Trait

com.databricks.labs.automl.model.tools.structures

ModelConfigGenerators

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trait ModelConfigGenerators extends SeedGenerator

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Type Members

  1. case class MLPCModelingConfig(layerCount: Int, layers: Array[Int], maxIter: Int, solver: String, stepSize: Double, tolerance: Double, hiddenLayerSizeAdjust: Int) extends Product with Serializable

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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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    protected[java.lang]
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    @throws( ... )
  6. def convertGBTResultToConfig(predictionDataFrame: DataFrame): Array[GBTConfig]

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  7. def convertLightGBMResultToConfig(predictionDataFrame: DataFrame): Array[LightGBMConfig]

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  8. def convertLinearRegressionResultToConfig(predictionDataFrame: DataFrame): Array[LinearRegressionConfig]

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  9. def convertLogisticRegressionResultToConfig(predictionDataFrame: DataFrame): Array[LogisticRegressionConfig]

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  10. def convertMLPCResultToConfig(predictionDataFrame: DataFrame, inputFeatureSize: Int, distinctClasses: Int): Array[MLPCConfig]

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  11. def convertRandomForestResultToConfig(predictionDataFrame: DataFrame): Array[RandomForestConfig]

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    Helper method for converting a Dataframe of predicted hyper parameters into configurations that can be used by models (for post-run hyper parameter optimization)

    Helper method for converting a Dataframe of predicted hyper parameters into configurations that can be used by models (for post-run hyper parameter optimization)

    predictionDataFrame

    The predicted sets of highest probability hyper parameter collections

    returns

    An Array of RandomForest Configurations to be used in generating model runs.

  12. def convertSVMResultToConfig(predictionDataFrame: DataFrame): Array[SVMConfig]

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  13. def convertToLog(minScale: Double, maxScale: Double, value: Double): Double

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    Definition Classes
    SeedGenerator
  14. def convertTreesResultToConfig(predictionDataFrame: DataFrame): Array[TreesConfig]

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  15. def convertXGBoostResultToConfig(predictionDataFrame: DataFrame): Array[XGBoostConfig]

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

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  17. def equals(arg0: Any): Boolean

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  18. def extractContinuousBoundaries(parameter: (Double, Double)): NumericBoundaries

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    Attributes
    protected[com.databricks.labs.automl.model.tools]
    Definition Classes
    SeedGenerator
  19. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  20. def gbtConfigGenerator(gbtPermutationCollection: GBTPermutationCollection): Array[GBTConfig]

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  21. def gbtNumericArrayGenerator(config: PermutationConfiguration): GBTNumericArrays

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  22. def gbtPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[GBTConfig]

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  23. def generateArraySpace(layerBoundaryLow: Int, layerBoundaryHigh: Int, hiddenBoundaryLow: Int, hiddenBoundaryHigh: Int, inputFeatureSize: Int, distinctClasses: Int, generatorCount: Int): Array[Array[Int]]

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    Definition Classes
    SeedGenerator
  24. def generateLinearIntSpace(boundaries: NumericBoundaries, generatorCount: Int): Array[Double]

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    Definition Classes
    SeedGenerator
  25. def generateLinearSpace(boundaries: NumericBoundaries, generatorCount: Int): Array[Double]

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    Definition Classes
    SeedGenerator
  26. def generateLogSpace(boundaries: NumericBoundaries, generatorCount: Int): Array[Double]

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    Definition Classes
    SeedGenerator
  27. def getCaseClassNames[T](implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[T]): List[String]

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    Helper method for reading a case class definition, getting the defined names of each key, and returning them as an iterable list.

    Helper method for reading a case class definition, getting the defined names of each key, and returning them as an iterable list.

    T

    The class type as derived through reflection

    returns

    The List of all case class member names

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

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  29. def getNumberOfElements(numericBoundaries: Map[String, (Double, Double)]): Int

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    Definition Classes
    SeedGenerator
  30. def getPermutationCounts(targetIterations: Int, numberOfElements: Int): Int

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    SeedGenerator
  31. def hashCode(): Int

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  32. final def isInstanceOf[T0]: Boolean

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  33. def lightGBMConfigGenerator(lightGBMPermutationCollection: LightGBMPermutationCollection): Array[LightGBMConfig]

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  34. def lightGBMNumericArrayGenerator(config: PermutationConfiguration): LightGBMNumericArrays

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  35. def lightGBMPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[LightGBMConfig]

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  36. def linearRegressionConfigGenerator(linearRegressionPermutationCollection: LinearRegressionPermutationCollection): Array[LinearRegressionConfig]

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  37. def linearRegressionNumericArrayGenerator(config: PermutationConfiguration): LinearRegressionNumericArrays

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  38. def linearRegressionPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[LinearRegressionConfig]

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  39. def logisticRegressionConfigGenerator(logisticRegressionPermutationCollection: LogisticRegressionPermutationCollection): Array[LogisticRegressionConfig]

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  40. def logisticRegressionNumericArrayGenerator(config: PermutationConfiguration): LogisticRegressionNumericArrays

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  41. def logisticRegressionPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[LogisticRegressionConfig]

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  42. def mlpcConfigGenerator(mlpcPermutationCollection: MLPCPermutationCollection): Array[MLPCModelingConfig]

