pairwise ranking implemented
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kalenhaha
parent
0b1e584d73
commit
c62dea8325
@ -94,20 +94,20 @@ public:
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inline void InitTrainer( void ) {
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base_gbm.InitTrainer();
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if( mparam.loss_type == PAIRWISE) {
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evaluator_.AddEval( "pairwise" );
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evaluator_.AddEval( "PAIR" );
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} else if( mparam.loss_type == MAP) {
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evaluator_.AddEval( "MAP" );
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} else {
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evaluator_.AddEval( "NDCG" );
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}
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evaluator_.Init();
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sampler.AssignSampler(mparam.sampler_type);
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}
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/*!
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* \brief initialize the current data storage for model, if the model is used first time, call this function
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*/
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inline void InitModel( void ) {
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base_gbm.InitModel();
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mparam.AdjustBase();
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}
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/*!
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* \brief load model from stream
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@ -134,6 +134,7 @@ public:
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inline void DumpPath( FILE *fo, const RMatrix &data ) {
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base_gbm.DumpPath( fo, data.data );
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}
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/*!
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* \brief save model to stream
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* \param fo output stream
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@ -142,6 +143,7 @@ public:
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base_gbm.SaveModel( fo );
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fo.Write( &mparam, sizeof(ModelParam) );
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}
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/*!
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* \brief update the model for one iteration
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* \param iteration iteration number
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@ -177,7 +179,7 @@ public:
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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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for( unsigned j = 0; j < ndata; ++ j ) {
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preds[j] = mparam.base_score + base_gbm.Predict( data.data, j, -1 );
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preds[j] = base_gbm.Predict( data.data, j, -1 );
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}
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}
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@ -219,7 +221,7 @@ private:
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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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for( unsigned j = 0; j < ndata; ++ j ) {
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preds[j] = mparam.base_score + base_gbm.InteractPredict( data.data, j, buffer_offset + j );
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preds[j] = base_gbm.InteractPredict( data.data, j, buffer_offset + j );
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}
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}
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/*! \brief repredict trial */
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@ -238,7 +240,7 @@ private:
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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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for( unsigned j = 0; j < ndata; ++ j ) {
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preds[j] = mparam.base_score + base_gbm.Predict( data.data, j, buffer_offset + j );
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preds[j] = base_gbm.Predict( data.data, j, buffer_offset + j );
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}
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}
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@ -254,13 +256,13 @@ private:
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bool j_better;
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float pred_diff,pred_diff_exp,first_order_gradient,second_order_gradient;
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for(int i = 0; i < group_index.size() - 1; i++){
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xgboost::rank::sample::PairSamplerSet sampler;
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xgboost::rank::sample::Pairs pairs = sampler.GenPairs(preds,labels,group_index[i],group_index[i+1]);
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sample::Pairs pairs = sampler.GenPairs(preds,labels,group_index[i],group_index[i+1]);
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for(int j = group_index[i]; j < group_index[i + 1]; j++){
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std::vector<int> pair_instance = pairs.GetPairs(j);
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for(int k = 0; k < pair_instance.size(); k++){
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j_better = labels[j] > labels[pair_instance[k]];
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if(j_better){
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for(int k = 0; k < pair_instance.size(); k++){
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pred_diff = preds[preds[j] - pair_instance[k]];
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pred_diff_exp = j_better? expf(-pred_diff):expf(pred_diff);
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first_order_gradient = mparam.FirstOrderGradient(pred_diff_exp);
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@ -285,17 +287,16 @@ private:
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/*! \brief training parameter for regression */
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struct ModelParam {
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/* \brief global bias */
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float base_score;
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/* \brief type of loss function */
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int loss_type;
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/* \brief number of features */
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int num_feature;
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/*! \brief reserved field */
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int reserved[ 16 ];
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/*! \brief sampler type */
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int sampler_type;
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/*! \brief constructor */
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ModelParam( void ) {
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base_score = 0.5f;
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loss_type = 0;
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num_feature = 0;
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memset( reserved, 0, sizeof( reserved ) );
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@ -306,36 +307,11 @@ private:
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* \param val value of the parameter
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*/
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inline void SetParam( const char *name, const char *val ) {
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if( !strcmp("base_score", name ) ) base_score = (float)atof( val );
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if( !strcmp("loss_type", name ) ) loss_type = atoi( val );
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if( !strcmp("bst:num_feature", name ) ) num_feature = atoi( val );
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}
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/*!
