[Spark-7422][MLLIB] Add argmax to Vector, SparseVector

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

@@ -150,6 +150,12 @@ sealed trait Vector extends Serializable {
       toDense
     }
   }
+
+  /**
+   * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
+   * Returns -1 if vector has length 0.
+   */
+  def argmax: Int
 }
 
 /**
@@ -588,11 +594,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
-  /**
-   * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
-   * Returns -1 if vector has length 0.
-   */
-  private[spark] def argmax: Int = {
+  override def argmax: Int = {
     if (size == 0) {
       -1
     } else {
@@ -717,6 +719,51 @@ class SparseVector(
       new SparseVector(size, ii, vv)
     }
   }
+
+  override def argmax: Int = {
+    if (size == 0) {
+      -1
+    } else {
+      // Find the max active entry.
+      var maxIdx = indices(0)
+      var maxValue = values(0)
+      var maxJ = 0
+      var j = 1
+      val na = numActives
+      while (j < na) {
+        val v = values(j)
+        if (v > maxValue) {
+          maxValue = v
+          maxIdx = indices(j)
+          maxJ = j
+        }
+        j += 1
+      }
+
+      // If the max active entry is nonpositive and there exists inactive ones, find the first zero.
+      if (maxValue <= 0.0 && na < size) {
+        if (maxValue == 0.0) {
+          // If there exists an inactive entry before maxIdx, find it and return its index.
+          if (maxJ < maxIdx) {
+            var k = 0
+            while (k < maxJ && indices(k) == k) {
+              k += 1
+            }
+            maxIdx = k
+          }
+        } else {
+          // If the max active value is negative, find and return the first inactive index.
+          var k = 0
+          while (k < na && indices(k) == k) {
+            k += 1
+          }
+          maxIdx = k
+        }
+      }
+
+      maxIdx
+    }
+  }
 }
 
 object SparseVector {

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

@@ -62,11 +62,50 @@ class VectorsSuite extends SparkFunSuite with Logging {
     assert(vec.toArray.eq(arr))
   }
 
+  test("dense argmax") {
+    val vec = Vectors.dense(Array.empty[Double]).asInstanceOf[DenseVector]
+    assert(vec.argmax === -1)
+
+    val vec2 = Vectors.dense(arr).asInstanceOf[DenseVector]
+    assert(vec2.argmax === 3)
+
+    val vec3 = Vectors.dense(Array(-1.0, 0.0, -2.0, 1.0)).asInstanceOf[DenseVector]
+    assert(vec3.argmax === 3)
+  }
+
   test("sparse to array") {
     val vec = Vectors.sparse(n, indices, values).asInstanceOf[SparseVector]
     assert(vec.toArray === arr)
   }
 
+  test("sparse argmax") {
+    val vec = Vectors.sparse(0, Array.empty[Int], Array.empty[Double]).asInstanceOf[SparseVector]
+    assert(vec.argmax === -1)
+
+    val vec2 = Vectors.sparse(n, indices, values).asInstanceOf[SparseVector]
+    assert(vec2.argmax === 3)
+
+    val vec3 = Vectors.sparse(5, Array(2, 3, 4), Array(1.0, 0.0, -.7))
+    assert(vec3.argmax === 2)
+
+    // check for case that sparse vector is created with
+    // only negative values {0.0, 0.0,-1.0, -0.7, 0.0}
+    val vec4 = Vectors.sparse(5, Array(2, 3), Array(-1.0, -.7))
+    assert(vec4.argmax === 0)
+
+    val vec5 = Vectors.sparse(11, Array(0, 3, 10), Array(-1.0, -.7, 0.0))
+    assert(vec5.argmax === 1)
+
+    val vec6 = Vectors.sparse(11, Array(0, 1, 2), Array(-1.0, -.7, 0.0))
+    assert(vec6.argmax === 2)
+
+    val vec7 = Vectors.sparse(5, Array(0, 1, 3), Array(-1.0, 0.0, -.7))
+    assert(vec7.argmax === 1)
+
+    val vec8 = Vectors.sparse(5, Array(1, 2), Array(0.0, -1.0))
+    assert(vec8.argmax === 0)
+  }
+
   test("vector equals") {
     val dv1 = Vectors.dense(arr.clone())
     val dv2 = Vectors.dense(arr.clone())

project/MimaExcludes.scala

@@ -95,6 +95,10 @@ object MimaExcludes {
               "org.apache.spark.api.r.StringRRDD.this"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.r.BaseRRDD.this")
+          ) ++ Seq(
+            // SPARK-7422 add argmax for sparse vectors
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.mllib.linalg.Vector.argmax")
           )
 
         case v if v.startsWith("1.4") =>