def compute(filename, topics=2):
doc = Document(LDATester.PATH + filename + ".txt")
gold_doc = Document(LDATester.PATH + filename + "_gold.txt")
topics = len(gold_doc.sentences)
ldaSummary = LDATester.getSummary(doc, topics)
# print ldaSummary
return BLEU.computeNormalize(gold_doc.document, ldaSummary, ignore=True)
def model_eval(epoch_file):
print('=============================================')
print()
#Getting model's information
model_version_number = epoch_file.split('/')[5].split('_')[1]
print('Testing model version: ', model_version_number)
#Loading the model
model = CaptionGenerator.load_from_checkpoint(checkpoint_path=epoch_file,
pad_idx=pad_idx)
model.eval()
with open(r'../../data/caption_generator/lightning_logs/version_' +
model_version_number + '/hparams.yaml') as file:
parameters = yaml.load(file, Loader=yaml.FullLoader)
print('With parameters: ', parameters)
captions = [
" ".join(model.caption_image(image, dataset.vocab)[1:-1])
for image in imgs
]
# Putting the file names and their corresponding captions together in a DataFrame to then save as .tsv
df = pd.DataFrame(data={'image': file_names, 'caption': captions})
df.to_csv('../../data/caption_generator/version_' + model_version_number +
'_outputs.tsv',
index=False,
sep='\t')
#Generating BLEU scores
evaluation = BLEU('../../data/caption_generator/version_' +
model_version_number + '_outputs.tsv')
azul = evaluation.get_bleu_score()
#Generating captions for the selected examples
examples = get_examples(model, dataset)
print('The model achieved the following performance on the test set: ')
print('BLEU-4 average (rounded) score: ' + '{:.3f}'.format(azul))
print()
print('=============================================')
print()
return model_version_number, parameters, azul, examples
def evaluate(loader, seq2seq, criterion, max_len):
losses = utils.AverageMeter()
ppls = utils.AverageMeter()
seq2seq.eval()
bleu = BLEU()
tot_st = time.time()
bleu_time = 0.
with torch.no_grad():
for i, example in enumerate(loader):
src, src_lens, tgt, tgt_lens = parse(example)
B = src.size(0)
dec_outs, attn_ws = seq2seq(src,
src_lens,
tgt,
tgt_lens,
teacher_forcing=0.)
loss, ppl = criterion(dec_outs, tgt[:, 1:])
losses.update(loss, B)
ppls.update(ppl, B)
# BLEU
bleu_st = time.time()
# convert logits to preds
preds = dec_outs.max(-1)[1]
# get pred lens by finding EOS token
pred_lens = get_lens(preds, max_len)
for pred, target, pred_len, target_len in zip(
preds, tgt, pred_lens, tgt_lens):
# target_len include SOS & EOS token => 1:target_len-1.
bleu.add_sentence(pred[:pred_len].cpu().numpy(),
target[1:target_len - 1].cpu().numpy())
bleu_time += time.time() - bleu_st
total_time = time.time() - tot_st
logger.debug("TIME: tot = {:.3f}\t bleu = {:.3f}".format(
total_time, bleu_time))
return losses.avg, ppls.avg, bleu.score()
def compute(filename):
gold_doc = Document(LDATester.PATH + filename + "_gold.txt")
doc = Document(LDATester.PATH + filename + ".txt")
## Get random summary
indices = [x for x in range(len(doc.sentences))]
random.shuffle(indices)
indices = indices[0 : len(gold_doc.sentences)]
sentences = [doc.sentences[i] for i in indices]
calibration = [doc.getSentenceOrginal(sentence) for sentence in sentences]
calibration = " ".join(calibration)
return BLEU.computeNormalize(gold_doc.document, calibration)
def test_bleu(self, N=300, gram=4):
all_score = []
for i in range(N):
input_indices = self.show(self.dp.X_test[i], self.dp.X_id2w)
o = self.model.infer(input_indices)[0]
