本文整理汇总了Scala中org.apache.spark.ml.feature.PolynomialExpansion类的典型用法代码示例。如果您正苦于以下问题：Scala PolynomialExpansion类的具体用法？Scala PolynomialExpansion怎么用？Scala PolynomialExpansion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PolynomialExpansion类的3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: LocalPolynomialExpansion
//设置package包名称以及导入依赖的类
package io.hydrosphere.spark_ml_serving.preprocessors
import io.hydrosphere.spark_ml_serving._
import org.apache.spark.ml.feature.PolynomialExpansion
import org.apache.spark.ml.linalg.{Vector, Vectors}
class LocalPolynomialExpansion(override val sparkTransformer: PolynomialExpansion) extends LocalTransformer[PolynomialExpansion] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val method = classOf[PolynomialExpansion].getMethod("createTransformFunc")
val newData = column.data.map(r => {
val row = r.asInstanceOf[List[Any]].map(_.toString.toDouble).toArray
val vector: Vector = Vectors.dense(row)
method.invoke(sparkTransformer).asInstanceOf[Vector => Vector](vector)
})
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, newData))
case None => localData
}
}
}
object LocalPolynomialExpansion extends LocalModel[PolynomialExpansion] {
override def load(metadata: Metadata, data: Map[String, Any]): PolynomialExpansion = {
new PolynomialExpansion(metadata.uid)
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
.setDegree(metadata.paramMap("degree").asInstanceOf[Number].intValue())
}
override implicit def getTransformer(transformer: PolynomialExpansion): LocalTransformer[PolynomialExpansion] = new LocalPolynomialExpansion(transformer)
} 示例2: LocalPolynomialExpansion
//设置package包名称以及导入依赖的类
package io.hydrosphere.mist.api.ml.preprocessors
import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.feature.PolynomialExpansion
import org.apache.spark.ml.linalg.{Vector, Vectors}
class LocalPolynomialExpansion(override val sparkTransformer: PolynomialExpansion) extends LocalTransformer[PolynomialExpansion] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val method = classOf[PolynomialExpansion].getMethod("createTransformFunc")
val newData = column.data.map(r => {
val row = r.asInstanceOf[List[Any]].map(_.toString.toDouble).toArray
val vector: Vector = Vectors.dense(row)
method.invoke(sparkTransformer).asInstanceOf[Vector => Vector](vector)
})
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, newData))
case None => localData
}
}
}
object LocalPolynomialExpansion extends LocalModel[PolynomialExpansion] {
override def load(metadata: Metadata, data: Map[String, Any]): PolynomialExpansion = {
new PolynomialExpansion(metadata.uid)
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
.setDegree(metadata.paramMap("degree").asInstanceOf[Number].intValue())
}
override implicit def getTransformer(transformer: PolynomialExpansion): LocalTransformer[PolynomialExpansion] = new LocalPolynomialExpansion(transformer)
} 示例3: PolynomialExpansionJob
//设置package包名称以及导入依赖的类
import io.hydrosphere.mist.api._
import io.hydrosphere.mist.api.ml._
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.PolynomialExpansion
import org.apache.spark.ml.linalg.{DenseVector, Vectors}
import org.apache.spark.sql.SparkSession
object PolynomialExpansionJob extends MLMistJob {
def session: SparkSession = SparkSession
.builder()
.appName(context.appName)
.config(context.getConf)
.getOrCreate()
def train(savePath: String): Map[String, Any] = {
val data = Array(
Vectors.dense(2.0, 1.0),
Vectors.dense(0.0, 0.0),
Vectors.dense(3.0, -1.0)
)
val df = session.createDataFrame(data.map(Tuple1.apply)).toDF("features")
val polyExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
.setDegree(3)
val pipeline = new Pipeline().setStages(Array(polyExpansion))
val model = pipeline.fit(df)
model.write.overwrite().save(savePath)
Map.empty[String, Any]
}
def serve(modelPath: String, features: List[List[Double]]): Map[String, Any] = {
import LocalPipelineModel._
val pipeline = PipelineLoader.load(modelPath)
val data = LocalData(LocalDataColumn("features", features))
val result = pipeline.transform(data)
val column = result.column("polyFeatures").map(_.asInstanceOf[LocalDataColumn[DenseVector]])
val response = column.map(c => c.data.map(_.toArray))
Map("result" -> response)
}
}