[SPARK-6263] [MLLIB] Python MLlib API missing items: Utils

Implement missing API in pyspark.

MLUtils
* appendBias
* loadVectors

`kFold` is also missing however I am not sure `ClassTag` can be passed or restored through python.

Author: lewuathe <lewuathe@me.com>

Closes #5707 from Lewuathe/SPARK-6263 and squashes the following commits:

16863ea [lewuathe] Merge master
3fc27e7 [lewuathe] Merge branch 'master' into SPARK-6263
6084e9c [lewuathe] Resolv conflict
d2aa2a0 [lewuathe] Resolv conflict
9c329d8 [lewuathe] Fix efficiency
3a12a2d [lewuathe] Merge branch 'master' into SPARK-6263
1d4714b [lewuathe] Fix style
b29e2bc [lewuathe] Remove scipy dependencies
e32eb40 [lewuathe] Merge branch 'master' into SPARK-6263
25d3c9d [lewuathe] Remove unnecessary imports
7ec04db [lewuathe] Resolv conflict
1502d13 [lewuathe] Resolv conflict
d6bd416 [lewuathe] Check existence of scipy.sparse
5d555b1 [lewuathe] Construct scipy.sparse matrix
c345a44 [lewuathe] Merge branch 'master' into SPARK-6263
b8b5ef7 [lewuathe] Fix unnecessary sort method
d254be7 [lewuathe] Merge branch 'master' into SPARK-6263
62a9c7e [lewuathe] Fix appendBias return type
454c73d [lewuathe] Merge branch 'master' into SPARK-6263
a353354 [lewuathe] Remove unnecessary appendBias implementation
44295c2 [lewuathe] Merge branch 'master' into SPARK-6263
64f72ad [lewuathe] Merge branch 'master' into SPARK-6263
c728046 [lewuathe] Fix style
2980569 [lewuathe] [SPARK-6263] Python MLlib API missing items: Utils

mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala

@@ -75,6 +75,15 @@ private[python] class PythonMLLibAPI extends Serializable {
       minPartitions: Int): JavaRDD[LabeledPoint] =
     MLUtils.loadLabeledPoints(jsc.sc, path, minPartitions)
 
+  /**
+   * Loads and serializes vectors saved with `RDD#saveAsTextFile`.
+   * @param jsc Java SparkContext
+   * @param path file or directory path in any Hadoop-supported file system URI
+   * @return serialized vectors in a RDD
+   */
+  def loadVectors(jsc: JavaSparkContext, path: String): RDD[Vector] =
+    MLUtils.loadVectors(jsc.sc, path)
+
   private def trainRegressionModel(
       learner: GeneralizedLinearAlgorithm[_ <: GeneralizedLinearModel],
       data: JavaRDD[LabeledPoint],

python/pyspark/mllib/tests.py

@@ -54,6 +54,7 @@
 from pyspark.mllib.feature import IDF
 from pyspark.mllib.feature import StandardScaler, ElementwiseProduct
 from pyspark.mllib.util import LinearDataGenerator
+from pyspark.mllib.util import MLUtils
 from pyspark.serializers import PickleSerializer
 from pyspark.streaming import StreamingContext
 from pyspark.sql import SQLContext
@@ -1290,6 +1291,48 @@ def func(rdd):
         self.assertTrue(mean_absolute_errors[1] - mean_absolute_errors[-1] > 2)
 
 
+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

@@ -169,6 +169,28 @@ def loadLabeledPoints(sc, path, minPartitions=None):
         minPartitions = minPartitions or min(sc.defaultParallelism, 2)
         return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
 
+    @staticmethod
+    def appendBias(data):
+        """
+        Returns a new vector with `1.0` (bias) appended to
+        the end of the input vector.
+        """
+        vec = _convert_to_vector(data)
+        if isinstance(vec, SparseVector):
+            newIndices = np.append(vec.indices, len(vec))
+            newValues = np.append(vec.values, 1.0)
+            return SparseVector(len(vec) + 1, newIndices, newValues)
+        else:
+            return _convert_to_vector(np.append(vec.toArray(), 1.0))
+
+    @staticmethod
+    def loadVectors(sc, path):
+        """
+        Loads vectors saved using `RDD[Vector].saveAsTextFile`
+        with the default number of partitions.
+        """
+        return callMLlibFunc("loadVectors", sc, path)
+
 
 class Saveable(object):
     """