def testFastText(self):
class LeeReader(object):
def __init__(self, fn):
self.fn = fn
def __iter__(self):
with smart_open(self.fn, 'r', encoding="latin_1") as infile:
for line in infile:
yield line.lower().strip().split()
model = FastText(LeeReader(datapath('lee.cor')))
model.init_sims()
index = self.indexer(model, 10)
self.assertVectorIsSimilarToItself(model.wv, index)
self.assertApproxNeighborsMatchExact(model, model.wv, index)
self.assertIndexSaved(index)
self.assertLoadedIndexEqual(index, model)
def get_fasttext_embedding_matrix(word_index, max_nb_words):
model = fText.load_fasttext_format(FASTTEXT_FILE)
nb_words = max_nb_words
word_embedding_matrix = np.zeros((nb_words + 1, EMBEDDING_DIM))
for word, i in word_index.items():
if i > max_nb_words:
continue
embedding_vector = model.wv[word]
if embedding_vector is not None:
word_embedding_matrix[i] = embedding_vector
print('Null word embeddings: %d' % np.sum(np.sum(word_embedding_matrix, axis=1) == 0))
return word_embedding_matrix, nb_words
def _train(self):
"""
Train the np2vec model.
"""
if self.word_embedding_type == 'word2vec':
self.model = Word2Vec(
self._sentences,
sg=self.sg,
size=self.size,
window=self.window,
alpha=self.alpha,
min_alpha=self.min_alpha,
min_count=self.min_count,
sample=self.sample,
workers=self.workers,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
iter=self.iter)
elif self.word_embedding_type == 'fasttext':
self.model = FastText(
self._sentences,
sg=self.sg,
size=self.size,
window=self.window,
alpha=self.alpha,
min_alpha=self.min_alpha,
min_count=self.min_count,
sample=self.sample,
workers=self.workers,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
iter=iter,
min_n=self.min_n,
max_n=self.max_n,
word_ngrams=self.word_ngrams)
else:
logger.error(
'invalid word embedding type: ' +
self.word_embedding_type)
sys.exit(0)
def load(cls, np2vec_model_file, binary=False, word_ngrams=0):
"""
Load the np2vec model.
Args:
np2vec_model_file (str): the file containing the np2vec model to load
binary (bool): boolean indicating whether the np2vec model to load is in binary format
word_ngrams (int {1,0}): If 1, np2vec model to load uses word vectors with subword (
ngrams) information.
Returns:
np2vec model to load
"""
if word_ngrams == 0:
return KeyedVectors.load_word2vec_format(
np2vec_model_file, binary=binary)
elif word_ngrams == 1:
return FastText.load(np2vec_model_file)
else:
logger.error('invalid value for \'word_ngrams\'')
def word_embedding(self):
return FastText(self.tokens, size=100, min_count=5, workers=multiprocessing.cpu_count(), sg=1)
def main():
class MyIter(object):
def __iter__(self):
# absolute_path = r'C:\Users\NA\Desktop\Workspace\GJAI_WarmingUpProject\AIJOA_Project\wiki.ko'
file_name = 'voice.txt'
path = datapath(absolute_path + '/' + file_name) # 학습시킬 데이터의 절대경로
with utils.open(path, 'r', encoding='UTF-8') as fin:
for line in fin:
yield list(tokenize(line))
# 데이터 불러오기
# absolute_path = r'C:\Users\NA\Desktop\Workspace\GJAI_WarmingUpProject\AIJOA_Project\wiki.ko'
file_name = 'wiki.ko.bin'
# file_name = 'cc.ko.300.bin' # wiki.ko.bin
corpus_file = datapath(absolute_path + '/' +
file_name) # 데이터의 절대경로 상대경로는 안되나? 응 안돼
# 모델 구축
# model 객체화 # 우측 링크의 매개변수 참조 : https://radimrehurek.com/gensim/models/fasttext.html
print("모델 객체 생성")
# model = FastText(size=1000, window=3, min_count=3, workers=4, sg=1)
model = models.fasttext.load_facebook_model(corpus_file)
# 불러온 모델에 이어서 학습하기
# MyIter()로 불러온 데이터 new_sentences 리스트에 담기
new_sentences = []
for i in MyIter():
new_sentences.append(i)
print(new_sentences)
# MyIter()로 불러온 데이터양 늘리기
new_sentences = new_sentences * 10000
print(len(new_sentences))
# corpus_total_words (및 corpus_count) 모델 속성을 추가 설정
# 이어서 학습시킬때에는 update=True로 설정해야 기존 학습한 어휘가 추가된체로 학습함.
print("추가 어휘구성")
model.build_vocab(new_sentences, update=True) # Update the vocabulary
print("추가 어휘구성 끝")
# 추가 학습
print("추가 학습")
model.train(new_sentences,
total_examples=len(new_sentences),
epochs=model.epochs)
print("추가 학습 끝")
# 모델 저장 : 저장경로는 절대 절대 절대 경로로 설정 안그러면 어만데 저장됨.
save_name = "wiki_ko_v3.model"
fname = get_tmpfile(absolute_path + '/' + save_name)
print("model save start")
model.save(fname)
print("model save end")
