import os import pathlib import pickle from config.load_config import get_config from config.config_utils import initialize_config_preproc, initialize_config_split, initialize_config_training from preproc.preprocess import generate_labels, correct_data_label from preproc.train_val_test_split import prepare_data_for_train from train_single_model.run_training import run_training if __name__ == '__main__': # Configuring the files here for now cfg_template = get_config(filename=pathlib.Path(os.getcwd()).parent / 'config' / 'default_config.yml') cfg_template.user = '******' cfg_template.load_mode = 'csv' cfg_template.overwrite = True cfg_template = initialize_config_preproc(cfg_template) # now load the actual cfg generated from the data vec_idx_patient = [1, 310] f_cfg_handle = "preproc_cfg_{}_{}.pkl".format(vec_idx_patient[0], vec_idx_patient[1]) f_cfg = cfg_template.d_preproc / f_cfg_handle with open(str(f_cfg), 'rb') as handle: cfg = pickle.load(handle) # name of particular feature that will be used # note if want to test for disease label then have to specify this to be 'disease' # otherwise it has to be one of ['IRF/SRF', 'Scar', 'GA', 'CNV', 'Large PED'] cfg.str_feature = 'disease'
import os from pathlib import Path import numpy as np from config.load_config import get_config from preproc import preprocess from modeling.model import get_model, get_callbacks from analysis.plotting import plot_norm_conf_matrix, plot_raw_conf_matrix from scipy.stats import mode # Configuring the files here for now cfg = get_config(filename=Path(os.getcwd()) / 'config' / 'default_config.yml') cfg.d_data = Path('/home/jyao/local/data/orig/amd_octa/') cfg.d_model = Path('/home/jyao/local/data/orig/amd_octa/trained_models/') cfg.str_healthy = 'Normal' cfg.label_healthy = 0 cfg.str_dry_amd = 'Dry AMD' cfg.label_dry_amd = 1 cfg.str_cnv = 'CNV' cfg.label_cnv = 2 cfg.num_classes = 3 cfg.vec_str_labels = ['Normal', 'Dry Amd', 'CNV'] cfg.num_octa = 5 cfg.str_angiography = 'Angiography' cfg.str_structure = 'Structure' cfg.str_bscan = 'B-Scan' cfg.vec_str_layer = [ 'Deep', 'Avascular', 'ORCC', 'Choriocapillaris', 'Choroid'