:py:mod:`bluecast.blueprints.orchestration`
===========================================

.. py:module:: bluecast.blueprints.orchestration

.. autoapi-nested-parse::

   Module containing model orchestration tools.



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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.blueprints.orchestration.ModelMatchMaker




.. py:class:: ModelMatchMaker


   Matching the incoming data with the best model based on the adversarial validation score.

   .. py:method:: append_model_and_dataset(bluecast_instance: Union[bluecast.blueprints.cast.BlueCast, bluecast.blueprints.cast_regression.BlueCastRegression, bluecast.blueprints.cast_cv.BlueCastCV, bluecast.blueprints.cast_cv_regression.BlueCastCVRegression], df: pandas.DataFrame)

      Append the model and the dataset to the matchmaker.

      :param bluecast_instance: The BlueCast instance to append.
      :param df: The dataset to append.


   .. py:method:: find_best_match(df: pandas.DataFrame, use_cols: List[Union[int, float, str]], cat_columns: Optional[List], delta: float, train_on_device: str = 'cpu') -> Tuple[Optional[Union[bluecast.blueprints.cast.BlueCast, bluecast.blueprints.cast_regression.BlueCastRegression, bluecast.blueprints.cast_cv.BlueCastCV, bluecast.blueprints.cast_cv_regression.BlueCastCVRegression]], Optional[pandas.DataFrame]]

      Find the best match based on the adversarial validation score.
      :param df: Dataset to match.
      :param use_cols: Columns to use for the adversarial validation. Numerical columns are allowed only.
      :param delta: Maximum delta for the adversarial validation score to be away from 0.5. If no dataset reaches this
       delta, (None, None) is returned.
      :param cat_columns: (Optional) List with names of categorical columns.
      :param train_on_device: Device to train the model on. Options are 'cpu' and 'gpu'. (Default is 'cpu')
      :return: If a match is found, the BlueCast instance and the dataset are returned. Otherwise, (None, None) is
          returned.