synthcity.plugins.core.models.time_series_survival.ts_surv_coxph module

class CoxTimeSeriesSurvival(alpha: float = 0.05, penalizer: float = 0.1, device: Any = device(type='cpu'), emb_n_iter: int = 1000, emb_batch_size: int = 100, emb_lr: float = 0.001, emb_n_layers_hidden: int = 1, emb_n_units_hidden: int = 40, emb_split: int = 100, emb_rnn_type: str = 'GRU', emb_output_type: str = 'MLP', emb_alpha: float = 0.34, emb_beta: float = 0.27, emb_sigma: float = 0.21, emb_dropout: float = 0.06, emb_patience: int = 20, n_iter: Optional[int] = None, random_state: int = 0)

Bases: synthcity.plugins.core.models.time_series_survival._base.TimeSeriesSurvivalPlugin

fit(static: Optional[numpy.ndarray], temporal: numpy.ndarray, observation_times: numpy.ndarray, T: numpy.ndarray, E: numpy.ndarray) → synthcity.plugins.core.models.time_series_survival._base.TimeSeriesSurvivalPlugin

Training logic

static hyperparameter_space(*args: Any, **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: numpy.ndarray, observation_times: numpy.ndarray, time_horizons: List) → numpy.ndarray

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