本文整理汇总了Scala中org.apache.spark.ml.feature.NGram类的典型用法代码示例。如果您正苦于以下问题：Scala NGram类的具体用法？Scala NGram怎么用？Scala NGram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NGram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: LocalNGram
//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.NGram
class LocalNGram(override val sparkTransformer: NGram) extends LocalTransformer[NGram] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val method = classOf[NGram].getMethod("createTransformFunc")
val f = method.invoke(sparkTransformer).asInstanceOf[Seq[String] => Seq[String]]
val data = column.data.head.asInstanceOf[Seq[String]]
val newData = f.apply(data).toList
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, List(newData)))
case None => localData
}
}
}
object LocalNGram extends LocalModel[NGram] {
override def load(metadata: Metadata, data: Map[String, Any]): NGram = {
new NGram(metadata.uid)
.setN(metadata.paramMap("n").asInstanceOf[Number].intValue())
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
}
override implicit def getTransformer(transformer: NGram): LocalTransformer[NGram] = new LocalNGram(transformer)
} 示例2: LocalNGram
//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.NGram
class LocalNGram(override val sparkTransformer: NGram) extends LocalTransformer[NGram] {
override def transform(localData: LocalData): LocalData = {
localData.column(sparkTransformer.getInputCol) match {
case Some(column) =>
val method = classOf[NGram].getMethod("createTransformFunc")
val f = method.invoke(sparkTransformer).asInstanceOf[Seq[String] => Seq[String]]
val data = column.data.head.asInstanceOf[Seq[String]]
val newData = f.apply(data).toList
localData.withColumn(LocalDataColumn(sparkTransformer.getOutputCol, List(newData)))
case None => localData
}
}
}
object LocalNGram extends LocalModel[NGram] {
override def load(metadata: Metadata, data: Map[String, Any]): NGram = {
new NGram(metadata.uid)
.setN(metadata.paramMap("n").asInstanceOf[Number].intValue())
.setInputCol(metadata.paramMap("inputCol").asInstanceOf[String])
.setOutputCol(metadata.paramMap("outputCol").asInstanceOf[String])
}
override implicit def getTransformer(transformer: NGram): LocalTransformer[NGram] = new LocalNGram(transformer)
} 示例3: NgramJob
//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.NGram
import org.apache.spark.sql.SparkSession
object NgramJob 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, Array("Provectus", "is", "such", "a", "cool", "company")),
(1, Array("Big", "data", "rules", "the", "world")),
(2, Array("Cloud", "solutions", "are", "our", "future"))
)).toDF("id", "words")
val ngram = new NGram().setN(2).setInputCol("words").setOutputCol("ngrams")
val pipeline = new Pipeline().setStages(Array(ngram))
val model = pipeline.fit(df)
model.write.overwrite().save(savePath)
Map.empty[String, Any]
}
def serve(modelPath: String, features: List[String]): Map[String, Any] = {
import LocalPipelineModel._
val pipeline = PipelineLoader.load(modelPath)
val data = LocalData(
LocalDataColumn("words", List(features))
)
val result: LocalData = pipeline.transform(data)
Map("result" -> result.select("words", "ngrams").toMapList)
}
}