Examples with RandomCutForest com.amazon.randomcutforest.RandomCutForest used on opensource projects

Example 31 with RandomCutForest

use of com.amazon.randomcutforest.RandomCutForest in project ml-commons by opensearch-project.

the class BatchRandomCutForest method train.

@Override
public Model train(DataFrame dataFrame) {
    RandomCutForest forest = createRandomCutForest(dataFrame);
    Integer actualTrainingDataSize = trainingDataSize == null ? dataFrame.size() : trainingDataSize;
    process(dataFrame, forest, actualTrainingDataSize);
    Model model = new Model();
    model.setName(FunctionName.BATCH_RCF.name());
    model.setVersion(1);
    RandomCutForestState state = rcfMapper.toState(forest);
    model.setContent(ModelSerDeSer.serialize(state));
    return model;
}

Example 32 with RandomCutForest

use of com.amazon.randomcutforest.RandomCutForest in project ml-commons by opensearch-project.

the class BatchRandomCutForest method trainAndPredict.

@Override
public MLOutput trainAndPredict(DataFrame dataFrame) {
    RandomCutForest forest = createRandomCutForest(dataFrame);
    Integer actualTrainingDataSize = trainingDataSize == null ? dataFrame.size() : trainingDataSize;
    List<Map<String, Object>> predictResult = process(dataFrame, forest, actualTrainingDataSize);
    return MLPredictionOutput.builder().predictionResult(DataFrameBuilder.load(predictResult)).build();
}

Example 33 with RandomCutForest

use of com.amazon.randomcutforest.RandomCutForest in project ml-commons by opensearch-project.

the class BatchRandomCutForest method process.

private List<Map<String, Object>> process(DataFrame dataFrame, RandomCutForest forest, Integer actualTrainingDataSize) {
    List<Double> pointList = new ArrayList<>();
    ColumnMeta[] columnMetas = dataFrame.columnMetas();
    List<Map<String, Object>> predictResult = new ArrayList<>();
    for (int rowNum = 0; rowNum < dataFrame.size(); rowNum++) {
        for (int i = 0; i < columnMetas.length; i++) {
            Row row = dataFrame.getRow(rowNum);
            ColumnValue value = row.getValue(i);
            pointList.add(value.doubleValue());
        }
        double[] point = pointList.stream().mapToDouble(d -> d).toArray();
        pointList.clear();
        double anomalyScore = forest.getAnomalyScore(point);
        if (actualTrainingDataSize == null || rowNum < actualTrainingDataSize) {
            forest.update(point);
        }
        Map<String, Object> result = new HashMap<>();
        result.put("score", anomalyScore);
        result.put("anomalous", anomalyScore > anomalyScoreThreshold);
        predictResult.add(result);
    }
    return predictResult;
}