def load(self, embedding_fname, embedding_url=None, *args, **kwargs):
"""
Method initializes dict of embeddings from file
Args:
fname: file name
Returns:
Nothing
"""
if not embedding_fname:
raise RuntimeError('No pretrained fasttext intent_model provided')
fasttext_model_file = embedding_fname
if not Path(fasttext_model_file).is_file():
emb_path = embedding_url
if not emb_path:
raise RuntimeError(
'No pretrained fasttext intent_model provided')
embedding_fname = Path(fasttext_model_file).name
try:
download_path = './'
download_untar(embedding_url, download_path)
except Exception as e:
raise RuntimeError(
'Looks like the `EMBEDDINGS_URL` variable is set incorrectly',
e)
self.model = FastText.load_fasttext_format(fasttext_model_file)
return
def prepare(self):
self.word2idx = defaultdict(int)
# to make sure start_symbol, end_symbol, pad, and unk will be included
self.word2idx[self.START_SYMBOL] = self.word2idx[
self.END_SYMBOL] = self.word2idx[self.UNK] = self.word2idx[
self.PAD] = self.min_word_freq
for dataset_type in ["train", "val"]:
caps = dset.CocoCaptions(
root=FilePathManager.resolve(f'data/{dataset_type}'),
annFile=FilePathManager.resolve(
f"data/annotations/captions_{dataset_type}2017.json"),
transform=transforms.ToTensor())
for _, captions in caps:
for capt in captions:
tokens = self.tokenize(capt)
for token in tokens:
self.word2idx[token] += 1
temp = {}
embeddings = {}
fast_text = FastText.load(
FilePathManager.resolve("data/fasttext.model"), mmap="r")
for k, v in self.word2idx.items():
if v >= self.min_word_freq:
temp[k] = len(temp)
embeddings[k] = fast_text[k] if k in fast_text else fast_text[
self.UNK]
self.word2idx = temp
# swap keys and values
self.idx2word = dict(zip(self.word2idx.values(), self.word2idx.keys()))
self.fast_text = embeddings
def get_embedding_model():
global _embedding_model
if not _embedding_model:
try:
_embedding_model = FastText.load_word2vec_format(project.aux_dir +
FASTTEXT_VOCAB)
except IOError:
_embedding_model = None
return _embedding_model
def fit(self, X, y=None):
X.to_csv(self.inputFile, index=False)
corpus_file = datapath(self.inputFile)
self.model_wrapper = FT_wrapper.train(self.ft_home,
self.inputFile,
model=self.model,
size=self.size,
word_ngrams=self.word_ngrams)
return self
def load_fasttext_format(cls, *args, **kwargs):
"""Load a :class:`~gensim.models.fasttext.FastText` model from a format compatible with
the original fasttext implementation.
Parameters
----------
fname : str
Path to the file.
