def transformer_tf_test():
# Transformer tf
transformer_tf = transformations_tf.Transformer()
start_time = time.time()
_ = transformer_test(transformer_tf)
print("Time transformer_test(transformer_tf) %s" %
utils.timer(start_time, time.time()),
flush=True)
"""
def transformer_traditional_test():
# Transformer
transformer = transformations.Transformer()
start_time = time.time()
_ = transformer_test(transformer)
print("Time transformer_test(transformer) %s" %
utils.timer(start_time, time.time()),
flush=True)
"""
def transformer_tf_returning_test():
# Transformer tf
transformer_tf = transformations_tf.Transformer()
start_time = time.time()
data = transformer_test(transformer_tf)
print("Time transformer_test(transformer_tf) %s" %
utils.timer(start_time, time.time()),
flush=True)
del transformer_tf
return data
transformer = Transformer()
model = TransformODSimpleModel(data_loader=ztf_od_loader,
transformer=transformer,
input_shape=x_train.shape[1:])
model.build(tuple([None] + list(x_train.shape[1:])))
# print(model.network.model().summary())
weight_path = os.path.join(PROJECT_PATH, 'results', model.name,
'my_checkpoint_simple.h5')
if os.path.exists(weight_path):
model.load_weights(weight_path)
else:
model.fit(x_train, x_val)
start_time = time.time()
met_dict = model.evaluate_od(x_train, x_test, y_test, 'ztf-real-bog-v1',
'real', x_val)
print("Time model.evaluate_od %s" % utils.timer(start_time, time.time()),
flush=True)
print('\nroc_auc')
for key in met_dict.keys():
print(key, met_dict[key]['roc_auc'])
print('\nacc_at_percentil')
for key in met_dict.keys():
print(key, met_dict[key]['acc_at_percentil'])
print('\nmax_accuracy')
for key in met_dict.keys():
print(key, met_dict[key]['max_accuracy'])
model.save_weights(weight_path)
# N_RUNS),
# (
# hits_outlier_dataset, kernel_transformer, 'hits', 'real',
# N_RUNS),
(hits_outlier_dataset, kernel_plus_transformer, 'hits', 'real', N_RUNS
),
# (
# ztf_outlier_dataset_63, trans_transformer, 'ztf-real-bog-v1-63', 'real',
# N_RUNS),
# (
# ztf_outlier_dataset_63, kernel_transformer, 'ztf-real-bog-v1-63',
# 'real',
# N_RUNS),
# (
# ztf_outlier_dataset_63, transformer, 'ztf-real-bog-v1-63', 'real',
# N_RUNS),
# (
# ztf_outlier_dataset_63, kernel_plus_transformer, 'ztf-real-bog-v1-63',
# 'real',
# N_RUNS),
]
start_time = time.time()
for data_loader, transformer, dataset_name, class_name, run_i in experiments_list:
run_experiments(data_loader, transformer, dataset_name, class_name,
run_i)
print("Time elapsed to train everything: " +
utils.timer(start_time, time.time()))
# metrics_to_create_table = {}
# create_auc_table()