Fix multi-output with alternating strategies. (#9933)

---------

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>

tests/python/test_multi_target.py

@@ -0,0 +1,105 @@
+from typing import Any, Dict
+
+from hypothesis import given, note, settings, strategies
+
+import xgboost as xgb
+from xgboost import testing as tm
+from xgboost.testing.params import (
+    exact_parameter_strategy,
+    hist_cache_strategy,
+    hist_multi_parameter_strategy,
+    hist_parameter_strategy,
+)
+from xgboost.testing.updater import ResetStrategy, train_result
+
+
+class TestTreeMethodMulti:
+    @given(
+        exact_parameter_strategy, strategies.integers(1, 20), tm.multi_dataset_strategy
+    )
+    @settings(deadline=None, print_blob=True)
+    def test_exact(self, param: dict, num_rounds: int, dataset: tm.TestDataset) -> None:
+        if dataset.name.endswith("-l1"):
+            return
+        param["tree_method"] = "exact"
+        param = dataset.set_params(param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)
+        assert tm.non_increasing(result["train"][dataset.metric])
+
+    @given(
+        exact_parameter_strategy,
+        hist_parameter_strategy,
+        hist_cache_strategy,
+        strategies.integers(1, 20),
+        tm.multi_dataset_strategy,
+    )
+    @settings(deadline=None, print_blob=True)
+    def test_approx(
+        self,
+        param: Dict[str, Any],
+        hist_param: Dict[str, Any],
+        cache_param: Dict[str, Any],
+        num_rounds: int,
+        dataset: tm.TestDataset,
+    ) -> None:
+        param["tree_method"] = "approx"
+        param = dataset.set_params(param)
+        param.update(hist_param)
+        param.update(cache_param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)
+        note(str(result))
+        assert tm.non_increasing(result["train"][dataset.metric])
+
+    @given(
+        exact_parameter_strategy,
+        hist_multi_parameter_strategy,
+        hist_cache_strategy,
+        strategies.integers(1, 20),
+        tm.multi_dataset_strategy,
+    )
+    @settings(deadline=None, print_blob=True)
+    def test_hist(
+        self,
+        param: Dict[str, Any],
+        hist_param: Dict[str, Any],
+        cache_param: Dict[str, Any],
+        num_rounds: int,
+        dataset: tm.TestDataset,
+    ) -> None:
+        if dataset.name.endswith("-l1"):
+            return
+        param["tree_method"] = "hist"
+        param = dataset.set_params(param)
+        param.update(hist_param)
+        param.update(cache_param)
+        result = train_result(param, dataset.get_dmat(), num_rounds)
+        note(str(result))
+        assert tm.non_increasing(result["train"][dataset.metric])
+
+
+def test_multiclass() -> None:
+    X, y = tm.datasets.make_classification(
+        128, n_features=12, n_informative=10, n_classes=4
+    )
+    clf = xgb.XGBClassifier(
+        multi_strategy="multi_output_tree", callbacks=[ResetStrategy()], n_estimators=10
+    )
+    clf.fit(X, y, eval_set=[(X, y)])
+    assert clf.objective == "multi:softprob"
+    assert tm.non_increasing(clf.evals_result()["validation_0"]["mlogloss"])
+
+    proba = clf.predict_proba(X)
+    assert proba.shape == (y.shape[0], 4)
+
+
+def test_multilabel() -> None:
+    X, y = tm.datasets.make_multilabel_classification(128)
+    clf = xgb.XGBClassifier(
+        multi_strategy="multi_output_tree", callbacks=[ResetStrategy()], n_estimators=10
+    )
+    clf.fit(X, y, eval_set=[(X, y)])
+    assert clf.objective == "binary:logistic"
+    assert tm.non_increasing(clf.evals_result()["validation_0"]["logloss"])
+
+    proba = clf.predict_proba(X)
+    assert proba.shape == y.shape