Merge approx tests. (dmlc#10583)

tests/cpp/tree/test_approx.cc

@@ -4,10 +4,12 @@
 #include <gtest/gtest.h>
 
 #include "../../../src/tree/common_row_partitioner.h"
+#include "../../../src/tree/param.h"    // for TrainParam
 #include "../collective/test_worker.h"  // for TestDistributedGlobal
 #include "../helpers.h"
 #include "test_column_split.h"  // for TestColumnSplit
 #include "test_partitioner.h"
+#include "xgboost/tree_model.h"  // for RegTree
 
 namespace xgboost::tree {
 namespace {
@@ -76,6 +78,53 @@ TEST(Approx, Partitioner) {
   }
 }
 
+TEST(Approx, InteractionConstraint) {
+  auto constexpr kRows = 32;
+  auto constexpr kCols = 16;
+  auto p_dmat = GenerateCatDMatrix(kRows, kCols, 0.6f, false);
+  Context ctx;
+
+  linalg::Matrix<GradientPair> gpair({kRows}, ctx.Device());
+  gpair.Data()->Copy(GenerateRandomGradients(kRows));
+
+  ObjInfo task{ObjInfo::kRegression};
+  {
+    // With constraints
+    RegTree tree{1, kCols};
+
+    std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
+    TrainParam param;
+    param.UpdateAllowUnknown(
+        Args{{"interaction_constraints", "[[0, 1]]"}, {"num_feature", std::to_string(kCols)}});
+    std::vector<HostDeviceVector<bst_node_t>> position(1);
+    updater->Configure(Args{});
+    updater->Update(&param, &gpair, p_dmat.get(), position, {&tree});
+
+    ASSERT_EQ(tree.NumExtraNodes(), 4);
+    ASSERT_EQ(tree[0].SplitIndex(), 1);
+
+    ASSERT_EQ(tree[tree[0].LeftChild()].SplitIndex(), 0);
+    ASSERT_EQ(tree[tree[0].RightChild()].SplitIndex(), 0);
+  }
+  {
+    // Without constraints
+    RegTree tree{1u, kCols};
+
+    std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
+    std::vector<HostDeviceVector<bst_node_t>> position(1);
+    TrainParam param;
+    param.Init(Args{});
+    updater->Configure(Args{});
+    updater->Update(&param, &gpair, p_dmat.get(), position, {&tree});
+
+    ASSERT_EQ(tree.NumExtraNodes(), 10);
+    ASSERT_EQ(tree[0].SplitIndex(), 1);
+
+    ASSERT_NE(tree[tree[0].LeftChild()].SplitIndex(), 0);
+    ASSERT_NE(tree[tree[0].RightChild()].SplitIndex(), 0);
+  }
+}
+
 namespace {
 void TestColumnSplitPartitioner(size_t n_samples, size_t base_rowid, std::shared_ptr<DMatrix> Xy,
                                 std::vector<float>* hess, float min_value, float mid_value,

tests/cpp/tree/test_column_split.h

@@ -23,9 +23,13 @@ inline std::shared_ptr<DMatrix> GenerateCatDMatrix(std::size_t rows, std::size_t
     for (size_t i = 0; i < ft.size(); ++i) {
       ft[i] = (i % 3 == 0) ? FeatureType::kNumerical : FeatureType::kCategorical;
     }
-    return RandomDataGenerator(rows, cols, 0.6f).Seed(3).Type(ft).MaxCategory(17).GenerateDMatrix();
+    return RandomDataGenerator(rows, cols, sparsity)
+        .Seed(3)
+        .Type(ft)
+        .MaxCategory(17)
+        .GenerateDMatrix();
   } else {
-    return RandomDataGenerator{rows, cols, 0.6f}.Seed(3).GenerateDMatrix();
+    return RandomDataGenerator{rows, cols, sparsity}.Seed(3).GenerateDMatrix();
   }
 }