#include <XGBooster.h>

Public Member Functions
void	addFeature (std::string name)

float	predict (const int iterationEnd=0)

float	predict (const std::vector< float > &features, const int iterationEnd=0) const

void	reset ()
	Reset feature values. More...

void	set (std::string name, float value)

	XGBooster (std::string model_file)

	XGBooster (std::string model_file, std::string model_features)

Private Attributes
BoosterHandle	booster_

std::map< std::string, unsigned int >	feature_name_to_index_

std::vector< float >	features_

Detailed Description

Definition at line 11 of file XGBooster.h.

Constructor & Destructor Documentation

◆ XGBooster() [1/2]

XGBooster::XGBooster ( std::string model_file )

Definition at line 47 of file XGBooster.cc.

References booster_, and mps_update::status.

                                          {
   int status = XGBoosterCreate(nullptr, 0, &booster_);
   if (status != 0)
     throw std::runtime_error("Failed to create XGBooster");
   status = XGBoosterLoadModel(booster_, model_file.c_str());
   if (status != 0)
     throw std::runtime_error("Failed to load XGBoost model");
   XGBoosterSetParam(booster_, "nthread", "1");
 }

◆ XGBooster() [2/2]

XGBooster::XGBooster	(	std::string	model_file,
		std::string	model_features
	)

Definition at line 57 of file XGBooster.cc.

References addFeature(), Skims_PA_cff::content, lowptgsfeleseed::features(), geometryDiff::file, read_features(), and AlCaHLTBitMon_QueryRunRegistry::string.

                                                                    : XGBooster(model_file) {
   std::ifstream file(model_features);
   if (!file.is_open())
     throw std::runtime_error("Failed to open file: " + model_features);
 
   std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
   file.close();
 
   std::vector<std::string> features = read_features(content);
 
   for (const auto& feature : features) {
     addFeature(feature);
   }
 }

Member Function Documentation

◆ addFeature()

void XGBooster::addFeature ( std::string name )

Features need to be entered in the order they are used in the model

Definition at line 74 of file XGBooster.cc.

References feature_name_to_index_, features_, and Skims_PA_cff::name.

Referenced by XGBooster().

                                          {
   features_.push_back(0);
   feature_name_to_index_[name] = features_.size() - 1;
 }

◆ predict() [1/2]

float XGBooster::predict ( const int iterationEnd = 0 )

Definition at line 81 of file XGBooster.cc.

References feature_name_to_index_, features_, mps_fire::i, CommonMethods::isnan(), reset(), runTheMatrix::ret, and AlCaHLTBitMon_QueryRunRegistry::string.

Referenced by pat::computeSoftMvaRun3().

                                                {
   // check if all feature values are set properly
   for (unsigned int i = 0; i < features_.size(); ++i)
     if (std::isnan(features_.at(i))) {
       std::string feature_name;
       for (const auto& pair : feature_name_to_index_) {
         if (pair.second == i) {
           feature_name = pair.first;
           break;
         }
       }
       throw std::runtime_error("Feature is not set: " + feature_name);
     }
 
   float const ret = predict(features_, iterationEnd);
 
   reset();
 
   return ret;
 }

◆ predict() [2/2]

float XGBooster::predict	(	const std::vector< float > &	features,
		const int	iterationEnd = `0`
	)		const

Definition at line 102 of file XGBooster.cc.

References cms::cuda::assert(), booster_, feature_name_to_index_, lowptgsfeleseed::features(), mps_fire::result, runTheMatrix::ret, and offlineSlimmedPrimaryVertices_cfi::score.

                                                                                        {
   float result{-999.};
 
   if (features.empty()) {
     throw std::runtime_error("Vector of input features is empty");
   }
 
   if (feature_name_to_index_.size() != features.size())
     throw std::runtime_error("Feature size mismatch");
 
   DMatrixHandle dvalues;
   XGDMatrixCreateFromMat(&features[0], 1, features.size(), 9e99, &dvalues);
 
   bst_ulong out_len = 0;
   const float* score = nullptr;
 
   char json[256];  // Make sure the buffer is large enough to hold the resulting JSON string
 
   // Use snprintf to format the JSON string with the external value
   std::snprintf(json,
                 sizeof(json),
                 R"({
     "type": 0,
     "training": false,
     "iteration_begin": 0,
     "iteration_end": %d,
     "strict_shape": false
    })",
                 iterationEnd);
 
   // Shape of output prediction
   bst_ulong const* out_shape = nullptr;
 
   auto ret = XGBoosterPredictFromDMatrix(booster_, dvalues, json, &out_shape, &out_len, &score);
 
   if (ret == 0) {
     assert(out_len == 1 && "Unexpected prediction format");
     result = score[0];
   }
 
   XGDMatrixFree(dvalues);
 
   return result;
 }