org.apache.spark.ml.feature.IndexToString Scala Examples

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.attribute.Attribute
import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
// $example off$
import org.apache.spark.sql.SparkSession

object IndexToStringExample {
  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("IndexToStringExample")
      .getOrCreate()

    // $example on$
    val df = spark.createDataFrame(Seq(
      (0, "a"),
      (1, "b"),
      (2, "c"),
      (3, "a"),
      (4, "a"),
      (5, "c")
    )).toDF("id", "category")

    val indexer = new StringIndexer()
      .setInputCol("category")
      .setOutputCol("categoryIndex")
      .fit(df)
    val indexed = indexer.transform(df)

    println(s"Transformed string column '${indexer.getInputCol}' " +
        s"to indexed column '${indexer.getOutputCol}'")
    indexed.show()

    val inputColSchema = indexed.schema(indexer.getOutputCol)
    println(s"StringIndexer will store labels in output column metadata: " +
        s"${Attribute.fromStructField(inputColSchema).toString}\n")

    val converter = new IndexToString()
      .setInputCol("categoryIndex")
      .setOutputCol("originalCategory")

    val converted = converter.transform(indexed)

    println(s"Transformed indexed column '${converter.getInputCol}' back to original string " +
        s"column '${converter.getOutputCol}' using labels in metadata")
    converted.select("id", "categoryIndex", "originalCategory").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println 

package io.hydrosphere.spark_ml_serving.preprocessors

import io.hydrosphere.spark_ml_serving.TypedTransformerConverter
import io.hydrosphere.spark_ml_serving.common._
import org.apache.spark.SparkException
import org.apache.spark.ml.feature.IndexToString

class LocalIndexToString(override val sparkTransformer: IndexToString)
  extends LocalTransformer[IndexToString] {
  override def transform(localData: LocalData): LocalData = {
    localData.column(sparkTransformer.getInputCol) match {
      case Some(column) =>
        val labels = sparkTransformer.getLabels
        val indexer = (index: Double) => {
          val idx = index.toInt
          if (0 <= idx && idx < labels.length) {
            labels(idx)
          } else {
            throw new SparkException(s"Unseen index: $index ??")
          }
        }
        val newColumn = LocalDataColumn(
          sparkTransformer.getOutputCol,
          column.data map {
            case i: Int    => indexer(i.toDouble)
            case d: Double => indexer(d)
            case d         => throw new IllegalArgumentException(s"Unknown data to index: $d")
          }
        )
        localData.withColumn(newColumn)
      case None => localData
    }
  }
}

object LocalIndexToString
  extends SimpleModelLoader[IndexToString]
  with TypedTransformerConverter[IndexToString] {
  override def build(metadata: Metadata, data: LocalData): IndexToString = {
    val ctor = classOf[IndexToString].getDeclaredConstructor(classOf[String])
    ctor.setAccessible(true)
    ctor
      .newInstance(metadata.uid)
      .setLabels(metadata.paramMap("labels").asInstanceOf[Seq[String]].toArray)
      .setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
      .setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
  }

  override implicit def toLocal(transformer: IndexToString) =
    new LocalIndexToString(transformer)
} 

package org.apache.spark.ml.bundle.ops.feature

import ml.combust.bundle.BundleContext
import ml.combust.bundle.op.OpModel
import ml.combust.bundle.dsl._
import ml.combust.mleap.core.types.{DataShape, ScalarShape}
import org.apache.spark.ml.attribute.{Attribute, BinaryAttribute, NominalAttribute, NumericAttribute}
import org.apache.spark.ml.bundle._
import org.apache.spark.ml.feature.IndexToString
import org.apache.spark.sql.types.StructField
import ml.combust.mleap.runtime.types.BundleTypeConverters._

import scala.util.{Failure, Try}


object ReverseStringIndexerOp {
  def labelsForField(field: StructField): Array[String] = {
    val attr = Attribute.fromStructField(field)

