set the precision of empty labset to 0.0

mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala

@@ -41,7 +41,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    * computed as #(relevant items retrived) / k. This formula also applies when the size of the
    * ground truth set is less than k.
    *
-   * If a query has an empty ground truth set, one will be returned together with a log warning.
+   * If a query has an empty ground truth set, zero will be returned together with a log warning.
    *
    * See the following paper for detail:
    *
@@ -67,7 +67,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
       }
       if (labSet.size == 0) {
         logWarning("Empty ground truth set, check input data")
-        1.0
+        0.0
       } else {
         cnt.toDouble / k
       }
@@ -76,7 +76,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
 
   /**
    * Returns the mean average precision (MAP) of all the queries.
-   * If a query has an empty ground truth set, the average precision will be 1.0 and a log
+   * If a query has an empty ground truth set, the average precision will be zero and a log
    * warining is generated.
    */
   lazy val meanAveragePrecision: Double = {
@@ -97,7 +97,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
       }
       if (labSetSize == 0) {
         logWarning("Empty ground truth set, check input data")
-        1.0
+        0.0
       } else {
         precSum / labSet.size
       }

mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala

@@ -35,15 +35,15 @@ class RankingMetricsSuite extends FunSuite with LocalSparkContext {
     val metrics = new RankingMetrics(predictionAndLabels)
     val map = metrics.meanAveragePrecision
 
-    assert(metrics.precisionAt(1) ~== 2.0/3 absTol eps)
-    assert(metrics.precisionAt(2) ~== 2.0/3 absTol eps)
-    assert(metrics.precisionAt(3) ~== 2.0/3 absTol eps)
-    assert(metrics.precisionAt(4) ~== 1.75/3 absTol eps)
-    assert(metrics.precisionAt(5) ~== 0.6 absTol eps)
-    assert(metrics.precisionAt(10) ~== 0.6 absTol eps)
-    assert(metrics.precisionAt(15) ~== 23.0/45 absTol eps)
-
-    assert(map ~== 0.6883598 absTol eps)
+    assert(metrics.precisionAt(1) ~== 1.0/3 absTol eps)
+    assert(metrics.precisionAt(2) ~== 1.0/3 absTol eps)
+    assert(metrics.precisionAt(3) ~== 1.0/3 absTol eps)
+    assert(metrics.precisionAt(4) ~== 0.75/3 absTol eps)
+    assert(metrics.precisionAt(5) ~== 0.8/3 absTol eps)
+    assert(metrics.precisionAt(10) ~== 0.8/3 absTol eps)
+    assert(metrics.precisionAt(15) ~== 8.0/45 absTol eps)
+
+    assert(map ~== 0.355026 absTol eps)
 
     assert(metrics.ndcgAt(3) ~== 1.0/3 absTol eps)
     assert(metrics.ndcgAt(5) ~== 0.328788 absTol eps)