/** * Copyright 2017-2023 by XGBoost contributors */ #include #include #include #include #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> args; std::unique_ptr 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> args; std::unique_ptr 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> args; std::unique_ptr 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> args; std::unique_ptr 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 io_preds = {0, 0.1f, 0.5f, 0.9f, 1}; std::vector 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(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> args; std::unique_ptr 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}); } } // namespace xgboost