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/test_gpu_ranking.py

@@ -5,6 +5,10 @@ import itertools
 import shutil
 import urllib.request
 import zipfile
+import sys
+sys.path.append("tests/python")
+
+import testing as tm            # noqa
 
 
 class TestRanking:
@@ -15,9 +19,9 @@ class TestRanking:
         """
         from sklearn.datasets import load_svmlight_files
         # download the test data
-        cls.dpath = 'demo/rank/'
+        cls.dpath = os.path.join(tm.PROJECT_ROOT, "demo/rank/")
         src = 'https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip'
-        target = cls.dpath + '/MQ2008.zip'
+        target = os.path.join(cls.dpath, "MQ2008.zip")
 
         if os.path.exists(cls.dpath) and os.path.exists(target):
             print("Skipping dataset download...")
@@ -79,8 +83,8 @@ class TestRanking:
         Cleanup test artifacts from download and unpacking
         :return:
         """
-        os.remove(cls.dpath + "MQ2008.zip")
-        shutil.rmtree(cls.dpath + "MQ2008")
+        os.remove(os.path.join(cls.dpath, "MQ2008.zip"))
+        shutil.rmtree(os.path.join(cls.dpath, "MQ2008"))
 
     @classmethod
     def __test_training_with_rank_objective(cls, rank_objective, metric_name, tolerance=1e-02):