def __create_xy_train(self,
tag_file,
embedding_file,
data_size=1,
look_back=5,
threshold=0,
suffix=None):
x_train = []
y_train = []
corpus = DataUtils.load_corpus(tag_file)
tag_emb = DataUtils.create_onehot_vectors(
DataUtils.extract_tag_list(corpus))
word_emb = DataUtils.load_embeddings(embedding_file)
if suffix is not None:
word_emb = DataUtils.add_suffix_embeddings(word_emb, suffix[0],
suffix[1])
words = DataUtils.extract_word_data(corpus)
word_keys = DataUtils.normalize_cases(word_emb.keys(), words)
tag_dict = DataUtils.extract_tag_dict(corpus, threshold)
data_size = int(len(words) * min(data_size, 1)) - int(
len(words) * min(data_size, 1)) % look_back
data_size = 53750
for idx in np.arange(0, data_size, look_back):
dict_tag_inputs = [tag_dict[words[idx]]]
word_inputs = [
word_emb[word_keys[idx]]
] if word_keys[idx] in word_emb else [word_emb["UNK"]]
for widx in range(1, look_back):
word_inputs = np.append(
word_inputs, [word_emb[word_keys[idx + widx]]] if
word_keys[idx + widx] in word_emb else [word_emb["UNK"]],
axis=0)
dict_tag_inputs.append(tag_dict[words[idx + widx]])
dict_tag_inputs = DataUtils.cartesian(np.array(dict_tag_inputs))
for jdx in range(len(dict_tag_inputs)):
tag_inputs = [tag_emb[tag] for tag in dict_tag_inputs[jdx]]
if idx == 0 and jdx == 0:
x_train = [word_inputs]
y_train = [tag_inputs]
else:
x_train = np.append(x_train, [word_inputs], axis=0)
y_train = np.append(y_train, [tag_inputs], axis=0)
if idx % int(data_size / (10 * look_back)) == 0:
DataUtils.update_message(str(int(idx / data_size * 100)))
x_train = np.array(x_train)
y_train = np.array(y_train)
return x_train, y_train
def __create_xy_test(self,
tag_file,
embedding_file,
data_size=1,
look_back=5,
suffix=None):
x_test = []
y_test = []
corpus = DataUtils.load_corpus(tag_file)
tag_emb = DataUtils.create_onehot_vectors(
DataUtils.extract_tag_list(corpus))
word_emb = DataUtils.load_embeddings(embedding_file)
if suffix is not None:
word_emb = DataUtils.add_suffix_embeddings(word_emb, suffix[0],
suffix[1])
words, tags = DataUtils.extract_data(corpus)
word_keys = DataUtils.normalize_cases(word_emb.keys(), words)
data_size = int(len(words) * min(data_size, 1)) - int(
len(words) * min(data_size, 1)) % look_back
for idx in np.arange(0, data_size, look_back):
x_timestep = []
y_timestep = []
for jdx in range(look_back):
word_input = word_emb[word_keys[idx + jdx]] if word_keys[
idx + jdx] in word_emb else word_emb["UNK"]
tag_input = tag_emb[tags[idx + jdx]]
if (jdx == 0):
x_timestep = [word_input]
y_timestep = [tag_input]
else:
x_timestep = np.append(x_timestep, [word_input], axis=0)
y_timestep = np.append(y_timestep, [tag_input], axis=0)
x_timestep = np.array(x_timestep)
y_timestep = np.array(y_timestep)
if (idx == 0):
x_test = [x_timestep]
y_test = [y_timestep]
else:
x_test = np.append(x_test, [x_timestep], axis=0)
y_test = np.append(y_test, [y_timestep], axis=0)
if idx % int(data_size / (10 * look_back)) == 0:
DataUtils.update_message(str(int(idx / data_size * 100)))
x_test = np.array(x_test)
y_test = np.array(y_test)
return x_test, y_test
def __create_xy(self, tag_file, embedding_file, data_size, window_size,
available_tags, suffix):
x = []
y = []
corpus = DataUtils.load_corpus(tag_file)
tag_emb = DataUtils.create_onehot_vectors(
DataUtils.extract_tag_list(corpus))
word_emb = DataUtils.load_embeddings(embedding_file)
if suffix is not None:
word_emb = DataUtils.add_suffix_embeddings(word_emb, suffix[0],
suffix[1])
words, tags = DataUtils.extract_data(corpus)
word_keys = DataUtils.normalize_cases(word_emb.keys(), words)
data_size = int(len(words) * data_size)
for idx in range(data_size):
tag = tags[idx + int(window_size / 2)]
if len(available_tags) == 0 or tag in available_tags:
word_input = word_emb[word_keys[idx]] if word_keys[
idx] in word_emb else word_emb["UNK"]
for widx in range(1, window_size):
word_input = np.append(
word_input,
word_emb[word_keys[idx + widx]] if
word_keys[idx + widx] in word_emb else word_emb["UNK"],
axis=0)
tag_input = tag_emb[tag]
if (idx == 0):
x = [word_input]
y = [tag_input]
else:
x = np.append(x, [word_input], axis=0)
y = np.append(y, [tag_input], axis=0)
if idx % int(data_size / 10) == 0:
DataUtils.update_message(str(int(idx / data_size * 100)))
return x, y
def __create_xy_train(self, tag_file, embedding_file, data_size, window_size, threshold, suffix):
x_train = []
y_train = []
corpus = DataUtils.load_corpus(tag_file)
tag_emb = DataUtils.create_onehot_vectors(DataUtils.extract_tag_list(corpus))
word_emb = DataUtils.load_embeddings(embedding_file)
if suffix is not None:
word_emb = DataUtils.add_suffix_embeddings(word_emb, suffix[0], suffix[1])
words = DataUtils.extract_word_data(corpus)
word_keys = DataUtils.normalize_cases(word_emb.keys(), words)
tag_dict = DataUtils.extract_tag_dict(corpus, threshold)
data_size = min((int(len(words)*data_size), len(words)-window_size))
for idx in range(data_size):
word_input = word_emb[word_keys[idx]] if word_keys[idx] in word_emb else word_emb["UNK"]
for widx in range(1, window_size):
word_input = np.append(word_input, word_emb[word_keys[idx+widx]] if word_keys[idx+widx] in word_emb else word_emb["UNK"], axis = 0)
tag_inputs = [tag_emb[tag] for tag in tag_dict[words[idx+int(window_size/2)]]]
for tidx in range(len(tag_inputs)):
tag_input = tag_inputs[tidx]
if idx == 0 and tidx == 0:
x_train = [word_input]
y_train = [tag_input]
else:
x_train = np.append(x_train, [word_input], axis=0)
y_train = np.append(y_train, [tag_input], axis=0)
if idx%int(data_size/100) == 0:
DataUtils.update_message(str(int(idx/data_size*100)))
x_train = np.array(x_train)
y_train = np.array(y_train)
return x_train, y_train
