Predictors

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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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8. def finalize(): Unit

   

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

   

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10. def getPredictor(): Model

    

    Retrieves a single DataRobot model, if it is the only one in the specified classloader

    Retrieves a single DataRobot model, if it is the only one in the specified classloader

    returns

    Spark compatible Scoring Code model

11. def getPredictor(modelId: String): Model

    

    Retrieves a DataRobot model by a modelId

    Retrieves a DataRobot model by a modelId

    modelId

    unique model id for the Scoring Code model. The model should be in a classpath

    returns

    Spark compatible Scoring Code model

12. def getPredictor(filePath: String, modelId: String): Model

    

    Retrieves a DataRobot model from a Scoring Code model jar file on a local file system

    Retrieves a DataRobot model from a Scoring Code model jar file on a local file system

    filePath

    an absolute or relative path to a local file. Example: ../testcases/iris/5e0b41c1a6e3dd003dcfc268.jar

    modelId

    unique model id for the Scoring Code model

    returns

    Spark compatible Scoring Code model

13. def getPredictor(modelBytes: Array[Byte], modelId: String): Model

    

    Retrieves a DataRobot model from a byte array out of a Scoring Code model jar file

    Retrieves a DataRobot model from a byte array out of a Scoring Code model jar file

    modelBytes

    byte array from a Scoring Code model file

    modelId

    unique model id for the Scoring Code model

    returns

    Spark compatible Scoring Code model

14. def getPredictorFromHdfs(hdfsPath: String, spark: SparkSession, modelId: String): Model

    

    Retrieves a DataRobot model from a Scoring Code model jar file on an HDFS file system

    Retrieves a DataRobot model from a Scoring Code model jar file on an HDFS file system

    hdfsPath

    absolute or relative path to a model jar on HDFS

    spark

    SparkSession instance to acquire HDFS configuration from

    modelId

    unique model id for the Scoring Code model

    returns

    Spark compatible Scoring Code model

15. def getPredictorFromServer(url: String, token: String): Model

    

    Downloads a model from a DataRobot instance and creates a Spark compatible Scoring Code model

    Downloads a model from a DataRobot instance and creates a Spark compatible Scoring Code model

    url

    ScoringCode model URL. e.g "https://app.datarobot.com/projects/5e0b2c400334190d3e6bb810/models/5e0b2c974322bb0a3c3354e7/blueprint"

    token

    user access token in a DataRobot instance. e.g "wu74mU2FvdvdZV8gNbTanc-PDrK4El3H"

    returns

    Spark compatible Scoring Code model

16. def getPredictorFromServer(host: String, projectId: String, modelId: String, token: String): Model

    

    Downloads a model from a DataRobot instance and creates a Spark compatible Scoring Code model

    Downloads a model from a DataRobot instance and creates a Spark compatible Scoring Code model

    host

    DataRobot host. e.g "https://staging.datarobot.com"

    projectId

    DataRobot projectId. e.g "5d67c1a53893470196c3ca7e"

    modelId

    unique modelId. e.g "5d67c1f938934701a8c3c9f1"

    token

    user access token in a DataRobot instance. e.g "wu74mU2FvdvdZV8gNbTanc-PDrK4El3H"

    returns

    Spark compatible Scoring Code model

17. def hashCode(): Int

    

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18. final def isInstanceOf[T0]: Boolean

    

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

    

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20. final def notify(): Unit

    

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21. final def notifyAll(): Unit

    

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

    

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23. def toString(): String

    

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24. final def wait(): Unit

    

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

    

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

    

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