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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.ParseField;
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 abstract class NXYSignificanceHeuristic extends SignificanceHeuristic {
protected static final ParseField BACKGROUND_IS_SUPERSET = new ParseField("background_is_superset");
protected static final ParseField INCLUDE_NEGATIVES_FIELD = new ParseField("include_negatives");
protected static final String SCORE_ERROR_MESSAGE = ", does your background filter not include all documents in the bucket? If so and it is intentional, set \"" + BACKGROUND_IS_SUPERSET.getPreferredName() + "\": false";
protected final boolean backgroundIsSuperset;
/**
* Some heuristics do not differentiate between terms that are descriptive for subset or for
* the background without the subset. We might want to filter out the terms that are appear much less often
* in the subset than in the background without the subset.
*/
protected final boolean includeNegatives;
public NXYSignificanceHeuristic(boolean includeNegatives, boolean backgroundIsSuperset) {
this.includeNegatives = includeNegatives;
this.backgroundIsSuperset = backgroundIsSuperset;
}
@Override
public void writeTo(StreamOutput out) throws IOException {
out.writeBoolean(includeNegatives);
out.writeBoolean(backgroundIsSuperset);
}
@Override
public boolean equals(Object other) {
return ((NXYSignificanceHeuristic) other).includeNegatives == includeNegatives && ((NXYSignificanceHeuristic) other).backgroundIsSuperset == backgroundIsSuperset;
}
@Override
public int hashCode() {
int result = (includeNegatives ? 1 : 0);
result = 31 * result + (backgroundIsSuperset ? 1 : 0);
return result;
}
protected static class Frequencies {
double N00, N01, N10, N11, N0_, N1_, N_0, N_1, N;
}
protected Frequencies computeNxys(long subsetFreq, long subsetSize, long supersetFreq, long supersetSize, String scoreFunctionName) {
checkFrequencies(subsetFreq, subsetSize, supersetFreq, supersetSize, scoreFunctionName);
Frequencies frequencies = new Frequencies();
if (backgroundIsSuperset) {
//documents not in class and do not contain term
frequencies.N00 = supersetSize - supersetFreq - (subsetSize - subsetFreq);
//documents in class and do not contain term
frequencies.N01 = (subsetSize - subsetFreq);
// documents not in class and do contain term
frequencies.N10 = supersetFreq - subsetFreq;
// documents in class and do contain term
frequencies.N11 = subsetFreq;
//documents that do not contain term
frequencies.N0_ = supersetSize - supersetFreq;
//documents that contain term
frequencies.N1_ = supersetFreq;
//documents that are not in class
frequencies.N_0 = supersetSize - subsetSize;
//documents that are in class
frequencies.N_1 = subsetSize;
//all docs
frequencies.N = supersetSize;
} else {
//documents not in class and do not contain term
frequencies.N00 = supersetSize - supersetFreq;
//documents in class and do not contain term
frequencies.N01 = subsetSize - subsetFreq;
// documents not in class and do contain term
frequencies.N10 = supersetFreq;
// documents in class and do contain term
frequencies.N11 = subsetFreq;
//documents that do not contain term
frequencies.N0_ = supersetSize - supersetFreq + subsetSize - subsetFreq;
//documents that contain term
frequencies.N1_ = supersetFreq + subsetFreq;
//documents that are not in class
frequencies.N_0 = supersetSize;
//documents that are in class
frequencies.N_1 = subsetSize;
//all docs
frequencies.N = supersetSize + subsetSize;
}
return frequencies;
}
protected void checkFrequencies(long subsetFreq, long subsetSize, long supersetFreq, long supersetSize, String scoreFunctionName) {
checkFrequencyValidity(subsetFreq, subsetSize, supersetFreq, supersetSize, scoreFunctionName);
if (backgroundIsSuperset) {
if (subsetFreq > supersetFreq) {
throw new ElasticsearchIllegalArgumentException("subsetFreq > supersetFreq" + SCORE_ERROR_MESSAGE);
}
if (subsetSize > supersetSize) {
throw new ElasticsearchIllegalArgumentException("subsetSize > supersetSize" + SCORE_ERROR_MESSAGE);
}
if (supersetFreq - subsetFreq > supersetSize - subsetSize) {
throw new ElasticsearchIllegalArgumentException("supersetFreq - subsetFreq > supersetSize - subsetSize" + SCORE_ERROR_MESSAGE);
}
}
}
public static abstract class NXYParser implements SignificanceHeuristicParser {
@Override
public SignificanceHeuristic parse(XContentParser parser) throws IOException, QueryParsingException {
String givenName = parser.currentName();
boolean includeNegatives = false;
boolean backgroundIsSuperset = true;
XContentParser.Token token = parser.nextToken();
while (!token.equals(XContentParser.Token.END_OBJECT)) {
if (INCLUDE_NEGATIVES_FIELD.match(parser.currentName())) {
parser.nextToken();
includeNegatives = parser.booleanValue();
} else if (BACKGROUND_IS_SUPERSET.match(parser.currentName())) {
parser.nextToken();
backgroundIsSuperset = parser.booleanValue();
} else {
throw new ElasticsearchParseException("Field " + parser.currentName().toString() + " unknown for " + givenName);
}
token = parser.nextToken();
}
return newHeuristic(includeNegatives, backgroundIsSuperset);
}
protected abstract SignificanceHeuristic newHeuristic(boolean includeNegatives, boolean backgroundIsSuperset);
}
protected abstract static class NXYBuilder implements SignificanceHeuristicBuilder {
protected boolean includeNegatives = true;
protected boolean backgroundIsSuperset = true;
public NXYBuilder(boolean includeNegatives, boolean backgroundIsSuperset) {
this.includeNegatives = includeNegatives;
this.backgroundIsSuperset = backgroundIsSuperset;
}
protected void build(XContentBuilder builder) throws IOException {
builder.field(INCLUDE_NEGATIVES_FIELD.getPreferredName(), includeNegatives)
.field(BACKGROUND_IS_SUPERSET.getPreferredName(), backgroundIsSuperset);
}
}
}