Add validation checks

include/LightGBM/boosting.h

@@ -79,7 +79,7 @@ class LIGHTGBM_EXPORT Boosting {
   /*!
   * \brief Change the leaf values of a tree and update the scores
   */
-  virtual void RefitTreeManual(int tree_idx, const double *vals) = 0;
+  virtual void RefitTreeManual(int tree_idx, const double *vals, const int vals_size) = 0;
 
   /*!
   * \brief Training logic

include/LightGBM/c_api.h

@@ -782,7 +782,8 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle,
  */
 LIGHTGBM_C_EXPORT int LGBM_BoosterRefitTreeManual(BoosterHandle handle,
                                                   int32_t tree_idx,
-                                                  const double *values);
+                                                  const double *vals,
+                                                  const int vals_size);
 
 /*!
  * \brief Update the model by specifying gradient and Hessian directly

python-package/lightgbm/basic.py

@@ -4936,14 +4936,12 @@ def refit_tree_manual(
         """
         values = _list_to_1d_numpy(values, dtype=np.float64, name="leaf_values")
 
-        if len(values) != self.num_leaves(tree_id):
-            raise ValueError("Length of values should be equal to the number of leaves in the tree")
-
         _safe_call(
             _LIB.LGBM_BoosterRefitTreeManual(
                 self._handle,
                 ctypes.c_int(tree_id),
-                values.ctypes.data_as(ctypes.POINTER(ctypes.c_double))
+                values.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
+                ctypes.c_int(len(values)),
             )
         )
         self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]

src/boosting/gbdt.cpp

@@ -289,8 +289,8 @@ void GBDT::RefitTree(const int* tree_leaf_prediction, const size_t nrow, const s
   }
 }
 
-void GBDT::RefitTreeManual(int tree_idx, const double *vals) {
-  CHECK(tree_idx >= 0 && static_cast<size_t>(tree_idx) < models_.size());
+void GBDT::RefitTreeManual(int tree_idx, const double *vals, const int vals_size) {
+  CHECK(tree_idx >= 0 && static_cast<size_t>(tree_idx) < models_.size() && vals_size == models_[tree_idx]->num_leaves());
   // reset score
   for (int leaf_id = 0; leaf_id < models_[tree_idx]->num_leaves(); ++leaf_id) {
     models_[tree_idx]->SetLeafOutput(leaf_id, vals[leaf_id] - models_[tree_idx]->LeafOutput(leaf_id));

src/boosting/gbdt.h

@@ -145,7 +145,7 @@ class GBDT : public GBDTBase {
 
   void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) override;
 
-  void RefitTreeManual(int tree_idx, const double *vals) override;
+  void RefitTreeManual(int tree_idx, const double *vals, const int vals_size) override;
 
   /*!
   * \brief Training logic

src/c_api.cpp

@@ -412,9 +412,9 @@ class Booster {
     boosting_->RefitTree(leaf_preds, nrow, ncol);
   }
 
-  void RefitTreeManual(int tree_idx, const double *vals) {
+  void RefitTreeManual(int tree_idx, const double *vals, const int vals_size) {
     UNIQUE_LOCK(mutex_)
-    boosting_->RefitTreeManual(tree_idx, vals);
+    boosting_->RefitTreeManual(tree_idx, vals, vals_size);
   }
 
   bool TrainOneIter(const score_t* gradients, const score_t* hessians) {
@@ -2065,10 +2065,11 @@ int LGBM_BoosterRefit(BoosterHandle handle, const int32_t* leaf_preds, int32_t n
 
 int LGBM_BoosterRefitTreeManual(BoosterHandle handle,
                                 int tree_idx,
-                                const double *val) {
+                                const double *vals,
+                                const int vals_size) {
   API_BEGIN();
   Booster* ref_booster = reinterpret_cast<Booster*>(handle);
-  ref_booster->RefitTreeManual(tree_idx, val);
+  ref_booster->RefitTreeManual(tree_idx, vals, vals_size);
   API_END();
 }