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[SPARK-23085][ML] API parity for mllib.linalg.Vectors.sparse #20275

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[SPARK-23085][ML] API parity for mllib.linalg.Vectors.sparse #20275

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7c8247e
nit
zhengruifeng Jan 16, 2018
b590f09
update pr
zhengruifeng Jan 16, 2018
ac067e7
add tests
zhengruifeng Jan 17, 2018
f3a4329
round out tests
zhengruifeng Jan 19, 2018
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2 changes: 1 addition & 1 deletion mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -565,7 +565,7 @@ class SparseVector @Since("2.0.0") (

// validate the data
{
require(size >= 0, "The size of the requested sparse vector must be greater than 0.")
require(size >= 0, "The size of the requested sparse vector must be no less than 0.")
require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
s" indices match the dimension of the values. You provided ${indices.length} indices and " +
s" ${values.length} values.")
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3 changes: 1 addition & 2 deletions mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
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Expand Up @@ -326,8 +326,6 @@ object Vectors {
*/
@Since("1.0.0")
def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = {
require(size > 0, "The size of the requested sparse vector must be greater than 0.")

val (indices, values) = elements.sortBy(_._1).unzip
var prev = -1
indices.foreach { i =>
Expand Down Expand Up @@ -758,6 +756,7 @@ class SparseVector @Since("1.0.0") (
@Since("1.0.0") val indices: Array[Int],
@Since("1.0.0") val values: Array[Double]) extends Vector {

require(size >= 0, "The size of the requested sparse vector must be no less than 0.")
require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
s" indices match the dimension of the values. You provided ${indices.length} indices and " +
s" ${values.length} values.")
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7 changes: 7 additions & 0 deletions mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
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Expand Up @@ -113,6 +113,13 @@ class VectorsSuite extends SparkFunSuite with Logging {
assert(vec.toArray === arr)
}

test("zero-length sparse vector") {
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We may as well add the same test to ml.linalg

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@MLnick MLnick Jan 18, 2018 •
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While we're doing this we may as well also add a test to intercept the exception for negative size (since the other sparse vector construction tests check for the other constraints but a size test is missing), for both ml and mllib

val v1 = Vectors.sparse(0, Array.emptyIntArray, Array.emptyDoubleArray)
val v2 = Vectors.sparse(0, Array.empty[(Int, Double)])
assert(v1.size === 0)
assert(v2.size === 0)
}

test("sparse argmax") {
val vec = Vectors.sparse(0, Array.empty[Int], Array.empty[Double]).asInstanceOf[SparseVector]
assert(vec.argmax === -1)
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