def main(): config = get_args() if config.load_model is not None: model, features, target_feature = load_model(config) data_loader = DataLoader(config, split=False, pretrained=True) data_loader.setup(features, target_feature) evaluator = Evaluator(config) evaluator.evaluate_pretrianed(model, data_loader, target_feature) exit(0) if config.load_checkpoint: auc, acc, pred, classes, completed = load_checkpoint(config) data_loader = DataLoader(config, split=not config.active_features) evaluator = Evaluator(config) trainer = Trainer(config, data_loader, evaluator) if config.load_checkpoint: evaluator.set_checkpoint(auc, acc, pred, classes) trainer.set_completed(completed) trainer.train() if not config.active_features: print(f"AUC ({config.evaluation_mode}): {evaluator.get_auc()}") print(f"Accuracy ({config.evaluation_mode}): {evaluator.get_accuracy()}") evaluator.save(data_loader.getFeatures()) display_runtime(config)