stepsel.modeling.prep.helper
============================

.. py:module:: stepsel.modeling.prep.helper

.. autoapi-nested-parse::

   Helper functions for modeling prep. Smaller functions that are used in multiple functions.



Functions
---------

.. autoapisummary::

   stepsel.modeling.prep.helper.get_interaction_type
   stepsel.modeling.prep.helper.relevel_categorical_variable
   stepsel.modeling.prep.helper.parse_model_formula
   stepsel.modeling.prep.helper.recognize_variable_types


Module Contents
---------------

.. py:function:: get_interaction_type(interaction: str, interaction_numerical_variables: list, interaction_categorical_variables: list)

   Get the interaction type of an interaction.

   :param interaction: The interaction to get the type of.
   :type interaction: str
   :param interaction_numerical_variables: The numerical variables that are used in the interactions.
   :type interaction_numerical_variables: list
   :param interaction_categorical_variables: The categorical variables that are used in the interactions.
   :type interaction_categorical_variables: list

   :returns: **interaction_type** -- The interaction type. One of "numerical_numerical", "categorical_categorical", "numerical_categorical", "categorical_numerical".
   :rtype: str

   :raises ValueError: If the interaction does not contain exactly one '*' character.
       If the interaction variables are not in exactly one of the two lists.

   .. rubric:: Examples

   >>> get_interaction_type("a * b", ["a"], ["b"])
   "numerical_categorical"


.. py:function:: relevel_categorical_variable(series: pandas.Series, new_order: list)

   Relevel a categorical variable.

   :param series: The categorical variable to relevel.
   :type series: pd.Series
   :param new_order: The new order of the categories.
   :type new_order: list

   :returns: **series** -- The relevelled categorical variable.
   :rtype: pd.Series

   :raises ValueError: If the new order is not a subset of the current categories.
       If the new order contains duplicates.


.. py:function:: parse_model_formula(formula: str)

   Parse a model formula into its components.

   :param formula: The model formula to parse.
   :type formula: str

   :returns: * **left_side_variables** (*list*) -- The variables on the left side of the formula.
             * **interaction_variables** (*list*) -- The interaction variables on the right side of the formula.
             * **non_interaction_variables** (*list*) -- The non-interaction variables on the right side of the formula.

   :raises ValueError: If the formula does not contain exactly one '~' character.

   .. rubric:: Examples

   >>> parse_model_formula("y ~ a + b + a * b")
   (["y"], ["a * b"], ["a", "b"])


.. py:function:: recognize_variable_types(data: pandas.DataFrame, interaction_variables: list, non_interaction_variables: list)

   Recognize the types of the variables.

   :param data: The data to recognize the variable types from.
   :type data: pd.DataFrame
   :param interaction_variables: The interaction variables to recognize the types from.
   :type interaction_variables: list
   :param non_interaction_variables: The non-interaction variables to recognize the types from.
   :type non_interaction_variables: list

   :returns: **dictionary** -- A dictionary containing the variable types.

             interaction_numerical_variables : list
                 The numerical variables in the interaction variables.

             interaction_categorical_variables : list
                 The categorical variables in the interaction variables.

             non_interaction_numerical_variables : list
                 The numerical variables in the non-interaction variables.

             non_interaction_categorical_variables : list
                 The categorical variables in the non-interaction variables.

             interaction_variables : list
                 The interaction variables.
   :rtype: dict

   :raises ValueError: If the interaction variables are not either numerical or categorical.
       If the non-interaction variables are not either numerical or categorical.

   .. rubric:: Examples

   >>> recognize_variable_types(data, ["a * b"], ["a", "b", "c"])
   (["a"], ["b"], [], [], ["a * b"])
   {"non_interaction_numerical_variables": ["a"],
    "non_interaction_categorical_variables": ["b"],
    "interaction_numerical_variables": ["a"],
    "interaction_categorical_variables": ["b", "c"],
    "interaction_variables": ["a * b"]}