Class

org.pmml4s.model

AnomalyDetectionModel

Related Doc: package model

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class AnomalyDetectionModel extends Model with HasWrappedAnomalyDetectionAttributes

Anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a data set. Traditional approaches comprise of distance and density-based approaches. Examples of common ways to define distance or density are distance to the k-nearest neighbors or count of points within a given fixed radius. These methods however are unable to handle data sets with regions of different densities and do not scale well for large data. Other algorithms have been proposed which are better able to handle such cases; the PMML standard at this time supports three such algorithms:

- Isolation Forest - One Class SVM - Clustering mean distance based anomaly detection model - Other models can also be used if their scoring follows PMML standard rules.

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Inherited
  1. AnomalyDetectionModel
  2. HasWrappedAnomalyDetectionAttributes
  3. HasAnomalyDetectionAttributes
  4. Model
  5. PmmlElement
  6. Serializable
  7. Serializable
  8. HasExtensions
  9. HasModelVerification
  10. Predictable
  11. HasTargetFields
  12. ModelLocation
  13. FieldScope
  14. HasField
  15. HasLocalTransformations
  16. HasTargets
  17. HasModelExplanation
  18. HasModelStats
  19. HasOutput
  20. HasMiningSchema
  21. HasWrappedModelAttributes
  22. HasModelAttributes
  23. HasVersion
  24. HasParent
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new AnomalyDetectionModel(parent: Model, attributes: AnomalyDetectionAttributes, miningSchema: MiningSchema, model: Model, meanClusterDistances: Option[MeanClusterDistances] = None, output: Option[Output] = None, localTransformations: Option[LocalTransformations] = None, modelVerification: Option[ModelVerification] = None, extensions: Seq[Extension] = immutable.Seq.empty)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. val affinityIndex: Int

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  5. def algorithmName: Option[String]

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    The algorithm name is free-type and can be any description for the specific algorithm that produced the model.

    The algorithm name is free-type and can be any description for the specific algorithm that produced the model. This attribute is for information only.

    Definition Classes
    HasWrappedModelAttributesHasModelAttributes
  6. def algorithmType: AlgorithmType

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    Defines a model type used by the anomaly model.

    Defines a model type used by the anomaly model.

    Definition Classes
    HasWrappedAnomalyDetectionAttributesHasAnomalyDetectionAttributes
  7. def anyMissing(series: Series): Boolean

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    Returns true if there are any missing values of all input fields in the specified series.

    Returns true if there are any missing values of all input fields in the specified series.

    Attributes
    protected
    Definition Classes
    Model
  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  9. val attributes: AnomalyDetectionAttributes

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    Common attributes of this model

    Common attributes of this model

    Definition Classes
    AnomalyDetectionModelHasWrappedAnomalyDetectionAttributesHasWrappedModelAttributes
  10. def candidateOutputFields: Array[OutputField]

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    Definition Classes
    HasOutput
  11. def candidateOutputSchema: StructType

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    The schema of candidate outputs.

    The schema of candidate outputs.

    Definition Classes
    Model
  12. def classes(name: String): Array[Any]

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    Returns class labels of the specified target.

    Returns class labels of the specified target.

    Definition Classes
    Model
  13. lazy val classes: Array[Any]

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    The class labels in a classification model.

    The class labels in a classification model.

    Definition Classes
    Model
  14. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  15. def combineOutputFields(listA: Array[OutputField], listB: Array[OutputField]): Array[OutputField]

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    Definition Classes
    HasOutput
  16. def containInterResults: Boolean

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    Definition Classes
    HasOutput
  17. def createOutputs(): AnomalyDetectionOutput

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    Creates an object of subclass of ModelOutputs that is for writing into an output series.

    Creates an object of subclass of ModelOutputs that is for writing into an output series.

    Definition Classes
    AnomalyDetectionModelModel
  18. var customOutputFields: Array[OutputField]

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    User-defined custom output fields, both the internal output of PMML and predefined output are ignored when the field is specified.

    User-defined custom output fields, both the internal output of PMML and predefined output are ignored when the field is specified.

    Definition Classes
    HasOutput
  19. def dVersion: Double

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    Returns PMML version as a double value

    Returns PMML version as a double value

    Definition Classes
    HasVersion
  20. def dataDictionary: DataDictionary

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    The data dictionary of this model.

    The data dictionary of this model.

    Definition Classes
    Model
  21. def defaultOutputFields: Array[OutputField]

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    Returns all candidates output fields of this model when there is no output specified explicitly.

    Returns all candidates output fields of this model when there is no output specified explicitly.

    Definition Classes
    AnomalyDetectionModelModelHasOutput
  22. def encode(series: Series): DSeries

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    Encodes the input series.

    Encodes the input series.

