deprecated = False
fontsize_1 = 35
fontsize_2 = 27.5
fig_size = (22, 7.5)
"""
GETTING PATHS
"""
# trainings stuff
folder_paths = [
get_path(
results_dir=results_dir,
learning_type=learning_type,
algorithm_or_model_name=algorithm_or_model_name,
epoching=epoching,
fold_type=fold_type,
n_folds=x,
deprecated=deprecated
)
for x in n_folds_list
]
n_trainings = len(folder_paths)
# saving stuff
savings_dir = join(dirname(dirname(folder_paths[0])), 'learning_curve')
touch_dir(savings_dir)
"""
SUBJECTS STUFF
"""
from hgdecode.utils import check_significant_digits
"""
SET HERE YOUR PARAMETERS
"""
ival = (-500, 4000)
frozen_layers_list = [1, 2, 3, 4, 5, 6, -5, -4, -3, -2, -1]
reference = 0 # 0 for ML cross, 1 for DL cross, 2 for TL4 ecc.
p_flag = False # if true, it will print p value too.
"""
GETTING PATHS
"""
folder_paths = [
get_path(results_dir=None,
learning_type='dl',
algorithm_or_model_name=None,
epoching=ival,
fold_type='cross_subject',
n_folds=None,
deprecated=False)
]
folder_paths += [
get_path(results_dir=None,
learning_type='dl',
algorithm_or_model_name=None,
epoching=ival,
fold_type='transfer_learning',
n_folds=128,
deprecated=False)
]
n_folds = 12
deprecated = True
balanced_fold = True
# metrics parameter
label = 'Feet' # Feet, LeftHand, Rest or RightHand
metric_type = 'overall' # label or overall
metric = 'acc'
"""
GETTING PATHS
"""
# getting folder path
folder_path = get_path(results_dir=results_dir,
learning_type=learning_type,
algorithm_or_model_name=algorithm_or_model_name,
epoching=epoching,
fold_type=fold_type,
n_folds=n_folds,
deprecated=deprecated,
balanced_folds=balanced_fold)
# getting file_path
file_path = os.path.join(folder_path, 'statistics', 'tables')
if metric_type == 'overall':
file_path = os.path.join(file_path, metric + '.csv')
else:
file_path = os.path.join(file_path, label, metric + '.csv')
"""
COMPUTATION START HERE
"""
with open(file_path) as f:
csv = list(reader(f))
"""
TRAINING 1
"""
results_dir = None
learning_type = 'dl'
algorithm_or_model_name = None
epoching = '-1000_1000'
fold_type_1 = 'single_subject'
n_folds_list = [12] # must be a list of integer
deprecated = False
balanced_folds = True
folder_paths_1 = [
get_path(results_dir=results_dir,
learning_type=learning_type,
algorithm_or_model_name=algorithm_or_model_name,
epoching=epoching,
fold_type=fold_type_1,
n_folds=x,
deprecated=deprecated,
balanced_folds=balanced_folds) for x in n_folds_list
]
"""
TRAINING 2
"""
results_dir = None
learning_type = 'ml'
algorithm_or_model_name = None
epoching = '-500_4000'
fold_type_2 = 'single_subject'
n_folds_list = [12] # must be a list of integer
deprecated = False
balanced_folds = True
ival = (-1000, 1000)
"""
GETTING CROSS-SUBJECT MODELS DIR PATH
-------------------------------------
"""
# setting cross_subj_dir_path: data from cross-subj computation are stored here
learning_type = 'dl'
algorithm_or_model_name = None
epoching = ival
fold_type = 'cross_subject'
n_folds = None
deprecated = False
cross_subj_dir_path = get_path(results_dir=dirname(results_dir),
learning_type=learning_type,
algorithm_or_model_name=algorithm_or_model_name,
epoching=epoching,
fold_type=fold_type,
n_folds=n_folds,
deprecated=deprecated)
"""
COMPUTATION
-----------
"""
for subject_id in subject_ids:
# creating a log object
subj_results_dir = create_log(results_dir=results_dir,
learning_type='dl',
algorithm_or_model_name=model_name,
subject_id=subject_id,
output_on_file=False,
use_last_result_directory=False)
