:py:mod:`bluecast.ml_modelling.parameter_tuning_utils`
======================================================

.. py:module:: bluecast.ml_modelling.parameter_tuning_utils


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


Functions
~~~~~~~~~

.. autoapisummary::

   bluecast.ml_modelling.parameter_tuning_utils.update_params_based_on_tree_method
   bluecast.ml_modelling.parameter_tuning_utils.get_params_based_on_device_xgboost
   bluecast.ml_modelling.parameter_tuning_utils.get_params_based_on_device_catboost
   bluecast.ml_modelling.parameter_tuning_utils.update_params_with_best_params
   bluecast.ml_modelling.parameter_tuning_utils.sample_data



.. py:function:: update_params_based_on_tree_method(param: Dict[str, Any], trial: optuna.Trial, xgboost_params: Union[bluecast.config.training_config.XgboostTuneParamsConfig, bluecast.config.training_config.XgboostTuneParamsRegressionConfig]) -> Dict[str, Any]

   Update parameters based on tree method.


.. py:function:: get_params_based_on_device_xgboost(conf_training: bluecast.config.training_config.TrainingConfig, conf_params_xgboost: Union[bluecast.config.training_config.XgboostFinalParamConfig, bluecast.config.training_config.XgboostRegressionFinalParamConfig], conf_xgboost: Union[bluecast.config.training_config.XgboostTuneParamsConfig, bluecast.config.training_config.XgboostTuneParamsRegressionConfig]) -> Dict[str, Any]

   Get parameters based on available or chosen device.


.. py:function:: get_params_based_on_device_catboost(conf_training: bluecast.config.training_config.TrainingConfig, conf_params_catboost: Union[bluecast.config.training_config.CatboostFinalParamConfig, bluecast.config.training_config.CatboostRegressionFinalParamConfig], conf_catboost: Union[bluecast.config.training_config.CatboostTuneParamsConfig, bluecast.config.training_config.CatboostTuneParamsRegressionConfig]) -> Dict[str, Any]

   Get parameters based on available or chosen device.


.. py:function:: update_params_with_best_params(param: Dict[str, Any], best_params: Dict[str, Any], model_type: str = 'xgboost') -> Dict[str, Any]

   Update parameters based on best parameters after tuning.


.. py:function:: sample_data(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series, conf_training: bluecast.config.training_config.TrainingConfig) -> Tuple[pandas.DataFrame, pandas.DataFrame, pandas.Series, pandas.Series]