[SPARK-10261][DOCUMENTATION, ML] Fixed @Since annotation to ml.evaluation

Author: Tijo Thomas <tijoparacka@gmail.com>
Author: tijo <tijo@ezzoft.com>

Closes #8554 from tijoparacka/SPARK-10261-2.

mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
@@ -30,10 +30,12 @@ import org.apache.spark.sql.types.DoubleType
  * :: Experimental ::
  * Evaluator for binary classification, which expects two input columns: rawPrediction and label.
  */
+@Since("1.2.0")
 @Experimental
-class BinaryClassificationEvaluator(override val uid: String)
+class BinaryClassificationEvaluator @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Evaluator with HasRawPredictionCol with HasLabelCol {
 
+  @Since("1.2.0")
   def this() = this(Identifiable.randomUID("binEval"))
 
   /**
@@ -41,6 +43,7 @@ class BinaryClassificationEvaluator(override val uid: String)
    * Default: areaUnderROC
    * @group param
    */
+  @Since("1.2.0")
   val metricName: Param[String] = {
     val allowedParams = ParamValidators.inArray(Array("areaUnderROC", "areaUnderPR"))
     new Param(
@@ -48,12 +51,15 @@ class BinaryClassificationEvaluator(override val uid: String)
   }
 
   /** @group getParam */
+  @Since("1.2.0")
   def getMetricName: String = $(metricName)
 
   /** @group setParam */
+  @Since("1.2.0")
   def setMetricName(value: String): this.type = set(metricName, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
 
   /**
@@ -61,13 +67,16 @@ class BinaryClassificationEvaluator(override val uid: String)
    * @deprecated use [[setRawPredictionCol()]] instead
    */
   @deprecated("use setRawPredictionCol instead", "1.5.0")
+  @Since("1.2.0")
   def setScoreCol(value: String): this.type = set(rawPredictionCol, value)
 
   /** @group setParam */
+  @Since("1.2.0")
   def setLabelCol(value: String): this.type = set(labelCol, value)
 
   setDefault(metricName -> "areaUnderROC")
 
+  @Since("1.2.0")
   override def evaluate(dataset: DataFrame): Double = {
     val schema = dataset.schema
     SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT)
@@ -87,10 +96,12 @@ class BinaryClassificationEvaluator(override val uid: String)
     metric
   }
 
+  @Since("1.5.0")
   override def isLargerBetter: Boolean = $(metricName) match {
     case "areaUnderROC" => true
     case "areaUnderPR" => true
   }
 
+  @Since("1.4.1")
   override def copy(extra: ParamMap): BinaryClassificationEvaluator = defaultCopy(extra)
 }

mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.sql.DataFrame
 
@@ -25,6 +25,7 @@ import org.apache.spark.sql.DataFrame
  * :: DeveloperApi ::
  * Abstract class for evaluators that compute metrics from predictions.
  */
+@Since("1.5.0")
 @DeveloperApi
 abstract class Evaluator extends Params {
 
@@ -35,6 +36,7 @@ abstract class Evaluator extends Params {
    * @param paramMap parameter map that specifies the input columns and output metrics
    * @return metric
    */
+  @Since("1.5.0")
   def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = {
     this.copy(paramMap).evaluate(dataset)
   }
@@ -44,6 +46,7 @@ abstract class Evaluator extends Params {
    * @param dataset a dataset that contains labels/observations and predictions.
    * @return metric
    */
+  @Since("1.5.0")
   def evaluate(dataset: DataFrame): Double
 
   /**
@@ -51,7 +54,9 @@ abstract class Evaluator extends Params {
    * or minimized (false).
    * A given evaluator may support multiple metrics which may be maximized or minimized.
    */
+  @Since("1.5.0")
   def isLargerBetter: Boolean = true
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): Evaluator
 }

mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.param.{ParamMap, ParamValidators, Param}
 import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol}
 import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
@@ -29,10 +29,12 @@ import org.apache.spark.sql.types.DoubleType
  * :: Experimental ::
  * Evaluator for multiclass classification, which expects two input columns: score and label.
  */
+@Since("1.5.0")
 @Experimental
-class MulticlassClassificationEvaluator (override val uid: String)
+class MulticlassClassificationEvaluator @Since("1.5.0") (@Since("1.5.0") override val uid: String)
   extends Evaluator with HasPredictionCol with HasLabelCol {
 
