PR review fixes

h2oai · Oct 7, 2016 · ec2039d · ec2039d
1 parent 61d4769
commit ec2039d
Show file tree

Hide file tree

Showing 2 changed files with 10 additions and 9 deletions.
diff --git a/ml/src/main/scala/org/apache/spark/ml/FrameMLUtils.scala b/ml/src/main/scala/org/apache/spark/ml/FrameMLUtils.scala
@@ -120,13 +120,13 @@ object FrameMLUtils {
   }
 
   private[ml] def toDouble(value: Any, fieldStruct: StructField, domain: Array[String]): Double = {
-    fieldStruct.dataType match {
-      case DataTypes.ByteType => value.asInstanceOf[Byte].doubleValue
-      case DataTypes.ShortType => value.asInstanceOf[Short].doubleValue
-      case DataTypes.IntegerType => value.asInstanceOf[Integer].doubleValue
-      case DataTypes.DoubleType => value.asInstanceOf[Double]
-      case DataTypes.StringType => domain.indexOf(value)
-      case _ => throw new IllegalArgumentException("Target column has to be an enum or a number. " + fieldStruct.toString)
+    value match {
+      case b: Byte if fieldStruct.dataType == DataTypes.ByteType => b.doubleValue
+      case s: Short if fieldStruct.dataType == DataTypes.ShortType => s.doubleValue
+      case i: Int if fieldStruct.dataType == DataTypes.IntegerType => i.doubleValue
+      case d: Double if fieldStruct.dataType == DataTypes.DoubleType => d
+      case string: String if fieldStruct.dataType == DataTypes.StringType => domain.indexOf(string)
+      case _ => throw new IllegalArgumentException("Target column has to be an enum or a number. " + fieldStruct)
     }
   }
 }
diff --git a/ml/src/main/scala/org/apache/spark/ml/spark/models/svm/SVM.java b/ml/src/main/scala/org/apache/spark/ml/spark/models/svm/SVM.java
@@ -99,7 +99,7 @@ public void init(boolean expensive) {
             }
         }
 
-        if(MissingValuesHandling.NotAllowed.equals(_parms._missing_values_handling)) {
+        if(MissingValuesHandling.NotAllowed == _parms._missing_values_handling) {
             for (int i = 0; i < _train.numCols(); i++) {
                 Vec vec = _train.vec(i);
                 String vecName = _train.name(i);
@@ -186,7 +186,8 @@ public void computeImpl() {
             RDD<LabeledPoint> training = points._1();
             training.cache();
 
-            if(training.count() == 0 && MissingValuesHandling.Skip.equals(_parms._missing_values_handling)) {
+            if(training.count() == 0 &&
+                    MissingValuesHandling.Skip == _parms._missing_values_handling) {
                 throw new H2OIllegalArgumentException("No rows left in the dataset after filtering out rows with missing values. Ignore columns with many NAs or set missing_values_handling to 'MeanImputation'.");
             }