/*!
* Original work Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Modified work Copyright (c) 2020-2024 Fabio Sigrist. All rights reserved.
* Licensed under the Apache License Version 2.0 See LICENSE file in the project root for license information.
*/
#include "gbdt.h"

#include <LightGBM/metric.h>
#include <LightGBM/network.h>
#include <LightGBM/objective_function.h>
#include <LightGBM/prediction_early_stop.h>
#include <LightGBM/utils/common.h>
#include <LightGBM/utils/openmp_wrapper.h>

#include <chrono>
#include <ctime>
#include <sstream>
#include <cstring>

namespace LightGBM {

	Common::Timer global_timer;

	int LGBM_config_::current_device = lgbm_device_cpu;
	int LGBM_config_::current_learner = use_cpu_learner;

	GBDT::GBDT()
		: iter_(0),
		train_data_(nullptr),
		config_(nullptr),
		objective_function_(nullptr),
		early_stopping_round_(0),
		es_first_metric_only_(false),
		max_feature_idx_(0),
		num_tree_per_iteration_(1),
		num_class_(1),
		num_iteration_for_pred_(0),
		shrinkage_rate_(0.1f),
		num_init_iteration_(0),
		need_re_bagging_(false),
		balanced_bagging_(false),
		bagging_runner_(0, bagging_rand_block_) {
		average_output_ = false;
		tree_learner_ = nullptr;
		linear_tree_ = false;
	}

	GBDT::~GBDT() {
	}

	void GBDT::Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function,
		const std::vector<const Metric*>& training_metrics) {
		CHECK_NOTNULL(train_data);
		train_data_ = train_data;
		if (!config->monotone_constraints.empty()) {
			CHECK_EQ(static_cast<size_t>(train_data_->num_total_features()), config->monotone_constraints.size());
		}
		if (!config->feature_contri.empty()) {
			CHECK_EQ(static_cast<size_t>(train_data_->num_total_features()), config->feature_contri.size());
		}
		iter_ = 0;
		num_iteration_for_pred_ = 0;
		max_feature_idx_ = 0;
		num_class_ = config->num_class;
		if (objective_function != nullptr) {
			if (std::strcmp(objective_function->GetName(), "mean_scale_regression") == 0) {
				num_class_ = 2;
			}
		}
		config_ = std::unique_ptr<Config>(new Config(*config));
		early_stopping_round_ = config_->early_stopping_round;
		es_first_metric_only_ = config_->first_metric_only;
		shrinkage_rate_ = config_->learning_rate;
		use_nesterov_acc_ = config_->use_nesterov_acc;
		momentum_schedule_version_ = config_->momentum_schedule_version;
		nesterov_acc_rate_ = config_->nesterov_acc_rate;
		momentum_offset_ = config_->momentum_offset;
		leaves_newton_update_ = config_->leaves_newton_update;
		optimal_lr_ = 1.;

		if (config_->device_type == std::string("cuda")) {
			LGBM_config_::current_learner = use_cuda_learner;
		}

		// load forced_splits file
		if (!config->forcedsplits_filename.empty()) {
			std::ifstream forced_splits_file(config->forcedsplits_filename.c_str());
			std::stringstream buffer;
			buffer << forced_splits_file.rdbuf();
			std::string err;
			forced_splits_json_ = Json::parse(buffer.str(), &err);
		}

		objective_function_ = objective_function;
		num_tree_per_iteration_ = num_class_;
		if (objective_function_ != nullptr) {
			if (objective_function_->HasGPModel()) {
				num_tree_per_iteration_ = objective_function_->GetNumSetsFixedEffects();
			}
			else {
				num_tree_per_iteration_ = objective_function_->NumModelPerIteration();
			}
			if (objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) {
				Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName());
			}
		}

		is_constant_hessian_ = GetIsConstHessian(objective_function);

		tree_learner_ = std::unique_ptr<TreeLearner>(TreeLearner::CreateTreeLearner(config_->tree_learner, config_->device_type,
			config_.get()));

		// init tree learner
		tree_learner_->Init(train_data_, is_constant_hessian_);
		tree_learner_->SetForcedSplit(&forced_splits_json_);

		// push training metrics
		training_metrics_.clear();
		for (const auto& metric : training_metrics) {
			training_metrics_.push_back(metric);
		}
		training_metrics_.shrink_to_fit();

		train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_,
			use_nesterov_acc_));

		num_data_ = train_data_->num_data();
		// create buffer for gradients and hessians
		if (objective_function_ != nullptr) {
			size_t total_size = static_cast<size_t>(num_data_)* num_tree_per_iteration_;
			gradients_.resize(total_size);
			hessians_.resize(total_size);
		}
		// get max feature index
		max_feature_idx_ = train_data_->num_total_features() - 1;
		// get label index
		label_idx_ = train_data_->label_idx();
		// get feature names
		feature_names_ = train_data_->feature_names();
		feature_infos_ = train_data_->feature_infos();
		monotone_constraints_ = config->monotone_constraints;

