/*! * Copyright 2018-2019 by Contributors */ #include #include #include "../helpers.h" #include "test_json_io.h" #include "../../../src/gbm/gblinear_model.h" #include "xgboost/base.h" namespace xgboost { TEST(Linear, Shotgun) { size_t constexpr kRows = 10; size_t constexpr kCols = 10; auto p_fmat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(); auto lparam = xgboost::CreateEmptyGenericParam(GPUIDX); LearnerModelParam mparam; mparam.num_feature = kCols; mparam.num_output_group = 1; mparam.base_score = 0.5; { auto updater = std::unique_ptr( xgboost::LinearUpdater::Create("shotgun", &lparam)); updater->Configure({{"eta", "1."}}); xgboost::HostDeviceVector gpair( p_fmat->Info().num_row_, xgboost::GradientPair(-5, 1.0)); xgboost::gbm::GBLinearModel model{&mparam}; model.LazyInitModel(); updater->Update(&gpair, p_fmat.get(), &model, gpair.Size()); ASSERT_EQ(model.Bias()[0], 5.0f); } { auto updater = std::unique_ptr( xgboost::LinearUpdater::Create("shotgun", &lparam)); EXPECT_ANY_THROW(updater->Configure({{"feature_selector", "random"}})); } } TEST(Shotgun, JsonIO) { TestUpdaterJsonIO("shotgun"); } TEST(Linear, coordinate) { size_t constexpr kRows = 10; size_t constexpr kCols = 10; auto p_fmat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(); auto lparam = xgboost::CreateEmptyGenericParam(GPUIDX); LearnerModelParam mparam; mparam.num_feature = kCols; mparam.num_output_group = 1; mparam.base_score = 0.5; auto updater = std::unique_ptr( xgboost::LinearUpdater::Create("coord_descent", &lparam)); updater->Configure({{"eta", "1."}}); xgboost::HostDeviceVector gpair( p_fmat->Info().num_row_, xgboost::GradientPair(-5, 1.0)); xgboost::gbm::GBLinearModel model{&mparam}; model.LazyInitModel(); updater->Update(&gpair, p_fmat.get(), &model, gpair.Size()); ASSERT_EQ(model.Bias()[0], 5.0f); } TEST(Coordinate, JsonIO){ TestUpdaterJsonIO("coord_descent"); } } // namespace xgboost