Function print_model

utils.py:180–183

(model)

        return param_group["lr"]

def print_model(model):
    print(model)
    n_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print(f"Model has {n_trainable_params} parameters")

def get_survival_data_for_BS(df, time_col_name, censorship_col_name='censorship'):
    # To compute one survival metric the Brier score (BS), you need a specific format of censorship and times