/*!
 * Copyright 2018 by Contributors
 * \author Rory Mitchell
 */

#include <boxhed_kernel/linear_updater.h>
#include "./param.h"
#include "../common/timer.h"
#include "coordinate_common.h"
#include "boxhed_kernel/json.h"

namespace boxhed_kernel {
namespace linear {

DMLC_REGISTER_PARAMETER(CoordinateParam);
DMLC_REGISTRY_FILE_TAG(updater_coordinate);

// training parameter
/**
 * \class CoordinateUpdater
 *
 * \brief Coordinate descent algorithm that updates one feature per iteration
 */

class CoordinateUpdater : public LinearUpdater {
 public:
  // set training parameter
  void Configure(Args const& args) override {
    const std::vector<std::pair<std::string, std::string> > rest {
      tparam_.UpdateAllowUnknown(args)
    };
    cparam_.UpdateAllowUnknown(rest);
    selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
    monitor_.Init("CoordinateUpdater");
  }

  void LoadConfig(Json const& in) override {
    auto const& config = get<Object const>(in);
    FromJson(config.at("linear_train_param"), &tparam_);
    FromJson(config.at("coordinate_param"), &cparam_);
  }
  void SaveConfig(Json* p_out) const override {
    auto& out = *p_out;
    out["linear_train_param"] = ToJson(tparam_);
    out["coordinate_param"] = ToJson(cparam_);
  }

  void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
              gbm::GBLinearModel *model, double sum_instance_weight) override {
    tparam_.DenormalizePenalties(sum_instance_weight);
    const int ngroup = model->learner_model_param->num_output_group;
    // update bias
    for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
      auto grad = GetBiasGradientParallel(group_idx, ngroup,
                                          in_gpair->ConstHostVector(), p_fmat);
      auto dbias = static_cast<float>(tparam_.learning_rate *
                                      CoordinateDeltaBias(grad.first, grad.second));
      model->Bias()[group_idx] += dbias;
      UpdateBiasResidualParallel(group_idx, ngroup,
                                 dbias, &in_gpair->HostVector(), p_fmat);
    }
    // prepare for updating the weights
    selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
                    tparam_.reg_alpha_denorm,
                    tparam_.reg_lambda_denorm, cparam_.top_k);
    // update weights
    for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
      for (unsigned i = 0U; i < model->learner_model_param->num_feature; i++) {
        int fidx = selector_->NextFeature
          (i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
           tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
        if (fidx < 0) break;
        this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), p_fmat, model);
      }
    }
    monitor_.Stop("UpdateFeature");
  }

  inline void UpdateFeature(int fidx, int group_idx, std::vector<GradientPair> *in_gpair,
                            DMatrix *p_fmat, gbm::GBLinearModel *model) {
    const int ngroup = model->learner_model_param->num_output_group;
    bst_float &w = (*model)[fidx][group_idx];
    auto gradient =
        GetGradientParallel(group_idx, ngroup, fidx, *in_gpair, p_fmat);
    auto dw = static_cast<float>(
        tparam_.learning_rate *
        CoordinateDelta(gradient.first, gradient.second, w, tparam_.reg_alpha_denorm,
                        tparam_.reg_lambda_denorm));
    w += dw;
    UpdateResidualParallel(fidx, group_idx, ngroup, dw, in_gpair, p_fmat);
  }

 private:
  CoordinateParam cparam_;
  // training parameter
  LinearTrainParam tparam_;
  std::unique_ptr<FeatureSelector> selector_;
  common::Monitor monitor_;
};

XGBOOST_REGISTER_LINEAR_UPDATER(CoordinateUpdater, "coord_descent")
    .describe("Update linear model according to coordinate descent algorithm.")
    .set_body([]() { return new CoordinateUpdater(); });
}  // namespace linear
}  // namespace boxhed_kernel
