Class

com.salesforce.op.local

OpWorkflowRunnerLocal

Related Doc: package local

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class OpWorkflowRunnerLocal extends Serializable

A class for running TransmogrifAI Workflow without Spark.

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Instance Constructors

  1. new OpWorkflowRunnerLocal(workflow: OpWorkflow)

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    workflow

    the workflow that you want to run (Note: the workflow should have the resultFeatures set)

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. def score(params: OpParams): ScoreFunction

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    Load the model & prepare a score function for local scoring

    Load the model & prepare a score function for local scoring

    Note: since we use Spark native org.apache.spark.ml.util.MLWriter interface to load stages the Spark session is being created internally. So if you would not like to have an open SparkSession please make sure to stop it after creating the score function:

    val scoreFunction = new OpWorkflowRunnerLocal(workflow).score(params) // stop the session after creating the scoreFunction if needed SparkSession.builder().getOrCreate().stop()

    params

    params to use during scoring

    returns

    score function for local scoring

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

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  17. def toString(): String

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  18. final def wait(): Unit

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

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

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  21. val workflow: OpWorkflow

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    the workflow that you want to run (Note: the workflow should have the resultFeatures set)

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