Remove unnecessary imports

python/pyspark/mllib/tests.py

@@ -49,6 +49,7 @@
 from pyspark.mllib.feature import IDF
 from pyspark.mllib.feature import StandardScaler
 from pyspark.mllib.feature import ElementwiseProduct
+from pyspark.mllib.util import MLUtils
 from pyspark.serializers import PickleSerializer
 from pyspark.streaming import StreamingContext
 from pyspark.sql import SQLContext
@@ -1010,6 +1011,48 @@ def collect(rdd):
         self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
 
 
+class MLUtilsTests(MLlibTestCase):
+    def test_append_bias(self):
+        data = [2.0, 2.0, 2.0]
+        ret = MLUtils.appendBias(data)
+        self.assertEqual(ret[3], 1.0)
+        self.assertEqual(type(ret), DenseVector)
+
+    def test_append_bias_with_vector(self):
+        data = Vectors.dense([2.0, 2.0, 2.0])
+        ret = MLUtils.appendBias(data)
+        self.assertEqual(ret[3], 1.0)
+        self.assertEqual(type(ret), DenseVector)
+
+    def test_append_bias_with_sp_vector(self):
+        data = Vectors.sparse(3, {0: 2.0, 2: 2.0})
+        expected = Vectors.sparse(4, {0: 2.0, 2: 2.0, 3: 1.0})
+        # Returned value must be SparseVector
+        ret = MLUtils.appendBias(data)
+        self.assertEqual(ret, expected)
+        self.assertEqual(type(ret), SparseVector)
+
+    def test_load_vectors(self):
+        import shutil
+        data = [
+            [1.0, 2.0, 3.0],
+            [1.0, 2.0, 3.0]
+        ]
+        temp_dir = tempfile.mkdtemp()
+        load_vectors_path = os.path.join(temp_dir, "test_load_vectors")
+        try:
+            self.sc.parallelize(data).saveAsTextFile(load_vectors_path)
+            ret_rdd = MLUtils.loadVectors(self.sc, load_vectors_path)
+            ret = ret_rdd.collect()
+            self.assertEqual(len(ret), 2)
+            self.assertEqual(ret[0], DenseVector([1.0, 2.0, 3.0]))
+            self.assertEqual(ret[1], DenseVector([1.0, 2.0, 3.0]))
+        except:
+            self.fail()
+        finally:
+            shutil.rmtree(load_vectors_path)
+
+
 if __name__ == "__main__":
     if not _have_scipy:
         print("NOTE: Skipping SciPy tests as it does not seem to be installed")

python/pyspark/mllib/util.py

@@ -29,7 +29,7 @@
     xrange = range
 
 from pyspark.mllib.common import callMLlibFunc, inherit_doc
-from pyspark.mllib.linalg import Vector, Vectors, DenseVector, SparseVector, _convert_to_vector
+from pyspark.mllib.linalg import Vector, Vectors, SparseVector, _convert_to_vector
 
 
 class MLUtils(object):
@@ -183,15 +183,11 @@ def appendBias(data):
         """
         vec = _convert_to_vector(data)
         if isinstance(vec, SparseVector):
-            if _have_scipy:
-                l = scipy.sparse.csc_matrix(np.append(vec.toArray(), 1.0))
-                return _convert_to_vector(l.T)
-            else:
-                raise TypeError("Cannot append bias %s into sparce "
-                                "vector because of lack of scipy" % type(vec))
+            l = scipy.sparse.csc_matrix(np.append(vec.toArray(), 1.0))
+            return _convert_to_vector(l.T)
         elif isinstance(vec, Vector):
             vec = vec.toArray()
-        return np.append(vec, 1.0).tolist()
+        return _convert_to_vector(np.append(vec, 1.0).tolist())
 
     @staticmethod
     def loadVectors(sc, path):