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.
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- @Unstable() @Since( "2.4.0" )
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- ReadWrite.scala
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abstract
def
format(): String
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.
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- @Since( "2.4.0" )
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2.4.0
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abstract
def
stageName(): String
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.
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- @Since( "2.4.0" )
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2.4.0
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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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- MLWriterFormat
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- @Since( "2.4.0" )
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