fdf533f2b9
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction. For l1 error, the optimal value is the median (50 percentile). This is marked as experimental support for the following reasons: - The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors. - Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
72 lines
2.1 KiB
C++
72 lines
2.1 KiB
C++
#include <gtest/gtest.h>
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#include <xgboost/tree_model.h>
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#include <xgboost/tree_updater.h>
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#include "../helpers.h"
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namespace xgboost {
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namespace tree {
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TEST(GrowHistMaker, InteractionConstraint) {
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size_t constexpr kRows = 32;
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size_t constexpr kCols = 16;
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GenericParameter param;
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param.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
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auto p_dmat = RandomDataGenerator{kRows, kCols, 0.6f}.Seed(3).GenerateDMatrix();
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HostDeviceVector<GradientPair> gradients (kRows);
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std::vector<GradientPair>& h_gradients = gradients.HostVector();
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xgboost::SimpleLCG gen;
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xgboost::SimpleRealUniformDistribution<bst_float> dist(0.0f, 1.0f);
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for (size_t i = 0; i < kRows; ++i) {
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bst_float grad = dist(&gen);
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bst_float hess = dist(&gen);
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h_gradients[i] = GradientPair(grad, hess);
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}
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{
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// With constraints
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RegTree tree;
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tree.param.num_feature = kCols;
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std::unique_ptr<TreeUpdater> updater{
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TreeUpdater::Create("grow_histmaker", ¶m, ObjInfo{ObjInfo::kRegression})};
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updater->Configure(Args{
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{"interaction_constraints", "[[0, 1]]"},
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{"num_feature", std::to_string(kCols)}});
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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updater->Update(&gradients, p_dmat.get(), position, {&tree});
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ASSERT_EQ(tree.NumExtraNodes(), 4);
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ASSERT_EQ(tree[0].SplitIndex(), 1);
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ASSERT_EQ(tree[tree[0].LeftChild()].SplitIndex(), 0);
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ASSERT_EQ(tree[tree[0].RightChild()].SplitIndex(), 0);
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}
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{
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// Without constraints
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RegTree tree;
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tree.param.num_feature = kCols;
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std::unique_ptr<TreeUpdater> updater{
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TreeUpdater::Create("grow_histmaker", ¶m, ObjInfo{ObjInfo::kRegression})};
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updater->Configure(Args{{"num_feature", std::to_string(kCols)}});
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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updater->Update(&gradients, p_dmat.get(), position, {&tree});
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ASSERT_EQ(tree.NumExtraNodes(), 10);
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ASSERT_EQ(tree[0].SplitIndex(), 1);
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ASSERT_NE(tree[tree[0].LeftChild()].SplitIndex(), 0);
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ASSERT_NE(tree[tree[0].RightChild()].SplitIndex(), 0);
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}
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}
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} // namespace tree
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} // namespace xgboost
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