		// if need bagging, create buffer
		ResetBaggingConfig(config_.get(), true);

		class_need_train_ = std::vector<bool>(num_tree_per_iteration_, true);
		if (objective_function_ != nullptr && objective_function_->SkipEmptyClass()) {
			CHECK_EQ(num_tree_per_iteration_, num_class_);
			for (int i = 0; i < num_class_; ++i) {
				class_need_train_[i] = objective_function_->ClassNeedTrain(i);
			}
		}

		if (config_->linear_tree) {
			linear_tree_ = true;
		}
	}

	void GBDT::AddValidDataset(const Dataset* valid_data,
		const std::vector<const Metric*>& valid_metrics) {
		if (!train_data_->CheckAlign(*valid_data)) {
			Log::Fatal("Cannot add validation data, since it has different bin mappers with training data");
		}
		// for a validation dataset, we need its score and metric
		auto new_score_updater = std::unique_ptr<ScoreUpdater>(new ScoreUpdater(valid_data, num_tree_per_iteration_,
			use_nesterov_acc_));
		// update score
		for (int i = 0; i < iter_; ++i) {
			for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
				auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id;
				new_score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);
			}
		}
		valid_score_updater_.push_back(std::move(new_score_updater));
		valid_metrics_.emplace_back();
		for (const auto& metric : valid_metrics) {
			valid_metrics_.back().push_back(metric);
			if ((metric->GetName()[0] == std::string("test_neg_log_likelihood") || metric->GetName()[0] == std::string("crps_gaussian")) &&
				!(objective_function_->UseGPModelForValidation()) && !(std::strcmp(objective_function_->GetName(), "mean_scale_regression") == 0)) {
				calculate_residual_variance_ = true;
			}
		}
		valid_metrics_.back().shrink_to_fit();

		if (early_stopping_round_ > 0) {
			auto num_metrics = valid_metrics.size();
			if (es_first_metric_only_) { num_metrics = 1; }
			best_iter_.emplace_back(num_metrics, 0);
			best_score_.emplace_back(num_metrics, kMinScore);
			best_msg_.emplace_back(num_metrics);
		}
	}

	void GBDT::Boosting() {
		Common::FunctionTimer fun_timer("GBDT::Boosting", global_timer);
		if (objective_function_ == nullptr) {
			Log::Fatal("No object function provided");
		}
		// objective function will calculate gradients and hessians
		int64_t num_score = 0;
		objective_function_->
			GetGradients(GetTrainingScore(&num_score), gradients_.data(), hessians_.data());
	}

	data_size_t GBDT::BaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) {
		if (cnt <= 0) {
			return 0;
		}
		data_size_t cur_left_cnt = 0;
		data_size_t cur_right_pos = cnt;
		// random bagging, minimal unit is one record
		for (data_size_t i = 0; i < cnt; ++i) {
			auto cur_idx = start + i;
			if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < config_->bagging_fraction) {
				buffer[cur_left_cnt++] = cur_idx;
			}
			else {
				buffer[--cur_right_pos] = cur_idx;
			}
		}
		return cur_left_cnt;
	}

	data_size_t GBDT::BalancedBaggingHelper(data_size_t start, data_size_t cnt,
		data_size_t* buffer) {
		if (cnt <= 0) {
			return 0;
		}
		auto label_ptr = train_data_->metadata().label();
		data_size_t cur_left_cnt = 0;
		data_size_t cur_right_pos = cnt;
		// random bagging, minimal unit is one record
		for (data_size_t i = 0; i < cnt; ++i) {
			auto cur_idx = start + i;
			bool is_pos = label_ptr[start + i] > 0;
			bool is_in_bag = false;
			if (is_pos) {
				is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() <
					config_->pos_bagging_fraction;
			}
			else {
				is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() <
					config_->neg_bagging_fraction;
			}
			if (is_in_bag) {
				buffer[cur_left_cnt++] = cur_idx;
			}
			else {
				buffer[--cur_right_pos] = cur_idx;
			}
		}
		return cur_left_cnt;
	}

	void GBDT::Bagging(int iter) {
		Common::FunctionTimer fun_timer("GBDT::Bagging", global_timer);
		// if need bagging
		if ((bag_data_cnt_ < num_data_ && iter % config_->bagging_freq == 0) ||
			need_re_bagging_) {
			need_re_bagging_ = false;
			auto left_cnt = bagging_runner_.Run<true>(
				num_data_,
				[=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left,
					data_size_t*) {
						data_size_t cur_left_count = 0;
						if (balanced_bagging_) {
							cur_left_count =
								BalancedBaggingHelper(cur_start, cur_cnt, left);
						}
						else {
							cur_left_count = BaggingHelper(cur_start, cur_cnt, left);
						}
						return cur_left_count;
				},
				bag_data_indices_.data());
			bag_data_cnt_ = left_cnt;
			Log::Debug("Re-bagging, using %d data to train", bag_data_cnt_);
			// set bagging data to tree learner
			if (!is_use_subset_) {
				tree_learner_->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_);
			}
			else {
				// get subset
				tmp_subset_->ReSize(bag_data_cnt_);
				tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(),
					bag_data_cnt_, false);
				tree_learner_->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(),
					bag_data_cnt_);
			}
		}
	}

