Handle the new device parameter in dask and demos. (#9386)

* Handle the new `device` parameter in dask and demos.

- Check no ordinal is specified in the dask interface.
- Update demos.
- Update dask doc.
- Update the condition for QDM.

tests/python/test_quantile_dmatrix.py

@@ -322,3 +322,15 @@ class TestQuantileDMatrix:
             X: np.ndarray = np.array(orig, dtype=dtype)
             with pytest.raises(ValueError):
                 xgb.QuantileDMatrix(X)
+
+    def test_changed_max_bin(self) -> None:
+        n_samples = 128
+        n_features = 16
+        csr, y = make_sparse_regression(n_samples, n_features, 0.5, False)
+        Xy = xgb.QuantileDMatrix(csr, y, max_bin=9)
+        booster = xgb.train({"max_bin": 9}, Xy, num_boost_round=2)
+
+        Xy = xgb.QuantileDMatrix(csr, y, max_bin=11)
+
+        with pytest.raises(ValueError, match="consistent"):
+            xgb.train({}, Xy, num_boost_round=2, xgb_model=booster)

tests/python/test_updaters.py

@@ -27,7 +27,7 @@ def train_result(param, dmat, num_rounds):
         param,
         dmat,
         num_rounds,
-        [(dmat, "train")],
+        evals=[(dmat, "train")],
         verbose_eval=False,
         evals_result=result,
     )
@@ -169,13 +169,21 @@ class TestTreeMethod:
         hist_res = {}
         exact_res = {}
 
-        xgb.train(ag_param, ag_dtrain, 10,
-                  [(ag_dtrain, 'train'), (ag_dtest, 'test')],
-                  evals_result=hist_res)
+        xgb.train(
+            ag_param,
+            ag_dtrain,
+            10,
+            evals=[(ag_dtrain, "train"), (ag_dtest, "test")],
+            evals_result=hist_res
+        )
         ag_param["tree_method"] = "exact"
-        xgb.train(ag_param, ag_dtrain, 10,
-                  [(ag_dtrain, 'train'), (ag_dtest, 'test')],
-                  evals_result=exact_res)
+        xgb.train(
+            ag_param,
+            ag_dtrain,
+            10,
+            evals=[(ag_dtrain, "train"), (ag_dtest, "test")],
+            evals_result=exact_res
+        )
         assert hist_res['train']['auc'] == exact_res['train']['auc']
         assert hist_res['test']['auc'] == exact_res['test']['auc']
 

tests/python/test_with_sklearn.py

@@ -1349,10 +1349,11 @@ def test_multilabel_classification() -> None:
     np.testing.assert_allclose(clf.predict(X), predt)
 
 
-def test_data_initialization():
+def test_data_initialization() -> None:
     from sklearn.datasets import load_digits
+
     X, y = load_digits(return_X_y=True)
-    validate_data_initialization(xgb.DMatrix, xgb.XGBClassifier, X, y)
+    validate_data_initialization(xgb.QuantileDMatrix, xgb.XGBClassifier, X, y)
 
 
 @parametrize_with_checks([xgb.XGBRegressor()])