[Feature] add set params function (#3)

imlightgbm/engine.py

@@ -1,13 +1,12 @@
 from collections.abc import Iterable
-from copy import deepcopy
 from typing import Any, Callable, Literal
 
 import lightgbm as lgb
 import numpy as np
 from sklearn.model_selection import BaseCrossValidator
 
-from imlightgbm.objective import set_fobj_feval
-from imlightgbm.utils import docstring, logger
+from imlightgbm.objective import set_params
+from imlightgbm.utils import docstring
 
 
 @docstring(lgb.train.__doc__)
@@ -23,17 +22,7 @@ def train(
     keep_training_booster: bool = False,
     callbacks: list[Callable] | None = None,
 ) -> lgb.Booster:
-    _params = deepcopy(params)
-    if "objective" in _params:
-        logger.warning("'objective' exists in params will not used.")
-        del _params["objective"]
-
-    _alpha = _params.pop("alpha", 0.05)
-    _gamma = _params.pop("gamma", 0.01)
-
-    fobj, feval = set_fobj_feval(train_set=train_set, alpha=_alpha, gamma=_gamma)
-    _params.update({"objective": fobj})
-
+    _params, feval = set_params(params=params, train_set=train_set)
     return lgb.train(
         params=_params,
         train_set=train_set,
@@ -72,16 +61,7 @@ def cv(
     eval_train_metric: bool = False,
     return_cvbooster: bool = False,
 ) -> dict[str, list[float] | lgb.CVBooster]:
-    _params = deepcopy(params)
-    if "objective" in _params:
-        logger.warning("'objective' exists in params will not used.")
-        del _params["objective"]
-
-    _alpha = _params.pop("alpha", 0.05)
-    _gamma = _params.pop("gamma", 0.01)
-
-    fobj, feval = set_fobj_feval(train_set=train_set, alpha=_alpha, gamma=_gamma)
-    _params.update({"objective": fobj})
+    _params, feval = set_params(params=params, train_set=train_set)
     return lgb.cv(
         params=_params,
         train_set=train_set,
@@ -91,7 +71,7 @@ def cv(
         stratified=stratified,
         shuffle=shuffle,
         metrics=metrics,
-        feavl=feval,
+        feval=feval,
         init_model=init_model,
         feature_name=feature_name,
         categorical_feature=categorical_feature,

imlightgbm/objective.py

@@ -1,38 +1,44 @@
+from copy import deepcopy
 from functools import partial
-from typing import Callable
+from typing import Any, Callable
 
 import numpy as np
 from lightgbm import Dataset
 from sklearn.utils.multiclass import type_of_target
 
+from imlightgbm.utils import logger
+
 EvalLike = Callable[[np.ndarray, Dataset], tuple[str, float, bool]]
 ObjLike = Callable[[np.ndarray, Dataset], tuple[np.ndarray, np.ndarray]]
+ALPHA_DEFAULT: float = 0.05
+GAMMA_DEFAULT: float = 0.05
+OBJECTIVE_STR: str = "objective"
 
 
 def binary_focal_eval(
     pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float
-):
+) -> tuple[str, float, bool]:
     is_higher_better = False
     return "binary_focal", ..., is_higher_better
 
 
 def binary_focal_objective(
     pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float
-):
+) -> tuple[np.ndarray, np.ndarray]:
     # TODO
     return ...
 
 
 def multiclass_focal_eval(
     pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float
-):
+) -> tuple[str, float, bool]:
     # TODO
     return
 
 
 def multiclass_focal_objective(
     pred: np.ndarray, train_data: Dataset, alpha: float, gamma: float
-):
+) -> tuple[np.ndarray, np.ndarray]:
     # TODO
     return
 
@@ -57,3 +63,19 @@ def set_fobj_feval(
     feval = eval_mapper[inferred_task]
 
     return fobj, feval
+
+
+def set_params(
+    params: dict[str, Any], train_set: Dataset
+) -> tuple[dict[str, Any], EvalLike]:
+    _params = deepcopy(params)
+    if OBJECTIVE_STR in _params:
+        logger.warning(f"'{OBJECTIVE_STR}' exists in params will not used.")
+        del _params[OBJECTIVE_STR]
+
+    _alpha = _params.pop("alpha", ALPHA_DEFAULT)
+    _gamma = _params.pop("gamma", GAMMA_DEFAULT)
+
+    fobj, feval = set_fobj_feval(train_set=train_set, alpha=_alpha, gamma=_gamma)
+    _params.update({OBJECTIVE_STR: fobj})
+    return _params, feval