Class/Object

com.databricks.labs.automl.exploration.tools

PCAReducer

Related Docs: object PCAReducer | package tools

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class PCAReducer extends SparkSessionWrapper

API wrapper for conducting a 2-component PCA for visualizing a data set's feature relationships in a way that can be readily visualized. Provides DataFrame export types for both the raw data with PC1 and PC2 values, as well as the eigen vector values.

Linear Supertypes
SparkSessionWrapper, Serializable, Serializable, AnyRef, Any
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  1. PCAReducer
  2. SparkSessionWrapper
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new PCAReducer()

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Value Members

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

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    Definition Classes
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  2. final def ##(): Int

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

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    Definition Classes
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  4. final val HANDLE_MODE: String("keep")

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  5. final val MINMAX_MAX: Double(1.0)

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  6. final val MINMAX_MIN: Double(-1.0)

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  7. final val PCA_DIM_1_NAME: String("PCA1")

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  8. final val PCA_DIM_2_NAME: String("PCA2")

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  9. final val PCA_INTERNAL_ARRAY_NAME: String("pcaArrayColumn")

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  10. final val SI_NAME: String("_si")

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  11. final val STANDARD_SCALER_MEAN: Boolean(true)

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  12. final val STANDARD_SCALER_STDDEV: Boolean(true)

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  13. final val TEMP_LABEL_NAME: String("tempLabel")

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  14. final val VECTOR_INTERNAL_NAME: String("preScaledFeatures")

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  15. final def asInstanceOf[T0]: T0

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    Definition Classes
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  16. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )

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

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    Definition Classes
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  18. def equals(arg0: Any): Boolean

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    Definition Classes
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  19. def executePipeline(data: DataFrame): PCAReducerResult

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    Main Method for getting a two dimensional PCA analysis for charting of the principle components in a scatter plot and to get the explained variances and the principle components matrix

    Main Method for getting a two dimensional PCA analysis for charting of the principle components in a scatter plot and to get the explained variances and the principle components matrix

    data

    raw dataframe to analyze

    returns

    PCAReducerResult, consisting of the 2 dimensional principle components of the data, the explained variances, and the PC matrix

    Since

    0.7.2

  20. var featureColumn: String

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  21. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

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

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  23. def getFeatureColumn: String

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  24. def getLabelColumn: String

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  25. def getPcaFeatureColumn: String

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  26. def getScalerType: String

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  27. def hashCode(): Int

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    Definition Classes
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  28. final def isInstanceOf[T0]: Boolean

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  29. var labelColumn: String

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

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    Definition Classes
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  31. final def notify(): Unit

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    Definition Classes
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  32. final def notifyAll(): Unit

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  33. var pcaFeatureColumn: String

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  34. var reportMode: String

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  35. lazy val sc: SparkContext

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    Definition Classes
    SparkSessionWrapper

  36. var scalerType: String

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  37. def setFeatureColumn(value: String): PCAReducer.this.type

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  38. def setLabelColumn(value: String): PCAReducer.this.type

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  39. def setPcaFeatureColumn(value: String): PCAReducer.this.type

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  40. def setScalerType(value: String): PCAReducer.this.type

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  41. lazy val spark: SparkSession

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    Definition Classes
    SparkSessionWrapper

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

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    Definition Classes
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  43. def toString(): String

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  44. final def wait(): Unit

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    @throws( ... )

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

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

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  47. def withConvertedColumns: PCAReducer.this.type

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  48. def withMaxAbsScaling: PCAReducer.this.type

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  49. def withMinMaxScaling: PCAReducer.this.type

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  50. def withOriginalColumns: PCAReducer.this.type

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  51. def withStandardScaling: PCAReducer.this.type

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Inherited from SparkSessionWrapper

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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