fix rebase.

dmlc · Mar 10, 2023 · 543002c · 543002c
1 parent 31cf9e8
commit 543002c
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Showing 6 changed files with 14 additions and 17 deletions.
diff --git a/demo/guide-python/multioutput_regression.py b/demo/guide-python/multioutput_regression.py
@@ -95,7 +95,7 @@ def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
         {
             "tree_method": "hist",
             "num_target": y.shape[1],
-            "multi_strategy": "mono",
+            "multi_strategy": "monolithic",
         },
         dtrain=Xy,
         num_boost_round=128,
@@ -116,8 +116,8 @@ 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, "compo")
+    rmse_model(args.plot == 1, "composite")
     # one model for all outputs
-    rmse_model(args.plot == 1, "mono")
+    rmse_model(args.plot == 1, "monolithic")
     # Train with custom objective.
     custom_rmse_model(args.plot == 1)
diff --git a/doc/parameter.rst b/doc/parameter.rst
@@ -226,12 +226,12 @@ 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 = ``compo``]
+* ``multi_strategy``, [default = ``composite``]
 
   - The strategy used for training multi-target models.
 
-    - ``compo``: One model for each target.
-    - ``mono``:  Use multi-target trees.
+    - ``composite``: One model for each target.
+    - ``monolithic``:  Use multi-target trees.
 
 .. _cat-param:
 

diff --git a/doc/tutorials/multioutput.rst b/doc/tutorials/multioutput.rst
@@ -54,12 +54,12 @@ Training with Vector Leaf
 
 XGBoost can optionally build multi-output trees with the size of leaf equals to the number
 of targets. The behavior can be controlled by the ``multi_strategy`` training
-parameter. It can take the value `compo` (the default) or `mono`. Specify `mono` and use
-``tree_method=hist`` to enable this feature.
+parameter. It can take the value `composite` (the default) or `monolithic`. Specify
+`monolithic` and use ``tree_method=hist`` to enable this feature.
 
 
 .. code-black:: python
 
-  clf = xgb.XGBClassifier(tree_method="hist", multi_strategy="mono")
+  clf = xgb.XGBClassifier(tree_method="hist", multi_strategy="monolithic")
 
 See :ref:`sphx_glr_python_examples_multioutput_regression.py` for a worked example.
diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
@@ -624,6 +624,7 @@ 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 +671,7 @@ 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

diff --git a/src/learner.cc b/src/learner.cc
@@ -67,11 +67,6 @@ const char* kMaxDeltaStepDefaultValue = "0.7";
 
 DECLARE_FIELD_ENUM_CLASS(xgboost::MultiStrategy);
 
-namespace xgboost {
-std::string StrategyStr(Strategy s) { return s == Strategy::kComposite ? "compo" : "mono"; }
-}  // namespace xgboost
-DECLARE_FIELD_ENUM_CLASS(xgboost::Strategy);
-
 namespace xgboost {
 Learner::~Learner() = default;
 namespace {

diff --git a/tests/cpp/predictor/test_gpu_predictor.cu b/tests/cpp/predictor/test_gpu_predictor.cu
@@ -36,7 +36,7 @@ TEST(GPUPredictor, Basic) {
 
     Context ctx;
     ctx.gpu_id = 0;
-    LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.gpu_id)};
+    LearnerModelParam mparam{MakeMP(n_col, .5, ctx.gpu_id)};
     gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
 
     // Test predict batch
@@ -150,7 +150,7 @@ TEST(GPUPredictor, ShapStump) {
 
   Context ctx;
   ctx.gpu_id = 0;
-  LearnerModelParam mparam{MakeMP(1, .5, 1, 1, ctx.gpu_id)};
+  LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.gpu_id)};
   gbm::GBTreeModel model(&mparam, &ctx);
 
   std::vector<std::unique_ptr<RegTree>> trees;
@@ -177,7 +177,7 @@ TEST(GPUPredictor, ShapStump) {
 TEST(GPUPredictor, Shap) {
   Context ctx;
   ctx.gpu_id = 0;
-  LearnerModelParam mparam{MakeMP(1, .5, 1, 1, ctx.gpu_id)};
+  LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.gpu_id)};
   gbm::GBTreeModel model(&mparam, &ctx);
 
   std::vector<std::unique_ptr<RegTree>> trees;