less expensive access pattern

python-package/lightgbm/basic.py

@@ -2850,8 +2850,9 @@ def get_label(self) -> Optional[_LGBM_LabelType]:
 
         Returns
         -------
-        label : numpy array or None
+        label : list, numpy 1-D array, pandas Series / one-column DataFrame or None
             The label information from the Dataset.
+            For a constructed ``Dataset``, this will only return a numpy array.
         """
         if self.label is None:
             self.label = self.get_field('label')
@@ -2862,8 +2863,9 @@ def get_weight(self) -> Optional[_LGBM_WeightType]:
 
         Returns
         -------
-        weight : numpy array or None
+        weight : list, numpy 1-D array, pandas Series or None
             Weight for each data point from the Dataset. Weights should be non-negative.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.weight is None:
             self.weight = self.get_field('weight')
@@ -2874,8 +2876,9 @@ def get_init_score(self) -> Optional[_LGBM_InitScoreType]:
 
         Returns
         -------
-        init_score : numpy array or None
+        init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None
             Init score of Booster.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.init_score is None:
             self.init_score = self.get_field('init_score')
@@ -2918,12 +2921,13 @@ def get_group(self) -> Optional[_LGBM_GroupType]:
 
         Returns
         -------
-        group : numpy array or None
+        group : list, numpy 1-D array, pandas Series or None
             Group/query data.
             Only used in the learning-to-rank task.
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.group is None:
             self.group = self.get_field('group')
@@ -2937,8 +2941,9 @@ def get_position(self) -> Optional[_LGBM_PositionType]:
 
         Returns
         -------
-        position : numpy 1-D array or None
+        position : numpy 1-D array, pandas Series or None
             Position of items used in unbiased learning-to-rank task.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.position is None:
             self.position = self.get_field('position')

python-package/lightgbm/sklearn.py

@@ -86,6 +86,26 @@
 _LGBM_ScikitValidSet = Tuple[_LGBM_ScikitMatrixLike, _LGBM_LabelType]
 
 
+def _get_label_from_constructed_dataset(dataset: Dataset) -> np.ndarray:
+    label = dataset.get_label()
+    error_msg = (
+        "Estimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. "
+        "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues."
+    )
+    assert isinstance(label, np.ndarray), error_msg
+    return label
+
+
+def _get_weight_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]:
+    weight = dataset.get_weight()
+    error_msg = (
+        "Estimators in lightgbm.sklearn should only retrieve weights from a constructed Dataset. "
+        "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues."
+    )
+    assert (weight is None or isinstance(weight, np.ndarray)), error_msg
+    return weight
+
+
 class _ObjectiveFunctionWrapper:
     """Proxy class for objective function."""
 
@@ -151,21 +171,25 @@ def __call__(self, preds: np.ndarray, dataset: Dataset) -> Tuple[np.ndarray, np.
             The value of the second order derivative (Hessian) of the loss
             with respect to the elements of preds for each sample point.
         """
-        labels = dataset.get_field("label")
+        labels = _get_label_from_constructed_dataset(dataset)
         argc = len(signature(self.func).parameters)
         if argc == 2:
             grad, hess = self.func(labels, preds)  # type: ignore[call-arg]
-        elif argc == 3:
-            grad, hess = self.func(labels, preds, dataset.get_field("weight"))  # type: ignore[call-arg]
-        elif argc == 4:
+            return grad, hess
+
+        weight = _get_weight_from_constructed_dataset(dataset)
+        if argc == 3:
+            grad, hess = self.func(labels, preds, weight)  # type: ignore[call-arg]
+            return grad, hess
+
+        if argc == 4:
             group = dataset.get_field("group")
             if group is not None:
-                return self.func(labels, preds, dataset.get_field("weight"), np.diff(group))  # type: ignore[call-arg]
+                return self.func(labels, preds, weight, np.diff(group))  # type: ignore[call-arg]
             else:
-                return self.func(labels, preds, dataset.get_field("weight"), group)  # type: ignore[call-arg]
-        else:
-            raise TypeError(f"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}")
-        return grad, hess
+                return self.func(labels, preds, weight, group)  # type: ignore[call-arg]
+
+        raise TypeError(f"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}")
 
 
 class _EvalFunctionWrapper:
@@ -233,20 +257,23 @@ def __call__(
         is_higher_better : bool
             Is eval result higher better, e.g. AUC is ``is_higher_better``.
         """
-        labels = dataset.get_field("label")
+        labels = _get_label_from_constructed_dataset(dataset)
         argc = len(signature(self.func).parameters)
         if argc == 2:
             return self.func(labels, preds)  # type: ignore[call-arg]
-        elif argc == 3:
-            return self.func(labels, preds, dataset.get_field("weight"))  # type: ignore[call-arg]
-        elif argc == 4:
+
+        weight = _get_weight_from_constructed_dataset(dataset)
+        if argc == 3:
+            return self.func(labels, preds, weight)  # type: ignore[call-arg]
+
+        if argc == 4:
             group = dataset.get_field("group")
             if group is not None:
-                return self.func(labels, preds, dataset.get_field("weight"), np.diff(group))  # type: ignore[call-arg]
+                return self.func(labels, preds, weight, np.diff(group))  # type: ignore[call-arg]
             else:
-                return self.func(labels, preds, dataset.get_field("weight"), group)  # type: ignore[call-arg]
-        else:
-            raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}")
+                return self.func(labels, preds, weight, group)  # type: ignore[call-arg]
+
+        raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}")
 
 
 # documentation templates for LGBMModel methods are shared between the classes in