stepsel.modeling.prep.interaction
=================================

.. py:module:: stepsel.modeling.prep.interaction

.. autoapi-nested-parse::

   Preprocessing functions for interaction terms.



Functions
---------

.. autoapisummary::

   stepsel.modeling.prep.interaction.interaction_categorical_numerical
   stepsel.modeling.prep.interaction.interaction_categorical_categorical
   stepsel.modeling.prep.interaction.interaction_numerical_numerical


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

.. py:function:: interaction_categorical_numerical(series1: pandas.Series, series2: pandas.Series)

   Create an interaction term between a categorical and a numerical variable.

   :param series1: The first series.
   :type series1: pandas.Series
   :param series2: The second series.
   :type series2: pandas.Series

   :returns: **interaction_df** -- A DataFrame with the interaction terms.
   :rtype: pandas.DataFrame

   :raises ValueError: If one (and only one) of the series is not categorical.
       If one (and only one) of the series is not numerical.

   .. rubric:: Notes

   The function will create dummy variables for the categorical variable and multiply them by the numerical variable.
   The dummy variables will be named "categorical_variable: category * numerical_variable".

   .. rubric:: Examples

   >>> import pandas as pd
   >>> import numpy as np
   >>> from stepsel.modeling.prep import interaction_categorical_numerical
   >>> categorical_series = pd.Series(np.random.choice(["A", "B", "C"], size=10), name="categorical").astype("category")
   >>> numerical_series = pd.Series(np.random.normal(size=10), name="numerical")
   >>> interaction_categorical_numerical(categorical_series, numerical_series)
           categorical: A * numerical  categorical: B * numerical  categorical: C * numerical
   0                        -0.626453                    0.417258                    0.619825
   1                         0.183643                   -0.720788                   -0.720788
   2                         0.835979                   -0.632650                   -0.632650
   ...


.. py:function:: interaction_categorical_categorical(series1: pandas.Series, series2: pandas.Series)

   Create an interaction term between two categorical variables.

   :param series1: The first series.
   :type series1: pandas.Series
   :param series2: The second series.
   :type series2: pandas.Series

   :returns: **interaction** -- A Series with the interaction terms.
   :rtype: pandas.Series

   :raises ValueError: If one series1 is not categorical.
       If one series2 is not categorical.

   .. rubric:: Notes

   The function will create an interaction term between the two categorical variables.
   The interaction term will be named "categorical_variable1 * categorical_variable2".
   The interactions will be in form of "category1 * category2".

   .. rubric:: Examples

   >>> import pandas as pd
   >>> import numpy as np
   >>> from stepsel.modeling.prep import interaction_categorical_categorical
   >>> categorical_series1 = pd.Series(["A", "B", "C"], name="categorical1").astype("category")
   >>> categorical_series2 = pd.Series(["X", "Y", "Z"], name="categorical2").astype("category")
   >>> interaction_categorical_categorical(categorical_series1, categorical_series2)
   0    A * X
   1    B * Y
   2    C * Z
   Name: categorical1 * categorical2, dtype: category
   Categories (3, object): ['A * X', 'B * Y', 'C * Z']


.. py:function:: interaction_numerical_numerical(series1: pandas.Series, series2: pandas.Series)

   Create an interaction term between two numerical variables.

   :param series1: The first series.
   :type series1: pandas.Series
   :param series2: The second series.
   :type series2: pandas.Series

   :returns: **interaction** -- A Series with the interaction terms.
   :rtype: pandas.Series

   :raises ValueError: If one series1 is not numerical.
       If one series2 is not numerical.

   .. rubric:: Notes

   The function will create an interaction term between the two numerical variables.
   The interaction term will be named "numerical_variable1 * numerical_variable2".

   .. rubric:: Examples

   >>> import pandas as pd
   >>> import numpy as np
   >>> from stepsel.modeling.prep import interaction_numerical_numerical
   >>> numerical_series1 = pd.Series(np.random.normal(size=10), name="numerical1")
   >>> numerical_series2 = pd.Series(np.random.normal(size=10), name="numerical2")
   >>> interaction_numerical_numerical(numerical_series1, numerical_series2)
   0    -0.626453
   1    -0.720788
   2    -0.632650
   ...
   Name: numerical1 * numerical2, dtype: float64