no normalization

src/main/scala/com/databricks/spark/sql/perf/mllib/BenchmarkAlgorithm.scala

@@ -30,6 +30,10 @@ trait BenchmarkAlgorithm extends Logging {
       ctx: MLBenchContext,
       trainingSet: DataFrame): Transformer
 
+  /**
+   * The unnormalized score of the training procedure on a dataset. The normalization is
+   * performed by the caller.
+   */
   @throws[Exception]("if scoring fails")
   def score(
       ctx: MLBenchContext,

src/main/scala/com/databricks/spark/sql/perf/mllib/MLTransformerBenchmarkable.scala

@@ -16,6 +16,7 @@ class MLTransformerBenchmarkable(
 
   private var testData: DataFrame = null
   private var trainingData: DataFrame = null
+  private var testDataCount: Option[Long] = None
   private val param = MLBenchContext(params, sqlContext)
 
   override val name = test.name
@@ -27,7 +28,7 @@ class MLTransformerBenchmarkable(
     try {
       testData = test.testDataSet(param)
       testData.cache()
-      testData.count()
+      testDataCount = Some(testData.count())
       trainingData = test.trainingDataSet(param)
       trainingData.cache()
       trainingData.count()
@@ -57,7 +58,7 @@ class MLTransformerBenchmarkable(
         trainingTime = Some(trainingTime.toMillis),
         trainingMetric = Some(scoreTraining),
         testTime = Some(scoreTestTime.toMillis),
-        testMetric = Some(scoreTest))
+        testMetric = Some(scoreTest / testDataCount.get))
 
       BenchmarkResult(
         name = name,

src/main/scala/com/databricks/spark/sql/perf/mllib/classification/LogisticRegression.scala

@@ -38,6 +38,7 @@ object LogisticRegression extends BenchmarkAlgorithm
     import ctx.params._
     val lr = new ml.classification.LogisticRegression()
       .setTol(tol)
+      .setMaxIter(maxIter)
       .setRegParam(regParam)
     lr.fit(trainingSet)
   }