synthcity.plugins.core.models.survival_analysis.benchmarks module

evaluate_survival_model(estimator: Any, X: pandas.core.frame.DataFrame, T: pandas.core.frame.DataFrame, Y: pandas.core.frame.DataFrame, time_horizons: List, n_folds: int = 3, metrics: List[str] = ['c_index', 'brier_score', 'aucroc'], random_state: int = 0, pretrained: bool = False) → Dict

Helper for evaluating survival analysis tasks.

Parameters

• model_name – str The model to evaluate

• model_args – dict The model args to use

• X – DataFrame The covariates

• T – Series time to event

• Y – Series event or censored

• time_horizons – list Horizons where to evaluate the performance.

• n_folds – int Number of folds for cross validation

• metrics – list Available metrics: “c_index”, “brier_score”, “aucroc”

• random_state – int Random seed

• pretrained – bool If the estimator was trained or not