def vary_batch_size():
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
global logger
with open(CONFIG_FILE) as f:
CONFIG = yaml.safe_load(f)
DATA_DIR = os.path.join(CONFIG['DATA_DIR'], _DIR)
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
logger.info('-------------------')
logger.info('DIR ' + _DIR)
train_x_pos, train_x_neg, _, _, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=1)
testing_dict = {}
for _c in range(1, 3 + 1):
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=_c)
testing_dict[_c] = [test_pos, test_anomaly]
DOMAIN_DIMS = domain_dims
print('Data shape', train_x_pos.shape)
for bs in [64, 128, 256, 512, 1024]:
process_all(CONFIG,
_DIR,
train_x_pos,
train_x_neg,
testing_dict,
batch_size=bs)
logger.info('-------------------')
def main():
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
time_1 = time.time()
with open(CONFIG_FILE) as f:
CONFIG = yaml.safe_load(f)
_DIR = CONFIG['_DIR']
DATA_DIR = CONFIG['DATA_DIR'] + '/' + _DIR
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
train_x_pos, train_x_neg, test_pos, test_anomaly, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=2)
DOMAIN_DIMS = domain_dims
print('Data shape', train_x_pos.shape)
time_1 = time.time()
process(CONFIG, _DIR, train_x_pos, train_x_neg, test_pos, test_anomaly)
time_2 = time.time()
print('time taken ', time_2 - time_1)
def main(exec_dir=None, ablation_flag=False):
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
global logger
_DIR = exec_dir
DATA_DIR = os.path.join(CONFIG['DATA_DIR'], _DIR)
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
logger.info('-------------------')
logger.info('DIR ' + exec_dir)
logger.info(' Ablation ')
logger.info(ablation_flag)
train_x_pos, train_x_neg, _, _, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=1)
testing_dict = {}
for _c in range(1, 3 + 1):
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=_c)
testing_dict[_c] = [test_pos, test_anomaly]
print('Data pos shape', train_x_pos.shape)
print('Data neg shape', train_x_neg.shape)
time_1 = time.time()
process(CONFIG, _DIR, train_x_pos, train_x_neg, testing_dict,
ablation_flag)
time_2 = time.time()
logger.info('-------------------')
print('time taken ', time_2 - time_1)
def main():
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
global logger
DATA_DIR = os.path.join(CONFIG['DATA_DIR'], _DIR)
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
logger.info('-------------------')
logger.info('DIR ' + _DIR)
train_x_pos, train_x_neg, _, _, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=1)
testing_dict = {}
for _c in range(1, 3 + 1):
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=_c)
testing_dict[_c] = [test_pos, test_anomaly]
DOMAIN_DIMS = domain_dims
print('Data shape', train_x_pos.shape)
process_all(CONFIG, _DIR, train_x_pos, train_x_neg, testing_dict)
logger.info('-------------------')
def get_data(data_dir, dir):
train_x_pos, _, _, _, domain_dims = data_fetcher.get_data_v3(data_dir,
dir,
c=1)
test_dict_cIdx_data = {}
for c in range(1, 3 + 1):
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(data_dir,
dir,
c=c)
test_pos_idList = test_pos[0]
test_pos_x = test_pos[1]
test_anomaly_idList = test_anomaly[0]
test_anomaly_x = test_anomaly[1]
test_ids = list(np.hstack([test_pos_idList, test_anomaly_idList]))
test_data_x = np.vstack([test_pos_x, test_anomaly_x])
test_dict_cIdx_data[c] = [test_ids, test_data_x, test_anomaly_idList]
return train_x_pos, test_dict_cIdx_data
def get_data(data_dir, dir):
def stringify_data(arr) -> np.array:
tmp1 = []
for i in range(arr.shape[0]):
tmp2 = []
for j in range(arr.shape[1]):
tmp2.append(str(arr[i][j]) + '_' + str(j))
tmp1.append(tmp2)
tmp1 = np.array(tmp1)
return tmp1
train_x_pos, _, _, _, domain_dims = data_fetcher.get_data_v3(data_dir,
dir,
c=1)
train_x_pos = stringify_data(train_x_pos)
test_dict_cIdx_data = {}
for c in range(1, 3 + 1):
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(data_dir,
dir,
c=c)
test_pos_idList = test_pos[0]
test_pos_x = test_pos[1]
test_anomaly_idList = test_anomaly[0]
test_anomaly_x = test_anomaly[1]
test_ids = list(np.hstack([test_pos_idList, test_anomaly_idList]))
test_data_x = np.vstack([test_pos_x, test_anomaly_x])
test_data_x = stringify_data(test_data_x)
test_dict_cIdx_data[c] = [test_ids, test_data_x, test_anomaly_idList]
return train_x_pos, test_dict_cIdx_data
