本文整理汇总了Scala中org.apache.spark.ml.feature.Normalizer类的典型用法代码示例。如果您正苦于以下问题：Scala Normalizer类的具体用法？Scala Normalizer怎么用？Scala Normalizer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Normalizer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: LocalNormalizer
//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.Normalizer
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
class LocalNormalizer(override val sparkTransformer: Normalizer) extends LocalTransformer[Normalizer] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val method = classOf[Normalizer].getMethod("createTransformFunc")
val newData = column.data.map(r => {
val vector = r match {
case x: List[Any] => Vectors.dense(x.map(_.toString.toDouble).toArray)
case x: SparseVector => x
case x: DenseVector => x
case unknown =>
throw new IllegalArgumentException(s"Unknown data type for LocalMaxAbsScaler: ${unknown.getClass}")
}
method.invoke(sparkTransformer).asInstanceOf[Vector => Vector](vector)
})
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, newData))
case None => localData
}
}
}
object LocalNormalizer extends LocalModel[Normalizer] {
override def load(metadata: Metadata, data: Map[String, Any]): Normalizer = {
new Normalizer(metadata.uid)
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
.setP(metadata.paramMap("p").toString.toDouble)
}
override implicit def getTransformer(transformer: Normalizer): LocalTransformer[Normalizer] = new LocalNormalizer(transformer)
} 示例2: LocalNormalizer
//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.Normalizer
import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
class LocalNormalizer(override val sparkTransformer: Normalizer) extends LocalTransformer[Normalizer] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val method = classOf[Normalizer].getMethod("createTransformFunc")
val newData = column.data.map(r => {
val vector = r match {
case x: List[Any] => Vectors.dense(x.map(_.toString.toDouble).toArray)
case x: SparseVector => x
case x: DenseVector => x
case unknown =>
throw new IllegalArgumentException(s"Unknown data type for LocalMaxAbsScaler: ${unknown.getClass}")
}
method.invoke(sparkTransformer).asInstanceOf[Vector => Vector](vector)
})
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, newData))
case None => localData
}
}
}
object LocalNormalizer extends LocalModel[Normalizer] {
override def load(metadata: Metadata, data: Map[String, Any]): Normalizer = {
new Normalizer(metadata.uid)
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
.setP(metadata.paramMap("p").toString.toDouble)
}
override implicit def getTransformer(transformer: Normalizer): LocalTransformer[Normalizer] = new LocalNormalizer(transformer)
} 示例3: NormalizerJob
//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.linalg.{Vector => LVector}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.Normalizer
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession
object NormalizerJob extends MLMistJob{
def session: SparkSession = SparkSession
.builder()
.appName(context.appName)
.config(context.getConf)
.getOrCreate()
def train(savePath: String): Map[String, Any] = {
val df = session.createDataFrame(Seq(
(0, Vectors.dense(1.0, 0.5, -1.0)),
(1, Vectors.dense(2.0, 1.0, 1.0)),
(2, Vectors.dense(4.0, 10.0, 2.0))
)).toDF("id", "features")
val normalizer = new Normalizer()
.setInputCol("features")
.setOutputCol("normFeatures")
.setP(1.0)
val pipeline = new Pipeline().setStages(Array(normalizer))
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 response = pipeline.transform(data).toMapList.map(rowMap => {
val conv = rowMap("normFeatures").asInstanceOf[LVector].toArray
rowMap + ("normFeatures" -> conv)
})
Map("result" -> response)
}
}