    (attr match {
      case nominal: NominalAttribute =>
        if (nominal.values.isDefined) {
          Try(nominal.values.get)
        } else {
          Failure(new RuntimeException(s"invalid nominal value for field ${field.name}"))
        }
      case _: BinaryAttribute =>
        Failure(new RuntimeException(s"invalid binary attribute for field ${field.name}"))
      case _: NumericAttribute =>
        Failure(new RuntimeException(s"invalid numeric attribute for field ${field.name}"))
      case _ =>
        Failure(new RuntimeException(s"unsupported attribute for field ${field.name}")) // optimistic about unknown attributes
    }).get
  }
}

class ReverseStringIndexerOp extends SimpleSparkOp[IndexToString] {
  override val Model: OpModel[SparkBundleContext, IndexToString] = new OpModel[SparkBundleContext, IndexToString] {
    override val klazz: Class[IndexToString] = classOf[IndexToString]

    override def opName: String = Bundle.BuiltinOps.feature.reverse_string_indexer

    override def store(model: Model, obj: IndexToString)
                      (implicit context: BundleContext[SparkBundleContext]): Model = {
      val labels = obj.get(obj.labels).getOrElse {
        assert(context.context.dataset.isDefined, BundleHelper.sampleDataframeMessage(klazz))
        val df = context.context.dataset.get
        ReverseStringIndexerOp.labelsForField(df.schema(obj.getInputCol))
      }

      model.withValue("labels", Value.stringList(labels)).
        withValue("input_shape", Value.dataShape(ScalarShape(false)))
    }

    override def load(model: Model)
                     (implicit context: BundleContext[SparkBundleContext]): IndexToString = {
      model.getValue("input_shape").map(_.getDataShape: DataShape).foreach {
        shape =>
          require(shape.isScalar, "cannot deserialize non-scalar input to Spark IndexToString model")
      }
      
      new IndexToString(uid = "").setLabels(model.value("labels").getStringList.toArray)
    }
  }

  override def sparkLoad(uid: String, shape: NodeShape, model: IndexToString): IndexToString = {
    new IndexToString(uid = uid).setLabels(model.getLabels)
  }

  override def sparkInputs(obj: IndexToString): Seq[ParamSpec] = {
    Seq("input" -> obj.inputCol)
  }

  override def sparkOutputs(obj: IndexToString): Seq[SimpleParamSpec] = {
    Seq("output" -> obj.outputCol)
  }
} 

package org.apache.spark.ml.parity.feature

import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
import org.apache.spark.ml.parity.SparkParityBase
import org.apache.spark.ml.{Pipeline, Transformer}
import org.apache.spark.sql.DataFrame


class ReverseStringIndexerParitySpec extends SparkParityBase {
  override val dataset: DataFrame = baseDataset.select("state")
  override val sparkTransformer: Transformer = {
    val stringIndexer = new StringIndexer().
      setInputCol("state").
      setOutputCol("state_index").
      fit(dataset)
    val reverseStringIndexer = new IndexToString().
      setInputCol("state_index").
      setOutputCol("state_reverse").
      setLabels(stringIndexer.labels)
    new Pipeline().setStages(Array(stringIndexer, reverseStringIndexer)).fit(dataset)
  }

  override val unserializedParams = Set("stringOrderType")
} 

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.attribute.Attribute
import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
// $example off$
import org.apache.spark.sql.SparkSession

object IndexToStringExample {
  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("IndexToStringExample")
      .getOrCreate()

    // $example on$
    val df = spark.createDataFrame(Seq(
      (0, "a"),
      (1, "b"),
      (2, "c"),
      (3, "a"),
      (4, "a"),
      (5, "c")
    )).toDF("id", "category")

    val indexer = new StringIndexer()
      .setInputCol("category")
      .setOutputCol("categoryIndex")
      .fit(df)
    val indexed = indexer.transform(df)

    println(s"Transformed string column '${indexer.getInputCol}' " +
        s"to indexed column '${indexer.getOutputCol}'")
    indexed.show()

    val inputColSchema = indexed.schema(indexer.getOutputCol)
    println(s"StringIndexer will store labels in output column metadata: " +
        s"${Attribute.fromStructField(inputColSchema).toString}\n")

