def l2_log_loss(event_times, predicted_event_times, event_observed=None):
    r"""
    Calculates the l2 log-loss of predicted event times to true event times for *non-censored*
    individuals only.

    .. math::  1/N \sum_{i} (log(t_i) - log(q_i))**2

    Parameters
    ----------
      event_times: a (n,) array of observed survival times.
      predicted_event_times: a (n,) array of predicted survival times.
      event_observed: a (n,) array of censorship flags, 1 if observed,
                      0 if not. Default None assumes all observed.

    Returns
    -------
      l2-log-loss: a scalar
    """
    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()
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 ( )
r"""
    Calculates the l2 log-loss of predicted event times to true event times for *non-censored*
    individuals only.

    .. math::  1/N \sum_{i} (log(t_i) - log(q_i))**2

    Parameters
    ----------
      event_times: a (n,) array of observed survival times.
      predicted_event_times: a (n,) array of predicted survival times.
      event_observed: a (n,) array of censorship flags, 1 if observed,
                      0 if not. Default None assumes all observed.

    Returns
    -------
      l2-log-loss: a scalar