chore(judge): refactor classifier parameters (#289)

spinnaker · Apr 13, 2018 · af5638c · af5638c
1 parent fd367cd
commit af5638c
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Showing 3 changed files with 15 additions and 15 deletions.
diff --git a/kayenta-judge/src/main/scala/com/netflix/kayenta/judge/NetflixACAJudge.scala b/kayenta-judge/src/main/scala/com/netflix/kayenta/judge/NetflixACAJudge.scala
@@ -152,7 +152,7 @@ class NetflixACAJudge extends CanaryJudge with StrictLogging {
     //=============================================
     // Metric Classification
     // ============================================
-    val mannWhitney = new MannWhitneyClassifier(fraction = 0.25, confLevel = 0.98)
+    val mannWhitney = new MannWhitneyClassifier(tolerance = 0.25, confLevel = 0.98)
 
     val resultBuilder = CanaryAnalysisResult.builder()
       .name(metric.getName)

diff --git a/...e/src/main/scala/com/netflix/kayenta/judge/classifiers/metric/MannWhitneyClassifier.scala b/...e/src/main/scala/com/netflix/kayenta/judge/classifiers/metric/MannWhitneyClassifier.scala
@@ -22,7 +22,7 @@ import org.apache.commons.math3.stat.StatUtils
 
 case class MannWhitneyResult(lowerConfidence: Double, upperConfidence: Double, estimate: Double)
 
-class MannWhitneyClassifier(fraction: Double=0.25, confLevel: Double=0.95) extends BaseMetricClassifier{
+class MannWhitneyClassifier(tolerance: Double=0.25, confLevel: Double=0.95) extends BaseMetricClassifier{
 
   /**
     * Mann-Whitney U Test
@@ -48,7 +48,7 @@ class MannWhitneyClassifier(fraction: Double=0.25, confLevel: Double=0.95) exten
     */
   def calculateBounds(testResult: MannWhitneyResult): (Double, Double) = {
     val estimate = math.abs(testResult.estimate)
-    val criticalValue = fraction * estimate
+    val criticalValue = tolerance * estimate
 
     val lowerBound = -1 * criticalValue
     val upperBound = criticalValue

diff --git a/kayenta-judge/src/test/scala/com/netflix/kayenta/judge/ClassifierSuite.scala b/kayenta-judge/src/test/scala/com/netflix/kayenta/judge/ClassifierSuite.scala
@@ -49,7 +49,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("pass-metric", experimentData, "canary")
     val controlMetric = Metric("pass-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Either)
 
     assert(result.classification == Pass)
@@ -81,7 +81,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("high-metric", experimentData, "canary")
     val controlMetric = Metric("high-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Either)
 
     assert(result.classification == High)
@@ -113,7 +113,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("low-metric", experimentData, "canary")
     val controlMetric = Metric("low-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Either)
 
     assert(result.classification == Low)
@@ -145,7 +145,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("pass-metric", experimentData, "canary")
     val controlMetric = Metric("pass-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Increase)
 
     assert(result.classification == Pass)
@@ -177,7 +177,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("pass-metric", experimentData, "canary")
     val controlMetric = Metric("pass-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Decrease)
 
     assert(result.classification == Pass)
@@ -209,7 +209,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("high-metric", experimentData, "canary")
     val controlMetric = Metric("high-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Increase)
 
     assert(result.classification == High)
@@ -241,7 +241,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("low-metric", experimentData, "canary")
     val controlMetric = Metric("low-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Decrease)
 
     assert(result.classification == Low)
@@ -303,7 +303,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("pass-metric", experimentData, "canary")
     val controlMetric = Metric("pass-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Either)
 
     assert(result.classification == High)
@@ -316,7 +316,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("test-metric", experimentData, "canary")
     val controlMetric = Metric("test-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Decrease)
 
     assert(result.classification == Pass)
@@ -329,7 +329,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("test-metric", experimentData, "canary")
     val controlMetric = Metric("test-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Decrease)
 
     assert(result.classification == Pass)
@@ -342,7 +342,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("test-metric", experimentData, "canary")
     val controlMetric = Metric("test-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Decrease)
 
     assert(result.classification == Pass)
@@ -355,7 +355,7 @@ class ClassifierSuite extends FunSuite{
     val experimentMetric = Metric("test-metric", experimentData, "canary")
     val controlMetric = Metric("test-metric", controlData, "baseline")
 
-    val classifier = new MannWhitneyClassifier(fraction = 0.10, confLevel = 0.95)
+    val classifier = new MannWhitneyClassifier(tolerance = 0.10, confLevel = 0.95)
     val result = classifier.classify(controlMetric, experimentMetric, MetricDirection.Decrease)
 
     assert(result.classification == Nodata)