Package co.elastic.clients.elasticsearch.ml

Class DataframeAnalysisOutlierDetection

java.lang.Object
co.elastic.clients.elasticsearch.ml.DataframeAnalysisOutlierDetection
All Implemented Interfaces:
DataframeAnalysisVariant, JsonpSerializable, UnionVariant
@JsonpDeserializable
public final class DataframeAnalysisOutlierDetection
extends java.lang.Object
implements DataframeAnalysisVariant, JsonpSerializable

  • Field Details

  • Constructor Details

  • Method Details

    • _variantType

      public java.lang.String _variantType()
      DataframeAnalysis variant type
      Specified by:
      _variantType in interface UnionVariant

    • computeFeatureInfluence

      @Nullable public java.lang.Boolean computeFeatureInfluence()
      Specifies whether the feature influence calculation is enabled.

      API name: compute_feature_influence

    • featureInfluenceThreshold

      @Nullable public java.lang.Double featureInfluenceThreshold()
      The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1.

      API name: feature_influence_threshold

    • method

      @Nullable public java.lang.String method()
      The method that outlier detection uses. Available methods are lof, ldof, distance_kth_nn, distance_knn, and ensemble. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score.

      API name: method

    • nNeighbors

      @Nullable public java.lang.Integer nNeighbors()
      Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.

      API name: n_neighbors

    • outlierFraction

      @Nullable public java.lang.Double outlierFraction()
      The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.

      API name: outlier_fraction

    • standardizationEnabled

      @Nullable public java.lang.Boolean standardizationEnabled()
      If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).

      API name: standardization_enabled

    • serialize

      public void serialize​(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)
      Serialize this object to JSON.
      Specified by:
      serialize in interface JsonpSerializable

    • serializeInternal

      protected void serializeInternal​(jakarta.json.stream.JsonGenerator generator, JsonpMapper mapper)

    • setupDataframeAnalysisOutlierDetectionDeserializer

      protected static void setupDataframeAnalysisOutlierDetectionDeserializer​(DelegatingDeserializer<DataframeAnalysisOutlierDetection.Builder> op)