104 lines
3.8 KiB
C++
104 lines
3.8 KiB
C++
/*!
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* Copyright 2018 by Contributors
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* \author Tianqi Chen, Rory Mitchell
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*/
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#include <xgboost/linear_updater.h>
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#include "coordinate_common.h"
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namespace xgboost {
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namespace linear {
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DMLC_REGISTRY_FILE_TAG(updater_shotgun);
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class ShotgunUpdater : public LinearUpdater {
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public:
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// set training parameter
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void Configure(Args const& args) override {
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param_.UpdateAllowUnknown(args);
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if (param_.feature_selector != kCyclic &&
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param_.feature_selector != kShuffle) {
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LOG(FATAL) << "Unsupported feature selector for shotgun updater.\n"
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<< "Supported options are: {cyclic, shuffle}";
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}
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selector_.reset(FeatureSelector::Create(param_.feature_selector, ctx_->Threads()));
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}
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void LoadConfig(Json const& in) override {
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auto const& config = get<Object const>(in);
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FromJson(config.at("linear_train_param"), ¶m_);
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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["linear_train_param"] = ToJson(param_);
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}
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void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
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gbm::GBLinearModel *model, double sum_instance_weight) override {
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auto &gpair = in_gpair->HostVector();
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param_.DenormalizePenalties(sum_instance_weight);
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const int ngroup = model->learner_model_param->num_output_group;
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// update bias
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for (int gid = 0; gid < ngroup; ++gid) {
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auto grad = GetBiasGradientParallel(gid, ngroup, in_gpair->ConstHostVector(), p_fmat,
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ctx_->Threads());
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auto dbias = static_cast<bst_float>(param_.learning_rate *
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CoordinateDeltaBias(grad.first, grad.second));
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model->Bias()[gid] += dbias;
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UpdateBiasResidualParallel(gid, ngroup, dbias, &in_gpair->HostVector(), p_fmat,
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ctx_->Threads());
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}
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// lock-free parallel updates of weights
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selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
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param_.reg_alpha_denorm, param_.reg_lambda_denorm, 0);
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for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
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auto page = batch.GetView();
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const auto nfeat = static_cast<bst_omp_uint>(batch.Size());
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common::ParallelFor(nfeat, ctx_->Threads(), [&](auto i) {
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int ii = selector_->NextFeature(i, *model, 0, in_gpair->ConstHostVector(), p_fmat,
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param_.reg_alpha_denorm, param_.reg_lambda_denorm);
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if (ii < 0) return;
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const bst_uint fid = ii;
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auto col = page[ii];
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for (int gid = 0; gid < ngroup; ++gid) {
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double sum_grad = 0.0, sum_hess = 0.0;
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for (auto &c : col) {
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const GradientPair &p = gpair[c.index * ngroup + gid];
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if (p.GetHess() < 0.0f) continue;
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const bst_float v = c.fvalue;
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sum_grad += p.GetGrad() * v;
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sum_hess += p.GetHess() * v * v;
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}
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bst_float &w = (*model)[fid][gid];
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auto dw = static_cast<bst_float>(
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param_.learning_rate * CoordinateDelta(sum_grad, sum_hess, w, param_.reg_alpha_denorm,
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param_.reg_lambda_denorm));
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if (dw == 0.f) continue;
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w += dw;
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// update grad values
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for (auto &c : col) {
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GradientPair &p = gpair[c.index * ngroup + gid];
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if (p.GetHess() < 0.0f) continue;
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p += GradientPair(p.GetHess() * c.fvalue * dw, 0);
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}
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}
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});
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}
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}
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protected:
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// training parameters
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LinearTrainParam param_;
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std::unique_ptr<FeatureSelector> selector_;
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};
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XGBOOST_REGISTER_LINEAR_UPDATER(ShotgunUpdater, "shotgun")
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.describe(
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"Update linear model according to shotgun coordinate descent "
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"algorithm.")
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.set_body([]() { return new ShotgunUpdater(); });
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} // namespace linear
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} // namespace xgboost
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