# "+ scaling" # main_compute([shallow_args, sleepstager_args], # [dl_dataset_args, dl_dataset_args], # train_sample, valid_sample, test_sample, # sample_size_list, saving_params) train_sample, valid_sample, test_sample = get_epochs_data( train_subjects=range(0, 10), valid_subjects=range(10, 15), test_subjects=range(15, 25), preprocessing=["microvolt_scaling", "filtering"]) dl_dataset_args_with_transforms["transform_list"] = [ ["add_noise_to_signal"]] for magnitude in [0, 0.2, 0.4, 0.6, 0.8, 1, 2, 3]: transforms_args["magnitude"] = magnitude dl_dataset_args_with_transforms["transform_type"] = "gaussian noise, "\ "scaling, filtering" \ "+ magnitude : " + str(magnitude) main_compute([sleepstager_args], [dl_dataset_args_with_transforms], transforms_args, train_sample, valid_sample, test_sample, sample_size_list, saving_params) # dl_dataset_args["transform_type"] = "raw (no transforms)" \ # "+ scaling, filtering" # dl_dataset_args_with_transforms["transform_type"] = \ # "masking + scaling, filtering" # run_handcrafted_features(train_sample, test_sample)
def dummy_handcrafted_features(train_sample, valid_sample, test_sample): main_compute([hf_args], [hf_dataset_args], transforms_args, train_sample, valid_sample, test_sample, sample_size_list, saving_params)
def dummy_sleepstagernet(train_sample, valid_sample, test_sample): main_compute([sleepstager_args], [dl_dataset_args], transforms_args, train_sample, valid_sample, test_sample, sample_size_list, saving_params)
def dummy_shallownet_with_transf(train_sample, valid_sample, test_sample): main_compute([shallow_args], [dl_dataset_args_with_transforms], transforms_args, train_sample, valid_sample, test_sample, sample_size_list, saving_params)