result_dir[-1] != '/':
result_dir = result_dir+'/'
if not train_lang:
train_lang = language
if not os.path.exists(result_dir):
os.makedirs(result_dir)
if viterbi:
fname = result_dir+train_lang+'_for_'+language+'_with_viterbi'
else:
fname = result_dir+train_lang+'_for_'+language
if masking:
fname = fname + '_with_masking'
else:
pass
if 'large' in pretrained:
fname = fname + '_large_tgt_result.txt'
else:
fname = fname + '_tgt_result.txt'
print('### Your result would be saved to:', fname)
trn, dev, tst = dataio.load_data(srl=srl, language=language)
print('### EVALUATION')
print('MODE:', srl)
print('target LANGUAGE:', language)
print('trained LANGUAGE:', train_lang)
print('Viterbi:', viterbi)
print('masking:', masking)
print('using TGT token:', tgt)
tic()
models = glob.glob(model_path+'*.pt')
eval_result = []
for m in models:
print('model:', m)
model = parser.ShallowSemanticParser(srl=srl,gold_pred=True, model_path=m, viterbi=viterbi,
masking=masking, language=language, tgt=tgt,
pretrained=pretrained)
gold_senses, pred_senses, gold_args, pred_args = [],[],[],[]
gold_full_all, pred_full_all = [],[]
for instance in tst:
result = model.parser(instance)
gold_sense = [i for i in instance[2] if i != '_'][0]
pred_sense = [i for i in result[0][2] if i != '_'][0]
gold_arg = [i for i in instance[3] if i != 'X']
pred_arg = [i for i in result[0][3]]
gold_senses.append(gold_sense)
pred_senses.append(pred_sense)
gold_args.append(gold_arg)
pred_args.append(pred_arg)
if srl == 'framenet':
gold_full = []
gold_full.append(gold_sense)
gold_full.append(gold_sense)
weighted_gold_args = weighting(gold_sense, gold_arg)
gold_full += weighted_gold_args
pred_full = []
pred_full.append(pred_sense)
pred_full.append(pred_sense)
weighted_pred_args = weighting(pred_sense, pred_arg)
pred_full += weighted_pred_args
gold_full_all.append(gold_full)
pred_full_all.append(pred_full)
# break
acc = accuracy_score(gold_senses, pred_senses)
arg_f1 = f1_score(gold_args, pred_args)
arg_precision = precision_score(gold_args, pred_args)
arg_recall = recall_score(gold_args, pred_args)
epoch = m.split('/')[-1].split('-')[1]
print('# EPOCH:', epoch)
print("SenseId Accuracy: {}".format(acc))
print("ArgId Precision: {}".format(arg_precision))
print("ArgId Recall: {}".format(arg_recall))
print("ArgId F1: {}".format(arg_f1))
if srl == 'framenet':
full_f1 = f1_score(gold_full_all, pred_full_all)
full_precision = precision_score(gold_full_all, pred_full_all)
full_recall = recall_score(gold_full_all, pred_full_all)
print("full-structure Precision: {}".format(full_precision))
print("full-structure Recall: {}".format(full_recall))
print("full-structure F1: {}".format(full_f1))
print('-----processing time:', tac())
print('')
model_result = []
model_result.append(epoch)
model_result.append(acc)
model_result.append(arg_precision)
model_result.append(arg_recall)
model_result.append(arg_f1)
if srl == 'framenet':
model_result.append(full_precision)
model_result.append(full_recall)
model_result.append(full_f1)
model_result = [str(i) for i in model_result]
eval_result.append(model_result)
# break
# print(eval_result)
with open(fname,'w') as f:
if srl == 'framenet':
f.write('epoch'+'\t''SenseID'+'\t'+'Arg_P'+'\t'+'Arg_R'+'\t'+'ArgF1'+'\t'+'full_P'+'\t'+'full_R'+'\t'+'full_F1'+'\n')
else:
f.write('epoch'+'\t''SenseID'+'\t'+'Arg_P'+'\t'+'Arg_R'+'\t'+'ArgF1'+'\n')
for i in eval_result:
line = '\t'.join(i)
f.write(line+'\n')
# In[1]:
# print('\t### Ko-SRL')
# srl = 'propbank-dp'
# language = 'ko'
# model_dir = '/disk/data/models/ko-srl-tgt-1117/'
# result_dir = '/disk/data/models/results/srl/'
# test(srl=srl, language=language, masking=False, viterbi=False, tgt=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###ko-for-ko-masking')
# srl = 'framenet'
# language = 'ko'
# model_dir = '/disk/data/models/ko-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=True, viterbi=False, tgt=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###en-for-en-masking')
# srl = 'framenet'
# language = 'en'
# model_dir = '/disk/data/models/en-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=True, viterbi=False, tgt=True, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###ko-for-ko-no-masking')
# srl = 'framenet'
# language = 'ko'
# model_dir = '/disk/data/models/ko-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=False, viterbi=False, tgt=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###en-for-en-no-masking')
# srl = 'framenet'
# language = 'en'
# model_dir = '/disk/data/models/en-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=False, viterbi=False, tgt=True, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
print('\t###en-large-for-en-masking')
