qolmat.imputations.imputers.ImputerRpcaNoisy¶
- class qolmat.imputations.imputers.ImputerRpcaNoisy(groups: Tuple[str, ...] = (), columnwise: bool = False, random_state: Optional[Union[int, RandomState]] = None, period: int = 1, mu: Optional[float] = None, rank: Optional[int] = None, tau: Optional[float] = None, lam: Optional[float] = None, list_periods: Tuple[int, ...] = (), list_etas: Tuple[float, ...] = (), max_iterations: int = 10000, tolerance: float = 1e-06, norm: Optional[str] = 'L2', verbose: bool = False)[source]¶
Noise RPCA imputer.
This class implements the Robust Principal Component Analysis imputation with added noise. The imputation minimizes a loss function combining a low-rank criterium on the dataframe and a L1 penalization on the residuals.
- Parameters
- groups: Tuple[str, …]
List of column names to group by, by default []
- columnwisebool
For the RPCA method to be applied columnwise (with reshaping of each column into an array) or to be applied directly on the dataframe. By default, the value is set to False.
- random_stateRandomSetting, optional
Controls the randomness of the fit_transform, by default None
- __init__(groups: Tuple[str, ...] = (), columnwise: bool = False, random_state: Optional[Union[int, RandomState]] = None, period: int = 1, mu: Optional[float] = None, rank: Optional[int] = None, tau: Optional[float] = None, lam: Optional[float] = None, list_periods: Tuple[int, ...] = (), list_etas: Tuple[float, ...] = (), max_iterations: int = 10000, tolerance: float = 1e-06, norm: Optional[str] = 'L2', verbose: bool = False) None[source]¶
