TestFeatureBuilder

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. object DefaultFeatureNames extends Product with Serializable

   

5. def apply(data: Seq[FeatureType]*)(implicit spark: SparkSession): (DataFrame, Array[Feature[_ <: FeatureType]])

   

   Build a dataset with arbitrary amount of features of specified types

   Build a dataset with arbitrary amount of features of specified types

   data

   data

   spark

   spark session

   returns

   dataset with arbitrary amount of features of specified types

6. def apply[F1 <: FeatureType, F2 <: FeatureType, F3 <: FeatureType, F4 <: FeatureType, F5 <: FeatureType](data: Seq[(F1, F2, F3, F4, F5)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], arg2: scala.reflect.api.JavaUniverse.TypeTag[F3], arg3: scala.reflect.api.JavaUniverse.TypeTag[F4], arg4: scala.reflect.api.JavaUniverse.TypeTag[F5], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2], Feature[F3], Feature[F4], Feature[F5])

   

   Build a dataset with five features of specified types

   Build a dataset with five features of specified types

   F1

   1st feature type

   F2

   2nd feature type

   F3

   3rd feature type

   F4

   4th feature type

   F5

   5th feature type

   data

   data

   spark

   spark session

   returns

   dataset with five features of specified types

7. def apply[F1 <: FeatureType, F2 <: FeatureType, F3 <: FeatureType, F4 <: FeatureType, F5 <: FeatureType](f1name: String, f2name: String, f3name: String, f4name: String, f5name: String, data: Seq[(F1, F2, F3, F4, F5)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], arg2: scala.reflect.api.JavaUniverse.TypeTag[F3], arg3: scala.reflect.api.JavaUniverse.TypeTag[F4], arg4: scala.reflect.api.JavaUniverse.TypeTag[F5], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2], Feature[F3], Feature[F4], Feature[F5])

   

   Build a dataset with five features of specified types

   Build a dataset with five features of specified types

   F1

   1st feature type

   F2

   2nd feature type

   F3

   3rd feature type

   F4

   4th feature type

   F5

   5th feature type

   f1name

   1st feature name

   f2name

   2nd feature name

   f3name

   3rd feature name

   f4name

   4th feature name

   f5name

   5th feature name

   data

   data

   spark

   spark session

   returns

   dataset with five features of specified types

8. def apply[F1 <: FeatureType, F2 <: FeatureType, F3 <: FeatureType, F4 <: FeatureType](data: Seq[(F1, F2, F3, F4)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], arg2: scala.reflect.api.JavaUniverse.TypeTag[F3], arg3: scala.reflect.api.JavaUniverse.TypeTag[F4], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2], Feature[F3], Feature[F4])

   

   Build a dataset with four features of specified types

   Build a dataset with four features of specified types

   F1

   1st feature type

   F2

   2nd feature type

   F3

   3rd feature type

   F4

   4th feature type

   data

   data

   spark

   spark session

   returns

   dataset with four features of specified types

9. def apply[F1 <: FeatureType, F2 <: FeatureType, F3 <: FeatureType, F4 <: FeatureType](f1name: String, f2name: String, f3name: String, f4name: String, data: Seq[(F1, F2, F3, F4)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], arg2: scala.reflect.api.JavaUniverse.TypeTag[F3], arg3: scala.reflect.api.JavaUniverse.TypeTag[F4], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2], Feature[F3], Feature[F4])

   

   Build a dataset with four features of specified types

   Build a dataset with four features of specified types

   F1

   1st feature type

   F2

   2nd feature type

   F3

   3rd feature type

   F4

   4th feature type

   f1name

   1st feature name

   f2name

   2nd feature name

   f3name

   3rd feature name

   f4name

   4th feature name

   data

   data

   spark

   spark session

   returns

   dataset with four features of specified types

10. def apply[F1 <: FeatureType, F2 <: FeatureType, F3 <: FeatureType](data: Seq[(F1, F2, F3)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], arg2: scala.reflect.api.JavaUniverse.TypeTag[F3], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2], Feature[F3])

    

    Build a dataset with three features of specified types

    Build a dataset with three features of specified types

    F1

    1st feature type

    F2

    2nd feature type

    F3

    3rd feature type

    data

    data

    spark

    spark session

    returns

    dataset with three features of specified types

11. def apply[F1 <: FeatureType, F2 <: FeatureType, F3 <: FeatureType](f1name: String, f2name: String, f3name: String, data: Seq[(F1, F2, F3)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], arg2: scala.reflect.api.JavaUniverse.TypeTag[F3], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2], Feature[F3])

    

    Build a dataset with three features of specified types

    Build a dataset with three features of specified types

    F1

    1st feature type

    F2

    2nd feature type

    F3

    3rd feature type

    f1name

    1st feature name

    f2name

    2nd feature name

    f3name

    3rd feature name

    data

    data

    spark

    spark session

    returns

    dataset with three features of specified types

12. def apply[F1 <: FeatureType, F2 <: FeatureType](data: Seq[(F1, F2)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2])

    

    Build a dataset with two features of specified types

    Build a dataset with two features of specified types

    F1

    1st feature type

    F2

    2nd feature type

    data

    data

    spark

    spark session

    returns

    dataset with two features of specified types

13. def apply[F1 <: FeatureType, F2 <: FeatureType](f1name: String, f2name: String, data: Seq[(F1, F2)])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], arg1: scala.reflect.api.JavaUniverse.TypeTag[F2], spark: SparkSession): (DataFrame, Feature[F1], Feature[F2])

