FeatureInteraction

Value Members

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

   

   Definition Classes

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2. final def ##(): Int

   

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

   

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4. final val AGGREGATE_COLUMN: String

   

   Definition Classes

   FeatureInteractionBase

5. final val COUNT_COLUMN: String

   

   Definition Classes

   FeatureInteractionBase

6. final val ENTROPY_COLUMN: String

   

   Definition Classes

   FeatureInteractionBase

7. final val FIELD_ENTROPY_COLUMN: String

   

   Definition Classes

   FeatureInteractionBase

8. final val INDEXED_SUFFIX: String

   

   Definition Classes

   FeatureInteractionBase

9. final val QUANTILE_PRECISION: Double

   

   Definition Classes

   FeatureInteractionBase

10. final val QUANTILE_THRESHOLD: Double

    

    Definition Classes

    FeatureInteractionBase

11. final val RATIO_COLUMN: String

    

    Definition Classes

    FeatureInteractionBase

12. final val TOTAL_RATIO_COLUMN: String

    

    Definition Classes

    FeatureInteractionBase

13. final val VARIANCE_STATISTIC: String

    

    Definition Classes

    FeatureInteractionBase

14. final def asInstanceOf[T0]: T0

    

    Definition Classes

    Any

15. def calculatePercentageChange(before: Double, after: Double): Double

    

    Method for evaluating the percentage change to the score metric to normalize.

    Method for evaluating the percentage change to the score metric to normalize.

    before

    Score of a parent feature

    after

    Score of an interaction feature

    returns

    the percentage change

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

    Since

    0.6.2

16. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

17. def createCandidates(df: DataFrame, nominalFields: Array[String], continuousFields: Array[String]): FeatureInteractionCollection

    

    Method for determining feature interaction candidates, apply those candidates as new fields to the DataFrame, and return a configuration payload that has the information about the interactions that can be used in a Pipeline.

    Method for determining feature interaction candidates, apply those candidates as new fields to the DataFrame, and return a configuration payload that has the information about the interactions that can be used in a Pipeline.

    df

    DataFrame to be used to calculate and potentially add feature interactions to

    nominalFields

    Fields from the DataFrame that were originally non-numeric (Character, String, etc.)

    continuousFields

    Fields from the DataFrame that were originally numeric, continuous types.

    returns

    FeatureInteractionCollection -> the DataFrame with candidate feature interactions added in and the payload of interaction features and their constituent parents in order to recreate for a Pipeline.

    Since

    0.6.2

18. def createCandidatesAndAddToVector(df: DataFrame, nominalFields: Array[String], continuousFields: Array[String], featureVectorColumn: String): FeatureInteractionOutputPayload

    

    Method for generating interaction candidates and re-building a feature vector

    Method for generating interaction candidates and re-building a feature vector

    df

    DataFrame to interact features with (that has a feature vector already built)

    nominalFields

    Array of column names for nominal (string indexed) values

    continuousFields

    Array of column names for continuous numeric values

    featureVectorColumn

    Name of the feature vector column

    returns

    DataFrame with a re-built feature vector that includes the interacted feature columns as part of it.

    Since

    0.6.2

19. def createPipeline(df: DataFrame, nominalFields: Array[String], continuousFields: Array[String], featureVectorColumn: String): PipelineInteractionOutput

    

    Method for generating a pipeline-friendly feature interaction to support serialization of the automl pipeline properly.

    Method for generating a pipeline-friendly feature interaction to support serialization of the automl pipeline properly. Utilizes the InteractionTransformer to generate the fields required for inference

    df

    DataFrame to be used for generating the interaction candidates and pipeline

    nominalFields

    Nominal type numeric fields that are part of the vector

    continuousFields

    Continuous type numeric fields that are part of the vector

    featureVectorColumn

    Name of the current feature vector column

    returns

    PipelineInteractionOutput which contains the pipeline to be applied to the automl pipeline flow.