refer4bleu = [[
' '.join(
[self.dp.Y_id2w.get(w, u'&') for w in self.dp.Y_test[i]])
]]
candi = [' '.join(w for w in o)]
score = BLEU(candi, refer4bleu, gram=gram)
all_score.append(score)
return np.mean(all_score)
def run(step, phase, summary_version=True):
shuffler = np.random.permutation(FLAGS.batch_size)
current_caption_matrix = captions_list[step][shuffler]
current_images = images_list[step][shuffler]
current_mask_matrix = mask_list[step][shuffler]
current_maxlen = maxlen[step]
context, sentence, mask, train_op, loss_op, gen_words_op, l, h = operations[
current_maxlen]
# current_images : [batch_size, 1, 4096]
# current_caption_matrix : [batch_size, n_lstm_steps]
# mask : [batch_size, n_lstm_steps]
if summary_version:
_, loss, words, summary_string = sess.run(
[train_op, loss_op, gen_words_op, summary_op],
feed_dict={
context: current_images,
sentence: current_caption_matrix,
mask: current_mask_matrix
})
else:
_, loss, words, logits, onehot_labels = sess.run(
[train_op, loss_op, gen_words_op, l, h],
feed_dict={
context: current_images,
sentence: current_caption_matrix,
mask: current_mask_matrix
})
avg_score = 0.0
sentences = []
for (w, c) in zip(words, current_caption_matrix):
score, gen_sentence, ref_sentence = \
BLEU.bleu_score(w, c, ix_to_word)
avg_score += score
sentences.append((gen_sentence, ref_sentence))
avg_score /= len(sentences)
if summary_version:
return loss, avg_score, sentences, summary_string
else:
return loss, avg_score, sentences
def __init__(self, is_test_mode, transform=None):
self.test_mode = is_test_mode
# train_dataset
# self.train_max_length_s, self.train_max_length_t, self.train_transform, self.train_dataset, self.train_data_loader = LoadSentenceData(DATA_SET_PATH)
# _, _, _, _, self.train_disc_data_loader = LoadSentenceData(DATA_SET_PATH, transform=self.train_transform)
# self.test_max_length_s, self.test_max_length_t, self.test_transform, self.test_dataset, self.test_data_loader = LoadSentenceData(TEST_SET_PATH, transform=self.train_transform, _shuffle=False)
self.train_max_length_s, self.train_max_length_t, self.train_transform, self.train_dataset, self.train_data_loader = LoadTranslateData(
)
_, _, _, _, self.train_disc_data_loader = LoadTranslateData(
transform=self.train_transform)
self.test_max_length_s, self.test_max_length_t, self.test_transform, self.test_dataset, self.test_data_loader = LoadTranslateData(
mode="test", transform=self.train_transform, _shuffle=False)
self.train_data_num = len(self.train_dataset)
self.test_data_num = 200
# calcurate device
self.device = DEVICE
# num of vocablary
self.vocab_size = len(self.train_transform.w2i)
self.emb_vec = LoadEmbVec("data/word2vec/translate_row.vec.pt",
self.train_transform,
self.vocab_size).to(self.device)
self.connect_char_tensor = torch.tensor([
self.train_transform.w2i[CONNECT_SYMBOL] for i in range(BATCH_SIZE)
]).unsqueeze(1).to(self.device)
# model
self.bce_loss = nn.CrossEntropyLoss(ignore_index=0)
self.generator = Generator(self.vocab_size, self.emb_vec)
self.blue = BLEU(4)
# optimizer
self.optimizer_gen = torch.optim.Adam(self.generator.parameters(),
lr=START_LEARNING_RATE_G,
betas=(0.5, 0.999))
self.one = torch.tensor(1, dtype=torch.float).to(self.device)
self.mone = (self.one * -1).to(self.device)
def run(step, phase, summary_version = True):
shuffler = np.random.permutation(FLAGS.batch_size)
current_caption_matrix = captions_list[step][shuffler]
current_images = images_list[step][shuffler]
