synthcity.plugins.core.models.time_to_event.benchmarks module

evaluate_model(model_name: str, model_args: dict, X: pandas.core.frame.DataFrame, T: pandas.core.frame.DataFrame, E: pandas.core.frame.DataFrame, n_folds: int = 3, random_state: int = 0) → tuple

generate_score(metric: numpy.ndarray) → tuple

generate_score_str(metric: numpy.ndarray) → str

objective_meta(model_name: str, X: pandas.core.frame.DataFrame, T: pandas.core.frame.DataFrame, E: pandas.core.frame.DataFrame, metric: str, pruner: synthcity.utils.optimizer.ParamRepeatPruner, n_folds: int = 3) → Callable

select_uncensoring_model(X: pandas.core.frame.DataFrame, T: pandas.core.frame.DataFrame, E: pandas.core.frame.DataFrame, random_states: List[str] = ['weibull_aft', 'cox_ph', 'random_survival_forest', 'survival_xgboost', 'deephit', 'tenn', 'date'], n_folds: int = 2, n_trials: int = 10, timeout: int = 120, random_state: int = 0) → Any