def sequentialize_data_no_padding(self, train_contents, feature_mode, val_contents=[], tokenizer=None,
max_length=None,
Max_Vocab_Size=None):
if Max_Vocab_Size is None:
Vocab_Size = MAX_VOCAB_SIZE
else:
Vocab_Size = Max_Vocab_Size
info("Max Vocab Size is {}".format(Vocab_Size))
if tokenizer is None:
if feature_mode == 0:
tokenizer = text.Tokenizer(num_words=Vocab_Size,
char_level=True,
oov_token="UNK")
elif feature_mode == 1:
tokenizer = text.Tokenizer(num_words=Vocab_Size)
tokenizer.fit_on_texts(train_contents)
_max_length = max_length
word_index = tokenizer.word_index
num_features = min(len(word_index) + 1, Vocab_Size)
info("vacab_word:", len(word_index))
if val_contents:
return word_index, num_features, tokenizer, _max_length
else:
return word_index, num_features, tokenizer, _max_length
def get_nlp_test_numpy_prefetch(self):
if self.test_tfds is None:
error("Error: test_tfds is None.")
return self.accum_test_x, self.accum_test_y
X, Y = [], []
if len(self.accum_test_x) == 0:
time_test_np_start = time.time()
tfds_test_os_iterator = self.test_tfds.make_one_shot_iterator()
tfds_test_iter_next = tfds_test_os_iterator.get_next()
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
while True:
try:
example, labels = sess.run(tfds_test_iter_next)
example = np.squeeze(example, (2, 3))
example = np.squeeze(example, axis=-1)
example = example.astype(np.int)
X.extend(example)
Y.extend(labels)
self.accum_test_x.extend(example)
self.accum_test_y.extend(labels)
# self.accm_test_cnt += 1
self.accm_test_cnt += example.shape[0]
except tf.errors.OutOfRangeError:
break
time_test_np_end = time.time()
info("note: now take test accm_test_cnt={}, cost_time={}s".format(self.accm_test_cnt, round(
time_test_np_end - time_test_np_start, 3)))
# self.accum_test_y = np.array(self.accum_test_y)
return X, Y
def sample_train_index_add(self, add_index):
train_label_distribution = np.sum(np.array(self.update_y), 0)
print("new sample train_distribution: ", train_label_distribution) # 获取对应label的分布
self.max_sample_num_per_class = int(
np.max(train_label_distribution) * 4 / 5)
if self.sample_num_per_class is None:
if self.num_samples_train < MAX_SAMPLE_TRIAN:
self.sample_num_per_class = self.max_sample_num_per_class
else:
self.sample_num_per_class = min(self.max_sample_num_per_class, self.MAX_TRAIN_PERCLASS_SAMPLE)
info("start sample data")
max_sample_num = min(self.sample_num_per_class, int(np.mean(train_label_distribution)))
if self.imbalance_flg:
max_sample_num = int(max_sample_num * self.normal_std)
# max_sample_num = self.sample_num_per_class
print("max_sample_num is {}".format(max_sample_num))
meta_train_add_index = []
for i in range(self.num_classes): # 按label类别抽取
if len(add_index[i]) == 0:
continue
elif len(add_index[i]) < self.sample_num_per_class and len(add_index[i]) > 0:
if self.imbalance_flg:
if len(add_index[i]) < max_sample_num:
tmp = add_index[i] * int(
max_sample_num / len(add_index[i]))
tmp += random.sample(add_index[i],
max_sample_num - len(tmp))
else:
tmp = random.sample(
add_index[i], max_sample_num)
# tmp = add_index[i] * int(
# self.sample_num_per_class / len(add_index[i]))
# tmp += random.sample(add_index[i],
# self.sample_num_per_class - len(tmp)) # 再采样n个样本， n取差值
else:
tmp = add_index[i] * int(
self.sample_num_per_class / len(add_index[i]))
tmp += random.sample(add_index[i],
self.sample_num_per_class - len(tmp))
meta_train_add_index += tmp
else: # 随机抽取
if self.imbalance_flg:
meta_train_add_index += random.sample(
add_index[i], max_sample_num)
else:
meta_train_add_index += random.sample(
add_index[i], self.sample_num_per_class)
info("end sample data")
random.shuffle(meta_train_add_index)
self.meta_train_add_index = meta_train_add_index
return meta_train_add_index
def get_train_numpy(self, update_train_num):
if self.train_tfds is None:
error("Error: train_tfds is None.")
