5b1161bb64
* Add a new ctor to tensor for `initilizer_list`. * Change labels from host device vector to tensor. * Rename the field from `labels_` to `labels` since it's a public member.
101 lines
3.2 KiB
Plaintext
101 lines
3.2 KiB
Plaintext
/*!
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* Copyright 2018-2019 by Contributors
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* \file hinge.cc
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* \brief Provides an implementation of the hinge loss function
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* \author Henry Gouk
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*/
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#include "xgboost/objective.h"
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#include "xgboost/json.h"
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#include "xgboost/span.h"
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#include "xgboost/host_device_vector.h"
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#include "../common/math.h"
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#include "../common/transform.h"
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#include "../common/common.h"
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namespace xgboost {
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namespace obj {
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#if defined(XGBOOST_USE_CUDA)
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DMLC_REGISTRY_FILE_TAG(hinge_obj_gpu);
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#endif // defined(XGBOOST_USE_CUDA)
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class HingeObj : public ObjFunction {
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public:
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HingeObj() = default;
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void Configure(
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const std::vector<std::pair<std::string, std::string> > &args) override {}
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ObjInfo Task() const override { return {ObjInfo::kRegression, false}; }
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void GetGradient(const HostDeviceVector<bst_float> &preds,
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const MetaInfo &info,
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int iter,
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HostDeviceVector<GradientPair> *out_gpair) override {
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CHECK_NE(info.labels.Size(), 0U) << "label set cannot be empty";
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CHECK_EQ(preds.Size(), info.labels.Size())
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<< "labels are not correctly provided"
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<< "preds.size=" << preds.Size()
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<< ", label.size=" << info.labels.Size();
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const size_t ndata = preds.Size();
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const bool is_null_weight = info.weights_.Size() == 0;
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if (!is_null_weight) {
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CHECK_EQ(info.weights_.Size(), ndata)
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<< "Number of weights should be equal to number of data points.";
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}
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out_gpair->Resize(ndata);
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common::Transform<>::Init(
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[=] XGBOOST_DEVICE(size_t _idx,
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common::Span<GradientPair> _out_gpair,
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common::Span<const bst_float> _preds,
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common::Span<const bst_float> _labels,
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common::Span<const bst_float> _weights) {
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bst_float p = _preds[_idx];
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bst_float w = is_null_weight ? 1.0f : _weights[_idx];
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bst_float y = _labels[_idx] * 2.0 - 1.0;
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bst_float g, h;
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if (p * y < 1.0) {
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g = -y * w;
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h = w;
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} else {
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g = 0.0;
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h = std::numeric_limits<bst_float>::min();
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}
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_out_gpair[_idx] = GradientPair(g, h);
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},
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common::Range{0, static_cast<int64_t>(ndata)},
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tparam_->gpu_id).Eval(
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out_gpair, &preds, info.labels.Data(), &info.weights_);
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}
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void PredTransform(HostDeviceVector<bst_float> *io_preds) const override {
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common::Transform<>::Init(
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[] XGBOOST_DEVICE(size_t _idx, common::Span<bst_float> _preds) {
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_preds[_idx] = _preds[_idx] > 0.0 ? 1.0 : 0.0;
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},
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common::Range{0, static_cast<int64_t>(io_preds->Size()), 1},
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io_preds->DeviceIdx())
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.Eval(io_preds);
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}
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const char* DefaultEvalMetric() const override {
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return "error";
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}
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void SaveConfig(Json* p_out) const override {
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auto& out = *p_out;
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out["name"] = String("binary:hinge");
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}
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void LoadConfig(Json const& in) override {}
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};
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// register the objective functions
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XGBOOST_REGISTER_OBJECTIVE(HingeObj, "binary:hinge")
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.describe("Hinge loss. Expects labels to be in [0,1f]")
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.set_body([]() { return new HingeObj(); });
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} // namespace obj
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} // namespace xgboost
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