co.elastic.clients.elasticsearch.ml.DataframeAnalysisOutlierDetection Maven / Gradle / Ivy
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/*
* Licensed to Elasticsearch B.V. under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch B.V. licenses this file to you under
* the Apache License, Version 2.0 (the "License"); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package co.elastic.clients.elasticsearch.ml;
import co.elastic.clients.json.JsonpDeserializable;
import co.elastic.clients.json.JsonpDeserializer;
import co.elastic.clients.json.JsonpMapper;
import co.elastic.clients.json.JsonpSerializable;
import co.elastic.clients.json.JsonpUtils;
import co.elastic.clients.json.ObjectBuilderDeserializer;
import co.elastic.clients.json.ObjectDeserializer;
import co.elastic.clients.util.ObjectBuilder;
import co.elastic.clients.util.WithJsonObjectBuilderBase;
import jakarta.json.stream.JsonGenerator;
import java.lang.Boolean;
import java.lang.Double;
import java.lang.Integer;
import java.lang.String;
import java.util.Objects;
import java.util.function.Function;
import javax.annotation.Nullable;
//----------------------------------------------------------------
// THIS CODE IS GENERATED. MANUAL EDITS WILL BE LOST.
//----------------------------------------------------------------
//
// This code is generated from the Elasticsearch API specification
// at https://github.com/elastic/elasticsearch-specification
//
// Manual updates to this file will be lost when the code is
// re-generated.
//
// If you find a property that is missing or wrongly typed, please
// open an issue or a PR on the API specification repository.
//
//----------------------------------------------------------------
// typedef: ml._types.DataframeAnalysisOutlierDetection
/**
*
* @see API
* specification
*/
@JsonpDeserializable
public class DataframeAnalysisOutlierDetection implements DataframeAnalysisVariant, JsonpSerializable {
@Nullable
private final Boolean computeFeatureInfluence;
@Nullable
private final Double featureInfluenceThreshold;
@Nullable
private final String method;
@Nullable
private final Integer nNeighbors;
@Nullable
private final Double outlierFraction;
@Nullable
private final Boolean standardizationEnabled;
// ---------------------------------------------------------------------------------------------
private DataframeAnalysisOutlierDetection(Builder builder) {
this.computeFeatureInfluence = builder.computeFeatureInfluence;
this.featureInfluenceThreshold = builder.featureInfluenceThreshold;
this.method = builder.method;
this.nNeighbors = builder.nNeighbors;
this.outlierFraction = builder.outlierFraction;
this.standardizationEnabled = builder.standardizationEnabled;
}
public static DataframeAnalysisOutlierDetection of(
Function> fn) {
return fn.apply(new Builder()).build();
}
/**
* DataframeAnalysis variant kind.
*/
@Override
public DataframeAnalysis.Kind _dataframeAnalysisKind() {
return DataframeAnalysis.Kind.OutlierDetection;
}
/**
* Specifies whether the feature influence calculation is enabled.
*
* API name: {@code compute_feature_influence}
*/
@Nullable
public final Boolean computeFeatureInfluence() {
return this.computeFeatureInfluence;
}
/**
* The minimum outlier score that a document needs to have in order to calculate
* its feature influence score. Value range: 0-1.
*
* API name: {@code feature_influence_threshold}
*/
@Nullable
public final Double featureInfluenceThreshold() {
return this.featureInfluenceThreshold;
}
/**
* 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: {@code method}
*/
@Nullable
public final String method() {
return this.method;
}
/**
* 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: {@code n_neighbors}
*/
@Nullable
public final Integer nNeighbors() {
return this.nNeighbors;
}
/**
* 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: {@code outlier_fraction}
*/
@Nullable
public final Double outlierFraction() {
return this.outlierFraction;
}
/**
* If true, the following operation is performed on the columns before computing
* outlier scores: (x_i - mean(x_i)) / sd(x_i).
*
* API name: {@code standardization_enabled}
*/
@Nullable
public final Boolean standardizationEnabled() {
return this.standardizationEnabled;
}
/**
* Serialize this object to JSON.