return self.accum_train_x, self.accum_train_y
if self.tfds_train_os_iterator is None:
self.tfds_train_os_iterator = self.train_tfds.make_one_shot_iterator()
self.tfds_train_iter_next =self.tfds_train_os_iterator.get_next()
X, Y = [], []
cur_get_cnt = 0
cur_data_y = list()
if self.accm_train_cnt < self.train_num:
info("show the accum_train_cnt {}".format(self.accm_train_cnt))
time_train_np_start = time.time()
while True:
try:
example, labels = self.tfds_convertor_sess.run(self.tfds_train_iter_next)
example = np.squeeze(example, (2, 3))
example = np.squeeze(example, axis=-1)
# print("Note:time example shape={}".format(example.shape))
example = example.astype(np.int)
self.accum_train_x.extend(example)
# cur_data_y.extend(labels)
X.extend(example)
Y.extend(labels)
# cur_get_cnt += 1
# self.accm_train_cnt += 1
cur_get_cnt += example.shape[0]
self.accm_train_cnt += example.shape[0]
# example_batch_num += 1
if cur_get_cnt >= update_train_num or self.accm_train_cnt >= self.train_num:
time_train_np_end = time.time()
info("note: now take train update={}, accm_train_cnt={}, cost_time={}s".format(cur_get_cnt,
self.accm_train_cnt,
time_train_np_end - time_train_np_start))
break
except tf.errors.OutOfRangeError:
break
if self.accum_train_y is None:
self.accum_train_y = np.array(cur_data_y)
else:
print("check accum_train_y shape {}, cur_data_y shape {}".format(self.accum_train_y.shape,
np.array(cur_data_y).shape))
# self.accum_train_y = np.concatenate((self.accum_train_y, np.array(cur_data_y)), axis=0)
else:
self.tfds_convertor_sess.close()
return X, Y
def __init__(self,
x_train, y_train,
metadata,
imbalance_level=-1,
multi_label=False):
self.meta_data_x, self.meta_data_y = x_train, y_train
# 第一次增量的数据就是初始的数据
self.update_x, self.update_y = x_train, y_train
self.metadata = metadata
self.num_classes = self.metadata['class_num']
self.num_samples_train = self.metadata['train_num']
self.language = metadata['language']
self.multi_label = multi_label
print("num_samples_train:", self.num_samples_train)
print("num_class_train:", self.num_classes)
self.val_index = None
self.tokenizer = None
self.max_length = None
self.sample_num_per_class = None
self.data_feature = {}
self.eda_feature = {}
self.pseudo_x_train_size = 0
self.full_x = []
self.full_y = np.array([])
self.x_dict = {i: [] for i in range(self.num_classes)}
self.imbalance_flg = False
self.do_generate_sample = False
self.empty_class_ = []
self.meta_train_x = []
self.meta_train_y = np.array([])
self.full_index = None
self.imbalance_level = imbalance_level
self.MAX_TRAIN_PERCLASS_SAMPLE = MAX_TRAIN_PERCLASS_SAMPLE
info("Init Data Manager! Imbalance_level is {}".format(self.imbalance_level))
if self.num_classes <= 5 and self.imbalance_level <= 1 and self.num_classes > 2:
self.MAX_TRAIN_PERCLASS_SAMPLE = 3000
elif self.num_classes == 2 and self.imbalance_level <= 1:
self.MAX_TRAIN_PERCLASS_SAMPLE = 3500
if self.multi_label:
if self.num_classes<50: #类别不是特别多，每类取100条
self.MAX_TRAIN_PERCLASS_SAMPLE = 100
elif self.num_classes<100:
self.MAX_TRAIN_PERCLASS_SAMPLE = 50
else:
self.MAX_TRAIN_PERCLASS_SAMPLE = 20
info("Init Data Manager! MAX_TRAIN_PERCLASS_SAMPLE is {}".format(self.MAX_TRAIN_PERCLASS_SAMPLE))
def generate_pseudo_samples(self, train_x, train_y):
info("Do Radam Create Samples!")