# 저장했던 모델 불러오기 : 절대 경로임.
# absolute_path = r'C:\Users\NA\Desktop\Workspace\GJAI_WarmingUpProject\AIJOA_Project\wiki.ko'
file_name = "wiki_ko_v3.model"
fname = get_tmpfile(absolute_path + '/' + file_name)
print("model load start")
model = FastText.load(fname)
print("model load end")
menu_list = {
'폴더버거 핫치킨': [
'골드버거 치킨', '오늘도 봐봐 치킨', '오늘도 보고 와 치킨', '불도 먹었어 치킨', '골드버거 핫치킨',
'골드버거 치킨', '월드 보고 아침에', '오늘도 보고 와 치킨', '폴더 버거 킹', '홀더 버거 치킨',
'뭘 더 먹어 치킨', '너 먹어 치킨', '뭐 먹어 치킨'
],
'폴더버거 비프': [
'골드버그 비프', '올더 버거 비프', '폴더 버거 비프', '골드버그 비프 세트', '올더 버거 비프 세트',
'어디서 먹어 핑크색', '물 더 먹어 비트 세트', '골드버그 비프 세트', '올 더 버거 비틀 세트',
'홀더 버거 비프', '뭘 더 먹어 비프', '너 먹어 피프 세트', '뭐 먹어 비프'
],
'리아미라클버거':
['리아미라클버거', '미아 미라클버거', '리아미라클버거 세트', '미라클버거 세트', '리아 미라클 버거 세트'],
'와규 에디션 투': [
'외규에디션 2', '마귀 에디션 2', '와규에디션 2', '와 귀신 전화', '월요일 좀 주세요 전화',
'와규에디션 2 세트', '와규에디션 2 세트', '목요일 샘플 세트'
],
'더블엑스투': [
'브렉시트', '더블엑스 2', '더블 X2 세트', '저번에 지출 세트', '버그 렉스필드 전화',
'노래 치킨 세트', '더블 X2 세트', '더블엑스 풀세트', '더블엑스 두 세트'
],
'티렉스': [
'티렉스 버거', '티렉스 버거 세트', '티렉스버거세트', '티렉스버거 찾아', '티렉스 버거 세트 두 개',
'티렉스버거세트 두 개'
],
'클래식 치즈버거': [
'클래식 치즈버거', '클래식 치즈버거 세트 하나', '클래식 치즈버거 틀어', '클래식 치즈버거 세트',
'클래식 치즈버거 세트 두 개'
],
'한우불고기': [
'한우 불고기', '한우불고기 세트 하나', '한우 불고기 집 전화', '한우 불고기 제주 TV',
'한우불고기 두 개', '한우불고기 세트 두 개'
],
'모짜렐라 인 더 버거 베이컨': [
'모짜렐라인 더 버거', '모짜렐라인 더 버거 세트 하나', '모짜렐라 인더버거 베트남',
'모짜렐라인 더 버거 세트 두 개', '모짜렐라인 더 버거 세트'
],
# '모짜렐라 in the 버거':['모짜렐라인 더 버거', '모짜렐라인 더 버거 세트 하나',
# '모짜렐라 인더버거 베트남', '모짜렐라인 더 버거 세트 두 개',
# '모짜렐라인 더 버거 세트'],
# '모짜렐라 인 더 버거':['모짜렐라인 더 버거', '모짜렐라인 더 버거 세트 하나',
# '모짜렐라 인더버거 베트남', '모짜렐라인 더 버거 세트 두 개',
# '모짜렐라인 더 버거 세트'],
'에이지버거': [
'az버거', 'az버거 세트', '에이지버거', '에이지버거 세트', '아재버거', '아재버거 세트 하나',
'거제도 가서 찾아', '아재 동생 두 개', '아재버거세트 두 개', '아재버거 세트 두 개'
],
'az버거': [
'az버거', 'az버거 세트', '에이지버거', '에이지버거 세트', '아재버거', '아재버거 세트 하나',
'거제도 가서 찾아', '아재 동생 두 개', '아재버거세트 두 개', '아재버거 세트 두 개'
],
'에이제트버거': [
'az버거', 'az버거 세트', '에이제트버거', '에이제트버거 세트', '아재버거', '아재버거 세트 하나',
'거제도 가서 찾아', '아재 동생 두 개', '아재버거세트 두 개', '아재버거 세트 두 개'
],
'원조 빅불': [
'원조빅불', '원조빅불 세트', '원조빅불세트', '언제 도착하나', '물제 를 풀 세트', '원조 빅불 세트',
'오늘 이불세트'
],
'핫크리스피버거': [
'핫크리스피버거', '하트스피가방', '핫크리스피버거 세트', '크리스피 버거 세트', '핫 크리스피버거 세트',
'하트 스트커 세트'
],
'불고기버거': ['불고기 버거', '불고기 버거 세트 하나', '불고기 버거 세트', '불고기버거 세트 두 개'],
'데리버거': [
'데리버거', '데리버거 세트 하나', '데리버거 찾아', '데리버거 두 개', '데리버거 세트 두 개',
'데리버거세트 두 개'
],
'치킨버거':
['치킨버거', '치킨 먹어', '치킨 버거 세트', '치킨 먹었다', '치킨 먹어서 두 개', '치킨 버거 세트 두 개'],
'새우버거': ['새우버거', '재봉 설탕', '새우버거 세트', '일본어 태어나', '여보 가서 켜', '새우버거 속']
}
# 메뉴오탈과 메들 사이의 유사도
print("메뉴오탈과 메들 사이의 유사도 비교 파일 생성 시작")
for key1 in menu_list.keys():
file_name = f'{key1}메뉴의 오탈자와 전체메뉴들과의 유사도.txt'
for value in menu_list[key1]:
for key2 in menu_list.keys():
# print(f"메뉴'{key1}'의 오탈자 '{value}'와 메뉴명'{key2}'와의 유사도: {model.wv.similarity(value, key2)}")
if file_name not in os.listdir(absolute_path):
with open(absolute_path + '/' + file_name,
'w',
encoding='utf-8') as file_data:
file_data.write(
f"'{key1}'의 오탈자 '{value}'와 메뉴명'{key2}'와의 유사도: {model.wv.similarity(value, key2)}\n"
)
else:
with open(absolute_path + '/' + file_name,
'a',
encoding='utf-8') as file_data:
file_data.write(
f"'{key1}'의 오탈자 '{value}'와 메뉴명'{key2}'와의 유사도: {model.wv.similarity(value, key2)}\n"
)
print("메뉴오탈과 메들 사이의 유사도 비교 파일 생성 종료")
def getResult(text):
print("START READ AND PREPROCESSING", text)
# kitab = np.load('dataset/numpy/kitabsave.npy', allow_pickle=True)
kitab = np.load(resource_path('./data/dataset/kitabsave.npy'), allow_pickle=True)
kitab = kitab.tolist()
# sentence_clear = np.load('dataset/numpy/sentence_clearsave.npy', allow_pickle=True)
sentence_clear = np.load(resource_path('./data/dataset/sentence_clearsave.npy'), allow_pickle=True)
sentence_clear = sentence_clear.tolist()
# kategori = np.load('dataset/numpy/kategori.npy', allow_pickle=True)
kategori = np.load(resource_path('./data/dataset/kategori.npy'), allow_pickle=True)
kategori = kategori.tolist()
# namakitab = np.load('dataset/numpy/namakitab.npy', allow_pickle=True)
namakitab = np.load(resource_path('./data/dataset/namakitab.npy'), allow_pickle=True)
namakitab = namakitab.tolist()
# kitab[0][1]
# modelFT = ft.load('Model/modelFT.model')
modelFT = ft.load(resource_path('./data/model/modelFT.model'))
#TF-IDF
tfidf_vectorizer = TfidfVectorizer()
norm_tf=[]
for isikitab in kitab:
for ktb in isikitab:
norm_tfidf = normalizeArabic(ktb)
norm_tf.append(norm_tfidf)
tfidf_doc = tfidf_vectorizer.fit_transform(norm_tf)
tfidf_word=tfidf_vectorizer.get_feature_names()
PIFQvectorizer = CountVectorizer()
vectoreTF = PIFQvectorizer.fit_transform(norm_tf)
featureTf = PIFQvectorizer.get_feature_names()
cosimhasil = []
cosimhasilnilai = []
hasilqenilaidicosim = []
namakitabcosim = []
halamankitabcosim = []
isikitabcosim = []
inputandicosim = []
pifqhasil = []
pifqhasilnilai = []
hasilqenilaidipifq = []
namakitabpifq = []
halamankitabpifq = []
isikitabpifq = []
inputandipifq = []
gabunganhasil = []
gabunganhasilnilai = []
hasilqenilaidigabungan = []
namakitabgabungan= []
halamankitabgabungan = []
isikitabgabungan = []
inputandigabungan = []
# MOST SIMILAR WE
hasilQE = modelFT.wv.most_similar(text)
hasilQE = [(strip_tashkeel(''.join(c for c in hasilQE[i][0] if not ud.category(c).startswith('P'))), hasilQE[i][1]) for i in range(len(hasilQE))]
# print(hasilQE)
cosim = []
hasilpifq = []
hasilgabungan = []
nilaihasilgabungan = []
nilaihasilpifq = []
nilaicosim = []
QEpakai = hasilQE[0:3]
for i in QEpakai:
tes=i[0]
tfidf_query = tfidf_vectorizer.transform([tes])
cos=0.0
#hitung kedekatan query pada masing masing dokumen
cos=cosine_similarity(tfidf_doc,tfidf_query)
# print(type(cos))
cosim.append(max(cos))
