def l1_log_loss(event_times, predicted_event_times, event_observed=None):
    if event_observed is None:
        event_observed = np.ones_like(event_times)

    ix = event_observed.astype(bool)
    return np.abs(np.log(event_times[ix]) - np.log(predicted_event_times[ix])).mean()
Calculates the l1 log-loss of predicted event times to true event times for *non-censored*
dataset/ETHPy150Open CamDavidsonPilon/lifelines/lifelines/utils/__init__.py/l1_log_loss