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Scorers

siapy.optimizers.scorers

Scorer 

Scorer(scorer)
PARAMETER DESCRIPTION
scorer

Source code in siapy/optimizers/scorers.py 
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def __init__(self, scorer):
    self._scorer = scorer

init_cross_validator_scorer classmethod 

init_cross_validator_scorer(scoring: str | ScorerFuncType | None = None, cv: int | BaseCrossValidator | _RepeatedSplits | Iterable | Literal['RepeatedKFold', 'RepeatedStratifiedKFold'] | None = None) -> Scorer
PARAMETER DESCRIPTION
scoring

TYPE: str | ScorerFuncType | None DEFAULT: None

cv

TYPE: int | BaseCrossValidator | _RepeatedSplits | Iterable | Literal['RepeatedKFold', 'RepeatedStratifiedKFold'] | None DEFAULT: None

Source code in siapy/optimizers/scorers.py 
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@classmethod
def init_cross_validator_scorer(
    cls,
    scoring: str | ScorerFuncType | None = None,
    cv: int
    | model_selection.BaseCrossValidator
    | model_selection._split._RepeatedSplits
    | Iterable
    | Literal["RepeatedKFold", "RepeatedStratifiedKFold"]
    | None = None,
) -> "Scorer":
    if isinstance(cv, str) and cv in ["RepeatedKFold", "RepeatedStratifiedKFold"]:
        cv = initialize_object(
            module=model_selection,
            module_name=cv,
            n_splits=3,
            n_repeats=5,
            random_state=0,
        )
    scorer = partial(
        cross_validation,
        scoring=scoring,
        cv=cv,  # type: ignore
        groups=None,
        n_jobs=1,
        verbose=0,
        fit_params=None,
        pre_dispatch=1,
        error_score=0,
    )
    return cls(scorer)

init_hold_out_scorer classmethod 

init_hold_out_scorer(scoring: str | ScorerFuncType | None = None, test_size: float | None = 0.2, stratify: ndarray | None = None) -> Scorer
PARAMETER DESCRIPTION
scoring

TYPE: str | ScorerFuncType | None DEFAULT: None

test_size

TYPE: float | None DEFAULT: 0.2

stratify

TYPE: ndarray | None DEFAULT: None

Source code in siapy/optimizers/scorers.py 
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@classmethod
def init_hold_out_scorer(
    cls,
    scoring: str | ScorerFuncType | None = None,
    test_size: float | None = 0.2,
    stratify: np.ndarray | None = None,
) -> "Scorer":
    scorer = partial(
        hold_out_validation,
        scoring=scoring,
        test_size=test_size,
        random_state=0,
        shuffle=True,
        stratify=stratify,
    )
    return cls(scorer)