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Scala DecisionTreeRegressionModel类代码示例

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

本文整理汇总了Scala中org.apache.spark.ml.regression.DecisionTreeRegressionModel类的典型用法代码示例。如果您正苦于以下问题：Scala DecisionTreeRegressionModel类的具体用法？Scala DecisionTreeRegressionModel怎么用？Scala DecisionTreeRegressionModel使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。



在下文中一共展示了DecisionTreeRegressionModel类的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Scala代码示例。

示例1: LocalDecisionTreeRegressionModel

//设置package包名称以及导入依赖的类
package io.hydrosphere.spark_ml_serving.regression

import io.hydrosphere.spark_ml_serving._
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.tree.Node

class LocalDecisionTreeRegressionModel(override val sparkTransformer: DecisionTreeRegressionModel) extends LocalTransformer[DecisionTreeRegressionModel] {
  override def transform(localData: LocalData): LocalData = {
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val method = classOf[DecisionTreeRegressionModel].getMethod("predict", classOf[Vector])
        method.setAccessible(true)
        val newColumn = LocalDataColumn(sparkTransformer.getPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map { vector =>
          method.invoke(sparkTransformer, vector).asInstanceOf[Double]
        })
        localData.withColumn(newColumn)
      case None => localData
    }
  }
}

object LocalDecisionTreeRegressionModel extends LocalModel[DecisionTreeRegressionModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): DecisionTreeRegressionModel = {
    createTree(metadata, data)
  }

  def createTree(metadata: Metadata, data: Map[String, Any]): DecisionTreeRegressionModel = {
    val ctor = classOf[DecisionTreeRegressionModel].getDeclaredConstructor(classOf[String], classOf[Node], classOf[Int])
    ctor.setAccessible(true)
    val inst = ctor.newInstance(
      metadata.uid,
      DataUtils.createNode(0, metadata, data),
      metadata.numFeatures.get.asInstanceOf[java.lang.Integer]
    )
    inst
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
    inst
      .set(inst.seed, metadata.paramMap("seed").toString.toLong)
      .set(inst.cacheNodeIds, metadata.paramMap("cacheNodeIds").toString.toBoolean)
      .set(inst.maxDepth, metadata.paramMap("maxDepth").toString.toInt)
      .set(inst.labelCol, metadata.paramMap("labelCol").toString)
      .set(inst.minInfoGain, metadata.paramMap("minInfoGain").toString.toDouble)
      .set(inst.checkpointInterval, metadata.paramMap("checkpointInterval").toString.toInt)
      .set(inst.minInstancesPerNode, metadata.paramMap("minInstancesPerNode").toString.toInt)
      .set(inst.maxMemoryInMB, metadata.paramMap("maxMemoryInMB").toString.toInt)
      .set(inst.maxBins, metadata.paramMap("maxBins").toString.toInt)
      .set(inst.impurity, metadata.paramMap("impurity").toString)
  }

  override implicit def getTransformer(transformer: DecisionTreeRegressionModel): LocalTransformer[DecisionTreeRegressionModel] = new LocalDecisionTreeRegressionModel(transformer)
}
开发者ID:Hydrospheredata，项目名称:spark-ml-serving，代码行数:54，代码来源:LocalDecisionTreeRegressionModel.scala


示例2: LocalRandomForestRegressionModel

//设置package包名称以及导入依赖的类
package io.hydrosphere.spark_ml_serving.regression

import io.hydrosphere.spark_ml_serving._
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, RandomForestRegressionModel}


class LocalRandomForestRegressionModel(override val sparkTransformer: RandomForestRegressionModel) extends LocalTransformer[RandomForestRegressionModel] {
  override def transform(localData: LocalData): LocalData = {
    val cls = classOf[RandomForestRegressionModel]
    val predict = cls.getMethod("predict", classOf[Vector])
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val predictionCol = LocalDataColumn(sparkTransformer.getPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map{ vector =>
          predict.invoke(sparkTransformer, vector).asInstanceOf[Double]
        })
        localData.withColumn(predictionCol)
      case None => localData
    }
  }
}

object LocalRandomForestRegressionModel extends LocalModel[RandomForestRegressionModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): RandomForestRegressionModel = {
    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]
      LocalDecisionTreeRegressionModel.createTree(
        meta,
        data(treeKv._1).asInstanceOf[Map[String, Any]]
      )
    }
    val ctor = classOf[RandomForestRegressionModel].getDeclaredConstructor(classOf[String], classOf[Array[DecisionTreeRegressionModel]], classOf[Int])
    ctor.setAccessible(true)
    val inst = ctor
      .newInstance(
        metadata.uid,
        trees.to[Array],
        metadata.numFeatures.get.asInstanceOf[java.lang.Integer]
      )
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])

    inst
      .set(inst.seed, metadata.paramMap("seed").toString.toLong)
      .set(inst.subsamplingRate, metadata.paramMap("subsamplingRate").toString.toDouble)
      .set(inst.impurity, metadata.paramMap("impurity").toString)
  }

  override implicit def getTransformer(transformer: RandomForestRegressionModel): LocalTransformer[RandomForestRegressionModel] = new LocalRandomForestRegressionModel(transformer)
}
开发者ID:Hydrospheredata，项目名称:spark-ml-serving，代码行数:53，代码来源:LocalRandomForestRegressionModel.scala


