fix local scoring with multipicklist features

features/src/main/scala/com/salesforce/op/features/types/FeatureTypeSparkConverter.scala

@@ -196,7 +196,7 @@ case object FeatureTypeSparkConverter {
 
       // Sets
       case wt if wt <:< weakTypeOf[t.MultiPickList] => (value: Any) =>
-        if (value == null) FeatureTypeDefaults.MultiPickList.value else value.asInstanceOf[MWrappedArray[String]].toSet
+        if (value == null) FeatureTypeDefaults.MultiPickList.value else value.asInstanceOf[Seq[String]].toSet
 
       // Everything else
       case _ => identity

local/src/test/scala/com/salesforce/op/local/OpWorkflowRunnerLocalTest.scala

@@ -32,12 +32,15 @@ package com.salesforce.op.local
 
 import java.nio.file.Paths
 
+import com.salesforce.op.features.Feature
 import com.salesforce.op.features.types._
 import com.salesforce.op.readers.DataFrameFieldNames._
-import com.salesforce.op.stages.impl.classification.BinaryClassificationModelSelector
-import com.salesforce.op.stages.impl.classification.BinaryClassificationModelsToTry._
+import com.salesforce.op.stages.base.unary.UnaryLambdaTransformer
+import com.salesforce.op.stages.impl.classification.{BinaryClassificationModelSelector, OpLogisticRegression}
+import com.salesforce.op.stages.impl.classification.BinaryClassificationModelsToTry
 import com.salesforce.op.stages.impl.feature.StringIndexerHandleInvalid
-import com.salesforce.op.test.{PassengerSparkFixtureTest, TestCommon}
+import com.salesforce.op.test.{PassengerSparkFixtureTest, TestCommon, TestFeatureBuilder}
+import com.salesforce.op.testkit._
 import com.salesforce.op.utils.spark.RichDataset._
 import com.salesforce.op.utils.spark.RichRow._
 import com.salesforce.op.{OpParams, OpWorkflow}
@@ -60,7 +63,7 @@ class OpWorkflowRunnerLocalTest extends FlatSpec with PassengerSparkFixtureTest
     .indexed(handleInvalid = StringIndexerHandleInvalid.Skip)
 
   val prediction = BinaryClassificationModelSelector.withTrainValidationSplit(
-    splitter = None, modelTypesToUse = Seq(OpLogisticRegression)
+    splitter = None, modelTypesToUse = Seq(BinaryClassificationModelsToTry.OpLogisticRegression)
   ).setInput(survivedNum, features).getOutput()
 
   val workflow = new OpWorkflow().setResultFeatures(prediction, survivedNum, indexed).setReader(dataReader)
@@ -117,4 +120,47 @@ class OpWorkflowRunnerLocalTest extends FlatSpec with PassengerSparkFixtureTest
     } score shouldBe expected
   }
 
+  it should "handle multi picklist features without throwing an exception" in {
+    // First set up the raw features:
+    val currencyData: Seq[Currency] = RandomReal.logNormal[Currency](mean = 10.0, sigma = 1.0).limit(1000)
+    val domain = List("Strawberry Milk", "Chocolate Milk", "Soy Milk", "Almond Milk")
+    val picklistData: Seq[PickList] = RandomText.pickLists(domain).limit(1000)
+    val domainSize = 20
+    val maxChoices = 2
+    val multiPickListData: Seq[MultiPickList] = RandomMultiPickList.of(
+      RandomText.textFromDomain(domain = List.range(0, domainSize).map(_.toString)), minLen = 0, maxLen = maxChoices
+    ).limit(1000)
+
+    // Generate the raw features and corresponding dataframe
+    val generatedData: Seq[(Currency, MultiPickList, PickList)] =
+      currencyData.zip(multiPickListData).zip(picklistData).map {
+        case ((cu, mp), pm) => (cu, mp, pm)
+      }
+    val (rawDF, rawCurrency, rawMultiPickList, rawPicklist) =
+      TestFeatureBuilder("currency", "multipicklist", "picklist", generatedData)
+
+    val labelTransformer = new UnaryLambdaTransformer[Currency, RealNN](operationName = "labelFunc",
+      transformFn = p => if (p.value.exists(_ >= 10.0)) 1.0.toRealNN else 0.0.toRealNN
+    )
+
+    val labelData = labelTransformer.setInput(rawCurrency).getOutput().asInstanceOf[Feature[RealNN]]
+      .copy(isResponse = true)
+
+    val genFeatureVector = Seq(rawCurrency, rawMultiPickList, rawPicklist).transmogrify()
+
+    val prediction = new OpLogisticRegression().setInput(labelData, genFeatureVector).getOutput()
+
+    val workflow = new OpWorkflow().setResultFeatures(prediction)
+
+    val model = workflow.setInputDataset(rawDF).train()
+
+    noException should be thrownBy
+      model.scoreFunction(Map("currency" -> 10.0, "multipicklist" -> Seq("0", "19"), "picklist" -> "Soy Milk"))
+
+    noException should be thrownBy
+      model.scoreFunction(Map("currency" -> 10.0, "multipicklist" -> Nil, "picklist" -> "Soy Milk"))
+
+    noException should be thrownBy
+      model.scoreFunction(Map("currency" -> 10.0, "multipicklist" -> null, "picklist" -> "Soy Milk"))
+  }
 }