org.apache.spark.ml.clustering

Class InternalKMeansModelWriter

Object

org.apache.spark.ml.clustering.InternalKMeansModelWriter

All Implemented Interfaces:

MLFormatRegister, MLWriterFormat

public class InternalKMeansModelWriter
extends Object
implements MLWriterFormat, MLFormatRegister

A writer for KMeans that handles the "internal" (or default) format

Constructor Summary

Constructors

Constructor and Description
InternalKMeansModelWriter()

Method Summary

Modifier and Type	Method and Description
String	format() The string that represents the format that this format provider uses.
String	stageName() The string that represents the stage type that this writer supports.
void	write(String path, SparkSession sparkSession, scala.collection.mutable.Map<String,String> optionMap, PipelineStage stage) Function to write the provided pipeline stage out.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.util.MLFormatRegister

shortName

Constructor Detail

InternalKMeansModelWriter

public InternalKMeansModelWriter()

Method Detail

format

public String format()

Description copied from interface: MLFormatRegister

The string that represents the format that this format provider uses. This is, along with stageName, is overridden by children to provide a nice alias for the writer. For example:

   override def format(): String =
       "pmml"
 

Indicates that this format is capable of saving a pmml model.

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

Format discovery is done using a ServiceLoader so make sure to list your format in META-INF/services.

Specified by:

format in interface MLFormatRegister

Returns:

(undocumented)

stageName

public String stageName()

Description copied from interface: MLFormatRegister

The string that represents the stage type that this writer supports. This is, along with format, is overridden by children to provide a nice alias for the writer. For example:

   override def stageName(): String =
       "org.apache.spark.ml.regression.LinearRegressionModel"
 

Indicates that this format is capable of saving Spark's own PMML model.

Format discovery is done using a ServiceLoader so make sure to list your format in META-INF/services.

Specified by:

stageName in interface MLFormatRegister

Returns:

(undocumented)

write

public void write(String path,
                  SparkSession sparkSession,
                  scala.collection.mutable.Map<String,String> optionMap,
                  PipelineStage stage)

Description copied from interface: MLWriterFormat

Function to write the provided pipeline stage out.

Specified by:

write in interface MLWriterFormat

Parameters:

path - The path to write the result out to.

sparkSession - SparkSession associated with the write request.

optionMap - User provided options stored as strings.

stage - The pipeline stage to be saved.