def l2_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.power(np.log(event_times[ix]) - np.log(predicted_event_times[ix]), 2).mean()
Execute only if the ArcGIS Spatial Analyst extension is available.
dataset/ETHPy150Open CamDavidsonPilon/lifelines/lifelines/utils/__init__.py/l2_log_loss--crossover with--dataset/ETHPy150Open Esri/solutions-geoprocessing-toolbox/suitability/toolboxes/scripts/MultidimensionSupplementalTools/MultidimensionSupplementalTools/Scripts/mds/tools/multidimensional_zonal_statistics.py/MultidimensionalZonalStatistics.isLicensed