naming is hard.

demo/guide-python/multioutput_regression.py

@@ -10,7 +10,8 @@
 
 .. note::
 
-    The feature is experimental. For the monolithic strategy, many features are missing.
+    The feature is experimental. For the `multi_output_tree` strategy, many features are
+    missing.
 
 """
 
@@ -121,15 +122,15 @@ def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
     args = parser.parse_args()
     # Train with builtin RMSE objective
     # - One model per output.
-    rmse_model(args.plot == 1, "composite")
+    rmse_model(args.plot == 1, "one_output_per_tree")
 
     # - One model for all outputs, this is still working in progress, many features are
     # missing.
-    rmse_model(args.plot == 1, "monolithic")
+    rmse_model(args.plot == 1, "multi_output_tree")
 
     # Train with custom objective.
     # - One model per output.
-    custom_rmse_model(args.plot == 1, "composite")
+    custom_rmse_model(args.plot == 1, "one_output_per_tree")
     # - One model for all outputs, this is still working in progress, many features are
     # missing.
-    custom_rmse_model(args.plot == 1, "monolithic")
+    custom_rmse_model(args.plot == 1, "multi_output_tree")

doc/parameter.rst

@@ -226,7 +226,7 @@ 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.
 
-* ``multi_strategy``, [default = ``composite``]
+* ``multi_strategy``, [default = ``one_output_per_tree``]
 
   .. versionadded:: 2.0.0
 
@@ -235,8 +235,8 @@ Parameters for Tree Booster
   - The strategy used for training multi-target models, including multi-target regression
   and multi-class classification. See :doc:`/tutorials/multioutput` for more information.
 
-    - ``composite``: One model for each target.
-    - ``monolithic``:  Use multi-target trees.
+    - ``one_output_per_tree``: One model for each target.
+    - ``multi_output_tree``:  Use multi-target trees.
 
 .. _cat-param:
 

doc/tutorials/multioutput.rst

@@ -53,12 +53,13 @@ Training with Vector Leaf
 
 XGBoost can optionally build multi-output trees with the size of leaf equals to the number
 of targets when the tree method `hist` is used. The behavior can be controlled by the
-``multi_strategy`` training parameter, which can take the value `composite` (the default)
-for building one model per-target or `monolithic` for building multi-output trees.
+``multi_strategy`` training parameter, which can take the value `one_output_per_tree` (the
+default) for building one model per-target or `multi_output_tree` for building
+multi-output trees.
 
 .. code-block:: python
 
-  clf = xgb.XGBClassifier(tree_method="hist", multi_strategy="monolithic")
+  clf = xgb.XGBClassifier(tree_method="hist", multi_strategy="multi_output_tree")
 
 See :ref:`sphx_glr_python_examples_multioutput_regression.py` for a worked example with
 regression.

include/xgboost/learner.h

@@ -286,8 +286,8 @@ struct LearnerModelParamLegacy;
  * \brief Strategy for building multi-target models.
  */
 enum class MultiStrategy : std::int32_t {
-  kComposite = 0,
-  kMonolithic = 1,
+  kOneOutputPerTree = 0,
+  kMultiOutputTree = 1,
 };
 
 /**
@@ -317,7 +317,7 @@ struct LearnerModelParam {
   /**
    * \brief Strategy for building multi-target models.
    */
-  MultiStrategy multi_strategy{MultiStrategy::kComposite};
+  MultiStrategy multi_strategy{MultiStrategy::kOneOutputPerTree};
 
   LearnerModelParam() = default;
   // As the old `LearnerModelParamLegacy` is still used by binary IO, we keep
@@ -338,7 +338,7 @@ struct LearnerModelParam {
 
   void Copy(LearnerModelParam const& that);
   [[nodiscard]] bool IsVectorLeaf() const noexcept {
-    return multi_strategy == MultiStrategy::kMonolithic;
+    return multi_strategy == MultiStrategy::kMultiOutputTree;
   }
   [[nodiscard]] bst_target_t OutputLength() const noexcept { return this->num_output_group; }
   [[nodiscard]] bst_target_t LeafLength() const noexcept {

python-package/xgboost/sklearn.py

@@ -322,8 +322,8 @@ def task(i: int) -> float:
         regression and multi-class classification. See :doc:`/tutorials/multioutput` for
         more information.
 
-        - ``composite``: One model for each target.
-        - ``monolithic``:  Use multi-target trees.
+        - ``one_output_per_tree``: One model for each target.
+        - ``multi_output_tree``:  Use multi-target trees.
 
     eval_metric : Optional[Union[str, List[str], Callable]]
 

python-package/xgboost/testing/params.py

@@ -4,8 +4,8 @@
 
 import pytest
 
-hypothesis = pytest.importorskip("hypothesis")
-from hypothesis import strategies  # pylint:disable=wrong-import-position
+strategies = pytest.importorskip("hypothesis.strategies")
+
 
 exact_parameter_strategy = strategies.fixed_dictionaries(
     {
@@ -46,7 +46,9 @@
         "max_depth": strategies.integers(1, 11),
         "max_leaves": strategies.integers(0, 1024),
         "max_bin": strategies.integers(2, 512),
-        "multi_strategy": strategies.sampled_from(["monolithic", "composite"]),
+        "multi_strategy": strategies.sampled_from(
+            ["multi_output_tree", "one_output_per_tree"]
+        ),
         "grow_policy": strategies.sampled_from(["lossguide", "depthwise"]),
         "min_child_weight": strategies.floats(0.5, 2.0),
         # We cannot enable subsampling as the training loss can increase

src/learner.cc

@@ -326,7 +326,7 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
   std::string booster;
   std::string objective;
   // This is a training parameter and is not saved (nor loaded) in the model.
-  MultiStrategy multi_strategy{MultiStrategy::kComposite};
+  MultiStrategy multi_strategy{MultiStrategy::kOneOutputPerTree};
 
