add auc evaluation metric
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tqchen
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7f9637aae4
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ea354683b4
@ -172,7 +172,7 @@ namespace xgboost{
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = mparam.PredTransform
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(mparam.base_score + base_gbm.Predict(data.data, j, -1));
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@ -213,7 +213,7 @@ namespace xgboost{
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inline void InteractPredict(std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset){
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = mparam.PredTransform
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(mparam.base_score + base_gbm.InteractPredict(data.data, j, buffer_offset + j));
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@ -222,7 +222,7 @@ namespace xgboost{
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/*! \brief repredict trial */
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inline void InteractRePredict(const DMatrix &data, unsigned buffer_offset){
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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base_gbm.InteractRePredict(data.data, j, buffer_offset + j);
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}
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@ -233,7 +233,7 @@ namespace xgboost{
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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preds[j] = mparam.PredTransform
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(mparam.base_score + base_gbm.Predict(data.data, j, buffer_offset + j));
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@ -242,13 +242,13 @@ namespace xgboost{
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels */
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inline void GetGradient(const std::vector<float> &preds,
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const std::vector<float> &labels,
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std::vector<float> &grad,
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std::vector<float> &hess){
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const std::vector<float> &labels,
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std::vector<float> &grad,
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std::vector<float> &hess){
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grad.resize(preds.size()); hess.resize(preds.size());
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const unsigned ndata = static_cast<unsigned>(preds.size());
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#pragma omp parallel for schedule( static )
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j){
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grad[j] = mparam.FirstOrderGradient(preds[j], labels[j]);
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hess[j] = mparam.SecondOrderGradient(preds[j], labels[j]);
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@ -11,6 +11,7 @@
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#include <algorithm>
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#include "../utils/xgboost_utils.h"
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#include "../utils/xgboost_omp.h"
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#include "../utils/xgboost_random.h"
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namespace xgboost{
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namespace regression{
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@ -30,10 +31,10 @@ namespace xgboost{
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/*! \brief RMSE */
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struct EvalRMSE : public IEvaluator{
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virtual float Eval(const std::vector<float> &preds,
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const std::vector<float> &labels) const{
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const std::vector<float> &labels) const{
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const unsigned ndata = static_cast<unsigned>(preds.size());
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float sum = 0.0;
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#pragma omp parallel for reduction(+:sum) schedule( static )
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#pragma omp parallel for reduction(+:sum) schedule( static )
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for (unsigned i = 0; i < ndata; ++i){
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float diff = preds[i] - labels[i];
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sum += diff * diff;
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@ -48,10 +49,10 @@ namespace xgboost{
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/*! \brief Error */
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struct EvalError : public IEvaluator{
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virtual float Eval(const std::vector<float> &preds,
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const std::vector<float> &labels) const{
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const std::vector<float> &labels) const{
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const unsigned ndata = static_cast<unsigned>(preds.size());
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unsigned nerr = 0;
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#pragma omp parallel for reduction(+:nerr) schedule( static )
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#pragma omp parallel for reduction(+:nerr) schedule( static )
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for (unsigned i = 0; i < ndata; ++i){
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if (preds[i] > 0.5f){
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if (labels[i] < 0.5f) nerr += 1;
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@ -67,14 +68,46 @@ namespace xgboost{
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}
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};
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/*! \brief Area under curve */
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struct EvalAuc : public IEvaluator{
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inline static bool CmpFirst( const std::pair<float,float> &a, const std::pair<float,float> &b ){
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return a.first > b.first;
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}
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virtual float Eval( const std::vector<float> &preds,
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const std::vector<float> &labels ) const{
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const unsigned ndata = static_cast<unsigned>( preds.size() );
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std::vector< std::pair<float, float> > rec;
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for( unsigned i = 0; i < ndata; ++ i ){
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rec.push_back( std::make_pair( preds[i], labels[i]) );
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}
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random::Shuffle( rec );
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std::sort( rec.begin(), rec.end(), CmpFirst );
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long npos = 0, nhit = 0;
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for( unsigned i = 0; i < ndata; ++ i ){
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if( rec[i].second > 0.5f ) {
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++ npos;
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}else{
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// this is the number of correct pairs
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nhit += npos;
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}
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}
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long nneg = ndata - npos;
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utils::Assert( nneg > 0, "the dataset only contains pos samples" );
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return static_cast<float>(nhit) / nneg / npos;
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}
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virtual const char *Name( void ) const{
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return "auc";
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}
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};
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/*! \brief Error */
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struct EvalLogLoss : public IEvaluator{
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virtual float Eval(const std::vector<float> &preds,
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const std::vector<float> &labels) const{
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const std::vector<float> &labels) const{
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const unsigned ndata = static_cast<unsigned>(preds.size());
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unsigned nerr = 0;
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#pragma omp parallel for reduction(+:nerr) schedule( static )
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#pragma omp parallel for reduction(+:nerr) schedule( static )
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for (unsigned i = 0; i < ndata; ++i){
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const float y = labels[i];
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const float py = preds[i];
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@ -96,14 +129,15 @@ namespace xgboost{
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if (!strcmp(name, "rmse")) evals_.push_back(&rmse_);
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if (!strcmp(name, "error")) evals_.push_back(&error_);
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if (!strcmp(name, "logloss")) evals_.push_back(&logloss_);
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if (!strcmp( name, "auc")) evals_.push_back( &auc_ );
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}
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inline void Init(void){
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std::sort(evals_.begin(), evals_.end());
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evals_.resize(std::unique(evals_.begin(), evals_.end()) - evals_.begin());
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}
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inline void Eval(FILE *fo, const char *evname,
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const std::vector<float> &preds,
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const std::vector<float> &labels) const{
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const std::vector<float> &preds,
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const std::vector<float> &labels) const{
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for (size_t i = 0; i < evals_.size(); ++i){
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float res = evals_[i]->Eval(preds, labels);
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fprintf(fo, "\t%s-%s:%f", evname, evals_[i]->Name(), res);
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@ -112,6 +146,7 @@ namespace xgboost{
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private:
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EvalRMSE rmse_;
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EvalError error_;
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EvalAuc auc_;
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EvalLogLoss logloss_;
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std::vector<const IEvaluator*> evals_;
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};
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