nilaicosim.append(cos)
# print('tfidf')
# ================
countTF = []
s = ''.join(c for c in tes if not ud.category(c).startswith('P'))
s = strip_tashkeel(s)
for k in range(len(featureTf)):
if featureTf[k] == s:
# print(k)
for j in range(vectoreTF.shape[0]):
countTF.append(vectoreTF[j,k])
#PIFQ
nilaipifq = []
for k in countTF:
if sum(countTF) == 0:
nilaipifq.append(0)
else:
nilaipifq.append(1 + np.log10(1 + (k / sum(countTF))) + 0.5)
nilaihasilpifq.append(nilaipifq)
hasilpifq.append(max(nilaipifq))
# print('pifq')
#gabungan
nilaigabungan = []
for k in range(vectoreTF.shape[0]):
nilaigabungan.append(nilaipifq[k] * cos[k][0])
nilaihasilgabungan.append(nilaigabungan)
hasilgabungan.append(max(nilaigabungan))
# print("======= hasil Cosim ===========")
angka = 0
for i in nilaicosim:
# print(hasilQE[angka][0])
for j in range(len(i)):
if i[j] == cosim[angka]:
panjangkitab= 0;
for iterkitab in range(len(kitab)):
panjangkitab = panjangkitab + len(kitab[iterkitab])
if j <= panjangkitab:
tessplit = kitab[iterkitab][0].split(',')
# print('Nama Kitab {} halaman ke {}'.format(namakitab[iterkitab],tessplit[4]))
# print('isi kitab : ', tessplit[5])
cosimhasil.append(hasilQE[angka][0])
hasilqenilaidicosim.append(hasilQE[angka][1])
cosimhasilnilai.append(cosim[angka])
namakitabcosim.append(namakitab[iterkitab])
halamankitabcosim.append(tessplit[4])
isikitabcosim.append(tessplit[5])
# inputandicosim.append(kata)
break;
angka += 1
# print("====== hasil pifq ==========")
angka = 0
for i in nilaihasilpifq:
# print(hasilQE[angka][0])
for j in range(len(i)):
if i[j] == hasilpifq[angka]:
panjangkitab= 0;
for iterkitab in range(len(kitab)):
panjangkitab = panjangkitab + len(kitab[iterkitab])
if j <= panjangkitab:
tessplit = kitab[iterkitab][0].split(',')
# print('Nama Kitab {} halaman ke {}'.format(namakitab[iterkitab],tessplit[4]))
# print('isi kitab : ', tessplit[5])
pifqhasil.append(hasilQE[angka][0])
pifqhasilnilai.append(hasilpifq[angka])
hasilqenilaidipifq.append(hasilQE[angka][1])
namakitabpifq.append(namakitab[iterkitab])
halamankitabpifq.append(tessplit[4])
isikitabpifq.append(tessplit[5])
# inputandipifq.append(kata)
break;
angka += 1
# print("============== Hasil Gabungan ===============")
print("============== selesai ===============")
angka = 0
for i in nilaihasilgabungan:
# print(hasilQE[angka][0])
for j in range(len(i)):
if i[j] == hasilgabungan[angka]:
panjangkitab= 0;
for iterkitab in range(len(kitab)):
panjangkitab = panjangkitab + len(kitab[iterkitab])
if j <= panjangkitab:
tessplit = kitab[iterkitab][0].split(',')
# print('Nama Kitab {} halaman ke {}'.format(namakitab[iterkitab],tessplit[4]))
# print('isi kitab : ', tessplit[5])
gabunganhasil.append(hasilQE[angka][0])
gabunganhasilnilai.append(hasilgabungan[angka])
hasilqenilaidigabungan.append(hasilQE[angka][1])
namakitabgabungan.append(namakitab[iterkitab])
halamankitabgabungan.append(tessplit[4])
isikitabgabungan.append(tessplit[5])
# inputandigabungan.append(kata)
break;
angka += 1
nilaihasilcosim = []
for i in cosimhasilnilai:
nilaihasilcosim.append(i[0])
def train_model(clxn):
sents = chain(*(tokenize(item.text) for item in clxn))
model = FastText(sents, size=300)
return model
x.append(ls[0])
temp = []
#print(ls[0])
for j in tokenizer.tokenize(ls[0].decode('utf-8')):
#print(j)
temp.append(j)
data.append(temp)
lent.append(len(temp))
y_test.append(int(ls[1]))
f.close()
pad_len = max(lent)
#model_FT = FastText(data, size=10, window=5, min_count=1, workers=5, sg=1,max_vocab_size=10000)
#model_FT.save("SG_fasttext.model")
model_FT = FastText.load("SG_fasttext.model")
print "SG FT model_done!"
voc = list(model_FT.wv.vocab)
print(len(voc))
XVAL = fit_transform(data)
print("Transformed!!")
x_train = []
x_train = XVAL[:m]
print(np.array(x_train).shape)
x_test = []
x_test = XVAL[m:]
print(np.array(x_test).shape)
test_size=0.3,
random_state=2018)
x_pos_train, x_pos_test, __, ___ = train_test_split(x_pos,
y,
test_size=0.3,
random_state=2018)
print('==== training BiLSTM-CRF ====')
# %%
# from gensim.models import Word2Vec
# model_ted = Word2Vec(sentences=vocab, size=50, window=3, min_count=1, workers=4, sg=1)
# model_ted.save('word2vec.model')
from gensim.models import FastText, Word2Vec
model_ted = FastText(sentences=vocab,
size=EMBED_DIM // 4,
window=3,
min_count=1,
workers=4)
# model_ted = Word2Vec(sent, size=2 * (EMBED_DIM // 5), window=3, min_count=1, workers=4, sg=0)
# model_ted = Word2Vec(sent, size=2 * (EMBED_DIM // 5), window=3, min_count=1, workers=4, sg=1)
def get_weight_matrix(embedding, vocab):
# total vocabulary size plus 0 for unknown words
vocab_size = len(vocab)
# define weight matrix dimensions with all 0
weight_matrix = numpy.zeros((vocab_size, 50))
# step vocab, store vectors using the Tokenizer's integer mapping
#wordvectors = embedding.wv
for i, word in enumerate(vocab):
#weight_matrix[i] = wordvectors.word_vec(word)
sims = sorted(enumerate(sims), key=lambda item: -item[1])
df2 = pd.DataFrame(sims, columns=['attack_id', 'sim_score'])
for i in range(0, len(df2)):
df2.text[i] = df.Description[df2.attack_id[i]]
from gensim.models import Word2Vec
model_hack = Word2Vec(sentences=texts,
size=100,
window=5,
min_count=2,
workers=4,
sg=0)
model_hack.wv.most_similar('researchers')
from gensim.models import FastText
model_hack = FastText(texts, size=100, window=5, min_count=5, workers=4, sg=1)
model_hack.wv.most_similar('researchers')
from elasticsearch import Elasticsearch
es = Elasticsearch(['http://cloudweb01.isi.edu/es/'],
http_auth=('effect', 'c@use!23'),
port=80)
print(es.info())
#search for blogs/news since certain timeframe.
