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package root

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root
package org

Definition Classes
root
package apache

Definition Classes
org
package spark
Core Spark functionality.
Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.
In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.
Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.
Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.
Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

Definition Classes
apache
package ml
DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.
DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

Definition Classes
spark
package util

Definition Classes
ml
DefaultParamsReadable
DefaultParamsWritable
GeneralMLWritable
GeneralMLWriter
Identifiable
MLFormatRegister
MLReadable
MLReader
MLWritable
MLWriter
MLWriterFormat

org.apache.spark.ml.util

MLWriterFormat

trait MLWriterFormat extends AnyRef

Abstract class to be implemented by objects that provide ML exportability.

A new instance of this class will be instantiated each time a save call is made.

Must have a valid zero argument constructor which will be called to instantiate.

Annotations: @Unstable() @Since( "2.4.0" )
Since: 2.4.0

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MLFormatRegister

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abstract def write(path: String, session: SparkSession, optionMap: Map[String, String], stage: PipelineStage): Unit
Function to write the provided pipeline stage out.
Function to write the provided pipeline stage out.
path
The path to write the result out to.
session
SparkSession associated with the write request.
optionMap
User provided options stored as strings.
stage
The pipeline stage to be saved.

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@Since( "2.4.0" )

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MLWriterFormat

trait MLWriterFormat extends AnyRef

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MLWriterFormat 

trait MLWriterFormat extends AnyRef

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MLWriterFormat