"""
return Ft_Wrapper.load_fasttext_format(*args, **kwargs)
def test_sg_hs_against_wrapper(self):
if self.ft_path is None:
logger.info("FT_HOME env variable not set, skipping test")
return
tmpf = get_tmpfile('gensim_fasttext.tst')
model_wrapper = FT_wrapper.train(
ft_path=self.ft_path,
corpus_file=datapath('lee_background.cor'),
output_file=tmpf,
model='skipgram',
size=50,
alpha=0.025,
window=5,
min_count=5,
word_ngrams=1,
loss='hs',
sample=1e-3,
negative=0,
iter=5,
min_n=3,
max_n=6,
sorted_vocab=1,
threads=12)
model_gensim = FT_gensim(size=50,
sg=1,
cbow_mean=1,
alpha=0.025,
window=5,
hs=1,
negative=0,
min_count=5,
iter=5,
batch_words=1000,
word_ngrams=1,
sample=1e-3,
min_n=3,
max_n=6,
sorted_vocab=1,
workers=1,
min_alpha=0.0)
lee_data = LineSentence(datapath('lee_background.cor'))
model_gensim.build_vocab(lee_data)
orig0 = np.copy(model_gensim.wv.syn0[0])
model_gensim.train(lee_data,
total_examples=model_gensim.corpus_count,
epochs=model_gensim.iter)
self.assertFalse((orig0 == model_gensim.wv.syn0[0]
).all()) # vector should vary after training
self.compare_with_wrapper(model_gensim, model_wrapper)
def make_w2v(vocab):
d = {}
if opt.lang == 'en_w2v':
model = KeyedVectors.load_word2vec_format(
'../../../GoogleNews-vectors-negative300.bin', binary=True)
if opt.lang == 'en_fast':
model = KeyedVectors.load_word2vec_format(
'../../../wiki-news-300d-1M.vec')
if opt.lang == 'es':
model = FastText.load_fasttext_format('../../../cc.es.300.bin')
if opt.lang == 'fr':
model = FastText.load_fasttext_format('../../../cc.fr.300.bin')
for i in range(4, vocab.size()):
word = vocab.idxToLabel[i]
#if opt.lang == 'en_w2v':
#if model.emb(word)[0] != None:
#if model.emb(word)[0] != None:
#d[i] = model.emb(word)
#d[i] = model[word]
if word in model:
d[i] = model[word]
return d
def reduce_fasttext_embedding(fasttext_path, words):
model = FT_wrapper.load(fasttext_path)
print(model)
word_to_embedding = {}
coverage = 0
for word in words:
key = word.lower()
if word in model:
coverage = coverage + 1
word_to_embedding[key] = model[key]
else:
word_to_embedding[key] = None
print('fastText cache: {}/{} words'.format(coverage, len(words)))
return word_to_embedding
def make_embedding_matrix(train_captions):
tokenizer.fit_on_texts(train_captions)
model = FastText.load_fasttext_format(cfg.fasttext)
#---------embedding matrix 만듬--------
vocab_size = len(tokenizer.word_index)
embedding_matrix = np.random.random((vocab_size, 256))
for word,i in tokenizer.word_index.items(): # 1부터 시작함
try:
embedding_vector = model[word]
except:
#min count 이하 등장 단어
#print(word, 'not found')
pass
if embedding_vector is not None:
embedding_matrix[i-1] = embedding_vector
return embedding_matrix
def load_pretrained_fasttext():
# Set FastText home to the path to the FastText executable
ft_home = '/home/dev/fastText/fasttext'
# Set file names for train and test data
train_file = config.pos_path
# Use FaceBook Corpus
#model = FastText.load_word2vec_format('/home/dev/wiki.ko.vec')
model = FastText.train(ft_home, train_file, min_count=1)
print(model)
result = model.most_similar(positive=['김승우'])
print(result)
return model
def test_sg_hs_against_wrapper(self):
if self.ft_path is None:
logger.info("FT_HOME env variable not set, skipping test")
return
model_wrapper = FT_wrapper.train(ft_path=self.ft_path, corpus_file=datapath('lee_background.cor'),
output_file=testfile(), model='skipgram', size=50, alpha=0.025, window=5, min_count=5, word_ngrams=1,
loss='hs', sample=1e-3, negative=0, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12)
model_gensim = FT_gensim(size=50, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0,
min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6,
sorted_vocab=1, workers=1, min_alpha=0.0)
lee_data = LineSentence(datapath('lee_background.cor'))
model_gensim.build_vocab(lee_data)
orig0 = np.copy(model_gensim.wv.syn0[0])
model_gensim.train(lee_data, total_examples=model_gensim.corpus_count, epochs=model_gensim.iter)
self.assertFalse((orig0 == model_gensim.wv.syn0[0]).all()) # vector should vary after training
self.compare_with_wrapper(model_gensim, model_wrapper)
def embedding_weights_load(words_map, embedding_weights_path):
pre_trained_embedding = None
try:
model = FastText.load_fasttext_format(embedding_weights_path)
pre_trained_embedding = "bin"
except:
print("fastText binary file (.bin) is not found!")