    

    Build a dataset with two features of specified types

    Build a dataset with two features of specified types

    F1

    1st feature type

    F2

    2nd feature type

    f1name

    1st feature name

    f2name

    2nd feature name

    data

    data

    spark

    spark session

    returns

    dataset with two features of specified types

14. def apply[F1 <: FeatureType](data: Seq[F1])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], spark: SparkSession): (DataFrame, Feature[F1])

    

    Build a dataset with one feature of specified type

    Build a dataset with one feature of specified type

    F1

    feature type

    data

    data

    spark

    spark session

    returns

    dataset with one feature of specified type

15. def apply[F1 <: FeatureType](f1name: String, data: Seq[F1])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[F1], spark: SparkSession): (DataFrame, Feature[F1])

    

    Build a dataset with one feature of specified type

    Build a dataset with one feature of specified type

    F1

    feature type

    f1name

    feature name

    data

    data

    spark

    spark session

    returns

    dataset with one feature of specified type

16. final def asInstanceOf[T0]: T0

    

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17. def clone(): AnyRef

    

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    protected[java.lang]

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

    

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19. def equals(arg0: Any): Boolean

    

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20. def finalize(): Unit

    

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

    

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22. final def isInstanceOf[T0]: Boolean

    

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

    

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24. final def notify(): Unit

    

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25. final def notifyAll(): Unit

    

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26. def random(numOfRows: Int = 10)(vectors: ⇒ Seq[OPVector] = ..., textLists: ⇒ Seq[TextList] = ..., dateLists: ⇒ Seq[DateList] = ..., dateTimeLists: ⇒ Seq[DateList] = ..., geoLocations: ⇒ Seq[Geolocation] = ..., base64Maps: ⇒ Seq[Base64Map] = ..., binaryMaps: ⇒ Seq[BinaryMap] = ..., comboBoxMaps: ⇒ Seq[ComboBoxMap] = ..., currencyMaps: ⇒ Seq[CurrencyMap] = ..., dateMaps: ⇒ Seq[DateMap] = ..., dateTimeMaps: ⇒ Seq[DateTimeMap] = ..., emailMaps: ⇒ Seq[EmailMap] = ..., idMaps: ⇒ Seq[IDMap] = ..., integralMaps: ⇒ Seq[IntegralMap] = ..., multiPickListMaps: ⇒ Seq[MultiPickListMap] = ..., percentMaps: ⇒ Seq[PercentMap] = ..., phoneMaps: ⇒ Seq[PhoneMap] = ..., pickListMaps: ⇒ Seq[PickListMap] = ..., realMaps: ⇒ Seq[RealMap] = ..., textAreaMaps: ⇒ Seq[TextAreaMap] = ..., textMaps: ⇒ Seq[TextMap] = ..., urlMaps: ⇒ Seq[URLMap] = ..., countryMaps: ⇒ Seq[CountryMap] = ..., stateMaps: ⇒ Seq[StateMap] = ..., cityMaps: ⇒ Seq[CityMap] = ..., postalCodeMaps: ⇒ Seq[PostalCodeMap] = ..., streetMaps: ⇒ Seq[StreetMap] = ..., nameStats: ⇒ Seq[NameStats] = ..., geoLocationMaps: ⇒ Seq[GeolocationMap] = ..., binaries: ⇒ Seq[Binary] = RandomBinary(0.5).limit(numOfRows), currencies: ⇒ Seq[Currency] = ..., dates: ⇒ Seq[Date] = ..., dateTimes: ⇒ Seq[DateTime] = ..., integrals: ⇒ Seq[Integral] = ..., percents: ⇒ Seq[Percent] = ..., reals: ⇒ Seq[Real] = ..., realNNs: ⇒ Seq[RealNN] = ..., multiPickLists: ⇒ Seq[MultiPickList] = ..., base64s: ⇒ Seq[Base64] = ..., comboBoxes: ⇒ Seq[ComboBox] = ..., emails: ⇒ Seq[Email] = ..., ids: ⇒ Seq[ID] = RandomText.ids.limit(numOfRows), phones: ⇒ Seq[Phone] = RandomText.phones.limit(numOfRows), pickLists: ⇒ Seq[PickList] = ..., texts: ⇒ Seq[Text] = ..., textAreas: ⇒ Seq[TextArea] = ..., urls: ⇒ Seq[URL] = RandomText.urls.limit(numOfRows), countries: ⇒ Seq[Country] = ..., states: ⇒ Seq[State] = RandomText.states.limit(numOfRows), cities: ⇒ Seq[City] = RandomText.cities.limit(numOfRows), postalCodes: ⇒ Seq[PostalCode] = ..., streets: ⇒ Seq[Street] = RandomText.streets.limit(numOfRows))(implicit spark: SparkSession): (DataFrame, Array[Feature[_ <: FeatureType]])

    

    Build a dataset with random features of specified size

    Build a dataset with random features of specified size

    numOfRows

    number of rows to generate (must be positive)

    spark

    spark session

    returns

    dataset with random features of specified size

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

    

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28. final def wait(): Unit

    

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

    

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

    

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