Fix handling of normalization at leaf node

dmlc · Feb 16, 2024 · e3c0263 · e3c0263
1 parent 4f46aba
commit e3c0263
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Showing 2 changed files with 19 additions and 5 deletions.
diff --git a/src/model_loader/sklearn.cc b/src/model_loader/sklearn.cc
@@ -14,6 +14,7 @@
 
 #include <algorithm>
 #include <cstdint>
+#include <experimental/mdspan>
 #include <limits>
 #include <memory>
 #include <numeric>
@@ -23,6 +24,11 @@ namespace treelite::model_loader::sklearn {
 
 namespace detail {
 
+namespace stdex = std::experimental;
+// Multidimensional array views. Use row-major (C) layout
+template <typename ElemT>
+using Array2DView = stdex::mdspan<ElemT, stdex::dextents<std::uint64_t, 2>, stdex::layout_right>;
+
 class RandomForestRegressorMixIn {
  public:
   void HandleMetadata(model_builder::ModelBuilder& builder, int n_trees, int n_features,
@@ -80,16 +86,24 @@ class RandomForestClassifierMixIn {
   }
 
   void HandleLeafNode(model_builder::ModelBuilder& builder, int tree_id, int node_id,
-      double const** value, [[maybe_unused]] int const* n_classes) const {
+      double const** value, std::int32_t const* n_classes) const {
     TREELITE_CHECK_GT(n_targets_, 0)
         << "n_targets not yet initialized. Was HandleMetadata() called?";
     TREELITE_CHECK_GT(max_num_class_, 0)
         << "max_num_class not yet initialized. Was HandleMetadata() called?";
     std::vector<double> leafvec(&value[tree_id][node_id * n_targets_ * max_num_class_],
         &value[tree_id][(node_id + 1) * n_targets_ * max_num_class_]);
     // Compute the probability distribution over label classes
-    double const norm_factor = std::accumulate(leafvec.begin(), leafvec.end(), 0.0);
-    std::for_each(leafvec.begin(), leafvec.end(), [norm_factor](double& e) { e /= norm_factor; });
+    auto leaf_view = Array2DView<double>(leafvec.data(), n_targets_, max_num_class_);
+    for (int target_id = 0; target_id < n_targets_; ++target_id) {
+      double norm_factor = 0.0;
+      for (std::int32_t class_id = 0; class_id < n_classes[target_id]; ++class_id) {
+        norm_factor += leaf_view(target_id, class_id);
+      }
+      for (std::int32_t class_id = 0; class_id < n_classes[target_id]; ++class_id) {
+        leaf_view(target_id, class_id) /= norm_factor;
+      }
+    }
     builder.LeafVector(leafvec);
   }
 
@@ -116,7 +130,7 @@ class IsolationForestMixIn {
   }
 
   void HandleLeafNode(model_builder::ModelBuilder& builder, int tree_id, int node_id,
-      double const** value, [[maybe_unused]] int const* n_classes) const {
+      double const** value, [[maybe_unused]] std::int32_t const* n_classes) const {
     builder.LeafScalar(value[tree_id][node_id]);
   }
 

diff --git a/tests/python/test_sklearn_integration.py b/tests/python/test_sklearn_integration.py
@@ -163,7 +163,7 @@ def test_skl_multitarget_multiclass_rf(n_classes, n_estimators):
 
     tl_model = treelite.sklearn.import_model(clf)
     out_prob = treelite.gtil.predict(tl_model, X)
-    expected_prob = clf.predict_proba(X)
+    expected_prob = np.transpose(clf.predict_proba(X), axes=(1, 0, 2))
     np.testing.assert_almost_equal(out_prob, expected_prob, decimal=5)