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net.sansa_stack.ml.spark.anomalydetection

MultiFeatureAnomalyDetection

class MultiFeatureAnomalyDetection extends AnyRef

This class runs multi-feature anomaly detection based on IsolationForest algorithm

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Instance Constructors

  1. new MultiFeatureAnomalyDetection(spark: SparkSession, originalDataRDD: RDD[Triple], config: DistADConfig)

    spark

    the initiated Spark session

    originalDataRDD

    RDD[Triple]

    config

    the config file object

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. def addClusterIdToData(data: DataFrame, predictions: DataFrame): DataFrame

    Adds the cluster ID to data

    Adds the cluster ID to data

    data

    data

    predictions

    dataframe containing cluster ids

    returns

    a new dataframe with cluster ids

  5. final def asInstanceOf[T0]: T0
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  6. def calculateAnomaliesForMultipleFeature(data: Dataset[Row]): DataFrame
  7. def clone(): AnyRef
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  10. final def getClass(): Class[_]
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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 run(): DataFrame

    The main function

    The main function

    returns

    the dataframe containing all the anomalies

  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def toString(): String
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  19. final def wait(arg0: Long, arg1: Int): Unit
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  21. final def wait(): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated @deprecated
    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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