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application.cpp
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application.cpp
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/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#include <LightGBM/application.h>
#include <LightGBM/boosting.h>
#include <LightGBM/dataset.h>
#include <LightGBM/dataset_loader.h>
#include <LightGBM/metric.h>
#include <LightGBM/network.h>
#include <LightGBM/objective_function.h>
#include <LightGBM/prediction_early_stop.h>
#include <LightGBM/cuda/vector_cudahost.h>
#include <LightGBM/utils/common.h>
#include <LightGBM/utils/openmp_wrapper.h>
#include <LightGBM/utils/text_reader.h>
#include <string>
#include <chrono>
#include <cstdio>
#include <ctime>
#include <fstream>
#include <sstream>
#include <utility>
#include "predictor.hpp"
namespace LightGBM {
Common::Timer global_timer;
Application::Application(int argc, char** argv) {
LoadParameters(argc, argv);
// set number of threads for openmp
if (config_.num_threads > 0) {
omp_set_num_threads(config_.num_threads);
}
if (config_.data.size() == 0 && config_.task != TaskType::kConvertModel) {
Log::Fatal("No training/prediction data, application quit");
}
if (config_.device_type == std::string("cuda")) {
LGBM_config_::current_device = lgbm_device_cuda;
}
}
Application::~Application() {
if (config_.is_parallel) {
Network::Dispose();
}
}
void Application::LoadParameters(int argc, char** argv) {
std::unordered_map<std::string, std::string> params;
for (int i = 1; i < argc; ++i) {
Config::KV2Map(¶ms, argv[i]);
}
// check for alias
ParameterAlias::KeyAliasTransform(¶ms);
// read parameters from config file
if (params.count("config") > 0) {
TextReader<size_t> config_reader(params["config"].c_str(), false);
config_reader.ReadAllLines();
if (!config_reader.Lines().empty()) {
for (auto& line : config_reader.Lines()) {
// remove str after "#"
if (line.size() > 0 && std::string::npos != line.find_first_of("#")) {
line.erase(line.find_first_of("#"));
}
line = Common::Trim(line);
if (line.size() == 0) {
continue;
}
Config::KV2Map(¶ms, line.c_str());
}
} else {
Log::Warning("Config file %s doesn't exist, will ignore",
params["config"].c_str());
}
}
// check for alias again
ParameterAlias::KeyAliasTransform(¶ms);
// load configs
config_.Set(params);
Log::Info("Finished loading parameters");
}
void Application::LoadData() {
auto start_time = std::chrono::high_resolution_clock::now();
std::unique_ptr<Predictor> predictor;
// prediction is needed if using input initial model(continued train)
PredictFunction predict_fun = nullptr;
// need to continue training
if (boosting_->NumberOfTotalModel() > 0 && config_.task != TaskType::KRefitTree) {
predictor.reset(new Predictor(boosting_.get(), 0, -1, true, false, false, false, -1, -1));
predict_fun = predictor->GetPredictFunction();
}
// sync up random seed for data partition
if (config_.is_data_based_parallel) {
config_.data_random_seed = Network::GlobalSyncUpByMin(config_.data_random_seed);
}
Log::Debug("Loading train file...");
DatasetLoader dataset_loader(config_, predict_fun,
config_.num_class, config_.data.c_str());
// load Training data
if (config_.is_data_based_parallel) {
// load data for parallel training
train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(),
Network::rank(), Network::num_machines()));
} else {
// load data for single machine
train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1));
}
// need save binary file
if (config_.save_binary) {
train_data_->SaveBinaryFile(nullptr);
}
// create training metric
if (config_.is_provide_training_metric) {
for (auto metric_type : config_.metric) {
auto metric = std::unique_ptr<Metric>(Metric::CreateMetric(metric_type, config_));
if (metric == nullptr) { continue; }
metric->Init(train_data_->metadata(), train_data_->num_data());
train_metric_.push_back(std::move(metric));
}
}
train_metric_.shrink_to_fit();
if (!config_.metric.empty()) {
// only when have metrics then need to construct validation data
// Add validation data, if it exists
for (size_t i = 0; i < config_.valid.size(); ++i) {
Log::Debug("Loading validation file #%zu...", (i + 1));
// add
auto new_dataset = std::unique_ptr<Dataset>(
dataset_loader.LoadFromFileAlignWithOtherDataset(
config_.valid[i].c_str(),
train_data_.get()));
valid_datas_.push_back(std::move(new_dataset));
// need save binary file
if (config_.save_binary) {
valid_datas_.back()->SaveBinaryFile(nullptr);
}
// add metric for validation data
