本文整理汇总了Scala中org.apache.spark.ml.feature.Binarizer类的典型用法代码示例。如果您正苦于以下问题：Scala Binarizer类的具体用法？Scala Binarizer怎么用？Scala Binarizer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Binarizer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: LocalBinarizer
//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.Binarizer
class LocalBinarizer(override val sparkTransformer: Binarizer) extends LocalTransformer[Binarizer] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val treshhold: Double = sparkTransformer.getThreshold
val newData = column.data.map(r => {
if (r.asInstanceOf[Number].doubleValue() > treshhold) 1.0 else 0.0
})
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, newData))
case None => localData
}
}
}
object LocalBinarizer extends LocalModel[Binarizer] {
override def load(metadata: Metadata, data: Map[String, Any]): Binarizer = {
new Binarizer(metadata.uid)
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
.setThreshold(metadata.paramMap("threshold").toString.toDouble)
}
override implicit def getTransformer(transformer: Binarizer): LocalTransformer[Binarizer] = new LocalBinarizer(transformer)
} 示例2: LocalBinarizer
//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.Binarizer
class LocalBinarizer(override val sparkTransformer: Binarizer) extends LocalTransformer[Binarizer] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val treshhold: Double = sparkTransformer.getThreshold
val newData = column.data.map(r => {
if (r.asInstanceOf[Number].doubleValue() > treshhold) 1.0 else 0.0
})
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, newData))
case None => localData
}
}
}
object LocalBinarizer extends LocalModel[Binarizer] {
override def load(metadata: Metadata, data: Map[String, Any]): Binarizer = {
new Binarizer(metadata.uid)
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
.setThreshold(metadata.paramMap("threshold").toString.toDouble)
}
override implicit def getTransformer(transformer: Binarizer): LocalTransformer[Binarizer] = new LocalBinarizer(transformer)
} 示例3: BinarizerJob
//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.Binarizer
import org.apache.spark.sql.SparkSession
object BinarizerJob extends MLMistJob {
def session: SparkSession = SparkSession
.builder()
.appName(context.appName)
.config(context.getConf)
.getOrCreate()
def train(savePath: String): Map[String, Any] = {
val data = Array((0, 0.1), (1, 0.8), (2, 0.2))
val dataFrame = session.createDataFrame(data).toDF("id", "feature")
val binarizer: Binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
.setThreshold(5.0)
val pipeline = new Pipeline().setStages(Array(binarizer))
val model = pipeline.fit(dataFrame)
model.write.overwrite().save(savePath)
Map.empty[String, Any]
}
def serve(modelPath: String, features: List[Double]): Map[String, Any] = {
import LocalPipelineModel._
val pipeline = PipelineLoader.load(modelPath)
val data = LocalData(LocalDataColumn("feature", features))
val result: LocalData = pipeline.transform(data)
Map("result" -> result.select("feature", "binarized_feature").toMapList)
}
}