Examples with SignificanceHeuristic org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic used on opensource projects

Example 1 with SignificanceHeuristic

use of org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic in project elasticsearch by elastic.

the class SignificantTermsTests method createTestAggregatorBuilder.

@Override
protected SignificantTermsAggregationBuilder createTestAggregatorBuilder() {
    String name = randomAsciiOfLengthBetween(3, 20);
    SignificantTermsAggregationBuilder factory = new SignificantTermsAggregationBuilder(name, null);
    String field = randomAsciiOfLengthBetween(3, 20);
    int randomFieldBranch = randomInt(2);
    switch(randomFieldBranch) {
        case 0:
            factory.field(field);
            break;
        case 1:
            factory.field(field);
            factory.script(new Script("_value + 1"));
            break;
        case 2:
            factory.script(new Script("doc[" + field + "] + 1"));
            break;
        default:
            fail();
    }
    if (randomBoolean()) {
        factory.missing("MISSING");
    }
    if (randomBoolean()) {
        factory.bucketCountThresholds().setRequiredSize(randomIntBetween(1, Integer.MAX_VALUE));
    }
    if (randomBoolean()) {
        factory.bucketCountThresholds().setShardSize(randomIntBetween(1, Integer.MAX_VALUE));
    }
    if (randomBoolean()) {
        int minDocCount = randomInt(4);
        switch(minDocCount) {
            case 0:
                break;
            case 1:
            case 2:
            case 3:
            case 4:
                minDocCount = randomIntBetween(0, Integer.MAX_VALUE);
                break;
        }
        factory.bucketCountThresholds().setMinDocCount(minDocCount);
    }
    if (randomBoolean()) {
        int shardMinDocCount = randomInt(4);
        switch(shardMinDocCount) {
            case 0:
                break;
            case 1:
            case 2:
            case 3:
            case 4:
                shardMinDocCount = randomIntBetween(0, Integer.MAX_VALUE);
                break;
            default:
                fail();
        }
        factory.bucketCountThresholds().setShardMinDocCount(shardMinDocCount);
    }
    if (randomBoolean()) {
        factory.executionHint(randomFrom(executionHints));
    }
    if (randomBoolean()) {
        factory.format("###.##");
    }
    if (randomBoolean()) {
        IncludeExclude incExc = null;
        switch(randomInt(5)) {
            case 0:
                incExc = new IncludeExclude(new RegExp("foobar"), null);
                break;
            case 1:
                incExc = new IncludeExclude(null, new RegExp("foobaz"));
                break;
            case 2:
                incExc = new IncludeExclude(new RegExp("foobar"), new RegExp("foobaz"));
                break;
            case 3:
                SortedSet<BytesRef> includeValues = new TreeSet<>();
                int numIncs = randomIntBetween(1, 20);
                for (int i = 0; i < numIncs; i++) {
                    includeValues.add(new BytesRef(randomAsciiOfLengthBetween(1, 30)));
                }
                SortedSet<BytesRef> excludeValues = null;
                incExc = new IncludeExclude(includeValues, excludeValues);
                break;
            case 4:
                SortedSet<BytesRef> includeValues2 = null;
                SortedSet<BytesRef> excludeValues2 = new TreeSet<>();
                int numExcs2 = randomIntBetween(1, 20);
                for (int i = 0; i < numExcs2; i++) {
                    excludeValues2.add(new BytesRef(randomAsciiOfLengthBetween(1, 30)));
                }
                incExc = new IncludeExclude(includeValues2, excludeValues2);
                break;
            case 5:
                SortedSet<BytesRef> includeValues3 = new TreeSet<>();
                int numIncs3 = randomIntBetween(1, 20);
                for (int i = 0; i < numIncs3; i++) {
                    includeValues3.add(new BytesRef(randomAsciiOfLengthBetween(1, 30)));
                }
                SortedSet<BytesRef> excludeValues3 = new TreeSet<>();
                int numExcs3 = randomIntBetween(1, 20);
                for (int i = 0; i < numExcs3; i++) {
                    excludeValues3.add(new BytesRef(randomAsciiOfLengthBetween(1, 30)));
                }
                incExc = new IncludeExclude(includeValues3, excludeValues3);
                break;
            default:
                fail();
        }
        factory.includeExclude(incExc);
    }
    if (randomBoolean()) {
        SignificanceHeuristic significanceHeuristic = null;
        switch(randomInt(5)) {
            case 0:
                significanceHeuristic = new PercentageScore();
                break;
            case 1:
                significanceHeuristic = new ChiSquare(randomBoolean(), randomBoolean());
                break;
            case 2:
                significanceHeuristic = new GND(randomBoolean());
                break;
            case 3:
                significanceHeuristic = new MutualInformation(randomBoolean(), randomBoolean());
                break;
            case 4:
                significanceHeuristic = new ScriptHeuristic(new Script("foo"));
                break;
            case 5:
                significanceHeuristic = new JLHScore();
                break;
            default:
                fail();
        }
        factory.significanceHeuristic(significanceHeuristic);
    }
    if (randomBoolean()) {
        factory.backgroundFilter(QueryBuilders.termsQuery("foo", "bar"));
    }
    return factory;
}

Example 2 with SignificanceHeuristic

use of org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic in project elasticsearch by elastic.

the class SignificanceHeuristicTests method getRandomSignificanceheuristic.

