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/**
* Copyright 2017-2023 by XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h>
#include <xgboost/json.h>
#include <xgboost/objective.h>
#include <numeric> // for iota
#include "../../../src/common/linalg_op.h" // for begin, end
#include "../../../src/objective/adaptive.h"
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h"
#include "xgboost/base.h"
#include "xgboost/data.h"
#include "xgboost/linalg.h"
#include "test_regression_obj.h"
namespace xgboost {
void TestLinearRegressionGPair(const Context* ctx) {
std::string obj_name = "reg:squarederror";
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args);
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{0, 0, 0, 0, 1, 1, 1, 1},
{1, 1, 1, 1, 1, 1, 1, 1},
{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
{1, 1, 1, 1, 1, 1, 1, 1});
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{0, 0, 0, 0, 1, 1, 1, 1},
{}, // empty weight
{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
{1, 1, 1, 1, 1, 1, 1, 1});
ASSERT_NO_THROW(obj->DefaultEvalMetric());
}
void TestSquaredLog(const Context* ctx) {
std::string obj_name = "reg:squaredlogerror";
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args);
CheckConfigReload(obj, obj_name);
CheckObjFunction(obj,
{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
CheckObjFunction(obj,
{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{}, // empty weights
{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"});
}
void TestLogisticRegressionGPair(const Context* ctx) {
std::string obj_name = "reg:logistic";
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args);
CheckConfigReload(obj, obj_name);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1}, // preds
{ 0, 0, 0, 0, 1, 1, 1, 1}, // labels
{ 1, 1, 1, 1, 1, 1, 1, 1}, // weights
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, // out_grad
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess
}
void TestLogisticRegressionBasic(const Context* ctx) {
std::string obj_name = "reg:logistic";
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create(obj_name, ctx)};
obj->Configure(args);
CheckConfigReload(obj, obj_name);
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
<< "Expected error when label not in range [0,1f] for LogisticRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
EXPECT_ANY_THROW((void)obj->ProbToMargin(10))
<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
void TestsLogisticRawGPair(const Context* ctx) {
std::string obj_name = "binary:logitraw";
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {ObjFunction::Create(obj_name, ctx)};
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
}
void TestPoissonRegressionGPair(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("count:poisson", ctx)
};
args.emplace_back("max_delta_step", "0.1f");
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{}, // Empty weight
{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
}
void TestPoissonRegressionBasic(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("count:poisson", ctx)
};
obj->Configure(args);
CheckConfigReload(obj, "count:poisson");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
<< "Expected error when label < 0 for PoissonRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
void TestGammaRegressionGPair(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj {
ObjFunction::Create("reg:gamma", ctx)
};
obj->Configure(args);
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{2, 2, 2, 2, 1, 1, 1, 1},
{1, 1, 1, 1, 1, 1, 1, 1},
{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
CheckObjFunction(obj,
{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{2, 2, 2, 2, 1, 1, 1, 1},
{}, // Empty weight
{-1, -0.809, 0.187, 0.264, 0, 0.09f, 0.59f, 0.63f},
{2, 1.809, 0.813, 0.735, 1, 0.90f, 0.40f, 0.36f});
}
void TestGammaRegressionBasic(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:gamma", ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:gamma");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {0}, {1}, {0}, {0}))
<< "Expected error when label = 0 for GammaRegression";
EXPECT_ANY_THROW(CheckObjFunction(obj, {-1}, {-1}, {1}, {-1}, {-3}))
<< "Expected error when label < 0 for GammaRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
void TestTweedieRegressionGPair(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:tweedie", ctx)};
args.emplace_back("tweedie_variance_power", "1.1f");
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{}, // Empty weight.
