Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/12663#discussion_r61003261
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala ---
    @@ -62,6 +65,76 @@ abstract class Classifier[
       def setRawPredictionCol(value: String): E = set(rawPredictionCol, 
value).asInstanceOf[E]
     
       // TODO: defaultEvaluator (follow-up PR)
    +
    +  /**
    +   * Extract [[labelCol]] and [[featuresCol]] from the given dataset,
    +   * and put it in an RDD with strong types.
    +   * @throws SparkException  if any label is not an integer >= 0
    +   */
    +  override protected def extractLabeledPoints(dataset: Dataset[_]): 
RDD[LabeledPoint] = {
    +    dataset.select(col($(labelCol)).cast(DoubleType), 
col($(featuresCol))).rdd.map {
    +      case Row(label: Double, features: Vector) =>
    +        require(label % 1 == 0 && label >= 0, s"Classifier was given 
dataset with invalid label" +
    +          s" $label.  Labels must be integers in range [0, 1, ..., 
numClasses-1]")
    +        LabeledPoint(label, features)
    +    }
    +  }
    +
    +  /**
    +   * Get the number of classes.  This looks in column metadata first, and 
if that is missing,
    +   * then this assumes classes are indexed 0,1,...,numClasses-1 and 
computes numClasses
    +   * by finding the maximum label value.
    +   *
    +   * Label validation (ensuring all labels are integers >= 0) needs to be 
handled elsewhere,
    +   * such as in [[extractLabeledPoints()]].
    +   *
    +   * @param dataset  Dataset which contains a column [[labelCol]]
    +   * @param maxNumClasses  Maximum number of classes allowed when inferred 
from data.  If numClasses
    +   *                       is specified in the metadata, then 
maxNumClasses is ignored.
    +   * @return  number of classes
    +   * @throws IllegalArgumentException  if metadata does not specify 
numClasses, and the
    +   *                                   actual numClasses exceeds 
maxNumClasses
    +   */
    +  protected def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 
1000): Int = {
    +    MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
    +      case Some(n: Int) => n
    +      case None =>
    --- End diff --
    
    Actually, I'm worried about a warning being annoying.  Maybe I'll just do 
logInfo and decrease maxNumClasses to 100.


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