:py:mod:`bluecast.preprocessing.encode_target_labels`
=====================================================

.. py:module:: bluecast.preprocessing.encode_target_labels

.. autoapi-nested-parse::

   A module for encoding target column labels.

   This is a convenience feature. It is only relevant when target column values are categorical.
   In such cases they will be converted to numerical values, but reverse-transformed for the end-user at the end of the
   pipeline.



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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.preprocessing.encode_target_labels.TargetLabelEncoder



Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.preprocessing.encode_target_labels.cast_bool_to_int



.. py:function:: cast_bool_to_int(df: pandas.DataFrame, column_name: str) -> pandas.DataFrame

   This function takes a DataFrame and a column name. If the column is of type bool, it casts it to int and returns the DataFrame.

   :param df: The input DataFrame.
   :param column_name: The name of the column to check and possibly cast.

   returns: The modified DataFrame with the column cast to bool if it was of type int.


.. py:class:: TargetLabelEncoder


   Encode target column labels.

   This function is only relevant when target column values are categorical. In such cases they will be converted
   into numerical representation. This encoding can also be reversed to translate back.

   .. py:method:: fit_label_encoder(targets: pandas.DataFrame) -> Dict[str, int]

      Iterate through target values and map them to numerics.


   .. py:method:: label_encoder_transform(targets: pandas.DataFrame, mapping: Dict[str, int], target_col: Optional[Union[str, int, float]] = None) -> pandas.DataFrame

      Transform target column from categorical to numerical representation.


   .. py:method:: fit_transform_target_labels(targets: pandas.DataFrame) -> pandas.DataFrame

      Wrapper function that creates the mapping and transforms the target column.


   .. py:method:: transform_target_labels(targets: pandas.DataFrame, target_col: Optional[Union[str, int, float]] = None) -> pandas.DataFrame

      Transform the target column based on already created mappings.


   .. py:method:: label_encoder_reverse_transform(targets: pandas.Series) -> pandas.DataFrame

      Reverse numerical encodings back to original categories.