cursus.processing.categorical.categorical_imputation_processor

Categorical Imputation Processor for Missing Values

This module provides atomic categorical imputation with configurable defaults. Extracted from TSA default value handling logic.

class CategoricalImputationProcessor(default_values=None, missing_indicators=None, strategy='default', constant_value='UNKNOWN')

Bases: Processor

Handles missing categorical values with configurable defaults.

Extracted from TSA default value handling logic.

Parameters:

• default_values (Dict[str, Any] | None) – Dictionary of field -> default value mappings

• missing_indicators (List[Any] | None) – Values that indicate missing data

• strategy (str) – ‘default’, ‘mode’, ‘constant’

• constant_value (str) – Value to use for constant strategy

fit(data)

Learn default values from data if needed

process(input_data)

Apply categorical imputation

add_missing_indicator(indicator)

Add a new missing indicator

remove_missing_indicator(indicator)

Remove a missing indicator

get_missing_statistics(data)

Get statistics about missing values in the data

get_config()

Return processor configuration