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(data, ignore_null_cols=True)[source]#

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

Parameters

data (pd.DataFrame, pd.Series) – Feature data.
ignore_null_cols (bool) – Whether to ignore downcasting columns with null values or not. Defaults to True.

Returns

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

Return type

data

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#