d5992dd881
* Replaced std::vector-based interfaces with HostDeviceVector-based interfaces. - replacement was performed in the learner, boosters, predictors, updaters, and objective functions - only interfaces used in training were replaced; interfaces like PredictInstance() still use std::vector - refactoring necessary for replacement of interfaces was also performed, such as using HostDeviceVector in prediction cache * HostDeviceVector-based interfaces for custom objective function example plugin.
71 lines
2.9 KiB
Plaintext
71 lines
2.9 KiB
Plaintext
/*!
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* Copyright 2017 XGBoost contributors
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*/
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#include <xgboost/objective.h>
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#include "../helpers.h"
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TEST(Objective, GPULinearRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu: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, GPULogisticRegressionGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu: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, GPULogisticRegressionBasic) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu: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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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.data_h();
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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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}
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TEST(Objective, GPULogisticRawGPair) {
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xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("gpu: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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