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
