本文作者为 tieying zhang，有任何问题请联系 zhangtiey@gmail.com
Lightgbm 以轻量著称，所以在实际的 C ++ 程序中，常常需要使用。但是官方文档并没有介绍如何在 C ++ 中调用 lightgbm 接口，也没有任何例子可供参考，网上的文档也基本没有。这篇文章中我介绍下如何在 C ++ 中调用 lightgbm。有任何问题请联系 zhangtiey@gmail.com
具体步骤如下：
首先需要下载 lightgbm 的源码包，从官网下载即可。官网也给出了如何编译，但是最后一定要 sudo make install（这个官网没有给出）。
C++ 调用的代码片段如下。首先 load 已经 train 好的 model（以 txt 的形式存在磁盘上），之后用该模型进行 inference，需要预测的数据可以是文件形式直接指定目录，也可以直接多行数据塞给模型。
编译 C ++ 文件：g++ -g -Wall -std=c++11 test.cpp -l_lightgbm 注意，用到了 l_lightgbm，这个.so 库是上面 make install 直接放入到了 /usr/local/lib 下。如果找不到该库，需要 whereis 查看一下，把相应目录加入到 lib path 里如：export LD_LIBRARY_PATH=/lib:/usr/lib:/usr/local/lib
#include <LightGBM/c_api.h>
#include <iostream>
#include <vector>
std::string predict(std::string data)
{
std::string pred_result = “”;
int temp;
int p = 1;
BoosterHandle handle;
// load model
temp = LGBM_BoosterCreateFromModelfile(“test_model1.txt”, &p, &handle);
std::cout <<“load result value is “<<temp <<std::endl;
// file data
const char* para = “None”;
int res = LGBM_BoosterPredictForFile(handle, “test_data.csv”, 0, C_API_PREDICT_NORMAL, 0, para, “result”);
std::cout << “file predict result is ” << res << std::endl;
// row data
std::vector<int> row(40, 0);
void* in_p = static_cast<void*>(row.data());
std::vector<double> out(1, 0);
double* out_result = static_cast<double*>(out.data());
int64_t out_len;
res = LGBM_BoosterPredictForMat(handle, in_p, C_API_DTYPE_FLOAT32, 1, 40, 1, C_API_PREDICT_NORMAL, 50, “None”, &out_len, out_result);
std::cout << “row predict return is ” << res << std::endl;
std::cout << “row predict result size is ” << out.size() << ” value is ” << out[0] << std::endl;
return pred_result;
/*I know the above return statement is completely insignificant. But i wanted to use the loaded model to predict the data points further.*/
}
int main() {
predict(“hahaha”);
std::cout << “Ok complete!”<< std::endl;
return 0;
}
遇到的问题汇总：
lib_lightgbm.so: cannot open shared object file: No such file or directory
export LD_LIBRARY_PATH=/lib:/usr/lib:/usr/local/lib
代码参照
data_size_t 定义在 include/LightGBM/meta.h：
typedef int32_t data_size_t;
用 C ++ 解析输入 file 可以借鉴已有 code：在 application/predictor.hpp 中。注意比较重要的是 TextReader<data_size_t> predict_data_reader(data_filename, header) 使用了 utils 下面的 utils/text_reader.h
真正的 predict 函数在 application/predictor.cpp 里：
/*!
brief predicting on data, then saving result to disk
param data_filename Filename of data
param result_filename Filename of output result
*/
void Predict(const char* data_filename, const char* result_filename, bool header) {
auto writer = VirtualFileWriter::Make(result_filename);
if (!writer->Init()) {
Log::Fatal(“Prediction results file %s cannot be found”, result_filename);
}
auto parser = std::unique_ptr<Parser>(Parser::CreateParser(data_filename, header, boosting_->MaxFeatureIdx() + 1, boosting_->LabelIdx()));
if (parser == nullptr) {
Log::Fatal(“Could not recognize the data format of data file %s”, data_filename);
}
TextReader<data_size_t> predict_data_reader(data_filename, header);
std::unordered_map<int, int> feature_names_map_;
bool need_adjust = false;
if (header) {
std::string first_line = predict_data_reader.first_line();
std::vector<std::string> header_words = Common::Split(first_line.c_str(), “\t,”);
header_words.erase(header_words.begin() + boosting_->LabelIdx());
for (int i = 0; i < static_cast<int>(header_words.size()); ++i) {
for (int j = 0; j < static_cast<int>(boosting_->FeatureNames().size()); ++j) {
if (header_words[i] == boosting_->FeatureNames()[j]) {
feature_names_map_[i] = j;
break;
}
}
}
for (auto s : feature_names_map_) {
if (s.first != s.second) {
need_adjust = true;
break;
}
}
}
// function for parse data
std::function<void(const char*, std::vector<std::pair<int, double>>*)> parser_fun;
double tmp_label;
parser_fun = [&]
(const char* buffer, std::vector<std::pair<int, double>>* feature) {
parser->ParseOneLine(buffer, feature, &tmp_label);
if (need_adjust) {
int i = 0, j = static_cast<int>(feature->size());
while (i < j) {
if (feature_names_map_.find((*feature)[i].first) != feature_names_map_.end()) {
(*feature)[i].first = feature_names_map_[(*feature)[i].first];
++i;
} else {
//move the non-used features to the end of the feature vector
std::swap((*feature)[i], (*feature)[–j]);
}
}
feature->resize(i);
}
};
std::function<void(data_size_t, const std::vector<std::string>&)> process_fun = [&]
(data_size_t, const std::vector<std::string>& lines) {
std::vector<std::pair<int, double>> oneline_features;
std::vector<std::string> result_to_write(lines.size());
OMP_INIT_EX();
#pragma omp parallel for schedule(static) firstprivate(oneline_features)
for (data_size_t i = 0; i < static_cast<data_size_t>(lines.size()); ++i) {
OMP_LOOP_EX_BEGIN();
oneline_features.clear();
// parser
parser_fun(lines[i].c_str(), &oneline_features);
// predict
std::vector<double> result(num_pred_one_row_);
predict_fun_(oneline_features, result.data());
auto str_result = Common::Join<double>(result, “\t”);
result_to_write[i] = str_result;
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
for (data_size_t i = 0; i < static_cast<data_size_t>(result_to_write.size()); ++i) {
writer->Write(result_to_write[i].c_str(), result_to_write[i].size());
writer->Write(“\n”, 1);
}
};
predict_data_reader.ReadAllAndProcessParallel(process_fun);
}