Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Use matrix for gradient. (#9508)

Browse Source 
- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
This commit is contained in:
Jiaming Yuan
2023-08-24 05:29:52 +08:00
committed by GitHub
parent 6103dca0bb
commit 972730cde0
77 changed files with 1052 additions and 651 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
tests/cpp/tree/test_histmaker.cc
View File

@@ -26,9 +26,11 @@ TEST(GrowHistMaker, InteractionConstraint) {
auto constexpr kRows = 32;
auto constexpr kCols = 16;
auto p_dmat = GenerateDMatrix(kRows, kCols);
auto p_gradients = GenerateGradients(kRows);
Context ctx;
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
ObjInfo task{ObjInfo::kRegression};
{
// With constraints
@@ -40,7 +42,7 @@ TEST(GrowHistMaker, InteractionConstraint) {
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, p_gradients.get(), p_dmat.get(), position, {&tree});
updater->Update(&param, &gpair, p_dmat.get(), position, {&tree});
ASSERT_EQ(tree.NumExtraNodes(), 4);
ASSERT_EQ(tree[0].SplitIndex(), 1);
@@ -57,7 +59,7 @@ TEST(GrowHistMaker, InteractionConstraint) {
TrainParam param;
param.Init(Args{});
updater->Configure(Args{});
updater->Update(&param, p_gradients.get(), p_dmat.get(), position, {&tree});
updater->Update(&param, &gpair, p_dmat.get(), position, {&tree});
ASSERT_EQ(tree.NumExtraNodes(), 10);
ASSERT_EQ(tree[0].SplitIndex(), 1);
@@ -70,9 +72,12 @@ TEST(GrowHistMaker, InteractionConstraint) {
namespace {
void VerifyColumnSplit(int32_t rows, bst_feature_t cols, bool categorical,
RegTree const& expected_tree) {
auto p_dmat = GenerateDMatrix(rows, cols, categorical);
auto p_gradients = GenerateGradients(rows);
Context ctx;
auto p_dmat = GenerateDMatrix(rows, cols, categorical);
linalg::Matrix<GradientPair> gpair({rows}, ctx.Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(rows));
ObjInfo task{ObjInfo::kRegression};
std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
std::vector<HostDeviceVector<bst_node_t>> position(1);
@@ -84,7 +89,7 @@ void VerifyColumnSplit(int32_t rows, bst_feature_t cols, bool categorical,
TrainParam param;
param.Init(Args{});
updater->Configure(Args{});
updater->Update(&param, p_gradients.get(), sliced.get(), position, {&tree});
updater->Update(&param, &gpair, sliced.get(), position, {&tree});
Json json{Object{}};
tree.SaveModel(&json);
@@ -100,15 +105,16 @@ void TestColumnSplit(bool categorical) {
RegTree expected_tree{1u, kCols};
ObjInfo task{ObjInfo::kRegression};
{
auto p_dmat = GenerateDMatrix(kRows, kCols, categorical);
auto p_gradients = GenerateGradients(kRows);
Context ctx;
auto p_dmat = GenerateDMatrix(kRows, kCols, categorical);
linalg::Matrix<GradientPair> gpair({kRows}, ctx.Ordinal());
gpair.Data()->Copy(GenerateRandomGradients(kRows));
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, p_gradients.get(), p_dmat.get(), position, {&expected_tree});
updater->Update(&param, &gpair, p_dmat.get(), position, {&expected_tree});
}
auto constexpr kWorldSize = 2;