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* \brief adjust base_score
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*/
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inline void AdjustBase( void ) {
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if( loss_type == 1 || loss_type == 2 ) {
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utils::Assert( base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain" );
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base_score = - logf( 1.0f / base_score - 1.0f );
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}
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if( !strcmp("rank:sampler",name)) sampler = atoi( val );
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}
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/*!
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* \brief transform the linear sum to prediction
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* \param x linear sum of boosting ensemble
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* \return transformed prediction
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*/
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inline float PredTransform( float x ) {
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switch( loss_type ) {
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case PAIRWISE:
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case MAP:
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case NDCG:
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return 1.0f/(1.0f + expf(-x));
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default:
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utils::Error("unknown loss_type");
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return 0.0f;
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}
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}
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/*!
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* \brief calculate first order gradient of pairwise loss function(f(x) = ln(1+exp(-x)),
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@ -358,57 +334,11 @@ private:
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inline float SecondOrderGradient( float pred_diff_exp ) const {
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return pred_diff_exp/pow(1 + pred_diff_exp,2);
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}
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/*!
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* \brief calculating the loss, given the predictions, labels and the loss type
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* \param preds the given predictions
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* \param labels the given labels
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* \return the specified loss
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*/
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inline float Loss(const std::vector<float> &preds, const std::vector<float> &labels) const {
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switch( loss_type ) {
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case kLinearSquare:
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return SquareLoss(preds,labels);
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case kLogisticNeglik:
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case kLogisticClassify:
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return NegLoglikelihoodLoss(preds,labels);
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default:
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utils::Error("unknown loss_type");
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return 0.0f;
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}
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}
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
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inline float SquareLoss(const std::vector<float> &preds, const std::vector<float> &labels) const {
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float ans = 0.0;
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for(size_t i = 0; i < preds.size(); i++) {
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float dif = preds[i] - labels[i];
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ans += dif * dif;
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}
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return ans;
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}
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
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inline float NegLoglikelihoodLoss(const std::vector<float> &preds, const std::vector<float> &labels) const {
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float ans = 0.0;
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for(size_t i = 0; i < preds.size(); i++)
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ans -= labels[i] * logf(preds[i]) + ( 1 - labels[i] ) * logf(1 - preds[i]);
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return ans;
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}
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};
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private:
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int silent;
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RankEvalSet evaluator_;
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sample::PairSamplerWrapper sampler;
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booster::GBMBase base_gbm;
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ModelParam mparam;
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const RMatrix *train_;
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@ -46,6 +46,14 @@ bool PairValueComparer(const Pair &a, const Pair &b){
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return a.value_ < b.value_;
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}
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struct EvalPair : public IRankEvaluator{
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virtual float Eval( const std::vector<float> &preds,
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const std::vector<float> &labels,
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const std::vector<int> &group_index ) const {
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return 0;
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}
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};
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/*! \brief Mean Average Precision */
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struct EvalMAP : public IRankEvaluator {
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virtual float Eval( const std::vector<float> &preds,
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@ -130,6 +138,7 @@ namespace rank {
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struct RankEvalSet {
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public:
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inline void AddEval( const char *name ) {
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if( !strcmp( name, "PAIR" )) evals_.push_back( &pair_);
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if( !strcmp( name, "MAP") ) evals_.push_back( &map_ );
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if( !strcmp( name, "NDCG") ) evals_.push_back( &ndcg_ );
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}
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@ -144,12 +153,13 @@ public:
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const std::vector<float> &labels,
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const std::vector<int> &group_index ) 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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float res = evals_[i]->Eval( preds, labels,group_index );
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fprintf( fo, "\t%s-%s:%f", evname, evals_[i]->Name(), res );
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}
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}
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private:
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EvalPair pair_;
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EvalMAP map_;
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EvalNDCG ndcg_;
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std::vector<const IRankEvaluator*> evals_;
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@ -1,14 +1,30 @@
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#ifndef _XGBOOST_SAMPLE_H_
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#define _XGBOOST_SAMPLE_H_
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#include<vector>
