Source code for evalml.objectives.utils
"""Utility methods for EvalML objectives."""
from evalml import objectives
from evalml.exceptions import ObjectiveCreationError, ObjectiveNotFoundError
from evalml.objectives.objective_base import ObjectiveBase
from evalml.problem_types import handle_problem_types
from evalml.utils.gen_utils import _get_subclasses
[docs]def 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
"""
return [
objectives.CostBenefitMatrix,
objectives.FraudCost,
objectives.LeadScoring,
objectives.Recall,
objectives.RecallMacro,
objectives.RecallMicro,
objectives.RecallWeighted,
objectives.MAPE,
objectives.MeanSquaredLogError,
objectives.RootMeanSquaredLogError,
objectives.SensitivityLowAlert,
]
def _all_objectives_dict():
all_objectives = _get_subclasses(ObjectiveBase)
objectives_dict = {}
for objective in all_objectives:
if "evalml.objectives" not in objective.__module__:
continue
objectives_dict[objective.name.lower()] = objective
return objectives_dict
[docs]def get_all_objective_names():
"""Get a list of the names of all objectives.
Returns:
list (str): Objective names
"""
all_objectives_dict = _all_objectives_dict()
return list(all_objectives_dict.keys())
[docs]def get_core_objective_names():
"""Get a list of all valid core objectives.
Returns:
list[str]: Objective names.
"""
all_objectives = _all_objectives_dict()
non_core = get_non_core_objectives()
return [
name
for name, class_name in all_objectives.items()
if class_name not in non_core
]
[docs]def get_objective(objective, return_instance=False, **kwargs):
"""Returns the Objective class corresponding to a given objective name.
Args:
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.
"""
if objective is None:
raise TypeError("Objective parameter cannot be NoneType")
if isinstance(objective, ObjectiveBase):
return objective
all_objectives_dict = _all_objectives_dict()
if not isinstance(objective, str):
raise TypeError(
"If parameter objective is not a string, it must be an instance of ObjectiveBase!",
)
if objective.lower() not in all_objectives_dict:
raise ObjectiveNotFoundError(
f"{objective} is not a valid Objective! "
"Use evalml.objectives.get_all_objective_names()"
"to get a list of all valid objective names. ",
)
objective_class = all_objectives_dict[objective.lower()]
if return_instance:
try:
return objective_class(**kwargs)
except TypeError as e:
raise ObjectiveCreationError(
f"In get_objective, cannot pass in return_instance=True for {objective} because {str(e)}",
)
return objective_class
[docs]def 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.
Args:
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
"""
problem_type = handle_problem_types(problem_type)
all_objectives_dict = _all_objectives_dict()
objectives = [
obj()
for obj in all_objectives_dict.values()
if obj.is_defined_for_problem_type(problem_type)
and obj not in get_non_core_objectives()
]
return objectives