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Elasticsearch Expanded Pack Plugin - Core
/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the Elastic License
* 2.0; you may not use this file except in compliance with the Elastic License
* 2.0.
*/
package org.elasticsearch.xpack.core.ml.job.results;
import org.elasticsearch.common.ParseField;
import org.elasticsearch.common.io.stream.StreamInput;
import org.elasticsearch.common.io.stream.StreamOutput;
import org.elasticsearch.common.io.stream.Writeable;
import org.elasticsearch.common.xcontent.ConstructingObjectParser;
import org.elasticsearch.common.xcontent.ObjectParser.ValueType;
import org.elasticsearch.common.xcontent.ToXContentObject;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.xpack.core.ml.job.config.Job;
import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
import org.elasticsearch.xpack.core.common.time.TimeUtils;
import java.io.IOException;
import java.util.Date;
import java.util.Objects;
public class BucketInfluencer implements ToXContentObject, Writeable {
/**
* Result type
*/
public static final String RESULT_TYPE_VALUE = "bucket_influencer";
public static final ParseField RESULT_TYPE_FIELD = new ParseField(RESULT_TYPE_VALUE);
/**
* Field names
*/
public static final ParseField INFLUENCER_FIELD_NAME = new ParseField("influencer_field_name");
public static final ParseField INITIAL_ANOMALY_SCORE = new ParseField("initial_anomaly_score");
public static final ParseField ANOMALY_SCORE = new ParseField("anomaly_score");
public static final ParseField RAW_ANOMALY_SCORE = new ParseField("raw_anomaly_score");
public static final ParseField PROBABILITY = new ParseField("probability");
public static final ParseField BUCKET_SPAN = new ParseField("bucket_span");
/**
* The influencer field name used for time influencers
*/
public static final String BUCKET_TIME = "bucket_time";
public static final ConstructingObjectParser STRICT_PARSER = createParser(false);
public static final ConstructingObjectParser LENIENT_PARSER = createParser(true);
private static ConstructingObjectParser createParser(boolean ignoreUnknownFields) {
ConstructingObjectParser parser = new ConstructingObjectParser<>(RESULT_TYPE_FIELD.getPreferredName(),
ignoreUnknownFields, a -> new BucketInfluencer((String) a[0], (Date) a[1], (long) a[2]));
parser.declareString(ConstructingObjectParser.constructorArg(), Job.ID);
parser.declareField(ConstructingObjectParser.constructorArg(),
p -> TimeUtils.parseTimeField(p, Result.TIMESTAMP.getPreferredName()), Result.TIMESTAMP, ValueType.VALUE);
parser.declareLong(ConstructingObjectParser.constructorArg(), BUCKET_SPAN);
parser.declareString((bucketInfluencer, s) -> {}, Result.RESULT_TYPE);
parser.declareString(BucketInfluencer::setInfluencerFieldName, INFLUENCER_FIELD_NAME);
parser.declareDouble(BucketInfluencer::setInitialAnomalyScore, INITIAL_ANOMALY_SCORE);
parser.declareDouble(BucketInfluencer::setAnomalyScore, ANOMALY_SCORE);
parser.declareDouble(BucketInfluencer::setRawAnomalyScore, RAW_ANOMALY_SCORE);
parser.declareDouble(BucketInfluencer::setProbability, PROBABILITY);
parser.declareBoolean(BucketInfluencer::setIsInterim, Result.IS_INTERIM);
return parser;
}
private final String jobId;
private String influenceField;
private double initialAnomalyScore;
private double anomalyScore;
private double rawAnomalyScore;
private double probability;
private boolean isInterim;
private final Date timestamp;
private final long bucketSpan;
public BucketInfluencer(String jobId, Date timestamp, long bucketSpan) {
this.jobId = jobId;
this.timestamp = ExceptionsHelper.requireNonNull(timestamp, Result.TIMESTAMP.getPreferredName());