    Attributes
    protected
    Definition Classes
    Model
  23. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  24. def equals(arg0: Any): Boolean

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    Definition Classes
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  25. val extensions: Seq[Extension]

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    Definition Classes
    AnomalyDetectionModelHasExtensions
  26. def field(name: String): Field

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    Returns the field of a given name.

    Returns the field of a given name.

    Definition Classes
    HasField
    Exceptions thrown

    FieldNotFoundException if a field with the given name does not exist

  27. def fieldsOfUsageType(typ: UsageType): Array[Field]

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    Get fields by its usage type: 'active', 'target', 'predicted', 'group' and so on

    Get fields by its usage type: 'active', 'target', 'predicted', 'group' and so on

    Definition Classes
    Model
  28. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  29. def functionName: MiningFunction

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    Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.

    Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.

    Definition Classes
    HasWrappedModelAttributesHasModelAttributes
  30. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  31. def getField(name: String): Option[Field]

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    Returns the field of a given name, None if a field with the given name does not exist.

    Returns the field of a given name, None if a field with the given name does not exist.

    Definition Classes
    ModelHasField
  32. def hasExtensions: Boolean

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    Definition Classes
    HasExtensions
  33. def hasTarget: Boolean

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    Definition Classes
    HasTargetFields
  34. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  35. def header: Header

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    The header of this model.

    The header of this model.

    Definition Classes
    Model
  36. lazy val implicitInputDerivedFields: Array[Field]

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    Implicit referenced derived fields for the sub-model except ones defined in the mining schema.

    Implicit referenced derived fields for the sub-model except ones defined in the mining schema.

    Definition Classes
    Model
  37. def importances: Map[String, Double]

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    Returns importances of predictors.

    Returns importances of predictors.

    Definition Classes
    Model
  38. def inferClasses: Array[Any]

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    The sub-classes can override this method to provide classes of target inside model.

    The sub-classes can override this method to provide classes of target inside model.

    Definition Classes
    Model
  39. lazy val inputDerivedFields: Array[Field]

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    Referenced derived fields.

    Referenced derived fields.

    Definition Classes
    Model
  40. lazy val inputFields: Array[Field]

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    All input fields in an array.

    All input fields in an array.

    Definition Classes
    Model
  41. lazy val inputNames: Array[String]

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    All input names in an array.

    All input names in an array.

    Definition Classes
    Model
  42. lazy val inputSchema: StructType

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    The schema of inputs.

    The schema of inputs.

    Definition Classes
    Model
  43. def isAssociationRules: Boolean

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    Tests if this is a association rules model.

    Tests if this is a association rules model.

    Definition Classes
    HasModelAttributes
  44. def isBinary: Boolean

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    Tests if the target is a binary field

    Tests if the target is a binary field

    Definition Classes
    Model
  45. def isClassification(name: String): Boolean

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    Tests if this is a classification model of the specified target, it's applicable for multiple targets.

    Tests if this is a classification model of the specified target, it's applicable for multiple targets.

    Definition Classes
    Model
  46. def isClassification: Boolean

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    Tests if this is a classification model.

    Tests if this is a classification model.

    Definition Classes
    ModelHasModelAttributes
  47. def isClustering: Boolean

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    Tests if this is a clustering model.

    Tests if this is a clustering model.

    Definition Classes
    HasModelAttributes
  48. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  49. def isMixed: Boolean

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    Tests if this is a mixed model.

    Tests if this is a mixed model.

    Definition Classes
    HasModelAttributes
  50. def isOrdinal: Boolean

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    Tests if the target is an ordinal field

    Tests if the target is an ordinal field

    Definition Classes
    Model
  51. def isPredictionOnly: Boolean

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    Definition Classes
    HasOutput
  52. def isRegression(name: String): Boolean

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    Tests if this is a regression model of the specified target, it's applicable for multiple targets.

    Tests if this is a regression model of the specified target, it's applicable for multiple targets.

    Definition Classes
    Model
  53. def isRegression: Boolean

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    Tests if this is a regression model.

    Tests if this is a regression model.

    Definition Classes
    ModelHasModelAttributes
  54. def isScorable: Boolean

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    Indicates if the model is valid for scoring.

    Indicates if the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results.

    Definition Classes
    HasWrappedModelAttributesHasModelAttributes
  55. def isSequences: Boolean

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    Tests if this is a sequences model.

    Tests if this is a sequences model.

    Definition Classes
    HasModelAttributes
  56. def isSubModel: Boolean

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    Definition Classes
    ModelLocation
  57. def isTimeSeries: Boolean

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    Tests if this is a time series model.

    Tests if this is a time series model.

    Definition Classes
    HasModelAttributes
  58. def isTopLevelModel: Boolean

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    Definition Classes
    ModelLocation
  59. val localTransformations: Option[LocalTransformations]

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    The optional local transformations.

    The optional local transformations.

