def __init__(self, globals_dict: dict) -> None: super().__init__( id="kappa", name="Kappa", score_func=metrics.cohen_kappa_score, scorer=metrics.make_scorer(metrics.cohen_kappa_score), )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="mcc", name="MCC", score_func=metrics.matthews_corrcoef, scorer=metrics.make_scorer(metrics.matthews_corrcoef), )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="f1", name="F1", score_func=metrics.f1_score, scorer=metrics.make_scorer(metrics.f1_score, average="weighted"), args={"average": "weighted"}, )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="recall", name="Recall", score_func=metrics.recall_score, scorer=metrics.make_scorer(metrics.recall_score, average="macro"), args={"average": "macro"}, )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="precision", name="Precision", display_name="Prec.", score_func=metrics.precision_score, scorer=metrics.make_scorer(metrics.precision_score, average="weighted"), args={"average": "weighted"}, )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="f1", name="F1", score_func=pycaret.internal.metrics.binary_multiclass_score_func( metrics.f1_score), scorer=metrics.make_scorer( pycaret.internal.metrics.binary_multiclass_score_func( metrics.f1_score), average="weighted", ), args={"average": "weighted"}, )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="recall", name="Recall", score_func=pycaret.internal.metrics.binary_multiclass_score_func( metrics.recall_score), scorer=metrics.make_scorer( pycaret.internal.metrics.binary_multiclass_score_func( metrics.recall_score), average="macro", ), args={"average": "macro"}, )
def __init__(self, globals_dict: dict) -> None: super().__init__( id="precision", name="Precision", display_name="Prec.", score_func=pycaret.internal.metrics.binary_multiclass_score_func( metrics.precision_score), scorer=metrics.make_scorer( pycaret.internal.metrics.binary_multiclass_score_func( metrics.precision_score), average="weighted", ), args={"average": "weighted"}, )
def __init__( self, id: str, name: str, score_func: type, scorer: Optional[Union[str, _BaseScorer]] = None, target: str = "pred", args: Dict[str, Any] = None, display_name: Optional[str] = None, greater_is_better: bool = True, is_multiclass: bool = True, is_custom: bool = False, ) -> None: if not args: args = {} if not isinstance(args, dict): raise TypeError("args needs to be a dictionary.") allowed_targets = ["pred", "pred_proba", "threshold"] if not target in allowed_targets: raise ValueError(f"Target must be one of {', '.join(allowed_targets)}.") self.id = id self.name = name self.score_func = score_func self.target = target self.scorer = ( scorer if scorer else metrics.make_scorer( score_func, needs_proba=target == "pred_proba", needs_threshold=target == "threshold", greater_is_better=greater_is_better, **args, ) ) self.display_name = display_name if display_name else name self.args = args self.greater_is_better = greater_is_better self.is_multiclass = is_multiclass self.is_custom = is_custom