def __create_xy(self, dependency_tree, embedding_file, data_size, look_back, test=False):
sentences, words, tags = DataUtils.parse_dependency_tree(dependency_tree)
word_vectors = DataUtils.create_onehot_vectors(words)
#word_int = DataUtils.create_int_dict(words)
word_emb = DataUtils.load_embeddings(embedding_file)
tag_int = DataUtils.create_int_dict(tags)
data_size = int(len(sentences)*min(data_size, 1))
if test:
sentences.reverse()
if look_back == 0:
for sentence in sentences[:data_size]:
look_back = max(look_back, len(sentence))
self.look_back = look_back
self.distinct_words = len(words)
self.distinct_tags = len(tags)
word_input_forward = []
word_input_backward = []
word_head_forward = []
word_head_backward = []
tag_input_forward = []
tag_input_backward = []
tag_head_forward = []
tag_head_backward = []
probability = []
progress = 0
for sentence in sentences[:data_size]:
parts = [sentence[i:i+look_back] for i in range(0,len(sentence),look_back)]
for part in parts:
word_temp = np.zeros((2,look_back,300))
tag_temp = np.zeros((2,look_back,),dtype="int32")
prob_temp = np.zeros((look_back,),dtype="float32")
for idx in range(len(part)):
word = part[idx]["word"]
word_temp[0][look_back-len(part)+idx] = word_emb[word] if word in word_emb else word_emb["UNK"]
word_temp[1][look_back-idx-1] = word_emb[word] if word in word_emb else word_emb["UNK"]
tag = part[idx]["tag"]
tag_temp[0][look_back-len(part)+idx] = tag_int[tag]
tag_temp[1][look_back-idx-1] = tag_int[tag]
word_instance = np.zeros((len(part),2,look_back,300))
tag_instance = np.zeros((len(part),2,look_back,),dtype="int32")
head_instance = np.zeros((look_back,1), dtype="float32")
for idx in range(len(part)):
word_instance[idx][0][look_back-idx-1:] = word_temp[0][look_back-len(part):look_back-len(part)+idx+1]
word_instance[idx][1][look_back-len(part)+idx:] = word_temp[1][look_back-len(part):look_back-idx]
tag_instance[idx][0][look_back-idx-1:] = tag_temp[0][look_back-len(part):look_back-len(part)+idx+1]
tag_instance[idx][1][look_back-len(part)+idx:] = tag_temp[1][look_back-len(part):look_back-idx]
for idx in range(len(part)):
word_input = np.zeros((2,2,look_back,300))
tag_input = np.zeros((2,2,look_back,),dtype="int32")
prob_temp = 0.0
for jdx in range(len(part)):
if idx != jdx:
if part[idx]["head"] == part[jdx]["word"]:
prob_temp = 1.0
word_input[0] = word_instance[idx]
tag_input[0] = tag_instance[idx]
word_input[1] = word_instance[jdx]
tag_input[1] = tag_instance[jdx]
if len(word_input_forward) == 0:
word_input_forward = [word_input[0][0]]
word_input_backward = [word_input[0][1]]
word_head_forward = [word_instance[1][0]]
word_head_backward = [word_instance[1][1]]
tag_input_forward = [tag_input[0][0]]
tag_input_backward = [tag_input[0][1]]
tag_head_forward = [tag_input[1][0]]
tag_head_backward = [tag_input[1][1]]
probability = [prob_temp]
else:
word_input_forward = np.append(word_input_forward,[word_input[0][0]], axis=0)
word_input_backward = np.append(word_input_backward,[word_input[0][1]], axis=0)
word_head_forward = np.append(word_head_forward,[word_instance[1][0]], axis=0)
word_head_backward = np.append(word_head_backward,[word_instance[1][1]], axis=0)
tag_input_forward = np.append(tag_input_forward,[tag_input[0][0]], axis=0)
tag_input_backward = np.append(tag_input_backward,[tag_input[0][1]], axis=0)
tag_head_forward = np.append(tag_head_forward,[tag_input[1][0]], axis=0)
tag_head_backward = np.append(tag_head_backward,[tag_input[1][1]], axis=0)
probability = np.append(probability, [prob_temp], axis=0)
DataUtils.update_message(str(progress)+"/"+str(data_size))
progress += 1
word_data = [(word_input_forward, word_input_backward), (word_head_forward, word_head_backward)]
tag_data = [(tag_input_forward, tag_input_backward), (tag_head_forward, tag_head_backward)]
return word_data, tag_data, probability
