MNAR multivariate: uMNAR Class

mMNAR

mMNAR

mMNAR(X: DataFrame, y: ndarray, threshold: float = 0, n_xmiss: int = 2, missTarget: bool = False, n_Threads: int = 1)

A class to generate missing values in a dataset based on the Missing Not At Random (MNAR) mechanism for multiple features simultaneously.

Args: X (pd.DataFrame): The dataset to receive the missing data. y (np.array): The label values from dataset missing_rate (int, optional): The rate of missing data to be generated. Default is 10.

Keyword Args: n_xmiss (int, optional): The number of features in the dataset that will receive missing values. Default is the number of features in dataset. threshold (float, optional): The threshold to select the locations in feature (xmiss) to receive missing values where 0 indicates de lowest and 1 highest values. Default is 0 missTarget (bool, optional): A flag to generate missing into the target.

Example Usage:

# Create an instance of the MNAR class
generator = MNAR(X, y)

# Generate missing values using the random strategy
data_md = generator.random()

random

random(missing_rate: int = 10, deterministic: bool = True)

Generate missing data using parallel processing.

correlated

correlated(missing_rate: int = 10, deterministic: bool = False)

Generate missing data using parallel processing.

median

median(missing_rate: int = 10, deterministic: bool = False)

Generate missing data using parallel processing.

MBOUV

MBOUV(missing_rate: int = 10, depend_on_external=None, ascending=True)

Generate missing data using parallel processing.

MBOV_randomness

MBOV_randomness(missing_rate: int = 10, randomness: float = 0, columns: list = None)

Generate missing data using parallel processing.

MBOV_median

MBOV_median(missing_rate: int = 10, columns: list = None)

Generate missing data using parallel processing.

MBIR

MBIR(missing_rate: int = 10, columns: list = None, statistical_method: str = 'Mann-Whitney')

Generate missing data using parallel processing.