SignificanceHeuristic getRandomSignificanceheuristic() {
    List<SignificanceHeuristic> heuristics = new ArrayList<>();
    heuristics.add(new JLHScore());
    heuristics.add(new MutualInformation(randomBoolean(), randomBoolean()));
    heuristics.add(new GND(randomBoolean()));
    heuristics.add(new ChiSquare(randomBoolean(), randomBoolean()));
    return heuristics.get(randomInt(3));
}

Example 3 with SignificanceHeuristic

use of org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic in project elasticsearch by elastic.

the class SignificanceHeuristicTests method createInternalAggregations.

// Create aggregations as they might come from three different shards and return as list.
private List<InternalAggregation> createInternalAggregations() {
    SignificanceHeuristic significanceHeuristic = getRandomSignificanceheuristic();
    TestAggFactory<?, ?> factory = randomBoolean() ? new StringTestAggFactory() : new LongTestAggFactory();
    List<InternalAggregation> aggs = new ArrayList<>();
    aggs.add(factory.createAggregation(significanceHeuristic, 4, 10, 1, (f, i) -> f.createBucket(4, 4, 5, 10, 0)));
    aggs.add(factory.createAggregation(significanceHeuristic, 4, 10, 1, (f, i) -> f.createBucket(4, 4, 5, 10, 1)));
    aggs.add(factory.createAggregation(significanceHeuristic, 8, 10, 2, (f, i) -> f.createBucket(4, 4, 5, 10, i)));
    return aggs;
}

Example 4 with SignificanceHeuristic

use of org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic in project elasticsearch by elastic.

the class SearchModule method registerSignificanceHeuristic.

private void registerSignificanceHeuristic(SearchExtensionSpec<SignificanceHeuristic, SignificanceHeuristicParser> heuristic) {
    significanceHeuristicParserRegistry.register(heuristic.getParser(), heuristic.getName());
    namedWriteables.add(new NamedWriteableRegistry.Entry(SignificanceHeuristic.class, heuristic.getName().getPreferredName(), heuristic.getReader()));
}

Example 5 with SignificanceHeuristic

use of org.elasticsearch.search.aggregations.bucket.significant.heuristics.SignificanceHeuristic in project elasticsearch by elastic.

the class InternalSignificantTerms method doReduce.

@Override
public InternalAggregation doReduce(List<InternalAggregation> aggregations, ReduceContext reduceContext) {
    long globalSubsetSize = 0;
    long globalSupersetSize = 0;
    // top-level Aggregations from each shard
    for (InternalAggregation aggregation : aggregations) {
        @SuppressWarnings("unchecked") InternalSignificantTerms<A, B> terms = (InternalSignificantTerms<A, B>) aggregation;
        globalSubsetSize += terms.getSubsetSize();
        globalSupersetSize += terms.getSupersetSize();
    }
    Map<String, List<B>> buckets = new HashMap<>();
    for (InternalAggregation aggregation : aggregations) {
        @SuppressWarnings("unchecked") InternalSignificantTerms<A, B> terms = (InternalSignificantTerms<A, B>) aggregation;
        for (B bucket : terms.getBucketsInternal()) {
            List<B> existingBuckets = buckets.get(bucket.getKeyAsString());
            if (existingBuckets == null) {
                existingBuckets = new ArrayList<>(aggregations.size());
                buckets.put(bucket.getKeyAsString(), existingBuckets);
            }
            // Adjust the buckets with the global stats representing the
            // total size of the pots from which the stats are drawn
            existingBuckets.add(bucket.newBucket(bucket.getSubsetDf(), globalSubsetSize, bucket.getSupersetDf(), globalSupersetSize, bucket.aggregations));
        }
    }
    SignificanceHeuristic heuristic = getSignificanceHeuristic().rewrite(reduceContext);
    final int size = reduceContext.isFinalReduce() == false ? buckets.size() : Math.min(requiredSize, buckets.size());
    BucketSignificancePriorityQueue<B> ordered = new BucketSignificancePriorityQueue<>(size);
    for (Map.Entry<String, List<B>> entry : buckets.entrySet()) {
        List<B> sameTermBuckets = entry.getValue();
        final B b = sameTermBuckets.get(0).reduce(sameTermBuckets, reduceContext);
        b.updateScore(heuristic);
        if (((b.score > 0) && (b.subsetDf >= minDocCount)) || reduceContext.isFinalReduce() == false) {
            ordered.insertWithOverflow(b);
        }
    }
    B[] list = createBucketsArray(ordered.size());
    for (int i = ordered.size() - 1; i >= 0; i--) {
        list[i] = ordered.pop();
    }
    return create(globalSubsetSize, globalSupersetSize, Arrays.asList(list));
}