Refactor RF objectives by objective family

cpp/CMakeLists.txt

@@ -352,18 +352,10 @@ if(BUILD_CUML_CPP_LIBRARY)
   if(all_algo OR decisiontree_algo)
     target_sources(
       cuml_objs
-      PRIVATE src/decisiontree/batched-levelalgo/kernels/entropy-double.cu
-              src/decisiontree/batched-levelalgo/kernels/entropy-float.cu
-              src/decisiontree/batched-levelalgo/kernels/gamma-double.cu
-              src/decisiontree/batched-levelalgo/kernels/gamma-float.cu
-              src/decisiontree/batched-levelalgo/kernels/gini-double.cu
-              src/decisiontree/batched-levelalgo/kernels/gini-float.cu
-              src/decisiontree/batched-levelalgo/kernels/inverse_gaussian-double.cu
-              src/decisiontree/batched-levelalgo/kernels/inverse_gaussian-float.cu
-              src/decisiontree/batched-levelalgo/kernels/mse-double.cu
-              src/decisiontree/batched-levelalgo/kernels/mse-float.cu
-              src/decisiontree/batched-levelalgo/kernels/poisson-double.cu
-              src/decisiontree/batched-levelalgo/kernels/poisson-float.cu
+      PRIVATE src/decisiontree/batched-levelalgo/kernels/classification-double.cu
+              src/decisiontree/batched-levelalgo/kernels/classification-float.cu
+              src/decisiontree/batched-levelalgo/kernels/regression-double.cu
+              src/decisiontree/batched-levelalgo/kernels/regression-float.cu
               src/decisiontree/decisiontree.cu
     )
   endif()

cpp/src/decisiontree/batched-levelalgo/builder.cuh

@@ -519,7 +519,7 @@ struct Builder {
     int len_histograms = n_bins * n_classes * n_blocks_dimy * n_large_nodes;
     RAFT_CUDA_TRY(cudaMemsetAsync(histograms, 0, sizeof(BinT) * len_histograms, builder_stream));
     // create the objective function object
-    ObjectiveT objective(dataset.num_outputs, params.min_samples_leaf);
+    ObjectiveT objective(dataset.num_outputs, params.min_samples_leaf, params.split_criterion);
     // call the computeSplitKernel
     raft::common::nvtx::range kernel_scope("computeSplitKernel @builder.cuh [batched-levelalgo]");
     launchComputeSplitKernel<DataT, LabelT, IdxT, TPB_DEFAULT>(histograms,
@@ -556,7 +556,7 @@ struct Builder {
     rmm::device_uvector<InstanceRange> d_instance_ranges(max_batch_size, builder_stream);
     rmm::device_uvector<DataT> d_leaves(max_batch_size * dataset.num_outputs, builder_stream);
 
-    ObjectiveT objective(dataset.num_outputs, params.min_samples_leaf);
+    ObjectiveT objective(dataset.num_outputs, params.min_samples_leaf, params.split_criterion);
     for (std::size_t batch_begin = 0; batch_begin < tree->sparsetree.size();
          batch_begin += max_batch_size) {
       std::size_t batch_end  = min(batch_begin + max_batch_size, tree->sparsetree.size());

cpp/src/decisiontree/batched-levelalgo/kernels/entropy-double.cu → cpp/src/decisiontree/batched-levelalgo/kernels/classification-double.cu

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -13,7 +13,7 @@ namespace DT {
 using _DataT      = double;
 using _LabelT     = int;
 using _IdxT       = int;
-using _ObjectiveT = EntropyObjectiveFunction<_DataT, _LabelT, _IdxT>;
+using _ObjectiveT = ClassificationObjectiveFunction<_DataT, _LabelT, _IdxT>;
 using _BinT       = CountBin;
 using _DatasetT   = Dataset<_DataT, _LabelT, _IdxT>;
 using _NodeT      = SparseTreeNode<_DataT, _LabelT, _IdxT>;

cpp/src/decisiontree/batched-levelalgo/kernels/entropy-float.cu → cpp/src/decisiontree/batched-levelalgo/kernels/classification-float.cu

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -13,7 +13,7 @@ namespace DT {
 using _DataT      = float;
 using _LabelT     = int;
 using _IdxT       = int;
-using _ObjectiveT = EntropyObjectiveFunction<_DataT, _LabelT, _IdxT>;
+using _ObjectiveT = ClassificationObjectiveFunction<_DataT, _LabelT, _IdxT>;
 using _BinT       = CountBin;
 using _DatasetT   = Dataset<_DataT, _LabelT, _IdxT>;
 using _NodeT      = SparseTreeNode<_DataT, _LabelT, _IdxT>;

cpp/src/decisiontree/batched-levelalgo/kernels/mse-double.cu → cpp/src/decisiontree/batched-levelalgo/kernels/regression-double.cu

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -13,7 +13,7 @@ namespace DT {
 using _DataT      = double;
 using _LabelT     = double;
 using _IdxT       = int;
-using _ObjectiveT = MSEObjectiveFunction<_DataT, _LabelT, _IdxT>;
+using _ObjectiveT = RegressionObjectiveFunction<_DataT, _LabelT, _IdxT>;
 using _BinT       = AggregateBin;
 using _DatasetT   = Dataset<_DataT, _LabelT, _IdxT>;
 using _NodeT      = SparseTreeNode<_DataT, _LabelT, _IdxT>;

cpp/src/decisiontree/batched-levelalgo/kernels/mse-float.cu → cpp/src/decisiontree/batched-levelalgo/kernels/regression-float.cu

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2019-2024, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -13,7 +13,7 @@ namespace DT {
 using _DataT      = float;
 using _LabelT     = float;
 using _IdxT       = int;
-using _ObjectiveT = MSEObjectiveFunction<_DataT, _LabelT, _IdxT>;
+using _ObjectiveT = RegressionObjectiveFunction<_DataT, _LabelT, _IdxT>;
 using _BinT       = AggregateBin;
 using _DatasetT   = Dataset<_DataT, _LabelT, _IdxT>;
 using _NodeT      = SparseTreeNode<_DataT, _LabelT, _IdxT>;