current_mask_matrix = mask_list[step][shuffler]
current_maxlen = maxlen[step]
context, sentence, mask, train_op, loss_op, gen_words_op, l, h = operations[current_maxlen]
# current_images : [batch_size, 1, 4096]
# current_caption_matrix : [batch_size, n_lstm_steps]
# mask : [batch_size, n_lstm_steps]
if summary_version:
_, loss, words, summary_string = sess.run(
[train_op, loss_op, gen_words_op, summary_op],
feed_dict = {context:current_images,
sentence:current_caption_matrix,
mask:current_mask_matrix})
else:
_, loss, words, logits, onehot_labels = sess.run(
[train_op, loss_op, gen_words_op, l, h],
feed_dict = {context:current_images,
sentence:current_caption_matrix,
mask:current_mask_matrix})
avg_score = 0.0
sentences = []
for (w, c) in zip(words, current_caption_matrix):
score, gen_sentence, ref_sentence = \
BLEU.bleu_score(w, c, ix_to_word)
avg_score += score
sentences.append((gen_sentence, ref_sentence))
avg_score /= len(sentences)
if summary_version:
return loss, avg_score, sentences, summary_string
else:
return loss, avg_score, sentences
hyps = [['i', 'am', 'a', 'boy', 'and', 'test'],
['this', 'is', 'the', 'game', 'of', 'the', 'throne'],
[
'hyp', 'is', 'long', 'and', 'this', 'is', 'the', 'game', 'of',
'the', 'throne'
], ['what', 'the', 'f**k', 'this', 'hmm'], ['the', 'short']]
refses = [
['i', 'am', 'a', 'boy', 'and', 'girl', 'and', 'long'],
['i', 'like', 'this', 'is', 'the', 'game', 'of', 'the', 'throne'],
['this', 'is', 'the', 'game', 'of', 'the', 'throne'],
['what', 'a', 'f*****g', 'serious', '?'], ['too', 'short', 'lang']
]
for hyp, refs in zip(hyps, refses):
# stats
mine_stats = BLEU.compute_stats(hyp, refs)
org_stats = bleu_stats(hyp, refs)
assert (mine_stats.flatten().astype(np.int) == org_stats).all()
# bleu
mine_bleu = BLEU.compute_bleu(mine_stats)
org_bleu = bleu(org_stats)
#print(mine_bleu, org_bleu)
assert mine_bleu == org_bleu
# total bleu score
org = get_bleu(hyps, refses)
bleu = BLEU()
bleu.add_corpus(hyps, refses)
print("org:", org)
!mkdir -p trained_models/bigan_20
bigan.save('trained_models/bigan_20/')
x_gen = bigan.generate()
x_gen.shape
text ='The Sri Lankan team will play three ODI'
translator = bigan.BiGAN
params['build_model'] = True
gan = cls(**params)
gan.generator = load_model(os.path.join(path, "generator.h5"))
gan.discriminator = load_model(os.path.join(path, "discriminator.h5"))
gan.encoder = load_model(os.path.join(path, "encoder.h5"))
gan.bigan_generator = load_model(os.path.join(path, "bigan_generator.h5"))
for key, value in destination_language.items():
print(translator.output(text,dest = value).text)
import bleu
from bleu import BLEU
candidate, references = fetch_data(candidate,BGAN)
bleu = BLEU(candidate, BGAN.op)
print (bleu)
out = open('bleu_out.txt', 'w')
out.write(str(bleu))
out.close()
hdf = hdf.loc[hdf['LP'] == lp] hdf = hdf.loc[hdf['SYSTEM'] == sys] hdf.reset_index(drop=True, inplace=True) cands = [] fc = open(csdir + '/' + cs, "r", encoding='utf-8') while True: line = fc.readline() if not line: break cands.append(wmt_data_cands(line)) assert len(cands) == len(refs) for i in range(len(cands)): bleu.append(BLEU(refs[i], cands[i], 4)) outlist.append([lp, sys, sum(bleu)/len(bleu), hdf['HUMAN'].item()]) sz = len(cses) pees = [row[2] for row in outlist[-sz:]] hues = [row[3] for row in outlist[-sz:]] lissy = [csdir[-5:]] src = spearmanr(pees, hues) pcc = pearsonr(pees, hues) ktc = kendalltau(pees, hues) lissy += [src.correlation, pcc[0], ktc[0]] finlist.append(lissy)
def pattern_baseline():
v = Vocab()
# files for evaluating BLEU
pred_path, gold_path = 'candidate.txt', 'reference.txt'
pred, gold = open(pred_path, 'w+'), open(gold_path, 'w')
ftest = open('../nlpcc-iccpol-2016.kbqa.testing-data', 'r')
# separate files for ROUGE
# here we use different gold file from seq2seq because the extracted templates from training set
# can't cover all the predicates from testing set
gold_for_ROUGE = "../run/evaluation/gold_temp/question_"
pred_for_ROUGE = "../run/evaluation/pred_temp/question_"
# patterns extracted from training set
trainAP = open('trainPattern.txt', 'r')
rel_dic = {}
for line in trainAP:
line = line.strip()
pattern, rel = line.split('\t')[0], line.split('\t')[-2]
if rel not in rel_dic:
rel_dic[rel] = [pattern]
else:
rel_dic[rel].append(pattern)
pattern = re.compile(r'[·•\-\s]|(\[[0-9]*\])')
cnt = 0
gold_all, pred_all = [], []
for line in ftest:
if line.find('<q') == 0: # question line
qRaw = line[line.index('>') + 2:].strip()
continue
elif line.find('<t') == 0: # triple line
triple = line[line.index('>') + 2:]
s = triple[:triple.index(' |||')].strip() # topic word
triNS = triple[triple.index(' |||') + 5:]
p = triNS[:triNS.index(' |||')] # predicate
p, num = pattern.subn('', p)
if p not in rel_dic:
with open(pred_for_ROUGE+str(cnt), 'w+') as sw:
sw.write('\n')
with open(gold_for_ROUGE+str(cnt), 'w+') as sw:
sw.write('\n')
pred_all.append([])
gold_all.append([])
else:
sp = random.sample(rel_dic[p],1)[0]
sp = sp.replace('(SUB)', s)
pred_list, gold_list = [], []
for char in sp:
wid = v.word2id(char)
pred_list.append(str(wid)) # replace unk in pred list with 0
pred_all.append(pred_list)
pred.write(' '.join(pred_list) + '\n')
with open(pred_for_ROUGE + str(cnt), 'w+') as sw:
sw.write(' '.join(pred_list) + '\n')
for char in qRaw:
wid = v.word2id(char)
gold_list.append(str(-1 if wid == 0 else wid)) # replace unk in gold list with -1
gold_all.append([gold_list])
gold.write(' '.join(gold_list) + '\n')
with open(gold_for_ROUGE + str(cnt), 'w+') as sw:
sw.write(' '.join(gold_list) + '\n')
cnt += 1
else:
continue
pred.close()
gold.close()
print("number of questions in test set: " + str(len(pred_all)))
pred_set = [pred_for_ROUGE + str(i) for i in range(cnt)]
gold_set = [[gold_for_ROUGE + str(i)] for i in range(cnt)]
bleu = BLEU(pred_path, gold_path)
print("Bleu: %s" % (str(bleu)))
recall, precision, F_measure = PythonROUGE(pred_set, gold_set, ngram_order=4)
print("F_measure: %s Recall: %s Precision: %s\n" % (str(F_measure), str(recall), str(precision)))
r2g = open('../data/relation2group.txt', 'r')
tfidf, cc = 0.0, 0
for line in r2g:
items = line.strip().split()
if len(items) > 2:
cc += 1
tmp = []
for item in items:
tmp.append(pred_all[int(item)])
try:
sm = SentenceSimilarity(tmp)
sm.TfidfModel()
tfidf += sm.similarity()
except ValueError:
pass
else:
pass
print("number of question clusters (under the same predicate): " + str(cc))
tfidf /= cc
print("Tf-idf DIVERSE: %s" % str(tfidf))
r2g = open('../data/relation2group.txt', 'r')
tfidf, cc = 0.0, 0
for line in r2g:
items = line.strip().split()
if len(items) > 2:
cc += 1
tmp = []
for item in items:
tmp.append(pred_all[int(item)])
try:
sm = SentenceSimilarity(tmp)
sm.TfidfModel()
tfidf += sm.similarity()
except ValueError:
pass
else:
pass
print("number of question clusters (under the same predicate): " + str(cc))
tfidf /= cc
print("Tf-idf DIVERSE: %s" % str(tfidf))
if __name__ == '__main__':
print('extracting answer patterns from training set ...')