def __create_xy(self, parse_tree_file, data_size, seq_len, test=False):
sentences, words, tags = DataUtils.parse_dependency_tree(
parse_tree_file)
word_int = DataUtils.create_int_dict(words)
tag_int = DataUtils.create_onehot_vectors(tags)
self.seq_len = seq_len
self.distinct_words = len(words)
self.distinct_tags = len(tags)
data_len = 0
for i in range(len(sentences)):
data_len += int(np.ceil(
len(sentences[i]) / seq_len)) * seq_len * seq_len
forward = np.zeros((
2,
data_len,
seq_len,
), dtype="int32")
backward = np.zeros((
2,
data_len,
seq_len,
), dtype="int32")
probability = np.zeros((data_len, ), dtype="float32")
tags = np.zeros((data_len, 18))
idx = 0
for sentence in sentences:
parts = [
sentence[i:i + seq_len]
for i in range(0, len(sentence), seq_len)
]
for part in parts:
part_len = len(part)
word_forward = np.zeros((seq_len, seq_len), dtype="int32")
word_backward = np.zeros((seq_len, seq_len), dtype="int32")
for jdx in range(part_len):
word_forward[jdx][seq_len - jdx - 1:] = [
word_int[part[i]["word"]] for i in range(jdx + 1)
]
word_backward[jdx][seq_len - part_len + jdx:] = [
word_int[part[part_len - i - 1]["word"]]
for i in range(part_len - jdx)
]
for jdx in range(part_len):
for zdx in range(part_len):
tags[idx] = tag_int[part[jdx]["tag"]]
forward[0][idx] = word_forward[jdx]
forward[1][idx] = word_forward[zdx]
backward[0][idx] = word_backward[jdx]
backward[1][idx] = word_backward[zdx]
probability[idx] = 1.0 if part[jdx]["head"] == part[
zdx]["word"] else 0.0
idx += 1
if idx % int(data_len / 100) == 0:
DataUtils.update_message(
str(int(idx / data_len * 100)))
if test:
forward = [
np.array(forward[0][5000:10000]),
np.array(forward[1][5000:10000])
]
backward = [
np.array(backward[0][5000:10000]),
np.array(backward[1][5000:10000])
]
probability = np.array(probability[5000:10000])
tags = np.array(tags[5000:10000])
else:
forward = [
np.array(forward[0][:5000]),
np.array(forward[1][:5000])
]
backward = [
np.array(backward[0][:5000]),
np.array(backward[1][:5000])
]
probability = np.array(probability[:5000])
tags = np.array(tags[:5000])
return [forward[0], backward[0], forward[1],
backward[1]], [tags, probability]
def __create_xy(self, embedding_file, data_size, look_back, test=False):
sentences, words, tags = DataUtils.parse_dependency_tree(self.language)
word_vectors = DataUtils.create_onehot_vectors(words)
#word_int = DataUtils.create_int_dict(words)
word_emb = None
if self.language == "turkish":
word_emb = DataUtils.load_embeddings(embedding_file, "fasttext")
else:
word_emb = DataUtils.load_embeddings(embedding_file)
tag_int = DataUtils.create_int_dict(tags)
data_size = int(len(sentences) * min(data_size, 1))
if test:
sentences.reverse()
if look_back == 0:
for sentence in sentences[:data_size]:
look_back = max(look_back, len(sentence))
self.look_back = look_back
self.distinct_words = len(words)
self.distinct_tags = len(tags)
word_full_forward = []
word_full_backward = []
word_instance_forward = []
word_instance_backward = []
tag_full_forward = []
tag_full_backward = []
tag_instance_forward = []
tag_instance_backward = []
head = []
progress = 0
for sentence in sentences[:data_size]:
parts = [
sentence[i:i + look_back]
for i in range(0, len(sentence), look_back)
]
for part in parts:
word_temp = np.zeros((2, look_back, 300))
tag_temp = np.zeros((
2,
look_back,
), dtype="int32")
head_instance = np.zeros((look_back, 1), dtype="float32")
for idx in range(len(part)):
word = part[idx]["word"]
word_temp[0][look_back - len(part) + idx] = word_emb[
word] if word in word_emb else word_emb["UNK"]
word_temp[1][look_back - idx - 1] = word_emb[
word] if word in word_emb else word_emb["UNK"]
tag = part[idx]["tag"]
tag_temp[0][look_back - len(part) + idx] = tag_int[tag]
tag_temp[1][look_back - idx - 1] = tag_int[tag]
word_instance = np.zeros((2, look_back, 300))
tag_instance = np.zeros((
2,
look_back,