results = es.search(index="effect/socialmedia",
scroll='1d',
size=20000,
body={
"query": {
"range": {
def error_correction_fasttext_with_retrain_wiki(self, model_type,
datasets_type, dataparam_1,
dataparam_2):
total_error = 0
total_error_to_repaired = 0
total_repaired = 0
if model_type == "Fasttext_All_Domain": #every time it will load the pretrained model to test new wiki table
error_correction = self.prepare_testing_datasets_wiki(
dataparam_1, dataparam_2
) #dataparam_1 : json_list, dataparam_2: path of json_filelist
model_fasttext = FastText.load("model/Fasttext_All_Domain.w2v")
if model_type == "Fasttext_CV_Fold":
model_fasttext = FastText.load("model/Fasttext_CV_Fold.w2v")
if model_type == "Fasttext_Domain_Location":
model_fasttext = FastText.load(
"model/Fasttext_Location_Domain.w2v")
error_correction = self.prepare_domain_testing_datasets_wiki(
dataparam_1, dataparam_2, "location")
total_error = self.calculate_total_error_wiki(
dataparam_1, dataparam_2)
if datasets_type == "wiki":
train_data_rows = []
for rf in dataparam_1:
if rf.endswith(".json"):
try:
revision_list = json.load(
io.open(os.path.join(dataparam_2, rf),
encoding="utf-8"))
one_item = revision_list[-1]
old_value = str(one_item[0]['old_value'].strip())
new_value = str(one_item[0]['new_value'].strip())
vicinity = one_item[0]['vicinity']
vicinity = remove_markup(str(vicinity))
vicinity = ast.literal_eval(vicinity)
#print('Before: ',vicinity)
train_vicinity_index = vicinity.index(old_value)
del vicinity[train_vicinity_index]
vicinity.append(new_value)
vicinity = [
x for x in vicinity
if not any(x1.isdigit() for x1 in x)
]
vicinity = [x for x in vicinity if len(x) != 0
] #remove empty item from list
#vicinity=[re.sub('[^a-zA-Z0-9.-]+', ' ', _) for _ in vicinity]
#print('After: ', vicinity)
#row=list(filter(None, row))
dirty_table = one_item[0]['dirty_table']
for index, row in enumerate(dirty_table):
if index == 0:
continue
shape = len(row)
row = remove_markup(str(row))
row = ast.literal_eval(row)
row = list(filter(None, row))
#remove all digit
row = [
x for x in row
if not any(x1.isdigit() for x1 in x)
]
row = [x for x in row if len(x) != 0
] #remove empty item from list
if row:
row = [
re.sub('[^a-zA-Z0-9.-]+', ' ', _)
for _ in row
]
train_data_rows.append(row)
except Exception as e:
print('Exception: ', str(e))
if train_data_rows:
model_fasttext.build_vocab(train_data_rows, update=True)
model_fasttext.train(sentences=train_data_rows,
total_examples=len(train_data_rows),
epochs=5)
for error_value, actual_value in zip(error_correction['error'],
error_correction['actual']):
try:
if model_type == "Fasttext_Domain_Location":
pass
else:
total_error = total_error + 1
if not any(x1.isdigit() for x1 in error_value):
total_error_to_repaired = total_error_to_repaired + 1
similar_value = model_fasttext.most_similar(
error_value)
#print('Actual value: ', actual_value,'Most similar value of : ',error_value, ' ' , similar_value)
first, b = similar_value[0]
#print('Error : ', error_value, ' Fixed: ', first, ' Actual: ', actual_value)
first = first.strip()
actual_value = actual_value.strip()
if first == actual_value:
print('Error : ', error_value, ' Fixed: ', first,
' Actual: ', actual_value)
total_repaired = total_repaired + 1
except:
continue
print(total_error, total_error_to_repaired, total_repaired)
model_type = model_type + ' retrain wiki '
self.evaluate_model(model_type, total_error, total_error_to_repaired,
total_repaired)
def loadEmbedding(filename):
print("Loading Word Embedding...")
it_model = FastText.load_fasttext_format(filename, full_model=False)
print("...Done!")
print("Building Sqlite DB....")
return it_model
def main(argv):
topic = argv[0]
filelang = argv[1]
mainlang = argv[2]
path = "/home/oyku/embeddings/fasttext/wiki." + filelang + ".align.vec"
dictionary = load_vec(path)
mono_path = "/home/oyku/monolingual_fasttext/cc." + filelang + ".300"
mono_wv = fText.load_fasttext_format(mono_path)
file = "/home/oyku/myversion/oov_words/" + mainlang + "/" + topic + "_" + filelang + ".txt"
f = open(file, 'r', encoding='utf8')
content = f.readlines()
cont = set()
for el in content:
if not el.strip().isdigit():
cont.add(el.strip())
print("The number of OOVs: " + str(len(content)))
print("The number of word OOVs: " + str(len(cont)))
## Morphologic
morphs = {}
for blob in cont:
if not blob.isdigit():
text = Text(blob)
text.language = filelang
morphemes = []
for morp in text.morphemes:
if len(morp) > 3 and morp in dictionary:
morphemes.append(morp)
if len(morphemes) != 0:
morphs[blob] = morphemes
print("Morphologic check is over")
left = cont.difference(morphs)
## Spelling
spellex = {}
for oov in left:
if len(oov) > 2:
possibles = []
for inv in dictionary:
if stringdist.rdlevenshtein(oov, inv) == 1:
possibles.append(inv)
if len(possibles) == 1:
spellex[oov] = possibles
print("Spelling check is over")
next_left = left.difference(spellex)
fasttext_bin = {}
for oov in next_left:
try:
similars = mono_wv.wv.most_similar(oov.strip())
most_sim = ""
for sim in similars:
if sim[0] in dictionary and sim[1] > 0.5:
most_sim = sim[0]
break
if most_sim != "":
fasttext_bin[oov.strip()] = [most_sim]
except:
continue
print("Fasttext check is over")
print("-----------------------------------------------")
print("Identified with morphologic analysis: " + str(len(morphs)))
print("Identified with spell analysis: " + str(len(spellex)))
print("Identified with Fasttext: " + str(len(fasttext_bin)))
union = union3(morphs, spellex, fasttext_bin)
print("Total: " + str(len(union)))
saved_path = "/home/oyku/myversion/oov_matches/" + mainlang + "/" + topic + "_" + filelang + ".p"
pickle.dump(union, open(saved_path, "wb"))
def Updates():
try:
print("updating Doc2Vec")
print(updating)
a = stem.snowball.ArabicStemmer()
stopwords_list = stopwords.words('arabic')
df = pd.read_csv('textc-Copy1.csv', encoding='utf-8')
df["contenu"].fillna("محتوى فارغ", inplace=True)
df["article"].fillna("محتوى فارغ", inplace=True)
y = df['ToF']
df = df.drop('ToF', axis=1)
text = []
for i in range(df.shape[0]):
x = nltk.tokenize.wordpunct_tokenize(df.contenu[i])
text1 = [a.stem(word) for word in x]
text.append(text1)
titre = [
a.stem(word) for word in df.article if word not in stopwords_list
]
#doc2vec
docs = []
analyzedDocument = namedtuple('AnalyzedDocument', 'words tags')
for i, te in enumerate(text):
tags = [i]
docs.append(analyzedDocument(te, tags))
model = doc2vec.Doc2Vec(docs,
vector_size=300,
non_negative=True,
window=8,
min_count=1,
workers=4,
dm=1)
from gensim.test.utils import get_tmpfile
fname = get_tmpfile("doc2vec.model")
model.save(fname)
model = doc2vec.Doc2Vec.load(fname)
print("updating fastext")
class MyItera(object):
def __iter__(self):
for line in Corpus.article:
filtered_sentence = []
for w in tokenize(line):
if w not in stop_words:
filtered_sentence.append(w)
yield filtered_sentence
class MyIter(object):
def __iter__(self):
for line in Corpus.contenu:
filtered_sentence = []
for w in tokenize(line):
if w not in stop_words:
filtered_sentence.append(w)
yield filtered_sentence
model = FastText(size=150, window=3, min_count=1)
model.build_vocab(sentences=MyIter())
total_examples = model.corpus_count
model.train(sentences=MyIter(),
total_examples=total_examples,
epochs=5)
except:
Update()
def loadfasttext():
fname = get_tmpfile("fasttext.model")
model_fasttext = FastText.load(fname)
return model_fasttext
def make_model_fasttext(dataset, setting):
pert_sent = list(perturbed_iterator(dataset, setting))
model = FastText(pert_sent, workers=effective_n_jobs(-1))
model.save(f'fasttext_models/{setting}.bin')
from gensim.models import FastText
print('Starting to load fasttext embeddings...')
