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

.. py:module:: bluecast.tests.test_cast


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

Classes
~~~~~~~

.. autoapisummary::

   bluecast.tests.test_cast.CustomModel



Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.tests.test_cast.synthetic_train_test_data
   bluecast.tests.test_cast.synthetic_calibration_data
   bluecast.tests.test_cast.synthetic_multiclass_train_test_data
   bluecast.tests.test_cast.test_blueprint_xgboost
   bluecast.tests.test_cast.test_bluecast_with_custom_model
   bluecast.tests.test_cast.bluecast_instance
   bluecast.tests.test_cast.test_enable_feature_selection_warning
   bluecast.tests.test_cast.test_hypertuning_cv_folds_warning
   bluecast.tests.test_cast.test_missing_feature_selector_warning
   bluecast.tests.test_cast.test_missing_xgboost_tune_params_config_warning
   bluecast.tests.test_cast.test_shap_values_and_ml_algorithm_warning
   bluecast.tests.test_cast.test_cat_encoding_via_ml_algorithm_and_ml_model_warning
   bluecast.tests.test_cast.test_precise_cv_tuning_warnings
   bluecast.tests.test_cast.test_class_problem_mismatch_warnings
   bluecast.tests.test_cast.test_categorical_encoding_not_supported_by_exact_tree_method



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


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


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


.. py:function:: test_blueprint_xgboost(synthetic_train_test_data, synthetic_multiclass_train_test_data, synthetic_calibration_data)

   Test that tests the BlueCast class


.. py:class:: CustomModel


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

   Base class for all ML models.

   Enforces the implementation of the fit and predict methods.
   If hyperparameter tuning is required, then the fit method should implement the tuning.

   .. 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) -> Tuple[bluecast.ml_modelling.base_classes.PredictedProbas, bluecast.ml_modelling.base_classes.PredictedClasses]

      Predict on unseen data.

      :return tuple of predicted probabilities and predicted classes



.. py:function:: test_bluecast_with_custom_model()


.. py:function:: bluecast_instance()


.. py:function:: test_enable_feature_selection_warning(bluecast_instance)


.. py:function:: test_hypertuning_cv_folds_warning(bluecast_instance)


.. py:function:: test_missing_feature_selector_warning()


.. py:function:: test_missing_xgboost_tune_params_config_warning()


.. py:function:: test_shap_values_and_ml_algorithm_warning(bluecast_instance)


.. py:function:: test_cat_encoding_via_ml_algorithm_and_ml_model_warning()


.. py:function:: test_precise_cv_tuning_warnings(bluecast_instance)


.. py:function:: test_class_problem_mismatch_warnings(bluecast_instance)


.. py:function:: test_categorical_encoding_not_supported_by_exact_tree_method(bluecast_instance)