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apache · Jun 19, 2015 · c3dd8d9 · c3dd8d9
1 parent b5f52c1
commit c3dd8d9
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Showing 3 changed files with 11 additions and 26 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
@@ -70,13 +70,13 @@ final class RegressionEvaluator(override val uid: String)
     val metrics = new RegressionMetrics(predictionAndLabels)
     val metric = $(metricName) match {
       case "rmse" =>
-        metrics.rootMeanSquaredError
+        1 / metrics.rootMeanSquaredError
       case "mse" =>
-        metrics.meanSquaredError
+        1 / metrics.meanSquaredError
       case "r2" =>
         metrics.r2
       case "mae" =>
-        metrics.meanAbsoluteError
+        1 / metrics.meanAbsoluteError
     }
     metric
   }

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -76,20 +76,6 @@ private[ml] trait CrossValidatorParams extends Params {
   def getNumFolds: Int = $(numFolds)
 
   setDefault(numFolds -> 3)
-
-  /**
-   * Param for whether maximize the evaluation value during cross validation.
-   * If false, turn to minimize the evaluation value.
-   * Default: true
-   * @group param
-   */
-  val useMax: BooleanParam = new BooleanParam(this, "useMax",
-    "whether maximize the evaluation value durin cross validation")
-
-  /** @group getParam */
-  def getUseMax: Boolean = $(useMax)
-
-  setDefault(useMax -> true)
 }
 
 /**
@@ -116,9 +102,6 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
   /** @group setParam */
   def setNumFolds(value: Int): this.type = set(numFolds, value)
 
-  /** @group setParam */
-  def setUseMax(value: Boolean): this.type = set(useMax, value)
-
   override def fit(dataset: DataFrame): CrossValidatorModel = {
     val schema = dataset.schema
     transformSchema(schema, logging = true)
@@ -148,11 +131,7 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
     }
     f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1)
     logInfo(s"Average cross-validation metrics: ${metrics.toSeq}")
-    val (bestMetric, bestIndex) = if ($(useMax)) {
-      metrics.zipWithIndex.maxBy(_._1)
-    } else {
-      metrics.zipWithIndex.minBy(_._1)
-    }
+    val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
     logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
     logInfo(s"Best cross-validation metric: $bestMetric.")
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]

diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -76,12 +76,18 @@ class CrossValidatorSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setEstimatorParamMaps(lrParamMaps)
       .setEvaluator(eval)
       .setNumFolds(3)
-      .setUseMax(false)
     val cvModel = cv.fit(dataset)
     val parent = cvModel.bestModel.parent.asInstanceOf[LinearRegression]
     assert(parent.getRegParam === 0.001)
     assert(parent.getMaxIter === 10)
     assert(cvModel.avgMetrics.length === lrParamMaps.length)
+
+    eval.setMetricName("r2")
+    val cvModel2 = cv.fit(dataset)
+    val parent2 = cvModel2.bestModel.parent.asInstanceOf[LinearRegression]
+    assert(parent2.getRegParam === 0.001)
+    assert(parent2.getMaxIter === 10)
+    assert(cvModel2.avgMetrics.length === lrParamMaps.length)
   }
 
   test("validateParams should check estimatorParamMaps") {