Implement multi-target for hist.

- Add new hist tree builder.
- Move data fetchers for tests.
- Dispatch function calls in gbm base on the tree type.

demo/guide-python/multioutput_regression.py

@@ -7,6 +7,11 @@
 https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py
 
 See :doc:`/tutorials/multioutput` for more information.
+
+.. note::
+
+    The feature is experimental. For the monolithic strategy, many features are missing.
+
 """
 
 import argparse
@@ -40,19 +45,26 @@ def gen_circle() -> Tuple[np.ndarray, np.ndarray]:
     return X, y
 
 
-def rmse_model(plot_result: bool):
+def rmse_model(plot_result: bool, strategy: str):
     """Draw a circle with 2-dim coordinate as target variables."""
     X, y = gen_circle()
     # Train a regressor on it
-    reg = xgb.XGBRegressor(tree_method="hist", n_estimators=64)
+    reg = xgb.XGBRegressor(
+        tree_method="hist",
+        n_estimators=128,
+        n_jobs=16,
+        max_depth=8,
+        multi_strategy=strategy,
+        subsample=0.6,
+    )
     reg.fit(X, y, eval_set=[(X, y)])
 
     y_predt = reg.predict(X)
     if plot_result:
         plot_predt(y, y_predt, "multi")
 
 
-def custom_rmse_model(plot_result: bool) -> None:
+def custom_rmse_model(plot_result: bool, strategy: str) -> None:
     """Train using Python implementation of Squared Error."""
 
     # As the experimental support status, custom objective doesn't support matrix as
@@ -88,9 +100,10 @@ def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
         {
             "tree_method": "hist",
             "num_target": y.shape[1],
+            "multi_strategy": strategy,
         },
         dtrain=Xy,
-        num_boost_round=100,
+        num_boost_round=128,
         obj=squared_log,
         evals=[(Xy, "Train")],
         evals_result=results,
@@ -107,6 +120,16 @@ def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
     parser.add_argument("--plot", choices=[0, 1], type=int, default=1)
     args = parser.parse_args()
     # Train with builtin RMSE objective
-    rmse_model(args.plot == 1)
+    # - One model per output.
+    rmse_model(args.plot == 1, "composite")
+
+    # - One model for all outputs, this is still working in progress, many features are
+    # missing.
+    rmse_model(args.plot == 1, "monolithic")
+
     # Train with custom objective.
-    custom_rmse_model(args.plot == 1)
+    # - One model per output.
+    custom_rmse_model(args.plot == 1, "composite")
+    # - One model for all outputs, this is still working in progress, many features are
+    # missing.
+    custom_rmse_model(args.plot == 1, "monolithic")

doc/parameter.rst

@@ -226,6 +226,18 @@ 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``]
+
+  .. versionadded:: 2.0.0
+
+  .. note:: This parameter is working-in-progress.
+
+  - 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.
+
 .. _cat-param:
 
 Parameters for Categorical Feature

doc/tutorials/multioutput.rst

@@ -11,7 +11,11 @@ can be simultaneously classified as both sci-fi and comedy.  For detailed explan
 terminologies related to different multi-output models please refer to the
 :doc:`scikit-learn user guide <sklearn:modules/multiclass>`.
 
-Internally, XGBoost builds one model for each target similar to sklearn meta estimators,
+**********************************
+Training with One-Model-Per-Target
+**********************************
+
+By default, XGBoost builds one model for each target similar to sklearn meta estimators,
 with the added benefit of reusing data and other integrated features like SHAP.  For a
 worked example of regression, see
 :ref:`sphx_glr_python_examples_multioutput_regression.py`. For multi-label classification,
@@ -36,3 +40,25 @@ dense matrix for labels.
 
 
 The feature is still under development with limited support from objectives and metrics.
+
+*************************
+Training with Vector Leaf
+*************************
+
+.. versionadded:: 2.0
+
+.. note::
+
+   This is still working-in-progress, and many features are missing.
+
+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.
+
+.. code-block:: python
+
+  clf = xgb.XGBClassifier(tree_method="hist", multi_strategy="monolithic")
+
+See :ref:`sphx_glr_python_examples_multioutput_regression.py` for a worked example with
+regression.

include/xgboost/linalg.h

@@ -530,17 +530,17 @@ class TensorView {
   /**
    * \brief Number of items in the tensor.
    */
-  LINALG_HD [[nodiscard]] std::size_t Size() const { return size_; }
+  [[nodiscard]] LINALG_HD std::size_t Size() const { return size_; }
   /**
    * \brief Whether this is a contiguous array, both C and F contiguous returns true.
    */
-  LINALG_HD [[nodiscard]] bool Contiguous() const {
+  [[nodiscard]] LINALG_HD bool Contiguous() const {
     return data_.size() == this->Size() || this->CContiguous() || this->FContiguous();
   }
   /**
    * \brief Whether it's a c-contiguous array.
    */
-  LINALG_HD [[nodiscard]] bool CContiguous() const {
+  [[nodiscard]] LINALG_HD bool CContiguous() const {
     StrideT stride;
     static_assert(std::is_same<decltype(stride), decltype(stride_)>::value);
     // It's contiguous if the stride can be calculated from shape.
@@ -550,7 +550,7 @@ class TensorView {
   /**
    * \brief Whether it's a f-contiguous array.
    */
-  LINALG_HD [[nodiscard]] bool FContiguous() const {
+  [[nodiscard]] LINALG_HD bool FContiguous() const {
     StrideT stride;
     static_assert(std::is_same<decltype(stride), decltype(stride_)>::value);
     // It's contiguous if the stride can be calculated from shape.

python-package/xgboost/sklearn.py

@@ -312,6 +312,19 @@ def task(i: int) -> float:
         needs to be set to have categorical feature support. See :doc:`Categorical Data
         </tutorials/categorical>` and :ref:`cat-param` for details.
 
+    multi_strategy : Optional[str]
+
+        .. versionadded:: 2.0.0
+
+        .. note:: This parameter is working-in-progress.
+
+        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.
+
     eval_metric : Optional[Union[str, List[str], Callable]]
 
         .. versionadded:: 1.6.0
@@ -624,6 +637,7 @@ def __init__(
         feature_types: Optional[FeatureTypes] = None,
         max_cat_to_onehot: Optional[int] = None,
         max_cat_threshold: Optional[int] = None,
+        multi_strategy: Optional[str] = None,
         eval_metric: Optional[Union[str, List[str], Callable]] = None,
         early_stopping_rounds: Optional[int] = None,
         callbacks: Optional[List[TrainingCallback]] = None,
@@ -670,6 +684,7 @@ def __init__(
         self.feature_types = feature_types
         self.max_cat_to_onehot = max_cat_to_onehot
         self.max_cat_threshold = max_cat_threshold
+        self.multi_strategy = multi_strategy
         self.eval_metric = eval_metric
         self.early_stopping_rounds = early_stopping_rounds
         self.callbacks = callbacks