fix the logic for secure vertical inference, each client save a diffe…

…rent model

src/learner.cc

@@ -1287,7 +1287,7 @@ class LearnerImpl : public LearnerIO {
     monitor_.Stop("GetGradient");
 
 
-
+/*
       if(collective::GetRank()==0){
           //print the total number of samples
             std::cout << "Total number of samples: " << train->Info().labels.Size() << std::endl;
@@ -1320,7 +1320,7 @@ class LearnerImpl : public LearnerIO {
       }
     }
     }
-
+*/
 
 
 
@@ -1372,9 +1372,6 @@ class LearnerImpl : public LearnerIO {
       std::shared_ptr<DMatrix> m = data_sets[i];
       auto &predt = prediction_container_.Cache(m, ctx_.Device());
       this->ValidateDMatrix(m.get(), false);
-        if(collective::GetRank()==0){
-            std::cout << "data size = " << data_sets[i]->Info().num_row_ << std::endl;
-        }
       this->PredictRaw(m.get(), &predt, false, 0, 0);
 
       auto &out = output_predictions_.Cache(m, ctx_.Device()).predictions;
@@ -1383,15 +1380,7 @@ class LearnerImpl : public LearnerIO {
 
       obj_->EvalTransform(&out);
       for (auto& ev : metrics_) {
-
-        auto metric = ev->Evaluate(out, m);
-
-          if(collective::GetRank()==0){
-              std::cout << "eval result = " << metric << std::endl;
-          }
-
-
-        os << '\t' << data_names[i] << '-' << ev->Name() << ':' << metric; //ev->Evaluate(out, m);
+        os << '\t' << data_names[i] << '-' << ev->Name() << ':' << ev->Evaluate(out, m);
       }
     }
 
@@ -1496,11 +1485,6 @@ class LearnerImpl : public LearnerIO {
     CHECK(gbm_ != nullptr) << "Predict must happen after Load or configuration";
     this->CheckModelInitialized();
     this->ValidateDMatrix(data, false);
-
-    if(collective::GetRank()==0){
-    std::cout << "PredictRaw training ? " << training << std::endl;
-    }
-
     gbm_->PredictBatch(data, out_preds, training, layer_begin, layer_end);
   }
 

src/tree/common_row_partitioner.h

@@ -117,6 +117,12 @@ class CommonRowPartitioner {
       if (is_index) {
         // if the split_pt is already recorded as a bin_id, use it directly
         (*split_conditions)[i] = static_cast<int32_t>(split_pt);
+        // at this point, each participants received the best split index,
+        // therefore can recover the split_pt from bin_id, update tree info
+        auto split_pt_local = vals[split_pt];
+        // make updates to the tree, replacing the existing index
+        // with cut value, note that we modified const here, carefully
+        const_cast<RegTree::Node&>(tree.GetNodes()[nidx]).SetSplit(fidx, split_pt_local);
       }
       else {
         // otherwise find the bin_id that corresponds to split_pt

src/tree/hist/evaluate_splits.h

@@ -270,6 +270,7 @@ class HistEvaluator {
           }
           else {
             // secure mode: record the best split point, rather than the actual value since it is not accessible
+            // at this point (active party finding best-split)
             best.Update(loss_chg, fidx, i, d_step == -1, false, left_sum, right_sum);
           }