Add DataFrame.values_host and remove uses of DataFrame.as_matrix from…

… tests.

python/cudf/cudf/core/dataframe.py

@@ -1005,6 +1005,18 @@ def values(self):
         """
         return cupy.asarray(self.as_gpu_matrix())
 
+    @property
+    def values_host(self):
+        """
+        Return a NumPy representation of the data.
+
+        Returns
+        -------
+        out : numpy.ndarray
+            A host representation of the underlying data.
+        """
+        return self.values.get()
+
     def __array__(self, dtype=None):
         raise TypeError(
             "Implicit conversion to a host NumPy array via __array__ is not "

python/cudf/cudf/tests/test_dataframe.py

@@ -291,7 +291,7 @@ def test_dataframe_basic():
     np.testing.assert_equal(df["vals"].to_array(), hvals)
 
     # As matrix
-    mat = df.as_matrix()
+    mat = df.values_host
 
     expect = np.vstack([hkeys, hvals]).T
 
@@ -1150,7 +1150,7 @@ def test_dataframe_hash_partition(nrows, nparts, nkeys):
     for p in got:
         if len(p):
             # Take rows of the keycolumns and build a set of the key-values
-            unique_keys = set(map(tuple, p.as_matrix(columns=keycols)))
+            unique_keys = set(map(tuple, p[keycols].values_host))
             # Ensure that none of the key-values have occurred in other groups
             assert not (unique_keys & part_unique_keys)
             part_unique_keys |= unique_keys

python/cudf/cudf/tests/test_onehot.py

@@ -31,7 +31,7 @@ def test_onehot_simple():
     assert df2.columns[0] == "vals"
     for i in range(1, len(df2.columns)):
         assert df2.columns[i] == "vals_%s" % (i - 1)
-    got = df2.as_matrix(columns=df2.columns[1:])
+    got = df2[df2.columns[1:]].values_host
     expect = np.identity(got.shape[0])
     np.testing.assert_equal(got, expect)
 
@@ -45,7 +45,7 @@ def test_onehot_random():
     df2 = df.one_hot_encoding(
         column="src", prefix="out_", cats=tuple(range(10, 17))
     )
-    mat = df2.as_matrix(columns=df2.columns[1:])
+    mat = df2[df2.columns[1:]].values_host
 
     for val in range(low, high):
         colidx = val - low