Re-implement ROC-AUC. (#6747)

* Re-implement ROC-AUC.

* Binary
* MultiClass
* LTR
* Add documents.

This PR resolves a few issues:
  - Define a value when the dataset is invalid, which can happen if there's an
  empty dataset, or when the dataset contains only positive or negative values.
  - Define ROC-AUC for multi-class classification.
  - Define weighted average value for distributed setting.
  - A correct implementation for learning to rank task.  Previous
  implementation is just binary classification with averaging across groups,
  which doesn't measure ordered learning to rank.

tests/python-gpu/conftest.py

@@ -42,6 +42,7 @@ def local_cuda_cluster(request, pytestconfig):
 def pytest_addoption(parser):
     parser.addoption('--use-rmm-pool', action='store_true', default=False, help='Use RMM pool')
 
+
 def pytest_collection_modifyitems(config, items):
     if config.getoption('--use-rmm-pool'):
         blocklist = [
@@ -53,3 +54,9 @@ def pytest_collection_modifyitems(config, items):
         for item in items:
             if any(item.nodeid.startswith(x) for x in blocklist):
                 item.add_marker(skip_mark)
+
+    # mark dask tests as `mgpu`.
+    mgpu_mark = pytest.mark.mgpu
+    for item in items:
+        if item.nodeid.startswith("python-gpu/test_gpu_with_dask.py"):
+            item.add_marker(mgpu_mark)