[Feature] add custom booster (#20)

imlightgbm/engine.py

@@ -3,12 +3,52 @@
 
 import lightgbm as lgb
 import numpy as np
+from scipy.sparse import spmatrix
+from scipy.special import expit
 from sklearn.model_selection import BaseCrossValidator
 
 from imlightgbm.docstring import add_docstring
 from imlightgbm.objective.engine import set_params
 
 
+class ImbalancedBooster(lgb.Booster):
+    def predict(
+        self,
+        data: lgb.basic._LGBM_PredictDataType,
+        start_iteration: int = 0,
+        num_iteration: int | None = None,
+        raw_score: bool = False,
+        pred_leaf: bool = False,
+        pred_contrib: bool = False,
+        data_has_header: bool = False,
+        validate_features: bool = False,
+        **kwargs: Any,
+    ) -> np.ndarray | spmatrix | list[spmatrix]:
+        _predict = super().predict(
+            data=data,
+            start_iteration=start_iteration,
+            num_iteration=num_iteration,
+            raw_score=raw_score,
+            pred_leaf=pred_leaf,
+            pred_contrib=pred_contrib,
+            data_has_header=data_has_header,
+            validate_features=validate_features,
+            **kwargs,
+        )
+        if (
+            raw_score
+            or pred_leaf
+            or pred_contrib
+            or isinstance(_predict, spmatrix | list[spmatrix])
+        ):
+            return _predict
+
+        if len(_predict.shape) == 1:
+            return expit(_predict)
+
+        return _predict  # TODO: multiclass
+
+
 @add_docstring("train")
 def train(
     params: dict[str, Any],
@@ -19,9 +59,9 @@ def train(
     init_model: str | lgb.Path | lgb.Booster | None = None,
     keep_training_booster: bool = False,
     callbacks: list[Callable] | None = None,
-) -> lgb.Booster:
+) -> ImbalancedBooster:
     _params = set_params(params=params, train_set=train_set)
-    return lgb.train(
+    _booster = lgb.train(
         params=_params,
         train_set=train_set,
         num_boost_round=num_boost_round,
@@ -31,6 +71,8 @@ def train(
         keep_training_booster=keep_training_booster,
         callbacks=callbacks,
     )
+    _booster_str = _booster.model_to_string()
+    return ImbalancedBooster(model_str=_booster_str)
 
 
 @add_docstring("cv")

imlightgbm/sklearn.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 from lightgbm.sklearn import LGBMClassifier, _LGBM_ScikitMatrixLike
+from scipy.sparse import spmatrix
 from scipy.special import expit
 
 from imlightgbm.base import ALPHA_DEFAULT, GAMMA_DEFAULT, Objective
@@ -99,7 +100,7 @@ def predict(
         pred_contrib: bool = False,
         validate_features: bool = False,
         **kwargs: Any,
-    ):
+    ) -> np.ndarray | spmatrix | list[spmatrix]:
         """Docstring is inherited from the LGBMClassifier."""
         result = super().predict(
             X=X,