	void GBDT::Train(int snapshot_freq, const std::string& model_output_path) {
		Common::FunctionTimer fun_timer("GBDT::Train", global_timer);
		bool is_finished = false;
		auto start_time = std::chrono::steady_clock::now();
		for (int iter = 0; iter < config_->num_iterations && !is_finished; ++iter) {
			is_finished = TrainOneIter(nullptr, nullptr);
			if (!is_finished) {
				is_finished = EvalAndCheckEarlyStopping();
			}
			auto end_time = std::chrono::steady_clock::now();
			// output used time per iteration
			Log::Info("%f seconds elapsed, finished iteration %d", std::chrono::duration<double,
				std::milli>(end_time - start_time) * 1e-3, iter + 1);
			if (snapshot_freq > 0
				&& (iter + 1) % snapshot_freq == 0) {
				std::string snapshot_out = model_output_path + ".snapshot_iter_" + std::to_string(iter + 1);
				SaveModelToFile(0, -1, config_->saved_feature_importance_type, snapshot_out.c_str());
			}
		}
	}

	void GBDT::RefitTree(const std::vector<std::vector<int>>& tree_leaf_prediction) {
		CHECK_GT(tree_leaf_prediction.size(), 0);
		CHECK_EQ(static_cast<size_t>(num_data_), tree_leaf_prediction.size());
		CHECK_EQ(static_cast<size_t>(models_.size()), tree_leaf_prediction[0].size());
		int num_iterations = static_cast<int>(models_.size() / num_tree_per_iteration_);
		std::vector<int> leaf_pred(num_data_);
		if (linear_tree_) {
			std::vector<int> max_leaves_by_thread = std::vector<int>(OMP_NUM_THREADS(), 0);
#pragma omp parallel for schedule(static)
			for (int i = 0; i < static_cast<int>(tree_leaf_prediction.size()); ++i) {
				int tid = omp_get_thread_num();
				for (size_t j = 0; j < tree_leaf_prediction[i].size(); ++j) {
					max_leaves_by_thread[tid] = std::max(max_leaves_by_thread[tid], tree_leaf_prediction[i][j]);
				}
			}
			int max_leaves = *std::max_element(max_leaves_by_thread.begin(), max_leaves_by_thread.end());
			max_leaves += 1;
			tree_learner_->InitLinear(train_data_, max_leaves);
		}
		for (int iter = 0; iter < num_iterations; ++iter) {
			Boosting();
			for (int tree_id = 0; tree_id < num_tree_per_iteration_; ++tree_id) {
				int model_index = iter * num_tree_per_iteration_ + tree_id;
#pragma omp parallel for schedule(static)
				for (int i = 0; i < num_data_; ++i) {
					leaf_pred[i] = tree_leaf_prediction[i][model_index];
					CHECK_LT(leaf_pred[i], models_[model_index]->num_leaves());
				}
				size_t offset = static_cast<size_t>(tree_id)* num_data_;
				auto grad = gradients_.data() + offset;
				auto hess = hessians_.data() + offset;
				auto new_tree = tree_learner_->FitByExistingTree(models_[model_index].get(), leaf_pred, grad, hess);
				train_score_updater_->AddScore(tree_learner_.get(), new_tree, tree_id);
				models_[model_index].reset(new_tree);
			}
		}
	}

	/* If the custom "average" is implemented it will be used inplace of the label average (if enabled)
	*
	* An improvement to this is to have options to explicitly choose
	* (i) standard average
	* (ii) custom average if available
	* (iii) any user defined scalar bias (e.g. using a new option "init_score" that overrides (i) and (ii) )
	*
	* (i) and (ii) could be selected as say "auto_init_score" = 0 or 1 etc..
	*
	*/
	double ObtainAutomaticInitialScore(const ObjectiveFunction* fobj, int class_id, const double* fixed_effects) {
		double init_score = 0.0;
		if (fobj != nullptr) {
			if (class_id == 0) {
				fobj->FindInitScoreGP(fixed_effects);
			}
			init_score = fobj->BoostFromScore(class_id);
		}
		if (Network::num_machines() > 1) {
			init_score = Network::GlobalSyncUpByMean(init_score);
		}
		return init_score;
	}

