zhengruifeng commented on code in PR #36049:
URL: https://github.com/apache/spark/pull/36049#discussion_r841303261
##########
mllib/src/main/scala/org/apache/spark/ml/util/DatasetUtils.scala:
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@@ -138,4 +140,61 @@ private[spark] object DatasetUtils {
case Row(point: Vector) => OldVectors.fromML(point)
}
}
+
+ /**
+ * 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
+ */
+ private[ml] def getNumClasses(
+ dataset: Dataset[_],
+ labelCol: String,
+ maxNumClasses: Int = 100): 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(checkClassificationLabels(labelCol,
Some(maxNumClasses))))
+ .take(1)
+ if (maxLabelRow.isEmpty || maxLabelRow(0).get(0) == null) {
+ throw new SparkException("ML algorithm was given empty dataset.")
+ }
+ val maxDoubleLabel: Double = maxLabelRow.head.getDouble(0)
+ require((maxDoubleLabel + 1).isValidInt, s"Classifier found max label
value =" +
+ s" $maxDoubleLabel but requires integers in range [0, ...
${Int.MaxValue})")
+ val numClasses = maxDoubleLabel.toInt + 1
+ require(numClasses <= maxNumClasses, s"Classifier inferred $numClasses
from label values" +
+ s" in column $labelCol, but this exceeded the max numClasses
($maxNumClasses) allowed" +
+ s" to be inferred from values. To avoid this error for labels with
> $maxNumClasses" +
+ s" classes, specify numClasses explicitly in the metadata; this can
be done by applying" +
+ s" StringIndexer to the label column.")
+ logInfo(this.getClass.getCanonicalName + s" inferred $numClasses
classes for" +
+ s" labelCol=$labelCol since numClasses was not specified in the
column metadata.")
+ numClasses
+ }
+ }
+
+ /**
+ * Obtain the number of features in a vector column.
+ * If no metadata is available, extract it from the dataset.
+ */
+ private[ml] def getNumFeatures(dataset: Dataset[_], vectorCol: String): Int
= {
+ MetadataUtils.getNumFeatures(dataset.schema(vectorCol)) match {
Review Comment:
ok, will swith back to getOrElse
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