Revert back since tag for traits and fix docs.

docs/ml-features.md

@@ -1189,8 +1189,8 @@ that the number of buckets used will be smaller than this value, for example, if
 distinct values of the input to create enough distinct quantiles.
 
 NaN values:
-NaN values will be removed from the column when `QuantileDiscretizer` fitting. This will produce
-a `Bucketizer` model for making prediction and transformation. During the transformation, `Bucketizer`
+NaN values will be removed from the column during `QuantileDiscretizer` fitting. This will produce
+a `Bucketizer` model for making predictions. During the transformation, `Bucketizer`
 will raise an error when it finds NaN values in the dataset, but the user can also choose to either
 keep or remove NaN values within the dataset by setting `handleInvalid`. If the user chooses to keep
 NaN values, they will be handled specially and placed into their own bucket, for example, if 4 buckets

mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala

@@ -49,13 +49,15 @@ private[feature] trait ChiSqSelectorParams extends Params
    *
    * @group param
    */
+  @Since("1.6.0")
   final val numTopFeatures = new IntParam(this, "numTopFeatures",
     "Number of features that selector will select, ordered by ascending p-value. If the" +
       " number of features is < numTopFeatures, then this will select all features.",
     ParamValidators.gtEq(1))
   setDefault(numTopFeatures -> 50)
 
   /** @group getParam */
+  @Since("1.6.0")
   def getNumTopFeatures: Int = $(numTopFeatures)
 
   /**
@@ -64,12 +66,14 @@ private[feature] trait ChiSqSelectorParams extends Params
    * Default value is 0.1.
    * @group param
    */
+  @Since("2.1.0")
   final val percentile = new DoubleParam(this, "percentile",
     "Percentile of features that selector will select, ordered by ascending p-value.",
     ParamValidators.inRange(0, 1))
   setDefault(percentile -> 0.1)
 
   /** @group getParam */
+  @Since("2.1.0")
   def getPercentile: Double = $(percentile)
 
   /**
@@ -78,25 +82,29 @@ private[feature] trait ChiSqSelectorParams extends Params
    * Default value is 0.05.
    * @group param
    */
+  @Since("2.1.0")
   final val fpr = new DoubleParam(this, "fpr", "The highest p-value for features to be kept.",
     ParamValidators.inRange(0, 1))
   setDefault(fpr -> 0.05)
 
   /** @group getParam */
+  @Since("2.1.0")
   def getFpr: Double = $(fpr)
 
   /**
    * The selector type of the ChisqSelector.
    * Supported options: "numTopFeatures" (default), "percentile", "fpr".
    * @group param
    */
+  @Since("2.1.0")
   final val selectorType = new Param[String](this, "selectorType",
     "The selector type of the ChisqSelector. " +
       "Supported options: " + OldChiSqSelector.supportedSelectorTypes.mkString(", "),
     ParamValidators.inArray[String](OldChiSqSelector.supportedSelectorTypes))
   setDefault(selectorType -> OldChiSqSelector.NumTopFeatures)
 
   /** @group getParam */
+  @Since("2.1.0")
   def getSelectorType: String = $(selectorType)
 }
 

mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala

@@ -73,13 +73,15 @@ private[feature] trait QuantileDiscretizerBase extends Params
    * @group param
    */
   // TODO: SPARK-18619 Make QuantileDiscretizer inherit from HasHandleInvalid.
+  @Since("2.1.0")
   val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "how to handle " +
     "invalid entries. Options are skip (filter out rows with invalid values), " +
     "error (throw an error), or keep (keep invalid values in a special additional bucket).",
     ParamValidators.inArray(Bucketizer.supportedHandleInvalids))
   setDefault(handleInvalid, Bucketizer.ERROR_INVALID)
 
   /** @group getParam */
+  @Since("2.1.0")
   def getHandleInvalid: String = $(handleInvalid)
 
 }
@@ -91,8 +93,8 @@ private[feature] trait QuantileDiscretizerBase extends Params
  * are too few distinct values of the input to create enough distinct quantiles.
  *
  * NaN handling:
- * NaN values will be removed from the column when `QuantileDiscretizer` fitting. This will produce
- * a `Bucketizer` model for making prediction and transformation. During the transformation,
+ * NaN values will be removed from the column during `QuantileDiscretizer` fitting. This will
+ * produce a `Bucketizer` model for making predictions. During the transformation,
  * `Bucketizer` will raise an error when it finds NaN values in the dataset, but the user can
  * also choose to either keep or remove NaN values within the dataset by setting `handleInvalid`.
  * If the user chooses to keep NaN values, they will be handled specially and placed into their own