rank pass toy

Makefile

@@ -12,6 +12,8 @@ export LDFLAGS= -pthread -lm
 
 xgboost: regression/xgboost_reg_main.cpp regression/*.h booster/*.h booster/*/*.hpp booster/*.hpp
 
+#xgboost: rank/xgboost_rank_main.cpp base/*.h rank/*.h booster/*.h booster/*/*.hpp booster/*.hpp
+
 $(BIN) : 
 	$(CXX) $(CFLAGS) $(LDFLAGS) -o $@ $(filter %.cpp %.o %.c, $^)
 

booster/xgboost_gbmbase.h

@@ -288,7 +288,8 @@ namespace xgboost{
                     booster_info.push_back(0);
                     this->ConfigBooster(boosters.back());
                     boosters.back()->InitModel();
-                }else{
+                }
+                else{
                     this->ConfigBooster(boosters.back());
                 }
                 return boosters.back();

demo/rank/README

@@ -0,0 +1,13 @@
+Demonstrating how to use XGBoost accomplish regression tasks on computer hardware dataset https://archive.ics.uci.edu/ml/datasets/Computer+Hardware
+
+Run: ./runexp.sh
+
+Format of input: LIBSVM format
+
+Format of ```featmap.txt: <featureid> <featurename> <q or i or int>\n ```:
+  - Feature id must be from 0 to number of features, in sorted order.
+  - i means this feature is binary indicator feature
+  - q means this feature is a quantitative value, such as age, time, can be missing
+  - int means this feature is integer value (when int is hinted, the decision boundary will be integer)
+
+Explainations: https://github.com/tqchen/xgboost/wiki/Regression

demo/rank/runexp.sh

@@ -0,0 +1,16 @@
+#!/bin/bash
+# map the data to features. For convenience we only use 7 original attributes and encode them as features in a trivial way 
+python mapfeat.py
+# split train and test
+python mknfold.py machine.txt 1
+# training and output the models
+../../xgboost machine.conf
+# output predictions of test data
+../../xgboost machine.conf task=pred model_in=0002.model
+# print the boosters of 0002.model in dump.raw.txt
+../../xgboost machine.conf task=dump model_in=0002.model name_dump=dump.raw.txt
+# print the boosters of 0002.model in dump.nice.txt with feature map
+../../xgboost machine.conf task=dump model_in=0002.model fmap=featmap.txt name_dump=dump.nice.txt 
+
+# cat the result
+cat dump.nice.txt

demo/rank/toy.eval

@@ -0,0 +1,5 @@
+1 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+1 0:2 1:3 2:2

demo/rank/toy.eval.group

@@ -0,0 +1,2 @@
+2
+3

demo/rank/toy.test

@@ -0,0 +1,5 @@
+1 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+1 0:2 1:3 2:2

demo/rank/toy.test.group

@@ -0,0 +1,2 @@
+2
+3

demo/rank/toy.train

@@ -0,0 +1,5 @@
+1 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+0 0:2 1:3 2:2
+1 0:2 1:3 2:2

demo/rank/toy.train.group

@@ -0,0 +1,2 @@
+2
+3

dev/base/xgboost_boost_task.h

@@ -20,6 +20,7 @@ namespace xgboost{
         class BoostTask{
         public:
             inline int Run(int argc, char *argv[]){
+
                 if (argc < 2){
                     printf("Usage: <config>\n");
                     return 0;
@@ -34,6 +35,7 @@ namespace xgboost{
                         this->SetParam(name, val);
                     }
                 }
+		
                 this->InitData();
                 this->InitLearner();
                 if (task == "dump"){
@@ -128,6 +130,7 @@ namespace xgboost{
 
 
             inline void InitData(void){
+	        
                 if (name_fmap != "NULL") fmap.LoadText(name_fmap.c_str());
                 if (task == "dump") return;
                 if (learning_task == RANKING){
@@ -140,6 +143,7 @@ namespace xgboost{
                         // training 
                         sscanf(train_path.c_str(), "%[^;];%s", instance_path, group_path);
 		        data.CacheLoad(instance_path, group_path, silent != 0, use_buffer != 0);
+                        
 			utils::Assert(eval_data_names.size() == eval_data_paths.size());
                         for (size_t i = 0; i < eval_data_names.size(); ++i){
                             deval.push_back(new DMatrix());
@@ -147,8 +151,6 @@ namespace xgboost{
                             deval.back()->CacheLoad(instance_path, group_path, silent != 0, use_buffer != 0);
                         }
                     }
-
-
                 }
                 else{
                     if (task == "pred" || task == "dumppath"){
@@ -166,7 +168,9 @@ namespace xgboost{
                 }
 
