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Scala Node类代码示例

本文整理汇总了Scala中org.apache.spark.ml.tree.Node类的典型用法代码示例。如果您正苦于以下问题：Scala Node类的具体用法？Scala Node怎么用？Scala Node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Node类的5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，您的评价将有助于系统推荐出更棒的Scala代码示例。

示例1: LocalDecisionTreeClassificationModel

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

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

class LocalDecisionTreeClassificationModel(override val sparkTransformer: DecisionTreeClassificationModel) extends LocalTransformer[DecisionTreeClassificationModel] {
  override def transform(localData: LocalData): LocalData = {
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val method = classOf[DecisionTreeClassificationModel].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 LocalDecisionTreeClassificationModel extends LocalModel[DecisionTreeClassificationModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): DecisionTreeClassificationModel = {
    createTree(metadata, data)
  }

  def createTree(metadata: Metadata, data: Map[String, Any]): DecisionTreeClassificationModel = {
    val ctor = classOf[DecisionTreeClassificationModel].getDeclaredConstructor(classOf[String], classOf[Node], classOf[Int], classOf[Int])
    ctor.setAccessible(true)
    val inst = ctor.newInstance(
      metadata.uid,
      DataUtils.createNode(0, metadata, data),
      metadata.numFeatures.get.asInstanceOf[java.lang.Integer],
      metadata.numClasses.get.asInstanceOf[java.lang.Integer]
    )
    inst
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
      .setProbabilityCol(metadata.paramMap("probabilityCol").asInstanceOf[String])
      .setRawPredictionCol(metadata.paramMap("rawPredictionCol").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: DecisionTreeClassificationModel): LocalTransformer[DecisionTreeClassificationModel] = new LocalDecisionTreeClassificationModel(transformer)
}
开发者ID:Hydrospheredata，项目名称:spark-ml-serving，代码行数:57，代码来源:LocalDecisionTreeClassificationModel.scala

示例2: 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3: LocalDecisionTreeClassificationModel

//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
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.tree.Node

class LocalDecisionTreeClassificationModel(override val sparkTransformer: DecisionTreeClassificationModel) extends LocalTransformer[DecisionTreeClassificationModel] {
  override def transform(localData: LocalData): LocalData = {
    localData.column(sparkTransformer.getFeaturesCol) match {
      case Some(column) =>
        val method = classOf[DecisionTreeClassificationModel].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 LocalDecisionTreeClassificationModel extends LocalModel[DecisionTreeClassificationModel] {
  override def load(metadata: Metadata, data: Map[String, Any]): DecisionTreeClassificationModel = {
    createTree(metadata, data)
  }

  def createTree(metadata: Metadata, data: Map[String, Any]): DecisionTreeClassificationModel = {
    val ctor = classOf[DecisionTreeClassificationModel].getDeclaredConstructor(classOf[String], classOf[Node], classOf[Int], classOf[Int])
    ctor.setAccessible(true)
    val inst = ctor.newInstance(
      metadata.uid,
      DataUtils.createNode(0, metadata, data),
      metadata.numFeatures.get.asInstanceOf[java.lang.Integer],
      metadata.numClasses.get.asInstanceOf[java.lang.Integer]
    )
    inst
      .setFeaturesCol(metadata.paramMap("featuresCol").asInstanceOf[String])
      .setPredictionCol(metadata.paramMap("predictionCol").asInstanceOf[String])
      .setProbabilityCol(metadata.paramMap("probabilityCol").asInstanceOf[String])
      .setRawPredictionCol(metadata.paramMap("rawPredictionCol").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: DecisionTreeClassificationModel): LocalTransformer[DecisionTreeClassificationModel] = new LocalDecisionTreeClassificationModel(transformer)
}
开发者ID:Hydrospheredata，项目名称:mist，代码行数:57，代码来源:LocalDecisionTreeClassificationModel.scala

示例4: 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5: NodeToMleap

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

import com.truecar.mleap.core.linalg.{DenseVector, Vector}
import com.truecar.mleap.core.tree
import com.truecar.mleap.spark.MleapSparkSupport._
import org.apache.spark.ml.tree.{InternalNode, LeafNode, Node}


case class NodeToMleap(node: Node) {
  def toMleap(includeImpurityStats: Boolean): tree.Node = {
    node match {
      case node: InternalNode =>
        tree.InternalNode(node.prediction,
          node.impurity,
          node.gain,
          node.leftChild.toMleap(includeImpurityStats),
          node.rightChild.toMleap(includeImpurityStats),
          node.split.toMleap)
      case node: LeafNode =>
        val impurityStats = if(includeImpurityStats) {
          Some(DenseVector(node.impurityStats.stats))
        } else { None }
        tree.LeafNode(node.prediction, node.impurity, impurityStats)
    }
  }
}
开发者ID:TrueCar，项目名称:mleap，代码行数:27，代码来源:NodeToMleap.scala


注：本文中的org.apache.spark.ml.tree.Node类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，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。