valid_metrics_.emplace_back();
for (auto metric_type : config_.metric) {
auto metric = std::unique_ptr<Metric>(Metric::CreateMetric(metric_type, config_));
if (metric == nullptr) { continue; }
metric->Init(valid_datas_.back()->metadata(),
valid_datas_.back()->num_data());
valid_metrics_.back().push_back(std::move(metric));
}
valid_metrics_.back().shrink_to_fit();
}
valid_datas_.shrink_to_fit();
valid_metrics_.shrink_to_fit();
}
auto end_time = std::chrono::high_resolution_clock::now();
// output used time on each iteration
Log::Info("Finished loading data in %f seconds",
std::chrono::duration<double, std::milli>(end_time - start_time) * 1e-3);
}
void Application::InitTrain() {
if (config_.is_parallel) {
// need init network
Network::Init(config_);
Log::Info("Finished initializing network");
config_.feature_fraction_seed =
Network::GlobalSyncUpByMin(config_.feature_fraction_seed);
config_.feature_fraction =
Network::GlobalSyncUpByMin(config_.feature_fraction);
config_.drop_seed =
Network::GlobalSyncUpByMin(config_.drop_seed);
}
// create boosting
boosting_.reset(
Boosting::CreateBoosting(config_.boosting,
config_.input_model.c_str()));
// create objective function
objective_fun_.reset(
ObjectiveFunction::CreateObjectiveFunction(config_.objective,
config_));
// load training data
LoadData();
// initialize the objective function
objective_fun_->Init(train_data_->metadata(), train_data_->num_data());
// initialize the boosting
boosting_->Init(&config_, train_data_.get(), objective_fun_.get(),
Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
// add validation data into boosting
for (size_t i = 0; i < valid_datas_.size(); ++i) {
boosting_->AddValidDataset(valid_datas_[i].get(),
Common::ConstPtrInVectorWrapper<Metric>(valid_metrics_[i]));
Log::Debug("Number of data points in validation set #%zu: %zu", i + 1, valid_datas_[i]->num_data());
}
Log::Info("Finished initializing training");
}
void Application::Train() {
Log::Info("Started training...");
boosting_->Train(config_.snapshot_freq, config_.output_model);
boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type,
config_.output_model.c_str());
// convert model to if-else statement code
if (config_.convert_model_language == std::string("cpp")) {
boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str());
}
Log::Info("Finished training");
}
void Application::Predict() {
if (config_.task == TaskType::KRefitTree) {
// create predictor
Predictor predictor(boosting_.get(), 0, -1, false, true, false, false, 1, 1);
predictor.Predict(config_.data.c_str(), config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check);
TextReader<int> result_reader(config_.output_result.c_str(), false);
result_reader.ReadAllLines();
std::vector<std::vector<int>> pred_leaf(result_reader.Lines().size());
#pragma omp parallel for schedule(static)
for (int i = 0; i < static_cast<int>(result_reader.Lines().size()); ++i) {
pred_leaf[i] = Common::StringToArray<int>(result_reader.Lines()[i], '\t');
// Free memory
result_reader.Lines()[i].clear();
}
DatasetLoader dataset_loader(config_, nullptr,
config_.num_class, config_.data.c_str());
train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1));
train_metric_.clear();
objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,
config_));
objective_fun_->Init(train_data_->metadata(), train_data_->num_data());
boosting_->Init(&config_, train_data_.get(), objective_fun_.get(),
Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
boosting_->RefitTree(pred_leaf);
boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type,
config_.output_model.c_str());
Log::Info("Finished RefitTree");
} else {
// create predictor
Predictor predictor(boosting_.get(), config_.start_iteration_predict, config_.num_iteration_predict, config_.predict_raw_score,
config_.predict_leaf_index, config_.predict_contrib,
config_.pred_early_stop, config_.pred_early_stop_freq,
config_.pred_early_stop_margin);
predictor.Predict(config_.data.c_str(),
config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check);
Log::Info("Finished prediction");
}
}
void Application::InitPredict() {
boosting_.reset(
Boosting::CreateBoosting("gbdt", config_.input_model.c_str()));
Log::Info("Finished initializing prediction, total used %d iterations", boosting_->GetCurrentIteration());
}
void Application::ConvertModel() {
boosting_.reset(
Boosting::CreateBoosting(config_.boosting, config_.input_model.c_str()));
boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str());
}
} // namespace LightGBM