Trait

org.incal.spark_ml

SparkMLService

Related Doc: package spark_ml

Permalink

trait SparkMLService extends MLBase

Linear Supertypes
MLBase, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. SparkMLService
  2. MLBase
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Type Members

  1. case class EvaluatorWrapper[Q](metric: Q, evaluator: Evaluator) extends Product with Serializable

    Permalink
    Definition Classes
    MLBase

Abstract Value Members

  1. abstract val rcStatesWindowFactory: RCStatesWindowFactory

    Permalink

  2. abstract val setting: SparkMLServiceSetting

    Permalink

Concrete Value Members

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

    Permalink
    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any

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

    Permalink
    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any

  5. lazy val binaryClassifierInputName: String

    Permalink
    Attributes
    protected

  6. lazy val binaryPredictionVectorizer: IndexVectorizer

    Permalink
    Attributes
    protected

  7. val classificationEvaluators: Seq[EvaluatorWrapper[models.classification.ClassificationEvalMetric.Value]]

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  8. def classificationStages(setting: ClassificationLearningSetting): Seq[() ⇒ PipelineStage]

    Permalink
    Attributes
    protected

  9. def classify(df: DataFrame, classifier: Classifier, setting: ClassificationLearningSetting = ClassificationLearningSetting(), replicationDf: Option[DataFrame] = None): Future[ClassificationResultsHolder]

    Permalink

  10. def classifyAux(df: DataFrame, replicationDf: Option[DataFrame], classifier: Classifier, setting: ClassificationLearningSetting)(splitDataSet: (DataFrame) ⇒ (DataFrame, DataFrame), calcTestPredictions: (Transformer, Dataset[_], Dataset[_]) ⇒ DataFrame, crossValidatorCreatorWithProcessor: Option[CrossValidatorCreatorWithProcessor], initStages: Seq[() ⇒ PipelineStage], preTrainingStages: Seq[() ⇒ PipelineStage], paramGrids: Traversable[ParamGrid[_]] = Nil, kernelSize: (Int) ⇒ Int = identity): Future[ClassificationResultsHolder]

    Permalink
    Attributes
    protected

  11. def classifyTimeSeries(df: DataFrame, classifier: Classifier, setting: TemporalClassificationLearningSetting, groupIdColumnName: Option[String] = None, replicationDf: Option[DataFrame] = None): Future[ClassificationResultsHolder]

    Permalink

  12. def classifyWithStages(df: DataFrame, replicationDf: Option[DataFrame], classifier: Classifier, setting: ClassificationLearningSetting)(splitDataset: (DataFrame) ⇒ (DataFrame, DataFrame), calcTestPredictions: (Transformer, Dataset[_], Dataset[_]) ⇒ DataFrame, crossValidatorCreatorWithProcessor: Option[CrossValidatorCreatorWithProcessor], stages: Seq[() ⇒ PipelineStage], paramGrids: Traversable[ParamGrid[_]], kernelSize: (Int) ⇒ Int): Future[ClassificationResultsHolder]

    Permalink
    Attributes
    protected

  13. def clone(): AnyRef

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

  14. def cluster(df: DataFrame, mlModel: Clustering, featuresNormalizationType: Option[models.VectorScalerType.Value] = None, pcaDim: Option[Int] = None): DataFrame

    Permalink

  15. def createTimeSeriesStagesWithParamGrids(groupIdColumnName: Option[String], setting: TemporalLearningSetting): (Seq[() ⇒ PipelineStage], Traversable[ParamGrid[_]], (Int) ⇒ Int)

    Permalink
    Attributes
    protected

  16. val defaultClassificationCrossValidationEvalMetric: models.classification.ClassificationEvalMetric.Value

    Permalink
    Attributes
    protected

  17. val defaultRegressionCrossValidationEvalMetric: models.regression.RegressionEvalMetric.Value

    Permalink
    Attributes
    protected

  18. val defaultTrainingTestingSplitRatio: Double

    Permalink
    Attributes
    protected

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

    Permalink
    Definition Classes
    AnyRef

  20. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any

  21. def evaluate[Q](evaluatorWrappers: Traversable[EvaluatorWrapper[Q]], trainPredictions: DataFrame, testPredictions: Seq[DataFrame]): Traversable[(Q, Double, Seq[Double])]

