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Support learning rate for zero-hessian objectives. (#8866)

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This commit is contained in:
Jiaming Yuan
2023-03-06 20:33:28 +08:00
committed by GitHub
parent 173096a6a7
commit 228a46e8ad
34 changed files with 464 additions and 434 deletions
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32
tests/cpp/tree/test_histmaker.cc
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@@ -5,11 +5,10 @@
#include <xgboost/tree_model.h>
#include <xgboost/tree_updater.h>
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h"
namespace xgboost {
namespace tree {
namespace xgboost::tree {
std::shared_ptr<DMatrix> GenerateDMatrix(std::size_t rows, std::size_t cols){
return RandomDataGenerator{rows, cols, 0.6f}.Seed(3).GenerateDMatrix();
}
@@ -45,11 +44,11 @@ TEST(GrowHistMaker, InteractionConstraint)
std::unique_ptr<TreeUpdater> updater{
TreeUpdater::Create("grow_histmaker", &ctx, ObjInfo{ObjInfo::kRegression})};
updater->Configure(Args{
{"interaction_constraints", "[[0, 1]]"},
{"num_feature", std::to_string(kCols)}});
TrainParam param;
param.UpdateAllowUnknown(
Args{{"interaction_constraints", "[[0, 1]]"}, {"num_feature", std::to_string(kCols)}});
std::vector<HostDeviceVector<bst_node_t>> position(1);
updater->Update(p_gradients.get(), p_dmat.get(), position, {&tree});
updater->Update(&param, p_gradients.get(), p_dmat.get(), position, {&tree});
ASSERT_EQ(tree.NumExtraNodes(), 4);
ASSERT_EQ(tree[0].SplitIndex(), 1);
@@ -64,9 +63,10 @@ TEST(GrowHistMaker, InteractionConstraint)
std::unique_ptr<TreeUpdater> updater{
TreeUpdater::Create("grow_histmaker", &ctx, ObjInfo{ObjInfo::kRegression})};
updater->Configure(Args{{"num_feature", std::to_string(kCols)}});
std::vector<HostDeviceVector<bst_node_t>> position(1);
updater->Update(p_gradients.get(), p_dmat.get(), position, {&tree});
TrainParam param;
param.Init(Args{});
updater->Update(&param, p_gradients.get(), p_dmat.get(), position, {&tree});
ASSERT_EQ(tree.NumExtraNodes(), 10);
ASSERT_EQ(tree[0].SplitIndex(), 1);
@@ -83,7 +83,6 @@ void TestColumnSplit(int32_t rows, int32_t cols, RegTree const& expected_tree) {
Context ctx;
std::unique_ptr<TreeUpdater> updater{
TreeUpdater::Create("grow_histmaker", &ctx, ObjInfo{ObjInfo::kRegression})};
updater->Configure(Args{{"num_feature", std::to_string(cols)}});
std::vector<HostDeviceVector<bst_node_t>> position(1);
std::unique_ptr<DMatrix> sliced{
@@ -91,7 +90,9 @@ void TestColumnSplit(int32_t rows, int32_t cols, RegTree const& expected_tree) {
RegTree tree;
tree.param.num_feature = cols;
updater->Update(p_gradients.get(), sliced.get(), position, {&tree});
TrainParam param;
param.Init(Args{});
updater->Update(&param, p_gradients.get(), sliced.get(), position, {&tree});
EXPECT_EQ(tree.NumExtraNodes(), 10);
EXPECT_EQ(tree[0].SplitIndex(), 1);
@@ -115,14 +116,13 @@ TEST(GrowHistMaker, ColumnSplit) {
Context ctx;
std::unique_ptr<TreeUpdater> updater{
TreeUpdater::Create("grow_histmaker", &ctx, ObjInfo{ObjInfo::kRegression})};
updater->Configure(Args{{"num_feature", std::to_string(kCols)}});
std::vector<HostDeviceVector<bst_node_t>> position(1);
updater->Update(p_gradients.get(), p_dmat.get(), position, {&expected_tree});
TrainParam param;
param.Init(Args{});
updater->Update(&param, p_gradients.get(), p_dmat.get(), position, {&expected_tree});
}
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, TestColumnSplit, kRows, kCols, std::cref(expected_tree));
}
} // namespace tree
} // namespace xgboost
} // namespace xgboost::tree