   // declare parameters
   DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
@@ -339,12 +339,12 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
         .set_default("reg:squarederror")
         .describe("Objective function used for obtaining gradient.");
     DMLC_DECLARE_FIELD(multi_strategy)
-        .add_enum("composite", MultiStrategy::kComposite)
-        .add_enum("monolithic", MultiStrategy::kMonolithic)
-        .set_default(MultiStrategy::kComposite)
+        .add_enum("one_output_per_tree", MultiStrategy::kOneOutputPerTree)
+        .add_enum("multi_output_tree", MultiStrategy::kMultiOutputTree)
+        .set_default(MultiStrategy::kOneOutputPerTree)
         .describe(
-            "Strategy used for training multi-target models. `monolithic` means building one "
-            "single tree for all targets.");
+            "Strategy used for training multi-target models. `multi_output_tree` means building "
+            "one single tree for all targets.");
   }
 };
 

tests/cpp/helpers.h

@@ -471,7 +471,7 @@ inline LearnerModelParam MakeMP(bst_feature_t n_features, float base_score, uint
                                 int32_t device = Context::kCpuId) {
   size_t shape[1]{1};
   LearnerModelParam mparam(n_features, linalg::Tensor<float, 1>{{base_score}, shape, device},
-                           n_groups, 1, MultiStrategy::kComposite);
+                           n_groups, 1, MultiStrategy::kOneOutputPerTree);
   return mparam;
 }
 

tests/cpp/predictor/test_predictor.cc

@@ -428,7 +428,7 @@ void TestVectorLeafPrediction(Context const *ctx) {
 
   LearnerModelParam mparam{static_cast<bst_feature_t>(kCols),
                            linalg::Vector<float>{{0.5}, {1}, Context::kCpuId}, 1, 3,
-                           MultiStrategy::kMonolithic};
+                           MultiStrategy::kMultiOutputTree};
 
   std::vector<std::unique_ptr<RegTree>> trees;
   trees.emplace_back(new RegTree{mparam.LeafLength(), mparam.num_feature});

tests/cpp/test_multi_target.cc

@@ -124,11 +124,11 @@ TEST(MultiStrategy, Configure) {
   auto p_fmat = RandomDataGenerator{12ul, 3ul, 0.0}.GenerateDMatrix();
   p_fmat->Info().labels.Reshape(p_fmat->Info().num_row_, 2);
   std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
-  learner->SetParams(Args{{"multi_strategy", "monolithic"}, {"num_target", "2"}});
+  learner->SetParams(Args{{"multi_strategy", "multi_output_tree"}, {"num_target", "2"}});
   learner->Configure();
   ASSERT_EQ(learner->Groups(), 2);
 
-  learner->SetParams(Args{{"multi_strategy", "monolithic"}, {"num_target", "0"}});
+  learner->SetParams(Args{{"multi_strategy", "multi_output_tree"}, {"num_target", "0"}});
   ASSERT_THROW({ learner->Configure(); }, dmlc::Error);
 }
 }  // namespace xgboost

tests/python/test_basic_models.py

@@ -330,7 +330,7 @@ def run_model_json_io(self, parameters: dict, ext: str) -> None:
         from_ubjraw = xgb.Booster()
         from_ubjraw.load_model(ubj_raw)
 
-        if parameters.get("multi_strategy", None) != "monolithic":
+        if parameters.get("multi_strategy", None) != "multi_output_tree":
             # old binary model is not supported.
             old_from_json = from_jraw.save_raw(raw_format="deprecated")
             old_from_ubj = from_ubjraw.save_raw(raw_format="deprecated")
@@ -341,7 +341,7 @@ def run_model_json_io(self, parameters: dict, ext: str) -> None:
         pretty = json.dumps(json.loads(raw_json), indent=2) + "\n\n"
         bst.load_model(bytearray(pretty, encoding="ascii"))
 
-        if parameters.get("multi_strategy", None) != "monolithic":
+        if parameters.get("multi_strategy", None) != "multi_output_tree":
             # old binary model is not supported.
             old_from_json = from_jraw.save_raw(raw_format="deprecated")
             old_from_ubj = from_ubjraw.save_raw(raw_format="deprecated")
@@ -363,7 +363,7 @@ def test_model_json_io(self, ext: str) -> None:
         parameters = {
             "booster": "gbtree",
             "tree_method": "hist",
-            "multi_strategy": "monolithic",
+            "multi_strategy": "multi_output_tree",
             "objective": "multi:softmax",
         }
         self.run_model_json_io(parameters, ext)