                 learner_->SetData(&data, deval, eval_data_names);
+		if(!silent) printf("BoostTask:Data Initiation Done!\n");
             }
+            
             inline void InitLearner(void){
                 cfg.BeforeFirst();
                 while (cfg.Next()){
@@ -182,6 +186,7 @@ namespace xgboost{
                     learner_->InitModel();
                 }
                 learner_->InitTrainer();
+		if(!silent) printf("BoostTask:InitLearner Done!\n");
             }
 
             inline void TaskTrain(void){

dev/base/xgboost_data_instance.h

@@ -70,17 +70,27 @@ namespace xgboost{
                         (unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
                 }
                 fclose(file);
+		LoadGroup(fgroup,silent);
+	    }
             
+            inline void LoadGroup(const char* fgroup, bool silent = false){
 	      //if exists group data load it in
 	        FILE *file_group = fopen64(fgroup, "r");
+	
 		if (file_group != NULL){
 		    group_index.push_back(0);
-					int tmp = 0, acc = 0;
+                    int tmp = 0, acc = 0,cnt = 0;
-					while (fscanf(file_group, "%d", tmp) == 1){
+                    while (fscanf(file_group, "%d", &tmp) == 1){
 		        acc += tmp;
                         group_index.push_back(acc);
+			cnt++;
                     }
+                    if(!silent) printf("%d groups are loaded from %s\n",cnt,fgroup);
+		    fclose(file_group);
+                }else{
+		    if(!silent) printf("There is no group file\n");
 		}
+                
 	    }
             /*!
             * \brief load from binary file
@@ -100,24 +110,11 @@ namespace xgboost{
                 data.InitData();
 
                 if (!silent){
-					printf("%ux%u matrix with %lu entries is loaded from %s\n",
+		    printf("%ux%u matrix with %lu entries is loaded from %s as binary\n",
                         (unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
                 }
 
-				//if group data exists load it in
+                LoadGroupBinary(fgroup,silent);
-				FILE *file_group = fopen64(fgroup, "r");
-				if (file_group != NULL){
-					int group_index_size = 0;
-					utils::FileStream group_stream(file_group);
-					utils::Assert(group_stream.Read(&group_index_size, sizeof(int)) != 0, "Load group indice size");
-					group_index.resize(group_index_size);
-					utils::Assert(group_stream.Read(&group_index, sizeof(int)* group_index_size) != 0, "Load group indice");
-
-					if (!silent){
-						printf("the group index of %d groups is loaded from %s\n",
-							group_index_size - 1, fgroup);
-					}
-				}
                 return true;
             }
             /*!
@@ -134,16 +131,42 @@ namespace xgboost{
                 fs.Write(&labels[0], sizeof(float)* data.NumRow());
                 fs.Close();
                 if (!silent){
-					printf("%ux%u matrix with %lu entries is saved to %s\n",
+                    printf("%ux%u matrix with %lu entries is saved to %s as binary\n",
                         (unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
                 }
 
+                SaveGroupBinary(fgroup,silent);
+            }
+            
+            inline void SaveGroupBinary(const char* fgroup, bool silent = false){
 	      //save group data
                 if (group_index.size() > 0){
                     utils::FileStream file_group(utils::FopenCheck(fgroup, "wb"));
                     int group_index_size = group_index.size();
                     file_group.Write(&(group_index_size), sizeof(int));
                     file_group.Write(&group_index[0], sizeof(int) * group_index_size);
+		    file_group.Close();
+		    if(!silent){printf("Index info of %d groups is saved to %s as binary\n",group_index_size-1,fgroup);}
+                }
+	    }
+            
+            inline void LoadGroupBinary(const char* fgroup, bool silent = false){
+	      //if group data exists load it in
+                FILE *file_group = fopen64(fgroup, "r");
+                if (file_group != NULL){
+                    int group_index_size = 0;
+                    utils::FileStream group_stream(file_group);
+                    utils::Assert(group_stream.Read(&group_index_size, sizeof(int)) != 0, "Load group indice size");
+                    group_index.resize(group_index_size);
+                    utils::Assert(group_stream.Read(&group_index[0], sizeof(int) * group_index_size) != 0, "Load group indice");
+
+                    if (!silent){
+                        printf("Index info of %d groups is loaded from %s as binary\n",
+                            group_index.size() - 1, fgroup);
+                    }
+		    fclose(file_group);
+                }else{
+		    if(!silent){printf("The binary file of group info not exists");}
 		}
                 
