[SPARK-8151] [MLLIB] pipeline components should correctly implement copy

Otherwise, extra params get ignored in `PipelineModel.transform`. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes apache#6622 from mengxr/SPARK-8087 and squashes the following commits:

0e4c8c4 [Xiangrui Meng] fix merge issues
26fc1f0 [Xiangrui Meng] address comments
e607a04 [Xiangrui Meng] merge master
b85b57e [Xiangrui Meng] fix examples/compile
d6f7891 [Xiangrui Meng] rename defaultCopyWithParams to defaultCopy
84ec278 [Xiangrui Meng] remove setter checks due to generics
2cf2ed0 [Xiangrui Meng] snapshot
291814f [Xiangrui Meng] OneVsRest.copy
1dfe3bd [Xiangrui Meng] PipelineModel.copy should copy stages

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

@@ -156,6 +156,11 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset) {
     // Create a model, and return it.
     return new MyJavaLogisticRegressionModel(uid(), weights).setParent(this);
   }
+
+  @Override
+  public MyJavaLogisticRegression copy(ParamMap extra) {
+    return defaultCopy(extra);
+  }
 }
 
 /**

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

@@ -130,6 +130,8 @@ private class MyLogisticRegression(override val uid: String)
     // Create a model, and return it.
     new MyLogisticRegressionModel(uid, weights).setParent(this)
   }
+
+  override def copy(extra: ParamMap): MyLogisticRegression = defaultCopy(extra)
 }
 
 /**

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

@@ -78,7 +78,5 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage {
     paramMaps.map(fit(dataset, _))
   }
 
-  override def copy(extra: ParamMap): Estimator[M] = {
-    super.copy(extra).asInstanceOf[Estimator[M]]
-  }
+  override def copy(extra: ParamMap): Estimator[M]
 }

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

@@ -45,8 +45,5 @@ abstract class Model[M <: Model[M]] extends Transformer {
   /** Indicates whether this [[Model]] has a corresponding parent. */
   def hasParent: Boolean = parent != null
 
-  override def copy(extra: ParamMap): M = {
-    // The default implementation of Params.copy doesn't work for models.
-    throw new NotImplementedError(s"${this.getClass} doesn't implement copy(extra: ParamMap)")
-  }
+  override def copy(extra: ParamMap): M
 }

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

@@ -66,9 +66,7 @@ abstract class PipelineStage extends Params with Logging {
     outputSchema
   }
 
-  override def copy(extra: ParamMap): PipelineStage = {
-    super.copy(extra).asInstanceOf[PipelineStage]
-  }
+  override def copy(extra: ParamMap): PipelineStage
 }
 
 /**
@@ -198,6 +196,6 @@ class PipelineModel private[ml] (
   }
 
   override def copy(extra: ParamMap): PipelineModel = {
-    new PipelineModel(uid, stages)
+    new PipelineModel(uid, stages.map(_.copy(extra)))
   }
 }

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

@@ -90,9 +90,7 @@ abstract class Predictor[
     copyValues(train(dataset).setParent(this))
   }
 
-  override def copy(extra: ParamMap): Learner = {
-    super.copy(extra).asInstanceOf[Learner]
-  }
+  override def copy(extra: ParamMap): Learner
 
   /**
    * Train a model using the given dataset and parameters.

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

@@ -67,9 +67,7 @@ abstract class Transformer extends PipelineStage {
    */
   def transform(dataset: DataFrame): DataFrame
 
-  override def copy(extra: ParamMap): Transformer = {
-    super.copy(extra).asInstanceOf[Transformer]
-  }
+  override def copy(extra: ParamMap): Transformer
 }
 
 /**
@@ -120,4 +118,6 @@ abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
     dataset.withColumn($(outputCol),
       callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol))))
   }
+
+  override def copy(extra: ParamMap): T = defaultCopy(extra)
 }

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

@@ -18,6 +18,7 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.SchemaUtils

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

@@ -86,6 +86,8 @@ final class DecisionTreeClassifier(override val uid: String)
     super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity,
       subsamplingRate = 1.0)
   }
+
+  override def copy(extra: ParamMap): DecisionTreeClassifier = defaultCopy(extra)
 }
 