getAnswerPatten()
get_rel2group()
print('done ...')
pattern_baseline()
print(BLEU('candidate.txt', 'reference.txt'))
def compute(filename):
doc = Document(FrequencyTester.PATH + filename + ".txt")
gold_doc = Document(FrequencyTester.PATH + filename + "_gold.txt")
freqSummary = FrequencyTester.getSummary(doc, len(gold_doc.sentences))
return BLEU.computeNormalize(gold_doc.document, freqSummary, ignore=True)
import numpy as np
from pandas import DataFrame
from AES_processing import cand_data, ref_data
from bleu import BLEU
cand_df = pd.read_csv('../data/ASAP_AES/training_set_rel3.tsv',
sep='\t',
encoding='ISO-8859–1')
ref_df = pd.read_csv('../data/ASAP_AES/reference_3_aes.tsv', sep='\t')
n_gram = 4
cand_id, candidate_corpus, human_scores = cand_data(cand_df)
reference_corpus = ref_data(ref_df)
bleu_scores = []
for i in range(len(candidate_corpus)):
bleu = BLEU(reference_corpus, candidate_corpus[i], n_gram)
# print(bleu)
bleu_scores.append(bleu)
print(max(bleu_scores))
filename = "../results/asap_aes_results/BLEU_scores_aes.txt"
with open(filename, 'w') as f:
f.write("candidate_id\tsimilarity\tscore\n")
for i in range(len(candidate_corpus)):
f.write("{0}\t{1}\t{2}\n".format(cand_id[i], bleu_scores[i],
human_scores[i]))
from SAS_processing import data
from bleu import BLEU
df = pd.read_csv('../data/ASAP_SAS/train.tsv', sep='\t')
n_gram = 4
reference_corpus, candidate_corpus, reference_id, candidate_id, candidate_scores, max_scores = data(
df)
# Check the correctness of length
# for i in range(len(reference_corpus)):
# print(len(reference_corpus[i]), len(candidate_corpus[i]), len(reference_id[i]), len(candidate_id[i]))
# print(reference_id[2], candidate_id[2], candidate_scores[2], max_scores)
print(BLEU(reference_corpus[2], reference_corpus[2][50], 4))
# bleu_scores = []
# for i in range(len(reference_corpus)):
# bleu = []
# for j in range(len(candidate_corpus[i])):
# bleu.append(BLEU(reference_corpus[i], candidate_corpus[i][j], n_gram))
# # print(bleu)
# bleu_scores.append(list(bleu))
# # for i in range(len(bleu)):
# # print(bleu[i])
# for i in range(len(reference_corpus)):
# filename = "../results/BLEU_scores_"+"EssaySet_{}".format(i+1)+".txt"
# with open(filename, 'w') as f:
def evaluate_s2s(sess, dataloader, model):
global last_best
testset = dataloader.test_set
pred_list = []
k = 0
with open(pred_path, 'w') as sw1:
for x in dataloader.batch_iter(testset, FLAGS.batch_size, False):
predictions = model.generate(x, sess)
for summary in np.array(predictions):
summary = list(summary)
if 2 in summary: # 2(START/END) marks the end of generation
summary = summary[:summary.