for i in range(self.num_classes):
new_samples = self.new_generate_samples_idx[i]
if len(new_samples) == 0:
continue
train_x.extend(new_samples)
new_label = np.zeros((len(new_samples), self.num_classes))
new_label[:, i] = 1
train_y = np.concatenate([train_y, new_label], axis=0)
return train_x, train_y
def sample_dataset_pipeline(self, use_val=False, update_train=True, data_x=None, data_y=None):
"""
全局采样pipeline
:param use_val: 是否采用val数据
:param update_train: 是否更新全量train
:param data_x: 采样数据来源x：增量数据或者全量原始数据
:param data_y: 采样数据来源y：增量数据或者全量原始数据
:return: 均衡采样后的训练集/评估集，use_val为True时，评估集为空
"""
val_diff_x, val_diff_y = None, None
############################ 采样准备阶段 ###################################
if update_train:
# 增量更新（第一次样本即增量）
self.add_index, self.add_val_index = self.sample_val_index(data_y)
val_diff_x, val_diff_y = map_x_y(self.add_val_index, data_x, data_y)
# 此时的训练集没有进行采样
train_diff_x, train_diff_y = flat_map_x_y(index=self.add_index, x=data_x, y=data_y)
if use_val: # 如果采用val，即当前不分train valid，全部数据更新meta_train
info(color_msg(msg="use val is True", color='blue'))
train_diff_x = train_diff_x + val_diff_x
train_diff_y = np.concatenate([train_diff_y, val_diff_y], axis=0)
val_diff_x = None
val_diff_y = None
self._update_train_meta(train_diff_x, train_diff_y)
if val_diff_x:
val_label_distribution = np.sum(np.array(val_diff_y), 0)
info("val_distribution: {}".format(val_label_distribution))
info("Check val_diff_x size {}, val_diff_y size {}".format(len(val_diff_x),
val_diff_y.shape[0]))
info("Check meta_train_x size {}, meta_train_y size {}".format(len(self.meta_train_x),
self.meta_train_y.shape[0]))
info("Check meta_data_x size {}, meta_data_y size {}".format(len(self.meta_data_x),
self.meta_data_y.shape[0]))
############################ 进入采样阶段 ###################################
train_x, train_y = self.get_sampling_data_frm_full_train()
return train_x, train_y, val_diff_x, val_diff_y
def get_sampling_data_frm_full_train(self):
"""
从全局的train data中采样，只看当前的 meta_train_x, meta_train_y
:return:
"""
sample_index = get_sample_index(self.meta_train_y, self.num_classes)
train_label_distribution = np.sum(np.array(self.meta_train_y), 0)
info(color_msg("before sampling--train_distribution: {}".format(train_label_distribution),
color='yellow')) # 获取对应label的分布
self.balance_sampling_index(sample_index, train_label_distribution)
# 每次只看当前需要采样的数据是否均衡，是否需要生成伪样本
self.normal_std, self.empty_class_ = get_imbalance_statistic(train_label_distribution)
self.check_imbalance_level(train_label_distribution)
self.new_generate_samples_idx = self.generate_presudo_samples(sample_index)
self.show_data_info()
self.imbalance_flg = False
train_x, train_y = self.extend_train_data(x=self.meta_train_x, y=self.meta_train_y)
train_label_distribution = np.sum(np.array(train_y), 0)
info(color_msg("after sampling--train_distribution: {}".format(train_label_distribution),
color='yellow')) # 获取对应label的分布
return train_x, train_y
def _set_max_train_sample_num(self, train_label_distribution):
self.max_sample_num_per_class = int(
np.max(train_label_distribution) * 4 / 5)
if self.sample_num_per_class is None:
if self.num_samples_train < MAX_SAMPLE_TRIAN:
self.sample_num_per_class = self.max_sample_num_per_class
else:
self.sample_num_per_class = min(self.max_sample_num_per_class, self.MAX_TRAIN_PERCLASS_SAMPLE)
else:
# 避免类别数多的情况下，第一次进样少，导致后面连续采样过低
self.sample_num_per_class = max(self.max_sample_num_per_class, int(np.mean(train_label_distribution)))
info("check sample_num_per_class:{}".format(self.sample_num_per_class))
if self.imbalance_flg:
max_sample_num = min(self.sample_num_per_class, int(np.mean(train_label_distribution)))
max_sample_num = min(max_sample_num, self.MAX_TRAIN_PERCLASS_SAMPLE)
else:
max_sample_num = min(self.sample_num_per_class, self.MAX_TRAIN_PERCLASS_SAMPLE)
info("max_sample_num is {}".format(max_sample_num))
return max_sample_num
def show_data_info(self):
info("check empty class {}, imbalance_flg is {}, normalized std is {} and do generate sample flg is {}".format(
self.empty_class_, self.imbalance_flg,
round(self.normal_std, 6), self.do_generate_sample))
