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/**
* Copyright 2023 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/base.h> // for Args, bst_target_t
#include <xgboost/data.h> // for DMatrix, MetaInfo
#include <xgboost/json.h> // for Json, get, Object, String
#include <xgboost/learner.h> // for Learner
#include <algorithm> // for copy
#include <cstddef> // for size_t
#include <memory> // for shared_ptr, allocator, __shared_ptr_access
#include <numeric> // for accumulate
#include <string> // for stod, string
#include <vector> // for vector
#include "../../src/common/linalg_op.h" // for begin, cbegin, cend
#include "../../src/common/stats.h" // for Median
#include "../../src/common/transform_iterator.h" // for IndexTransformIter
#include "helpers.h" // for RandomDataGenerator
#include "xgboost/host_device_vector.h" // for HostDeviceVector
#include "xgboost/linalg.h" // for Tensor, All, TensorView, Vector
namespace xgboost {
class TestL1MultiTarget : public ::testing::Test {
std::shared_ptr<DMatrix> Xy_;
std::shared_ptr<DMatrix> Xyw_;
std::vector<std::shared_ptr<DMatrix>> single_;
std::vector<std::shared_ptr<DMatrix>> single_w_;
public:
void SetUp() override {
std::size_t constexpr kRows{256}, kCols{5}, kTargets{3};
auto make_fmat = [&](bool weighted) {
if (weighted) {
auto p_fmat =
RandomDataGenerator{kRows, kCols, 0.5f}.Targets(kTargets).GenerateDMatrix(true);
p_fmat->Info().weights_.Resize(kRows);
RandomDataGenerator{kRows, 1, 0.0f}.GenerateDense(&p_fmat->Info().weights_);
return p_fmat;
} else {
return RandomDataGenerator{kRows, kCols, 0.5f}.Targets(kTargets).GenerateDMatrix(true);
}
};
Xy_ = make_fmat(false);
Xyw_ = make_fmat(true);
ASSERT_EQ(Xy_->Info().labels.Shape(1), kTargets);
ASSERT_EQ(Xyw_->Info().labels.Shape(1), kTargets);
single_.clear();
single_w_.clear();
for (bst_target_t t{0}; t < kTargets; ++t) {
{
single_.emplace_back(make_fmat(false));
single_[t]->Info().labels.Reshape(kRows, 1);
auto h_labels = single_[t]->Info().labels.HostView();
auto in_labels = Xy_->Info().labels.HostView().Slice(linalg::All(), t);
std::copy(linalg::cbegin(in_labels), linalg::cend(in_labels), linalg::begin(h_labels));
}
{
single_w_.emplace_back(make_fmat(true));
single_w_[t]->Info().labels.Reshape(kRows, 1);
auto h_labels = single_w_[t]->Info().labels.HostView();
auto in_labels = Xyw_->Info().labels.HostView().Slice(linalg::All(), t);
std::copy(linalg::cbegin(in_labels), linalg::cend(in_labels), linalg::begin(h_labels));
}
}
}
void RunTest(Context const* ctx, std::string const& tree_method, bool weight) {
auto p_fmat = weight ? Xyw_ : Xy_;
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParams(Args{{"tree_method", tree_method},
{"objective", "reg:absoluteerror"},
{"device", ctx->DeviceName()}});
learner->Configure();
for (auto i = 0; i < 4; ++i) {
learner->UpdateOneIter(i, p_fmat);
}
ASSERT_EQ(learner->Groups(), 3);
Json config{Object{}};
learner->SaveConfig(&config);
auto base_score =
std::stod(get<String const>(config["learner"]["learner_model_param"]["base_score"]));
std::vector<float> base_scores;
for (bst_target_t t{0}; t < p_fmat->Info().labels.Shape(1); ++t) {
auto t_Xy = weight ? single_w_[t] : single_[t];
std::unique_ptr<Learner> sl{Learner::Create({t_Xy})};
sl->SetParams(Args{{"tree_method", tree_method},
{"objective", "reg:absoluteerror"},
{"device", ctx->DeviceName()}});
sl->Configure();
sl->UpdateOneIter(0, t_Xy);
Json s_config{Object{}};
sl->SaveConfig(&s_config);
auto s_base_score =
std::stod(get<String const>(s_config["learner"]["learner_model_param"]["base_score"]));
linalg::Vector<float> out;
common::Median(sl->Ctx(), t_Xy->Info().labels, t_Xy->Info().weights_, &out);
ASSERT_FLOAT_EQ(s_base_score, out(0));
base_scores.push_back(s_base_score);
}
auto mean = std::accumulate(base_scores.cbegin(), base_scores.cend(), .0f) /
static_cast<float>(base_scores.size());
ASSERT_FLOAT_EQ(mean, base_score);
}
void RunTest(Context const* ctx, std::string const& tree_method) {
this->RunTest(ctx, tree_method, false);
this->RunTest(ctx, tree_method, true);
}
};
TEST_F(TestL1MultiTarget, Hist) {
Context ctx;
this->RunTest(&ctx, "hist");
}
TEST_F(TestL1MultiTarget, Exact) {
Context ctx;
this->RunTest(&ctx, "exact");
}
TEST_F(TestL1MultiTarget, Approx) {
Context ctx;
this->RunTest(&ctx, "approx");
}
#if defined(XGBOOST_USE_CUDA)
TEST_F(TestL1MultiTarget, GpuHist) {
auto ctx = MakeCUDACtx(0);
this->RunTest(&ctx, "hist");
}
#endif // defined(XGBOOST_USE_CUDA)
TEST(MultiStrategy, Configure) {
auto p_fmat = RandomDataGenerator{12ul, 3ul, 0.0}.GenerateDMatrix();
p_fmat->Info().labels.Reshape(p_fmat->Info().num_row_, 2);
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParams(Args{{"multi_strategy", "multi_output_tree"}, {"num_target", "2"}});
learner->Configure();
ASSERT_EQ(learner->Groups(), 2);
learner->SetParams(Args{{"multi_strategy", "multi_output_tree"}, {"num_target", "0"}});
ASSERT_THROW({ learner->Configure(); }, dmlc::Error);
}
} // namespace xgboost
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