RichTextMapFeature

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. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. val f: FeatureLike[TextMap]

   

   FeatureLike

9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. def smartVectorize(maxCategoricalCardinality: Int, numHashes: Int, autoDetectLanguage: Boolean, minTokenLength: Int, toLowercase: Boolean, cleanKeys: Boolean = TransmogrifierDefaults.CleanKeys, cleanText: Boolean = TransmogrifierDefaults.CleanText, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls, trackTextLen: Boolean = TransmogrifierDefaults.TrackTextLen, topK: Int = TransmogrifierDefaults.TopK, minSupport: Int = TransmogrifierDefaults.MinSupport, unseenName: String = TransmogrifierDefaults.OtherString, hashWithIndex: Boolean = ..., binaryFreq: Boolean = TransmogrifierDefaults.BinaryFreq, prependFeatureName: Boolean = ..., autoDetectThreshold: Double = TextTokenizer.AutoDetectThreshold, hashSpaceStrategy: HashSpaceStrategy = ..., defaultLanguage: Language = TextTokenizer.DefaultLanguage, hashAlgorithm: HashAlgorithm = ..., textLengthType: TextLengthType = SmartTextVectorizer.LengthType, minLengthStdDev: Double = ..., stripHtml: Boolean = TextTokenizer.StripHtml, others: Array[FeatureLike[TextMap]] = Array.empty): FeatureLike[OPVector]

    

    Vectorize text map features by treating low cardinality text features as categoricals and applying hashing trick to high caridinality ones.

    Vectorize text map features by treating low cardinality text features as categoricals and applying hashing trick to high caridinality ones.

    maxCategoricalCardinality

    max cardinality for a text feature to be treated as categorical

    numHashes

    number of features (hashes) to generate

    autoDetectLanguage

    indicates whether to attempt language detection

    minTokenLength

    minimum token length, >= 1.

    toLowercase

    indicates whether to convert all characters to lowercase before analyzing

    cleanKeys

    clean map keys before pivoting

    cleanText

    indicates whether to ignore capitalization and punctuation

    trackNulls

    indicates whether or not to track null values in a separate column.

    trackTextLen

    indicates whether or not to track the length of the text in a separate column

    topK

    number of most common elements to be used as categorical pivots

    minSupport

    minimum number of occurrences an element must have to appear in pivot

    unseenName

    name to give indexes which do not have a label name associated with them

    hashWithIndex

    include indices when hashing a feature that has them (OPLists or OPVectors)

    binaryFreq

    if true, term frequency vector will be binary such that non-zero term counts will be set to 1.0

    prependFeatureName

    if true, prepends a input feature name to each token of that feature

    autoDetectThreshold

    Language detection threshold. If none of the detected languages have confidence greater than the threshold then defaultLanguage is used.

    hashSpaceStrategy

    strategy to determine whether to use shared hash space for all included features

    defaultLanguage

    default language to assume in case autoDetectLanguage is disabled or failed to make a good enough prediction.

    hashAlgorithm

    hash algorithm to use

    textLengthType

    Method to use for constructing text length distribution in TextStats. Current options are from the full entry or from the tokens

    minLengthStdDev

    minimum standard deviation of the lengths of tokens in a text field for it to be hashed instead of ignored

    stripHtml

    indicates whether to strip HTML tags from the text or not before analyzing

    others

    additional text features

    returns

    result feature of type Vector

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

    

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18. def toString(): String

    

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19. def vectorize(cleanText: Boolean, cleanKeys: Boolean = TransmogrifierDefaults.CleanKeys, shouldPrependFeatureName: Boolean = ..., allowListKeys: Array[String] = Array.empty, blockListKeys: Array[String] = Array.empty, others: Array[FeatureLike[TextMap]] = Array.empty, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls, trackTextLen: Boolean = TransmogrifierDefaults.TrackTextLen, numHashes: Int = ..., hashSpaceStrategy: HashSpaceStrategy = ...): FeatureLike[OPVector]

    

    Apply TextMapVectorizer on any OPMap that has string values

    Apply TextMapVectorizer on any OPMap that has string values

    cleanText

    clean text before pivoting

    cleanKeys

    clean map keys before pivoting

    shouldPrependFeatureName

    whether or not to prepend feature name hash to the tokens before hashing

    allowListKeys

    keys to allowlist

    blockListKeys

    keys to blocklist

    others

    other features of the same type

    trackNulls

    option to keep track of values that were missing

    trackTextLen

    option to add a column containing the text length to the feature vector

    numHashes

    size of hash space

    hashSpaceStrategy

    strategy to determine whether to use shared hash space for all included features

    returns

    an OPVector feature

20. final def wait(): Unit

    

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

    

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

    

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