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Scala DecisionTreeClassificationModel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.classification.DecisionTreeClassificationModel类的典型用法代码示例。如果您正苦于以下问题：Scala DecisionTreeClassificationModel类的具体用法？Scala DecisionTreeClassificationModel怎么用？Scala DecisionTreeClassificationModel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DecisionTreeClassificationModel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，您的评价将有助于我们的系统推荐出更棒的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: Forest

//设置package包名称以及导入依赖的类
package com.redislabs.provider.redis.ml

import org.apache.spark.ml.tree
import org.apache.spark.ml.classification.DecisionTreeClassificationModel
import redis.clients.jedis.Protocol.Command
import redis.clients.jedis.{Jedis, _}
import com.redislabs.client.redisml.MLClient
import org.apache.spark.ml.tree.{CategoricalSplit, ContinuousSplit, InternalNode}

class Forest(trees: Array[DecisionTreeClassificationModel]) {

  private def subtreeToRedisString(n: org.apache.spark.ml.tree.Node, path: String = "."): String = {
    val prefix: String = s",${path},"
    n.getClass.getSimpleName match {
      case "InternalNode" => {
        val in = n.asInstanceOf[InternalNode]
        val splitStr = in.split match {
          case contSplit: ContinuousSplit => s"numeric,${in.split.featureIndex},${contSplit.threshold}"
          case catSplit: CategoricalSplit => s"categoric,${in.split.featureIndex}," +
            catSplit.leftCategories.mkString(":")
        }
        prefix + splitStr + subtreeToRedisString(in.leftChild, path + "l") +
          subtreeToRedisString(in.rightChild, path + "r")
      }
      case "LeafNode" => {
        prefix + s"leaf,${n.prediction}" +
          s",stats,${n.getImpurityStats.mkString(":")}"
      }
    }
  }

  private def toRedisString: String = {
    trees.zipWithIndex.map { case (tree, treeIndex) =>
      s"${treeIndex}" + subtreeToRedisString(tree.rootNode, ".")
    }.fold("") { (a, b) => a + "\n" + b }
  }

  def toDebugArray: Array[String] = {
    toRedisString.split("\n").drop(1)
  }

  def loadToRedis(forestId: String = "test_forest", host: String = "localhost") {
    val jedis = new Jedis(host)
    val commands = toRedisString.split("\n").drop(1)
    jedis.getClient.sendCommand(Command.MULTI)
    jedis.getClient().getStatusCodeReply
    for (cmd <- commands) {
      val cmdArray = forestId +: cmd.split(",")
      jedis.getClient.sendCommand(MLClient.ModuleCommand.FOREST_ADD, cmdArray: _*)
      jedis.getClient().getStatusCodeReply
    }
    jedis.getClient.sendCommand(Command.EXEC)
    jedis.getClient.getMultiBulkReply
  }
}
开发者ID:RedisLabs，项目名称:spark-redis-ml，代码行数:56，代码来源:package.scala


示例3: LocalRandomForestClassificationModel

//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)
}
开发者ID:Hydrospheredata，项目名称:mist，代码行数:59，代码来源:LocalRandomForestClassificationModel.scala


示例4: 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5: RandomForestClassificationModelToMleap

//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)
  }
}
开发者ID:TrueCar，项目名称:mleap，代码行数:22，代码来源:RandomForestClassificationModelToMleap.scala



注：本文中的org.apache.spark.ml.classification.DecisionTreeClassificationModel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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