def greater_is_better_distinction(metric):
sign = Scorer.metric_sign(metric)
if sign == 1:
return True
elif sign == -1:
return False
else:
raise_PhotonaiError("Metric can not be of sign: {}".format(sign))
def metric_metadata(m: str = "accuracy",
metadata_id: int = METRIC_PKGID) -> Union[str, int]:
Scorer.is_metric(m)
if metadata_id < METRIC_METADATA_OUT_OF_BOUNDS and metadata_id > -1:
return METRIC_METADATA[m][metadata_id]
else:
raise_PhotonaiError(
"METRIC_METADATA_index OUT_OF_BOUNDS bounds: {}, was index: {}"
.format(METRIC_METADATA_OUT_OF_BOUNDS, metadata_id))
def is_score_type(score_type: str) -> bool:
"""
Parameters
----------
score_type
Returns
-------
True or PhotonaiError
"""
if score_type in Scorer.SCORE_TYPES:
return True
raise_PhotonaiError("Specify valid score_type:{} of [{}]".format(
score_type, Scorer.SCORE_TYPES))
def is_metric(metric: str) -> Union[bool, PhotonaiError]:
"""
Raises
--------
if not known metric
Parameters
----------
metric
Returns
-------
True or PhotonaiError
"""
if metric in Scorer.METRIC_METADATA:
return True
raise_PhotonaiError("Specify valid ml_type:{} of [{}]".format(
metric, Scorer.METRIC_METADATA))
def is_machine_learning_type(ml_type: str) -> Union[bool, PhotonaiError]:
"""
Parameters
----------
ml_type
Returns
-------
True or PhotonaiError
Raises
------
if not known machine_learning_type
"""
if ml_type in Scorer.ML_TYPES:
return True
raise_PhotonaiError(
"Specify valid ml_type. invalid :{} of supported: [{}]".format(
ml_type, Scorer.ML_TYPES))
def is_estimator_predict(estimator: str) -> Union[bool, PhotonaiError]:
"""
Parameters
----------
element_type
Returns
-------
True - if element_type is one of Scorer.ELEMENT_TYPES
Raises
-------
PhotonaiError
"""
if hasattr(estimator, Scorer.ESTIMATOR_PREDICT):
return True
raise_PhotonaiError(
"Estimator does not implement predict() method: {}]".format(
estimator))
def is_estimator(element_type: str) -> Union[bool, PhotonaiError]:
"""
Parameters
----------
element_type
Returns
-------
True - if element_type is one of Scorer.ELEMENT_TYPES
Raises
-------
PhotonaiError
"""
if hasattr(element_type, Scorer.ESTIMATOR):
return True
raise_PhotonaiError("Specify valid element_type:{} of [{}]".format(
element_type, Scorer.ELEMENT_TYPES))
def is_estimator_not_predict(
element_type: str) -> Union[bool, PhotonaiError]:
"""
Parameters
----------
element_type
Returns
-------
True - if element_type is one of Scorer.ELEMENT_TYPES
Raises
-------
PhotonaiError
"""
if not hasattr(element_type, Scorer.ESTIMATOR_PREDICT):
return True
raise_PhotonaiError(
"Element has predict() method but does not specify whether it is an estimator,\
Remember to inherit from <estimator>Mixin.")