示例3: LocalDecisionTreeRegressionModel

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

import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.regression.DecisionTreeRegressionModel
import org.apache.spark.ml.tree.Node

class LocalDecisionTreeRegressionModel(override val sparkTransformer: DecisionTreeRegressionModel) extends LocalTransformer[DecisionTreeRegressionModel] {
  override def transform(localData: LocalData): LocalData = {
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val method = classOf[DecisionTreeRegressionModel].getMethod("predict", classOf[Vector])
        method.setAccessible(true)
        val newColumn = LocalDataColumn(sparkTransformer.getPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map { vector =>
          method.invoke(sparkTransformer, vector).asInstanceOf[Double]
        })
        localData.withColumn(newColumn)
      case None => localData
    }
  }
}

object LocalDecisionTreeRegressionModel extends LocalModel[DecisionTreeRegressionModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): DecisionTreeRegressionModel = {
    createTree(metadata, data)
  }

  def createTree(metadata: Metadata, data: Map[String, Any]): DecisionTreeRegressionModel = {
    val ctor = classOf[DecisionTreeRegressionModel].getDeclaredConstructor(classOf[String], classOf[Node], classOf[Int])
    ctor.setAccessible(true)
    val inst = ctor.newInstance(
      metadata.uid,
      DataUtils.createNode(0, metadata, data),
      metadata.numFeatures.get.asInstanceOf[java.lang.Integer]
    )
    inst
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
    inst
      .set(inst.seed, metadata.paramMap("seed").toString.toLong)
      .set(inst.cacheNodeIds, metadata.paramMap("cacheNodeIds").toString.toBoolean)
      .set(inst.maxDepth, metadata.paramMap("maxDepth").toString.toInt)
      .set(inst.labelCol, metadata.paramMap("labelCol").toString)
      .set(inst.minInfoGain, metadata.paramMap("minInfoGain").toString.toDouble)
      .set(inst.checkpointInterval, metadata.paramMap("checkpointInterval").toString.toInt)
      .set(inst.minInstancesPerNode, metadata.paramMap("minInstancesPerNode").toString.toInt)
      .set(inst.maxMemoryInMB, metadata.paramMap("maxMemoryInMB").toString.toInt)
      .set(inst.maxBins, metadata.paramMap("maxBins").toString.toInt)
      .set(inst.impurity, metadata.paramMap("impurity").toString)
  }

  override implicit def getTransformer(transformer: DecisionTreeRegressionModel): LocalTransformer[DecisionTreeRegressionModel] = new LocalDecisionTreeRegressionModel(transformer)
}
开发者ID:Hydrospheredata，项目名称:mist，代码行数:54，代码来源:LocalDecisionTreeRegressionModel.scala


示例4: LocalRandomForestRegressionModel

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

import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, RandomForestRegressionModel}


class LocalRandomForestRegressionModel(override val sparkTransformer: RandomForestRegressionModel) extends LocalTransformer[RandomForestRegressionModel] {
  override def transform(localData: LocalData): LocalData = {
    val cls = classOf[RandomForestRegressionModel]
    val predict = cls.getMethod("predict", classOf[Vector])
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val predictionCol = LocalDataColumn(sparkTransformer.getPredictionCol, column.data.map(f => Vectors.dense(f.asInstanceOf[Array[Double]])).map{ vector =>
          predict.invoke(sparkTransformer, vector).asInstanceOf[Double]
        })
        localData.withColumn(predictionCol)
      case None => localData
    }
  }
}

object LocalRandomForestRegressionModel extends LocalModel[RandomForestRegressionModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): RandomForestRegressionModel = {
    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]
      LocalDecisionTreeRegressionModel.createTree(
        meta,
        data(treeKv._1).asInstanceOf[Map[String, Any]]
      )
    }
    val ctor = classOf[RandomForestRegressionModel].getDeclaredConstructor(classOf[String], classOf[Array[DecisionTreeRegressionModel]], classOf[Int])
    ctor.setAccessible(true)
    val inst = ctor
      .newInstance(
        metadata.uid,
        trees.to[Array],
        metadata.numFeatures.get.asInstanceOf[java.lang.Integer]
      )
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])

    inst
      .set(inst.seed, metadata.paramMap("seed").toString.toLong)
      .set(inst.subsamplingRate, metadata.paramMap("subsamplingRate").toString.toDouble)
      .set(inst.impurity, metadata.paramMap("impurity").toString)
  }

  override implicit def getTransformer(transformer: RandomForestRegressionModel): LocalTransformer[RandomForestRegressionModel] = new LocalRandomForestRegressionModel(transformer)
}
开发者ID:Hydrospheredata，项目名称:mist，代码行数:53，代码来源:LocalRandomForestRegressionModel.scala



注：本文中的org.apache.spark.ml.regression.DecisionTreeRegressionModel类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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