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

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

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

27
tests/cpp/tree/test_tree_stat.cc
View File

@@ -1,7 +1,11 @@
/**
* Copyright 2020-2023 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/tree_model.h>
#include <xgboost/tree_updater.h>
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h"
namespace xgboost {
@@ -21,6 +25,9 @@ class UpdaterTreeStatTest : public ::testing::Test {
}
void RunTest(std::string updater) {
tree::TrainParam param;
param.Init(Args{});
Context ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
auto up = std::unique_ptr<TreeUpdater>{
@@ -29,7 +36,7 @@ class UpdaterTreeStatTest : public ::testing::Test {
RegTree tree;
tree.param.num_feature = kCols;
std::vector<HostDeviceVector<bst_node_t>> position(1);
up->Update(&gpairs_, p_dmat_.get(), position, {&tree});
up->Update(&param, &gpairs_, p_dmat_.get(), position, {&tree});
tree.WalkTree([&tree](bst_node_t nidx) {
if (tree[nidx].IsLeaf()) {
@@ -69,28 +76,33 @@ class UpdaterEtaTest : public ::testing::Test {
void RunTest(std::string updater) {
Context ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
float eta = 0.4;
auto up_0 = std::unique_ptr<TreeUpdater>{
TreeUpdater::Create(updater, &ctx, ObjInfo{ObjInfo::kClassification})};
up_0->Configure(Args{{"eta", std::to_string(eta)}});
up_0->Configure(Args{});
tree::TrainParam param0;
param0.Init(Args{{"eta", std::to_string(eta)}});
auto up_1 = std::unique_ptr<TreeUpdater>{
TreeUpdater::Create(updater, &ctx, ObjInfo{ObjInfo::kClassification})};
up_1->Configure(Args{{"eta", "1.0"}});
tree::TrainParam param1;
param1.Init(Args{{"eta", "1.0"}});
for (size_t iter = 0; iter < 4; ++iter) {
RegTree tree_0;
{
tree_0.param.num_feature = kCols;
std::vector<HostDeviceVector<bst_node_t>> position(1);
up_0->Update(&gpairs_, p_dmat_.get(), position, {&tree_0});
up_0->Update(&param0, &gpairs_, p_dmat_.get(), position, {&tree_0});
}
RegTree tree_1;
{
tree_1.param.num_feature = kCols;
std::vector<HostDeviceVector<bst_node_t>> position(1);
up_1->Update(&gpairs_, p_dmat_.get(), position, {&tree_1});
up_1->Update(&param1, &gpairs_, p_dmat_.get(), position, {&tree_1});
}
tree_0.WalkTree([&](bst_node_t nidx) {
if (tree_0[nidx].IsLeaf()) {
@@ -139,17 +151,18 @@ class TestMinSplitLoss : public ::testing::Test {
// test gamma
{"gamma", std::to_string(gamma)}};
tree::TrainParam param;
param.UpdateAllowUnknown(args);
Context ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
std::cout << ctx.gpu_id << std::endl;
auto up = std::unique_ptr<TreeUpdater>{
TreeUpdater::Create(updater, &ctx, ObjInfo{ObjInfo::kRegression})};
up->Configure(args);
up->Configure({});
RegTree tree;
std::vector<HostDeviceVector<bst_node_t>> position(1);
up->Update(&gpair_, dmat_.get(), position, {&tree});
up->Update(&param, &gpair_, dmat_.get(), position, {&tree});
auto n_nodes = tree.NumExtraNodes();
return n_nodes;