Fix metainfo from DataFrame. (#5216)

* Fix metainfo from DataFrame.

* Unify helper functions for data and meta.

tests/python/test_dt.py

@@ -1,51 +1,54 @@
-# -*- coding: utf-8 -*-
-import unittest
-import pytest
-
-import testing as tm
-import xgboost as xgb
-
-try:
-    import datatable as dt
-    import pandas as pd
-except ImportError:
-    pass
-
-pytestmark = pytest.mark.skipif(
-    tm.no_dt()['condition'] or tm.no_pandas()['condition'],
-    reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
-
-
-class TestDataTable(unittest.TestCase):
-
-    def test_dt(self):
-        df = pd.DataFrame([[1, 2., True], [2, 3., False]],
-                          columns=['a', 'b', 'c'])
-        dtable = dt.Frame(df)
-        labels = dt.Frame([1, 2])
-        dm = xgb.DMatrix(dtable, label=labels)
-        assert dm.feature_names == ['a', 'b', 'c']
-        assert dm.feature_types == ['int', 'float', 'i']
-        assert dm.num_row() == 2
-        assert dm.num_col() == 3
-
-        # overwrite feature_names
-        dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]),
-                         feature_names=['x', 'y', 'z'])
-        assert dm.feature_names == ['x', 'y', 'z']
-        assert dm.num_row() == 2
-        assert dm.num_col() == 3
-
-        # incorrect dtypes
-        df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
-                          columns=['a', 'b', 'c'])
-        dtable = dt.Frame(df)
-        self.assertRaises(ValueError, xgb.DMatrix, dtable)
-
-        df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
-        dtable = dt.Frame(df)
-        dm = xgb.DMatrix(dtable)
-        assert dm.feature_names == ['A=1', 'A=2']
-        assert dm.feature_types == ['int', 'int']
-        assert dm.num_row() == 3
-        assert dm.num_col() == 2
+# -*- coding: utf-8 -*-
+import unittest
+import pytest
+import numpy as np
+
+import testing as tm
+import xgboost as xgb
+
+try:
+    import datatable as dt
+    import pandas as pd
+except ImportError:
+    pass
+
+pytestmark = pytest.mark.skipif(
+    tm.no_dt()['condition'] or tm.no_pandas()['condition'],
+    reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
+
+
+class TestDataTable(unittest.TestCase):
+
+    def test_dt(self):
+        df = pd.DataFrame([[1, 2., True], [2, 3., False]],
+                          columns=['a', 'b', 'c'])
+        dtable = dt.Frame(df)
+        labels = dt.Frame([1, 2])
+        dm = xgb.DMatrix(dtable, label=labels)
+        assert dm.feature_names == ['a', 'b', 'c']
+        assert dm.feature_types == ['int', 'float', 'i']
+        assert dm.num_row() == 2
+        assert dm.num_col() == 3
+
+        np.testing.assert_array_equal(np.array([1, 2]), dm.get_label())
+
+        # overwrite feature_names
+        dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]),
+                         feature_names=['x', 'y', 'z'])
+        assert dm.feature_names == ['x', 'y', 'z']
+        assert dm.num_row() == 2
+        assert dm.num_col() == 3
+
+        # incorrect dtypes
+        df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
+                          columns=['a', 'b', 'c'])
+        dtable = dt.Frame(df)
+        self.assertRaises(ValueError, xgb.DMatrix, dtable)
+
+        df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
+        dtable = dt.Frame(df)
+        dm = xgb.DMatrix(dtable)
+        assert dm.feature_names == ['A=1', 'A=2']
+        assert dm.feature_types == ['int', 'int']
+        assert dm.num_row() == 3
+        assert dm.num_col() == 2