	double GBDT::BoostFromAverage(int class_id, bool update_scorer) {
		Common::FunctionTimer fun_timer("GBDT::BoostFromAverage", global_timer);
		// boosting from average label; or customized "average" if implemented for the current objective
		// Note: if a GPModel with a non-Gaussian likelihood is attached, this initialization must still run even when the
		// user has supplied an init_score / offset: GPBoost needs to jointly find a proper initial value for the tree
		// ensemble and the random effects mode while correctly accounting for the offset (passed below as fixed_effects). 
		// This does not apply to the Gaussian likelihood, for which
		// BoostFromScore() does not (and need not) account for the offset.
		bool needs_gp_model_init = objective_function_ != nullptr && objective_function_->HasGPModel() &&
			!objective_function_->GetGPModel()->GaussLikelihood();
		if (models_.empty() && (!train_score_updater_->has_init_score() || needs_gp_model_init) && objective_function_ != nullptr) {
			if (config_->boost_from_average || (train_data_ != nullptr && train_data_->num_features() == 0) || needs_gp_model_init) {
				double init_score = ObtainAutomaticInitialScore(objective_function_, class_id, train_score_updater_->score());
				if (std::fabs(init_score) > kEpsilon) {
					if (update_scorer) {
						train_score_updater_->AddScore(init_score, class_id);
						train_score_updater_->InitializeScoreLag1();
						for (auto& score_updater : valid_score_updater_) {
							score_updater->AddScore(init_score, class_id);
							score_updater->InitializeScoreLag1();
						}
					}
					Log::Info("Start training from score %lf", init_score);
					return init_score;
				}
			}
			else if (std::string(objective_function_->GetName()) == std::string("regression_l1")
				|| std::string(objective_function_->GetName()) == std::string("quantile")
				|| std::string(objective_function_->GetName()) == std::string("mape")) {
				Log::Warning("Disabling boost_from_average in %s may cause the slow convergence", objective_function_->GetName());
			}
		}
		return 0.0f;
	}

	bool GBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) {
		Log::Debug("Boosting iteration number: %d", iter_);
		Common::FunctionTimer fun_timer("GBDT::TrainOneIter", global_timer);
		std::vector<double> init_scores(num_tree_per_iteration_, 0.0);
		// boosting first
		if (gradients == nullptr || hessians == nullptr) {
			for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
				init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true);
			}
			//Momentum step
			if (use_nesterov_acc_) {
				double mu = NesterovSchedule(iter_, momentum_schedule_version_, nesterov_acc_rate_, momentum_offset_);
				train_score_updater_->ApplyMomentumStep(mu);
				for (auto& score_updater : valid_score_updater_) {
					score_updater->ApplyMomentumStep(mu);
				}
			}
			if (!(objective_function_->HasGPModel()) || (iter_ == 0 && objective_function_->HasGPModel()) || use_nesterov_acc_) {
				//Calculate gradients if (i) there is no GP model ( = classical iid boosting) or
				//	(ii) if there is a GP model ( = Gaussian process boosting) in the first iteration or 
				//	(iii) if there is a GP model & Nesteroc acceleration is used. 
				//	If there is a GP model, this also finds optimal covariance parameters.
				//	But we only do this for the first iteration (when there is no Nesteroc acceleration), afterwards gradients are calculated at the end of every iteration 
				//		to avoid double calculation for validation losses.
				Boosting();//calculate gradients
			}
			gradients = gradients_.data();
			hessians = hessians_.data();
		}
		// bagging logic
		Bagging(iter_);

		bool should_continue = false;
		for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
			const size_t offset = static_cast<size_t>(cur_tree_id)* num_data_;
			std::unique_ptr<Tree> new_tree(new Tree(2, false, false));
			if (class_need_train_[cur_tree_id] && train_data_->num_features() > 0) {
				auto grad = gradients + offset;
				auto hess = hessians + offset;
				// need to copy gradients for bagging subset.
				if (is_use_subset_ && bag_data_cnt_ < num_data_) {
					for (int i = 0; i < bag_data_cnt_; ++i) {
						gradients_[offset + i] = grad[bag_data_indices_[i]];
						hessians_[offset + i] = hess[bag_data_indices_[i]];
					}
					grad = gradients_.data() + offset;
					hess = hessians_.data() + offset;
				}
				bool is_first_tree = models_.size() < static_cast<size_t>(num_tree_per_iteration_);
				new_tree.reset(tree_learner_->Train(grad, hess, is_first_tree));
			}

