Utils

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 buckBucket(bucketSize: Int): (String, String) ⇒ Int

   

6. def buckBuckets(bucketSize: Int)(col: String*): Int

   

7. def bucketizedColumn(boundaries: Array[Float]): (Float) ⇒ Int

   

8. def categoricalFromVocabList(vocabList: Array[String]): (String) ⇒ Int

   

9. def clone(): AnyRef

   

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

    

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11. def equals(arg0: Any): Boolean

    

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12. def finalize(): Unit

    

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

    

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14. def getDeepTensor(r: Row, columnInfo: ColumnFeatureInfo): Tensor[Float]

    

15. def getDeepTensors(r: Row, columnInfo: ColumnFeatureInfo): Array[Tensor[Float]]

    

    convert a row to tensors given column feature information of WideAndDeep model.

    convert a row to tensors given column feature information of WideAndDeep model.

    r

    Row of userId, itemId, features and label

    columnInfo

    ColumnFeatureInfo specify information of different features

    returns

    an array of tensors as input for deep part of a WideAndDeep model

16. def getNegativeSamples(indexed: DataFrame): DataFrame

    

    generate negative samples given a dataframe of positive records, label >=2.

    generate negative samples given a dataframe of positive records, label >=2.

    indexed

    dataframe positive of userId, itemId and label.

    returns

    a dataframe of negative samples(label=1) with the same size as indexed dataframe

17. def getWideTensor(r: Row, columnInfo: ColumnFeatureInfo): Tensor[Float]

    

    convert a row to tensor given column feature information of WideAndDeep model.

    convert a row to tensor given column feature information of WideAndDeep model.

    r

    Row of userId, itemId, features and label

    columnInfo

    ColumnFeatureInfo specify information of different features

    returns

    a tensor as input for wide part of a WideAndDeep model

18. def hashCode(): Int

    

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19. final def isInstanceOf[T0]: Boolean

    

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

    

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21. final def notify(): Unit

    

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22. final def notifyAll(): Unit

    

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23. def prePadding(maxLength: Int): (WrappedArray[Float]) ⇒ Array[Float]

    

24. def row2Sample(r: Row, columnInfo: ColumnFeatureInfo, modelType: String): Sample[Float]

    

    convert a row to sample given column information of WideAndDeep model.

    convert a row to sample given column information of WideAndDeep model.

    r

    Row of userId, itemId, features and label

    columnInfo

    ColumnFeatureInfo specify information of different features

    modelType

    support "wide_n_deep", "wide", "deep" only

    returns

    TensorSample as input for WideAndDeep model

25. def row2SampleSequential(r: Row, columnInfo: ColumnFeatureInfo, modelType: String): Sample[Float]

    

    convert a row to sample given column information of WideAndDeep Sequential model.

    convert a row to sample given column information of WideAndDeep Sequential model.

    r

    Row of userId, itemId, features and label

    columnInfo

    ColumnFeatureInfo specify information of different features

    modelType

    support "wide_n_deep", "wide", "deep" only

    returns

    TensorSample as input for WideAndDeep Sequential model

26. def row2sampleSession(r: Row, sessionLength: Int, includeHistory: Boolean, historyLength: Int): Sample[Float]

    

27. def slideSession(df: DataFrame, sessionLength: Int): DataFrame

    

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

    

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29. def toString(): String

    

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30. final def wait(): Unit

    

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

    

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

    

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