pass simple test

demo/regression/reg.conf

@@ -1,35 +1,22 @@
-boost_iterations=10
+num_round=10
 
 save_period=0
 
-train_path=C:\cygwin64\home\Chen\GitHub\xgboost\demo\regression\train.txt
-					
-model_dir_path=C:\cygwin64\home\Chen\GitHub\xgboost\demo\regression\model
-
-validation_paths=C:\cygwin64\home\Chen\GitHub\xgboost\demo\regression\validation.txt
-
-validation_names=validation
-
-test_paths=C:\cygwin64\home\Chen\GitHub\xgboost\demo\regression\test.txt
-
-test_names=test
+data = "train.txt"
+test:data = "test.txt"
+eval[valid] = "validation.txt"
 
 booster_type=1
 
-do_reboost=0
-
 bst:num_feature=3
 
-learning_rate=0.01
+bst:learning_rate=0.01
                 
-min_child_weight=1
+bst:min_child_weight=1
                 
-min_split_loss=0.1
+bst:min_split_loss=0.1
                 
-max_depth=3           
+bst:max_depth=3           
                 
-reg_lambda=0.1
+bst:reg_lambda=0.1
 
-subsample=1
-                
-use_layerwise=0

demo/regression/validation.txt

@@ -1,3 +1,4 @@
 1 0:1 1:2 2:1
 1 0:2 1:1 2:1
 0 0:5 1:0 2:0
+

regression/xgboost_reg.h

@@ -19,7 +19,9 @@ namespace xgboost{
         class RegBoostLearner{            
         public:
             /*! \brief constructor */
-            RegBoostLearner( void ){}
+            RegBoostLearner( void ){
+                silent = 0;            
+            }
             /*! 
             * \brief a regression booter associated with training and evaluating data 
             * \param train pointer to the training data
@@ -29,6 +31,7 @@ namespace xgboost{
             RegBoostLearner( const DMatrix *train,
                              const std::vector<DMatrix *> &evals,
                              const std::vector<std::string> &evname ){
+                silent = 0;
                 this->SetData(train,evals,evname);
             }
 
@@ -51,7 +54,10 @@ namespace xgboost{
                     buffer_size += static_cast<unsigned>( evals[i]->Size() );
                 }
                 char snum_pbuffer[25];
-                printf( snum_pbuffer, "%u", buffer_size );
+                sprintf( snum_pbuffer, "%u", buffer_size );
+                if( !silent ){
+                    printf( "buffer_size=%u\n", buffer_size );
+                }
                 base_model.SetParam( "num_pbuffer",snum_pbuffer );
             }
             /*! 
@@ -60,6 +66,7 @@ namespace xgboost{
             * \param val  value of the parameter
             */
             inline void SetParam( const char *name, const char *val ){
+                if( !strcmp( name, "silent") ) silent = atoi( val );
                 mparam.SetParam( name, val );
                 base_model.SetParam( name, val );
             }
@@ -173,7 +180,6 @@ namespace xgboost{
                 float base_score;
                 /* \brief type of loss function */
                 int loss_type;
-
                 ModelParam( void ){
                     base_score = 0.5f;
                     loss_type  = 0;
@@ -280,6 +286,7 @@ namespace xgboost{
                 }
             };
         private:
+            int silent;
             booster::GBMBaseModel base_model;
             ModelParam   mparam;
             const DMatrix *train_;

regression/xgboost_reg_main.cpp

@@ -96,11 +96,11 @@ namespace xgboost{
                     learner.SetParam( cfg.name(), cfg.val() );
                 }
                 if( strcmp( model_in.c_str(), "NULL" ) != 0 ){
-                    utils::Assert( !strcmp( task.c_str(), "train"), "model_in not specified" );
                     utils::FileStream fi( utils::FopenCheck( model_in.c_str(), "rb") );
                     learner.LoadModel( fi );
                     fi.Close();
                 }else{
+                    utils::Assert( !strcmp( task.c_str(), "train"), "model_in not specified" );
                     learner.InitModel();
                 }
                 learner.InitTrainer();
@@ -114,19 +114,19 @@ namespace xgboost{
                     learner.UpdateOneIter( i );
                     learner.EvalOneIter( i );
                     if( save_period != 0 && (i+1) % save_period == 0 ){
-                        SaveModel( i );
+                        this->SaveModel( i );
                     }
                     elapsed = (unsigned long)(time(NULL) - start); 
                 }
                 // always save final round
-                if( num_round % save_period != 0 ){
-                    SaveModel( num_round );
+                if( save_period == 0 || num_round % save_period != 0 ){
+                    this->SaveModel( num_round );
                 }
                 if( !silent ){
                     printf("\nupdating end, %lu sec in all\n", elapsed );
                 }
             }
-            inline void SaveModel( int i ){
+            inline void SaveModel( int i ) const{
                 char fname[256];
                 sprintf( fname ,"%s/%04d.model", model_dir_path.c_str(), i+1 );
                 utils::FileStream fo( utils::FopenCheck( fname, "wb" ) );
@@ -135,7 +135,9 @@ namespace xgboost{
             }
             inline void TaskTest( void ){
                 std::vector<float> preds;
+                if( !silent ) printf("start prediction...\n");
                 learner.Predict( preds, data );
+                if( !silent ) printf("writing prediction to %s\n", name_pred.c_str() );
                 FILE *fo = utils::FopenCheck( name_pred.c_str(), "w" );
                 for( size_t i = 0; i < preds.size(); i ++ ){
                     fprintf( fo, "%f\n", preds[i] );

regression/xgboost_regdata.h

@@ -122,7 +122,7 @@ namespace xgboost{
                 sprintf( bname, "%s.buffer", fname );
                 if( !this->LoadBinary( bname, silent ) ){
                     this->LoadText( fname, silent );
-                    this->SaveBinary( fname, silent );
+                    this->SaveBinary( bname, silent );
                 }                
             }
         private: