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'