def main():
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
global logger
global OP_DIR
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
logger.info('-------------------')
logger.info('DIR ' + _DIR)
train_x_pos, train_x_neg, test_pos, test_anomaly, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=2)
DOMAIN_DIMS = domain_dims
print('Data shape', train_x_pos.shape)
train_x_embeddings = embedding_analysis_v1(CONFIG, _DIR, train_x_pos,
train_x_neg, test_pos,
test_anomaly)
print(' >>>> ', train_x_embeddings.shape)
# Write out the train_x & its embedding to a file
op_data = [train_x_pos, train_x_embeddings]
with open(os.path.join(OP_DIR, 'train_embedding_values.pkl'), 'wb') as fh:
pickle.dump(op_data, fh, pickle.HIGHEST_PROTOCOL)
logger.info('-------------------')
def vary_num_neg_type(_type=None):
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
global logger
with open(CONFIG_FILE) as f:
CONFIG = yaml.safe_load(f)
DATA_DIR = os.path.join(CONFIG['DATA_DIR'], _DIR)
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
logger.info('-------------------')
logger.info('DIR ' + _DIR)
if _type is None:
_type = 'normal'
logger.info(' Negative samplying type ' + _type)
if _type == 'ape':
train_x_pos, train_x_neg, _, _, _, _, domain_dims = data_fetcher.get_data_v1(
CONFIG['DATA_DIR'], _DIR, c=1)
k = 3
_indices = np.arange(0, train_x_neg.shape[1], k)
train_x_neg = np.take(train_x_neg, _indices, axis=1)
else:
# ensure same number of samples as APE
_, tmp, _, _, _, _, _ = data_fetcher.get_data_v1(CONFIG['DATA_DIR'],
_DIR,
c=1)
k = 3
_count = int(tmp.shape[1] / k)
print(_count)
train_x_pos, train_x_neg, _, _, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=1)
train_x_neg = train_x_neg[:, :_count, :]
testing_dict = {}
for _c in range(1, 3 + 1):
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'], _DIR, c=_c)
testing_dict[_c] = [test_pos, test_anomaly]
DOMAIN_DIMS = domain_dims
print('Data shape', train_x_pos.shape)
process_all(CONFIG, _DIR, train_x_pos, train_x_neg, testing_dict)
logger.info('-------------------')
def main():
global embedding_dims
global SAVE_DIR
global _DIR
global DATA_DIR
global CONFIG
global CONFIG_FILE
global MODEL_NAME
global DOMAIN_DIMS
global logger
with open(CONFIG_FILE) as f:
CONFIG = yaml.safe_load(f)
DATA_DIR = os.path.join(CONFIG['DATA_DIR'], _DIR)
setup_general_config()
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(
os.path.join(SAVE_DIR, 'checkpoints')
)
# ------------ #
if not os.path.exists(os.path.join(SAVE_DIR, 'checkpoints')):
os.mkdir(os.path.join(SAVE_DIR, 'checkpoints'))
# ------------ #
logger.info('-------------------')
logger.info('DIR ' + _DIR)
train_x_pos, train_x_neg, _, _, domain_dims = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'],
_DIR,
c=1
)
train_x_neg = train_x_neg[:,:12,:]
testing_dict = {}
for _c in range(2, 3 + 1):
testing_dict[_c] = {}
for s in [1] :
_, _, test_pos, test_anomaly, _ = data_fetcher.get_data_v3(
CONFIG['DATA_DIR'],
_DIR,
c=_c
)
compreXdata_loc = './../../comprex/comprexData/' + _DIR
test_anomalies_ids_file = os.path.join(compreXdata_loc, "id_test_anomalies_c{}_sample{}.txt".format(_c,s))
df_0 = pd.read_csv(test_anomalies_ids_file,header=None)
test_anomalies_ids = list(df_0[0])
test_anomaly_idList = test_anomaly[0]
anomaly_data = test_anomaly[1]
tmp_df = pd.DataFrame(
np.hstack([np.reshape(test_anomaly_idList,[-1,1]),anomaly_data])
)
print(len(tmp_df))
tmp_df = tmp_df.loc[tmp_df[0].isin(test_anomalies_ids)]
print(len(tmp_df))
test_anomaly_idList = (tmp_df[0]).values
del tmp_df[0]
anomaly_data = tmp_df.values
test_anomaly = [test_anomaly_idList, anomaly_data]
# ----
test_set_ids_file = os.path.join(compreXdata_loc, "id_test_set_c{}_sample{}.txt".format(_c, s))
df_0 = pd.read_csv(test_set_ids_file, header=None)
test_set_ids = list(df_0[0])
test_normal_idList = test_pos[0]
test_x = test_pos[1]
tmp_df = pd.DataFrame(
np.hstack([np.reshape(test_normal_idList, [-1, 1]), test_x])
)
tmp_df = tmp_df.loc[tmp_df[0].isin(test_set_ids)]
test_normal_idList = (tmp_df[0]).values
del tmp_df[0]
test_x = tmp_df.values
test_pos = [test_normal_idList, test_x]
testing_dict[_c][s] = [test_pos, test_anomaly]
DOMAIN_DIMS = domain_dims
print('Data shape', train_x_pos.shape)
process_all(
CONFIG,
_DIR,
train_x_pos,
train_x_neg,
testing_dict
)
logger.info('-------------------')