path_to_fasttext_emb = '/tmp/wiki.ru.bin'
print('Done!')
ft_model = FastText.load_fasttext_format(path_to_fasttext_emb)
print(ft_model.wv['снег'])
from fse import IndexedList
from fse.models.average import FAST_VERSION, MAX_WORDS_IN_BATCH
from fse.models import SIF
from gensim.models import FastText
import logging
logging.basicConfig(
format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s',
level=logging.INFO)
w2v_model = "H:/Vietnamese word representations/Word_vector_data/VnNewsWord2Vec/VnNewsWord2Vec.bin"
lookup = FastText.load_fasttext_format(w2v_model, encoding='utf-8')
sentences = []
s = IndexedList(sentences)
print(len(s))
title_file = 'H:/Vietnamese word representations/News-titles-embedding/Data/tokenized_titles_cleaned'
with open(title_file, 'r', encoding='utf-8') as file:
for line in file:
sentences.append(line.split())
s = IndexedList(sentences)
model = SIF(lookup, workers=2)
model.train(s)
model.save('sent2vec')
def main():
# --- argument parsing ---
(
model_name, epochs, min_count, cores, checkpoint_every,
cache_in_memory, lowercase, fasttext, args
) = parse_args(default_model_name='w2v_default', default_epochs=100)
# --- init logging ---
logger = init_logging(name=model_name, basic=True, to_file=True, to_stdout=False)
log_args(logger, args)
input_dir = join(SMPL_PATH, 'dewiki')
model_dir = join(EMB_PATH, model_name)
if not exists(model_dir):
makedirs(model_dir)
logger.info('model dir: ' + model_dir)
t0 = time()
if cache_in_memory:
# needs approx. 25GB of RAM
logger.info('cache data in memory')
sentences = [s for s in Sentences(input_dir, logger, lowercase=lowercase)]
else:
sentences = Sentences(input_dir, logger, use_file_cache=True, lowercase=lowercase)
gc.collect()
# Model initialization
logger.info('Initializing new model')
if fasttext:
model = FastText(
size=300,
window=5,
min_count=min_count,
sample=1e-5,
negative=5,
sg=1,
seed=42,
iter=epochs,
workers=cores,
min_n=3,
max_n=6,
)
else:
model = Word2Vec(
size=300,
window=5,
min_count=min_count,
sample=1e-5,
negative=5,
sg=1,
seed=42,
iter=epochs,
workers=cores,
)
logger.info('Building vocab')
model.build_vocab(sentences, progress_per=100_000)
# Model Training
epoch_saver = EpochSaver(model_name, model_dir, checkpoint_every)
epoch_logger = EpochLogger(logger)
logger.info('Training {:d} epochs'.format(epochs))
model.train(
sentences,
total_examples=model.corpus_count,
epochs=model.epochs,
report_delay=60,
callbacks=[epoch_logger, epoch_saver],
)
# saving model
file_path = join(model_dir, model_name)
logger.info('Writing model to ' + file_path)
model.callbacks = ()
model.save(file_path)
t1 = int(time() - t0)
logger.info("all done in {:02d}:{:02d}:{:02d}".format(t1//3600, (t1//60) % 60, t1 % 60))
def error_correction_fasttext_with_retrain_realworld(
self, datasets_type, dataset_1, dataset_2, model_type):
total_error = 0
total_error_to_repaired = 0
total_repaired = 0
try:
if model_type == "Fasttext_All_Domain":
if datasets_type == "real_world":
error_correction = self.prepare_testing_datasets_real_world(
dataset_1,
dataset_2) #dataset_1 clean data for real world
model_fasttext = FastText.load("model/Fasttext_All_Domain.w2v")
if model_type == "Fasttext_Domain_Location":
error_correction = self.prepare_domain_testing_datasets_realworld(
dataset_1, dataset_2) #dataset_1 clean data for real world
total_error = self.calculate_total_error_realworld(
dataset_1, dataset_2)
model_fasttext = FastText.load(
"model/Fasttext_Location_Domain.w2v")
except Exception as e:
print('Model Error: ', str(e))
data_for_retrain = self.prepare_dataset_for_retrain_realworld(
dataset_1, dataset_2)
train_data_rows = []
try:
data_for_retrain = data_for_retrain.values.tolist()
for row in data_for_retrain:
row = list(map(str, row))
row = list(filter(None, row))
train_data_rows.append(row)
if train_data_rows:
if train_data_rows:
model_fasttext.build_vocab(train_data_rows, update=True)
model_fasttext.train(sentences=train_data_rows,
total_examples=len(train_data_rows),
epochs=5)
except Exception as e:
print("Exception from spell model : ", str(e))
for error_value, actual_value in zip(error_correction['error'],
error_correction['actual']):
if model_type == "Fasttext_Domain_Location" and datasets_type == "real_world":
pass
else:
total_error = total_error + 1
try:
if not any(x1.isdigit() for x1 in error_value):
total_error_to_repaired = total_error_to_repaired + 1
similar_value = model_fasttext.most_similar(error_value)
first, b = similar_value[0]
first = first.lower()
actual_value = actual_value.lower()
#print('Error : ', error_value, ' Fixed: ', first, ' Actual: ', actual_value)
first = first.strip()
actual_value = actual_value.strip()
if first == actual_value:
print('Error : ', error_value, ' Fixed: ', first,
' Actual: ', actual_value)
total_repaired = total_repaired + 1
except Exception as e:
print('Error correction model: ', str(e))
continue
self.evaluate_model(model_type, total_error, total_error_to_repaired,
total_repaired)
for ix in range(len(word_index)):
word = word_index[ix]
# word_encode = word_index[ix].encode(('utf-8'))
vec = word_vectors[word]
for j in range(int(dim)):
E[ix][j] = vec[j]
window_size = 10000
embedding = Embedding(100,
32,
weights=[E],
input_length=window_size,
trainable=False)
# https://gist.github.com/brandonrobertz/49424db4164edb0d8ab34f16a3b742d5
trained_model = FastText.load_fasttext_format('data_noun_token')
input_train = []
fin = open('data_noun_token.txt', 'r')
lines = fin.readlines()
print(len(lines))
for line in lines:
word_vector = []
for word in line.split():
try:
word_vector.append(trained_model[word])
except:
pass
input_train.append(word_vector)
fin.close()
for word in tokens:
if word not in diccionario:
diccionario[word] = 1
else:
diccionario[word] += 1
print("numero de oraciones presentes en el corpus " + str(len(sentences)))
print("numero de palabras unicas " + str(len(diccionario)))
num_features = [20, 50, 100] #Dimensionality of the resulting word vectors
min_word_count = 1 #Minimum word count threshold
num_workers = multiprocessing.cpu_count(
) #Number of threads to run in parallel
context_size = 5 #Context window length
seed = 1 #Seed for the RNG, to make the result reproducible
for p in num_features:
fasttext_model = FastText(
sentences=sentences,
size=p,
window=context_size,
min_count=min_word_count,
workers=num_workers,
sg=1 #skip-gram
)
fasttext_model.wv.save_word2vec_format(
'model/fasttext_skip-gram_model_bioinfer_' + str(p) + '.txt',
binary=False)
del fasttext_model
import numpy as np
import pickle
import utils
# Setting
max_size, max_seq_len = 50, 30
(modelno_to_goodsnm, modelno_to_goodsnms) = utils.model_basic_dict()
