:py:mod:`bluecast.tests.test_cast_cv_regression`
================================================

.. py:module:: bluecast.tests.test_cast_cv_regression


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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.tests.test_cast_cv_regression.CustomLRModel
   bluecast.tests.test_cast_cv_regression.MyCustomLastMilePreprocessing
   bluecast.tests.test_cast_cv_regression.CustomModel



Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.tests.test_cast_cv_regression.synthetic_train_test_data
   bluecast.tests.test_cast_cv_regression.synthetic_calibration_data
   bluecast.tests.test_cast_cv_regression.test_blueprint_cv_xgboost
   bluecast.tests.test_cast_cv_regression.test_bluecast_cv_fit_eval_with_custom_model
   bluecast.tests.test_cast_cv_regression.test_bluecast_cv_with_custom_objects



.. py:function:: synthetic_train_test_data() -> Tuple[pandas.DataFrame, pandas.DataFrame]


.. py:function:: synthetic_calibration_data() -> pandas.DataFrame


.. py:function:: test_blueprint_cv_xgboost(synthetic_train_test_data, synthetic_calibration_data)

   Test that tests the BlueCast cv class


.. py:class:: CustomLRModel


   Bases: :py:obj:`bluecast.ml_modelling.base_classes.BaseClassMlRegressionModel`

   .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> None


   .. py:method:: predict(df: pandas.DataFrame) -> numpy.ndarray



.. py:class:: MyCustomLastMilePreprocessing(trained_patterns: Optional[Any] = None)


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

   This class is an entry point for last mile computations before model training or tuning. It is an abstract class
   and must be extended by the user. For fit_transform x_train and y_train are passed. For transform x_test and y_test
   are passed in the BlueCast pipeline. Use prediction_mode = False to skip processing the missing targets.
   :param trained_patterns: Optional. Can we used to save anything from training to be loaded and used in transform.
   If more placeholders are needed, use a dictionary.

   .. py:method:: custom_function(df: pandas.DataFrame) -> pandas.DataFrame


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


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

      Use prediction mode to not process the missing target during inference.



.. py:class:: CustomModel


   Bases: :py:obj:`bluecast.ml_modelling.base_classes.BaseClassMlRegressionModel`

   .. py:method:: fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) -> None


   .. py:method:: predict(df: pandas.DataFrame) -> numpy.ndarray



.. py:function:: test_bluecast_cv_fit_eval_with_custom_model()


.. py:function:: test_bluecast_cv_with_custom_objects()