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rf.hpp
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rf.hpp
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/*!
* Copyright (c) 2017 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#ifndef LIGHTGBM_BOOSTING_RF_H_
#define LIGHTGBM_BOOSTING_RF_H_
#include <LightGBM/boosting.h>
#include <LightGBM/metric.h>
#include <string>
#include <cstdio>
#include <fstream>
#include <memory>
#include <utility>
#include <vector>
#include "gbdt.h"
#include "score_updater.hpp"
namespace LightGBM {
/*!
* \brief Rondom Forest implementation
*/
class RF : public GBDT {
public:
RF() : GBDT() {
average_output_ = true;
}
~RF() {}
void Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function,
const std::vector<const Metric*>& training_metrics) override {
CHECK(config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f);
CHECK(config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f);
GBDT::Init(config, train_data, objective_function, training_metrics);
if (num_init_iteration_ > 0) {
for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
MultiplyScore(cur_tree_id, 1.0f / num_init_iteration_);
}
} else {
CHECK(train_data->metadata().init_score() == nullptr);
}
CHECK(num_tree_per_iteration_ == num_class_);
// not shrinkage rate for the RF
shrinkage_rate_ = 1.0f;
// only boosting one time
Boosting();
if (is_use_subset_ && bag_data_cnt_ < num_data_) {
tmp_grad_.resize(num_data_);
tmp_hess_.resize(num_data_);
}
}
void ResetConfig(const Config* config) override {
CHECK(config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f);
CHECK(config->feature_fraction <= 1.0f && config->feature_fraction > 0.0f);
GBDT::ResetConfig(config);
// not shrinkage rate for the RF
shrinkage_rate_ = 1.0f;
}
void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,
const std::vector<const Metric*>& training_metrics) override {
GBDT::ResetTrainingData(train_data, objective_function, training_metrics);
if (iter_ + num_init_iteration_ > 0) {
for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
train_score_updater_->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id);
}
}
CHECK(num_tree_per_iteration_ == num_class_);
// only boosting one time
Boosting();
if (is_use_subset_ && bag_data_cnt_ < num_data_) {
tmp_grad_.resize(num_data_);
tmp_hess_.resize(num_data_);
}
}
void Boosting() override {
if (objective_function_ == nullptr) {
Log::Fatal("RF mode do not support custom objective function, please use built-in objectives.");
}
init_scores_.resize(num_tree_per_iteration_, 0.0);
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, false);
}
size_t total_size = static_cast<size_t>(num_data_) * num_tree_per_iteration_;
std::vector<double> tmp_scores(total_size, 0.0f);
#pragma omp parallel for schedule(static)
for (int j = 0; j < num_tree_per_iteration_; ++j) {
size_t offset = static_cast<size_t>(j)* num_data_;
for (data_size_t i = 0; i < num_data_; ++i) {
tmp_scores[offset + i] = init_scores_[j];
}
}
objective_function_->
GetGradients(tmp_scores.data(), gradients_.data(), hessians_.data());
}
bool TrainOneIter(const score_t* gradients, const score_t* hessians) override {
// bagging logic
Bagging(iter_);
CHECK(gradients == nullptr);
CHECK(hessians == nullptr);
gradients = gradients_.data();
hessians = hessians_.data();
for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
std::unique_ptr<Tree> new_tree(new Tree(2));
size_t offset = static_cast<size_t>(cur_tree_id)* num_data_;
if (class_need_train_[cur_tree_id]) {
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) {
tmp_grad_[i] = grad[bag_data_indices_[i]];
tmp_hess_[i] = hess[bag_data_indices_[i]];
}
grad = tmp_grad_.data();
hess = tmp_hess_.data();
}
new_tree.reset(tree_learner_->Train(grad, hess, is_constant_hessian_,
forced_splits_json_));
}
if (new_tree->num_leaves() > 1) {
double pred = init_scores_[cur_tree_id];
auto residual_getter = [pred](const label_t* label, int i) {return static_cast<double>(label[i]) - pred; };
tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,
num_data_, bag_data_indices_.data(), bag_data_cnt_);
if (std::fabs(init_scores_[cur_tree_id]) > kEpsilon) {
new_tree->AddBias(init_scores_[cur_tree_id]);
}
// update score
MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));
UpdateScore(new_tree.get(), cur_tree_id);
MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1));
} 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) {
output = objective_function_->BoostFromScore(cur_tree_id);
} else {
output = init_scores_[cur_tree_id];
}
}
new_tree->AsConstantTree(output);
MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));
UpdateScore(new_tree.get(), cur_tree_id);
MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1));
}
}
// add model
models_.push_back(std::move(new_tree));
}
++iter_;
return false;
}
void RollbackOneIter() override {
if (iter_ <= 0) { return; }
int cur_iter = iter_ + num_init_iteration_ - 1;
// reset score
for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
auto curr_tree = cur_iter * num_tree_per_iteration_ + cur_tree_id;
models_[curr_tree]->Shrinkage(-1.0);
MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));
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);
}
MultiplyScore(cur_tree_id, 1.0f / (iter_ + num_init_iteration_ - 1));
}
// remove model
for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
models_.pop_back();
}
--iter_;
}
void MultiplyScore(const int cur_tree_id, double val) {
train_score_updater_->MultiplyScore(val, cur_tree_id);
for (auto& score_updater : valid_score_updater_) {
score_updater->MultiplyScore(val, cur_tree_id);
}
}
void AddValidDataset(const Dataset* valid_data,
const std::vector<const Metric*>& valid_metrics) override {
GBDT::AddValidDataset(valid_data, valid_metrics);
if (iter_ + num_init_iteration_ > 0) {
for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
valid_score_updater_.back()->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id);
}
}
}
bool NeedAccuratePrediction() const override {
// No early stopping for prediction
return true;
};
private:
std::vector<score_t> tmp_grad_;
std::vector<score_t> tmp_hess_;
std::vector<double> init_scores_;
};
} // namespace LightGBM
#endif // LIGHTGBM_BOOSTING_RF_H_