Validate out of range categorical value.

doc/tutorials/categorical.rst

@@ -114,11 +114,11 @@ Miscellaneous
 
 By default, XGBoost assumes input categories are integers starting from 0 till the number
 of categories :math:`[0, n_categories)`. However, user might provide inputs with invalid
-values due to mistakes or missing values. It can be negative value, floating point value
-that can not be represented by 32-bit integer, or values that are larger than actual
-number of unique categories.  During training this is validated but for prediction it's
-treated as the same as missing value for performance reasons.  Lastly, missing values are
-treated as the same as numerical features.
+values due to mistakes or missing values. It can be negative value, integer values that
+can not be accurately represented by 32-bit floating point, or values that are larger than
+actual number of unique categories.  During training this is validated but for prediction
+it's treated as the same as missing value for performance reasons.  Lastly, missing values
+are treated as the same as numerical features (using the learned split direction).
 
 **********
 Next Steps

src/common/categorical.h

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2020-2021 by XGBoost Contributors
+ * Copyright 2020-2022 by XGBoost Contributors
  * \file categorical.h
  */
 #ifndef XGBOOST_COMMON_CATEGORICAL_H_
@@ -32,9 +32,17 @@ inline XGBOOST_DEVICE bool IsCat(Span<FeatureType const> ft, bst_feature_t fidx)
   return !ft.empty() && ft[fidx] == FeatureType::kCategorical;
 }
 
+constexpr inline bst_cat_t OutOfRangeCat() {
+  // See the round trip assert in `InvalidCat`.
+  return static_cast<bst_cat_t>(16777217) - static_cast<bst_cat_t>(1);
+}
 
 inline XGBOOST_DEVICE bool InvalidCat(float cat) {
-  return cat < 0 || cat > static_cast<float>(std::numeric_limits<bst_cat_t>::max());
+  constexpr auto kMaxCat = OutOfRangeCat();
+  static_assert(static_cast<bst_cat_t>(static_cast<float>(kMaxCat)) == kMaxCat, "");
+  static_assert(static_cast<bst_cat_t>(static_cast<float>(kMaxCat + 1)) != kMaxCat + 1, "");
+  static_assert(static_cast<float>(kMaxCat + 1) == kMaxCat, "");
+  return cat < 0 || cat >= kMaxCat;
 }
 
 /* \brief Whether should it traverse to left branch of a tree.
@@ -53,9 +61,13 @@ inline XGBOOST_DEVICE bool Decision(common::Span<uint32_t const> cats, float cat
 }
 
 inline void InvalidCategory() {
-  LOG(FATAL) << "Invalid categorical value detected.  Categorical value "
-                "should be non-negative, less than maximum size of int32 and less than total "
-                "number of categories in training data.";
+  // OutOfRangeCat() can be accurately represented, but everything after it will be
+  // rounded toward it, so we use >= for comparison check.  As a result, we require input
+  // values to be less than this last representable value.
+  auto str = std::to_string(OutOfRangeCat());
+  LOG(FATAL) << "Invalid categorical value detected.  Categorical value should be non-negative, "
+                "less than total umber of categories in training data and less than " +
+                    str;
 }
 
 /*!

src/common/quantile.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2020-2021 by XGBoost Contributors
+ * Copyright 2020-2022 by XGBoost Contributors
  */
 #include <limits>
 #include <utility>
@@ -27,6 +27,7 @@ SketchContainerImpl<WQSketch>::SketchContainerImpl(std::vector<bst_row_t> column
   sketches_.resize(columns_size_.size());
   CHECK_GE(n_threads_, 1);
   categories_.resize(columns_size_.size());
+  has_categorical_ = std::any_of(feature_types_.cbegin(), feature_types_.cend(), IsCatOp{});
 }
 