			if (new_tree->num_leaves() > 1) {
				should_continue = true;
				auto score_ptr = train_score_updater_->score() + offset;
				auto residual_getter = [score_ptr](const label_t* label, int i) {return static_cast<double>(label[i]) - score_ptr[i]; };
				tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,
					num_data_, bag_data_indices_.data(), bag_data_cnt_);
				//Do Newton update step for the tree leaves after the gradient step (only releveant for GPBoost algorithm, i.e. if objective_function_->HasGPModel()==true)
				if (objective_function_->HasGPModel() && leaves_newton_update_) {
					std::vector<int> data_leaf_index = std::vector<int>(num_data_);//leaf index of every data point
					tree_learner_->GetDataLeafIndices(new_tree.get(), data_leaf_index.data());
					std::vector<double> new_leaf_values(new_tree->num_leaves());
					objective_function_->NewtonUpdateLeafValues(data_leaf_index.data(), new_tree->num_leaves(), new_leaf_values.data());
					for (int i = 0; i < new_tree->num_leaves(); ++i) {
						new_tree->SetLeafOutput(i, new_leaf_values[i]);
					}
				}
				//Find optimal step length using a line search
				if (objective_function_->HasGPModel() && config_->line_search_step_length) {
					if (num_tree_per_iteration_ > 1) {
						Log::Fatal("the option 'line_search_step_length' is currently no implemented for this likelihood ");
					}
					CHECK(!is_use_subset_);//if not true, this is not yet implemented, see 'UpdateScore' how to extend to this
					CHECK(num_data_ == bag_data_cnt_);//if not true, this is not yet implemented, see 'UpdateScore' how to extend to this
					std::vector<double> new_score(num_data_, 0.0);
					tree_learner_->AddPredictionToScore(new_tree.get(), new_score.data());
					int64_t num_score = 0;
					const double* train_score = GetTrainingScore(&num_score);
					objective_function_->LineSearchLearningRate(train_score, new_score.data(), optimal_lr_);
					new_tree->Shrinkage(optimal_lr_);
				}
				// shrinkage by learning rate
				new_tree->Shrinkage(shrinkage_rate_);
				// update score
				UpdateScore(new_tree.get(), cur_tree_id);
				if (std::fabs(init_scores[cur_tree_id]) > kEpsilon) {
					new_tree->AddBias(init_scores[cur_tree_id]);
				}
			}
			else {
				// only add default score one-time
				if (models_.size() < static_cast<size_t>(num_tree_per_iteration_)) {
					double output = 0.0;
					if (!class_need_train_[cur_tree_id]) {
						if (objective_function_ != nullptr) {
							if (cur_tree_id == 0) {
								objective_function_->FindInitScoreGP(train_score_updater_->score());
							}
							output = objective_function_->BoostFromScore(cur_tree_id);
						}
					}
					else {
						output = init_scores[cur_tree_id];
					}
					new_tree->AsConstantTree(output);
					// updates scores
					train_score_updater_->AddScore(output, cur_tree_id);
					for (auto& score_updater : valid_score_updater_) {
						score_updater->AddScore(output, cur_tree_id);
					}
				}
			}
			// Calculate residual variance if needed
			if (calculate_residual_variance_) {
				int64_t num_score = 0;
				const double* train_score = GetTrainingScore(&num_score);
				CHECK(num_score == num_data_);
				auto label_ptr = train_data_->metadata().label();
				double residual_mean = 0.;
#pragma omp parallel for schedule(static) reduction(+:residual_mean)
				for (data_size_t i = 0; i < num_data_; ++i) {
					residual_mean += (label_ptr[i] - train_score[i]);
				}
				residual_mean /= num_data_;
				double residual_variance = 0.;
#pragma omp parallel for schedule(static) reduction(+:residual_variance)
				for (data_size_t i = 0; i < num_data_; ++i) {
					residual_variance += std::pow(label_ptr[i] - train_score[i] - residual_mean, 2);
				}
				residual_variance_ = residual_variance / (num_data_ - 1);
			}
			if (objective_function_->HasGPModel() && !(objective_function_->UseGPModelForValidation() && use_nesterov_acc_)) {
				// Calculate gradients & first find optimal covariance parameters if there is a GP model
				// When there is Nesterov acceleration and the re_model is not used for validation, it is not necessary to calculate this here
				//	since the gradients are calculated after the momentum step and this is not needed here.
				// TODO: if use_nesterov_acc_ && objective->UseGPModelForValidation(), the calculation of the gradient could be avoided and 
				//			it is enough to set the response data of re_model correctly to score - label = F_t - y
				Boosting();
			}
			// add model
			models_.push_back(std::move(new_tree));
		}

		if (!should_continue) {
			//Log::Debug("Stopped training because there are no more leaves that meet the split requirements");
			if (models_.size() > static_cast<size_t>(num_tree_per_iteration_)) {
				for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
					models_.pop_back();
				}
			}
			return true;
		}

