utils#

Utility methods for EvalML objectives.

Module Contents#

Functions#

`get_all_objective_names`	Get a list of the names of all objectives.
`get_core_objective_names`	Get a list of all valid core objectives.
`get_core_objectives`	Returns all core objective instances associated with the given problem type.
`get_default_recommendation_objectives`	Get the default recommendation score metrics for the given problem type.
`get_non_core_objectives`	Get non-core objective classes.
`get_objective`	Returns the Objective class corresponding to a given objective name.
`get_optimization_objectives`	Get objectives for optimization.
`get_ranking_objectives`	Get objectives for pipeline rankings.
`normalize_objectives`	Converts objectives from a [0, inf) scale to [0, 1] given a max and min for each objective.
`organize_objectives`	Generate objectives to consider, with optional modifications to the defaults.
`ranking_only_objectives`	Get ranking-only objective classes.
`recommendation_score`	Computes a recommendation score for a model given scores for a group of objectives.

Attributes Summary#

DEFAULT_RECOMMENDATION_OBJECTIVES

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 out-of-the-box 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 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

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 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

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.