hyperimpute.plugins.imputers.plugin_miwae module

class MIWAEPlugin(n_epochs: int = 500, batch_size: int = 256, latent_size: int = 1, n_hidden: int = 1, random_state: int = 0, K: int = 20)

Bases: ImputerPlugin

MIWAE imputation plugin

Parameters:

  • n_epochs – int Number of training iterations

  • batch_size – int Batch size

  • latent_size – int dimension of the latent space

  • n_hidden – int number of hidden units

  • K – int number of IS during training

  • random_state – int random seed

Example

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

Reference: “MIWAE: Deep Generative Modelling and Imputation of Incomplete Data”, Pierre-Alexandre Mattei, Jes Frellsen Original code: https://github.com/pamattei/miwae

_abc_impl = <_abc_data object>
_fit(X: DataFrame, *args: Any, **kwargs: Any) → MIWAEPlugin
_miwae_impute(iota_x: Tensor, mask: Tensor, L: int) → Tensor
_miwae_loss(iota_x: Tensor, mask: Tensor) → Tensor
_transform(X: DataFrame) → DataFrame
static hyperparameter_space(*args: Any, **kwargs: Any) → List[Params]
module_relative_path: Optional[Path]
static name() → str
plugin

alias of MIWAEPlugin

weights_init(layer: Any) → None