check buckets

Author: yinxusen

mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala

@@ -34,12 +34,30 @@ import org.apache.spark.sql.types.{DoubleType, StructType}
 @AlphaComponent
 final class Bucketizer extends Transformer with HasInputCol with HasOutputCol {
 
+  /**
+   * The given buckets should match 1) its size is larger than zero; 2) it is ordered in a non-DESC
+   * way.
+   */
+  private def checkBuckets(buckets: Array[Double]): Boolean = {
+    if (buckets.size == 0) false
+    else if (buckets.size == 1) true
+    else {
+      buckets.foldLeft((true, Double.MinValue)) { case ((validator, prevValue), currValue) =>
+        if (validator & prevValue <= currValue) {
+          (true, currValue)
+        } else {
+          (false, currValue)
+        }
+      }._1
+    }
+  }
+
   /**
    * Parameter for mapping continuous features into buckets.
    * @group param
    */
   val buckets: Param[Array[Double]] = new Param[Array[Double]](this, "buckets",
-    "Map continuous features into buckets.")
+    "Split points for mapping continuous features into buckets.", checkBuckets)
 
   /** @group getParam */
   def getBuckets: Array[Double] = $(buckets)
@@ -55,7 +73,7 @@ final class Bucketizer extends Transformer with HasInputCol with HasOutputCol {
 
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema)
-    val bucketizer = udf { feature: Double => binarySearchForBins($(buckets), feature) }
+    val bucketizer = udf { feature: Double => binarySearchForBuckets($(buckets), feature) }
     val outputColName = $(outputCol)
     val metadata = NominalAttribute.defaultAttr
       .withName(outputColName).withValues($(buckets).map(_.toString)).toMetadata()
@@ -65,7 +83,7 @@ final class Bucketizer extends Transformer with HasInputCol with HasOutputCol {
   /**
    * Binary searching in several buckets to place each data point.
    */
-  private def binarySearchForBins(splits: Array[Double], feature: Double): Double = {
+  private def binarySearchForBuckets(splits: Array[Double], feature: Double): Double = {
     val wrappedSplits = Array(Double.MinValue) ++ splits ++ Array(Double.MaxValue)
     var left = 0
     var right = wrappedSplits.length - 2