Remove no-longer-needed slice code and handle review comments

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

@@ -32,7 +32,7 @@ import org.apache.spark.rdd.RDD
  * :: DeveloperApi ::
  * The Java stubs necessary for the Python mllib bindings.
  *
- * See mllib/python/pyspark._common.py for the mutually agreed upon data format.
+ * See python/pyspark/mllib/_common.py for the mutually agreed upon data format.
  */
 @DeveloperApi
 class PythonMLLibAPI extends Serializable {

mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala

@@ -60,8 +60,6 @@ trait Vector extends Serializable {
    * @param i index
    */
   private[mllib] def apply(i: Int): Double = toBreeze(i)
-
-  private[mllib] def slice(start: Int, end: Int): Vector
 }
 
 /**
@@ -159,10 +157,6 @@ class DenseVector(val values: Array[Double]) extends Vector {
   private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values)
 
   override def apply(i: Int) = values(i)
-
-  private[mllib] override def slice(start: Int, end: Int): Vector = {
-    new DenseVector(values.slice(start, end))
-  }
 }
 
 /**
@@ -193,39 +187,4 @@ class SparseVector(
   }
 
   private[mllib] override def toBreeze: BV[Double] = new BSV[Double](indices, values, size)
-
-  override def apply(pos: Int): Double = {
-    // A more efficient apply() than creating a new Breeze vector
-    var i = 0
-    while (i < indices.length) {
-      if (indices(i) == pos) {
-        return values(i)
-      } else if (indices(i) > pos) {
-        return 0.0
-      }
-      i += 1
-    }
-    0.0
-  }
-
-  private[mllib] override def slice(start: Int, end: Int): Vector = {
-    require(start <= end, s"invalid range: ${start} to ${end}")
-    require(start >= 0, s"invalid range: ${start} to ${end}")
-    require(end <= size, s"invalid range: ${start} to ${end}")
-    // Figure out the range of indices that fall within the given bounds
-    var i = 0
-    var indexRangeStart = 0
-    var indexRangeEnd = 0
-    while (i < indices.length && indices(i) < start) {
-      i += 1
-    }
-    indexRangeStart = i
-    while (i < indices.length && indices(i) < end) {
-      i += 1
-    }
-    indexRangeEnd = i
-    val newIndices = indices.slice(indexRangeStart, indexRangeEnd).map(_ - start)
-    val newValues = values.slice(indexRangeStart, indexRangeEnd)
-    new SparseVector(end - start, newIndices, newValues)
-  }
 }

mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala

@@ -100,24 +100,4 @@ class VectorsSuite extends FunSuite {
     assert(vec2(6) === 4.0)
     assert(vec2(7) === 0.0)
   }
-
-  test("slicing dense vectors") {
-    val vec = Vectors.dense(1.0, 2.0, 3.0, 4.0)
-    val slice = vec.slice(1, 3)
-    assert(slice === Vectors.dense(2.0, 3.0))
-    assert(slice.isInstanceOf[DenseVector], "slice was not DenseVector")
-  }
-
-  test("slicing sparse vectors") {
-    val vec = Vectors.sparse(7, Array(0, 2, 4, 6), Array(1.0, 2.0, 3.0, 4.0))
-    val slice = vec.slice(1, 5)
-    assert(slice === Vectors.sparse(4, Array(1,3), Array(2.0, 3.0)))
-    assert(slice.isInstanceOf[SparseVector], "slice was not SparseVector")
-    val slice2 = vec.slice(1, 2)
-    assert(slice2 === Vectors.sparse(1, Array(), Array()))
-    assert(slice2.isInstanceOf[SparseVector], "slice was not SparseVector")
-    val slice3 = vec.slice(6, 7)
-    assert(slice3 === Vectors.sparse(1, Array(0), Array(4.0)))
-    assert(slice3.isInstanceOf[SparseVector], "slice was not SparseVector")
-  }
 }

python/pyspark/mllib/_common.py

@@ -17,7 +17,7 @@
 
 import struct
 import numpy
-from numpy import ndarray, float64, int64, int32, ones, array_equal, array, dot, shape, complex, issubdtype
+from numpy import ndarray, float64, int64, int32, array_equal, array
 from pyspark import SparkContext, RDD
 from pyspark.mllib.linalg import SparseVector
 from pyspark.serializers import Serializer
@@ -243,8 +243,6 @@ def _deserialize_double_matrix(ba):
 
 def _serialize_labeled_point(p):
     """Serialize a LabeledPoint with a features vector of any type."""
-    #from pyspark.mllib.regression import LabeledPoint
-    #assert type(p) == LabeledPoint, "Expected a LabeledPoint object"
     from pyspark.mllib.regression import LabeledPoint
     serialized_features = _serialize_double_vector(p.features)
     header = bytearray(9)
@@ -318,9 +316,9 @@ def _get_initial_weights(initial_weights, data):
             if initial_weights.ndim != 1:
                 raise TypeError("At least one data element has "
                         + initial_weights.ndim + " dimensions, which is not 1")
-            initial_weights = numpy.ones([initial_weights.shape[0]])
+            initial_weights = numpy.zeros([initial_weights.shape[0]])
         elif type(initial_weights) == SparseVector:
-            initial_weights = numpy.ones([initial_weights.size])
+            initial_weights = numpy.zeros([initial_weights.size])
     return initial_weights