[SPARK-7407][MLLIB] use uid + name to identify parameters

examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java

@@ -28,6 +28,7 @@
 import org.apache.spark.ml.classification.ClassificationModel;
 import org.apache.spark.ml.param.IntParam;
 import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.util.Identifiable$;
 import org.apache.spark.mllib.linalg.BLAS;
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.Vectors;
@@ -103,7 +104,23 @@ public static void main(String[] args) throws Exception {
  *       However, this should still compile and run successfully.
  */
 class MyJavaLogisticRegression
-    extends Classifier<Vector, MyJavaLogisticRegression, MyJavaLogisticRegressionModel> {
+  extends Classifier<Vector, MyJavaLogisticRegression, MyJavaLogisticRegressionModel> {
+
+  public MyJavaLogisticRegression() {
+    init();
+  }
+
+  public MyJavaLogisticRegression(String uid) {
+    this.uid_ = uid;
+    init();
+  }
+
+  private String uid_ = Identifiable$.MODULE$.randomUID("myJavaLogReg");
+
+  @Override
+  public String uid() {
+    return uid_;
+  }
 
   /**
    * Param for max number of iterations
@@ -117,7 +134,7 @@ class MyJavaLogisticRegression
 
   int getMaxIter() { return (Integer) getOrDefault(maxIter); }
 
-  public MyJavaLogisticRegression() {
+  private void init() {
     setMaxIter(100);
   }
 
@@ -137,7 +154,7 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset) {
     Vector weights = Vectors.zeros(numFeatures); // Learning would happen here.
 
     // Create a model, and return it.
-    return new MyJavaLogisticRegressionModel(this, weights);
+    return new MyJavaLogisticRegressionModel(uid(), weights).setParent(this);
   }
 }
 
@@ -149,17 +166,21 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset) {
  *       However, this should still compile and run successfully.
  */
 class MyJavaLogisticRegressionModel
-    extends ClassificationModel<Vector, MyJavaLogisticRegressionModel> {
-
-  private MyJavaLogisticRegression parent_;
-  public MyJavaLogisticRegression parent() { return parent_; }
+  extends ClassificationModel<Vector, MyJavaLogisticRegressionModel> {
 
   private Vector weights_;
   public Vector weights() { return weights_; }
 
-  public MyJavaLogisticRegressionModel(MyJavaLogisticRegression parent_, Vector weights_) {
-    this.parent_ = parent_;
-    this.weights_ = weights_;
+  public MyJavaLogisticRegressionModel(String uid, Vector weights) {
+    this.uid_ = uid;
+    this.weights_ = weights;
+  }
+
+  private String uid_ = Identifiable$.MODULE$.randomUID("myJavaLogReg");
+
+  @Override
+  public String uid() {
+    return uid_;
   }
 
   // This uses the default implementation of transform(), which reads column "features" and outputs
@@ -204,6 +225,6 @@ public Vector predictRaw(Vector features) {
    */
   @Override
   public MyJavaLogisticRegressionModel copy(ParamMap extra) {
-    return copyValues(new MyJavaLogisticRegressionModel(parent_, weights_), extra);
+    return copyValues(new MyJavaLogisticRegressionModel(uid(), weights_), extra);
   }
 }

examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala

@@ -20,6 +20,7 @@ package org.apache.spark.examples.ml
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams}
 import org.apache.spark.ml.param.{IntParam, ParamMap}
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
@@ -106,10 +107,12 @@ private trait MyLogisticRegressionParams extends ClassifierParams {
  *
  * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
  */
-private class MyLogisticRegression
+private class MyLogisticRegression(override val uid: String)
   extends Classifier[Vector, MyLogisticRegression, MyLogisticRegressionModel]
   with MyLogisticRegressionParams {
 
+  def this() = this(Identifiable.randomUID("myLogReg"))
+
   setMaxIter(100) // Initialize
 
   // The parameter setter is in this class since it should return type MyLogisticRegression.
@@ -125,7 +128,7 @@ private class MyLogisticRegression
     val weights = Vectors.zeros(numFeatures) // Learning would happen here.
 
     // Create a model, and return it.
-    new MyLogisticRegressionModel(this, weights)
+    new MyLogisticRegressionModel(uid, weights).setParent(this)
   }
 }
 
@@ -135,7 +138,7 @@ private class MyLogisticRegression
  * NOTE: This is private since it is an example.  In practice, you may not want it to be private.
  */
 private class MyLogisticRegressionModel(
-    override val parent: MyLogisticRegression,
+    override val uid: String,
     val weights: Vector)
   extends ClassificationModel[Vector, MyLogisticRegressionModel]
   with MyLogisticRegressionParams {
@@ -173,6 +176,6 @@ private class MyLogisticRegressionModel(
    * This is used for the default implementation of [[transform()]].
    */
   override def copy(extra: ParamMap): MyLogisticRegressionModel = {
-    copyValues(new MyLogisticRegressionModel(parent, weights), extra)
+    copyValues(new MyLogisticRegressionModel(uid, weights), extra)
   }
 }

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

@@ -32,7 +32,15 @@ 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] = _
+
+  /**
+   * Sets the parent of this model (Java API).
+   */
+  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
 
@@ -80,7 +81,9 @@ 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
@@ -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,
+    override 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 +140,7 @@ 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)
+    val uid = if (parent != null) parent.uid else Identifiable.randomUID("dtc")
+    new DecisionTreeClassificationModel(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:
@@ -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 +212,7 @@ 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)
+    val uid = if (parent != null) parent.uid else Identifiable.randomUID("gbtc")
+    new GBTClassificationModel(parent.uid, newTrees, oldModel.treeWeights)
   }
 }

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

@@ -26,6 +26,7 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction}
 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.linalg._
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -50,10 +51,12 @@ 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 with Logging {
 
+  def this() = this(Identifiable.randomUID("logreg"))
+
   /**
    * Set the regularization parameter.
    * Default is 0.0.
@@ -213,7 +216,7 @@ class LogisticRegression
       (weightsWithIntercept, 0.0)
     }
 
-    new LogisticRegressionModel(this, weights.compressed, intercept)
+    new LogisticRegressionModel(uid, weights.compressed, intercept)
   }
 }
 
@@ -224,7 +227,7 @@ 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]
@@ -276,7 +279,7 @@ 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 = {