if os.path.exists("./Word_embedding/wiki.en.vec"):
print("Using wikipedia(en) pre-trained word vectors.")
else:
print("Downloading wikipedia(en) pre-trained word vectors.")
chakin.download(number=2, save_dir="./Word_embedding")
print("Loading vectors...")
if os.path.exists("./Word_embedding_model.pkl"):
with open("./Word_embedding_model.pkl", mode="rb") as f:
model = pickle.load(f)
else:
model = KeyedVectors.load_word2vec_format(
'./Word_embedding/wiki.en.vec')
with open("Word_embedding_model.pkl", mode="wb") as f:
pickle.dump(model, f)
pre_trained_embedding = "txt"
vocab_size = len(words_map)
word_dimension = model['a'].shape[0]
w = np.zeros((vocab_size, word_dimension), dtype=np.float32)
for k, v in words_map.items():
word = k
word_number = v
try:
w[word_number][:] = model[word]
except KeyError as e:
if pre_trained_embedding == "bin":
w[word_number][:] = model.seeded_vector(word)
else:
np.random.seed(word_number)
w[word_number][:] = np.random.uniform(-0.25, 0.25,
word_dimension)
return w
def embedding_weights_load(words_map, embeddingWeights_path):
pre_trained_embedding = None
try:
model = FastText.load_fasttext_format(
embeddingWeights_path) #binファイルがある場合はそちらを読み込む
pre_trained_embedding = "bin"
except:
print("fastText binary file (.bin) is not found!"
) #ない場合はwikipediaの分散表現を使用する
if os.path.exists("./Word_embedding/wiki.en.vec"):
print("Using wikipedia(en) pre-trained word vectors.")
else:
print("Downloading wikipedia(en) pre-trained word vectors.")
chakin.download(number=2, save_dir="./Word_embedding")
print("Loading vectors...")
model = KeyedVectors.load_word2vec_format(
'./Word_embedding/wiki.en.vec')
pre_trained_embedding = "txt"
vocab_size = len(words_map)
word_dimension = model['a'].shape[0] #次元数を取得
W = np.zeros((vocab_size, word_dimension),
dtype=np.float32) #分散表現を格納するための行列
for k, v in words_map.items(): #kには単語，vには単語ID
word = k
word_number = v
#モデル中に存在しないチャンゴがある場合には、その単語の分散表現は乱数となる
try:
W[word_number][:] = model[word]
except KeyError as e:
if pre_trained_embedding == "bin":
W[word_number][:] = model.seeded_vector(word)
else:
np.random.seed(word_number)
W[word_number][:] = np.random.uniform(-0.25, 0.25,
word_dimension)
return W
def load(self):
self.logger.info("Loading model: {}".format(self.model_path))
self.model = FT_wrapper.load(self.model_path)
self.logger.info("Model loaded")
def get_fasttext():
global _fasttext
if _fasttext is None:
log.debug("Loading fasttext model..")
_fasttext = FastText.load_fasttext_format(FASTTEXT_PATH)
return _fasttext
# print('Training gensim fasttext model...')
# tstart = time.time()
# model_gensim.train(train_data, total_examples=model_gensim.corpus_count, epochs=model_gensim.iter)
# tend = time.time()
# print('Time elapsed for training wrapper model %.2f' % (tend - tstart))
# print(model_gensim)
with open(data_dir + 'questions_file.txt', 'w') as fw:
for line in train_data:
fw.write(line + '\n')
print('Text saved to %s' % (data_dir + 'questions_file.txt'))
# train the model
print('Training wrapper fasttext model...')
tstart = time.time()
model_wrapper = FT_wrapper.train(ft_home, data_dir + 'questions_file.txt')
tend = time.time()
print('Time elapsed for training wrapper model %.2f' % (tend - tstart))
print(model_wrapper)
# # saving a model trained via Gensim's fastText implementation
# print('Loading fasttext gensim model...')
# model_gensim.save(output_dir + 'saved_model_gensim')
# loaded_model = FT_gensim.load(output_dir + 'saved_model_gensim')
# print(loaded_model)
# saving a model trained via fastText wrapper
print('Loading fasttext wrapper model...')
