utils
=================================

.. py:module:: evalml.objectives.utils

.. autoapi-nested-parse::

   Utility methods for EvalML objectives.



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



Functions
~~~~~~~~~

.. autoapisummary::
   :nosignatures:

   evalml.objectives.utils.get_all_objective_names
   evalml.objectives.utils.get_core_objective_names
   evalml.objectives.utils.get_core_objectives
   evalml.objectives.utils.get_non_core_objectives
   evalml.objectives.utils.get_objective
   evalml.objectives.utils.get_optimization_objectives
   evalml.objectives.utils.get_ranking_objectives
   evalml.objectives.utils.ranking_only_objectives



Contents
~~~~~~~~~~~~~~~~~~~
.. py:function:: get_all_objective_names()

   Get a list of the names of all objectives.

   :returns: Objective names
   :rtype: list (str)


.. py:function:: get_core_objective_names()

   Get a list of all valid core objectives.

   :returns: Objective names.
   :rtype: list[str]


.. py:function:: get_core_objectives(problem_type)

   Returns all core objective instances associated with the given problem type.

   Core objectives are designed to work out-of-the-box for any dataset.

   :param problem_type: Type of problem
   :type problem_type: str/ProblemTypes

   :returns: List of ObjectiveBase instances

   .. rubric:: Examples

   >>> for objective in get_core_objectives("regression"):
   ...     print(objective.name)
   ExpVariance
   MaxError
   MedianAE
   MSE
   MAE
   R2
   Root Mean Squared Error
   >>> for objective in get_core_objectives("binary"):
   ...     print(objective.name)
   MCC Binary
   Log Loss Binary
   Gini
   AUC
   Precision
   F1
   Balanced Accuracy Binary
   Accuracy Binary


.. py:function:: get_non_core_objectives()

   Get non-core objective classes.

   Non-core objectives are objectives that are domain-specific. Users typically need to configure these objectives
   before using them in AutoMLSearch.

   :returns: List of ObjectiveBase classes


.. py:function:: get_objective(objective, return_instance=False, **kwargs)

   Returns the Objective class corresponding to a given objective name.

   :param objective: Name or instance of the objective class.
   :type objective: str or ObjectiveBase
   :param return_instance: Whether to return an instance of the objective. This only applies if objective
                           is of type str. Note that the instance will be initialized with default arguments.
   :type return_instance: bool
   :param kwargs: Any keyword arguments to pass into the objective. Only used when return_instance=True.
   :type kwargs: Any

   :returns: ObjectiveBase if the parameter objective is of type ObjectiveBase. If objective is instead a valid
             objective name, function will return the class corresponding to that name. If return_instance is True,
             an instance of that objective will be returned.

   :raises TypeError: If objective is None.
   :raises TypeError: If objective is not a string and not an instance of ObjectiveBase.
   :raises ObjectiveNotFoundError: If input objective is not a valid objective.
   :raises ObjectiveCreationError: If objective cannot be created properly.


.. py:function:: get_optimization_objectives(problem_type)

   Get objectives for optimization.

   :param problem_type: Type of problem
   :type problem_type: str/ProblemTypes

   :returns: List of ObjectiveBase instances


.. py:function:: get_ranking_objectives(problem_type)

   Get objectives for pipeline rankings.

   :param problem_type: Type of problem
   :type problem_type: str/ProblemTypes

   :returns: List of ObjectiveBase instances


.. py:function:: ranking_only_objectives()

   Get ranking-only objective classes.

   Ranking-only objectives are objectives that are useful for evaluating the performance of a model, but should not
   be used as an optimization objective during AutoMLSearch for various reasons.

   :returns: List of ObjectiveBase classes