{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"tweedie-nloglik@1.1"});
}
void TestTweedieRegressionBasic(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:tweedie", ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:tweedie");
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
<< "Expected error when label < 0 for TweedieRegression";
// test ProbToMargin
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
std::vector<bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
obj->PredTransform(&io_preds);
auto& preds = io_preds.HostVector();
for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
}
}
void TestCoxRegressionGPair(const Context* ctx) {
std::vector<std::pair<std::string, std::string>> args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("survival:cox", ctx)};
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, -2, -2, 2, 3, 5, -10, 100},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
{ 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
}
void TestAbsoluteError(const Context* ctx) {
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", ctx)};
obj->Configure({});
CheckConfigReload(obj, "reg:absoluteerror");
MetaInfo info;
std::vector<float> labels{0.f, 3.f, 2.f, 5.f, 4.f, 7.f};
info.labels.Reshape(6, 1);
info.labels.Data()->HostVector() = labels;
info.num_row_ = labels.size();
HostDeviceVector<float> predt{1.f, 2.f, 3.f, 4.f, 5.f, 6.f};
info.weights_.HostVector() = {1.f, 1.f, 1.f, 1.f, 1.f, 1.f};
CheckObjFunction(obj, predt.HostVector(), labels, info.weights_.HostVector(),
{1.f, -1.f, 1.f, -1.f, 1.f, -1.f}, info.weights_.HostVector());
RegTree tree;
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
HostDeviceVector<bst_node_t> position(labels.size(), 0);
auto& h_position = position.HostVector();
for (size_t i = 0; i < labels.size(); ++i) {
if (i < labels.size() / 2) {
h_position[i] = 1; // left
} else {
h_position[i] = 2; // right
}
}
auto& h_predt = predt.HostVector();
for (size_t i = 0; i < h_predt.size(); ++i) {
h_predt[i] = labels[i] + i;
}
tree::TrainParam param;
param.Init(Args{});
auto lr = param.learning_rate;
obj->UpdateTreeLeaf(position, info, param.learning_rate, predt, 0, &tree);
ASSERT_EQ(tree[1].LeafValue(), -1.0f * lr);
ASSERT_EQ(tree[2].LeafValue(), -4.0f * lr);
}
void TestAbsoluteErrorLeaf(const Context* ctx) {
bst_target_t constexpr kTargets = 3, kRows = 16;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:absoluteerror", ctx)};
obj->Configure({});
MetaInfo info;
info.num_row_ = kRows;
info.labels.Reshape(16, kTargets);
HostDeviceVector<float> predt(info.labels.Size());
for (bst_target_t t{0}; t < kTargets; ++t) {
auto h_labels = info.labels.HostView().Slice(linalg::All(), t);
std::iota(linalg::begin(h_labels), linalg::end(h_labels), 0);
auto h_predt =
linalg::MakeTensorView(ctx, predt.HostSpan(), kRows, kTargets).Slice(linalg::All(), t);
for (size_t i = 0; i < h_predt.Size(); ++i) {
h_predt(i) = h_labels(i) + i;
}
HostDeviceVector<bst_node_t> position(h_labels.Size(), 0);
auto& h_position = position.HostVector();
for (int32_t i = 0; i < 3; ++i) {
h_position[i] = ~i; // negation for sampled nodes.
}
for (size_t i = 3; i < 8; ++i) {
h_position[i] = 3;
}
// empty leaf for node 4
for (size_t i = 8; i < 13; ++i) {
h_position[i] = 5;
}
for (size_t i = 13; i < h_labels.Size(); ++i) {
h_position[i] = 6;
}
RegTree tree;
tree.ExpandNode(0, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
tree.ExpandNode(1, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
tree.ExpandNode(2, /*split_index=*/1, 2, true, 0.0f, 2.f, 3.f, 4.f, 2.f, 1.f, 1.f);
ASSERT_EQ(tree.GetNumLeaves(), 4);
auto empty_leaf = tree[4].LeafValue();
tree::TrainParam param;
param.Init(Args{});
auto lr = param.learning_rate;
obj->UpdateTreeLeaf(position, info, lr, predt, t, &tree);
ASSERT_EQ(tree[3].LeafValue(), -5.0f * lr);
ASSERT_EQ(tree[4].LeafValue(), empty_leaf * lr);
ASSERT_EQ(tree[5].LeafValue(), -10.0f * lr);
ASSERT_EQ(tree[6].LeafValue(), -14.0f * lr);
}
}
void TestPseudoHuber(const Context* ctx) {
Args args;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:pseudohubererror", ctx)};
obj->Configure(args);
CheckConfigReload(obj, "reg:pseudohubererror");
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{}, // empty weights
{-0.668965f, -0.624695f, -0.514496f, -0.196116f, 0.514496f}, // out_grad
{0.410660f, 0.476140f, 0.630510f, 0.9428660f, 0.630510f}); // out_hess
ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"mphe"});
obj->Configure({{"huber_slope", "0.1"}});
CheckConfigReload(obj, "reg:pseudohubererror");
CheckObjFunction(obj, {0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
{-0.099388f, -0.099228f, -0.098639f, -0.089443f, 0.098639f}, // out_grad
{0.0013467f, 0.001908f, 0.004443f, 0.089443f, 0.004443f}); // out_hess
}
} // namespace xgboost
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