chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*!
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* Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.
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* Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.
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* Licensed under the MIT License. See LICENSE file in the project root for license information.
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*/
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#include <LightGBM/objective_function.h>
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#include <string>
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#include "binary_objective.hpp"
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#include "multiclass_objective.hpp"
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#include "rank_objective.hpp"
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#include "regression_objective.hpp"
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#include "xentropy_objective.hpp"
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#include <LightGBM/cuda/cuda_objective_function.hpp>
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#include "cuda/cuda_binary_objective.hpp"
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#include "cuda/cuda_multiclass_objective.hpp"
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#include "cuda/cuda_rank_objective.hpp"
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#include "cuda/cuda_regression_objective.hpp"
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namespace LightGBM {
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#ifdef USE_CUDA
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ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunctionCUDA(const std::string& type, const Config& config) {
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if (type == std::string("regression")) {
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return new CUDARegressionL2loss(config);
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} else if (type == std::string("regression_l1")) {
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return new CUDARegressionL1loss(config);
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} else if (type == std::string("quantile")) {
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return new CUDARegressionQuantileloss(config);
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} else if (type == std::string("huber")) {
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return new CUDARegressionHuberLoss(config);
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} else if (type == std::string("fair")) {
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return new CUDARegressionFairLoss(config);
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} else if (type == std::string("poisson")) {
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return new CUDARegressionPoissonLoss(config);
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} else if (type == std::string("binary")) {
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return new CUDABinaryLogloss(config);
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} else if (type == std::string("lambdarank")) {
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return new CUDALambdarankNDCG(config);
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} else if (type == std::string("rank_xendcg")) {
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return new CUDARankXENDCG(config);
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} else if (type == std::string("multiclass")) {
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return new CUDAMulticlassSoftmax(config);
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} else if (type == std::string("multiclassova")) {
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return new CUDAMulticlassOVA(config);
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} else if (type == std::string("cross_entropy")) {
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Log::Warning("Objective cross_entropy is not implemented in cuda version. Fall back to boosting on CPU.");
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return new CrossEntropy(config);
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} else if (type == std::string("cross_entropy_lambda")) {
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Log::Warning("Objective cross_entropy_lambda is not implemented in cuda version. Fall back to boosting on CPU.");
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return new CrossEntropyLambda(config);
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} else if (type == std::string("mape")) {
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Log::Warning("Objective mape is not implemented in cuda version. Fall back to boosting on CPU.");
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return new RegressionMAPELOSS(config);
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} else if (type == std::string("gamma")) {
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Log::Warning("Objective gamma is not implemented in cuda version. Fall back to boosting on CPU.");
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return new RegressionGammaLoss(config);
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} else if (type == std::string("tweedie")) {
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Log::Warning("Objective tweedie is not implemented in cuda version. Fall back to boosting on CPU.");
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return new RegressionTweedieLoss(config);
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} else if (type == std::string("custom")) {
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Log::Warning("Using customized objective with cuda. This requires copying gradients from CPU to GPU, which can be slow.");
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return nullptr;
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}
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}
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#endif // USE_CUDA
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ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& type, const Config& config) {
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#ifdef USE_CUDA
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if (config.device_type == std::string("cuda") &&
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config.data_sample_strategy != std::string("goss") &&
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config.boosting != std::string("rf")) {
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return CreateObjectiveFunctionCUDA(type, config);
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} else {
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#endif // USE_CUDA
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if (type == std::string("regression")) {
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return new RegressionL2loss(config);
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} else if (type == std::string("regression_l1")) {
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return new RegressionL1loss(config);
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} else if (type == std::string("quantile")) {
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return new RegressionQuantileloss(config);
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} else if (type == std::string("huber")) {
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return new RegressionHuberLoss(config);
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} else if (type == std::string("fair")) {
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return new RegressionFairLoss(config);
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} else if (type == std::string("poisson")) {
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return new RegressionPoissonLoss(config);
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} else if (type == std::string("binary")) {
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return new BinaryLogloss(config);
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} else if (type == std::string("lambdarank")) {
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return new LambdarankNDCG(config);
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} else if (type == std::string("rank_xendcg")) {
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return new RankXENDCG(config);
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} else if (type == std::string("multiclass")) {
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return new MulticlassSoftmax(config);
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} else if (type == std::string("multiclassova")) {
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return new MulticlassOVA(config);
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} else if (type == std::string("cross_entropy")) {
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return new CrossEntropy(config);
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} else if (type == std::string("cross_entropy_lambda")) {
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return new CrossEntropyLambda(config);
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} else if (type == std::string("mape")) {
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return new RegressionMAPELOSS(config);
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} else if (type == std::string("gamma")) {
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return new RegressionGammaLoss(config);
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} else if (type == std::string("tweedie")) {
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return new RegressionTweedieLoss(config);
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} else if (type == std::string("custom")) {
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return nullptr;
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}
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#ifdef USE_CUDA
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}
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#endif // USE_CUDA
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Log::Fatal("Unknown objective type name: %s", type.c_str());
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return nullptr;
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}
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ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& str) {
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auto strs = Common::Split(str.c_str(), ' ');
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auto type = strs[0];
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if (type == std::string("regression")) {
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return new RegressionL2loss(strs);
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} else if (type == std::string("regression_l1")) {
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return new RegressionL1loss(strs);
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} else if (type == std::string("quantile")) {
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return new RegressionQuantileloss(strs);
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} else if (type == std::string("huber")) {
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return new RegressionHuberLoss(strs);
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} else if (type == std::string("fair")) {
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return new RegressionFairLoss(strs);
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} else if (type == std::string("poisson")) {
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return new RegressionPoissonLoss(strs);
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} else if (type == std::string("binary")) {
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return new BinaryLogloss(strs);
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} else if (type == std::string("lambdarank")) {
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return new LambdarankNDCG(strs);
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} else if (type == std::string("rank_xendcg")) {
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return new RankXENDCG(strs);
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} else if (type == std::string("multiclass")) {
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return new MulticlassSoftmax(strs);
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} else if (type == std::string("multiclassova")) {
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return new MulticlassOVA(strs);
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} else if (type == std::string("cross_entropy")) {
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return new CrossEntropy(strs);
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} else if (type == std::string("cross_entropy_lambda")) {
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return new CrossEntropyLambda(strs);
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} else if (type == std::string("mape")) {
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return new RegressionMAPELOSS(strs);
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} else if (type == std::string("gamma")) {
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return new RegressionGammaLoss(strs);
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} else if (type == std::string("tweedie")) {
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return new RegressionTweedieLoss(strs);
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} else if (type == std::string("custom")) {
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return nullptr;
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}
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Log::Fatal("Unknown objective type name: %s", type.c_str());
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return nullptr;
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}
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} // namespace LightGBM
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