    val converter = new IndexToString()
      .setInputCol("categoryIndex")
      .setOutputCol("originalCategory")

    val converted = converter.transform(indexed)

    println(s"Transformed indexed column '${converter.getInputCol}' back to original string " +
        s"column '${converter.getOutputCol}' using labels in metadata")
    converted.select("id", "categoryIndex", "originalCategory").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println 

package org.apache.spark.ml.r

import org.apache.hadoop.fs.Path
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.{MultilayerPerceptronClassificationModel, MultilayerPerceptronClassifier}
import org.apache.spark.ml.feature.{IndexToString, RFormula}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.r.RWrapperUtils._
import org.apache.spark.ml.util.{MLReadable, MLReader, MLWritable, MLWriter}
import org.apache.spark.sql.{DataFrame, Dataset}

private[r] class MultilayerPerceptronClassifierWrapper private (
    val pipeline: PipelineModel
  ) extends MLWritable {

  import MultilayerPerceptronClassifierWrapper._

  val mlpModel: MultilayerPerceptronClassificationModel =
    pipeline.stages(1).asInstanceOf[MultilayerPerceptronClassificationModel]

  val weights: Array[Double] = mlpModel.weights.toArray
  val layers: Array[Int] = mlpModel.layers

  def transform(dataset: Dataset[_]): DataFrame = {
    pipeline.transform(dataset)
      .drop(mlpModel.getFeaturesCol)
      .drop(mlpModel.getLabelCol)
      .drop(PREDICTED_LABEL_INDEX_COL)
  }

  
  override def read: MLReader[MultilayerPerceptronClassifierWrapper] =
    new MultilayerPerceptronClassifierWrapperReader

  override def load(path: String): MultilayerPerceptronClassifierWrapper = super.load(path)

  class MultilayerPerceptronClassifierWrapperReader
    extends MLReader[MultilayerPerceptronClassifierWrapper]{

    override def load(path: String): MultilayerPerceptronClassifierWrapper = {
      implicit val format = DefaultFormats
      val pipelinePath = new Path(path, "pipeline").toString

      val pipeline = PipelineModel.load(pipelinePath)
      new MultilayerPerceptronClassifierWrapper(pipeline)
    }
  }

  class MultilayerPerceptronClassifierWrapperWriter(instance: MultilayerPerceptronClassifierWrapper)
    extends MLWriter {

    override protected def saveImpl(path: String): Unit = {
      val rMetadataPath = new Path(path, "rMetadata").toString
      val pipelinePath = new Path(path, "pipeline").toString

      val rMetadata = "class" -> instance.getClass.getName
      val rMetadataJson: String = compact(render(rMetadata))
      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)

      instance.pipeline.save(pipelinePath)
    }
  }
} 

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.attribute.Attribute
import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
// $example off$
import org.apache.spark.sql.SparkSession

object IndexToStringExample {
  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("IndexToStringExample")
      .getOrCreate()

    // $example on$
    val df = spark.createDataFrame(Seq(
      (0, "a"),
      (1, "b"),
      (2, "c"),
      (3, "a"),
      (4, "a"),
      (5, "c")
    )).toDF("id", "category")

    val indexer = new StringIndexer()
      .setInputCol("category")
      .setOutputCol("categoryIndex")
      .fit(df)
    val indexed = indexer.transform(df)

    println(s"Transformed string column '${indexer.getInputCol}' " +
        s"to indexed column '${indexer.getOutputCol}'")
    indexed.show()

    val inputColSchema = indexed.schema(indexer.getOutputCol)
    println(s"StringIndexer will store labels in output column metadata: " +
        s"${Attribute.fromStructField(inputColSchema).toString}\n")

    val converter = new IndexToString()
      .setInputCol("categoryIndex")
      .setOutputCol("originalCategory")

    val converted = converter.transform(indexed)

    println(s"Transformed indexed column '${converter.getInputCol}' back to original string " +
        s"column '${converter.getOutputCol}' using labels in metadata")
    converted.select("id", "categoryIndex", "originalCategory").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println 