		++iter_;
		return false;
	}

	void GBDT::RollbackOneIter() {
		if (iter_ <= 0) { return; }
		// reset score
		for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
			auto curr_tree = models_.size() - num_tree_per_iteration_ + cur_tree_id;
			models_[curr_tree]->Shrinkage(-1.0);
			train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);
			for (auto& score_updater : valid_score_updater_) {
				score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);
			}
		}
		// remove model
		for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
			models_.pop_back();
		}
		--iter_;
	}

	bool GBDT::EvalAndCheckEarlyStopping() {
		bool is_met_early_stopping = false;
		// print message for metric
		auto best_msg = OutputMetric(iter_);


		is_met_early_stopping = !best_msg.empty();
		if (is_met_early_stopping) {
			Log::Info("Early stopping at iteration %d, the best iteration round is %d",
				iter_, iter_ - early_stopping_round_);
			Log::Info("Output of best iteration round:\n%s", best_msg.c_str());
			// pop last early_stopping_round_ models
			for (int i = 0; i < early_stopping_round_ * num_tree_per_iteration_; ++i) {
				models_.pop_back();
			}
		}
		return is_met_early_stopping;
	}

	void GBDT::UpdateScore(const Tree* tree, const int cur_tree_id) {
		Common::FunctionTimer fun_timer("GBDT::UpdateScore", global_timer);
		// update training score
		if (!is_use_subset_) {
			train_score_updater_->AddScore(tree_learner_.get(), tree, cur_tree_id);
			// we need to predict out-of-bag scores of data for boosting
			if (num_data_ - bag_data_cnt_ > 0) {
				train_score_updater_->AddScore(tree, bag_data_indices_.data() + bag_data_cnt_, num_data_ - bag_data_cnt_, cur_tree_id);
			}
		}
		else {
			train_score_updater_->AddScore(tree, cur_tree_id);
		}
		// update validation score
		for (auto& score_updater : valid_score_updater_) {
			score_updater->AddScore(tree, cur_tree_id);
		}
	}

	std::vector<double> GBDT::EvalOneMetric(const Metric* metric, const double* score) const {
		return metric->Eval(score, objective_function_, &residual_variance_);
	}

	std::string GBDT::OutputMetric(int iter) {
		bool need_output = (iter % config_->metric_freq) == 0;
		std::string ret = "";
		std::stringstream msg_buf;
		std::vector<std::pair<size_t, size_t>> meet_early_stopping_pairs;
		// print training metric
		if (need_output) {
			for (auto& sub_metric : training_metrics_) {
				auto name = sub_metric->GetName();
				auto scores = EvalOneMetric(sub_metric, train_score_updater_->score());
				for (size_t k = 0; k < name.size(); ++k) {
					std::stringstream tmp_buf;
					tmp_buf << "Iteration:" << iter
						<< ", training " << name[k]
						<< " : " << scores[k];
					Log::Info(tmp_buf.str().c_str());
					if (early_stopping_round_ > 0) {
						msg_buf << tmp_buf.str() << '\n';
					}
				}
			}
		}
		// print validation metric
		if (need_output || early_stopping_round_ > 0) {
			for (size_t i = 0; i < valid_metrics_.size(); ++i) {
				for (size_t j = 0; j < valid_metrics_[i].size(); ++j) {
					auto test_scores = EvalOneMetric(valid_metrics_[i][j], valid_score_updater_[i]->score());
					auto name = valid_metrics_[i][j]->GetName();
					for (size_t k = 0; k < name.size(); ++k) {
						std::stringstream tmp_buf;
						tmp_buf << "Iteration:" << iter
							<< ", valid_" << i + 1 << " " << name[k]
							<< " : " << test_scores[k];
						if (need_output) {
							Log::Info(tmp_buf.str().c_str());
						}
						if (early_stopping_round_ > 0) {
							msg_buf << tmp_buf.str() << '\n';
						}
					}
					if (es_first_metric_only_ && j > 0) { continue; }
					if (ret.empty() && early_stopping_round_ > 0) {
						auto cur_score = valid_metrics_[i][j]->factor_to_bigger_better() * test_scores.back();
						if (cur_score > best_score_[i][j]) {
							best_score_[i][j] = cur_score;
							best_iter_[i][j] = iter;
							meet_early_stopping_pairs.emplace_back(i, j);
						}
						else {
							if (iter - best_iter_[i][j] >= early_stopping_round_) { ret = best_msg_[i][j]; }
						}
					}
				}
			}
		}
		for (auto& pair : meet_early_stopping_pairs) {
			best_msg_[pair.first][pair.second] = msg_buf.str();
		}
		return ret;
	}

	/*! \brief Get eval result */
	std::vector<double> GBDT::GetEvalAt(int data_idx) const {
		CHECK(data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()));
		std::vector<double> ret;
		if (data_idx == 0) {
			for (auto& sub_metric : training_metrics_) {
				auto scores = EvalOneMetric(sub_metric, train_score_updater_->score());
				for (auto score : scores) {
					ret.push_back(score);
				}
			}
		}
		else {
			auto used_idx = data_idx - 1;
			for (size_t j = 0; j < valid_metrics_[used_idx].size(); ++j) {
				auto test_scores = EvalOneMetric(valid_metrics_[used_idx][j], valid_score_updater_[used_idx]->score());
				for (auto score : test_scores) {
					ret.push_back(score);
				}
			}
		}
		return ret;
	}

