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org.apache.spark.mllib.util

DataValidators

Related Doc: package util

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object DataValidators extends Logging

:: DeveloperApi :: A collection of methods used to validate data before applying ML algorithms.

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@DeveloperApi() @Since( "0.8.0" )
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DataValidators.scala
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  1. final def !=(arg0: Any): Boolean

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  5. val binaryLabelValidator: (RDD[LabeledPoint]) ⇒ Boolean

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    Function to check if labels used for classification are either zero or one.

    Function to check if labels used for classification are either zero or one.

    returns

    True if labels are all zero or one, false otherwise.

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    @Since( "1.0.0" )
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  14. def isTraceEnabled(): Boolean

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logDebug(msg: ⇒ String): Unit

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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logError(msg: ⇒ String): Unit

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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  22. def logName: String

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  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logWarning(msg: ⇒ String): Unit

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  27. def multiLabelValidator(k: Int): (RDD[LabeledPoint]) ⇒ Boolean

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    Function to check if labels used for k class multi-label classification are in the range of {0, 1, ..., k - 1}.

    Function to check if labels used for k class multi-label classification are in the range of {0, 1, ..., k - 1}.

    returns

    True if labels are all in the range of {0, 1, ..., k-1}, false otherwise.

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    @Since( "1.3.0" )
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  29. final def notify(): Unit

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