woodwork_utils ===================================== .. py:module:: evalml.utils.woodwork_utils .. autoapi-nested-parse:: Woodwork utility methods. Module Contents --------------- Functions ~~~~~~~~~ .. autoapisummary:: :nosignatures: evalml.utils.woodwork_utils.downcast_nullable_types evalml.utils.woodwork_utils.infer_feature_types Attributes Summary ~~~~~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.utils.woodwork_utils.numeric_and_boolean_ww Contents ~~~~~~~~~~~~~~~~~~~ .. py:function:: downcast_nullable_types(data, ignore_null_cols=True) Downcasts IntegerNullable, BooleanNullable types to Double, Boolean in order to support certain estimators like ARIMA, CatBoost, and LightGBM. :param data: Feature data. :type data: pd.DataFrame, pd.Series :param ignore_null_cols: Whether to ignore downcasting columns with null values or not. Defaults to True. :type ignore_null_cols: bool :returns: DataFrame or Series initialized with logical type information where BooleanNullable are cast as Double. :rtype: data .. py:function:: infer_feature_types(data, feature_types=None) Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns. If a column's type is not specified, it will be inferred by Woodwork. :param data: Input data to convert to a Woodwork data structure. :type data: pd.DataFrame, pd.Series :param feature_types: If data is a 2D structure, feature_types must be a dictionary mapping column names to the type of data represented in the column. If data is a 1D structure, then feature_types must be a Woodwork logical type or a string representing a Woodwork logical type ("Double", "Integer", "Boolean", "Categorical", "Datetime", "NaturalLanguage") :type feature_types: string, ww.logical_type obj, dict, optional :returns: A Woodwork data structure where the data type of each column was either specified or inferred. :raises ValueError: If there is a mismatch between the dataframe and the woodwork schema. .. py:data:: numeric_and_boolean_ww