#print("✱ the # of classes(catalogs): ", len(modelno_to_goodsnm)) # Not-Toy set
# Loading..
# ├ ⑴Toy dict.(pre-stored @toyData.py)
# ├ ⑵Word Embedding Model (pre-trained @wordEmbedding.py)
# └ ⑶LSTM model (pre-stored @lstm.py)
with open('dictionary/toyDict.pickle', 'rb') as handle:
toy_dict = pickle.load(handle)
fastText = FastText.load('model/FastText.bin')
preLSTM = load_model('model/lstm.h5')
LSTM = MyLSTM(toy_dict=toy_dict, embedding_model=fastText)
(X_train, Y_train, X_val, Y_val, X_test, Y_test,
toy_train_dict) = LSTM.split_train_test()
X_test = sequence.pad_sequences(np.array(X_test), maxlen=max_seq_len)
index_dict = LSTM.create_index_dict()
#---------------------Prediction---------------------#
X_new = X_test[:50]
Y_new = Y_test[:50]
Y_hat = list(preLSTM.predict_classes(X_new))
print("\nY_hat: {},\nY_new: {}\n".format(Y_hat, Y_new))
for i in range(len(Y_hat)):
def train_fasttext(corpus):
model = FastText(size=9, window=2, min_count=1)
model.build_vocab(sentences=corpus)
model.train(sentences=corpus, total_examples=len(corpus), epochs=10)
model.save(r'models\fasttext.model')
def main():
try:
with open("token_counts.json") as infile:
token_counts = json.load(infile)
except:
print(
"Failed to load token counts - did you `python train.py build-corpus $INFILE` yet?"
)
sys.exit(1)
if os.path.exists("vectors_out.sqlite"):
os.unlink("vectors_out.sqlite")
conn = sqlite3.connect("vectors_out.sqlite")
conn.execute("""
CREATE TABLE vector_meta (
vector_float_bytes integer,
embedding_dimensions integer,
vocab_size integer,
oov_token text,
build_parameters text
)
""")
conn.execute(
"INSERT INTO vector_meta VALUES (?, ?, ?, ?, ?)",
("float32", EMBED_DIM, VOCAB_SIZE, OOV_TOKEN, jsonify_build_params()),
)
conn.execute(
"CREATE TABLE vectors (token text primary key, vector_bytes blob);")
conn.execute(
"CREATE TABLE frequencies (token text primary key, count integer);")
print(f"Loaded {sum(token_counts.values())} words. Training model...")
ft_model = FastText(
corpus_file="corpus.txt",
size=EMBED_DIM,
window=WINDOW_LEN,
min_count=MIN_COUNT,
max_vocab_size=VOCAB_SIZE,
alpha=LEARNING_RATE,
sg=SKIPGRAM,
sorted_vocab=1,
iter=EPOCHS,
workers=NUM_WORKERS,
negative=NEGATIVE_SAMPLES,
ns_exponent=NEG_SAMP_DIST,
)
vectors = {}
vocab = {}
sorted_counts = sorted(token_counts.items(), key=lambda p: -p[1])
idx = 2
progress = tqdm(desc="Writing Vectors", total=VOCAB_SIZE)
oov_vector = ft_model.wv[OOV_TOKEN]
conn.execute("INSERT INTO vectors VALUES (?, ?)", (OOV_TOKEN, oov_vector))
conn.execute("INSERT INTO frequencies VALUES (?, ?)", (OOV_TOKEN, 0))
for token, count in sorted_counts:
try:
vector = ft_model.wv.get_vector(token)
except:
continue
vectors[token] = vector
vocab[token] = idx
vector_bytes = vector.astype("float32").tobytes()
conn.execute("INSERT INTO vectors VALUES (?, ?)",
(token, vector_bytes))
conn.execute("INSERT INTO frequencies VALUES (?, ?)", (token, count))
progress.update(1)
idx += 1
if idx >= VOCAB_SIZE:
break
print("Writing vectors_out.sqlite...")
conn.commit()
progress.close()
print("Writing vocab.json...")
with open("vocab.json", "w") as outfile:
json.dump(vocab, outfile)
print("Done!")
