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* Licensed to Elasticsearch under one or more contributor
* license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright
* ownership. Elasticsearch 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 org.elasticsearch.search.aggregations.bucket.significant.heuristics;
import org.elasticsearch.ElasticsearchIllegalArgumentException;
import org.elasticsearch.ElasticsearchParseException;
import org.elasticsearch.common.io.stream.StreamInput;
import org.elasticsearch.common.io.stream.StreamOutput;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.XContentParser;
import org.elasticsearch.index.query.QueryParsingException;
import java.io.IOException;
public class JLHScore extends SignificanceHeuristic {
public static final JLHScore INSTANCE = new JLHScore();
protected static final String[] NAMES = {"jlh"};
private JLHScore() {};
public static final SignificanceHeuristicStreams.Stream STREAM = new SignificanceHeuristicStreams.Stream() {
@Override
public SignificanceHeuristic readResult(StreamInput in) throws IOException {
return readFrom(in);
}
@Override
public String getName() {
return NAMES[0];
}
};
public static SignificanceHeuristic readFrom(StreamInput in) throws IOException {
return INSTANCE;
}
/**
* Calculates the significance of a term in a sample against a background of
* normal distributions by comparing the changes in frequency. This is the heart
* of the significant terms feature.
*/
@Override
public double getScore(long subsetFreq, long subsetSize, long supersetFreq, long supersetSize) {
checkFrequencyValidity(subsetFreq, subsetSize, supersetFreq, supersetSize, "JLHScore");
if ((subsetSize == 0) || (supersetSize == 0)) {
// avoid any divide by zero issues
return 0;
}
if (supersetFreq == 0) {
// If we are using a background context that is not a strict superset, a foreground
// term may be missing from the background, so for the purposes of this calculation
// we assume a value of 1 for our calculations which avoids returning an "infinity" result
supersetFreq = 1;
}
double subsetProbability = (double) subsetFreq / (double) subsetSize;
double supersetProbability = (double) supersetFreq / (double) supersetSize;
// Using absoluteProbabilityChange alone favours very common words e.g. you, we etc
// because a doubling in popularity of a common term is a big percent difference
// whereas a rare term would have to achieve a hundred-fold increase in popularity to
// achieve the same difference measure.
// In favouring common words as suggested features for search we would get high
// recall but low precision.
double absoluteProbabilityChange = subsetProbability - supersetProbability;
if (absoluteProbabilityChange <= 0) {
return 0;
}
// Using relativeProbabilityChange tends to favour rarer terms e.g.mis-spellings or
// unique URLs.
// A very low-probability term can very easily double in popularity due to the low
// numbers required to do so whereas a high-probability term would have to add many
// extra individual sightings to achieve the same shift.
// In favouring rare words as suggested features for search we would get high
// precision but low recall.
double relativeProbabilityChange = (subsetProbability / supersetProbability);
// A blend of the above metrics - favours medium-rare terms to strike a useful
// balance between precision and recall.
return absoluteProbabilityChange * relativeProbabilityChange;
}
@Override
public void writeTo(StreamOutput out) throws IOException {
out.writeString(STREAM.getName());
}
public static class JLHScoreParser implements SignificanceHeuristicParser {
@Override
public SignificanceHeuristic parse(XContentParser parser) throws IOException, QueryParsingException {
// move to the closing bracket
if (!parser.nextToken().equals(XContentParser.Token.END_OBJECT)) {
throw new ElasticsearchParseException("expected }, got " + parser.currentName() + " instead in jhl score");
}
return new JLHScore();
}
@Override
public String[] getNames() {
return NAMES;
}
}
public static class JLHScoreBuilder implements SignificanceHeuristicBuilder {
@Override
public void toXContent(XContentBuilder builder) throws IOException {
builder.startObject(STREAM.getName()).endObject();
}
}
}