utils#
Utility methods for EvalML objectives.
Module Contents#
Functions#
Get a list of the names of all objectives. 

Get a list of all valid core objectives. 

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

Get the default recommendation score metrics for the given problem type. 

Get noncore objective classes. 

Returns the Objective class corresponding to a given objective name. 

Get objectives for optimization. 

Get objectives for pipeline rankings. 

Converts objectives from a [0, inf) scale to [0, 1] given a max and min for each objective. 

Generate objectives to consider, with optional modifications to the defaults. 

Get rankingonly objective classes. 

Computes a recommendation score for a model given scores for a group of objectives. 
Attributes Summary#
Contents#
 evalml.objectives.utils.DEFAULT_RECOMMENDATION_OBJECTIVES#
 evalml.objectives.utils.get_all_objective_names()[source]#
Get a list of the names of all objectives.
 Returns
Objective names
 Return type
list (str)
 evalml.objectives.utils.get_core_objective_names()[source]#
Get a list of all valid core objectives.
 Returns
Objective names.
 Return type
list[str]
 evalml.objectives.utils.get_core_objectives(problem_type)[source]#
Returns all core objective instances associated with the given problem type.
Core objectives are designed to work outofthebox for any dataset.
 Parameters
problem_type (str/ProblemTypes) – Type of problem
 Returns
List of ObjectiveBase instances
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
 evalml.objectives.utils.get_default_recommendation_objectives(problem_type, imbalanced=False)[source]#
Get the default recommendation score metrics for the given problem type.
 Parameters
problem_type (str/ProblemType) – Type of problem
imbalanced (boolean) – For multiclass problems, if the classes are imbalanced. Defaults to False
 Returns
Set of string objective names that correspond to ObjectiveBase objectives
 evalml.objectives.utils.get_non_core_objectives()[source]#
Get noncore objective classes.
Noncore objectives are objectives that are domainspecific. Users typically need to configure these objectives before using them in AutoMLSearch.
 Returns
List of ObjectiveBase classes
 evalml.objectives.utils.get_objective(objective, return_instance=False, **kwargs)[source]#
Returns the Objective class corresponding to a given objective name.
 Parameters
objective (str or ObjectiveBase) – Name or instance of the objective class.
return_instance (bool) – 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.
kwargs (Any) – Any keyword arguments to pass into the objective. Only used when return_instance=True.
 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.
TypeError – If objective is not a string and not an instance of ObjectiveBase.
ObjectiveNotFoundError – If input objective is not a valid objective.
ObjectiveCreationError – If objective cannot be created properly.
 evalml.objectives.utils.get_optimization_objectives(problem_type)[source]#
Get objectives for optimization.
 Parameters
problem_type (str/ProblemTypes) – Type of problem
 Returns
List of ObjectiveBase instances
 evalml.objectives.utils.get_ranking_objectives(problem_type)[source]#
Get objectives for pipeline rankings.
 Parameters
problem_type (str/ProblemTypes) – Type of problem
 Returns
List of ObjectiveBase instances
 evalml.objectives.utils.normalize_objectives(objectives_to_normalize, max_objectives, min_objectives)[source]#
Converts objectives from a [0, inf) scale to [0, 1] given a max and min for each objective.
 Parameters
objectives_to_normalize (dict[str,float]) – A dictionary mapping objectives to values
max_objectives (dict[str,float]) – The mapping of objectives to the maximum values for normalization
min_objectives (dict[str,float]) – The mapping of objectives to the minimum values for normalization
 Returns
A dictionary mapping objective names to their new normalized values
 evalml.objectives.utils.organize_objectives(problem_type, include=None, exclude=None, imbalanced=False)[source]#
Generate objectives to consider, with optional modifications to the defaults.
 Parameters
problem_type (str/ProblemType) – Type of problem
include (list[str/ObjectiveBase]) – A list of objectives to include beyond the defaults. Defaults to None.
exclude (list[str/ObjectiveBase]) – A list of objectives to exclude from the defaults. Defaults to None.
imbalanced (boolean) – For multiclass problems, if the classes are imbalanced. Defaults to False
 Returns
List of string objective names that correspond to ObjectiveBase objectives
 Raises
ValueError – If any objectives to include or exclude are not valid for the problem type
ValueError – If an objective to exclude is not in the default objectives
 evalml.objectives.utils.ranking_only_objectives()[source]#
Get rankingonly objective classes.
Rankingonly 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
 evalml.objectives.utils.recommendation_score(objectives, prioritized_objective=None, custom_weights=None)[source]#
Computes a recommendation score for a model given scores for a group of objectives.
This recommendation score is a weighted average of the given objectives, by default all weighted equally. Passing in a prioritized objective will weight that objective with the prioritized weight, and all other objectives will split the remaining weight equally.
 Parameters
objectives (dict[str,float]) – A dictionary mapping objectives to their values. Objectives should be a float between 0 and 1, where higher is better. If the objective does not represent score this way, scores should first be normalized using the normalize_objectives function.
prioritized_objective (str) – An optional name of a priority objective that should be given heavier weight (50% of the total) than the other objectives contributing to the score. Defaults to None, where all objectives are weighted equally.
custom_weights (dict[str,float]) – A dictionary mapping objective names to corresponding weights between 0 and 1. If all objectives are listed, should add up to 1. If a subset of objectives are listed, should add up to less than 1, and remaining weight will be evenly distributed between the remaining objectives. Should not be used at the same time as prioritized_objective.
 Returns
A value between 0 and 100 representing how strongly we recommend a pipeline given a set of evaluated objectives
 Raises
ValueError – If the objective(s) to prioritize are not in the known objectives, or if the custom weight(s) are not a float between 0 and 1.