Revert rapidsai#3869 (rapidsai#3933)

Reverts rapidsai#3869, as it was shown to reduce the test accuracy in some cases. Closes rapidsai#3910 Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - Dante Gama Dessavre (https://github.com/dantegd) URL: rapidsai#3933
vimarsh6739 · Jun 2, 2021 · 8e04b65 · 8e04b65
1 parent bafbc3b
commit 8e04b65
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Showing 5 changed files with 12 additions and 46 deletions.
diff --git a/cpp/src/decisiontree/batched-levelalgo/kernels.cuh b/cpp/src/decisiontree/batched-levelalgo/kernels.cuh
@@ -89,28 +89,16 @@ struct ClsDeviceTraits {
       atomicAdd(shist + label, 1);
     }
     __syncthreads();
+    auto op = Int2Max();
+    int2 v = {-1, -1};
+    for (int i = tid; i < input.nclasses; i += blockDim.x) {
+      int2 tmp = {i, shist[i]};
+      v = op(v, tmp);
+    }
+    v = BlockReduceT(temp).Reduce(v, op);
+    __syncthreads();
     if (tid == 0) {
-      int max_class_idx = 0;
-      int max_count = 0;
-      int total_count = 0;
-      for (int i = 0; i < input.nclasses; ++i) {
-        int current_count = shist[i];
-        total_count += current_count;
-        if (current_count > max_count) {
-          max_class_idx = i;
-          max_count = current_count;
-        }
-      }
-      DataT aux = DataT(-1);
-      if (input.nclasses <= 2) {
-        // Special handling for binary classifiers
-        if (input.nclasses == 2) {
-          aux = static_cast<DataT>(shist[1]) / total_count;
-        } else {
-          aux = static_cast<DataT>(0);
-        }
-      }
-      nodes[0].makeLeaf(n_leaves, LabelT(max_class_idx), aux);
+      nodes[0].makeLeaf(n_leaves, LabelT(v.x));
     }
   }
 };  // struct ClsDeviceTraits

diff --git a/cpp/src/decisiontree/batched-levelalgo/node.cuh b/cpp/src/decisiontree/batched-levelalgo/node.cuh
@@ -67,11 +67,10 @@ struct Node {
    *
    * @note to be called only by one thread across all participating threadblocks
    */
-  DI void makeLeaf(IdxT* n_leaves, LabelT pred,
-                   DataT aux = DataT(-1)) volatile {
+  DI void makeLeaf(IdxT* n_leaves, LabelT pred) volatile {
     info.prediction = pred;
     info.colid = Leaf;
-    info.quesval = aux;
+    info.quesval = DataT(0);          // don't care for leaf nodes
     info.best_metric_val = DataT(0);  // don't care for leaf nodes
     info.left_child_id = Leaf;
     atomicAdd(n_leaves, 1);

diff --git a/cpp/src/decisiontree/decisiontree_impl.cuh b/cpp/src/decisiontree/decisiontree_impl.cuh
@@ -138,9 +138,6 @@ std::string get_node_json(const std::string &prefix,
     if (node.instance_count != UINT32_MAX) {
       oss << ", \"instance_count\": " << node.instance_count;
     }
-    if (node.quesval >= 0) {
-      oss << ", \"positive_fraction\": " << node.quesval;
-    }
     oss << "}";
   }
   return oss.str();
@@ -205,21 +202,7 @@ tl::Tree<T, T> build_treelite_tree(
 
       } else {
         if (num_class == 1) {
-          if (std::is_same<decltype(q_node.node->prediction), int>::value) {
-            // Binary classification; use fraction of the positive class
-            // to produce a "soft output"
-            // Note. The old RF backend doesn't provide this fraction
-            static_assert(std::is_floating_point<T>::value,
-                          "Expected T to be a floating-point type");
-            if (q_node.node->quesval >= 0) {
-              tl_tree.SetLeaf(node_id, static_cast<T>(q_node.node->quesval));
-            } else {
-              tl_tree.SetLeaf(node_id, static_cast<T>(q_node.node->prediction));
-            }
-          } else {
-            // Regression
-            tl_tree.SetLeaf(node_id, static_cast<T>(q_node.node->prediction));
-          }
+          tl_tree.SetLeaf(node_id, static_cast<T>(q_node.node->prediction));
         } else {
           std::vector<T> leaf_vector(num_class, 0);
           leaf_vector[q_node.node->prediction] = 1;

diff --git a/cpp/src/decisiontree/levelalgo/levelhelper_classifier.cuh b/cpp/src/decisiontree/levelalgo/levelhelper_classifier.cuh
@@ -321,7 +321,6 @@ void leaf_eval_classification(
     if (condition) {
       node_flag = 0xFFFFFFFF;
       sparsetree[sparsesize + sparse_nodeid].colid = -1;
-      sparsetree[sparsesize + sparse_nodeid].quesval = -1;
       sparsetree[sparsesize + sparse_nodeid].prediction =
         get_class_hist(nodehist, n_unique_labels);
     } else {

diff --git a/cpp/src/decisiontree/levelalgo/levelkernel_classifier.cuh b/cpp/src/decisiontree/levelalgo/levelkernel_classifier.cuh
@@ -452,7 +452,6 @@ __global__ void best_split_gather_classification_kernel(
       colid = -1;
       localnode.prediction =
         get_class_hist_shared(shmemhist_parent, n_unique_labels);
-      localnode.quesval = -1;
     }
     localnode.colid = colid;
     localnode.best_metric_val = parent_metric;
@@ -575,7 +574,6 @@ __global__ void best_split_gather_classification_minmax_kernel(
       colid = -1;
       localnode.prediction =
         get_class_hist_shared(shmemhist_parent, n_unique_labels);
-      localnode.quesval = -1;
     }
     localnode.colid = colid;
     localnode.best_metric_val = parent_metric;
@@ -613,7 +611,6 @@ __global__ void make_leaf_gather_classification_kernel(
     localnode.prediction =
       get_class_hist_shared(shmemhist_parent, n_unique_labels);
     localnode.colid = -1;
-    localnode.quesval = -1;
     localnode.best_metric_val = parent_metric;
     d_sparsenodes[d_nodelist[blockIdx.x]] = localnode;
   }