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  43. def mlpcLayersExtractor(layers: Array[Int]): (Int, Int)

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    Attributes
    protected[com.databricks.labs.automl.model.tools]
    Definition Classes
    SeedGenerator
  44. def mlpcNumericArrayGenerator(config: MLPCPermutationConfiguration): MLPCNumericArrays

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  45. def mlpcPermutationGenerator(config: MLPCPermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[MLPCModelingConfig]

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  46. def mlpcRandomIndexSelection(numericArrays: MLPCNumericArrays): MLPCArrayCollection

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    protected[com.databricks.labs.automl.model.tools]
    Definition Classes
    SeedGenerator
  47. def mlpcStaticIndexSelection(numericArrays: MLPCNumericArrays): MLPCArrayCollection

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    protected[com.databricks.labs.automl.model.tools]
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    SeedGenerator
  48. final def ne(arg0: AnyRef): Boolean

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  49. final def notify(): Unit

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  50. final def notifyAll(): Unit

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  51. def randomForestConfigGenerator(randomForestPermutationCollection: RandomForestPermutationCollection): Array[RandomForestConfig]

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    Method for taking a collection of permutations generated per each hyper parameter and converting them into a collection that can be used to execute models by building out all possible permutations of the generated hyper parameter collections.

    Method for taking a collection of permutations generated per each hyper parameter and converting them into a collection that can be used to execute models by building out all possible permutations of the generated hyper parameter collections.

    randomForestPermutationCollection

    The Array of values generated for possible hyper parameters for the permutation collection creation

    returns

    Array of Random Forest configurations based on permutations of each value within the arrays supplied.

  52. def randomForestNumericArrayGenerator(config: PermutationConfiguration): RandomForestNumericArrays

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    Method for generating linear and log spaces for potential hyper parameter values for the model

    Method for generating linear and log spaces for potential hyper parameter values for the model

    config

    Configuration value for the generation of permutation arrays

    returns

    Arrays for all numeric parameters that will be generated for input into the permutation generator

    Attributes
    protected[com.databricks.labs.automl.model.tools]
  53. def randomForestPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[RandomForestConfig]

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    Main accessor for generating permutations for a RandomForest Model

    Main accessor for generating permutations for a RandomForest Model

    config

    Configuration for holding the numeber of permutations to generate and the boundaries of the search space

    countTarget

    Total maximum count of permutations to return

    seed

    Seed for determining the random sample of permutations that are generated due to the sheer count of permutations that are generated to search the space effectively.

    returns

    An Array of RandomForest Configurations to be used in generating model runs.

  54. def randomIndexSelection(numericArrays: Array[Array[Double]]): NumericArrayCollection

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    Attributes
    protected[com.databricks.labs.automl.model.tools]
    Definition Classes
    SeedGenerator
  55. def randomSampleArray[T](hyperParameterArray: Array[T], sampleCount: Int, seed: Long = 42L)(implicit arg0: ClassTag[T]): Array[T]

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    Attributes
    protected[com.databricks.labs.automl.model.tools]
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  56. def selectCoinFlip(currentIterator: Int): Boolean

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    protected[com.databricks.labs.automl.model.tools]
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    SeedGenerator
  57. def selectStringIndex(availableParams: List[String], currentIterator: Int): StringSelectionReturn

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    Attributes
    protected[com.databricks.labs.automl.model.tools]
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    SeedGenerator
  58. def staticIndexSelection(numericArrays: Array[Array[Double]]): NumericArrayCollection

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    Attributes
    protected[com.databricks.labs.automl.model.tools]
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    SeedGenerator
  59. def stringBoundaryPermutationCalculator(stringBoundaries: Map[String, List[String]]): Int

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    Calculates the number of possible additional permutations to be added to the search space for string values

    Calculates the number of possible additional permutations to be added to the search space for string values

    stringBoundaries

    The string boundary payload for a modeling family

    returns

    Int representing any additional permutations on the numeric body that will need to be generated in order to attempt to reach the target unique hyperparameter search space

    Attributes
    protected[com.databricks.labs.automl.model.tools]
    Definition Classes
    SeedGenerator
  60. def svmConfigGenerator(svmPermutationCollection: SVMPermutationCollection): Array[SVMConfig]

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  61. def svmNumericArrayGenerator(config: PermutationConfiguration): SVMNumericArrays

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  62. def svmPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[SVMConfig]

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

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  64. def toString(): String

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  65. def treesConfigGenerator(treesPermutationCollection: TreesPermutationCollection): Array[TreesConfig]

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  66. def treesNumericArrayGenerator(config: PermutationConfiguration): TreesNumericArrays

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  67. def treesPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[TreesConfig]

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  68. final def wait(): Unit

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

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

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  71. def xgboostConfigGenerator(xgboostPermutationCollection: XGBoostPermutationCollection): Array[XGBoostConfig]

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  72. def xgboostNumericArrayGenerator(config: PermutationConfiguration): XGBoostNumericArrays

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  73. def xgboostPermutationGenerator(config: PermutationConfiguration, countTarget: Int, seed: Long = 42L): Array[XGBoostConfig]

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Inherited from SeedGenerator

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