Scala RandomForestClassificationModel类代码示例

//设置package包名称以及导入依赖的类
package io.hydrosphere.mist.api.ml.classification

import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, RandomForestClassificationModel}
import org.apache.spark.ml.linalg.{DenseVector, Vector, Vectors}

class LocalRandomForestClassificationModel(override val sparkTransformer: RandomForestClassificationModel) extends LocalTransformer[RandomForestClassificationModel] {
  override def transform(localData: LocalData): LocalData = {
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val cls = classOf[RandomForestClassificationModel]
        val rawPredictionCol = LocalDataColumn(sparkTransformer.getRawPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map { vector =>
          val predictRaw = cls.getDeclaredMethod("predictRaw", classOf[Vector])
          predictRaw.invoke(sparkTransformer, vector)
        })
        val probabilityCol = LocalDataColumn(sparkTransformer.getProbabilityCol, rawPredictionCol.data.map(_.asInstanceOf[DenseVector]).map { vector =>
          val raw2probabilityInPlace = cls.getDeclaredMethod("raw2probabilityInPlace", classOf[Vector])
          raw2probabilityInPlace.invoke(sparkTransformer, vector.copy)
        })
        val predictionCol = LocalDataColumn(sparkTransformer.getPredictionCol, rawPredictionCol.data.map(_.asInstanceOf[DenseVector]).map { vector =>
          val raw2prediction = cls.getMethod("raw2prediction", classOf[Vector])
          raw2prediction.invoke(sparkTransformer, vector.copy)
        })
        localData.withColumn(rawPredictionCol)
          .withColumn(probabilityCol)
          .withColumn(predictionCol)
      case None => localData
    }
  }
}

object LocalRandomForestClassificationModel extends LocalModel[RandomForestClassificationModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): RandomForestClassificationModel = {
    val treesMetadata = metadata.paramMap("treesMetadata").asInstanceOf[Map[String, Any]]
    val trees = treesMetadata map { treeKv =>
      val treeMeta = treeKv._2.asInstanceOf[Map[String, Any]]
      val meta = treeMeta("metadata").asInstanceOf[Metadata]
      LocalDecisionTreeClassificationModel.createTree(
        meta,
        data(treeKv._1).asInstanceOf[Map[String, Any]]
      )
    }
    val ctor = classOf[RandomForestClassificationModel].getDeclaredConstructor(classOf[String], classOf[Array[DecisionTreeClassificationModel]], classOf[Int], classOf[Int])
    ctor.setAccessible(true)
    ctor
      .newInstance(
        metadata.uid,
        trees.to[Array],
        metadata.numFeatures.get.asInstanceOf[java.lang.Integer],
        metadata.numClasses.get.asInstanceOf[java.lang.Integer]
      )
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
      .setProbabilityCol(metadata.paramMap("probabilityCol").asInstanceOf[String])
  }

  override implicit def getTransformer(transformer: RandomForestClassificationModel): LocalTransformer[RandomForestClassificationModel] = new LocalRandomForestClassificationModel(transformer)
} 

//设置package包名称以及导入依赖的类
package org.apache.spark.ml.mleap.converter.runtime.classification

import com.truecar.mleap.core.classification.RandomForestClassification
import com.truecar.mleap.runtime.transformer
import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, RandomForestClassificationModel}
import org.apache.spark.ml.mleap.converter.runtime.TransformerToMleap


object RandomForestClassificationModelToMleap extends TransformerToMleap[RandomForestClassificationModel, transformer.RandomForestClassificationModel] {
  override def toMleap(t: RandomForestClassificationModel): transformer.RandomForestClassificationModel = {
    val trees = t.trees.asInstanceOf[Array[DecisionTreeClassificationModel]]
      .map(tree => DecisionTreeClassificationModelToMleap(tree).toMleap)
    val model = RandomForestClassification(trees,
      t.numFeatures,
      t.numClasses)

    transformer.RandomForestClassificationModel(t.getFeaturesCol,
      t.getPredictionCol,
      model)
  }
} 

//设置package包名称以及导入依赖的类
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