Rewrite approx

amalgamation/xgboost-all0.cc

@@ -57,6 +57,7 @@
 #include "../src/tree/updater_refresh.cc"
 #include "../src/tree/updater_sync.cc"
 #include "../src/tree/updater_histmaker.cc"
+#include "../src/tree/updater_approx.cc"
 #include "../src/tree/constraints.cc"
 
 // linear

demo/guide-python/categorical.py

@@ -3,7 +3,8 @@
 =====================================
 
 Experimental support for categorical data.  After 1.5 XGBoost `gpu_hist` tree method has
-experimental support for one-hot encoding based tree split.
+experimental support for one-hot encoding based tree split, and in 1.6 `approx` supported
+was added.
 
 In before, users need to run an encoder themselves before passing the data into XGBoost,
 which creates a sparse matrix and potentially increase memory usage.  This demo showcases

doc/parameter.rst

@@ -154,7 +154,7 @@ Parameters for Tree Booster
 
 * ``sketch_eps`` [default=0.03]
 
-  - Only used for ``tree_method=approx``.
+  - Only used for ``updater=grow_local_histmaker``.
   - This roughly translates into ``O(1 / sketch_eps)`` number of bins.
     Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy.
   - Usually user does not have to tune this.
@@ -238,13 +238,27 @@ Parameters for Tree Booster
     list is a group of indices of features that are allowed to interact with each other.
     See :doc:`/tutorials/feature_interaction_constraint` for more information.
 
-Additional parameters for ``hist`` and ``gpu_hist`` tree method
-================================================================
+Additional parameters for ``hist``, ``gpu_hist`` and ``approx`` tree method
+===========================================================================
 
 * ``single_precision_histogram``, [default= ``false``]
 
   - Use single precision to build histograms instead of double precision.
 
+Additional parameters for ``approx`` tree method
+================================================
+
+* ``max_cat_to_onehot``
+
+  .. versionadded:: 1.6
+
+  .. note:: The support for this parameter is experimental.
+
+  - A threshold for deciding whether XGBoost should use one-hot encoding based split for
+    categorical data.  When number of categories is lesser than the threshold then one-hot
+    encoding is chosen, otherwise the categories will be partitioned into children nodes.
+    Only relevant for regression and binary classification with `approx` tree method.
+
 Additional parameters for Dart Booster (``booster=dart``)
 =========================================================
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressorSuite.scala

@@ -53,7 +53,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
       "max_depth" -> "6",
       "silent" -> "1",
       "objective" -> "reg:squarederror",
-      "max_bin" -> 16,
+      "max_bin" -> 64,
       "tree_method" -> treeMethod)
 
     val model1 = ScalaXGBoost.train(trainingDM, paramMap, round)

python-package/xgboost/sklearn.py

@@ -267,6 +267,16 @@ def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
             callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
                                                     save_best=True)]
 
+    max_cat_to_onehot : bool
+
+        .. versionadded:: 1.6.0
+
+        A threshold for deciding whether XGBoost should use one-hot encoding based split
+        for categorical data.  When number of categories is lesser than the threshold then
+        one-hot encoding is chosen, otherwise the categories will be partitioned into
+        children nodes.  Only relevant for regression and binary classification and
+        `approx` tree method.
+
     kwargs : dict, optional
         Keyword arguments for XGBoost Booster object.  Full documentation of parameters
         can be found :doc:`here </parameter>`.
@@ -483,6 +493,7 @@ def __init__(
         eval_metric: Optional[Union[str, List[str], Callable]] = None,
         early_stopping_rounds: Optional[int] = None,
         callbacks: Optional[List[TrainingCallback]] = None,
+        max_cat_to_onehot: Optional[int] = None,
         **kwargs: Any
     ) -> None:
         if not SKLEARN_INSTALLED:
@@ -522,6 +533,7 @@ def __init__(
         self.eval_metric = eval_metric
         self.early_stopping_rounds = early_stopping_rounds
         self.callbacks = callbacks
+        self.max_cat_to_onehot = max_cat_to_onehot
         if kwargs:
             self.kwargs = kwargs
 
@@ -800,8 +812,8 @@ def _duplicated(parameter: str) -> None:
             _duplicated("callbacks")
         callbacks = self.callbacks if self.callbacks is not None else callbacks
 
