synthcity.plugins.core.models.survival_analysis.surv_coxph module

class CoxPHSurvivalAnalysis(device: Any = device(type='cpu'), alpha: float = 0.05, fit_options: dict = {'step_size': 0.1}, **kwargs: Any)

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

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 estimation

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