pass compile

mllib/src/main/scala/org/apache/spark/ml/Model.scala

@@ -32,7 +32,12 @@ abstract class Model[M <: Model[M]] extends Transformer {
    * The parent estimator that produced this model.
    * Note: For ensembles' component Models, this value can be null.
    */
-  val parent: Estimator[M]
+  var parent: Estimator[M] = _
+
+  def setParent(parent: Estimator[M]): M = {
+    this.parent = parent
+    this.asInstanceOf[M]
+  }
 
   override def copy(extra: ParamMap): M = {
     // The default implementation of Params.copy doesn't work for models.

mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala

@@ -22,6 +22,7 @@ import scala.collection.mutable.ListBuffer
 import org.apache.spark.Logging
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
 import org.apache.spark.ml.param.{Param, ParamMap, Params}
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.types.StructType
 
@@ -80,13 +81,15 @@ abstract class PipelineStage extends Params with Logging {
  * an identity transformer.
  */
 @AlphaComponent
-class Pipeline extends Estimator[PipelineModel] {
+class Pipeline(override val uid: String) extends Estimator[PipelineModel] {
+
+  def this() = this(Identifiable.randomUID("pipeline"))
 
   /**
    * param for pipeline stages
    * @group param
    */
-  val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline")
+  val stages: Param[Array[PipelineStage]] = new Param(uid, "stages", "stages of the pipeline")
 
   /** @group setParam */
   def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this }
@@ -148,7 +151,7 @@ class Pipeline extends Estimator[PipelineModel] {
       }
     }
 
-    new PipelineModel(this, transformers.toArray)
+    new PipelineModel(uid, transformers.toArray).setParent(this)
   }
 
   override def copy(extra: ParamMap): Pipeline = {
@@ -171,7 +174,7 @@ class Pipeline extends Estimator[PipelineModel] {
  */
 @AlphaComponent
 class PipelineModel private[ml] (
-    override val parent: Pipeline,
+    val uid: String,
     val stages: Array[Transformer])
   extends Model[PipelineModel] with Logging {
 
@@ -190,6 +193,6 @@ class PipelineModel private[ml] (
   }
 
   override def copy(extra: ParamMap): PipelineModel = {
-    new PipelineModel(parent, stages)
+    new PipelineModel(uid, stages)
   }
 }

mllib/src/main/scala/org/apache/spark/ml/Predictor.scala

@@ -88,7 +88,7 @@ abstract class Predictor[
     // This handles a few items such as schema validation.
     // Developers only need to implement train().
     transformSchema(dataset.schema, logging = true)
-    copyValues(train(dataset))
+    copyValues(train(dataset).setParent(this))
   }
 
   override def copy(extra: ParamMap): Learner = {

mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala

@@ -21,7 +21,7 @@ import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{TreeClassifierParams, DecisionTreeParams, DecisionTreeModel, Node}
-import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree}
@@ -39,10 +39,12 @@ import org.apache.spark.sql.DataFrame
  * features.
  */
 @AlphaComponent
-final class DecisionTreeClassifier
+final class DecisionTreeClassifier(override val uid: String)
   extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel]
   with DecisionTreeParams with TreeClassifierParams {
 
+  def this() = this(Identifiable.randomUID("dtc"))
+
   // Override parameter setters from parent trait for Java API compatibility.
 
   override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value)
@@ -101,7 +103,7 @@ object DecisionTreeClassifier {
  */
 @AlphaComponent
 final class DecisionTreeClassificationModel private[ml] (
-    override val parent: DecisionTreeClassifier,
+    override val uid: String,
     override val rootNode: Node)
   extends PredictionModel[Vector, DecisionTreeClassificationModel]
   with DecisionTreeModel with Serializable {
@@ -114,7 +116,7 @@ final class DecisionTreeClassificationModel private[ml] (
   }
 
   override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
-    copyValues(new DecisionTreeClassificationModel(parent, rootNode), extra)
+    copyValues(new DecisionTreeClassificationModel(uid, rootNode), extra)
   }
 
