synthcity.plugins.core.models.time_to_event.tte_survival_time_series module

class TSSurvivalFunctionTimeToEvent(time_points: int = 100, survival_base_learner: str = 'Transformer', regression_base_learner: str = 'Transformer', device: Any = device(type='cpu'), random_state: int = 0, n_layers_hidden: int = 1, n_units_hidden: int = 250, nonlin: str = 'leaky_relu', n_iter: int = 500, dropout: float = 0, lr: float = 0.001, patience: int = 20, **kwargs: Any)

Bases: synthcity.plugins.core.models.time_to_event._base.TimeToEventPlugin

fit(static: Optional[numpy.ndarray], temporal: numpy.ndarray, observation_times: numpy.ndarray, T: numpy.ndarray, E: numpy.ndarray) → 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(static: Optional[numpy.ndarray], temporal: Union[List, numpy.ndarray], observation_times: Union[List, numpy.ndarray]) → pandas.core.series.Series

Predict time-to-event

predict_any(static: Optional[numpy.ndarray], temporal: Union[List, numpy.ndarray], observation_times: Union[List, numpy.ndarray], E: numpy.ndarray) → pandas.core.series.Series

Predict time-to-event or censoring

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