package org.apache.spark.ml.r

import org.apache.hadoop.fs.Path
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.{MultilayerPerceptronClassificationModel, MultilayerPerceptronClassifier}
import org.apache.spark.ml.feature.{IndexToString, RFormula}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.r.RWrapperUtils._
import org.apache.spark.ml.util.{MLReadable, MLReader, MLWritable, MLWriter}
import org.apache.spark.sql.{DataFrame, Dataset}

private[r] class MultilayerPerceptronClassifierWrapper private (
    val pipeline: PipelineModel
  ) extends MLWritable {

  import MultilayerPerceptronClassifierWrapper._

  val mlpModel: MultilayerPerceptronClassificationModel =
    pipeline.stages(1).asInstanceOf[MultilayerPerceptronClassificationModel]

  val weights: Array[Double] = mlpModel.weights.toArray
  val layers: Array[Int] = mlpModel.layers

  def transform(dataset: Dataset[_]): DataFrame = {
    pipeline.transform(dataset)
      .drop(mlpModel.getFeaturesCol)
      .drop(mlpModel.getLabelCol)
      .drop(PREDICTED_LABEL_INDEX_COL)
  }

  
  override def read: MLReader[MultilayerPerceptronClassifierWrapper] =
    new MultilayerPerceptronClassifierWrapperReader

  override def load(path: String): MultilayerPerceptronClassifierWrapper = super.load(path)

  class MultilayerPerceptronClassifierWrapperReader
    extends MLReader[MultilayerPerceptronClassifierWrapper]{

    override def load(path: String): MultilayerPerceptronClassifierWrapper = {
      implicit val format = DefaultFormats
      val pipelinePath = new Path(path, "pipeline").toString

      val pipeline = PipelineModel.load(pipelinePath)
      new MultilayerPerceptronClassifierWrapper(pipeline)
    }
  }

  class MultilayerPerceptronClassifierWrapperWriter(instance: MultilayerPerceptronClassifierWrapper)
    extends MLWriter {

    override protected def saveImpl(path: String): Unit = {
      val rMetadataPath = new Path(path, "rMetadata").toString
      val pipelinePath = new Path(path, "pipeline").toString

      val rMetadata = "class" -> instance.getClass.getName
      val rMetadataJson: String = compact(render(rMetadata))
      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)

      instance.pipeline.save(pipelinePath)
    }
  }
} 

package frameless
package ml
package feature

import frameless.ml.internals.UnaryInputsChecker
import org.apache.spark.ml.feature.IndexToString


final class TypedIndexToString[Inputs] private[ml](indexToString: IndexToString, inputCol: String)
  extends AppendTransformer[Inputs, TypedIndexToString.Outputs, IndexToString] {

  val transformer: IndexToString =
    indexToString
      .setInputCol(inputCol)
      .setOutputCol(AppendTransformer.tempColumnName)

}

object TypedIndexToString {
  case class Outputs(originalOutput: String)

  def apply[Inputs](labels: Array[String])
                    (implicit inputsChecker: UnaryInputsChecker[Inputs, Double]): TypedIndexToString[Inputs] = {
    new TypedIndexToString[Inputs](new IndexToString().setLabels(labels), inputsChecker.inputCol)
  }
} 

package com.salesforce.op.stages.impl.feature

import com.salesforce.op.features.types._
import com.salesforce.op.test.{SwTransformerSpec, TestFeatureBuilder}
import com.salesforce.op.utils.spark.RichDataset._
import org.apache.spark.ml.feature.IndexToString
import org.junit.runner.RunWith
import org.scalatest.junit.JUnitRunner