if embedding == 'w2v':
model = Word2Vec(sents,
size=dimension,
window=word_window,
workers=3,
sg=skip_grams,
hs=softmax,
negative=negative_sample,
iter=EPOCHS)
model.wv.save_word2vec_format(model_filename,
binary=False)
model.vocabulary.save(vocab_filename)
else:
model = FastText(sg=skip_grams,
hs=softmax,
size=dimension,
window=word_window,
workers=3,
negative=negative_sample)
model.build_vocab(sentences=sents)
model.train(sentences=sents,
total_examples=len(sents),
epochs=EPOCHS)
model.save(model_filename)
# model.vocabulary.save(vocab_filename)
print("Model saved at " + model_filename)
embedding_model = []
if embedding == 'w2v':
embedding_model = Embedding.from_word2vec(model_filename)
else:
def generate_fasttext(messages: list):
model = FastText(min_count=3, workers=8, window=3, min_n=1)
model.build_vocab(sentences=messages)
model.train(sentences=messages, total_examples=len(messages), epochs=10)
return model
help='corpus')
parser.add_argument('--base',
'-b',
dest='base',
action='store',
required=True,
help='base')
args = parser.parse_args()
corpus_file = args.base + "/" + args.corpus
base = args.base
#modelSG = Word2Vec(sentencia, sg=1,window=1,min_count=1)
output = base + "/gSG_" + base + ".vec"
modelSG = Word2Vec(corpus_file=corpus_file, sg=1, window=1, min_count=1)
modelSG.wv.save_word2vec_format(output, binary=False)
#modelCB = Word2Vec(sentencia, sg=0,window=1,min_count=1)
output = base + "/gCB_" + base + ".vec"
modelCB = Word2Vec(corpus_file=corpus_file, sg=0, window=1, min_count=1)
modelCB.wv.save_word2vec_format(output, binary=False)
output = base + "/gFT_" + base + ".vec"
modelFT = FastText(window=1, min_count=1, sg=1)
modelFT.build_vocab(
corpus_file=corpus_file) # scan over corpus to build the vocabulary
total_words = modelFT.corpus_total_words # number of words in the corpus
modelFT.train(corpus_file=corpus_file, total_words=total_words, epochs=5)
modelFT.wv.save_word2vec_format(output, binary=False)
#characters
chars = set([w_i for w in words for w_i in w])
n_chars = len(chars)
print("Number of Labels: ", n_chars)
tag2idx = {t: i + 1 for i, t in enumerate(tags)}
tag2idx["PAD"] = 0
# Vocabulary Key:tag_index -> Value:Label/Tag
idx2tag = {i: w for w, i in tag2idx.items()}
# Char Key:char -> Value:token_index
char2idx = {c: i + 2 for i, c in enumerate(chars)}
char2idx["UNK"] = 1
char2idx["PAD"] = 0
words_fast = FastText.load('model_fast30/model_fast.model')
#load pretrained word embedding
embedding_matrix = np.ones((len(word2idx), 100), dtype='float32')
embedding_matrix[0] = np.zeros(100, dtype='float32')
# with open('wiki-news-300d-1M.vec') as f:
for i in range(2, len(idx2word) - 2):
embedding_matrix[i] = words_fast[idx2word[i]]
# ordered_words_ft.append(s[0])
print('Found %s word vectors.' % len(embedding_matrix))
# for word, i in word2idx.items():
# embedding_vector = embeddings_index.get(word)
# if embedding_vector is not None:
# # words not found in embedding index will be all-zeros.
# embedding_matrix[i] = embedding_vector
""" `gensim.models.FastText` 使用示例 """ # gensim 示例 import gensim import numpy as np from gensim.test.utils import common_texts from gensim.models.keyedvectors import FastTextKeyedVectors from gensim.models._utils_any2vec import compute_ngrams, ft_hash from gensim.models import FastText # 构建 FastText 模型 sentences = [["Hello", "World", "!"], ["I", "am", "huay", "."]] min_ngrams, max_ngrams = 2, 4 # ngrams 范围 model = FastText(sentences, size=5, min_count=1, min_n=min_ngrams, max_n=max_ngrams) # 可以通过相同的方式获取每个单词以及任一个 n-gram 的向量 print(model.wv['hello']) print(model.wv['<h']) """ [-0.03481839 0.00606661 0.02581969 0.00188777 0.0325358 ] [ 0.04481247 -0.1784363 -0.03192253 0.07162753 0.16744071] """ print() # 词向量和 n-gram 向量是分开存储的 print(len(model.wv.vectors)) # 7 print(len(model.wv.vectors_ngrams)) # 57 # gensim 好像没有提供直接获取所有 ngrams tokens 的方法 print(model.wv.vocab.keys())
word2id[i[0]] = int(i[1])
read_file.close()
#参数配置
epcho = 1
batch_size = 256
num_to_ev = 400 # 训练多少批，在本地评测一次
vocab_size = len(word2id) # 词典大小
embedding_dim = 256 # 词向量维度
t_max_len = 22 #title的最大长度
q_max_len = 11 #query的最大长度
lr = 0.0001 #学习率
#加载验证集
val_data = load_esim_data_and_labels(
"/home/kesci/work/data/eval_data/19_eval.csv",
word2id,
q_max_len=q_max_len,
t_max_len=t_max_len)
# 每个词拼接使用128维的w2v和128维的fast向量到256维的组合向量表示
ce = np.random.uniform(-1, 1, [vocab_size + 1, embedding_dim])
word2vec_model = Word2Vec.load("/home/kesci/word2vec.model")
fast_model = FastText.load("./data/fast_w2v.model")
ce[0] = np.zeros(embedding_dim)
for i in word2id:
try:
ce[word2id[i]] = np.concatenate((word2vec_model[i], fast_model[i]))
except:
print(i)
set_b = set(b["PaperTitle"].to_list() + b["abstract"].to_list())
list_ab = list(set_a) + list(set_b)
cores = multiprocessing.cpu_count()
ps = PorterStemmer()
with Pool(cores) as p:
sentences = p.map(stem_lines, list_ab)
# sentences = [stem_lines(i) for i in list_ab]
# for s in tokenizer.pipe(list_ab):
# sentences.append([t.text for t in s if not t.check_flag(IS_PUNCT)])
print("Train the FastText model")
# word2vec_model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=cores, sg=0)
# word2vec_model.wv.save_word2vec_format("word2vec_model", binary=True) # binary format
print(sentences[0])
fastText_model = FastText(size=100,
window=5,
min_count=1,
sentences=sentences,
iter=5,
workers=cores)
fastText_model.wv.save("fastText_model")
print("finish training")
word_vectors = fastText_model.wv # KeyedVectors.load_word2vec_format("fastText_model")
b['embedding'] = b.apply(
lambda x: wv_papers(x['abstract'], x['PaperTitle'], word_vectors),
axis=1)
print(b.head())
print(len(b))
b.to_pickle("fasttext_embedding.pickle")
import os
from gensim.models import FastText
EXP_HOME = "F:/MyWorks/Thesis Works/Crowdsource_Knowledge_Base/DeepGenQR/experiment"
# EXP_HOME = "C:/My MSc/ThesisWorks/BigData_Code_Search/DeepGenQR/experiment"
model_file = EXP_HOME + '/pymodel/github-deepcs'
model = FastText.load(model_file)
# print(len(model.wv))
# quit()
word_file = EXP_HOME + '/w2vec-data/words.txt'
vec_file = EXP_HOME + '/w2vec-data/github-vector.txt'
vec_lines = list()
words = open(word_file, 'r')
for word in words:
try:
if model.wv.__contains__(word.strip()):
vector = model.wv[word.strip()]
line = word.strip() + " " + ' '.join(str(x) for x in vector)
vec_lines.append(line)
except IOError:
print("Could not found " + word)
pass
output_file = open(vec_file, 'w')
for content in vec_lines:
output_file.write("%s\n" % content)
output_file.close()
class NP2vec:
"""
Initialize the np2vec model, train it, save it and load it.
"""
def is_marked(self, s):
"""
Check if a string is marked.
Args:
s (str): string to check
"""
return len(s) > 0 and s[-1] == self.mark_char
def __init__(
self,
corpus,
corpus_format='txt',
mark_char='_',
word_embedding_type='word2vec',
sg=0,
size=100,
window=10,
alpha=0.025,
min_alpha=0.0001,
min_count=5,
sample=1e-5,
workers=20,
hs=0,
negative=25,
cbow_mean=1,
iter=15,
min_n=3,
max_n=6,
word_ngrams=1):
"""
Initialize np2vec model and train it.
