175 lines
7.1 KiB
C++
175 lines
7.1 KiB
C++
// Copyright by Contributors
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#include <xgboost/objective.h>
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#include "../helpers.h"
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TEST(Objective, LinearRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:linear");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{0, 0, 0, 0, 1, 1, 1, 1},
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{1, 1, 1, 1, 1, 1, 1, 1},
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{0, 0.1f, 0.9f, 1.0f, -1.0f, -0.9f, -0.1f, 0},
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{1, 1, 1, 1, 1, 1, 1, 1});
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ASSERT_NO_THROW(obj->DefaultEvalMetric());
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}
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TEST(Objective, LogisticRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
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}
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TEST(Objective, LogisticRegressionBasic) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
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<< "Expected error when label not in range [0,1f] for LogisticRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
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EXPECT_ANY_THROW(obj->ProbToMargin(10))
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<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<xgboost::bst_float> out_preds = {0.5f, 0.524f, 0.622f, 0.710f, 0.731f};
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obj->PredTransform(&preds);
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for (int i = 0; i < static_cast<int>(preds.size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(Objective, LogisticRawGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("binary:logitraw");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
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}
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TEST(Objective, PoissonRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson");
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std::vector<std::pair<std::string, std::string> > args;
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args.push_back(std::make_pair("max_delta_step", "0.1f"));
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 1, 1.10f, 2.45f, 2.71f, 0, 0.10f, 1.45f, 1.71f},
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{1.10f, 1.22f, 2.71f, 3.00f, 1.10f, 1.22f, 2.71f, 3.00f});
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}
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TEST(Objective, PoissonRegressionBasic) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
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<< "Expected error when label < 0 for PoissonRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<xgboost::bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
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obj->PredTransform(&preds);
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for (int i = 0; i < static_cast<int>(preds.size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(Objective, GammaRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:gamma");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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CheckObjFunction(obj,
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{0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{0, 0, 0, 0, 1, 1, 1, 1},
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{1, 1, 1, 1, 1, 1, 1, 1},
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{1, 1, 1, 1, 0, 0.09f, 0.59f, 0.63f},
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{0, 0, 0, 0, 1, 0.90f, 0.40f, 0.36f});
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}
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TEST(Objective, GammaRegressionBasic) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:gamma");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
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<< "Expected error when label < 0 for GammaRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.30f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), -0.69f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), -0.10f, 0.01f);
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<xgboost::bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
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obj->PredTransform(&preds);
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for (int i = 0; i < static_cast<int>(preds.size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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TEST(Objective, TweedieRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie");
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std::vector<std::pair<std::string, std::string> > args;
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args.push_back(std::make_pair("tweedie_variance_power", "1.1f"));
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
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{ 0, 0, 0, 0, 1, 1, 1, 1},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 1, 1.09f, 2.24f, 2.45f, 0, 0.10f, 1.33f, 1.55f},
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{0.89f, 0.98f, 2.02f, 2.21f, 1, 1.08f, 2.11f, 2.30f});
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}
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TEST(Objective, TweedieRegressionBasic) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie");
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std::vector<std::pair<std::string, std::string> > args;
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obj->Configure(args);
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// test label validation
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EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {-1}, {1}, {0}, {0}))
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<< "Expected error when label < 0 for TweedieRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1f), 0.10f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.5f), 0.5f, 0.01f);
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EXPECT_NEAR(obj->ProbToMargin(0.9f), 0.89f, 0.01f);
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1f, 0.5f, 0.9f, 1};
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std::vector<xgboost::bst_float> out_preds = {1, 1.10f, 1.64f, 2.45f, 2.71f};
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obj->PredTransform(&preds);
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for (int i = 0; i < static_cast<int>(preds.size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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}
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