def test_dh_transtransformer_1c():
transformer = transformations_tf.TransTransformer()
scores_list = []
delta_times_list = []
for i in range(N_RUNS):
mdl = create_simple_network(input_shape=(21, 21, 1),
num_classes=transformer.n_transforms,
dropout_rate=0.0)
mdl.compile('adam', 'categorical_crossentropy', ['acc'])
start_time = time.time()
scores = test_tf1_transformed_data_on_tf2_keras_model_diri(
mdl,
transformer,
dataset_name='hits-1-c',
tf_version='tf1',
transformer_name='transtransformed',
model_name='dh',
epochs=2)
end_time = time.time()
delta_times_list.append(end_time - start_time)
scores_list.append(scores)
del mdl
file_path = os.path.join(PROJECT_PATH, 'tests', 'aux_results',
'test_models_tf1-tf2.txt')
print_scores_times_to_file(
file_path,
'Data_transformer_tf1_models_diri_tf2_dh_transtransformer_functionModel\n NRUNS: %i'
% N_RUNS, scores_list, delta_times_list)
def test_tf2_resnet_transtransformer_unchanged_1c():
transformer = transformations_tf.TransTransformer()
scores_list = []
delta_times_list = []
for i in range(N_RUNS):
mdl = TransformODModel(data_loader=None,
transformer=transformer,
input_shape=(21, 21, 1))
start_time = time.time()
scores = test_tf1_normal_data_on_tf2_transformer_model_original(
mdl,
transformer,
dataset_name='hits-1-c',
tf_version='tf1',
epochs=2)
end_time = time.time()
delta_times_list.append(end_time - start_time)
scores_list.append(scores)
del mdl
file_path = os.path.join(PROJECT_PATH, 'tests', 'aux_results',
'test_models_tf1-tf2.txt')
print_scores_times_to_file(
file_path,
'Data_normal_tf1_models_and_transforms_tf2_unchanged_resnet_transtransformer_1c\n NRUNS: %i'
% N_RUNS, scores_list, delta_times_list)
def test_resnet_transtransformer():
transformer = transformations_tf.TransTransformer()
scores_list = []
delta_times_list = []
for i in range(N_RUNS):
mdl = AlreadyTransformODModel(transformer=transformer,
input_shape=(21, 21, 4))
start_time = time.time()
scores = test_tf1_transformed_data_on_tf2_model_original_diri(
mdl,
transformer,
dataset_name='hits-4-c',
tf_version='tf1',
transformer_name='transtransformed',
model_name='resnet',
epochs=2)
end_time = time.time()
delta_times_list.append(end_time - start_time)
scores_list.append(scores)
del mdl
file_path = os.path.join(PROJECT_PATH, 'tests', 'aux_results',
'test_models_tf1-tf2.txt')
print_scores_times_to_file(
file_path,
'Data_transformer_tf1_models_diri_tf2_resnet_transtransformer\n NRUNS: %i'
% N_RUNS, scores_list, delta_times_list)
def test_all_tf2_dh_transtransformer_1c():
transformer = transformations_tf.TransTransformer()
hits_params = {
loader_keys.DATA_PATH:
os.path.join(PROJECT_PATH, '../datasets/HiTS2013_300k_samples.pkl'),
loader_keys.N_SAMPLES_BY_CLASS:
10000,
loader_keys.TEST_PERCENTAGE:
0.2,
loader_keys.VAL_SET_INLIER_PERCENTAGE:
0.1,
loader_keys.USED_CHANNELS: [2],
loader_keys.CROP_SIZE:
21,
general_keys.RANDOM_SEED:
42,
loader_keys.TRANSFORMATION_INLIER_CLASS_VALUE:
1
}
hits_outlier_dataset = HiTSOutlierLoader(hits_params)
scores_list = []
delta_times_list = []
for i in range(N_RUNS):
mdl = TransformODSimpleModel(data_loader=None,
transformer=transformer,
input_shape=(21, 21, 1),
drop_rate=0.0)
start_time = time.time()
scores = test_all_tf2(mdl,
transformer,
hits_outlier_dataset,
dataset_name='hits-1-c',
epochs=2)
end_time = time.time()
delta_times_list.append(end_time - start_time)
scores_list.append(scores)
del mdl
file_path = os.path.join(PROJECT_PATH, 'tests', 'aux_results',
'test_models_tf1-tf2.txt')
print_scores_times_to_file(
file_path,
'all_tf2_unchanged_dh_transtransformer_1c_DP0.0_fast_no_prints\n NRUNS: %i'
% N_RUNS, scores_list, delta_times_list)
def best_score_evaluation(result_folder_name, epochs, patience=0):
trainer_params = {
param_keys.RESULTS_FOLDER_NAME: result_folder_name,
'epochs': epochs,
'patience': patience,
}
# data loaders
hits_params = {
loader_keys.DATA_PATH:
os.path.join(PROJECT_PATH, '../datasets/HiTS2013_300k_samples.pkl'),
loader_keys.N_SAMPLES_BY_CLASS:
10000,
loader_keys.TEST_PERCENTAGE:
0.2,
loader_keys.VAL_SET_INLIER_PERCENTAGE:
0.1,
loader_keys.USED_CHANNELS: [0, 1, 2, 3], # [2],#
loader_keys.CROP_SIZE:
21,
general_keys.RANDOM_SEED:
42,
loader_keys.TRANSFORMATION_INLIER_CLASS_VALUE:
1
}
hits_loader = HiTSOutlierLoader(hits_params)
ztf_params = {
loader_keys.DATA_PATH:
os.path.join(PROJECT_PATH, '../datasets/ztf_v1_bogus_added.pkl'),
loader_keys.VAL_SET_INLIER_PERCENTAGE:
0.1,
loader_keys.USED_CHANNELS: [0, 1, 2],
loader_keys.CROP_SIZE:
21,
general_keys.RANDOM_SEED:
42,
loader_keys.TRANSFORMATION_INLIER_CLASS_VALUE:
1
}
ztf_loader = ZTFOutlierLoader(ztf_params)
# transformers
transformer_72 = transformations_tf.Transformer()
trans_transformer = transformations_tf.TransTransformer()
kernel_transformer = transformations_tf.KernelTransformer()
plus_kernel_transformer = transformations_tf.PlusKernelTransformer()
# trainers
hits_trainer = Trainer(hits_loader, trainer_params)
ztf_trainer = Trainer(ztf_loader, trainer_params)
model_constructors_list = (TransformODModel, )
transformers_list = (
plus_kernel_transformer,
kernel_transformer,
transformer_72,
trans_transformer,
)
trainers_list = (hits_trainer, ) # (ztf_trainer, hits_trainer, )
trainer_model_transformer_tuples = list(
itertools.product(trainers_list, model_constructors_list,
transformers_list))
for trainer, model_constructor, transformer in trainer_model_transformer_tuples:
trainer.train_model_n_times(model_constructor,
transformer,
trainer_params,
train_times=TRAIN_TIME)
hits_trainer.create_tables_of_results_folders()