woodwork_utils#

Woodwork utility methods.

Module Contents#

Functions#

downcast_nullable_types

Downcasts IntegerNullable, BooleanNullable types to Double, Boolean in order to support certain estimators like ARIMA, CatBoost, and LightGBM.

infer_feature_types

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.

Attributes Summary#

numeric_and_boolean_ww

Contents#

evalml.utils.woodwork_utils.downcast_nullable_types(X, ignore_null_cols=True)[source]#

Downcasts IntegerNullable, BooleanNullable types to Double, Boolean in order to support certain estimators like ARIMA, CatBoost, and LightGBM.

Parameters

  • X (pd.DataFrame) – Feature data.

  • ignore_null_cols (bool) – Whether to ignore downcasting columns with null values or not. Defaults to True.

Returns

DataFrame initialized with logical type information where BooleanNullable are cast as Double.

Return type

X

evalml.utils.woodwork_utils.infer_feature_types(data, feature_types=None)[source]#

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.

Parameters

  • data (pd.DataFrame, pd.Series) – Input data to convert to a Woodwork data structure.

  • feature_types (string, ww.logical_type obj, dict, optional) – 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”)

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.

evalml.utils.woodwork_utils.numeric_and_boolean_ww#