srl = 'framenet'
language = 'en'
model_path = '/disk/data/models/en-framenet-tgt-large/'
result_dir = '/disk/data/models/results/tgt/'
test(srl=srl, language=language, masking=True, viterbi=False, tgt=True, train_lang='en',
model_path=model_path, result_dir=result_dir,
pretrained='bert-large-cased')
# In[ ]:
print('\t###en-large-for-en-no-masking')
srl = 'framenet'
language = 'en'
model_path = '/disk/data/models/en-framenet-tgt-large/'
result_dir = '/disk/data/models/results/tgt/'
test(srl=srl, language=language, masking=False, viterbi=False, tgt=True, train_lang='en',
model_path=model_path, result_dir=result_dir,
pretrained='bert-large-cased')
# In[ ]:
# print('\t###en-for-ko-masking')
# srl = 'framenet'
# language = 'ko'
# model_dir = '/disk/data/models/en-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=True, viterbi=False, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###en-for-ko-no-masking')
# srl = 'framenet'
# language = 'ko'
# model_dir = '/disk/data/models/en-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=False, viterbi=False, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###ko-for-en-no-masking')
# srl = 'framenet'
# language = 'en'
# model_dir = '/disk/data/models/ko-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=False, viterbi=False, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# print('\t###ko-for-en-masking')
# srl = 'framenet'
# language = 'en'
# model_dir = '/disk/data/models/ko-framenet-tgt-1117/'
# result_dir = '/disk/data/models/results/tgt/'
# test(srl=srl, language=language, masking=True, viterbi=False, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# In[ ]:
# srl = 'framenet'
# # srl = 'propbank-dp'
# # language = 'ko'
# language = 'en'
# masking = True
# model_dir = '/disk/data/models/enframenet_1105/'
# if language == 'en':
# fnversion = 1.7
# # PRETRAINED_MODEL = "bert-large-cased"
# MAX_LEN = 256
# batch_size = 1
# PRETRAINED_MODEL = "bert-base-multilingual-cased"
# else:
# fnversion = 1.1
# PRETRAINED_MODEL = "bert-base-multilingual-cased"
# MAX_LEN = 256
# batch_size = 1
# Korean Frame-semantic parsing using KFN
# print('\t###Korean Frame-semantic parsing using KFN')
# srl = 'framenet'
# language = 'ko'
# model_dir = '/disk/data/models/koframenet_1105/'
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=False, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=False, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# English Frame-semantic parsing using enFN
# print('\t###English Frame-semantic parsing using enFN')
# srl = 'framenet'
# language = 'en'
# model_dir = '/disk/data/models/enframenet_1105/'
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=False, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=False, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# Korean Frame-semantic parsing using enFN
# print('\t###Korean Frame-semantic parsing using enFN')
# srl = 'framenet'
# language = 'ko'
# model_dir = '/disk/data/models/enframenet_1105/'
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=False, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=True, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=False, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=True, train_lang='en', model_dir=model_dir, result_dir=result_dir)
# English Frame-semantic parsing using koFN
# print('\t###English Frame-semantic parsing using koFN')
# srl = 'framenet'
# language = 'en'
# model_dir = '/disk/data/models/koframenet_1105/'
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=False, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=True, viterbi=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=False, train_lang='ko', model_dir=model_dir, result_dir=result_dir)
# result_dir = '/disk/data/models/results/'
# test(srl=srl, language=language, masking=False, viterbi=True, train_lang='ko', model_dir=model_dir, result_dir=result_dir)