    Definition Classes
    AnomalyDetectionModelHasLocalTransformations
  60. val meanClusterDistances: Option[MeanClusterDistances]

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  61. val miningSchema: MiningSchema

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    Definition Classes
    AnomalyDetectionModelHasMiningSchema
  62. val model: Model

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  63. def modelElement: ModelElement

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    Model element type.

    Model element type.

    Definition Classes
    AnomalyDetectionModelModel
  64. def modelExplanation: Option[ModelExplanation]

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  65. def modelName: Option[String]

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    Identifies the model with a unique name in the context of the PMML file.

    Identifies the model with a unique name in the context of the PMML file. This attribute is not required. Consumers of PMML models are free to manage the names of the models at their discretion.

    Definition Classes
    HasWrappedModelAttributesHasModelAttributes
  66. def modelStats: Option[ModelStats]

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    Definition Classes
    AnomalyDetectionModelHasModelStats
  67. val modelVerification: Option[ModelVerification]

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  68. def multiTargets: Boolean

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    Definition Classes
    HasTargetFields
  69. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  70. final def notify(): Unit

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    Definition Classes
    AnyRef
  71. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  72. lazy val nullSeries: Series

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    A series with all null values is returned when can not produce a result.

    A series with all null values is returned when can not produce a result.

    Definition Classes
    Model
  73. def numClasses(name: String): Int

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    Returns the number of class labels of the specified target.

    Returns the number of class labels of the specified target.

    Definition Classes
    Model
  74. lazy val numClasses: Int

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    The number of class labels in a classification model.

    The number of class labels in a classification model.

    Definition Classes
    Model
  75. def opType(name: String): OpType

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    Returns optype of the specified target.

    Returns optype of the specified target.

    Definition Classes
    Model
  76. lazy val opType: OpType

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    When Target specifies optype then it overrides the optype attribute in a corresponding MiningField, if it exists.

    When Target specifies optype then it overrides the optype attribute in a corresponding MiningField, if it exists. If the target does not specify optype then the MiningField is used as default. And, in turn, if the MiningField does not specify an optype, it is taken from the corresponding DataField. In other words, a MiningField overrides a DataField, and a Target overrides a MiningField.

    Definition Classes
    Model
  77. val output: Option[Output]

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    Definition Classes
    AnomalyDetectionModelHasOutput
  78. def outputFields: Array[OutputField]

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    Definition Classes
    HasOutput
  79. def outputIndex(feature: ResultFeature, value: Option[Any] = None): Int

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    Definition Classes
    HasOutput
  80. def outputNames: Array[String]

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    Definition Classes
    HasOutput
  81. def outputSchema: StructType

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    The schema of final outputs.

    The schema of final outputs.

    Definition Classes
    Model
  82. var parent: Model

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    The parent model.

    The parent model.

    Definition Classes
    AnomalyDetectionModelHasParent
  83. def postClassification(name: String = null): (Any, Map[Any, Double])

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    Attributes
    protected
    Definition Classes
    Model
  84. def postPredictedValue(outputs: MutablePredictedValue, name: String = null): MutablePredictedValue

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    protected
    Definition Classes
    Model
  85. def postRegression(predictedValue: Any, name: String = null): Any

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    Attributes
    protected
    Definition Classes
    Model
  86. def predict(values: Series): Series

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    Predicts values for a given data series.

    Predicts values for a given data series.

    Definition Classes
    AnomalyDetectionModelModelPredictable
  87. def predict(it: Iterator[Series]): Iterator[Series]

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    Definition Classes
    Model
  88. def predict(json: String): String

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    Predicts one or multiple records in json format, there are two formats supported:

    Predicts one or multiple records in json format, there are two formats supported:

    - ‘records’ : list like [{column -> value}, … , {column -> value}] - ‘split’ : dict like {‘columns’ -> [columns], ‘data’ -> [values]}

    json

    Records in json

    returns

    Results in json

    Definition Classes
    Model
  89. def predict(values: List[Any]): List[Any]

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    Definition Classes
    Model
  90. def predict[T](values: Array[T]): Array[Any]

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    Predicts values for a given Array, and the order of those values is supposed as same as the input fields list

    Predicts values for a given Array, and the order of those values is supposed as same as the input fields list

    Definition Classes
    Model
  91. def predict(values: (String, Any)*): Seq[(String, Any)]

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    Predicts values for a given list of key/value pairs.

    Predicts values for a given list of key/value pairs.

    Definition Classes
    Model
  92. def predict(values: Map[String, Any]): Map[String, Any]

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    Predicts values for a given data map of Java.

    Predicts values for a given data map of Java.

    Definition Classes
    Model
  93. def predict(values: Map[String, Any]): Map[String, Any]

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    Predicts values for a given data map.

    Predicts values for a given data map.

