Package org.apache.spark.ml.regression
Class InternalLinearRegressionModelWriter
Object
org.apache.spark.ml.regression.InternalLinearRegressionModelWriter
- All Implemented Interfaces:
MLFormatRegister
,MLWriterFormat
public class InternalLinearRegressionModelWriter
extends Object
implements MLWriterFormat, MLFormatRegister
A writer for LinearRegression that handles the "internal" (or default) format
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionformat()
The string that represents the format that this format provider uses.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 java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.apache.spark.ml.util.MLFormatRegister
shortName
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Constructor Details
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InternalLinearRegressionModelWriter
public InternalLinearRegressionModelWriter()
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Method Details
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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:
Indicates that this format is capable of saving a pmml model.override def format(): String = "pmml"
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 interfaceMLFormatRegister
- Returns:
- (undocumented)
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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:
Indicates that this format is capable of saving Spark's own PMML model.override def stageName(): String = "org.apache.spark.ml.regression.LinearRegressionModel"
Format discovery is done using a ServiceLoader so make sure to list your format in META-INF/services.
- Specified by:
stageName
in interfaceMLFormatRegister
- Returns:
- (undocumented)
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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 interfaceMLWriterFormat
- 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.
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