model_wrapper.save(output_dir + 'saved_model_wrapper')
loaded_model = FT_wrapper.load(output_dir + 'saved_model_wrapper')
print(loaded_model)
def load(self, fname):
self.fasttext = FastText.load_fasttext_format(fname)
# In[1]:
from gensim.models import KeyedVectors
from gensim.models.wrappers.fasttext import FastText
# In[2]:
## load model
word2vec = KeyedVectors.load(
"../japanese-dataset/livedoor-news-corpus/model/vector-response-test/word2vec200.model"
)
word2vec_weighted = KeyedVectors.load(
"../japanese-dataset/livedoor-news-corpus/model/vector-response-test/word2vec_weighted.model"
)
fasttext = FastText.load_fasttext_format(
"../japanese-dataset/livedoor-news-corpus/model/vector-response-test/fasttext_model_200dim"
)
fasttext_weighted = KeyedVectors.load(
"../japanese-dataset/livedoor-news-corpus/model/vector-response-test/fasttext_weighted.model"
)
poincare_vec = KeyedVectors.load(
"../japanese-dataset/livedoor-news-corpus/model/vector-response-test/poincare_vec.model"
)
poincare_vec_weighted = KeyedVectors.load(
"../japanese-dataset/livedoor-news-corpus/model/vector-response-test/poincare_vec_weighted.model"
)
# In[3]:
len(word2vec.most_similar("独身"))
def load_fasttext_format(cls, *args, **kwargs):
return Ft_Wrapper.load_fasttext_format(*args, **kwargs)
start = time.time()
clf = lgb.LGBMClassifier(objective="multiclass")
clf.fit(plain_fasttext, train["class"])
Y_true, Y_pred = test["class"], clf.predict(plain_fasttext_test)
print("Report")
print(classification_report(Y_true, Y_pred, digits=6))
print("Accuracy: ", clf.score(plain_fasttext_test, test["class"]))
print("Time taken:", time.time() - start, "\n")
# In[ ]:
## SCDV based fasttext
from gensim.models.wrappers.fasttext import FastText
fasttext_model_200 = FastText.load_fasttext_format(
'../japanese-dataset/livedoor-news-corpus/for-fasttext/fasttext_model_200dim'
)
# In[ ]:
# Get wordvectors for all words in vocabulary.
word_vectors = fasttext_model_200.wv.syn0
# Set number of clusters.
num_clusters = 60
# Uncomment below line for creating new clusters.
idx, idx_proba = cluster_GMM(num_clusters, word_vectors)
# Uncomment below lines for loading saved cluster assignments and probabaility of cluster assignments.
# idx_name = "gmm_latestclusmodel_len2alldata.pkl"
# idx_proba_name = "gmm_prob_latestclusmodel_len2alldata.pkl"
total_examples=model_gensim.corpus_count,
epochs=model_gensim.epochs)
print(model_gensim)
# ### Using wrapper for fastText's C++ code
# In[*]
from gensim.models.wrappers.fasttext import FastText as FT_wrapper
# Set FastText home to the path to the FastText executable
ft_home = '/usr/local/bin/fasttext'
# train the model
model_wrapper = FT_wrapper.train(ft_home, lee_train_file)
print(model_wrapper)
# ### Training hyperparameters
# Hyperparameters for training the model follow the same pattern as Word2Vec. FastText supports the folllowing parameters from the original word2vec -
# - model: Training architecture. Allowed values: `cbow`, `skipgram` (Default `cbow`)
# - size: Size of embeddings to be learnt (Default 100)
# - alpha: Initial learning rate (Default 0.025)
# - window: Context window size (Default 5)
# - min_count: Ignore words with number of occurrences below this (Default 5)
# - loss: Training objective. Allowed values: `ns`, `hs`, `softmax` (Default `ns`)
# - sample: Threshold for downsampling higher-frequency words (Default 0.001)
# - negative: Number of negative words to sample, for `ns` (Default 5)
# - iter: Number of epochs (Default 5)
def embdReader(embd_path,
embd_dim,
word_index,
max_nb_words,
fasttext_source='',
ft_dim=0,
ft_home='/data2/tonyq/fastText/fasttext',
output_dir='/data2/tonyq/quora-output/',
skip_header=False,
initializer='glorot'):
########################################
## index word vectors
########################################
if not embd_path == '':
logger.info('Indexing word vectors...')
