Examples with Input org.opensearch.ml.common.parameter.Input used on opensource projects

Example 16 with Input

use of org.opensearch.ml.common.parameter.Input in project ml-commons by opensearch-project.

the class AnomalyLocalizerImpl method newSearchRequestForEntry.

private SearchRequest newSearchRequestForEntry(AnomalyLocalizationInput input, AggregationBuilder agg, AnomalyLocalizationOutput.Bucket bucket, Optional<Map<String, Object>> afterKey) {
    RangeQueryBuilder timeRangeFilter = new RangeQueryBuilder(input.getTimeFieldName()).from(bucket.getStartTime(), true).to(bucket.getEndTime(), true);
    BoolQueryBuilder filter = QueryBuilders.boolQuery().filter(timeRangeFilter);
    input.getFilterQuery().ifPresent(q -> filter.filter(q));
    CompositeAggregationBuilder compositeAgg = new CompositeAggregationBuilder(agg.getName(), input.getAttributeFieldNames().stream().map(name -> new TermsValuesSourceBuilder(name).field(name)).collect(Collectors.toList())).size(MAX_BUCKET_SETTING.get(this.settings));
    compositeAgg.subAggregation(agg);
    if (afterKey.isPresent()) {
        compositeAgg.aggregateAfter(afterKey.get());
    }
    SearchSourceBuilder search = new SearchSourceBuilder().size(0).query(filter).aggregation(compositeAgg);
    SearchRequest searchRequest = new SearchRequest(new String[] { input.getIndexName() }, search);
    return searchRequest;
}

Example 17 with Input

use of org.opensearch.ml.common.parameter.Input in project ml-commons by opensearch-project.

the class AnomalyLocalizerImpl method processNewEntry.

private void processNewEntry(AnomalyLocalizationInput input, AggregationBuilder agg, AnomalyLocalizationOutput.Result result, AnomalyLocalizationOutput.Bucket bucket, Optional<Map<String, Object>> afterKey, PriorityQueue<AnomalyLocalizationOutput.Entity> queue, AnomalyLocalizationOutput output, ActionListener<AnomalyLocalizationOutput> listener) {
    SearchRequest request = newSearchRequestForEntry(input, agg, bucket, afterKey);
    client.search(request, wrap(r -> onNewEntryResponse(r, input, agg, result, bucket, queue, output, listener), listener::onFailure));
}

Example 18 with Input

use of org.opensearch.ml.common.parameter.Input in project ml-commons by opensearch-project.

the class AnomalyLocalizerImpl method newSearchRequestForOverallAggregates.

private MultiSearchRequest newSearchRequestForOverallAggregates(AnomalyLocalizationInput input, AggregationBuilder agg, LocalizationTimeBuckets timeBuckets) {
    MultiSearchRequest multiSearchRequest = new MultiSearchRequest();
    timeBuckets.getAllIntervals().stream().map(i -> {
        RangeQueryBuilder timeRangeFilter = new RangeQueryBuilder(input.getTimeFieldName()).from(i.getKey(), true).to(i.getValue(), true);
        BoolQueryBuilder filter = QueryBuilders.boolQuery().filter(timeRangeFilter);
        input.getFilterQuery().ifPresent(q -> filter.filter(q));
        SearchSourceBuilder search = new SearchSourceBuilder().size(0).query(filter).aggregation(agg);
        SearchRequest searchRequest = new SearchRequest(new String[] { input.getIndexName() }, search);
        return searchRequest;
    }).forEach(multiSearchRequest::add);
    return multiSearchRequest;
}

Example 19 with Input

use of org.opensearch.ml.common.parameter.Input in project ml-commons by opensearch-project.

the class MLEngineTest method predictWithoutModel.

@Test
public void predictWithoutModel() {
    exceptionRule.expect(IllegalArgumentException.class);
    exceptionRule.expectMessage("No model found for linear regression prediction.");
    MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructLinearRegressionPredictionDataFrame()).build();
    Input mlInput = MLInput.builder().algorithm(FunctionName.LINEAR_REGRESSION).inputDataset(inputDataset).build();
    MLEngine.predict(mlInput, null);
}

Example 20 with Input

use of org.opensearch.ml.common.parameter.Input in project ml-commons by opensearch-project.

the class MLEngineTest method predictLinearRegression.

@Test
public void predictLinearRegression() {
    Model model = trainLinearRegressionModel();
    DataFrame predictionDataFrame = constructLinearRegressionPredictionDataFrame();
    MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(predictionDataFrame).build();
    Input mlInput = MLInput.builder().algorithm(FunctionName.LINEAR_REGRESSION).inputDataset(inputDataset).build();
    MLPredictionOutput output = (MLPredictionOutput) MLEngine.predict(mlInput, model);
    DataFrame predictions = output.getPredictionResult();
    Assert.assertEquals(2, predictions.size());
}