Class/Object

com.ibm.sparktc.sparkbench.workload.ml

KMeansWorkload

Related Docs: object KMeansWorkload | package ml

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case class KMeansWorkload(input: Option[String], output: Option[String], saveMode: String, k: Int, seed: Long, maxIterations: Int) extends Workload with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, Workload, AnyRef, Any
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Inherited
  1. KMeansWorkload
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Workload
  7. AnyRef
  8. Any
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Visibility
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Instance Constructors

  1. new KMeansWorkload(input: Option[String], output: Option[String], saveMode: String, k: Int, seed: Long, maxIterations: Int)

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  6. def doWorkload(df: Option[DataFrame], spark: SparkSession): DataFrame

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    Actually run the workload.

    Actually run the workload. Takes an optional DataFrame as input if the user supplies an inputDir, and returns the generated results DataFrame.

    Definition Classes
    KMeansWorkload → Workload

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

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    Definition Classes
    AnyRef

  8. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    AnyRef → Any

  10. val input: Option[String]

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    Definition Classes
    KMeansWorkload → Workload

  11. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  12. val k: Int

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  13. def loadToCache(df: DataFrame, spark: SparkSession): (Long, RDD[Vector])

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  14. val maxIterations: Int

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

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    Definition Classes
    AnyRef

  16. final def notify(): Unit

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    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  18. val output: Option[String]

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    Definition Classes
    KMeansWorkload → Workload

  19. def reconcileSchema(dataFrame: DataFrame): DataFrame

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    Validate that the data set has a correct schema and fix if necessary.

    Validate that the data set has a correct schema and fix if necessary. This is to solve issues such as the KMeans load-from-disk pathway returning a DataFrame with all the rows as StringType instead of DoubleType.

    Definition Classes
    Workload

  20. def run(spark: SparkSession): DataFrame

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    Definition Classes
    Workload

  21. def save(ds: RDD[Vector], model: KMeansModel, spark: SparkSession): Long

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  22. val saveMode: String

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    Definition Classes
    KMeansWorkload → Workload

  23. val seed: Long

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

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    Definition Classes
    AnyRef

  25. def test(model: KMeansModel, df: RDD[Vector], spark: SparkSession): (Long, Double)

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  26. def toMap: Map[String, Any]

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    Definition Classes
    Workload

  27. def train(df: RDD[Vector], spark: SparkSession): (Long, KMeansModel)

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  28. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Workload

Inherited from AnyRef

Inherited from Any

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