Extract dask and spark test into distributed test. (#8395)

- Move test files.
- Run spark and dask separately to prevent conflicts.
- Gather common code into the testing module.

tests/python/test_updaters.py

@@ -8,36 +8,10 @@ from hypothesis import given, note, settings, strategies
 
 import xgboost as xgb
 from xgboost import testing as tm
-
-exact_parameter_strategy = strategies.fixed_dictionaries({
-    'nthread': strategies.integers(1, 4),
-    'max_depth': strategies.integers(1, 11),
-    'min_child_weight': strategies.floats(0.5, 2.0),
-    'alpha': strategies.floats(1e-5, 2.0),
-    'lambda': strategies.floats(1e-5, 2.0),
-    'eta': strategies.floats(0.01, 0.5),
-    'gamma': strategies.floats(1e-5, 2.0),
-    'seed': strategies.integers(0, 10),
-    # We cannot enable subsampling as the training loss can increase
-    # 'subsample': strategies.floats(0.5, 1.0),
-    'colsample_bytree': strategies.floats(0.5, 1.0),
-    'colsample_bylevel': strategies.floats(0.5, 1.0),
-})
-
-hist_parameter_strategy = strategies.fixed_dictionaries({
-    'max_depth': strategies.integers(1, 11),
-    'max_leaves': strategies.integers(0, 1024),
-    'max_bin': strategies.integers(2, 512),
-    'grow_policy': strategies.sampled_from(['lossguide', 'depthwise']),
-}).filter(lambda x: (x['max_depth'] > 0 or x['max_leaves'] > 0) and (
-    x['max_depth'] > 0 or x['grow_policy'] == 'lossguide'))
-
-
-cat_parameter_strategy = strategies.fixed_dictionaries(
-    {
-        "max_cat_to_onehot": strategies.integers(1, 128),
-        "max_cat_threshold": strategies.integers(1, 128),
-    }
+from xgboost.testing.params import (
+    exact_parameter_strategy,
+    hist_parameter_strategy,
+    cat_parameter_strategy,
 )