972730cde0
- Use the `linalg::Matrix` for storing gradients. - New API for the custom objective. - Custom objective for multi-class/multi-target is now required to return the correct shape. - Custom objective for Python can accept arrays with any strides. (row-major, column-major)
104 lines
3.9 KiB
C++
104 lines
3.9 KiB
C++
/**
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* Copyright 2018-2023 by XGBoost Contributors
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* \author Rory Mitchell
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*/
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#include <xgboost/linear_updater.h>
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#include "./param.h"
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#include "../common/timer.h"
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#include "coordinate_common.h"
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#include "xgboost/json.h"
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namespace xgboost::linear {
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DMLC_REGISTER_PARAMETER(CoordinateParam);
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DMLC_REGISTRY_FILE_TAG(updater_coordinate);
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// training parameter
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/**
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* \class CoordinateUpdater
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*
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* \brief Coordinate descent algorithm that updates one feature per iteration
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*/
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class CoordinateUpdater : 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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const std::vector<std::pair<std::string, std::string> > rest {
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tparam_.UpdateAllowUnknown(args)
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};
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cparam_.UpdateAllowUnknown(rest);
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selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
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monitor_.Init("CoordinateUpdater");
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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"), &tparam_);
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FromJson(config.at("coordinate_param"), &cparam_);
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}
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void SaveConfig(Json *p_out) const override {
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LOG(DEBUG) << "Save config for CPU updater.";
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auto &out = *p_out;
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out["linear_train_param"] = ToJson(tparam_);
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out["coordinate_param"] = ToJson(cparam_);
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}
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void Update(linalg::Matrix<GradientPair> *in_gpair, DMatrix *p_fmat, gbm::GBLinearModel *model,
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double sum_instance_weight) override {
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auto gpair = in_gpair->Data();
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tparam_.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 group_idx = 0; group_idx < ngroup; ++group_idx) {
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auto grad = GetBiasGradientParallel(group_idx, ngroup, gpair->ConstHostVector(), p_fmat,
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ctx_->Threads());
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auto dbias = static_cast<float>(tparam_.learning_rate *
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CoordinateDeltaBias(grad.first, grad.second));
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model->Bias()[group_idx] += dbias;
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UpdateBiasResidualParallel(ctx_, group_idx, ngroup, dbias, &gpair->HostVector(), p_fmat);
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}
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// prepare for updating the weights
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selector_->Setup(ctx_, *model, gpair->ConstHostVector(), p_fmat, tparam_.reg_alpha_denorm,
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tparam_.reg_lambda_denorm, cparam_.top_k);
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// update weights
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for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
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for (unsigned i = 0U; i < model->learner_model_param->num_feature; i++) {
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int fidx =
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selector_->NextFeature(ctx_, i, *model, group_idx, gpair->ConstHostVector(), p_fmat,
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tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
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if (fidx < 0) break;
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this->UpdateFeature(fidx, group_idx, &gpair->HostVector(), p_fmat, model);
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}
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}
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monitor_.Stop("UpdateFeature");
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}
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void UpdateFeature(int fidx, int group_idx, std::vector<GradientPair> *in_gpair, DMatrix *p_fmat,
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gbm::GBLinearModel *model) {
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const int ngroup = model->learner_model_param->num_output_group;
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bst_float &w = (*model)[fidx][group_idx];
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auto gradient = GetGradientParallel(ctx_, group_idx, ngroup, fidx,
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*in_gpair, p_fmat);
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auto dw = static_cast<float>(
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tparam_.learning_rate *
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CoordinateDelta(gradient.first, gradient.second, w, tparam_.reg_alpha_denorm,
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tparam_.reg_lambda_denorm));
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w += dw;
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UpdateResidualParallel(ctx_, fidx, group_idx, ngroup, dw, in_gpair, p_fmat);
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}
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private:
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CoordinateParam cparam_;
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// training parameter
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LinearTrainParam tparam_;
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std::unique_ptr<FeatureSelector> selector_;
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common::Monitor monitor_;
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};
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XGBOOST_REGISTER_LINEAR_UPDATER(CoordinateUpdater, "coord_descent")
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.describe("Update linear model according to coordinate descent algorithm.")
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.set_body([]() { return new CoordinateUpdater(); });
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} // namespace xgboost::linear
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