本文整理汇总了Scala中org.apache.spark.ml.regression.RandomForestRegressionModel类的典型用法代码示例。如果您正苦于以下问题：Scala RandomForestRegressionModel类的具体用法？Scala RandomForestRegressionModel怎么用？Scala RandomForestRegressionModel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RandomForestRegressionModel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，您的评价将有助于系统推荐出更棒的Scala代码示例。
示例1: 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)
} 示例2: ScorePredictor
//设置package包名称以及导入依赖的类
package org.wikimedia.research.recommendation.job.translation
import java.io.File
import org.apache.log4j.{LogManager, Logger}
import org.apache.spark.ml.regression.RandomForestRegressionModel
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession}
import scala.collection.parallel.mutable.ParArray
object ScorePredictor {
val log: Logger = LogManager.getLogger(ScorePredictor.getClass)
def predictScores(spark: SparkSession,
modelsInputDir: File,
predictionsOutputDir: Option[File],
sites: ParArray[String],
featureData: DataFrame): Unit = {
log.info("Scoring items")
val predictions: Array[DataFrame] = sites.map(target => {
try {
log.info("Scoring for " + target)
log.info("Getting work data for " + target)
val workData: DataFrame = Utils.getWorkData(spark, featureData, target, exists = false)
log.info("Loading model for " + target)
val model = RandomForestRegressionModel.load(
new File(modelsInputDir, target).getAbsolutePath)
log.info("Scoring data for " + target)
val predictions = model
.setPredictionCol(target)
.transform(workData)
.select("id", target)
predictions
} catch {
case unknown: Throwable =>
log.error("Score for " + target + " failed", unknown)
val schema = StructType(Seq(
StructField("id", StringType, nullable = false),
StructField(target, DoubleType, nullable = true)))
spark.createDataFrame(spark.sparkContext.emptyRDD[Row], schema)
}
}).toArray
val predictedScores = predictions.reduce((left, right) => left.join(right, Seq("id"), "outer"))
log.info("Saving predictions")
predictionsOutputDir.foreach(f = o =>
predictedScores.coalesce(1)
.write
.mode(SaveMode.ErrorIfExists)
.option("header", value = true)
.option("compression", "bzip2")
.csv(new File(o, "allPredictions").getAbsolutePath))
}
} 示例3: 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)
} 示例4: BaseTransformerConverter
//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