-        # lastly check categorical data support.
-        if self.enable_categorical and params.get("tree_method", None) != "gpu_hist":
+        tree_method = params.get("tree_method", None)
+        if self.enable_categorical and tree_method not in ("gpu_hist", "approx"):
             raise ValueError(
                 "Experimental support for categorical data is not implemented for"
                 " current tree method yet."
@@ -876,8 +888,7 @@ def fit(
         feature_weights :
             Weight for each feature, defines the probability of each feature being
             selected when colsample is being used.  All values must be greater than 0,
-            otherwise a `ValueError` is thrown.  Only available for `hist`, `gpu_hist` and
-            `exact` tree methods.
+            otherwise a `ValueError` is thrown.
 
         callbacks :
             .. deprecated: 1.6.0
@@ -1750,8 +1761,7 @@ def fit(
         feature_weights :
             Weight for each feature, defines the probability of each feature being
             selected when colsample is being used.  All values must be greater than 0,
-            otherwise a `ValueError` is thrown.  Only available for `hist`, `gpu_hist` and
-            `exact` tree methods.
+            otherwise a `ValueError` is thrown.
 
         callbacks :
             .. deprecated: 1.6.0

src/common/threading_utils.h

@@ -130,8 +130,7 @@ class BlockedSpace2d {
 template <typename Func>
 void ParallelFor2d(const BlockedSpace2d& space, int nthreads, Func func) {
   const size_t num_blocks_in_space = space.Size();
-  nthreads = std::min(nthreads, omp_get_max_threads());
-  nthreads = std::max(nthreads, 1);
+  CHECK_GE(nthreads, 1);
 
   dmlc::OMPException exc;
 #pragma omp parallel num_threads(nthreads)
@@ -277,9 +276,10 @@ inline int32_t OmpSetNumThreadsWithoutHT(int32_t* p_threads) {
 
 inline int32_t OmpGetNumThreads(int32_t n_threads) {
   if (n_threads <= 0) {
-    n_threads = omp_get_num_procs();
+    n_threads = std::min(omp_get_num_procs(), omp_get_max_threads());
   }
   n_threads = std::min(n_threads, OmpGetThreadLimit());
+  n_threads = std::max(n_threads, 1);
   return n_threads;
 }
 }  // namespace common

src/gbm/gbtree.cc

@@ -168,7 +168,7 @@ void GBTree::ConfigureUpdaters() {
       // calling this function.
       break;
     case TreeMethod::kApprox:
-      tparam_.updater_seq = "grow_histmaker,prune";
+      tparam_.updater_seq = "grow_histmaker";
       break;
     case TreeMethod::kExact:
       tparam_.updater_seq = "grow_colmaker,prune";

src/tree/param.h

@@ -38,7 +38,7 @@ struct TrainParam : public XGBoostParameter<TrainParam> {
   enum TreeGrowPolicy { kDepthWise = 0, kLossGuide = 1 };
   int grow_policy;
 
-  uint32_t max_cat_to_onehot{1};
+  uint32_t max_cat_to_onehot{4};
 
   //----- the rest parameters are less important ----
   // minimum amount of hessian(weight) allowed in a child

src/tree/tree_model.cc

@@ -973,6 +973,7 @@ void RegTree::SaveCategoricalSplit(Json* p_out) const {
       }
       size_t size = categories.size() - begin;
       categories_sizes.emplace_back(static_cast<Integer::Int>(size));
+      CHECK_NE(size, 0);
     }
   }
 

src/tree/tree_updater.cc

@@ -35,6 +35,7 @@ DMLC_REGISTRY_LINK_TAG(updater_refresh);
 DMLC_REGISTRY_LINK_TAG(updater_prune);
 DMLC_REGISTRY_LINK_TAG(updater_quantile_hist);
 DMLC_REGISTRY_LINK_TAG(updater_histmaker);
+DMLC_REGISTRY_LINK_TAG(updater_approx);
 DMLC_REGISTRY_LINK_TAG(updater_sync);
 #ifdef XGBOOST_USE_CUDA
 DMLC_REGISTRY_LINK_TAG(updater_gpu_hist);