Trait

org.apache.spark.ml.util

MLFormatRegister

Related Doc: package util

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trait MLFormatRegister extends MLWriterFormat

ML export formats for should implement this trait so that users can specify a shortname rather than the fully qualified class name of the exporter.

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

Annotations
@Unstable() @Since( "2.4.0" )
Source
ReadWrite.scala
Since

2.4.0

Linear Supertypes
MLWriterFormat, AnyRef, Any
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Abstract Value Members

  1. abstract def format(): String

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    The string that represents the format that this format provider uses.

    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.

    Annotations
    @Since( "2.4.0" )
    Since

    2.4.0

  2. abstract def stageName(): String

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    The string that represents the stage type that this writer supports.

    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.

    Annotations
    @Since( "2.4.0" )
    Since

    2.4.0

  3. abstract def write(path: String, session: SparkSession, optionMap: Map[String, String], stage: PipelineStage): Unit

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    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.

    Definition Classes
    MLWriterFormat
    Annotations
    @Since( "2.4.0" )

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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  9. final def getClass(): Class[_]

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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  12. final def ne(arg0: AnyRef): Boolean

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. final def synchronized[T0](arg0: ⇒ T0): T0

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  16. def toString(): String

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  17. final def wait(): Unit

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  18. final def wait(arg0: Long, arg1: Int): Unit

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  19. final def wait(arg0: Long): Unit

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Inherited from MLWriterFormat

Inherited from AnyRef

Inherited from Any

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