Args:
corpus (str): path to the corpus.
corpus_format (str {json,txt,conll2000}): format of the input marked corpus; txt and json
formats are supported. For json format, the file should contain an iterable of
sentences. Each sentence is a list of terms (unicode strings) that will be used for
training.
mark_char (char): special character that marks NP's suffix.
word_embedding_type (str {word2vec,fasttext}): word embedding model type; word2vec and
fasttext are supported.
np2vec_model_file (str): path to the file where the trained np2vec model has to be
stored.
binary (bool): boolean indicating whether the model is stored in binary format; if
word_embedding_type is fasttext and word_ngrams is 1, binary should be set to True.
sg (int {0,1}): model training hyperparameter, skip-gram. Defines the training
algorithm. If 1, CBOW is used,otherwise, skip-gram is employed.
size (int): model training hyperparameter, size of the feature vectors.
window (int): model training hyperparameter, maximum distance between the current and
predicted word within a sentence.
alpha (float): model training hyperparameter. The initial learning rate.
min_alpha (float): model training hyperparameter. Learning rate will linearly drop to
`min_alpha` as training progresses.
min_count (int): model training hyperparameter, ignore all words with total frequency
lower than this.
sample (float): model training hyperparameter, threshold for configuring which
higher-frequency words are randomly downsampled, useful range is (0, 1e-5)
workers (int): model training hyperparameter, number of worker threads.
hs (int {0,1}): model training hyperparameter, hierarchical softmax. If set to 1,
hierarchical softmax will be used for model training. If set to 0, and `negative` is non-
zero, negative sampling will be used.
negative (int): model training hyperparameter, negative sampling. If > 0, negative
sampling will be used, the int for negative specifies how many "noise words" should be
drawn (usually between 5-20). If set to 0, no negative sampling is used.
cbow_mean (int {0,1}): model training hyperparameter. If 0, use the sum of the context
word vectors. If 1, use the mean, only applies when cbow is used.
iter (int): model training hyperparameter, number of iterations.
min_n (int): fasttext training hyperparameter. Min length of char ngrams to be used
for training word representations.
max_n (int): fasttext training hyperparameter. Max length of char ngrams to be used for
training word representations. Set `max_n` to be lesser than `min_n` to avoid char
ngrams being used.
word_ngrams (int {0,1}): fasttext training hyperparameter. If 1, uses enrich word
vectors with subword (ngrams) information. If 0, this is equivalent to word2vec training.
"""
self.mark_char = mark_char
self.word_embedding_type = word_embedding_type
self.sg = sg
self.size = size
self.window = window
self.alpha = alpha
self.min_alpha = min_alpha
self.min_count = min_count
self.sample = sample
self.workers = workers
self.hs = hs
self.negative = negative
self.cbow_mean = cbow_mean
self.iter = iter
self.min_n = min_n
self.max_n = max_n
self.word_ngrams = word_ngrams
if corpus_format == 'txt':
self._sentences = LineSentence(corpus)
elif corpus_format == 'json':
with open(corpus) as json_data:
self._sentences = json.load(json_data)
elif corpus_format == 'conll2000':
try:
self._sentences = list()
for chunked_sent in conll2000.chunked_sents(corpus):
tokens = list()
for chunk in chunked_sent:
if hasattr(chunk, '_label') and chunk._label == 'NP':
s = ''
for w in chunk:
s += w[0] + self.mark_char
tokens.append(s)
else:
if isinstance(chunk, nltk.Tree):
for w in chunk:
tokens.append(w[0])
else:
tokens.append(chunk[0])
self._sentences.append(tokens)
except Exception:
print('Conll2000 dataset is missing from NLTK. See downloading details in the '
'README file')
else:
logger.error('invalid corpus format: ' + corpus_format)
sys.exit(0)
if word_embedding_type == 'fasttext' and word_ngrams == 1:
# remove the marking character at the end for subword fasttext model training
for i, sentence in enumerate(self._sentences):
self._sentences[i] = [
w[:-1] if self.is_marked(w) else w for w in sentence]
logger.info('training np2vec model')
self._train()
def _train(self):
"""
Train the np2vec model.
"""
if self.word_embedding_type == 'word2vec':
self.model = Word2Vec(
self._sentences,
sg=self.sg,
size=self.size,
window=self.window,
alpha=self.alpha,
min_alpha=self.min_alpha,
min_count=self.min_count,
sample=self.sample,
workers=self.workers,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
iter=self.iter)
elif self.word_embedding_type == 'fasttext':
self.model = FastText(
self._sentences,
sg=self.sg,
size=self.size,
window=self.window,
alpha=self.alpha,
min_alpha=self.min_alpha,
min_count=self.min_count,
sample=self.sample,
workers=self.workers,
hs=self.hs,
negative=self.negative,
cbow_mean=self.cbow_mean,
iter=iter,
min_n=self.min_n,
max_n=self.max_n,
word_ngrams=self.word_ngrams)
else:
logger.error(
'invalid word embedding type: ' +
self.word_embedding_type)
sys.exit(0)
def save(self, np2vec_model_file='np2vec.model', binary=False):
"""
Save the np2vec model.
Args:
np2vec_model_file (str): the file containing the np2vec model to load
binary (bool): boolean indicating whether the np2vec model to load is in binary format
"""
if self.word_embedding_type == 'fasttext' and self.word_ngrams == 1:
if not binary:
logger.error(
"if word_embedding_type is fasttext and word_ngrams is 1, "
"binary should be set to True.")
sys.exit(0)
# not relevant to prune fasttext subword model
self.model.save(np2vec_model_file)
else:
# prune non NP terms
logger.info('pruning np2vec model')
total_vec = 0
vector_size = self.model.vector_size
for word in self.model.wv.vocab.keys():
if self.is_marked(word):
total_vec += 1
logger.info(
"storing %sx%s projection weights for NP's into %s" %
(total_vec, vector_size, np2vec_model_file))
with utils.smart_open(np2vec_model_file, 'wb') as fout:
fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size)))
# store NP vectors in sorted order: most frequent NP's at the top
for word, vocab in sorted(
iteritems(
self.model.wv.vocab), key=lambda item: -item[1].count):
if self.is_marked(word):
embedding_vec = self.model.wv.syn0[vocab.index]
if binary:
fout.write(
utils.to_utf8(word) + b" " + embedding_vec.tostring())
else:
fout.write(
utils.to_utf8(
"%s %s\n" %
(word, ' '.join(
"%f" %
val for val in embedding_vec))))
@classmethod
def load(cls, np2vec_model_file, binary=False, word_ngrams=0):
"""
Load the np2vec model.
Args:
np2vec_model_file (str): the file containing the np2vec model to load
binary (bool): boolean indicating whether the np2vec model to load is in binary format
word_ngrams (int {1,0}): If 1, np2vec model to load uses word vectors with subword (
ngrams) information.
Returns:
np2vec model to load
"""
if word_ngrams == 0:
return KeyedVectors.load_word2vec_format(
np2vec_model_file, binary=binary)
elif word_ngrams == 1:
return FastText.load(np2vec_model_file)
else:
logger.error('invalid value for \'word_ngrams\'')
