ml_grid.pipeline.main
=====================

.. py:module:: ml_grid.pipeline.main


Classes
-------

.. autoapisummary::

   ml_grid.pipeline.main.run


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

.. py:class:: run(ml_grid_object: ml_grid.pipeline.data.pipe, local_param_dict: Dict[str, Any])

   Initializes the run class.

   This class takes the main data pipeline object and a dictionary of local
   parameters to set up and prepare for executing a series of hyperparameter
   searches across multiple machine learning models.

   :param ml_grid_object: The main data pipeline object, which contains
                          the data (X_train, y_train, etc.) and a list of model classes
                          to be evaluated.
   :type ml_grid_object: pipe
   :param local_param_dict: A dictionary of parameters for the
                            current experimental run, such as `param_space_size`.
   :type local_param_dict: Dict[str, Any]


   .. py:attribute:: global_params
      :type:  ml_grid.util.global_params.global_parameters

      A reference to the global parameters singleton instance.


   .. py:attribute:: verbose
      :type:  int

      The verbosity level for logging, inherited from global parameters.


   .. py:attribute:: error_raise
      :type:  bool

      A flag to control error handling. If True, exceptions will be raised.


   .. py:attribute:: ml_grid_object
      :type:  ml_grid.pipeline.data.pipe

      The main data pipeline object, containing data and model configurations.


   .. py:attribute:: sub_sample_param_space_pct
      :type:  float

      The percentage of the parameter space to sample in a randomized search.


   .. py:attribute:: parameter_space_size
      :type:  str

      The size of the parameter space for base learners (e.g., 'medium', 'xsmall').


   .. py:attribute:: model_class_list
      :type:  List[Any]

      A list of instantiated model class objects to be evaluated in this run.


   .. py:attribute:: pg_list
      :type:  List[int]

      A list containing the calculated size of the parameter grid for each model.


   .. py:attribute:: mean_parameter_space_val
      :type:  float

      The mean size of the parameter spaces across all models in the run.


   .. py:attribute:: sub_sample_parameter_val
      :type:  int

      The calculated number of iterations for randomized search, based on `sub_sample_param_space_pct`.


   .. py:attribute:: arg_list
      :type:  List[Tuple]

      A list of argument tuples, one for each model, to be passed to the grid search function.


   .. py:attribute:: multiprocess
      :type:  bool

      A flag to enable or disable multiprocessing for running grid searches in parallel.


   .. py:attribute:: local_param_dict
      :type:  Dict[str, Any]

      A dictionary of parameters for the current experimental run.


   .. py:attribute:: model_error_list
      :type:  List[List[Any]]

      A list to store details of any errors encountered during model training.


   .. py:attribute:: highest_score
      :type:  float

      The highest score achieved across all successful model runs in the execute step.


   .. py:method:: execute() -> Tuple[List[List[Any]], float]

      Executes the grid search for each model in the list.

      This method iterates through the list of configured models and their
      parameter spaces, running a cross-validated grid search for each one.
      It captures any errors that occur during the process and returns a list
      of those errors along with the highest score achieved.

      :returns:

                A tuple containing:
                    - A list of model errors, where each error is a list containing
                      the algorithm instance, the exception, and the traceback.
                    - The highest score achieved across all successful model runs.
      :rtype: Tuple[List[List[Any]], float]