 	    }
@@ -161,11 +184,13 @@ namespace xgboost{
                 if (len > 8 && !strcmp(fname + len - 7, ".buffer")){
                     this->LoadBinary(fname, fgroup, silent); return;
                 }
-				char bname[1024];
+                char bname[1024],bgroup[1024];
                 sprintf(bname, "%s.buffer", fname);
-				if (!this->LoadBinary(bname, fgroup, silent)){
+                sprintf(bgroup, "%s.buffer", fgroup);
+                if (!this->LoadBinary(bname, bgroup, silent))
+		{
                     this->LoadText(fname, fgroup, silent);
-					if (savebuffer) this->SaveBinary(bname, fgroup, silent);
+                    if (savebuffer) this->SaveBinary(bname, bgroup, silent);
                 }
             }
         private:

dev/base/xgboost_learner.h

@@ -96,6 +96,7 @@ namespace xgboost {
             */
             inline void InitModel(void) {
                 base_gbm.InitModel();
+		if(!silent) printf("BoostLearner:InitModel Done!\n");
             }
             /*!
             * \brief load model from stream
@@ -210,7 +211,6 @@ namespace xgboost {
             /*! \brief get intransformed predictions, given data */
             virtual inline void PredictBuffer(std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset) {
                 preds.resize(data.Size());
-
 		const unsigned ndata = static_cast<unsigned>(data.Size());
 #pragma omp parallel for schedule( static )
                 for (unsigned j = 0; j < ndata; ++j) {

dev/rank/xgboost_rank_main.cpp

@@ -11,20 +11,11 @@
 #include "../base/xgboost_boost_task.h"
 #include "xgboost_rank.h"
 #include "../regression/xgboost_reg.h"
-
+#include "../regression/xgboost_reg_main.cpp"
+#include "../base/xgboost_data_instance.h"
 int main(int argc, char *argv[]) {
-	
   xgboost::random::Seed(0);
-	xgboost::base::BoostTask tsk;
+  xgboost::base::BoostTask rank_tsk;
-	xgboost::utils::ConfigIterator itr(argv[1]);
+  rank_tsk.SetLearner(new xgboost::rank::RankBoostLearner);
-/*	int learner_index = 0;
+  return rank_tsk.Run(argc, argv);
-	while (itr.Next()){
-		if (!strcmp(itr.name(), "learning_task")){
-			learner_index = atoi(itr.val());
-		}
-	}*/
-	xgboost::rank::RankBoostLearner* rank_learner = new xgboost::rank::RankBoostLearner;
-	xgboost::base::BoostLearner *parent = static_cast<xgboost::base::BoostLearner*>(rank_learner);
-	tsk.SetLearner(parent);
-	return tsk.Run(argc, argv);
 }

dev/rank/xgboost_sample.h

@@ -115,6 +115,7 @@ namespace xgboost {
                 Pairs GenPairs(const std::vector<float> &preds,
                     const std::vector<float> &labels,
                     int start, int end){
+		    utils::Assert(sampler_ != NULL,"Not config the sampler yet. Add rank:sampler in the config file\n");
                     return sampler_->GenPairs(preds, labels, start, end);
                 }
             private:

regression/xgboost_reg.h

@@ -94,7 +94,8 @@ namespace xgboost{
                 base_gbm.InitTrainer();
                 if (mparam.loss_type == kLogisticClassify){
                     evaluator_.AddEval("error");
-                }else{
+                }
+                else{
                     evaluator_.AddEval("rmse");
                 }
                 evaluator_.Init();

regression/xgboost_reg_data.h

@@ -52,7 +52,8 @@ namespace xgboost{
                     unsigned index; float value;
                     if (sscanf(tmp, "%u:%f", &index, &value) == 2){
                         findex.push_back(index); fvalue.push_back(value);
-                    }else{
+                    }
+                    else{
                         if (!init){
                             labels.push_back(label);
                             data.AddRow(findex, fvalue);

regression/xgboost_reg_eval.h

@@ -55,7 +55,8 @@ namespace xgboost{
                 for (unsigned i = 0; i < ndata; ++i){
                     if (preds[i] > 0.5f){
                         if (labels[i] < 0.5f) nerr += 1;
-                    }else{
+                    }
+                    else{
                         if (labels[i] > 0.5f) nerr += 1;
                     }
                 }