   override def toString: String = {
@@ -138,6 +140,6 @@ private[ml] object DecisionTreeClassificationModel {
       s"Cannot convert non-classification DecisionTreeModel (old API) to" +
         s" DecisionTreeClassificationModel (new API).  Algo is: ${oldModel.algo}")
     val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures)
-    new DecisionTreeClassificationModel(parent, rootNode)
+    new DecisionTreeClassificationModel(parent.uid, rootNode)
   }
 }

mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala

@@ -25,7 +25,7 @@ import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree.{GBTParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel}
-import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT}
@@ -44,10 +44,12 @@ import org.apache.spark.sql.DataFrame
  * Note: Multiclass labels are not currently supported.
  */
 @AlphaComponent
-final class GBTClassifier
+final class GBTClassifier(override val uid: String)
   extends Predictor[Vector, GBTClassifier, GBTClassificationModel]
   with GBTParams with TreeClassifierParams with Logging {
 
+  def this() = this(Identifiable.randomUID("gbtc"))
+
   // Override parameter setters from parent trait for Java API compatibility.
 
   // Parameters from TreeClassifierParams:
@@ -99,7 +101,7 @@ final class GBTClassifier
    * (default = logistic)
    * @group param
    */
-  val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" +
+  val lossType: Param[String] = new Param[String](uid, "lossType", "Loss function which GBT" +
     " tries to minimize (case-insensitive). Supported options:" +
     s" ${GBTClassifier.supportedLossTypes.mkString(", ")}",
     (value: String) => GBTClassifier.supportedLossTypes.contains(value.toLowerCase))
@@ -160,7 +162,7 @@ object GBTClassifier {
  */
 @AlphaComponent
 final class GBTClassificationModel(
-    override val parent: GBTClassifier,
+    override val uid: String,
     private val _trees: Array[DecisionTreeRegressionModel],
     private val _treeWeights: Array[Double])
   extends PredictionModel[Vector, GBTClassificationModel]
@@ -184,7 +186,7 @@ final class GBTClassificationModel(
   }
 
   override def copy(extra: ParamMap): GBTClassificationModel = {
-    copyValues(new GBTClassificationModel(parent, _trees, _treeWeights), extra)
+    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights), extra)
   }
 
   override def toString: String = {
@@ -210,6 +212,6 @@ private[ml] object GBTClassificationModel {
       // parent, fittingParamMap for each tree is null since there are no good ways to set these.
       DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
     }
-    new GBTClassificationModel(parent, newTrees, oldModel.treeWeights)
+    new GBTClassificationModel(parent.uid, newTrees, oldModel.treeWeights)
   }
 }

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -20,6 +20,7 @@ package org.apache.spark.ml.classification
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.DataFrame
@@ -41,10 +42,12 @@ private[classification] trait LogisticRegressionParams extends ProbabilisticClas
  * Currently, this class only supports binary classification.
  */
 @AlphaComponent
-class LogisticRegression
+class LogisticRegression(override val uid: String)
   extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel]
   with LogisticRegressionParams {
 
+  def this() = this(Identifiable.randomUID("logreg"))
+
   /** @group setParam */
   def setRegParam(value: Double): this.type = set(regParam, value)
 
@@ -72,7 +75,7 @@ class LogisticRegression
       .setRegParam($(regParam))
       .setNumIterations($(maxIter))
     val oldModel = lr.run(oldDataset)
-    val lrm = new LogisticRegressionModel(this, oldModel.weights, oldModel.intercept)
+    val lrm = new LogisticRegressionModel(uid, oldModel.weights, oldModel.intercept)
 
     if (handlePersistence) {
       oldDataset.unpersist()
@@ -89,7 +92,7 @@ class LogisticRegression
  */
 @AlphaComponent
 class LogisticRegressionModel private[ml] (
-    override val parent: LogisticRegression,
+    override val uid: String,
     val weights: Vector,
     val intercept: Double)
   extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
@@ -140,7 +143,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LogisticRegressionModel = {
-    copyValues(new LogisticRegressionModel(parent, weights, intercept), extra)
+    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra)
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {

mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala

@@ -23,7 +23,7 @@ import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel}
-import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
@@ -41,10 +41,12 @@ import org.apache.spark.sql.DataFrame
  * features.
  */
 @AlphaComponent
-final class RandomForestClassifier
+final class RandomForestClassifier(override val uid: String)
   extends Predictor[Vector, RandomForestClassifier, RandomForestClassificationModel]
   with RandomForestParams with TreeClassifierParams {
 
+  def this() = this(Identifiable.randomUID("rfc"))
+
   // Override parameter setters from parent trait for Java API compatibility.
 
