Ensure correct metrics despite model failures on some CV folds

core/src/main/scala/com/salesforce/op/stages/impl/tuning/OpCrossValidation.scala

@@ -25,14 +25,15 @@ import com.salesforce.op.evaluators.{EvaluationMetrics, OpEvaluatorBase}
 import com.salesforce.op.stages.OPStage
 import com.salesforce.op.stages.impl.selector.ModelSelectorNames
 import com.salesforce.op.utils.stages.FitStagesUtil._
-import com.twitter.algebird.Monoid._
 import com.twitter.algebird.Operators._
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Dataset, Row, SparkSession}
 import org.apache.spark.util.SparkThreadUtils
+import com.twitter.algebird._
+import com.twitter.algebird.Operators._
 
 import scala.concurrent.duration.Duration
 import scala.concurrent.{ExecutionContext, Future}
@@ -54,19 +55,33 @@ private[op] class OpCrossValidation[M <: Model[_], E <: Estimator[_]]
   override def getParams(): Map[String, Any] = Map("numFolds" -> numFolds, "seed" -> seed,
     "evaluator" -> evaluator.name.humanFriendlyName, "stratify" -> stratify, "parallelism" -> parallelism)
 
+  /**
+   * Should be called only on instances of the same model
+   */
   private def findBestModel(
     folds: Seq[ValidatedModel[E]]
   ): ValidatedModel[E] = {
-    val metrics = folds.map(_.metrics).reduce(_ + _)
-    blas.dscal(metrics.length, 1.0 / numFolds, metrics, 1)
-    val ValidatedModel(est, _, _, grid) = folds.head
-    log.info(s"Average cross-validation for $est metrics: {}", metrics.toSeq.mkString(","))
-    val (bestMetric, bestIndex) =
-      if (evaluator.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)
-      else metrics.zipWithIndex.minBy(_._1)
-    log.info(s"Best set of parameters:\n${grid(bestIndex)}")
+
+    val gridCounts = folds.flatMap(_.grids.map(_ -> 1)).sumByKey
+    val (_, maxFolds) = gridCounts.maxBy{ case (_, count) => count }
+    val gridsIn = gridCounts.filter{ case (_, foldCount) => foldCount == maxFolds }.keySet
+
+    implicit val doubleSemigroup = Semigroup.from[Double](_ + _)
+    implicit val mapDoubleMonoid = Monoid.mapMonoid[String, Double](doubleSemigroup)
+    val gridMetrics = folds.flatMap{
+      f => f.grids.zip(f.metrics).collect { case (pm, met) if gridsIn.contains(pm) => (pm, met / maxFolds) }
+    }.sumByKey
+
+    val ((bestGrid, bestMetric), bestIndex) =
+      if (evaluator.isLargerBetter) gridMetrics.zipWithIndex.maxBy{ case ((_, metric), _) => metric}
+      else gridMetrics.zipWithIndex.minBy{ case ((_, metric), _) => metric}
+
+    val ValidatedModel(est, _, _, _) = folds.head
+    log.info(s"Average cross-validation for $est metrics: {}", gridMetrics.mkString(","))
+    log.info(s"Best set of parameters:\n$bestGrid")
     log.info(s"Best cross-validation metric: $bestMetric.")
-    ValidatedModel(est, bestIndex, metrics, grid)
+    val (grid, metrics) = gridMetrics.unzip
+    ValidatedModel(est, bestIndex, metrics.toArray, grid.toArray)
   }
 
   private[op] override def validate[T](

core/src/test/scala/com/salesforce/op/stages/impl/regression/RegressionModelSelectorTest.scala

@@ -219,7 +219,7 @@ class RegressionModelSelectorTest extends FlatSpec with TestSparkContext
     justScores.length shouldEqual transformedData.count()
   }
 
-  it should "fit and predict for even when some models fail" in {
+  it should "fit and predict even when some models fail" in {
     val testEstimator = RegressionModelSelector
       .withCrossValidation(
         numFolds = 4,
@@ -240,8 +240,31 @@ class RegressionModelSelectorTest extends FlatSpec with TestSparkContext
       assert(metaData.trainEvaluation.toJson(false).contains(s"${metric.entryName}"),
         s"Metric ${metric.entryName} is not present in metadata: " + metaData)
     )
-    metaData.validationResults.foreach(println(_))
-    metaData.validationResults.size shouldBe 42
+    metaData.validationResults.size shouldBe 40
+  }
+
+
+  it should "fit and predict even when some parameter settings fail for one of the models" in {
+    val testEstimator = RegressionModelSelector
+      .withCrossValidation(
+        numFolds = 4,
+        validationMetric = Evaluators.Regression.mse(),
+        seed = 10L,
+        modelTypesToUse = Seq(RMT.OpGeneralizedLinearRegression)
+      )
+      .setInput(label, features)
+
+
+    val model = testEstimator.fit(data)
+    model.evaluateModel(data)
+
+    // evaluation metrics from train set should be in metadata
+    val metaData = ModelSelectorSummary.fromMetadata(model.getMetadata().getSummaryMetadata())
+    RegressionEvalMetrics.values.foreach(metric =>
+      assert(metaData.trainEvaluation.toJson(false).contains(s"${metric.entryName}"),
+        s"Metric ${metric.entryName} is not present in metadata: " + metaData)
+    )
+    metaData.validationResults.size shouldBe 32
   }
 
 

core/src/test/scala/com/salesforce/op/stages/impl/selector/ModelSelectorTest.scala

@@ -82,23 +82,23 @@ class ModelSelectorTest extends OpEstimatorSpec[Prediction, SelectedModel, Model
 
   private val lr = new OpLogisticRegression()
   private val lrParams = new ParamGridBuilder()
-    .addGrid(lr.regParam, Array(0.1, 100))
-    .addGrid(lr.elasticNetParam, Array(0, 0.5)).build()
+    .addGrid(lr.regParam, Array(0.1, 10000))
+    .addGrid(lr.elasticNetParam, Array(0.5)).build()
 
   private val rf = new OpRandomForestClassifier()
   private val rfParams = new ParamGridBuilder()
     .addGrid(rf.numTrees, Array(2, 4))
-    .addGrid(rf.minInfoGain, Array(100.0, 10.0)).build()
+    .addGrid(rf.minInfoGain, Array(1000.0, 100.0)).build()
 
   private val linR = new OpLinearRegression()
   private val linRParams = new ParamGridBuilder()
-    .addGrid(linR.regParam, Array(0.1, 100))
+    .addGrid(linR.regParam, Array(0.1, 1000))
     .addGrid(linR.maxIter, Array(10, 20)).build()
 
   private val rfR = new OpRandomForestRegressor()
   private val rfRParams = new ParamGridBuilder()
     .addGrid(rfR.numTrees, Array(2, 4))
-    .addGrid(rfR.minInfoGain, Array(100.0, 10.0)).build()
+    .addGrid(rfR.minInfoGain, Array(1000.0, 100.0)).build()
 
   val (inputData, rawFeature1, feature2) = TestFeatureBuilder("label", "features",
     Seq[(RealNN, OPVector)](