 @Experimental

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

@@ -141,6 +141,8 @@ final class GBTClassifier(override val uid: String)
     val oldModel = oldGBT.run(oldDataset)
     GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures)
   }
+
+  override def copy(extra: ParamMap): GBTClassifier = defaultCopy(extra)
 }
 
 @Experimental

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

@@ -220,6 +220,8 @@ class LogisticRegression(override val uid: String)
 
     new LogisticRegressionModel(uid, weights.compressed, intercept)
   }
+
+  override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra)
 }
 
 /**

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

@@ -24,7 +24,7 @@ import scala.language.existentials
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute._
-import org.apache.spark.ml.param.Param
+import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.sql.{DataFrame, Row}
@@ -133,6 +133,12 @@ final class OneVsRestModel private[ml] (
     aggregatedDataset.withColumn($(predictionCol), labelUdf.as($(predictionCol), labelMetadata))
       .drop(accColName)
   }
+
+  override def copy(extra: ParamMap): OneVsRestModel = {
+    val copied = new OneVsRestModel(
+      uid, labelMetadata, models.map(_.copy(extra).asInstanceOf[ClassificationModel[_, _]]))
+    copyValues(copied, extra)
+  }
 }
 
 /**
@@ -209,4 +215,12 @@ final class OneVsRest(override val uid: String)
     val model = new OneVsRestModel(uid, labelAttribute.toMetadata(), models).setParent(this)
     copyValues(model)
   }
+
+  override def copy(extra: ParamMap): OneVsRest = {
+    val copied = defaultCopy(extra).asInstanceOf[OneVsRest]
+    if (isDefined(classifier)) {
+      copied.setClassifier($(classifier).copy(extra))
+    }
+    copied
+  }
 }

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

@@ -97,6 +97,8 @@ final class RandomForestClassifier(override val uid: String)
       oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt)
     RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures)
   }
+
+  override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
 }
 
 @Experimental

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

@@ -79,4 +79,6 @@ class BinaryClassificationEvaluator(override val uid: String)
     metrics.unpersist()
     metric
   }
+
+  override def copy(extra: ParamMap): BinaryClassificationEvaluator = defaultCopy(extra)
 }

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

@@ -46,7 +46,5 @@ abstract class Evaluator extends Params {
    */
   def evaluate(dataset: DataFrame): Double
 
-  override def copy(extra: ParamMap): Evaluator = {
-    super.copy(extra).asInstanceOf[Evaluator]
-  }
+  override def copy(extra: ParamMap): Evaluator
 }

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

@@ -18,7 +18,7 @@
 package org.apache.spark.ml.evaluation
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.param.{Param, ParamValidators}
+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}
 import org.apache.spark.mllib.evaluation.RegressionMetrics
@@ -80,4 +80,6 @@ final class RegressionEvaluator(override val uid: String)
     }
     metric
   }
+
+  override def copy(extra: ParamMap): RegressionEvaluator = defaultCopy(extra)
 }

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

@@ -83,4 +83,6 @@ final class Binarizer(override val uid: String)
     val outputFields = inputFields :+ attr.toStructField()
     StructType(outputFields)
   }
+
+  override def copy(extra: ParamMap): Binarizer = defaultCopy(extra)
 }

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

@@ -89,6 +89,8 @@ final class Bucketizer(override val uid: String)
     SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
     SchemaUtils.appendColumn(schema, prepOutputField(schema))
   }
+
+  override def copy(extra: ParamMap): Bucketizer = defaultCopy(extra)
 }
 
 private[feature] object Bucketizer {

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

@@ -19,7 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.Param
+import org.apache.spark.ml.param.{ParamMap, Param}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}

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

@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.AttributeGroup
-import org.apache.spark.ml.param.{IntParam, ParamValidators}
+import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.feature
@@ -74,4 +74,6 @@ class HashingTF(override val uid: String) extends Transformer with HasInputCol w
     val attrGroup = new AttributeGroup($(outputCol), $(numFeatures))
     SchemaUtils.appendColumn(schema, attrGroup.toStructField())
   }
+
+  override def copy(extra: ParamMap): HashingTF = defaultCopy(extra)
 }

