try predpath

src/gbm/gbtree-inl.hpp

@@ -107,7 +107,7 @@ class GBTree : public IGradBooster {
                        int64_t buffer_offset,
                        const BoosterInfo &info,
                        std::vector<float> *out_preds,
-                       unsigned ntree_limit = 0) {
+                       unsigned ntree_limit = 0) {    
     int nthread;
     #pragma omp parallel
     {
@@ -117,6 +117,10 @@ class GBTree : public IGradBooster {
     for (int i = 0; i < nthread; ++i) {
       thread_temp[i].Init(mparam.num_feature);
     }
+    if (tparam.pred_path != 0) {
+      this->PredPath(p_fmat, info, out_preds);
+      return;
+    }
 
     std::vector<float> &preds = *out_preds;
     const size_t stride = info.num_row * mparam.num_output_group;
@@ -144,7 +148,7 @@ class GBTree : public IGradBooster {
         }
       }
     }
-  }
+  }  
   virtual std::vector<std::string> DumpModel(const utils::FeatMap& fmap, int option) {
     std::vector<std::string> dump;
     for (size_t i = 0; i < trees.size(); i++) {
@@ -258,6 +262,34 @@ class GBTree : public IGradBooster {
       out_pred[stride * (i + 1)] = vec_psum[i];
     }
   }
+  // predict independent leaf index
+  inline void PredPath(IFMatrix *p_fmat,
+                       const BoosterInfo &info,
+                       std::vector<float> *out_preds) {
+    std::vector<float> &preds = *out_preds;
+    preds.resize(info.num_row * mparam.num_trees);
+    // start collecting the prediction
+    utils::IIterator<RowBatch> *iter = p_fmat->RowIterator();
+    iter->BeforeFirst();
+    while (iter->Next()) {
+      const RowBatch &batch = iter->Value();
+      // parallel over local batch
+      const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
+      #pragma omp parallel for schedule(static)
+      for (bst_omp_uint i = 0; i < nsize; ++i) {
+        const int tid = omp_get_thread_num();
+        int64_t ridx = static_cast<int64_t>(batch.base_rowid + i);
+        tree::RegTree::FVec &feats = thread_temp[tid];
+        feats.Fill(batch[i]);
+        for (size_t j = 0; j < trees.size(); ++j) {
+          int tid = trees[i]->GetLeafIndex(feats, info.GetRoot(ridx));
+          preds[ridx * mparam.num_trees + j] = static_cast<float>(tid);
+        }
+        feats.Drop(batch[i]);
+      }
+    }
+  }
+                       
   // --- data structure ---
   /*! \brief training parameters */
   struct TrainParam {
@@ -268,6 +300,8 @@ class GBTree : public IGradBooster {
      *  use this option to support boosted random forest
      */
     int num_parallel_tree;
+    /*! \brief predict path in prediction */
+    int pred_path;
     /*! \brief whether updater is already initialized */
     int updater_initialized;
     /*! \brief tree updater sequence */
@@ -278,6 +312,7 @@ class GBTree : public IGradBooster {
       updater_seq = "grow_colmaker,prune";
       num_parallel_tree = 1;
       updater_initialized = 0;
+      pred_path = 0;
     }
     inline void SetParam(const char *name, const char *val){
       using namespace std;
@@ -292,6 +327,7 @@ class GBTree : public IGradBooster {
       if (!strcmp(name, "num_parallel_tree")) {
         num_parallel_tree = atoi(val);
       }
+      if (!strcmp(name, "pred_path")) pred_path = atoi(val);
     }
   };
   /*! \brief model parameters */