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 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 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 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 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_fasttext_format(cls, *args, **kwargs):
return Ft_Wrapper.load_fasttext_format(*args, **kwargs)
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"))
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"
def get_fasttext():
global _fasttext
if _fasttext is None:
log.debug("Loading fasttext model..")
_fasttext = FastText.load_fasttext_format(FASTTEXT_PATH)
return _fasttext
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)
] 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 = []