+  @Since("1.5.0")
   def this() = this(Identifiable.randomUID("mcEval"))
 
   /**
@@ -40,6 +42,7 @@ class MulticlassClassificationEvaluator (override val uid: String)
    * `"weightedPrecision"`, `"weightedRecall"`)
    * @group param
    */
+  @Since("1.5.0")
   val metricName: Param[String] = {
     val allowedParams = ParamValidators.inArray(Array("f1", "precision",
       "recall", "weightedPrecision", "weightedRecall"))
@@ -48,19 +51,24 @@ class MulticlassClassificationEvaluator (override val uid: String)
   }
 
   /** @group getParam */
+  @Since("1.5.0")
   def getMetricName: String = $(metricName)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setMetricName(value: String): this.type = set(metricName, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setLabelCol(value: String): this.type = set(labelCol, value)
 
   setDefault(metricName -> "f1")
 
+  @Since("1.5.0")
   override def evaluate(dataset: DataFrame): Double = {
     val schema = dataset.schema
     SchemaUtils.checkColumnType(schema, $(predictionCol), DoubleType)
@@ -81,6 +89,7 @@ class MulticlassClassificationEvaluator (override val uid: String)
     metric
   }
 
+  @Since("1.5.0")
   override def isLargerBetter: Boolean = $(metricName) match {
     case "f1" => true
     case "precision" => true
@@ -89,5 +98,6 @@ class MulticlassClassificationEvaluator (override val uid: String)
     case "weightedRecall" => true
   }
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): MulticlassClassificationEvaluator = defaultCopy(extra)
 }

mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared.{HasLabelCol, HasPredictionCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
@@ -29,10 +29,12 @@ import org.apache.spark.sql.types.DoubleType
  * :: Experimental ::
  * Evaluator for regression, which expects two input columns: prediction and label.
  */
+@Since("1.4.0")
 @Experimental
-final class RegressionEvaluator(override val uid: String)
+final class RegressionEvaluator @Since("1.4.0") (@Since("1.4.0") override val uid: String)
   extends Evaluator with HasPredictionCol with HasLabelCol {
 
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("regEval"))
 
   /**
@@ -43,25 +45,31 @@ final class RegressionEvaluator(override val uid: String)
    * we take and output the negative of this metric.
    * @group param
    */
+  @Since("1.4.0")
   val metricName: Param[String] = {
     val allowedParams = ParamValidators.inArray(Array("mse", "rmse", "r2", "mae"))
     new Param(this, "metricName", "metric name in evaluation (mse|rmse|r2|mae)", allowedParams)
   }
 
   /** @group getParam */
+  @Since("1.4.0")
   def getMetricName: String = $(metricName)
 
   /** @group setParam */
+  @Since("1.4.0")
   def setMetricName(value: String): this.type = set(metricName, value)
 
   /** @group setParam */
+  @Since("1.4.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
   /** @group setParam */
+  @Since("1.4.0")
   def setLabelCol(value: String): this.type = set(labelCol, value)
 
   setDefault(metricName -> "rmse")
 
+  @Since("1.4.0")
   override def evaluate(dataset: DataFrame): Double = {
     val schema = dataset.schema
     SchemaUtils.checkColumnType(schema, $(predictionCol), DoubleType)
@@ -81,6 +89,7 @@ final class RegressionEvaluator(override val uid: String)
     metric
   }
 
+  @Since("1.4.0")
   override def isLargerBetter: Boolean = $(metricName) match {
     case "rmse" => false
     case "mse" => false
@@ -88,5 +97,6 @@ final class RegressionEvaluator(override val uid: String)
     case "mae" => false
   }
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): RegressionEvaluator = defaultCopy(extra)
 }