Support feature names/types for cudf. (#4902)

* Implement most of the pandas procedure for cudf except for type conversion.
* Requires an array of interfaces in metainfo.

tests/cpp/data/test_metainfo.cu

@@ -31,9 +31,10 @@ std::string PrepareData(std::string typestr, thrust::device_vector<T>* out) {
         Json(Integer(reinterpret_cast<Integer::Int>(p_d_data))),
         Json(Boolean(false))};
   column["data"] = j_data;
+  Json array(std::vector<Json>{column});
 
   std::stringstream ss;
-  Json::Dump(column, &ss);
+  Json::Dump(array, &ss);
   std::string str = ss.str();
 
   return str;

tests/python-gpu/test_from_columnar.py

@@ -42,6 +42,7 @@ Arrow specification.'''
     @pytest.mark.skipif(**tm.no_cudf())
     def test_from_cudf(self):
         '''Test constructing DMatrix from cudf'''
+        import cudf
         dmatrix_from_cudf(np.float32, np.NAN)
         dmatrix_from_cudf(np.float64, np.NAN)
 
@@ -52,3 +53,19 @@ Arrow specification.'''
         dmatrix_from_cudf(np.int8, 2)
         dmatrix_from_cudf(np.int32, -2)
         dmatrix_from_cudf(np.int64, -3)
+
+        cd = cudf.DataFrame({'x': [1, 2, 3], 'y': [0.1, 0.2, 0.3]})
+        dtrain = xgb.DMatrix(cd)
+
+        assert dtrain.feature_names == ['x', 'y']
+        assert dtrain.feature_types == ['int', 'float']
+
+        series = cudf.DataFrame({'x': [1, 2, 3]}).iloc[:, 0]
+        assert isinstance(series, cudf.Series)
+        dtrain = xgb.DMatrix(series)
+
+        assert dtrain.feature_names == ['x']
+        assert dtrain.feature_types == ['int']
+
+        with pytest.raises(Exception):
+            dtrain = xgb.DMatrix(cd, label=cd)