vw

Type Members

1. class ContextualBanditMetrics extends Serializable

   

2. class ExampleStack extends AnyRef

   

3. trait HasAdditionalFeatures extends Params

   

   VW support multiple input columns which are mapped to namespaces.

   VW support multiple input columns which are mapped to namespaces. Note: when one wants to create quadratic features within VW you'd specify additionalFeatures. Each feature column is treated as one namespace. Using -q 'uc' for columns 'user' and 'content' you'd get all quadratics for features in user/content (the first letter is called feature group and VW users are used to it... before somebody starts complaining ;)

4. trait HasNumBits extends Params

   

   Controls hashing parameters such us number of bits (numbits) and how to handle collisions.

5. trait HasSumCollisions extends Params

   

   Controls hashing parameters such us number of bits (numbits) and how to handle collisions.

6. case class NamespaceInfo(hash: Int, featureGroup: Char, colIdx: Int) extends Product with Serializable

   

7. case class PathAndData(path: String, bytes: Array[Byte]) extends Product with Serializable

   

8. case class TrainingResult(model: Option[Array[Byte]], stats: TrainingStats) extends Product with Serializable

   

9. case class TrainingStats(partitionId: Int, arguments: String, learningRate: Double, powerT: Double, hashSeed: Int, numBits: Int, numberOfExamplesPerPass: Long, weightedExampleSum: Double, weightedLabelSum: Double, averageLoss: Double, bestConstant: Float, bestConstantLoss: Float, totalNumberOfFeatures: Long, timeTotalNs: Long, timeNativeIngestNs: Long, timeLearnNs: Long, timeMultipassNs: Long, ipsEstimate: Double, snipsEstimate: Double) extends Product with Serializable

   

10. class VectorZipper extends Transformer with HasInputCols with HasOutputCol with Wrappable with ComplexParamsWritable

    

    Combine one or more input columns into a sequence in the output column.

11. trait VowpalWabbitBase extends Wrappable with HasWeightCol with HasAdditionalFeatures with Logging

    

    Base implementation of VowpalWabbit learners.

    Base implementation of VowpalWabbit learners.

    Annotations

    @InternalWrapper()

    Note

    parameters that regularly are swept through are exposed as proper parameters.

12. trait VowpalWabbitBaseModel extends HasFeaturesCol with HasRawPredictionCol with HasAdditionalFeatures

    

    Base trait to wrap the model for prediction.

13. class VowpalWabbitClassificationModel extends ProbabilisticClassificationModel[Row, VowpalWabbitClassificationModel] with VowpalWabbitBaseModel with ComplexParamsWritable

    

    Annotations

    @InternalWrapper()

14. class VowpalWabbitClassifier extends ProbabilisticClassifier[Row, VowpalWabbitClassifier, VowpalWabbitClassificationModel] with VowpalWabbitBase with ComplexParamsWritable

    

    Annotations

    @InternalWrapper()

15. class VowpalWabbitContextualBandit extends Predictor[Row, VowpalWabbitContextualBandit, VowpalWabbitContextualBanditModel] with VowpalWabbitContextualBanditBase with HasParallelismInjected with ComplexParamsWritable

    

    Annotations

    @InternalWrapper()

16. trait VowpalWabbitContextualBanditBase extends VowpalWabbitBase

    

    Annotations

    @InternalWrapper()

17. class VowpalWabbitContextualBanditModel extends PredictionModel[Row, VowpalWabbitContextualBanditModel] with VowpalWabbitBaseModel with VowpalWabbitContextualBanditBase with ComplexParamsWritable

    

    Annotations

    @InternalWrapper()

18. class VowpalWabbitFeaturizer extends Transformer with HasInputCols with HasOutputCol with HasNumBits with HasSumCollisions with Wrappable with ComplexParamsWritable

    

19. class VowpalWabbitInteractions extends Transformer with HasInputCols with HasOutputCol with HasNumBits with HasSumCollisions with Wrappable with ComplexParamsWritable

    

    This transformer is not intended to be used with VW classifier or regressor, but rather to bring sparse interaction concept to other SparkML learners (e.g.

    This transformer is not intended to be used with VW classifier or regressor, but rather to bring sparse interaction concept to other SparkML learners (e.g. LR).

20. class VowpalWabbitMurmurWithPrefix extends Serializable

    

    VW style murmur hash with pre-hashing of an initially specified prefix.

21. class VowpalWabbitRegressionModel extends RegressionModel[Row, VowpalWabbitRegressionModel] with VowpalWabbitBaseModel with ComplexParamsWritable

    

    Annotations

    @InternalWrapper()

22. class VowpalWabbitRegressor extends BaseRegressor[Row, VowpalWabbitRegressor, VowpalWabbitRegressionModel] with VowpalWabbitBase with ComplexParamsWritable

    

    Annotations

    @InternalWrapper()