Class/Object

com.ibm.sparktc.sparkbench.workload.ml

LogisticRegressionWorkload

Related Docs: object LogisticRegressionWorkload | package ml

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case class LogisticRegressionWorkload(input: Option[String], output: Option[String], saveMode: String, testFile: String, numPartitions: Int, cacheEnabled: Boolean) extends Workload with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, Workload, AnyRef, Any
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Inherited
  1. LogisticRegressionWorkload
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Workload
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new LogisticRegressionWorkload(input: Option[String], output: Option[String], saveMode: String, testFile: String, numPartitions: Int, cacheEnabled: Boolean)

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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. val cacheEnabled: Boolean

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  6. def clone(): AnyRef

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

  7. 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
    LogisticRegressionWorkload → Workload

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

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    Definition Classes
    AnyRef

  9. def finalize(): Unit

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

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

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    Definition Classes
    AnyRef → Any

  11. val input: Option[String]

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    Definition Classes
    LogisticRegressionWorkload → Workload

  12. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    AnyRef

  14. final def notify(): Unit

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    Definition Classes
    AnyRef

  15. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  16. val numPartitions: Int

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  17. val output: Option[String]

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    Definition Classes
    LogisticRegressionWorkload → Workload

  18. 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

  19. def run(spark: SparkSession): DataFrame

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    Definition Classes
    Workload

  20. val saveMode: String

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    Definition Classes
    LogisticRegressionWorkload → Workload

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

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    Definition Classes
    AnyRef

  22. val testFile: String

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  23. def toMap: Map[String, Any]

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    Definition Classes
    Workload

  24. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  26. 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

Ungrouped