    Permalink
    Attributes
    protected

  22. def finalize(): Unit

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

  23. def fit[M <: Model[M]](estimator: Estimator[M], data: DataFrame): (M, DataFrame)

    Permalink
    Attributes
    protected

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

    Permalink
    Definition Classes
    AnyRef → Any

  25. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any

  26. val independentTestPredictions: (Transformer, Dataset[_], Dataset[_]) ⇒ DataFrame

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  27. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any

  28. val logger: Logger

    Permalink
    Attributes
    protected

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

    Permalink
    Definition Classes
    AnyRef

  30. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef

  31. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef

  32. val orderDependentTestPredictions: (String) ⇒ (Transformer, Dataset[_], Dataset[_]) ⇒ Dataset[Row]

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  33. val orderDependentTestPredictionsWithParams: (String) ⇒ (Transformer, Dataset[_], Dataset[_], ParamMap) ⇒ Dataset[Row]

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  34. val randomSplit: (Double) ⇒ (DataFrame) ⇒ (Dataset[Row], Dataset[Row])

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  35. def regress(df: DataFrame, regressor: Regressor, setting: RegressionLearningSetting = RegressionLearningSetting(), replicationDf: Option[DataFrame] = None): Future[RegressionResultsHolder]

    Permalink

  36. def regressAux(df: DataFrame, replicationDf: Option[DataFrame], regressor: Regressor, setting: RegressionLearningSetting)(splitDataSet: (DataFrame) ⇒ (DataFrame, DataFrame), calcTestPredictions: (Transformer, Dataset[_], Dataset[_]) ⇒ DataFrame, crossValidatorCreatorWithProcessor: Option[CrossValidatorCreatorWithProcessor], initStages: Seq[() ⇒ PipelineStage], preTrainingStages: Seq[() ⇒ PipelineStage], paramGrids: Traversable[ParamGrid[_]]): Future[RegressionResultsHolder]

    Permalink
    Attributes
    protected

  37. def regressTimeSeries(df: DataFrame, regressor: Regressor, setting: TemporalRegressionLearningSetting, groupIdColumnName: Option[String] = None, replicationDf: Option[DataFrame] = None): Future[RegressionResultsHolder]

    Permalink

  38. def regressWithStages(df: DataFrame, replicationDf: Option[DataFrame], regressor: Regressor, setting: RegressionLearningSetting)(splitDataset: (DataFrame) ⇒ (DataFrame, DataFrame), calcTestPredictions: (Transformer, Dataset[_], Dataset[_]) ⇒ DataFrame, crossValidatorCreatorWithProcessor: Option[CrossValidatorCreatorWithProcessor], stages: Seq[() ⇒ PipelineStage], paramGrids: Traversable[ParamGrid[_]]): Future[RegressionResultsHolder]

    Permalink
    Attributes
    protected

  39. val regressionEvaluators: Seq[EvaluatorWrapper[models.regression.RegressionEvalMetric.Value]]

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  40. def regressionStages(setting: RegressionLearningSetting): Seq[() ⇒ PipelineStage]

    Permalink
    Attributes
    protected

  41. lazy val repetitionParallelism: Int

    Permalink
    Attributes
    protected

  42. val seqSplit: (String) ⇒ (Double) ⇒ (DataFrame) ⇒ (Dataset[Row], Dataset[Row])

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

  43. val seriesOrderCol: String

    Permalink
    Attributes
    protected

  44. val splitByValue: (String) ⇒ (Double) ⇒ (DataFrame) ⇒ (Dataset[Row], Dataset[Row])

    Permalink
    Attributes
    protected
    Definition Classes
    MLBase

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

    Permalink
    Definition Classes
    AnyRef

  46. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any

  47. lazy val useConsecutiveOrderForDL: Boolean

    Permalink
    Attributes
    protected

  48. def verifyRocAndPrResults(predictionDf: DataFrame): Unit

    Permalink
    Attributes
    protected

  49. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from MLBase

Inherited from AnyRef

Inherited from Any

Ungrouped