85b2fb3eee
* Integrating a faster version of grow_gpu plugin 1. Removed the older files to reduce duplication 2. Moved all of the grow_gpu files under 'exact' folder 3. All of them are inside 'exact' namespace to avoid any conflicts 4. Fixed a bug in benchmark.py while running only 'grow_gpu' plugin 5. Added cub and googletest submodules to ease integration and unit-testing 6. Updates to CMakeLists.txt to directly build cuda objects into libxgboost * Added support for building gpu plugins through make flow 1. updated makefile and config.mk to add right targets 2. added unit-tests for gpu exact plugin code * 1. Added support for building gpu plugin using 'make' flow as well 2. Updated instructions for building and testing gpu plugin * Fix travis-ci errors for PR#2360 1. lint errors on unit-tests 2. removed googletest, instead depended upon dmlc-core provide gtest cache * Some more fixes to travis-ci lint failures PR#2360 * Added Rory's copyrights to the files containing code from both. * updated copyright statement as per Rory's request * moved the static datasets into a script to generate them at runtime * 1. memory usage print when silent=0 2. tests/ and test/ folder organization 3. removal of the dependency of googletest for just building xgboost 4. coding style updates for .cuh as well * Fixes for compilation warnings * add cuda object files as well when JVM_BINDINGS=ON
175 lines
7.0 KiB
C++
175 lines
7.0 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, 0.1, 0.9, 1, 0, 0.1, 0.9, 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, 0.1, 0.9, 1.0, -1.0, -0.9, -0.1, 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, 0.1, 0.9, 1, 0, 0.1, 0.9, 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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{ 0.5, 0.52, 0.71, 0.73, -0.5, -0.47, -0.28, -0.26},
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{0.25, 0.24, 0.20, 0.19, 0.25, 0.24, 0.20, 0.19});
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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 [0,1] for LogisticRegression";
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// test ProbToMargin
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EXPECT_NEAR(obj->ProbToMargin(0.1), -2.197, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.5), 0, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.9), 2.197, 0.01);
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EXPECT_ANY_THROW(obj->ProbToMargin(10))
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<< "Expected error when base_score not in range [0,1] for LogisticRegression";
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1, 0.5, 0.9, 1};
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std::vector<xgboost::bst_float> out_preds = {0.5, 0.524, 0.622, 0.710, 0.731};
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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], 0.01);
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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, 0.1, 0.9, 1, 0, 0.1, 0.9, 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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{ 0.5, 0.52, 0.71, 0.73, -0.5, -0.47, -0.28, -0.26},
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{0.25, 0.24, 0.20, 0.19, 0.25, 0.24, 0.20, 0.19});
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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", "0.1"));
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1, 0.9, 1, 0, 0.1, 0.9, 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, 1.10, 2.45, 2.71, 0, 0.10, 1.45, 1.71},
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{1.10, 1.22, 2.71, 3.00, 1.10, 1.22, 2.71, 3.00});
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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(0.1), -2.30, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.5), -0.69, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.9), -0.10, 0.01);
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1, 0.5, 0.9, 1};
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std::vector<xgboost::bst_float> out_preds = {1, 1.10, 1.64, 2.45, 2.71};
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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], 0.01);
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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, 0.1, 0.9, 1, 0, 0.1, 0.9, 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, 0.09, 0.59, 0.63},
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{0, 0, 0, 0, 1, 0.90, 0.40, 0.36});
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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(0.1), -2.30, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.5), -0.69, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.9), -0.10, 0.01);
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1, 0.5, 0.9, 1};
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std::vector<xgboost::bst_float> out_preds = {1, 1.10, 1.64, 2.45, 2.71};
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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], 0.01);
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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", "1.1"));
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obj->Configure(args);
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CheckObjFunction(obj,
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{ 0, 0.1, 0.9, 1, 0, 0.1, 0.9, 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, 1.09, 2.24, 2.45, 0, 0.10, 1.33, 1.55},
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{0.89, 0.98, 2.02, 2.21, 1, 1.08, 2.11, 2.30});
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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(0.1), 0.10, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.5), 0.5, 0.01);
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EXPECT_NEAR(obj->ProbToMargin(0.9), 0.89, 0.01);
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// test PredTransform
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std::vector<xgboost::bst_float> preds = {0, 0.1, 0.5, 0.9, 1};
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std::vector<xgboost::bst_float> out_preds = {1, 1.10, 1.64, 2.45, 2.71};
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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], 0.01);
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}
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}
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