synthcity.plugins.core.models.survival_analysis.surv_xgb module

class XGBSurvivalAnalysis(n_estimators: int = 100, colsample_bynode: float = 0.5, max_depth: int = 5, 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', bce_n_iter: int = 1000, time_points: int = 100, device: Any = device(type='cpu'), **kwargs: Any)

Bases: synthcity.plugins.core.models.survival_analysis._base.SurvivalAnalysisPlugin

booster = ['gbtree', 'gblinear', 'dart']

explain(X: pandas.core.frame.DataFrame) → numpy.ndarray

fit(X: pandas.core.frame.DataFrame, T: pandas.core.series.Series, Y: pandas.core.series.Series) → synthcity.plugins.core.models.survival_analysis._base.SurvivalAnalysisPlugin

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, time_horizons: List) → pandas.core.frame.DataFrame

Predict risk

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