    Since

    0.6.2

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

    

    Definition Classes

    AnyRef

21. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

22. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

23. def generateCandidates(df: DataFrame, nominalFields: Array[String], continuousFields: Array[String]): Array[InteractionPayloadExtract]

    

    Main method for generating a list of interaction candidates based on the configuration specified in the class configuration.

    Main method for generating a list of interaction candidates based on the configuration specified in the class configuration.

    df

    The DataFrame to process interactions for

    nominalFields

    The nominal fields (String Indexed) to be used for interaction

    continuousFields

    The continuous fields (Original Numeric Types) to be used for interaction

    returns

    Array[InteractionPayload] for candidate fields interactions that meet the acceptance criteria as set by configuration.

    Since

    0.6.2

24. def generateInteractionCandidates(featureColumns: Array[ColumnTypeData]): Array[InteractionPayload]

    

    Method for generating a collection of Interaction Candidates to be tested and applied to the feature set if the tests for inclusion pass.

    Method for generating a collection of Interaction Candidates to be tested and applied to the feature set if the tests for inclusion pass.

    featureColumns

    List of the columns that make up the feature vector

    returns

    Array of InteractionPayload values.

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

    Since

    0.6.2

25. def generateNominalIndexesInteractionFields(payload: FeatureInteractionCollection): NominalDataCollection

    

    Method for converting nominal interaction fields to a new StringIndexed value to preserve information type and eliminate the possibility of data distribution skew

    Method for converting nominal interaction fields to a new StringIndexed value to preserve information type and eliminate the possibility of data distribution skew

    payload

    FeatureInteractionCollection of the source parents and their interacted children fields

    returns

    NominalDataCollecction payload containing a DataFrame that has new StringIndexed fields for nominal interactions and the fields that need to be seen as included in the final feature vector

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

    Since

    0.6.2

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

    

    Definition Classes

    AnyRef → Any

27. def getFieldType(fieldType: String): structures.FieldEncodingType.Value

    

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

28. def getModelType(modelingType: String): structures.ModelingType.Value

    

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

29. def getRetentionMode(retentionMode: String): structures.InteractionRetentionMode.Value

    

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

30. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

31. def interactProduct(df: DataFrame, candidate: InteractionPayload): DataFrame

    

    Method for generating a product interaction between feature columns

    Method for generating a product interaction between feature columns

    df

    A DataFrame to add a field for an interaction between two columns

    candidate

    InteractionPayload information about the two parent columns and the name of the new interaction column to be created.

    returns

    A modified DataFrame with the new column.

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

    Since

    0.6.2

32. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

    Definition Classes

    AnyRef

34. final def notify(): Unit

    

    Definition Classes

    AnyRef

35. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

36. def regenerateFeatureVector(df: DataFrame, preInteractedFields: Array[String], interactedFields: Array[String], featureCol: String): VectorAssemblyOutput

    

    Helper method for recreating the feature vector after interactions have been completed on individual columns

    Helper method for recreating the feature vector after interactions have been completed on individual columns

    df

    DataFrame containing the interacted fields with the original feature vector dropped

    preInteractedFields

    Fields making up the original vector before interaction

    interactedFields

    Interaction candidate fields that have been selected to be included in the final feature vector

    featureCol

    Name of the feature vector field

    returns

    DataFrame with a new feature vector.

    Attributes

    protected[com.databricks.labs.automl.feature]

    Definition Classes

    FeatureInteractionBase

    Since

    0.6.2

37. def setContinuousDiscretizerBucketCount(value: Int): FeatureInteraction.this.type

    

38. def setLabelCol(value: String): FeatureInteraction.this.type

    

39. def setParallelism(value: Int): FeatureInteraction.this.type

    

40. def setTargetInteractionPercentage(value: Double): FeatureInteraction.this.type

    

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

    

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42. def toString(): String

    

    Definition Classes

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43. final def wait(): Unit

    

    Definition Classes

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    @throws( ... )

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

    

    Definition Classes

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    @throws( ... )

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

    

    Definition Classes

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    @throws( ... )