    Definition Classes
    Model
  94. val predictedIndex: Int

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  95. lazy val predictedValueIndex: Int

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    Definition Classes
    HasOutput
  96. def prepare(series: Series): (Series, Boolean)

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    Pre-process the input series.

    Pre-process the input series.

    Attributes
    protected
    Definition Classes
    Model
  97. def probabilitiesSupported: Boolean

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    Tests if probabilities of categories of target can be produced by this model.

    Tests if probabilities of categories of target can be produced by this model.

    Definition Classes
    Model
  98. def result(series: Series, modelOutputs: ModelOutputs, fields: Array[OutputField] = Array.empty): Series

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    Attributes
    protected
    Definition Classes
    Model
  99. def sampleDataSize: Option[Long]

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    A required parameter for isolation forest models.

    A required parameter for isolation forest models. It is the dataset size used to train the forest and is needed to normalize the tree search depth.

    Definition Classes
    HasWrappedAnomalyDetectionAttributesHasAnomalyDetectionAttributes
  100. def setOutputFields(outputFields: Array[OutputField]): AnomalyDetectionModel.this.type

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    Definition Classes
    HasOutput
  101. def setParent(parent: Model): AnomalyDetectionModel.this.type

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    Definition Classes
    HasParent
  102. def setSupplementOutput(value: Boolean): AnomalyDetectionModel.this.type

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    Definition Classes
    HasOutput
  103. def singleTarget: Boolean

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    Definition Classes
    HasTargetFields
  104. def size: Int

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    Definition Classes
    HasTargetFields
  105. var supplementOutput: Boolean

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    A flag for whether to return those predefined output fields not exist in the output element explicitly.

    A flag for whether to return those predefined output fields not exist in the output element explicitly.

    Definition Classes
    HasOutput
  106. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  107. lazy val targetClasses: Map[String, Array[Any]]

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    The class labels of all categorical targets.

    The class labels of all categorical targets.

    Definition Classes
    Model
  108. lazy val targetField: Field

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    The first target field for the supervised model.

    The first target field for the supervised model.

    Definition Classes
    Model
  109. lazy val targetFields: Array[Field]

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    All target fields in an array.

    All target fields in an array. Multiple target fields are allowed. It depends on the kind of the model whether prediction of multiple fields is supported.

    Definition Classes
    Model
  110. def targetFieldsOfResidual: Array[Field]

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    Returns targets that are residual values to be computed, the input data must include target values.

    Returns targets that are residual values to be computed, the input data must include target values.

    Definition Classes
    HasOutput
  111. def targetName: String

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    Name of the first target for the supervised model.

    Name of the first target for the supervised model.

    Definition Classes
    HasTargetFields
  112. lazy val targetNames: Array[String]

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    All target names in an array.

    All target names in an array.

    Definition Classes
    ModelHasTargetFields
  113. def targetNamesOfResidual: Array[String]

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    Definition Classes
    HasOutput
  114. def targets: Option[Targets]

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    Definition Classes
    AnomalyDetectionModelHasTargets
  115. def toString(): String

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    Definition Classes
    AnyRef → Any
  116. def transformationDictionary: Option[TransformationDictionary]

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    The optional transformation dictionary.

    The optional transformation dictionary.

    Definition Classes
    Model
  117. def unionCandidateOutputFields: Array[OutputField]

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    Definition Classes
    HasOutput
  118. def unionOutputFields: Array[OutputField]

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    Definition Classes
    HasOutput
  119. lazy val usedFields: Array[Field]

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    Setup indices to retrieve data from series faster by index instead of name, the index is immutable when model is built because the model object could run in multiple threads, so it's important make sure the model object is totally immutable.

    Setup indices to retrieve data from series faster by index instead of name, the index is immutable when model is built because the model object could run in multiple threads, so it's important make sure the model object is totally immutable.

    Setup indices of targets that are usually not used by the scoring process, they are only used when residual values to be computed.

    Definition Classes
    Model
  120. lazy val usedSchema: StructType

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    The schema of used fields.

    The schema of used fields.

    Definition Classes
    Model
  121. def version: String

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    PMML version.

    PMML version.

    Definition Classes
    HasVersion
  122. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  123. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  124. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Model

Inherited from PmmlElement

Inherited from Serializable

Inherited from Serializable

Inherited from HasExtensions

Inherited from HasModelVerification

Inherited from Predictable

Inherited from HasTargetFields

Inherited from ModelLocation

Inherited from FieldScope

Inherited from HasField

Inherited from HasLocalTransformations

Inherited from HasTargets

Inherited from HasModelExplanation

Inherited from HasModelStats

Inherited from HasOutput

Inherited from HasMiningSchema

Inherited from HasWrappedModelAttributes

Inherited from HasModelAttributes

Inherited from HasVersion

Inherited from HasParent

Inherited from AnyRef

Inherited from Any

Ungrouped