344 lines
14 KiB
C++
344 lines
14 KiB
C++
/*!
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* Copyright 2017-2019 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/objective.h>
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#include <xgboost/generic_parameters.h>
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#include "../helpers.h"
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TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
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xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj =
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xgboost::ObjFunction::Create("reg:squarederror", &tparam);
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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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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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{}, // empty weight
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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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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
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xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj =
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xgboost::ObjFunction::Create("reg:squaredlogerror", &tparam);
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obj->Configure(args);
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CheckObjFunction(obj,
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{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // weights
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{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
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{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
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CheckObjFunction(obj,
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{0.1f, 0.2f, 0.4f, 0.8f, 1.6f}, // pred
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{1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, // labels
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{}, // empty weights
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{-0.5435f, -0.4257f, -0.25475f, -0.05855f, 0.1009f},
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{ 1.3205f, 1.0492f, 0.69215f, 0.34115f, 0.1091f});
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ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"rmsle"});
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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
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xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic", &tparam);
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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}, // preds
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{ 0, 0, 0, 0, 1, 1, 1, 1}, // labels
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{ 1, 1, 1, 1, 1, 1, 1, 1}, // weights
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{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f}, // out_grad
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{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f}); // out_hess
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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic", &lparam);
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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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xgboost::HostDeviceVector<xgboost::bst_float> io_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(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("binary:logitraw", &lparam);
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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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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson", &lparam);
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args.emplace_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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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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{}, // Empty weight
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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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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson", &lparam);
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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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xgboost::HostDeviceVector<xgboost::bst_float> io_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(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:gamma", &lparam);
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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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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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{}, // Empty weight
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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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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:gamma", &lparam);
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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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xgboost::HostDeviceVector<xgboost::bst_float> io_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(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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delete obj;
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}
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TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie", &lparam);
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args.emplace_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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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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{}, // Empty weight.
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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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ASSERT_EQ(obj->DefaultEvalMetric(), std::string{"tweedie-nloglik@1.1"});
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delete obj;
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}
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#if defined(__CUDACC__)
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TEST(Objective, CPU_vs_CUDA) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, 1);
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xgboost::ObjFunction * obj =
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xgboost::ObjFunction::Create("reg:squarederror", &lparam);
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xgboost::HostDeviceVector<xgboost::GradientPair> cpu_out_preds;
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xgboost::HostDeviceVector<xgboost::GradientPair> cuda_out_preds;
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constexpr size_t kRows = 400;
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constexpr size_t kCols = 100;
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auto ppdmat = xgboost::CreateDMatrix(kRows, kCols, 0, 0);
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xgboost::HostDeviceVector<float> preds;
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preds.Resize(kRows);
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auto& h_preds = preds.HostVector();
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for (size_t i = 0; i < h_preds.size(); ++i) {
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h_preds[i] = static_cast<float>(i);
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}
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auto& info = (*ppdmat)->Info();
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info.labels_.Resize(kRows);
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auto& h_labels = info.labels_.HostVector();
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for (size_t i = 0; i < h_labels.size(); ++i) {
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h_labels[i] = 1 / (float)(i+1);
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}
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{
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// CPU
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lparam.n_gpus = 0;
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obj->GetGradient(preds, info, 0, &cpu_out_preds);
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}
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{
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// CUDA
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lparam.n_gpus = 1;
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obj->GetGradient(preds, info, 0, &cuda_out_preds);
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}
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auto& h_cpu_out = cpu_out_preds.HostVector();
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auto& h_cuda_out = cuda_out_preds.HostVector();
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float sgrad = 0;
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float shess = 0;
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for (size_t i = 0; i < kRows; ++i) {
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sgrad += std::pow(h_cpu_out[i].GetGrad() - h_cuda_out[i].GetGrad(), 2);
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shess += std::pow(h_cpu_out[i].GetHess() - h_cuda_out[i].GetHess(), 2);
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}
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ASSERT_NEAR(sgrad, 0.0f, xgboost::kRtEps);
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ASSERT_NEAR(shess, 0.0f, xgboost::kRtEps);
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delete ppdmat;
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delete obj;
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}
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#endif
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TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie", &lparam);
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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), -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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xgboost::HostDeviceVector<xgboost::bst_float> io_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(&io_preds);
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auto& preds = io_preds.HostVector();
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for (int i = 0; i < static_cast<int>(io_preds.Size()); ++i) {
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EXPECT_NEAR(preds[i], out_preds[i], 0.01f);
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}
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delete obj;
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}
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// CoxRegression not implemented in GPU code, no need for testing.
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#if !defined(__CUDACC__)
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TEST(Objective, CoxRegressionGPair) {
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xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, 0);
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std::vector<std::pair<std::string, std::string>> args;
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xgboost::ObjFunction * obj =
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xgboost::ObjFunction::Create("survival:cox", &lparam);
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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, -2, -2, 2, 3, 5, -10, 100},
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{ 1, 1, 1, 1, 1, 1, 1, 1},
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{ 0, 0, 0, -0.799f, -0.788f, -0.590f, 0.910f, 1.006f},
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{ 0, 0, 0, 0.160f, 0.186f, 0.348f, 0.610f, 0.639f});
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delete obj;
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}
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#endif
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