[Backport] Rename data to X in predict_proba. (#6555) (#6586)

* [Breaking] Rename `data` to `X` in `predict_proba`. (#6555)

New Scikit-Learn version uses keyword argument, and `X` is the predefined
keyword.

* Use pip to install latest Python graphviz on Windows CI.

* Suppress health check.

tests/python/test_with_dask.py

@@ -8,7 +8,8 @@ import asyncio
 from sklearn.datasets import make_classification
 import os
 import subprocess
-from hypothesis import given, settings, note
+import hypothesis
+from hypothesis import given, settings, note, HealthCheck
 from test_updaters import hist_parameter_strategy, exact_parameter_strategy
 
 if sys.platform.startswith("win"):
@@ -17,6 +18,12 @@ if tm.no_dask()['condition']:
     pytest.skip(msg=tm.no_dask()['reason'], allow_module_level=True)
 
 
+if hasattr(HealthCheck, 'function_scoped_fixture'):
+    suppress = [HealthCheck.function_scoped_fixture]
+else:
+    suppress = hypothesis.utils.conventions.not_set
+
+
 try:
     from distributed import LocalCluster, Client, get_client
     from distributed.utils_test import client, loop, cluster_fixture
@@ -668,14 +675,14 @@ class TestWithDask:
 
     @given(params=hist_parameter_strategy,
            dataset=tm.dataset_strategy)
-    @settings(deadline=None)
+    @settings(deadline=None, suppress_health_check=suppress)
     def test_hist(self, params, dataset, client):
         num_rounds = 30
         self.run_updater_test(client, params, num_rounds, dataset, 'hist')
 
     @given(params=exact_parameter_strategy,
            dataset=tm.dataset_strategy)
-    @settings(deadline=None)
+    @settings(deadline=None, suppress_health_check=suppress)
     def test_approx(self, client, params, dataset):
         num_rounds = 30
         self.run_updater_test(client, params, num_rounds, dataset, 'approx')
@@ -795,7 +802,6 @@ class TestDaskCallbacks:
                 merged = xgb.dask._get_workers_from_data(train, evals=[(valid, 'Valid')])
                 assert len(merged) == 2
 
-
     def test_data_initialization(self):
         '''Assert each worker has the correct amount of data, and DMatrix initialization doesn't
         generate unnecessary copies of data.