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[SPARK-21210][DOC][ML] Javadoc 8 fixes for ML shared param traits #18420

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[SPARK-21210][DOC][ML] Javadoc 8 fixes for ML shared param traits #18420

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5 changes: 4 additions & 1 deletion mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
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Expand Up @@ -20,6 +20,7 @@ package org.apache.spark.ml.param.shared
import java.io.PrintWriter

import scala.reflect.ClassTag
import scala.xml.Utility

/**
* Code generator for shared params (sharedParams.scala). Run under the Spark folder with
Expand Down Expand Up @@ -167,14 +168,16 @@ private[shared] object SharedParamsCodeGen {
"def"
}

val htmlCompliantDoc = Utility.escape(doc)

s"""
|/**
| * Trait for shared param $name$defaultValueDoc.
| */
|private[ml] trait Has$Name extends Params {
|
| /**
| * Param for $doc.
| * Param for $htmlCompliantDoc.
| * @group ${groupStr(0)}
| */
| final val $name: $Param = new $Param(this, "$name", "$doc"$isValid)
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2 changes: 1 addition & 1 deletion mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
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Expand Up @@ -176,7 +176,7 @@ private[ml] trait HasThreshold extends Params {
private[ml] trait HasThresholds extends Params {

/**
* Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
* Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
* @group param
*/
final val thresholds: DoubleArrayParam = new DoubleArrayParam(this, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold", (t: Array[Double]) => t.forall(_ >= 0) && t.count(_ == 0) <= 1)
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