synthcity.plugins.core.models.time_to_event.tte_xgb module
- class XGBTimeToEvent(model_search_n_iter: Optional[int] = None, n_estimators: int = 100, colsample_bynode: float = 0.5, max_depth: int = 8, subsample: float = 0.5, learning_rate: float = 0.05, min_child_weight: int = 50, tree_method: str = 'hist', booster: int = 0, random_state: int = 0, objective: str = 'aft', strategy: str = 'debiased_bce', time_points: int = 100, device: Any = device(type='cpu'), **kwargs: Any)
Bases:
synthcity.plugins.core.models.time_to_event._base.TimeToEventPlugin
- booster = ['gbtree', 'gblinear', 'dart']
- fit(X: pandas.core.frame.DataFrame, T: pandas.core.series.Series, Y: pandas.core.series.Series) synthcity.plugins.core.models.time_to_event._base.TimeToEventPlugin
Training logic
- static hyperparameter_space(**kwargs: Any) List[synthcity.plugins.core.distribution.Distribution]
Returns the hyperparameter space for the derived plugin.
- static load(buff: bytes) Any
- static load_dict(representation: dict) Any
- static name() str
The name of the plugin.
- predict(X: pandas.core.frame.DataFrame) pandas.core.series.Series
Predict time-to-event
- predict_any(X: pandas.core.frame.DataFrame, E: pandas.core.series.Series) pandas.core.series.Series
Predict time-to-event
- classmethod sample_hyperparameters(*args: Any, **kwargs: Any) Dict[str, Any]
Sample value from the hyperparameter space for the current plugin.
- save() bytes
- save_dict() dict
- save_to_file(path: pathlib.Path) bytes
- static version() str
API version