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

@@ -45,9 +45,6 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
   /** @group getParam */
   def getMinDocFreq: Int = $(minDocFreq)
 
-  /** @group setParam */
-  def setMinDocFreq(value: Int): this.type = set(minDocFreq, value)
-
   /**
    * Validate and transform the input schema.
    */
@@ -72,6 +69,9 @@ final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBa
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
+  /** @group setParam */
+  def setMinDocFreq(value: Int): this.type = set(minDocFreq, value)
+
   override def fit(dataset: DataFrame): IDFModel = {
     transformSchema(dataset.schema, logging = true)
     val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v }
@@ -82,6 +82,8 @@ final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBa
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }
+
+  override def copy(extra: ParamMap): IDF = defaultCopy(extra)
 }
 
 /**
@@ -109,4 +111,9 @@ class IDFModel private[ml] (
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }
+
+  override def copy(extra: ParamMap): IDFModel = {
+    val copied = new IDFModel(uid, idfModel)
+    copyValues(copied, extra)
+  }
 }

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

@@ -165,4 +165,6 @@ class OneHotEncoder(override val uid: String) extends Transformer
 
     dataset.select(col("*"), encode(col(inputColName).cast(DoubleType)).as(outputColName, metadata))
   }
+
+  override def copy(extra: ParamMap): OneHotEncoder = defaultCopy(extra)
 }

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

@@ -21,7 +21,7 @@ import scala.collection.mutable
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{IntParam, ParamValidators}
+import org.apache.spark.ml.param.{ParamMap, IntParam, ParamValidators}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.types.DataType
@@ -61,6 +61,8 @@ class PolynomialExpansion(override val uid: String)
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
+
+  override def copy(extra: ParamMap): PolynomialExpansion = defaultCopy(extra)
 }
 
 /**

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

@@ -92,6 +92,8 @@ class StandardScaler(override val uid: String) extends Estimator[StandardScalerM
     val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
     StructType(outputFields)
   }
+
+  override def copy(extra: ParamMap): StandardScaler = defaultCopy(extra)
 }
 
 /**
@@ -125,4 +127,9 @@ class StandardScalerModel private[ml] (
     val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
     StructType(outputFields)
   }
+
+  override def copy(extra: ParamMap): StandardScalerModel = {
+    val copied = new StandardScalerModel(uid, scaler)
+    copyValues(copied, extra)
+  }
 }

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

@@ -83,6 +83,8 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }
+
+  override def copy(extra: ParamMap): StringIndexer = defaultCopy(extra)
 }
 
 /**
@@ -144,4 +146,9 @@ class StringIndexerModel private[ml] (
       schema
     }
   }
+
+  override def copy(extra: ParamMap): StringIndexerModel = {
+    val copied = new StringIndexerModel(uid, labels)
+    copyValues(copied, extra)
+  }
 }

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

@@ -43,6 +43,8 @@ class Tokenizer(override val uid: String) extends UnaryTransformer[String, Seq[S
   }
 
   override protected def outputDataType: DataType = new ArrayType(StringType, false)
+
+  override def copy(extra: ParamMap): Tokenizer = defaultCopy(extra)
 }
 
 /**
@@ -112,4 +114,6 @@ class RegexTokenizer(override val uid: String)
   }
 
   override protected def outputDataType: DataType = new ArrayType(StringType, false)
+
+  override def copy(extra: ParamMap): RegexTokenizer = defaultCopy(extra)
 }

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

@@ -23,6 +23,7 @@ import org.apache.spark.SparkException
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute, UnresolvedAttribute}
+import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
@@ -117,6 +118,8 @@ class VectorAssembler(override val uid: String)
     }
     StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, false))
   }
+
+  override def copy(extra: ParamMap): VectorAssembler = defaultCopy(extra)
 }
 
 private object VectorAssembler {