add AlphaComponent annotation

apache · Nov 11, 2014 · 6d97fe6 · 6d97fe6
1 parent 731f0e4
commit 6d97fe6
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diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
@@ -20,13 +20,16 @@ package org.apache.spark.ml
 import scala.annotation.varargs
 import scala.collection.JavaConverters._
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param.{ParamMap, ParamPair, Params}
 import org.apache.spark.sql.SchemaRDD
 import org.apache.spark.sql.api.java.JavaSchemaRDD
 
 /**
+ * :: AlphaComponent ::
  * Abstract class for estimators that fit models to data.
  */
+@AlphaComponent
 abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
 
   /**

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala
@@ -17,12 +17,15 @@
 
 package org.apache.spark.ml
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.sql.SchemaRDD
 
 /**
+ * :: AlphaComponent ::
  * Abstract class for evaluators that compute metrics from predictions.
  */
+@AlphaComponent
 abstract class Evaluator extends Identifiable {
 
   /**

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala
@@ -19,9 +19,13 @@ package org.apache.spark.ml
 
 import java.util.UUID
 
+import org.apache.spark.annotation.AlphaComponent
+
 /**
+ * :: AlphaComponent ::
  * Object with a unique id.
  */
+@AlphaComponent
 trait Identifiable extends Serializable {
 
   /**

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala
@@ -17,13 +17,16 @@
 
 package org.apache.spark.ml
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param.ParamMap
 
 /**
+ * :: AlphaComponent ::
  * A fitted model, i.e., a [[Transformer]] produced by an [[Estimator]].
  *
  * @tparam M model type
  */
+@AlphaComponent
 abstract class Model[M <: Model[M]] extends Transformer {
   /**
    * The parent estimator that produced this model.

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -20,12 +20,15 @@ package org.apache.spark.ml
 import scala.collection.mutable.ListBuffer
 
 import org.apache.spark.Logging
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.sql.{SchemaRDD, StructType}
 
 /**
+ * :: AlphaComponent ::
  * A stage in a pipeline, either an Estimator or an Transformer.
  */
+@AlphaComponent
 abstract class PipelineStage extends Serializable with Logging {
 
   /**
@@ -49,6 +52,7 @@ abstract class PipelineStage extends Serializable with Logging {
 }
 
 /**
+ * :: AlphaComponent ::
  * A simple pipeline, which acts as an estimator. A Pipeline consists of a sequence of stages, each
  * of which is either an [[Estimator]] or a [[Transformer]]. When [[Pipeline.fit]] is called, the
  * stages are executed in order. If a stage is an [[Estimator]], its [[Estimator.fit]] method will
@@ -59,6 +63,7 @@ abstract class PipelineStage extends Serializable with Logging {
  * transformers, corresponding to the pipeline stages. If there are no stages, the pipeline acts as
  * an identity transformer.
  */
+@AlphaComponent
 class Pipeline extends Estimator[PipelineModel] {
 
   /** param for pipeline stages */
@@ -125,8 +130,10 @@ class Pipeline extends Estimator[PipelineModel] {
 }
 
 /**
+ * :: AlphaComponent ::
  * Represents a compiled pipeline.
  */
+@AlphaComponent
 class PipelineModel(
     override val parent: Pipeline,
     override val fittingParamMap: ParamMap,

diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
@@ -21,6 +21,7 @@ import scala.annotation.varargs
 import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.spark.Logging
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param._
 import org.apache.spark.sql.SchemaRDD
 import org.apache.spark.sql.api.java.JavaSchemaRDD
@@ -30,8 +31,10 @@ import org.apache.spark.sql.catalyst.dsl._
 import org.apache.spark.sql.catalyst.types._
 
 /**
+ * :: AlphaComponet ::
  * Abstract class for transformers that transform one dataset into another.
  */
+@AlphaComponent
 abstract class Transformer extends PipelineStage with Params {
 
   /**
@@ -80,9 +83,11 @@ abstract class Transformer extends PipelineStage with Params {
 }
 
 /**
+ * :: AlphaComponent ::
  * Abstract class for transformers that take one input column, apply transformation, and output the
  * result as a new column.
  */
+@AlphaComponent
 abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransformer[IN, OUT, T]]
   extends Transformer with HasInputCol with HasOutputCol with Logging {
 

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.classification
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
@@ -28,8 +29,10 @@ import org.apache.spark.sql.catalyst.dsl._
 import org.apache.spark.storage.StorageLevel
 
 /**
+ * :: AlphaComponent ::
  * Params for logistic regression.
  */
+@AlphaComponent
 private[classification] trait LogisticRegressionParams extends Params
   with HasRegParam with HasMaxIter with HasLabelCol with HasThreshold with HasFeaturesCol
   with HasScoreCol with HasPredictionCol {
@@ -108,8 +111,10 @@ class LogisticRegression extends Estimator[LogisticRegressionModel] with Logisti
 }
 
