hyperimpute.plugins.imputers.plugin_miracle module

class MiraclePlugin(lr: float = 0.001, batch_size: int = 1024, num_outputs: int = 1, n_hidden: int = 32, reg_lambda: float = 1, reg_beta: float = 1, DAG_only: bool = False, reg_m: float = 1.0, window: int = 10, max_steps: int = 400, seed_imputation: str = 'mean', random_state: int = 0)

Bases: ImputerPlugin

MIRACLE (Missing data Imputation Refinement And Causal LEarning) MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism and encouraging imputation to be consistent with the causal structure of the data.

Example

>>> import numpy as np
>>> from hyperimpute.plugins.imputers import Imputers
>>> plugin = Imputers().get("miracle")
>>> plugin.fit_transform([[1, 1, 1, 1], [np.nan, np.nan, np.nan, np.nan], [1, 2, 2, 1], [2, 2, 2, 2]])

Reference: “MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms”, Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar

_abc_impl = <_abc_data object>

_fit(X: DataFrame, *args: Any, **kwargs: Any) → MiraclePlugin

_get_seed_imputer(method: str) → ImputerPlugin

_transform(X: DataFrame) → DataFrame

static hyperparameter_space(*args: Any, **kwargs: Any) → List[Params]

classmethod load(buff: bytes) → MiraclePlugin

module_relative_path: Optional[Path]

static name() → str

save() → bytes

plugin: alias of MiraclePlugin