	/*! \brief Get training scores result */
	const double* GBDT::GetTrainingScore(int64_t* out_len) {
		*out_len = static_cast<int64_t>(train_score_updater_->num_data())* num_class_;
		return train_score_updater_->score();
	}

	void GBDT::PredictContrib(const double* features, double* output) const {
		// set zero
		const int num_features = max_feature_idx_ + 1;
		std::memset(output, 0, sizeof(double) * num_tree_per_iteration_ * (num_features + 1));
		const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;
		for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {
			// predict all the trees for one iteration
			for (int k = 0; k < num_tree_per_iteration_; ++k) {
				models_[i * num_tree_per_iteration_ + k]->PredictContrib(features, num_features, output + k * (num_features + 1));
			}
		}
	}

	void GBDT::PredictContribByMap(const std::unordered_map<int, double>& features,
		std::vector<std::unordered_map<int, double>>* output) const {
		const int num_features = max_feature_idx_ + 1;
		const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;
		for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {
			// predict all the trees for one iteration
			for (int k = 0; k < num_tree_per_iteration_; ++k) {
				models_[i * num_tree_per_iteration_ + k]->PredictContribByMap(features, num_features, &((*output)[k]));
			}
		}
	}

	void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) {
		CHECK(data_idx >= 0 && data_idx <= static_cast<int>(valid_score_updater_.size()));

		const double* raw_scores = nullptr;
		data_size_t num_data = 0;
		if (data_idx == 0) {
			raw_scores = GetTrainingScore(out_len);
			num_data = train_score_updater_->num_data();
		}
		else {
			auto used_idx = data_idx - 1;
			raw_scores = valid_score_updater_[used_idx]->score();
			num_data = valid_score_updater_[used_idx]->num_data();
			*out_len = static_cast<int64_t>(num_data)* num_class_;
		}
		if (objective_function_ != nullptr) {
#pragma omp parallel for schedule(static)
			for (data_size_t i = 0; i < num_data; ++i) {
				std::vector<double> tree_pred(num_tree_per_iteration_);
				for (int j = 0; j < num_tree_per_iteration_; ++j) {
					tree_pred[j] = raw_scores[j * num_data + i];
				}
				std::vector<double> tmp_result(num_class_);
				objective_function_->ConvertOutput(tree_pred.data(), tmp_result.data());
				for (int j = 0; j < num_class_; ++j) {
					out_result[j * num_data + i] = static_cast<double>(tmp_result[j]);
				}
			}
		}
		else {
#pragma omp parallel for schedule(static)
			for (data_size_t i = 0; i < num_data; ++i) {
				for (int j = 0; j < num_tree_per_iteration_; ++j) {
					out_result[j * num_data + i] = static_cast<double>(raw_scores[j * num_data + i]);
				}
			}
		}
	}

	double GBDT::GetUpperBoundValue() const {
		double max_value = 0.0;
		for (const auto& tree : models_) {
			max_value += tree->GetUpperBoundValue();
		}
		return max_value;
	}

	double GBDT::GetLowerBoundValue() const {
		double min_value = 0.0;
		for (const auto& tree : models_) {
			min_value += tree->GetLowerBoundValue();
		}
		return min_value;
	}

	void GBDT::ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,
		const std::vector<const Metric*>& training_metrics) {
		if (train_data != train_data_ && !train_data_->CheckAlign(*train_data)) {
			Log::Fatal("Cannot reset training data, since new training data has different bin mappers");
		}

		objective_function_ = objective_function;
		if (objective_function_ != nullptr) {
			CHECK_EQ(num_tree_per_iteration_, objective_function_->NumModelPerIteration());
			if (objective_function_->IsRenewTreeOutput() && !config_->monotone_constraints.empty()) {
				Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName());
			}
		}
		is_constant_hessian_ = GetIsConstHessian(objective_function);

		// push training metrics
		training_metrics_.clear();
		for (const auto& metric : training_metrics) {
			training_metrics_.push_back(metric);
		}
		training_metrics_.shrink_to_fit();

		if (train_data != train_data_) {
			train_data_ = train_data;
			// not same training data, need reset score and others
			// create score tracker
			train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_,
				use_nesterov_acc_));

			// update score
			for (int i = 0; i < iter_; ++i) {
				for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
					auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id;
					train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);
				}
			}

			num_data_ = train_data_->num_data();