this.bucketSpan = bucketSpan;
}
public BucketInfluencer(StreamInput in) throws IOException {
jobId = in.readString();
influenceField = in.readOptionalString();
initialAnomalyScore = in.readDouble();
anomalyScore = in.readDouble();
rawAnomalyScore = in.readDouble();
probability = in.readDouble();
isInterim = in.readBoolean();
timestamp = new Date(in.readLong());
bucketSpan = in.readLong();
}
@Override
public void writeTo(StreamOutput out) throws IOException {
out.writeString(jobId);
out.writeOptionalString(influenceField);
out.writeDouble(initialAnomalyScore);
out.writeDouble(anomalyScore);
out.writeDouble(rawAnomalyScore);
out.writeDouble(probability);
out.writeBoolean(isInterim);
out.writeLong(timestamp.getTime());
out.writeLong(bucketSpan);
}
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.startObject();
innerToXContent(builder, params);
builder.endObject();
return builder;
}
XContentBuilder innerToXContent(XContentBuilder builder, Params params) throws IOException {
builder.field(Job.ID.getPreferredName(), jobId);
builder.field(Result.RESULT_TYPE.getPreferredName(), RESULT_TYPE_VALUE);
if (influenceField != null) {
builder.field(INFLUENCER_FIELD_NAME.getPreferredName(), influenceField);
}
builder.field(INITIAL_ANOMALY_SCORE.getPreferredName(), initialAnomalyScore);
builder.field(ANOMALY_SCORE.getPreferredName(), anomalyScore);
builder.field(RAW_ANOMALY_SCORE.getPreferredName(), rawAnomalyScore);
builder.field(PROBABILITY.getPreferredName(), probability);
builder.timeField(Result.TIMESTAMP.getPreferredName(), Result.TIMESTAMP.getPreferredName() + "_string", timestamp.getTime());
builder.field(BUCKET_SPAN.getPreferredName(), bucketSpan);
builder.field(Result.IS_INTERIM.getPreferredName(), isInterim);
return builder;
}
/**
* Data store ID of this bucket influencer.
*/
public String getId() {
return jobId + "_bucket_influencer_" + timestamp.getTime() + "_" + bucketSpan
+ (influenceField == null ? "" : "_" + influenceField);
}
public String getJobId() {
return jobId;
}
public double getProbability() {
return probability;
}
public void setProbability(double probability) {
this.probability = probability;
}
public String getInfluencerFieldName() {
return influenceField;
}
public void setInfluencerFieldName(String fieldName) {
this.influenceField = fieldName;
}
public double getInitialAnomalyScore() {
return initialAnomalyScore;
}
public void setInitialAnomalyScore(double influenceScore) {
this.initialAnomalyScore = influenceScore;
}
public double getAnomalyScore() {
return anomalyScore;
}
public void setAnomalyScore(double score) {
anomalyScore = score;
}
public double getRawAnomalyScore() {
return rawAnomalyScore;
}
public void setRawAnomalyScore(double score) {
rawAnomalyScore = score;
}
public void setIsInterim(boolean isInterim) {
this.isInterim = isInterim;
}
public boolean isInterim() {
return isInterim;
}
public Date getTimestamp() {
return timestamp;
}
@Override
public int hashCode() {
return Objects.hash(influenceField, initialAnomalyScore, anomalyScore, rawAnomalyScore, probability, isInterim, timestamp, jobId,
bucketSpan);
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
BucketInfluencer other = (BucketInfluencer) obj;
return Objects.equals(influenceField, other.influenceField) && Double.compare(initialAnomalyScore, other.initialAnomalyScore) == 0
&& Double.compare(anomalyScore, other.anomalyScore) == 0 && Double.compare(rawAnomalyScore, other.rawAnomalyScore) == 0
&& Double.compare(probability, other.probability) == 0 && Objects.equals(isInterim, other.isInterim)
&& Objects.equals(timestamp, other.timestamp) && Objects.equals(jobId, other.jobId) && bucketSpan == other.bucketSpan;
}
}