   // Parameters from TreeClassifierParams:
@@ -118,7 +120,7 @@ object RandomForestClassifier {
  */
 @AlphaComponent
 final class RandomForestClassificationModel private[ml] (
-    override val parent: RandomForestClassifier,
+    override val uid: String,
     private val _trees: Array[DecisionTreeClassificationModel])
   extends PredictionModel[Vector, RandomForestClassificationModel]
   with TreeEnsembleModel with Serializable {
@@ -146,7 +148,7 @@ final class RandomForestClassificationModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
-    copyValues(new RandomForestClassificationModel(parent, _trees), extra)
+    copyValues(new RandomForestClassificationModel(uid, _trees), extra)
   }
 
   override def toString: String = {
@@ -172,6 +174,6 @@ private[ml] object RandomForestClassificationModel {
       // parent, fittingParamMap for each tree is null since there are no good ways to set these.
       DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures)
     }
-    new RandomForestClassificationModel(parent, newTrees)
+    new RandomForestClassificationModel(parent.uid, newTrees)
   }
 }

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

@@ -21,7 +21,7 @@ import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.Evaluator
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.{DataFrame, Row}
@@ -33,13 +33,16 @@ import org.apache.spark.sql.types.DoubleType
  * Evaluator for binary classification, which expects two input columns: score and label.
  */
 @AlphaComponent
-class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol {
+class BinaryClassificationEvaluator(override val uid: String)
+  extends Evaluator with HasRawPredictionCol with HasLabelCol {
+
+  def this() = this(Identifiable.randomUID("binEval"))
 
   /**
    * param for metric name in evaluation
    * @group param
    */
-  val metricName: Param[String] = new Param(this, "metricName",
+  val metricName: Param[String] = new Param(uid, "metricName",
     "metric name in evaluation (areaUnderROC|areaUnderPR)")
 
   /** @group getParam */

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

@@ -22,7 +22,7 @@ import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.BinaryAttribute
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
-import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, StructType}
@@ -32,7 +32,10 @@ import org.apache.spark.sql.types.{DoubleType, StructType}
  * Binarize a column of continuous features given a threshold.
  */
 @AlphaComponent
-final class Binarizer extends Transformer with HasInputCol with HasOutputCol {
+final class Binarizer(override val uid: String)
+  extends Transformer with HasInputCol with HasOutputCol {
+
+  def this() = this(Identifiable.randomUID("binarizer"))
 
   /**
    * Param for threshold used to binarize continuous features.
@@ -41,7 +44,7 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol {
    * @group param
    */
   val threshold: DoubleParam =
-    new DoubleParam(this, "threshold", "threshold used to binarize continuous features")
+    new DoubleParam(uid, "threshold", "threshold used to binarize continuous features")
 
   /** @group getParam */
   def getThreshold: Double = $(threshold)

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

@@ -20,6 +20,7 @@ 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, ParamValidators}
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.types.DataType
@@ -29,14 +30,16 @@ import org.apache.spark.sql.types.DataType
  * Maps a sequence of terms to their term frequencies using the hashing trick.
  */
 @AlphaComponent
-class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] {
+class HashingTF(override val uid: String) extends UnaryTransformer[Iterable[_], Vector, HashingTF] {
+
+  def this() = this(Identifiable.randomUID("hashingTF"))
 
   /**
    * Number of features.  Should be > 0.
    * (default = 2^18^)
    * @group param
    */
-  val numFeatures = new IntParam(this, "numFeatures", "number of features (> 0)",
+  val numFeatures = new IntParam(uid, "numFeatures", "number of features (> 0)",
     ParamValidators.gt(0))
 
   setDefault(numFeatures -> (1 << 18))