本文整理汇总了Scala中org.apache.spark.ml.regression.GBTRegressor类的典型用法代码示例。如果您正苦于以下问题：Scala GBTRegressor类的具体用法？Scala GBTRegressor怎么用？Scala GBTRegressor使用的例子？那么, 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GBTRegressor类的1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: GBTRegressionJob
//设置package包名称以及导入依赖的类
import GaussianMixtureJob.context
import io.hydrosphere.mist.api._
import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.VectorIndexer
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.regression.GBTRegressor
import org.apache.spark.sql.SparkSession
object GBTRegressionJob extends MLMistJob {
def session: SparkSession = SparkSession
.builder()
.appName(context.appName)
.config(context.getConf)
.getOrCreate()
def constructVector(params: Map[String, Any]): Vector = {
Vectors.sparse(
params("size").asInstanceOf[Int],
params("indices").asInstanceOf[List[Int]].toArray[Int],
params("values").asInstanceOf[List[Int]].map(_.toDouble).toArray[Double]
)
}
def train(savePath: String): Map[String, Any] = {
val data = session.read.format("libsvm").load("examples/resources/sample_libsvm_data.txt")
val gbt = new GBTRegressor()
.setLabelCol("label")
.setFeaturesCol("features")
.setMaxIter(10)
val pipeline = new Pipeline()
.setStages(Array(gbt))
val model = pipeline.fit(data)
model.write.overwrite().save(savePath)
Map.empty[String, Any]
}
def serve(modelPath: String, features: List[Map[String, Any]]): Map[String, Any] = {
import LocalPipelineModel._
val pipeline = PipelineLoader.load(modelPath)
val data = LocalData(LocalDataColumn("features", features.map(constructVector)))
val result = pipeline.transform(data)
val response = result.select("prediction").toMapList.map(rowMap => {
val mapped = rowMap("prediction").asInstanceOf[Double]
rowMap + ("prediction" -> mapped)
})
Map("result" -> response)
}
}