index(2)] if summary[0] != 2 else [2]
sw1.write(" ".join([str(t) for t in summary]) + '\n')
with open(pred_for_ROUGE + str(k), 'w+') as sw2:
sw2.write(" ".join([str(t) for t in summary]) + '\n')
k += 1
pred_list.append([str(t) for t in summary])
print("Total questions in Test:" + str(k))
# BLEU test
bleu = BLEU(pred_path, gold_path)
print("Bleu: %s" % (str(bleu)))
# ROUGE test
pred_set = [pred_for_ROUGE + str(i) for i in range(k)]
gold_set = [[gold_for_ROUGE + str(i)] for i in range(k)]
recall, precision, F_measure = PythonROUGE(pred_set,
gold_set,
ngram_order=4)
print("F_measure: %s Recall: %s Precision: %s\n" %
(str(F_measure), str(recall), str(precision)))
r2g = open('../data/relation2group.txt', 'r')
tfidf, cc = 0.0, 0
for line in r2g:
items = line.strip().split()
if len(items) > 2:
cc += 1
tmp = []
for item in items:
tmp.append(pred_list[int(item)])
try:
sm = SentenceSimilarity(tmp)
sm.TfidfModel()
tfidf += sm.similarity()
except ValueError:
pass
else:
pass
tfidf /= cc
print("number of question clusters (under the same predicate): " + str(cc))
print("Tf-idf DIVERSE: %s" % str(tfidf))
result = "BLEU: %s BLEU_beam: %s \nF_measure: %s Recall: %s Precision: %s\n Tf-idf: %s\n " % \
(str(bleu), str(0), str(F_measure), str(recall), str(precision), str(tfidf))
if float(bleu) > last_best:
last_best = float(bleu)
to_word(pred_list, save_dir)
return result
def evaluate(loader, seq2seq, criterion, max_len):
import time
losses = utils.AverageMeter()
ppls = utils.AverageMeter()
seq2seq.eval()
bleu = BLEU()
tot_st = time.time()
bleu_time = 0.
# BLEU time: 13k 개에 대해서 약 4s. multi-cpu parallelization 은 가능함.
def get_lens(tensor, max_len=max_len):
""" get first position (index) of EOS_idx in tensor
= length of each sentence
tensor: [B, T]
"""
# assume that former idx coming earlier in nonzero().
# tensor 가 [B, T] 이므로 nonzero 함수도 [i, j] 형태의 tuple 을 결과로 내놓는데,
# 이 결과가 i => j 순으로 sorting 되어 있다고 가정.
# e.g) nonzero() => [[1,1], [1,2], [2,1], [2,3], [2,5], ...]
nz = (tensor == EOS_idx).nonzero()
is_first = nz[:-1, 0] != nz[1:, 0]
is_first = torch.cat([torch.cuda.ByteTensor([1]),
is_first]) # first mask
# convert is_first from mask to indice by nonzero()
first_nz = nz[is_first.nonzero().flatten()]
lens = torch.full([tensor.size(0)], max_len, dtype=torch.long).cuda()
lens[first_nz[:, 0]] = first_nz[:, 1]
return lens
with torch.no_grad():
for i, (src, src_lens, tgt, tgt_lens) in enumerate(loader):
B = src.size(0)
src = src.cuda()
tgt = tgt.cuda()
dec_outs, attn_ws = seq2seq(src,
src_lens,
tgt,
tgt_lens,
teacher_forcing=0.)
loss, ppl = criterion(dec_outs, tgt)
losses.update(loss, B)
ppls.update(ppl, B)
# BLEU
bleu_st = time.time()
# convert logits to preds
preds = dec_outs.max(-1)[1]
# get pred lens by finding EOS token
pred_lens = get_lens(preds)
for pred, target, pred_len, target_len in zip(
preds, tgt, pred_lens, tgt_lens):
# target_len include EOS token => -1.
bleu.add_sentence(pred[:pred_len].cpu().numpy(),
target[:target_len - 1].cpu().numpy())
bleu_time += time.time() - bleu_st
total_time = time.time() - tot_st
logger.debug("TIME: tot = {:.3f}\t bleu = {:.3f}".format(
total_time, bleu_time))
return losses.avg, ppls.avg, bleu.score()