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)