 template <typename WQSketch>
@@ -187,7 +188,7 @@ void SketchContainerImpl<WQSketch>::PushRowPage(SparsePage const &page, MetaInfo
           if (is_dense) {
             for (size_t ii = begin; ii < end; ii++) {
               if (IsCat(feature_types_, ii)) {
-                categories_[ii].emplace(AsCat(p_inst[ii].fvalue));
+                categories_[ii].emplace(p_inst[ii].fvalue);
               } else {
                 sketches_[ii].Push(p_inst[ii].fvalue, w);
               }
@@ -197,7 +198,7 @@ void SketchContainerImpl<WQSketch>::PushRowPage(SparsePage const &page, MetaInfo
               auto const& entry = p_inst[i];
               if (entry.index >= begin && entry.index < end) {
                 if (IsCat(feature_types_, entry.index)) {
-                  categories_[entry.index].emplace(AsCat(entry.fvalue));
+                  categories_[entry.index].emplace(entry.fvalue);
                 } else {
                   sketches_[entry.index].Push(entry.fvalue, w);
                 }
@@ -352,10 +353,10 @@ void AddCutPoint(typename SketchType::SummaryContainer const &summary, int max_b
   }
 }
 
-void AddCategories(std::set<bst_cat_t> const &categories, HistogramCuts *cuts) {
+void AddCategories(std::set<float> const &categories, HistogramCuts *cuts) {
   auto &cut_values = cuts->cut_values_.HostVector();
   for (auto const &v : categories) {
-    cut_values.push_back(v);
+    cut_values.push_back(AsCat(v));
   }
 }
 
@@ -410,6 +411,15 @@ void SketchContainerImpl<WQSketch>::MakeCuts(HistogramCuts* cuts) {
     CHECK_GT(cut_size, cuts->cut_ptrs_.HostVector().back());
     cuts->cut_ptrs_.HostVector().push_back(cut_size);
   }
+
+  if (has_categorical_) {
+    for (auto const &feat : categories_) {
+      if (std::any_of(feat.cbegin(), feat.cend(), InvalidCat)) {
+        InvalidCategory();
+      }
+    }
+  }
+
   monitor_.Stop(__func__);
 }
 
@@ -457,7 +467,7 @@ void SortedSketchContainer::PushColPage(SparsePage const &page, MetaInfo const &
     // second pass
     if (IsCat(feature_types_, fidx)) {
       for (auto c : column) {
-        categories_[fidx].emplace(AsCat(c.fvalue));
+        categories_[fidx].emplace(c.fvalue);
       }
     } else {
       for (auto c : column) {

src/common/quantile.h

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2014-2021 by Contributors
+ * Copyright 2014-2022 by XGBoost Contributors
  * \file quantile.h
  * \brief util to compute quantiles
  * \author Tianqi Chen
@@ -706,13 +706,14 @@ template <typename WQSketch>
 class SketchContainerImpl {
  protected:
   std::vector<WQSketch> sketches_;
-  std::vector<std::set<bst_cat_t>> categories_;
+  std::vector<std::set<float>> categories_;
   std::vector<FeatureType> const feature_types_;
 
   std::vector<bst_row_t> columns_size_;
   int32_t max_bins_;
   bool use_group_ind_{false};
   int32_t n_threads_;
+  bool has_categorical_{false};
   Monitor monitor_;
 
  public:

src/tree/hist/evaluate_splits.h

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2021 by XGBoost Contributors
+ * Copyright 2021-2022 by XGBoost Contributors
  */
 #ifndef XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_
 #define XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_
@@ -303,8 +303,9 @@ template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
     if (candidate.split.is_cat) {
       std::vector<uint32_t> split_cats;
       if (candidate.split.cat_bits.empty()) {
-        CHECK_LT(candidate.split.split_value, std::numeric_limits<bst_cat_t>::max())
-            << "Categorical feature value too large.";
+        if (common::InvalidCat(candidate.split.split_value)) {
+          common::InvalidCategory();
+        }
         auto cat = common::AsCat(candidate.split.split_value);
         split_cats.resize(LBitField32::ComputeStorageSize(std::max(cat + 1, 1)), 0);
         LBitField32 cat_bits;