embeddings_index = {}
with open(embd_path, 'r', encoding='utf8') as f:
if skip_header or embd_path.endswith('.vec'):
next(f)
for line in tqdm(f):
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
logger.info('Found %d word vectors in embedding file.' %
len(embeddings_index))
########################################
## prepare fasttext
########################################
if not fasttext_source == '':
from gensim.models.wrappers.fasttext import FastText as FT_wrapper
if fasttext_source.endswith('.bin'):
loaded_model = FT_wrapper.load(fasttext_source)
print(loaded_model)
else:
_, train_question1, train_question2 = get_pdTable(fasttext_source,
notag=True)
train_question1, train_maxLen1 = text_cleaner(train_question1)
train_question2, train_maxLen2 = text_cleaner(train_question2)
train_data = train_question1 + train_question2
print('Train data lines %d' % len(train_data))
with open(output_dir + 'questions_file.txt', 'w') as fw:
for line in train_data:
fw.write(line + '\n')
print('Text saved to %s' % (output_dir + 'questions_file.txt'))
# train the model
print('Training wrapper fasttext model...')
tstart = time.time()
model_wrapper = FT_wrapper.train(ft_home,
output_dir + 'questions_file.txt',
size=ft_dim)
tend = time.time()
print('Time elapsed for training wrapper model %.2f' %
(tend - tstart))
print(model_wrapper)
# saving a model trained via fastText wrapper
print('Loading fasttext wrapper model...')
model_wrapper.save(output_dir + 'saved_model_wrapper.bin')
########################################
## prepare embeddings
########################################
logger.info('Preparing embedding matrix based on given word list...')
nb_words = min(max_nb_words, len(word_index)) + 1
w2v_oov = 0
ft_oov = []
if initializer == 'zero':
# zero initialization of embedding matrix
embedding_matrix = np.zeros((nb_words, embd_dim + ft_dim))
elif initializer == 'glorot':
# glorot uniform initialization of embedding matrix
scale = 1 / nb_words # fan_in
# scale = 1 / (embd_dim + ft_dim) # fan_out
limit = np.sqrt(3. * scale)
embedding_matrix = np.random.uniform(low=-limit,
high=limit,
size=(nb_words,
embd_dim + ft_dim))
else:
raise NotImplementedError
reverseDict = [''] * nb_words
for word, i in tqdm(word_index.items()):
if not embd_path == '':
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i][:embd_dim] = embedding_vector
reverseDict[i] = word
else:
reverseDict[i] = '<' + word + '>'
w2v_oov += 1
if not fasttext_source == '':
try:
embedding_matrix[i][embd_dim:] = model_wrapper[word]
reverseDict[i] = word
except KeyError:
reverseDict[i] = '<' + word + '>'
ft_oov.append(word)
logger.info('Word embeddings shape: %r (%d+%d)' %
(embedding_matrix.shape, embd_dim, ft_dim))
if not embd_path == '':
logger.info('Word2Vec null embeddings: %d' % w2v_oov)
if not fasttext_source == '':
logger.info('FastText null embeddings: %d' % len(ft_oov))
logger.info('FastText OOV: %r' % ft_oov)
return embedding_matrix, reverseDict
def train_wikipedia(ft_home, input_path, output_path, iterations=5, min_n=3, max_n=3):
model = FT_wrapper.train(ft_home, input_path, min_n=min_n, max_n=max_n, iter=iterations)
model.save(output_path)
# create vector
v = np.array(v)
print(np.shape(v))
text_len = np.array([len(s) for s in text]).reshape(len(text), 1)
X = np.concatenate((text_len, v), axis=1)
