Class

com.salesforce.op.dsl.RichListFeature

RichTextListFeature

Related Doc: package RichListFeature

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implicit class RichTextListFeature extends AnyRef

Enrichment functions for TextList Feature

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Instance Constructors

  1. new RichTextListFeature(f: FeatureLike[TextList])

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    f

    TextList Feature

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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  6. def countVec(binary: Boolean = false, minDF: Double = 1.0, minTF: Double = 1.0, vocabSize: Int = 1 << 18): FeatureLike[OPVector]

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    Converts array of strings into a count vector

    Converts array of strings into a count vector

    binary

    Binary toggle to control the output vector values. If True, all nonzero counts are set to 1.

    minDF

    Minimum number of documents a term must appear in to be included in the vocabulary. If this is an integer greater than or equal to 1, this specifies the number of documents the term must appear in. if this is a double in [0,1), then this specifies the fraction of documents.

    minTF

    Minimum number of times a term must appear in a document. If this is an integer greater than or equal to 1, then this specifies a count. If this is a double in [0,1), then this specifies a fraction.

    vocabSize

    Max size of the vocabulary. CountVectorizer will build a vocabulary that only considers the top vocabSize terms ordered by term frequency across the corpus.

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

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  8. def equals(arg0: Any): Boolean

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  9. val f: FeatureLike[TextList]

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    TextList Feature

  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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

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  15. def ngram(n: Int = 2): FeatureLike[TextList]

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    A feature transformer that converts the input array of strings into an array of n-grams.

    A feature transformer that converts the input array of strings into an array of n-grams. Note: Null values in the input list are ignored. It returns a list of n-grams where each n-gram is represented by a space-separated string of words.

    When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned.

    n

    number elements per n-gram (>=1)

  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def removeStopWords(stopWords: Array[String] = ..., caseSensitive: Boolean = false): FeatureLike[TextList]

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    A feature transformer that filters out stop words from input.

    A feature transformer that filters out stop words from input. Note: null values from input array are preserved unless adding null to stopWords explicitly.

    stopWords

    the words to be filtered out (default: English stop words) See StopWordsRemover.loadDefaultStopWords() for all supported languages.

    caseSensitive

    whether to do a case-sensitive comparison over the stop words

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

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  20. def tf(numTerms: Int = ..., binary: Boolean = TransmogrifierDefaults.BinaryFreq): FeatureLike[OPVector]

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    Apply hashing term frequency transformation.

    Apply hashing term frequency transformation.

    numTerms

    number of features (> 0)

    binary

    if true, all non zero counts are set to 1.0

  21. def tfidf(numTerms: Int = ..., binary: Boolean = TransmogrifierDefaults.BinaryFreq, minDocFreq: Int = 0): FeatureLike[OPVector]

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    Apply Term frequency-inverse document frequency (TF-IDF), a feature vectorization method to reflect the importance of a term to a document in the corpus

    Apply Term frequency-inverse document frequency (TF-IDF), a feature vectorization method to reflect the importance of a term to a document in the corpus

    numTerms

    number of features (> 0)

    binary

    if true, all non zero counts are set to 1.0

    minDocFreq

    minimum number of documents in which a term should appear for filtering

  22. def toString(): String

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  23. def vectorize(numTerms: Int, binary: Boolean, minDocFreq: Int, others: Array[FeatureLike[TextList]] = Array.empty): FeatureLike[OPVector]

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    Apply Term frequency-inverse document frequency (TF-IDF), a feature vectorization method to reflect the importance of a term to a document in the corpus.

    Apply Term frequency-inverse document frequency (TF-IDF), a feature vectorization method to reflect the importance of a term to a document in the corpus. Results in a vector representation of text

    numTerms

    number of features (> 0)

    binary

    if true, all non zero counts are set to 1.0

    minDocFreq

    minimum number of documents in which a term should appear for filtering

  24. final def wait(): Unit

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

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

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  27. def word2vec(maxIter: Int = 1, maxSentenceLength: Int = 1000, minCount: Int = 5, numPartition: Int = 1, stepSize: Double = 0.025, windowSize: Int = 5, vectorSize: Int = 100): FeatureLike[OPVector]

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    Convert array of strings into one vector using word2vec method

    Convert array of strings into one vector using word2vec method

    maxIter

    Number of iterations

    maxSentenceLength

    Maximum length (in words) of each sentence in the input data

    minCount

    Minimum number of times a token must appear to be included in the word2vec model's

    numPartition

    Number of partitions for sentences of words

    stepSize

    Initial learning rate

    windowSize

    Window size (context words from [-window, window])

    vectorSize

    Dimension of the code that you want to transform from words

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