for training in ['training_life_only', 'training_2samp']:
print(training)
root_path_template = os.path.join(in_data_path,'learning_out_' + date_str, training,'pdfs/single_source_model_reg_{reg}_predicted')
out_path_template = os.path.join(in_data_path, 'learning_out_' + date_str, training,'stacking/stacking_out_reg_{reg}')
for reg in [True, False]:
for selection_crit in selection_crit_list:
root_path = root_path_template.format(reg=reg)
out_path = out_path_template.format(reg=reg)
file_pref = target + '__' + selection_crit + '__'
source_dict = {
'aseg': os.path.join(root_path, file_pref + 'aseg_df_predicted.pkl'),
'ct': os.path.join(root_path, file_pref + 'lh_ct_fsav4_sm0__rh_ct_fsav4_sm0_df_predicted.pkl'),
'csa': os.path.join(root_path, file_pref + 'lh_csa_fsav4_sm0__rh_csa_fsav4_sm0_df_predicted.pkl'),
'basc197': os.path.join(root_path, file_pref + 'basc_197_df_predicted.pkl'),
'basc444': os.path.join(root_path, file_pref + 'basc_444_df_predicted.pkl'),
}
source_selection_dict = {'all': ['basc197', 'basc444', 'aseg', 'csa', 'ct'],
'rs': ['basc197', 'basc444'],
'fs': ['aseg', 'csa', 'ct'],
}
stacking(out_path, target, selection_crit, source_dict, source_selection_dict, rf=None)
for training in ['training_life_only']:
print(training)
root_path_template = os.path.join(in_data_path,'learning_out_reverse_split_' + date_str, training,'pdfs/single_source_model_reg_{reg}_predicted')
out_path_template = os.path.join(in_data_path, 'learning_out_reverse_split_' + date_str, training,'stacking/stacking_out_reg_{reg}')
for reg in [False]:
for selection_crit in selection_crit_list:
root_path = root_path_template.format(reg=reg)
out_path = out_path_template.format(reg=reg)
file_pref = target + '__' + selection_crit + '__'
source_dict = {
'aseg': os.path.join(root_path, file_pref + 'aseg_df_predicted.pkl'),
'ct': os.path.join(root_path, file_pref + 'lh_ct_fsav4_sm0__rh_ct_fsav4_sm0_df_predicted.pkl'),
'csa': os.path.join(root_path, file_pref + 'lh_csa_fsav4_sm0__rh_csa_fsav4_sm0_df_predicted.pkl'),
'basc197': os.path.join(root_path, file_pref + 'basc_197_df_predicted.pkl'),
'basc444': os.path.join(root_path, file_pref + 'basc_444_df_predicted.pkl'),
}
source_selection_dict = {'all': ['basc197', 'basc444', 'aseg', 'csa', 'ct'],
'rs': ['basc197', 'basc444'],
'fs': ['aseg', 'csa', 'ct'],
}
stacking(out_path, target, selection_crit, source_dict, source_selection_dict, rf=None)
root_path_template = os.path.join(in_data_path,
'learning_out_from_neuH_' + date_str + '/pdfs/single_source_model_reg_{reg}_predicted')
out_path_template = os.path.join(in_data_path,
'learning_out_from_neuH_' + date_str + '/stacking/stacking_out_reg_{reg}')
rf_root_template = os.path.join(in_data_path,
'learning_out_' + date_str + '/' + training + '/stacking/stacking_out_reg_{reg}')
for life_subjects_selection_crit in life_train_subjects_selection_crit_list:
rf_file_template = target + '__' + life_subjects_selection_crit + '__{stacking_crit}__stacking_fitted_model.pkl'
for reg in [True, False]:
root_path = root_path_template.format(reg=reg, life_subjects_selection_crit=life_subjects_selection_crit)
out_path = out_path_template.format(reg=reg, life_subjects_selection_crit=life_subjects_selection_crit)
file_pref = target + '__' + selection_crit_test + '__'
source_dict = {
'aseg': os.path.join(root_path, file_pref + 'aseg_df_predicted.pkl'),
'ct': os.path.join(root_path, file_pref + 'lh_ct_fsav4_sm0__rh_ct_fsav4_sm0_df_predicted.pkl'),
'csa': os.path.join(root_path, file_pref + 'lh_csa_fsav4_sm0__rh_csa_fsav4_sm0_df_predicted.pkl'),
'basc197': os.path.join(root_path, file_pref + 'basc_197_df_predicted.pkl'),
'basc444': os.path.join(root_path, file_pref + 'basc_444_df_predicted.pkl'),
}
source_selection_dict = {'all': ['basc197', 'basc444', 'aseg', 'csa', 'ct'],
'rs': ['basc197', 'basc444'],
'fs': ['aseg', 'csa', 'ct'],
}
rf_file = os.path.join(rf_root_template.format(reg=reg), rf_file_template)
stacking(out_path, target, life_subjects_selection_crit, source_dict, source_selection_dict, rf=rf_file)