def train_classifier(args):
'''
Function that performs the detection of Parkinson
:param args: Input arguments
:return:
'''
exp_name = args.exp_name
subfolder = os.path.join(
args.output,
exp_name + '_' + datetime.datetime.now().strftime("%m_%d"),
datetime.datetime.now().strftime("%H_%M"))
file_result_patients = os.path.join(subfolder, 'res_pat.csv')
file_result_segments = os.path.join(subfolder, 'res_seg.csv')
model_file = os.path.join(subfolder, "model.json")
if not os.path.exists(subfolder):
os.makedirs(subfolder)
val_results = Results(file_result_segments, file_result_patients)
datas = Data(args.input_data, 1, 100, pk_level=False)
for i in range(0, 10):
lr = 0.001
model = multiple_cnn1D(datas.X_data.shape[2])
model_json = model.to_json()
with open(model_file, "w") as json_file:
json_file.write(model_json)
print('fold', str(i))
datas.separate_fold(i)
log_filename = os.path.join(subfolder, "training_" + str(i) + ".csv")
w_filename = os.path.join(subfolder, "weights_" + str(i) + ".hdf5")
model = train(model, datas, lr, log_filename, w_filename)
print('Validation !!')
val_results.validate_patient(model, datas.X_val, datas.y_val,
datas.count_val)
def ablation_study(args):
'''
Function that performs the ablation study
:param args: Input arguments
:return:
'''
features = np.arange(1, 19)
folder = os.path.join(
args.output,
args.exp_name + '_' + datetime.datetime.now().strftime("%m_%d"),
datetime.datetime.now().strftime("%H_%M"))
if not os.path.exists(folder):
os.makedirs(folder)
for j in range(1, 9):
exp_name = args.exp_name + str(j)
subfolder = os.path.join(folder, 'feature_' + str(j))
file_result_patients = os.path.join(subfolder, 'res_pat.csv')
file_result_segments = os.path.join(subfolder, 'res_seg.csv')
#filename = subfolder + "weights.hdf5"
model_file = os.path.join(subfolder, "model.json")
if not os.path.exists(subfolder):
os.makedirs(subfolder)
feature_delete = j
feature_delete_r = j + 9
features_i = np.delete(features, [feature_delete, feature_delete_r])
val_results = Results(file_result_segments, file_result_patients)
#datas = Datas(args.input_data, 1, 100, features=features_i)
#datas.load(norm=None)
datas = Data(args.input_data, 1, 100, pk_level=False)
for i in range(0, 10):
lr = 0.001
print('fold', str(i))
log_filename = os.path.join(subfolder,
"training_" + str(i) + ".csv")
w_filename = os.path.join(subfolder, "weights_" + str(i) + ".hdf5")
datas.separate_fold(i)
model = multiple_cnn1D(datas.X_data.shape[2])
model_json = model.to_json()
with open(model_file, "w") as json_file:
json_file.write(model_json)
model = train(model, datas, lr, log_filename, w_filename)
print('Validation !!!!!!!')
val_results.validate_patient(model, datas.X_val, datas.y_val,
datas.count_val)