Package co.elastic.clients.elasticsearch.ml

Class DataframeAnalysisOutlierDetection.Builder

java.lang.Object

co.elastic.clients.util.ObjectBuilderBase

co.elastic.clients.util.WithJsonObjectBuilderBase<DataframeAnalysisOutlierDetection.Builder>

co.elastic.clients.elasticsearch.ml.DataframeAnalysisOutlierDetection.Builder

All Implemented Interfaces:

WithJson<DataframeAnalysisOutlierDetection.Builder>, ObjectBuilder<DataframeAnalysisOutlierDetection>

Enclosing class:

DataframeAnalysisOutlierDetection

public static class DataframeAnalysisOutlierDetection.Builder
extends WithJsonObjectBuilderBase<DataframeAnalysisOutlierDetection.Builder>
implements ObjectBuilder<DataframeAnalysisOutlierDetection>

Builder for DataframeAnalysisOutlierDetection.

Constructor Summary

Constructors

Constructor	Description
Builder()

Method Summary

Modifier and Type	Method	Description
DataframeAnalysisOutlierDetection	build()	Builds a DataframeAnalysisOutlierDetection.
final DataframeAnalysisOutlierDetection.Builder	computeFeatureInfluence(Boolean value)	Specifies whether the feature influence calculation is enabled.
final DataframeAnalysisOutlierDetection.Builder	featureInfluenceThreshold(Double value)	The minimum outlier score that a document needs to have in order to calculate its feature influence score.
final DataframeAnalysisOutlierDetection.Builder	method(String value)	The method that outlier detection uses.
final DataframeAnalysisOutlierDetection.Builder	nNeighbors(Integer value)	Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score.
final DataframeAnalysisOutlierDetection.Builder	outlierFraction(Double value)	The proportion of the data set that is assumed to be outlying prior to outlier detection.
protected DataframeAnalysisOutlierDetection.Builder	self()
final DataframeAnalysisOutlierDetection.Builder	standardizationEnabled(Boolean value)	If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).

Methods inherited from class co.elastic.clients.util.WithJsonObjectBuilderBase

withJson

Methods inherited from class co.elastic.clients.util.ObjectBuilderBase

_checkSingleUse, _listAdd, _listAddAll, _mapPut, _mapPutAll

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface co.elastic.clients.json.WithJson

withJson, withJson

Constructor Details

Builder

public Builder()

Method Details

computeFeatureInfluence

public final DataframeAnalysisOutlierDetection.Builder computeFeatureInfluence(@Nullable
 Boolean value)

Specifies whether the feature influence calculation is enabled.

API name: compute_feature_influence

featureInfluenceThreshold

public final DataframeAnalysisOutlierDetection.Builder featureInfluenceThreshold(@Nullable
 Double value)

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

public final DataframeAnalysisOutlierDetection.Builder method(@Nullable
 String value)

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

public final DataframeAnalysisOutlierDetection.Builder nNeighbors(@Nullable
 Integer value)

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

public final DataframeAnalysisOutlierDetection.Builder outlierFraction(@Nullable
 Double value)

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

public final DataframeAnalysisOutlierDetection.Builder standardizationEnabled(@Nullable
 Boolean value)

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

self

protected DataframeAnalysisOutlierDetection.Builder self()

Specified by:

self in class WithJsonObjectBuilderBase<DataframeAnalysisOutlierDetection.Builder>

build

public DataframeAnalysisOutlierDetection build()

Builds a DataframeAnalysisOutlierDetection.

Specified by:

build in interface ObjectBuilder<DataframeAnalysisOutlierDetection>

Throws:

NullPointerException - if some of the required fields are null.