regression/xgboost_reg_main.cpp

@@ -50,7 +50,8 @@ namespace xgboost{
                 }
                 if (task == "pred"){
                     this->TaskPred();
-                }else{                  
+                }
+                else{
                     this->TaskTrain();
                 }
                 return 0;
@@ -113,7 +114,8 @@ namespace xgboost{
                 if (task == "dump") return;
                 if (task == "pred" || task == "dumppath"){
                     data.CacheLoad(test_path.c_str(), silent != 0, use_buffer != 0);
-                }else{
+                }
+                else{
                     // training 
                     data.CacheLoad(train_path.c_str(), silent != 0, use_buffer != 0);
                     utils::Assert(eval_data_names.size() == eval_data_paths.size());
@@ -133,7 +135,8 @@ namespace xgboost{
                     utils::FileStream fi(utils::FopenCheck(model_in.c_str(), "rb"));
                     learner.LoadModel(fi);
                     fi.Close();
-                }else{
+                }
+                else{
                     utils::Assert(task == "train", "model_in not specified");
                     learner.InitModel();
                 }
@@ -156,7 +159,8 @@ namespace xgboost{
                 if (save_period == 0 || num_round % save_period != 0){
                     if (model_out == "NULL"){
                         this->SaveModel(num_round - 1);
-                    }else{
+                    }
+                    else{
                         this->SaveModel(model_out.c_str());
                     }
                 }
@@ -177,7 +181,8 @@ namespace xgboost{
                     if (!strcmp(cfg_batch.name(), "run")){
                         learner.UpdateInteract(interact_action);
                         batch_action += 1;
-                    } else{
+                    }
+                    else{
                         learner.SetParam(cfg_batch.name(), cfg_batch.val());
                     }
                 }
@@ -273,8 +278,8 @@ namespace xgboost{
     };
 };
 
-int main( int argc, char *argv[] ){
+// int main( int argc, char *argv[] ){
-    xgboost::random::Seed( 0 );
+//     xgboost::random::Seed( 0 );
-    xgboost::regression::RegBoostTask tsk;
+//     xgboost::regression::RegBoostTask tsk;
-    return tsk.Run( argc, argv );
+//     return tsk.Run( argc, argv );
-}
+// }

utils/xgboost_config.h

@@ -94,7 +94,8 @@ namespace xgboost{
                     case '\"':
                         if (i == 0){
                             ParseStr(tok); ch_buf = fgetc(fi); return new_line;
-                        }else{
+                        }
+                        else{
                             Error("token followed directly by string");
                         }
                     case '=':
@@ -102,7 +103,8 @@ namespace xgboost{
                             ch_buf = fgetc(fi);
                             tok[0] = '=';
                             tok[1] = '\0';
-                        }else{
+                        }
+                        else{
                             tok[i] = '\0';
                         }
                         return new_line;
@@ -155,7 +157,8 @@ namespace xgboost{
                 if (priority == 0){
                     names.push_back(std::string(name));
                     values.push_back(std::string(val));
-                }else{
+                }
+                else{
                     names_high.push_back(std::string(name));
                     values_high.push_back(std::string(val));
                 }
@@ -184,7 +187,8 @@ namespace xgboost{
                 size_t i = idx - 1;
                 if (i >= names.size()){
                     return names_high[i - names.size()].c_str();
-                }else{
+                }
+                else{
                     return names[i].c_str();
                 }
             }
@@ -197,7 +201,8 @@ namespace xgboost{
                 size_t i = idx - 1;
                 if (i >= values.size()){
                     return values_high[i - values.size()].c_str();
-                }else{
+                }
+                else{
                     return values[i].c_str();
                 }
             }

utils/xgboost_matrix_csr.h

@@ -50,7 +50,8 @@ namespace xgboost{
                 if (!UseAcList){
                     rptr.clear();
                     rptr.resize(nrows + 1, 0);
-                }else{
+                }
+                else{
                     Assert(nrows + 1 == rptr.size(), "rptr must be initialized already");
                     this->Cleanup();
                 }
@@ -79,7 +80,8 @@ namespace xgboost{
                         rptr[i] = start;
                         start += rlen;
                     }
-                }else{
+                }
+                else{
                     // case with active list
                     std::sort(aclist.begin(), aclist.end());
 

utils/xgboost_omp.h

@@ -10,7 +10,7 @@
 #if defined(_OPENMP)
 #include <omp.h>
 #else
-//#warning "OpenMP is not available, compile to single thread code"
+#warning "OpenMP is not available, compile to single thread code"
 inline int omp_get_thread_num() { return 0; }
 inline int omp_get_num_threads() { return 1; }
 inline void omp_set_num_threads(int nthread) {}

utils/xgboost_random.h

@@ -88,7 +88,8 @@ namespace xgboost{
                     u = NextDouble();
                 } while (u == 0.0);
                 return SampleGamma(alpha + 1.0, beta) * pow(u, 1.0 / alpha);
-            } else {
+            }
+            else {
                 double d, c, x, v, u;
                 d = alpha - 1.0 / 3.0;
                 c = 1.0 / sqrt(9.0 * d);