print(np.shape(X))
elif mode == 'ft':
# --------------------------------------------------------------------
# FastText
print('generating fasttext')
text = [clean_text(s).split() for s in text]
dim = 200
model = FastText(size=dim, iter=1)
#model.build_vocab(text)
model.train(text)
# then calculate word vector per paragraph
print('generating paragraph vectors')
v = []
for s in text:
ww = np.zeros((dim))
n = 0
for k, w in enumerate(s):
if w in model.wv:
ww += model.wv[w]
n += 1
if n > 0:
v.append(ww / n)
else:
import gensim
from gensim.models.wrappers.fasttext import FastText
from file_path_manager import FilePathManager
if __name__ == '__main__':
model = FastText.load_fasttext_format(FilePathManager.resolve("data/wiki.en"))
model.save(FilePathManager.resolve("data/fasttext.model"))
] and not c[1] in ["非自立", "代名詞"]:
words.append(cols[0])
return words
questions_src = []
questions = []
answers = []
for line in open(args.input, "r", encoding="utf-8", errors="ignore"):
cols = line.strip().split('\t')
#print(cols[0])
questions_src.append(cols[0])
questions.append(wakati(cols[0]))
answers.append(cols[1])
model = FastText.load_fasttext_format(args.model)
def part_minus(v):
#正と負で別のベクトルにする
tmp_v = np.zeros(DIM * 2)
for i in range(DIM):
if v[i] >= 0:
tmp_v[i] = v[i]
else:
tmp_v[i * 2] = -v[i]
return tmp_v
questions_vec = []
tf_vecs = []
def load_fasttext_format(cls, *args, **kwargs):
return Ft_Wrapper.load_fasttext_format(*args, **kwargs)
def train_fasttext(corpus_file,
fasttext_path=None,
save="../data/embeddings/",
dim=300):
"""
Input:
corpus_file:
the path to the file that has the embedding training dataset.
fasttext_path:
path to the FastText executable. If not given, we use the gensim
reimplementation instead.
save:
the directory where the embeddings will be saved.
dim:
number of dimensions for the embeddings.
Output:
A file with the embeddings both in gensim format and in word2vec format.
It also returns the model itself.
"""
print("Generating embeddings...")
if fasttext_path is not None:
# Run this if FastText is installed
print("FastText wrapper loaded")
# Set FastText home to the path to the FastText executable
ft_home = fasttext_path
print("\nCreating embeddings model...")
# train the model
model = FT_wrapper.train(ft_home, corpus_file, sg=1, size=dim)
print("Model created and trained")
else:
# Run this if using windows or if FastText is not installed
print("Gensim implementation loaded")
print("\nCreating embeddings model...")
model = FT_gensim(size=dim, sg=1)
print("Model created")
# build the vocabulary
print("\nGenerating vocabulary...")
model.build_vocab(corpus_file=corpus_file)
print("Vocabulary generated")
# train the model
print("\nTraining embeddings model")
model.train(corpus_file=corpus_file,
epochs=model.epochs,
total_examples=model.corpus_count,
total_words=model.corpus_total_words)
print("Model trained:")
print(model, "\n")
# saving a model
if save is not None:
path = save + "ft_embeddings." + str(dim)
model.save(path + ".model")
model.wv.save_word2vec_format(path + ".vec")
gg = open(path + ".txt", 'w', encoding="utf8")
for token in model.wv.vocab.keys():
string = token
for value in model.wv[token]:
string += " " + str(value)
gg.write(string + '\n')
gg.close()
print("Embeddings saved\n")
print("")
return model
