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

.. py:module:: bluecast.preprocessing.feature_selection


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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.preprocessing.feature_selection.BoostaRootaWrapper
   bluecast.preprocessing.feature_selection.BoostARoota



Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.preprocessing.feature_selection._create_shadow
   bluecast.preprocessing.feature_selection._reduce_vars_xgb
   bluecast.preprocessing.feature_selection._reduce_vars_sklearn
   bluecast.preprocessing.feature_selection._BoostARoota



.. py:class:: BoostaRootaWrapper(class_problem: Literal[binary, multiclass, regression], random_state)


   Bases: :py:obj:`bluecast.preprocessing.custom.CustomPreprocessing`

   .. py:method:: fit_transform(df: pandas.DataFrame, targets: pandas.Series) -> Tuple[pandas.DataFrame, Optional[pandas.Series]]


   .. py:method:: transform(df: pandas.DataFrame, target: Optional[pandas.Series] = None, predicton_mode: bool = False) -> Tuple[pandas.DataFrame, Optional[pandas.Series]]



.. py:class:: BoostARoota(metric=None, clf=None, cutoff=200, iters=10, max_rounds=100, delta=0.1, silent=True)


   Bases: :py:obj:`object`

   .. py:method:: fit(x, y)


   .. py:method:: transform(x)


   .. py:method:: fit_transform(x, y)



.. py:function:: _create_shadow(x_train)

   Take all X variables, creating copies and randomly shuffling them
   :param x_train: the dataframe to create shadow features on
   :return: dataframe 2x width and the names of the shadows for removing later


.. py:function:: _reduce_vars_xgb(x, y, metric, this_round, cutoff, n_iterations, delta, silent)

   Function to run through each
   :param x: Input dataframe - X
   :param y: Target variable
   :param metric: Metric to optimize in XGBoost
   :param this_round: Round so it can be printed to screen
   :return: tuple - stopping criteria and the variables to keep


.. py:function:: _reduce_vars_sklearn(x, y, clf, this_round, cutoff, n_iterations, delta, silent)

   Function to run through each
   :param x: Input dataframe - X
   :param y: Target variable
   :param clf: the fully specified classifier passed in by user
   :param this_round: Round so it can be printed to screen
   :return: tuple - stopping criteria and the variables to keep


.. py:function:: _BoostARoota(x, y, metric, clf, cutoff, iters, max_rounds, delta, silent)

   Function loops through, waiting for the stopping criteria to change
   :param x: X dataframe One Hot Encoded
   :param y: Labels for the target variable
   :param metric: The metric to optimize in XGBoost
   :return: names of the variables to keep