Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/12663#discussion_r60955574
--- 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 =>
+ // Get number of classes from dataset itself.
+ val maxLabelRow: Array[Row] =
dataset.select(max($(labelCol))).take(1)
+ if (maxLabelRow.isEmpty) {
+ throw new SparkException("ML algorithm was given empty dataset.")
+ }
+ val maxLabel: Int = maxLabelRow.head.getDouble(0).toInt
+ val numClasses = maxLabel + 1
+ require(numClasses <= maxNumClasses, s"Classifier inferred
$numClasses from label values" +
+ s" in column $labelCol since numClasses were not specified in
dataset metadata, but" +
+ s" this exceeded the max numClasses ($maxNumClasses) allowed to
be inferred from" +
+ s" values. To avoid this error, specify numClasses in metadata,
such as by applying" +
+ s" StringIndexer to the label column.")
+ numClasses
+ }
+ }
+}
+
+private[ml] object Classifier {
+ /**
+ * 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 and <
numClasses
+ */
+ def extractLabeledPoints(
--- End diff --
Note this is ```private[ml]``` instead of protected within class Classifier
since GBTClassifier does not yet implement Classifier.
---
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