src/tree/updater_gpu_hist.cu

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2017-2021 XGBoost contributors
+ * Copyright 2017-2022 XGBoost contributors
  */
 #include <thrust/copy.h>
 #include <thrust/reduce.h>
@@ -572,11 +572,11 @@ struct GPUHistMakerDevice {
     if (is_cat) {
       CHECK_LT(candidate.split.fvalue, std::numeric_limits<bst_cat_t>::max())
           << "Categorical feature value too large.";
-      auto cat = common::AsCat(candidate.split.fvalue);
-      if (common::InvalidCat(cat)) {
+      if (common::InvalidCat(candidate.split.fvalue)) {
         common::InvalidCategory();
       }
-      std::vector<uint32_t> split_cats(LBitField32::ComputeStorageSize(std::max(cat+1, 1)), 0);
+      auto cat = common::AsCat(candidate.split.fvalue);
+      std::vector<uint32_t> split_cats(LBitField32::ComputeStorageSize(std::max(cat + 1, 1)), 0);
       LBitField32 cats_bits(split_cats);
       cats_bits.Set(cat);
       dh::CopyToD(split_cats, &node_categories);

tests/python-gpu/test_gpu_updaters.py

@@ -60,20 +60,9 @@ def test_categorical_32_cat(self):
         rounds = 4
         self.cputest.run_categorical_basic(rows, cols, rounds, cats, "gpu_hist")
 
+    @pytest.mark.skipif(**tm.no_cupy())
     def test_invalid_categorical(self):
-        import cupy as cp
-        rng = np.random.default_rng()
-        X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
-        y = rng.normal(loc=0, scale=1, size=100)
-
-        # Check is performe during sketching.
-        Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
-        with pytest.raises(ValueError):
-            xgb.train({"tree_method": "gpu_hist"}, Xy)
-
-        X, y = cp.array(X), cp.array(y)
-        with pytest.raises(ValueError):
-            Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
+        self.cputest.run_invalid_category("gpu_hist")
 
     @pytest.mark.skipif(**tm.no_cupy())
     @given(parameter_strategy, strategies.integers(1, 20),

tests/python/test_updaters.py

@@ -133,6 +133,41 @@ def test_hist_degenerate_case(self):
         w = [0, 0, 1, 0]
         model.fit(X, y, sample_weight=w)
 
+    def run_invalid_category(self, tree_method: str) -> None:
+        rng = np.random.default_rng()
+        # too large
+        X = rng.integers(low=0, high=4, size=1000).reshape(100, 10)
+        y = rng.normal(loc=0, scale=1, size=100)
+        X[13, 7] = np.iinfo(np.int32).max + 1
+
+        # Check is performed during sketching.
+        Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
+        with pytest.raises(ValueError):
+            xgb.train({"tree_method": tree_method}, Xy)
+
+        X[13, 7] = 16777216
+        Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
+        with pytest.raises(ValueError):
+            xgb.train({"tree_method": tree_method}, Xy)
+
+        # mixed positive and negative values
+        X = rng.normal(loc=0, scale=1, size=1000).reshape(100, 10)
+        y = rng.normal(loc=0, scale=1, size=100)
+
+        Xy = xgb.DMatrix(X, y, feature_types=["c"] * 10)
+        with pytest.raises(ValueError):
+            xgb.train({"tree_method": tree_method}, Xy)
+
+        if tree_method == "gpu_hist":
+            import cupy as cp
+
+            X, y = cp.array(X), cp.array(y)
+            with pytest.raises(ValueError):
+                Xy = xgb.DeviceQuantileDMatrix(X, y, feature_types=["c"] * 10)
+
+    def test_invalid_category(self) -> None:
+        self.run_invalid_category("approx")
+
     def run_categorical_basic(self, rows, cols, rounds, cats, tree_method):
         onehot, label = tm.make_categorical(rows, cols, cats, True)
         cat, _ = tm.make_categorical(rows, cols, cats, False)