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#include"../utils/xgboost_utils.h"
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namespace xgboost {
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namespace rank {
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namespace sample {
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/*
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* \brief the data structure to maintain the sample pairs
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*/
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struct Pairs {
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/*
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* \brief constructor given the start and end offset of the sampling group
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* in overall instances
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* \param start the begin index of the group
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* \param end the end index of the group
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*/
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Pairs(int start,int end):start_(start),end_(end_){
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for(int i = start; i < end; i++){
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vector<int> v;
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pairs_.push_back(v);
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}
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}
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/*
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* \brief retrieve the related pair information of an data instances
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* \param index, the index of retrieved instance
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@ -16,6 +32,16 @@ namespace xgboost {
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*/
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std::vector<int> GetPairs(int index) {
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utils::assert(index >= start_ && index < end_,"The query index out of sampling bound");
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return pairs_[index-start_];
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}
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/*
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* \brief add in a sampled pair
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* \param index the index of the instance to sample a friend
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* \param paired_index the index of the instance sampled as a friend
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*/
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void push(int index,int paired_index){
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pairs_[index - start_].push_back(paired_index);
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}
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std::vector<std::vector<int>> pairs_;
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@ -23,6 +49,9 @@ namespace xgboost {
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int end_;
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};
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/*
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* \brief the interface of pair sampler
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*/
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struct IPairSampler {
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/*
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* \brief Generate sample pairs given the predcions, labels, the start and the end index
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@ -36,29 +65,61 @@ namespace xgboost {
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virtual Pairs GenPairs(const std::vector<float> &preds,
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const std::vector<float> &labels,
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int start,int end) = 0;
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};
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/*! \brief a set of evaluators */
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struct PairSamplerSet{
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public:
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inline void AssignSampler( const char *name ){
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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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}
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enum{
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BINARY_LINEAR_SAMPLER
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};
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/*! \brief A simple pair sampler when the rank relevence scale is binary
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* for each positive instance, we will pick a negative
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* instance and add in a pair. When using binary linear sampler,
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* we should guarantee the labels are 0 or 1
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*/
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struct BinaryLinearSampler:public IPairSampler{
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virtual Pairs GenPairs(const std::vector<float> &preds,
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const std::vector<float> &labels,
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int start,int end) {
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Pairs pairs(start,end);
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int pointer = 0, last_pointer = 0,index = start, interval = end - start;
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for(int i = start; i < end; i++){
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if(labels[i] == 1){
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while(true){
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index = (++pointer) % interval + start;
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if(labels[index] == 0) break;
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if(pointer - last_pointer > interval) return pairs;
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}
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pairs.push(i,index);
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pairs.push(index,i);
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last_pointer = pointer;
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}
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}
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return pairs;
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}
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};
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/*! \brief Pair Sampler Wrapper*/
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struct PairSamplerWrapper{
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public:
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inline void AssignSampler( int sampler_index ){
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switch(sampler_index){
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case BINARY_LINEAR_SAMPLER:sampler_ = &binary_linear_sampler;break;
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default:utils::Error("Cannot find the specified sampler");
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}
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}
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Pairs GenPairs(const std::vector<float> &preds,
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const std::vector<float> &labels,
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int start,int end){
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return sampler_.GenPairs(preds,labels,start,end);
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}
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private:
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EvalRMSE rmse_;
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EvalError error_;
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EvalLogLoss logloss_;
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std::vector<const IEvaluator*> evals_;
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BinaryLinearSampler binary_linear_sampler;
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IPairSampler *sampler_;
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};
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}
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}
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