Addressing reviewers comments mengxr. Added true positive rate and fa…

…lse positive rate. Test suite code style.

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

@@ -43,9 +43,24 @@ class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)]) extends Log
   private lazy val fpByClass: Map[Double, Int] = predictionsAndLabels
     .map { case (prediction, label) =>
       (prediction, if (prediction != label) 1 else 0)
-  }.reduceByKey(_ + _)
+    }.reduceByKey(_ + _)
     .collectAsMap()
 
+  /**
+   * Returns true positive rate for a given label (category)
+   * @param label the label.
+   */
+  def truePositiveRate(label: Double): Double = recall(label)
+
+  /**
+   * Returns false positive rate for a given label (category)
+   * @param label the label.
+   */
+  def falsePositiveRate(label: Double): Double = {
+    val fp = fpByClass.getOrElse(label, 0)
+    fp.toDouble / (labelCount - labelCountByClass(label))
+  }
+
   /**
    * Returns precision for a given label (category)
    * @param label the label.
@@ -65,6 +80,7 @@ class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)]) extends Log
   /**
    * Returns f-measure for a given label (category)
    * @param label the label.
+   * @param beta the beta parameter.
    */
   def fMeasure(label: Double, beta: Double): Double = {
     val p = precision(label)
@@ -113,15 +129,23 @@ class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)]) extends Log
     precision(category) * count.toDouble / labelCount
   }.sum
 
+  /**
+   * Returns weighted averaged f-measure
+   * @param beta the beta parameter.
+   */
+  def weightedFMeasure(beta: Double): Double = labelCountByClass.map { case (category, count) =>
+    fMeasure(category, beta) * count.toDouble / labelCount
+  }.sum
+
   /**
    * Returns weighted averaged f1-measure
    */
-  lazy val weightedF1Measure: Double = labelCountByClass.map { case (category, count) =>
-    fMeasure(category) * count.toDouble / labelCount
+  lazy val weightedFMeasure: Double = labelCountByClass.map { case (category, count) =>
+    fMeasure(category, 1.0) * count.toDouble / labelCount
   }.sum
 
   /**
    * Returns the sequence of labels in ascending order
    */
-  lazy val labels:Array[Double] = tpByClass.keys.toArray.sorted
+  lazy val labels: Array[Double] = tpByClass.keys.toArray.sorted
 }

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

@@ -36,15 +36,21 @@ class MulticlassMetricsSuite extends FunSuite with LocalSparkContext {
         (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)), 2)
     val metrics = new MulticlassMetrics(scoreAndLabels)
     val delta = 0.0000001
-    val precision0 = 2.0 / (2.0 + 1.0)
-    val precision1 = 3.0 / (3.0 + 1.0)
-    val precision2 = 1.0 / (1.0 + 1.0)
-    val recall0 = 2.0 / (2.0 + 2.0)
-    val recall1 = 3.0 / (3.0 + 1.0)
-    val recall2 = 1.0 / (1.0 + 0.0)
+    val fpRate0 = 1.0 / (9 - 4)
+    val fpRate1 = 1.0 / (9 - 4)
+    val fpRate2 = 1.0 / (9 - 1)
+    val precision0 = 2.0 / (2 + 1)
+    val precision1 = 3.0 / (3 + 1)
+    val precision2 = 1.0 / (1 + 1)
+    val recall0 = 2.0 / (2 + 2)
+    val recall1 = 3.0 / (3 + 1)
+    val recall2 = 1.0 / (1 + 0)
     val f1measure0 = 2 * precision0 * recall0 / (precision0 + recall0)
     val f1measure1 = 2 * precision1 * recall1 / (precision1 + recall1)
     val f1measure2 = 2 * precision2 * recall2 / (precision2 + recall2)
+    assert(math.abs(metrics.falsePositiveRate(0.0) - fpRate0) < delta)
+    assert(math.abs(metrics.falsePositiveRate(1.0) - fpRate1) < delta)
+    assert(math.abs(metrics.falsePositiveRate(2.0) - fpRate2) < delta)
     assert(math.abs(metrics.precision(0.0) - precision0) < delta)
     assert(math.abs(metrics.precision(1.0) - precision1) < delta)
     assert(math.abs(metrics.precision(2.0) - precision2) < delta)
@@ -55,16 +61,16 @@ class MulticlassMetricsSuite extends FunSuite with LocalSparkContext {
     assert(math.abs(metrics.fMeasure(1.0) - f1measure1) < delta)
     assert(math.abs(metrics.fMeasure(2.0) - f1measure2) < delta)
     assert(math.abs(metrics.recall -
-      (2.0 + 3.0 + 1.0) / ((2.0 + 3.0 + 1.0) + (1.0 + 1.0 + 1.0))) < delta)
+      (2.0 + 3.0 + 1.0) / ((2 + 3 + 1) + (1 + 1 + 1))) < delta)
     assert(math.abs(metrics.recall - metrics.precision) < delta)
     assert(math.abs(metrics.recall - metrics.fMeasure) < delta)
     assert(math.abs(metrics.recall - metrics.weightedRecall) < delta)
     assert(math.abs(metrics.weightedPrecision -
-      ((4.0 / 9.0) * precision0 + (4.0 / 9.0) * precision1 + (1.0 / 9.0) * precision2)) < delta)
+      ((4.0 / 9) * precision0 + (4.0 / 9) * precision1 + (1.0 / 9) * precision2)) < delta)
     assert(math.abs(metrics.weightedRecall -
-      ((4.0 / 9.0) * recall0 + (4.0 / 9.0) * recall1 + (1.0 / 9.0) * recall2)) < delta)
-    assert(math.abs(metrics.weightedF1Measure -
-      ((4.0 / 9.0) * f1measure0 + (4.0 / 9.0) * f1measure1 + (1.0 / 9.0) * f1measure2)) < delta)
+      ((4.0 / 9) * recall0 + (4.0 / 9) * recall1 + (1.0 / 9) * recall2)) < delta)
+    assert(math.abs(metrics.weightedFMeasure -
+      ((4.0 / 9) * f1measure0 + (4.0 / 9) * f1measure1 + (1.0 / 9) * f1measure2)) < delta)
     assert(metrics.labels.sameElements(labels))
   }
 }