			// create buffer for gradients and hessians
			if (objective_function_ != nullptr) {
				size_t total_size = static_cast<size_t>(num_data_)* num_tree_per_iteration_;
				gradients_.resize(total_size);
				hessians_.resize(total_size);
			}

			max_feature_idx_ = train_data_->num_total_features() - 1;
			label_idx_ = train_data_->label_idx();
			feature_names_ = train_data_->feature_names();
			feature_infos_ = train_data_->feature_infos();

			tree_learner_->ResetTrainingData(train_data, is_constant_hessian_);
			ResetBaggingConfig(config_.get(), true);
		}
		else {
			tree_learner_->ResetIsConstantHessian(is_constant_hessian_);
		}
	}

	void GBDT::ResetConfig(const Config* config) {
		auto new_config = std::unique_ptr<Config>(new Config(*config));
		if (!config->monotone_constraints.empty()) {
			CHECK_EQ(static_cast<size_t>(train_data_->num_total_features()), config->monotone_constraints.size());
		}
		if (!config->feature_contri.empty()) {
			CHECK_EQ(static_cast<size_t>(train_data_->num_total_features()), config->feature_contri.size());
		}
		if (objective_function_ != nullptr && objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) {
			Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName());
		}
		early_stopping_round_ = new_config->early_stopping_round;
		shrinkage_rate_ = new_config->learning_rate;
		if (tree_learner_ != nullptr) {
			tree_learner_->ResetConfig(new_config.get());
		}
		if (train_data_ != nullptr) {
			ResetBaggingConfig(new_config.get(), false);
		}
		if (config_.get() != nullptr && config_->forcedsplits_filename != new_config->forcedsplits_filename) {
			// load forced_splits file
			if (!new_config->forcedsplits_filename.empty()) {
				std::ifstream forced_splits_file(
					new_config->forcedsplits_filename.c_str());
				std::stringstream buffer;
				buffer << forced_splits_file.rdbuf();
				std::string err;
				forced_splits_json_ = Json::parse(buffer.str(), &err);
				tree_learner_->SetForcedSplit(&forced_splits_json_);
			}
			else {
				forced_splits_json_ = Json();
				tree_learner_->SetForcedSplit(nullptr);
			}
		}
		config_.reset(new_config.release());
	}

	void GBDT::ResetBaggingConfig(const Config* config, bool is_change_dataset) {
		// if need bagging, create buffer
		data_size_t num_pos_data = 0;
		if (objective_function_ != nullptr) {
			num_pos_data = objective_function_->NumPositiveData();
		}
		bool balance_bagging_cond = (config->pos_bagging_fraction < 1.0 || config->neg_bagging_fraction < 1.0) && (num_pos_data > 0);
		if ((config->bagging_fraction < 1.0 || balance_bagging_cond) && config->bagging_freq > 0) {
			need_re_bagging_ = false;
			if (!is_change_dataset &&
				config_.get() != nullptr && config_->bagging_fraction == config->bagging_fraction && config_->bagging_freq == config->bagging_freq
				&& config_->pos_bagging_fraction == config->pos_bagging_fraction && config_->neg_bagging_fraction == config->neg_bagging_fraction) {
				return;
			}
			if (balance_bagging_cond) {
				balanced_bagging_ = true;
				bag_data_cnt_ = static_cast<data_size_t>(num_pos_data * config->pos_bagging_fraction)
					+ static_cast<data_size_t>((num_data_ - num_pos_data) * config->neg_bagging_fraction);
			}
			else {
				bag_data_cnt_ = static_cast<data_size_t>(config->bagging_fraction * num_data_);
			}
			bag_data_indices_.resize(num_data_);
			bagging_runner_.ReSize(num_data_);
			bagging_rands_.clear();
			for (int i = 0;
				i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) {
				bagging_rands_.emplace_back(config_->bagging_seed + i);
			}

			double average_bag_rate =
				(static_cast<double>(bag_data_cnt_) / num_data_) / config->bagging_freq;
			is_use_subset_ = false;
			const int group_threshold_usesubset = 100;
			if (average_bag_rate <= 0.5
				&& (train_data_->num_feature_groups() < group_threshold_usesubset)) {
				if (tmp_subset_ == nullptr || is_change_dataset) {
					tmp_subset_.reset(new Dataset(bag_data_cnt_));
					tmp_subset_->CopyFeatureMapperFrom(train_data_);
				}
				is_use_subset_ = true;
				Log::Debug("Use subset for bagging");
			}

			need_re_bagging_ = true;

			if (is_use_subset_ && bag_data_cnt_ < num_data_) {
				if (objective_function_ == nullptr) {
					size_t total_size = static_cast<size_t>(num_data_)* num_tree_per_iteration_;
					gradients_.resize(total_size);
					hessians_.resize(total_size);
				}
			}
		}
		else {
			bag_data_cnt_ = num_data_;
			bag_data_indices_.clear();
			bagging_runner_.ReSize(0);
			is_use_subset_ = false;
		}
	}

}  // namespace LightGBM
