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| Original file line number | Diff line number | Diff line change |
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| /*! | ||
| * Copyright 2018-2019 by Contributors | ||
| * \file metric_param.cc | ||
| */ | ||
| #ifndef XGBOOST_METRIC_METRIC_COMMON_H_ | ||
| #define XGBOOST_METRIC_METRIC_COMMON_H_ | ||
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| #include <dmlc/parameter.h> | ||
| #include "../common/common.h" | ||
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| namespace xgboost { | ||
| namespace metric { | ||
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| // Created exclusively for GPU. | ||
| struct MetricParam : public dmlc::Parameter<MetricParam> { | ||
| int n_gpus; | ||
| int gpu_id; | ||
| DMLC_DECLARE_PARAMETER(MetricParam) { | ||
| DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll) | ||
| .describe("Number of GPUs to use for multi-gpu algorithms."); | ||
| DMLC_DECLARE_FIELD(gpu_id) | ||
| .set_lower_bound(0) | ||
| .set_default(0) | ||
| .describe("gpu to use for objective function evaluation"); | ||
| }; | ||
| }; | ||
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| class PackedReduceResult { | ||
| double residue_sum_; | ||
| double weights_sum_; | ||
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| public: | ||
| XGBOOST_DEVICE PackedReduceResult() : residue_sum_{0}, weights_sum_{0} {} | ||
| XGBOOST_DEVICE PackedReduceResult(double residue, double weight) | ||
| : residue_sum_{residue}, weights_sum_{weight} {} | ||
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| XGBOOST_DEVICE | ||
| PackedReduceResult operator+(PackedReduceResult const &other) const { | ||
| return PackedReduceResult{residue_sum_ + other.residue_sum_, | ||
| weights_sum_ + other.weights_sum_}; | ||
| } | ||
| PackedReduceResult &operator+=(PackedReduceResult const &other) { | ||
| this->residue_sum_ += other.residue_sum_; | ||
| this->weights_sum_ += other.weights_sum_; | ||
| return *this; | ||
| } | ||
| double Residue() const { return residue_sum_; } | ||
| double Weights() const { return weights_sum_; } | ||
| }; | ||
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| } // namespace metric | ||
| } // namespace xgboost | ||
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| #endif // XGBOOST_METRIC_METRIC_COMMON_H_ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,126 +1,8 @@ | ||
| /*! | ||
| * Copyright 2015 by Contributors | ||
| * \file multiclass_metric.cc | ||
| * \brief evaluation metrics for multiclass classification. | ||
| * \author Kailong Chen, Tianqi Chen | ||
| * Copyright 2019 XGBoost contributors | ||
| */ | ||
| #include <rabit/rabit.h> | ||
| #include <xgboost/metric.h> | ||
| #include <cmath> | ||
| #include "../common/math.h" | ||
| // Dummy file to keep the CUDA conditional compile trick. | ||
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| namespace xgboost { | ||
| namespace metric { | ||
| // tag the this file, used by force static link later. | ||
| DMLC_REGISTRY_FILE_TAG(multiclass_metric); | ||
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| /*! | ||
| * \brief base class of multi-class evaluation | ||
| * \tparam Derived the name of subclass | ||
| */ | ||
| template<typename Derived> | ||
| struct EvalMClassBase : public Metric { | ||
| bst_float Eval(const HostDeviceVector<bst_float> &preds, | ||
| const MetaInfo &info, | ||
| bool distributed) override { | ||
| CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty"; | ||
| CHECK(preds.Size() % info.labels_.Size() == 0) | ||
| << "label and prediction size not match"; | ||
| const size_t nclass = preds.Size() / info.labels_.Size(); | ||
| CHECK_GE(nclass, 1U) | ||
| << "mlogloss and merror are only used for multi-class classification," | ||
| << " use logloss for binary classification"; | ||