@RunWith(classOf[JUnitRunner])
class OpIndexToStringTest extends SwTransformerSpec[Text, IndexToString, OpIndexToString] {

  val (inputData, indF) = TestFeatureBuilder(Seq(0.0, 2.0, 1.0, 0.0, 0.0, 1.0).map(_.toRealNN))
  val labels = Array("a", "c", "b")

  val transformer = new OpIndexToString().setInput(indF).setLabels(labels)

  val expectedResult: Seq[Text] = Array("a", "b", "c", "a", "a", "c").map(_.toText)

  it should "correctly deindex a numeric column" in {
    val strs = transformer.transform(inputData).collect(transformer.getOutput())
    strs shouldBe expectedResult
  }

  it should "correctly deindex a numeric column (shortcut)" in {
    val str = indF.deindexed(labels, handleInvalid = IndexToStringHandleInvalid.Error)
    val strs = str.originStage.asInstanceOf[OpIndexToString].transform(inputData).collect(str)
    strs shouldBe expectedResult
  }

  it should "get labels" in {
    transformer.getLabels shouldBe labels
  }
} 

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.attribute.Attribute
import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
// $example off$
import org.apache.spark.sql.SparkSession

object IndexToStringExample {
  def main(args: Array[String]) {
    val spark = SparkSession
      .builder
      .appName("IndexToStringExample")
      .getOrCreate()

    // $example on$
    val df = spark.createDataFrame(Seq(
      (0, "a"),
      (1, "b"),
      (2, "c"),
      (3, "a"),
      (4, "a"),
      (5, "c")
    )).toDF("id", "category")

    val indexer = new StringIndexer()
      .setInputCol("category")
      .setOutputCol("categoryIndex")
      .fit(df)
    val indexed = indexer.transform(df)

    println(s"Transformed string column '${indexer.getInputCol}' " +
        s"to indexed column '${indexer.getOutputCol}'")
    indexed.show()

    val inputColSchema = indexed.schema(indexer.getOutputCol)
    println(s"StringIndexer will store labels in output column metadata: " +
        s"${Attribute.fromStructField(inputColSchema).toString}\n")

    val converter = new IndexToString()
      .setInputCol("categoryIndex")
      .setOutputCol("originalCategory")

    val converted = converter.transform(indexed)

    println(s"Transformed indexed column '${converter.getInputCol}' back to original string " +
        s"column '${converter.getOutputCol}' using labels in metadata")
    converted.select("id", "categoryIndex", "originalCategory").show()
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println 

package org.apache.spark.ml.r

import org.apache.hadoop.fs.Path
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._

import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.classification.{MultilayerPerceptronClassificationModel, MultilayerPerceptronClassifier}
import org.apache.spark.ml.feature.{IndexToString, RFormula}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.r.RWrapperUtils._
import org.apache.spark.ml.util.{MLReadable, MLReader, MLWritable, MLWriter}
import org.apache.spark.sql.{DataFrame, Dataset}

private[r] class MultilayerPerceptronClassifierWrapper private (
    val pipeline: PipelineModel
  ) extends MLWritable {

  import MultilayerPerceptronClassifierWrapper._

  private val mlpModel: MultilayerPerceptronClassificationModel =
    pipeline.stages(1).asInstanceOf[MultilayerPerceptronClassificationModel]

  lazy val weights: Array[Double] = mlpModel.weights.toArray
  lazy val layers: Array[Int] = mlpModel.layers

  def transform(dataset: Dataset[_]): DataFrame = {
    pipeline.transform(dataset)
      .drop(mlpModel.getFeaturesCol)
      .drop(mlpModel.getLabelCol)
      .drop(PREDICTED_LABEL_INDEX_COL)
  }

  
  override def read: MLReader[MultilayerPerceptronClassifierWrapper] =
    new MultilayerPerceptronClassifierWrapperReader

  override def load(path: String): MultilayerPerceptronClassifierWrapper = super.load(path)

  class MultilayerPerceptronClassifierWrapperReader
    extends MLReader[MultilayerPerceptronClassifierWrapper]{

    override def load(path: String): MultilayerPerceptronClassifierWrapper = {
      implicit val format = DefaultFormats
      val pipelinePath = new Path(path, "pipeline").toString

      val pipeline = PipelineModel.load(pipelinePath)
      new MultilayerPerceptronClassifierWrapper(pipeline)
    }
  }

  class MultilayerPerceptronClassifierWrapperWriter(instance: MultilayerPerceptronClassifierWrapper)
    extends MLWriter {

    override protected def saveImpl(path: String): Unit = {
      val rMetadataPath = new Path(path, "rMetadata").toString
      val pipelinePath = new Path(path, "pipeline").toString

      val rMetadata = "class" -> instance.getClass.getName
      val rMetadataJson: String = compact(render(rMetadata))
      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)

      instance.pipeline.save(pipelinePath)
    }
  }
} 

// scalastyle:off println
package org.apache.spark.examples.ml

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.ml.feature.{StringIndexer, IndexToString}
// $example off$

object IndexToStringExample {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("IndexToStringExample")
    val sc = new SparkContext(conf)

    val sqlContext = SQLContext.getOrCreate(sc)

