address comment

docs/mllib-evaluation-metrics.md

@@ -450,7 +450,7 @@ $$rel_D(r) = \begin{cases}1 & \text{if $r \in D$}, \\ 0 & \text{otherwise}.\end{
     <tr>
       <td>Mean Average Precision</td>
       <td>
-        $MAP=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{\left|R_i\right|} \sum_{j=0}^{\left|R_i\right|-1} \frac{rel_{D_i}(R_i(j))}{j + 1}}$
+        $MAP=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{\left|D_i\right|} \sum_{j=0}^{\left|R_i\right|-1} \frac{rel_{D_i}(R_i(j))}{j + 1}}$
       </td>
       <td>
         <a href="https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision">MAP</a> is a measure of how
@@ -462,10 +462,10 @@ $$rel_D(r) = \begin{cases}1 & \text{if $r \in D$}, \\ 0 & \text{otherwise}.\end{
       <td>Normalized Discounted Cumulative Gain</td>
       <td>
         $NDCG(k)=\frac{1}{M} \sum_{i=0}^{M-1} {\frac{1}{IDCG(D_i, k)}\sum_{j=0}^{n-1}
-          \frac{rel_{D_i}(R_i(j))}{log_2(j+2)}} \\
+          \frac{rel_{D_i}(R_i(j))}{\text{log}_2(j+2)}} \\
         \text{Where} \\
         \hspace{5 mm} n = \text{min}\left(\text{max}\left(|R_i|,|D_i|\right),k\right) \\
-        \hspace{5 mm} IDCG(D, k) = \sum_{j=0}^{\text{min}(\left|D\right|, k) - 1} \frac{1}{log_2(j+2)}$
+        \hspace{5 mm} IDCG(D, k) = \sum_{j=0}^{\text{min}(\left|D\right|, k) - 1} \frac{1}{\text{log}_2(j+2)}$
       </td>
       <td>
         <a href="https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG">NDCG at k</a> is a

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

@@ -100,7 +100,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
           }
           i += 1
         }
-        precSum / n
+        precSum / labSet.size
       } else {
         logWarning("Empty ground truth set, check input data")
         0.0

mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java

@@ -49,7 +49,7 @@ public void setUp() throws IOException {
   public void rankingMetrics() {
     @SuppressWarnings("unchecked")
     RankingMetrics<?> metrics = RankingMetrics.of(predictionAndLabels);
-    Assert.assertEquals(0.147989, metrics.meanAveragePrecision(), 1e-5);
+    Assert.assertEquals(0.355026, metrics.meanAveragePrecision(), 1e-5);
     Assert.assertEquals(0.75 / 3.0, metrics.precisionAt(4), 1e-5);
   }
 }

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

@@ -43,7 +43,7 @@ class RankingMetricsSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(metrics.precisionAt(10) ~== 0.8/3 absTol eps)
     assert(metrics.precisionAt(15) ~== 8.0/45 absTol eps)
 
-    assert(map ~== 0.147989 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)

python/pyspark/mllib/evaluation.py

@@ -324,7 +324,7 @@ class RankingMetrics(JavaModelWrapper):
     >>> metrics.precisionAt(15)
     0.17...
     >>> metrics.meanAveragePrecision
-    0.14...
+    0.35...
     >>> metrics.ndcgAt(3)
     0.33...
     >>> metrics.ndcgAt(10)