*/
public void serialize(JsonGenerator generator, JsonpMapper mapper) {
generator.writeStartObject();
serializeInternal(generator, mapper);
generator.writeEnd();
}
protected void serializeInternal(JsonGenerator generator, JsonpMapper mapper) {
if (this.computeFeatureInfluence != null) {
generator.writeKey("compute_feature_influence");
generator.write(this.computeFeatureInfluence);
}
if (this.featureInfluenceThreshold != null) {
generator.writeKey("feature_influence_threshold");
generator.write(this.featureInfluenceThreshold);
}
if (this.method != null) {
generator.writeKey("method");
generator.write(this.method);
}
if (this.nNeighbors != null) {
generator.writeKey("n_neighbors");
generator.write(this.nNeighbors);
}
if (this.outlierFraction != null) {
generator.writeKey("outlier_fraction");
generator.write(this.outlierFraction);
}
if (this.standardizationEnabled != null) {
generator.writeKey("standardization_enabled");
generator.write(this.standardizationEnabled);
}
}
@Override
public String toString() {
return JsonpUtils.toString(this);
}
// ---------------------------------------------------------------------------------------------
/**
* Builder for {@link DataframeAnalysisOutlierDetection}.
*/
public static class Builder extends WithJsonObjectBuilderBase
implements
ObjectBuilder {
@Nullable
private Boolean computeFeatureInfluence;
@Nullable
private Double featureInfluenceThreshold;
@Nullable
private String method;
@Nullable
private Integer nNeighbors;
@Nullable
private Double outlierFraction;
@Nullable
private Boolean standardizationEnabled;
/**
* Specifies whether the feature influence calculation is enabled.
*
* API name: {@code compute_feature_influence}
*/
public final Builder computeFeatureInfluence(@Nullable Boolean value) {
this.computeFeatureInfluence = value;
return this;
}
/**
* The minimum outlier score that a document needs to have in order to calculate
* its feature influence score. Value range: 0-1.
*
* API name: {@code feature_influence_threshold}
*/
public final Builder featureInfluenceThreshold(@Nullable Double value) {
this.featureInfluenceThreshold = value;
return this;
}
/**
* 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: {@code method}
*/
public final Builder method(@Nullable String value) {
this.method = value;
return this;
}
/**
* 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: {@code n_neighbors}
*/
public final Builder nNeighbors(@Nullable Integer value) {
this.nNeighbors = value;
return this;
}
/**
* 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: {@code outlier_fraction}
*/
public final Builder outlierFraction(@Nullable Double value) {
this.outlierFraction = value;
return this;
}
/**
* If true, the following operation is performed on the columns before computing
* outlier scores: (x_i - mean(x_i)) / sd(x_i).
*
* API name: {@code standardization_enabled}
*/
public final Builder standardizationEnabled(@Nullable Boolean value) {
this.standardizationEnabled = value;
return this;
}
@Override
protected Builder self() {
return this;
}
/**
* Builds a {@link DataframeAnalysisOutlierDetection}.
*
* @throws NullPointerException
* if some of the required fields are null.
*/
public DataframeAnalysisOutlierDetection build() {
_checkSingleUse();
return new DataframeAnalysisOutlierDetection(this);
}
}
// ---------------------------------------------------------------------------------------------
/**
* Json deserializer for {@link DataframeAnalysisOutlierDetection}
*/
public static final JsonpDeserializer _DESERIALIZER = ObjectBuilderDeserializer
.lazy(Builder::new, DataframeAnalysisOutlierDetection::setupDataframeAnalysisOutlierDetectionDeserializer);
protected static void setupDataframeAnalysisOutlierDetectionDeserializer(
ObjectDeserializer op) {
op.add(Builder::computeFeatureInfluence, JsonpDeserializer.booleanDeserializer(), "compute_feature_influence");
op.add(Builder::featureInfluenceThreshold, JsonpDeserializer.doubleDeserializer(),
"feature_influence_threshold");
op.add(Builder::method, JsonpDeserializer.stringDeserializer(), "method");
op.add(Builder::nNeighbors, JsonpDeserializer.integerDeserializer(), "n_neighbors");
op.add(Builder::outlierFraction, JsonpDeserializer.doubleDeserializer(), "outlier_fraction");
op.add(Builder::standardizationEnabled, JsonpDeserializer.booleanDeserializer(), "standardization_enabled");
}
}