    // $example on$
    val df = sqlContext.createDataFrame(Seq(
      (0, "a"),
      (1, "b"),
      (2, "c"),
      (3, "a"),
      (4, "a"),
      (5, "c")
    )).toDF("id", "category")

    val indexer = new StringIndexer()
      .setInputCol("category")
      .setOutputCol("categoryIndex")
      .fit(df)
    val indexed = indexer.transform(df)

    val converter = new IndexToString()
      .setInputCol("categoryIndex")
      .setOutputCol("originalCategory")

    val converted = converter.transform(indexed)
    converted.select("id", "originalCategory").show()
    // $example off$
    sc.stop()
  }
}
// scalastyle:on println 

package org.apache.spark.ml.mleap.converter.runtime

import com.truecar.mleap.runtime.transformer
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.classification.RandomForestClassificationModel
import org.apache.spark.ml.feature.{IndexToString, StandardScalerModel, StringIndexerModel, VectorAssembler}
import org.apache.spark.ml.mleap.classification.SVMModel
import org.apache.spark.ml.mleap.converter.runtime.classification.{RandomForestClassificationModelToMleap, SupportVectorMachineModelToMleap}
import org.apache.spark.ml.mleap.converter.runtime.feature.{IndexToStringToMleap, StandardScalerModelToMleap, StringIndexerModelToMleap, VectorAssemblerModelToMleap}
import org.apache.spark.ml.mleap.converter.runtime.regression.{LinearRegressionModelToMleap, RandomForestRegressionModelToMleap}
import org.apache.spark.ml.regression.{LinearRegressionModel, RandomForestRegressionModel}


trait BaseTransformerConverter extends SparkTransformerConverter {
  // regression
  implicit val mleapLinearRegressionModelToMleap: TransformerToMleap[LinearRegressionModel, transformer.LinearRegressionModel] =
    addConverter(LinearRegressionModelToMleap)
  implicit val mleapRandomForestRegressionModelToMleap: TransformerToMleap[RandomForestRegressionModel, transformer.RandomForestRegressionModel] =
    addConverter(RandomForestRegressionModelToMleap)

  // classification
  implicit val mleapRandomForestClassificationModelToMleap: TransformerToMleap[RandomForestClassificationModel, transformer.RandomForestClassificationModel] =
    addConverter(RandomForestClassificationModelToMleap)
  implicit val mleapSupportVectorMachineModelToMleap: TransformerToMleap[SVMModel, transformer.SupportVectorMachineModel] =
    addConverter(SupportVectorMachineModelToMleap)

  //feature
  implicit val mleapIndexToStringToMleap: TransformerToMleap[IndexToString, transformer.ReverseStringIndexerModel] =
    addConverter(IndexToStringToMleap)
  implicit val mleapStandardScalerModelToMleap: TransformerToMleap[StandardScalerModel, transformer.StandardScalerModel] =
    addConverter(StandardScalerModelToMleap)
  implicit val mleapStringIndexerModelToMleap: TransformerToMleap[StringIndexerModel, transformer.StringIndexerModel] =
    addConverter(StringIndexerModelToMleap)
  implicit val mleapVectorAssemblerToMleap: TransformerToMleap[VectorAssembler, transformer.VectorAssemblerModel] =
    addConverter(VectorAssemblerModelToMleap)

  // other
  implicit val mleapPipelineModelToMleap: TransformerToMleap[PipelineModel, transformer.PipelineModel] =
    addConverter(PipelineModelToMleap(this))
}
object BaseTransformerConverter extends BaseTransformerConverter