Examples with AnomalyFactory org.tribuo.anomaly.AnomalyFactory used on opensource projects

Example 1 with AnomalyFactory

use of org.tribuo.anomaly.AnomalyFactory in project ml-commons by opensearch-project.

the class AnomalyDetectionLibSVM method train.

@Override
public Model train(DataFrame dataFrame) {
    KernelType kernelType = parseKernelType();
    SVMParameters params = new SVMParameters<>(new SVMAnomalyType(SVMAnomalyType.SVMMode.ONE_CLASS), kernelType);
    Double gamma = Optional.ofNullable(parameters.getGamma()).orElse(DEFAULT_GAMMA);
    Double nu = Optional.ofNullable(parameters.getNu()).orElse(DEFAULT_NU);
    params.setGamma(gamma);
    params.setNu(nu);
    if (parameters.getCost() != null) {
        params.setCost(parameters.getCost());
    }
    if (parameters.getCoeff() != null) {
        params.setCoeff(parameters.getCoeff());
    }
    if (parameters.getEpsilon() != null) {
        params.setEpsilon(parameters.getEpsilon());
    }
    if (parameters.getDegree() != null) {
        params.setDegree(parameters.getDegree());
    }
    MutableDataset<Event> data = TribuoUtil.generateDataset(dataFrame, new AnomalyFactory(), "Anomaly detection LibSVM training data from OpenSearch", TribuoOutputType.ANOMALY_DETECTION_LIBSVM);
    LibSVMAnomalyTrainer trainer = new LibSVMAnomalyTrainer(params);
    LibSVMModel libSVMModel = trainer.train(data);
    ((LibSVMAnomalyModel) libSVMModel).getNumberOfSupportVectors();
    Model model = new Model();
    model.setName(FunctionName.AD_LIBSVM.name());
    model.setVersion(VERSION);
    model.setContent(ModelSerDeSer.serialize(libSVMModel));
    return model;
}

Example 2 with AnomalyFactory

use of org.tribuo.anomaly.AnomalyFactory in project ml-commons by opensearch-project.

the class AnomalyDetectionLibSVM method predict.

@Override
public MLOutput predict(DataFrame dataFrame, Model model) {
    if (model == null) {
        throw new IllegalArgumentException("No model found for KMeans prediction.");
    }
    List<Prediction<Event>> predictions;
    MutableDataset<Event> predictionDataset = TribuoUtil.generateDataset(dataFrame, new AnomalyFactory(), "Anomaly detection LibSVM prediction data from OpenSearch", TribuoOutputType.ANOMALY_DETECTION_LIBSVM);
    LibSVMModel libSVMAnomalyModel = (LibSVMModel) ModelSerDeSer.deserialize(model.getContent());
    predictions = libSVMAnomalyModel.predict(predictionDataset);
    List<Map<String, Object>> adResults = new ArrayList<>();
    predictions.forEach(e -> {
        Map<String, Object> result = new HashMap<>();
        result.put("score", e.getOutput().getScore());
        result.put("anomaly_type", e.getOutput().getType().name());
        adResults.add(result);
    });
    return MLPredictionOutput.builder().predictionResult(DataFrameBuilder.load(adResults)).build();
}