), dtype="int32")
for jdx in range(len(part)):
word_instance[0][look_back - jdx - 1:] = word_temp[
0][look_back - len(part):look_back - len(part) +
jdx + 1]
word_instance[1][look_back - len(part) +
jdx:] = word_temp[1][look_back -
len(part
):look_back -
jdx]
tag_instance[0][look_back - jdx -
1:] = tag_temp[0][look_back -
len(part):look_back -
len(part) + jdx + 1]
tag_instance[1][look_back - len(part) +
jdx:] = tag_temp[1][look_back -
len(part
):look_back -
jdx]
head_instance = np.zeros((look_back, 1),
dtype="float32")
for zdx in range(len(part)):
head_instance[zdx] = 1 if part[jdx][
"head"] == part[zdx]["word"] else 0
if len(word_full_forward) == 0:
word_full_forward = [word_temp[0]]
word_full_backward = [word_temp[1]]
word_instance_forward = [word_instance[0]]
word_instance_backward = [word_instance[1]]
tag_full_forward = [tag_temp[0]]
tag_full_backward = [tag_temp[1]]
tag_instance_forward = [tag_instance[0]]
tag_instance_backward = [tag_instance[1]]
head = [head_instance]
else:
word_full_forward = np.append(word_full_forward,
[word_temp[0]],
axis=0)
word_full_backward = np.append(word_full_backward,
[word_temp[1]],
axis=0)
word_instance_forward = np.append(
word_instance_forward, [word_instance[0]],
axis=0)
word_instance_backward = np.append(
word_instance_backward, [word_instance[1]],
axis=0)
tag_full_forward = np.append(tag_full_forward,
[tag_temp[0]],
axis=0)
tag_full_backward = np.append(tag_full_backward,
[tag_temp[1]],
axis=0)
tag_instance_forward = np.append(
tag_instance_forward, [tag_instance[0]],
axis=0)
tag_instance_backward = np.append(
tag_instance_backward, [tag_instance[1]],
axis=0)
head = np.append(head, [head_instance], axis=0)
DataUtils.update_message(str(progress) + "/" + str(data_size))
progress += 1
word_data = [(word_full_forward, word_full_backward),
(word_instance_forward, word_instance_backward)]
tag_data = [(tag_full_forward, tag_full_backward),
(tag_instance_forward, tag_instance_backward)]
print(word_full_forward.shape, word_instance_forward.shape, head.shape)
return word_data, tag_data, head
def __create_xy(self,
dependency_tree,
embedding_file,
data_size,
look_back,
test=False):
sentences, words, tags = DataUtils.parse_dependency_tree(
dependency_tree)
word_vectors = DataUtils.create_onehot_vectors(words)
word_emb = DataUtils.load_embeddings(embedding_file)
tag_int = DataUtils.create_int_dict(tags)
data_size = int(len(sentences) * min(data_size, 1))
if test:
sentences.reverse()
if look_back == 0:
for sentence in sentences[:data_size]:
look_back = max(look_back, len(sentence))
self.look_back = look_back
self.distinct_words = len(words)
self.distinct_tags = len(tags)
word_data = []
head_data = []
tag_data = []
progress = 0
for sentence in sentences[:data_size]:
word_timestep = np.zeros((look_back, 300))
head_timestep = np.zeros((look_back, len(words)))
tag_timestep = np.zeros((look_back, ), dtype="int32")
timestep = 0
for element in sentence:
word = element["word"]
if word != "ROOT":
word_timestep[timestep % look_back] = word_emb[
word] if word in word_emb else word_emb["UNK"]
head = element["head"]
head_timestep[timestep % look_back] = word_vectors[head]
tag = element["tag"]
tag_timestep[timestep % look_back] = tag_int[tag]
timestep += 1
if timestep % look_back == 0 or timestep == len(sentence):
if len(word_data) == 0:
word_data = [word_timestep]
head_data = [head_timestep]
tag_data = [tag_timestep]
else:
word_data = np.append(word_data, [word_timestep],
axis=0)
head_data = np.append(head_data, [head_timestep],
axis=0)
tag_data = np.append(tag_data, [tag_timestep], axis=0)
word_timestep.fill(0)
head_timestep.fill(0)
tag_timestep.fill(0)
DataUtils.update_message(str(progress) + "/" + str(data_size))
progress += 1
word_data = np.array(word_data)
head_data = np.array(head_data)
tag_data = np.array(tag_data)
return word_data, head_data, tag_data