| const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size()); | ||
| double sum = 0.0, wsum = 0.0; | ||
| int label_error = 0; | ||
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| const auto& labels = info.labels_.HostVector(); | ||
| const auto& weights = info.weights_.HostVector(); | ||
| const std::vector<bst_float>& h_preds = preds.HostVector(); | ||
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| #pragma omp parallel for reduction(+: sum, wsum) schedule(static) | ||
| for (bst_omp_uint i = 0; i < ndata; ++i) { | ||
| const bst_float wt = weights.size() > 0 ? weights[i] : 1.0f; | ||
| auto label = static_cast<int>(labels[i]); | ||
| if (label >= 0 && label < static_cast<int>(nclass)) { | ||
| sum += Derived::EvalRow(label, | ||
| h_preds.data() + i * nclass, | ||
| nclass) * wt; | ||
| wsum += wt; | ||
| } else { | ||
| label_error = label; | ||
| } | ||
| } | ||
| CHECK(label_error >= 0 && label_error < static_cast<int>(nclass)) | ||
| << "MultiClassEvaluation: label must be in [0, num_class)," | ||
| << " num_class=" << nclass << " but found " << label_error << " in label"; | ||
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| double dat[2]; dat[0] = sum, dat[1] = wsum; | ||
| if (distributed) { | ||
| rabit::Allreduce<rabit::op::Sum>(dat, 2); | ||
| } | ||
| return Derived::GetFinal(dat[0], dat[1]); | ||
| } | ||
| /*! | ||
| * \brief to be implemented by subclass, | ||
| * get evaluation result from one row | ||
| * \param label label of current instance | ||
| * \param pred prediction value of current instance | ||
| * \param nclass number of class in the prediction | ||
| */ | ||
| inline static bst_float EvalRow(int label, | ||
| const bst_float *pred, | ||
| size_t nclass); | ||
| /*! | ||
| * \brief to be overridden by subclass, final transformation | ||
| * \param esum the sum statistics returned by EvalRow | ||
| * \param wsum sum of weight | ||
| */ | ||
| inline static bst_float GetFinal(bst_float esum, bst_float wsum) { | ||
| return esum / wsum; | ||
| } | ||
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| private: | ||
| // used to store error message | ||
| const char *error_msg_; | ||
| }; | ||
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| /*! \brief match error */ | ||
| struct EvalMatchError : public EvalMClassBase<EvalMatchError> { | ||
| const char* Name() const override { | ||
| return "merror"; | ||
| } | ||
| inline static bst_float EvalRow(int label, | ||
| const bst_float *pred, | ||
| size_t nclass) { | ||
| return common::FindMaxIndex(pred, pred + nclass) != pred + static_cast<int>(label); | ||
| } | ||
| }; | ||
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| /*! \brief match error */ | ||
| struct EvalMultiLogLoss : public EvalMClassBase<EvalMultiLogLoss> { | ||
| const char* Name() const override { | ||
| return "mlogloss"; | ||
| } | ||
| inline static bst_float EvalRow(int label, | ||
| const bst_float *pred, | ||
| size_t nclass) { | ||
| const bst_float eps = 1e-16f; | ||
| auto k = static_cast<size_t>(label); | ||
| if (pred[k] > eps) { | ||
| return -std::log(pred[k]); | ||
| } else { | ||
| return -std::log(eps); | ||
| } | ||
| } | ||
| }; | ||
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| XGBOOST_REGISTER_METRIC(MatchError, "merror") | ||
| .describe("Multiclass classification error.") | ||
| .set_body([](const char* param) { return new EvalMatchError(); }); | ||
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| XGBOOST_REGISTER_METRIC(MultiLogLoss, "mlogloss") | ||
| .describe("Multiclass negative loglikelihood.") | ||
| .set_body([](const char* param) { return new EvalMultiLogLoss(); }); | ||
| } // namespace metric | ||
| } // namespace xgboost | ||
| #if !defined(XGBOOST_USE_CUDA) | ||
| #include "multiclass_metric.cu" | ||
| #endif // !defined(XGBOOST_USE_CUDA) |
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