[python-package] add Booster.set_leaf_output method (#5712)

python-package/lightgbm/basic.py

@@ -4043,6 +4043,38 @@ class Booster:
             ctypes.byref(ret)))
         return ret.value
 
+    def set_leaf_output(
+        self,
+        tree_id: int,
+        leaf_id: int,
+        value: float,
+    ) -> 'Booster':
+        """Set the output of a leaf.
+
+        Parameters
+        ----------
+        tree_id : int
+            The index of the tree.
+        leaf_id : int
+            The index of the leaf in the tree.
+        value : float
+            Value to set as the output of the leaf.
+
+        Returns
+        -------
+        self : Booster
+            Booster with the leaf output set.
+        """
+        _safe_call(
+            _LIB.LGBM_BoosterSetLeafValue(
+                self.handle,
+                ctypes.c_int(tree_id),
+                ctypes.c_int(leaf_id),
+                ctypes.c_double(value)
+            )
+        )
+        return self
+
     def _to_predictor(
         self,
         pred_parameter: Optional[Dict[str, Any]] = None

tests/python_package_test/test_basic.py

@@ -793,3 +793,15 @@ def test_feature_num_bin_with_max_bin_by_feature():
     ds = lgb.Dataset(X, params={'max_bin_by_feature': max_bin_by_feature}).construct()
     actual_num_bins = [ds.feature_num_bin(i) for i in range(X.shape[1])]
     np.testing.assert_equal(actual_num_bins, max_bin_by_feature)
+
+
+def test_set_leaf_output():
+    X, y = load_breast_cancer(return_X_y=True)
+    ds = lgb.Dataset(X, y)
+    bst = lgb.Booster({'num_leaves': 2}, ds)
+    bst.update()
+    y_pred = bst.predict(X)
+    for leaf_id in range(2):
+        leaf_output = bst.get_leaf_output(tree_id=0, leaf_id=leaf_id)
+        bst.set_leaf_output(tree_id=0, leaf_id=leaf_id, value=leaf_output + 1)
+    np.testing.assert_allclose(bst.predict(X), y_pred + 1)