 /**
+ * :: AlphaComponent ::
  * Model produced by [[LogisticRegression]].
  */
+@AlphaComponent
 class LogisticRegressionModel private[ml] (
     override val parent: LogisticRegression,
     override val fittingParamMap: ParamMap,

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -17,14 +17,17 @@
 
 package org.apache.spark.ml.evaluation
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.sql.{DoubleType, Row, SchemaRDD}
 
 /**
+ * :: AlphaComponent ::
  * Evaluator for binary classification, which expects two input columns: score and label.
  */
+@AlphaComponent
 class BinaryClassificationEvaluator extends Evaluator with Params
   with HasScoreCol with HasLabelCol {
 

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
@@ -17,14 +17,17 @@
 
 package org.apache.spark.ml.feature
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param.{IntParam, ParamMap}
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.Vector
 
 /**
+ * :: AlphaComponent ::
  * Maps a sequence of terms to their term frequencies using the hashing trick.
  */
+@AlphaComponent
 class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] {
 
   /** number of features */

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.feature
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.mllib.feature
@@ -31,9 +32,11 @@ import org.apache.spark.sql.catalyst.dsl._
 private[feature] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol
 
 /**
+ * :: AlphaComponent ::
  * Standardizes features by removing the mean and scaling to unit variance using column summary
  * statistics on the samples in the training set.
  */
+@AlphaComponent
 class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams {
 
   def setInputCol(value: String): this.type = set(inputCol, value)
@@ -66,8 +69,10 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP
 }
 
 /**
+ * :: AlphaComponent ::
  * Model fitted by [[StandardScaler]].
  */
+@AlphaComponent
 class StandardScalerModel private[ml] (
     override val parent: StandardScaler,
     override val fittingParamMap: ParamMap,

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -19,12 +19,15 @@ package org.apache.spark.ml.param
 
 import java.lang.reflect.Modifier
 
+import org.apache.spark.annotation.AlphaComponent
+
 import scala.annotation.varargs
 import scala.collection.mutable
 
 import org.apache.spark.ml.Identifiable
 
 /**
+ * :: AlphaComponent ::
  * A param with self-contained documentation and optionally default value. Primitive-typed param
  * should use the specialized versions, which are more friendly to Java users.
  *
@@ -33,6 +36,7 @@ import org.apache.spark.ml.Identifiable
  * @param doc documentation
  * @tparam T param value type
  */
+@AlphaComponent
 class Param[T] (
     val parent: Params,
     val name: String,
@@ -102,9 +106,11 @@ class BooleanParam(parent: Params, name: String, doc: String, defaultValue: Opti
 case class ParamPair[T](param: Param[T], value: T)
 
 /**
+ * :: AlphaComponent ::
  * Trait for components that take parameters. This also provides an internal param map to store
  * parameter values attached to the instance.
  */
+@AlphaComponent
 trait Params extends Identifiable with Serializable {
 
   /** Returns all params. */
@@ -198,8 +204,10 @@ private[ml] object Params {
 }
 
 /**
+ * :: AlphaComponent ::
  * A param to value map.
  */
+@AlphaComponent
 class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) extends Serializable {
 
   /**

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -20,6 +20,7 @@ package org.apache.spark.ml.tuning
 import com.github.fommil.netlib.F2jBLAS
 
 import org.apache.spark.Logging
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml._
 import org.apache.spark.ml.param.{IntParam, Param, ParamMap, Params}
 import org.apache.spark.mllib.util.MLUtils
@@ -49,8 +50,10 @@ private[ml] trait CrossValidatorParams extends Params {
 }
 
 /**
+ * :: AlphaComponent ::
  * K-fold cross validation.
  */
+@AlphaComponent
 class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorParams with Logging {
 
   private val f2jBLAS = new F2jBLAS
@@ -103,8 +106,10 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
 }
 
 /**
+ * :: AlphaComponent ::
  * Model from k-fold cross validation.
  */
+@AlphaComponent
 class CrossValidatorModel private[ml] (
     override val parent: CrossValidator,
     override val fittingParamMap: ParamMap,

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/ParamGridBuilder.scala
@@ -20,11 +20,14 @@ package org.apache.spark.ml.tuning
 import scala.annotation.varargs
 import scala.collection.mutable
 
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param._
 
 /**
+ * :: AlphaComponent ::
  * Builder for a param grid used in grid search-based model selection.
  */
+@AlphaComponent
 class ParamGridBuilder {
 
   private val paramGrid = mutable.Map.empty[Param[_], Iterable[_]]