2 files changed, 23 insertions, 3 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index f71726f110..a1d36c4bec 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -29,6 +29,8 @@ import org.apache.spark.sql.types.DoubleType
 /**
  * :: Experimental ::
  * Evaluator for binary classification, which expects two input columns: rawPrediction and label.
+ * The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1)
+ * or of type vector (length-2 vector of raw predictions, scores, or label probabilities).
  */
 @Since("1.2.0")
 @Experimental
@@ -78,13 +80,14 @@ class BinaryClassificationEvaluator @Since("1.4.0") (@Since("1.4.0") override va
   @Since("1.2.0")
   override def evaluate(dataset: DataFrame): Double = {
     val schema = dataset.schema
-    SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT)
+    SchemaUtils.checkColumnTypes(schema, $(rawPredictionCol), Seq(DoubleType, new VectorUDT))
     SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
 
     // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2.
     val scoreAndLabels = dataset.select($(rawPredictionCol), $(labelCol))
-      .map { case Row(rawPrediction: Vector, label: Double) =>
-        (rawPrediction(1), label)
+      .map {
+        case Row(rawPrediction: Vector, label: Double) => (rawPrediction(1), label)
+        case Row(rawPrediction: Double, label: Double) => (rawPrediction, label)
       }
     val metrics = new BinaryClassificationMetrics(scoreAndLabels)
     val metric = $(metricName) match {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala
index 76f651488a..e71dd9eee0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala
@@ -44,6 +44,23 @@ private[spark] object SchemaUtils {
   }
 
   /**
+    * Check whether the given schema contains a column of one of the require data types.
+    * @param colName  column name
+    * @param dataTypes  required column data types
+    */
+  def checkColumnTypes(
+      schema: StructType,
+      colName: String,
+      dataTypes: Seq[DataType],
+      msg: String = ""): Unit = {
+    val actualDataType = schema(colName).dataType
+    val message = if (msg != null && msg.trim.length > 0) " " + msg else ""
+    require(dataTypes.exists(actualDataType.equals),
+      s"Column $colName must be of type equal to one of the following types: " +
+        s"${dataTypes.mkString("[", ", ", "]")} but was actually of type $actualDataType.$message")
+  }
+
+  /**
    * Appends a new column to the input schema. This fails if the given output column already exists.
    * @param schema input schema
    * @param colName new column name. If this column name is an empty string "", this method returns