Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/15826#discussion_r87256910
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala ---
@@ -110,21 +110,20 @@ class NaiveBayes @Since("1.5.0") (
@Since("2.1.0")
def setWeightCol(value: String): this.type = set(weightCol, value)
+ override protected def train(dataset: Dataset[_]): NaiveBayesModel = {
+ trainWithLabelCheck(dataset)
+ }
+
/**
* ml assumes input labels in range [0, numClasses). But this
implementation
* is also called by mllib NaiveBayes which allows other kinds of input
labels
- * such as {-1, +1}. Here we use this parameter to switch between
different processing logic.
- * It should be removed when we remove mllib NaiveBayes.
+ * such as {-1, +1}. `positiveLabel` is used to determine whether the
label
+ * should be checked and it should be removed when we remove mllib
NaiveBayes.
*/
- private[spark] var isML: Boolean = true
-
- private[spark] def setIsML(isML: Boolean): this.type = {
- this.isML = isML
- this
- }
-
- override protected def train(dataset: Dataset[_]): NaiveBayesModel = {
- if (isML) {
+ private[spark] def trainWithLabelCheck(
+ dataset: Dataset[_],
+ positiveLabel: Boolean = true): NaiveBayesModel = {
--- End diff --
Since it is an internal method, I suggest to not use a default argument and
specify it explicitly above.
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