Examples with PreprocessorMapper com.amazon.randomcutforest.parkservices.state.preprocessor.PreprocessorMapper used on opensource projects

Example 1 with PreprocessorMapper

use of com.amazon.randomcutforest.parkservices.state.preprocessor.PreprocessorMapper in project random-cut-forest-by-aws by aws.

the class ThresholdedRandomCutForestMapper method toState.

@Override
public ThresholdedRandomCutForestState toState(ThresholdedRandomCutForest model) {
    ThresholdedRandomCutForestState state = new ThresholdedRandomCutForestState();
    RandomCutForestMapper randomCutForestMapper = new RandomCutForestMapper();
    randomCutForestMapper.setPartialTreeStateEnabled(true);
    randomCutForestMapper.setSaveTreeStateEnabled(true);
    randomCutForestMapper.setCompressionEnabled(true);
    randomCutForestMapper.setSaveCoordinatorStateEnabled(true);
    randomCutForestMapper.setSaveExecutorContextEnabled(true);
    state.setForestState(randomCutForestMapper.toState(model.getForest()));
    BasicThresholderMapper thresholderMapper = new BasicThresholderMapper();
    state.setThresholderState(thresholderMapper.toState(model.getThresholder()));
    PreprocessorMapper preprocessorMapper = new PreprocessorMapper();
    state.setPreprocessorStates(new PreprocessorState[] { preprocessorMapper.toState((Preprocessor) model.getPreprocessor()) });
    state.setTriggerFactor(model.getPredictorCorrector().getTriggerFactor());
    state.setIgnoreSimilar(model.getPredictorCorrector().isIgnoreSimilar());
    state.setIgnoreSimilarFactor(model.getPredictorCorrector().getIgnoreSimilarFactor());
    state.setNumberOfAttributors(model.getPredictorCorrector().getNumberOfAttributors());
    state.setForestMode(model.getForestMode().name());
    state.setTransformMethod(model.getTransformMethod().name());
    IRCFComputeDescriptor descriptor = model.getLastAnomalyDescriptor();
    state.setLastAnomalyTimeStamp(descriptor.getInternalTimeStamp());
    state.setLastAnomalyScore(descriptor.getRCFScore());
    state.setLastAnomalyAttribution(new DiVectorMapper().toState(descriptor.getAttribution()));
    state.setLastAnomalyPoint(descriptor.getRCFPoint());
    state.setLastExpectedPoint(descriptor.getExpectedRCFPoint());
    state.setLastRelativeIndex(descriptor.getRelativeIndex());
    return state;
}

Example 2 with PreprocessorMapper

use of com.amazon.randomcutforest.parkservices.state.preprocessor.PreprocessorMapper in project random-cut-forest-by-aws by aws.

the class ThresholdedRandomCutForestMapper method toModel.

@Override
public ThresholdedRandomCutForest toModel(ThresholdedRandomCutForestState state, long seed) {
    RandomCutForestMapper randomCutForestMapper = new RandomCutForestMapper();
    BasicThresholderMapper thresholderMapper = new BasicThresholderMapper();
    PreprocessorMapper preprocessorMapper = new PreprocessorMapper();
    RandomCutForest forest = randomCutForestMapper.toModel(state.getForestState());
    BasicThresholder thresholder = thresholderMapper.toModel(state.getThresholderState());
    Preprocessor preprocessor = preprocessorMapper.toModel(state.getPreprocessorStates()[0]);
    ForestMode forestMode = ForestMode.valueOf(state.getForestMode());
    TransformMethod transformMethod = TransformMethod.valueOf(state.getTransformMethod());
    RCFComputeDescriptor descriptor = new RCFComputeDescriptor(null, 0L);
    descriptor.setRCFScore(state.getLastAnomalyScore());
    descriptor.setInternalTimeStamp(state.getLastAnomalyTimeStamp());
    descriptor.setAttribution(new DiVectorMapper().toModel(state.getLastAnomalyAttribution()));
    descriptor.setRCFPoint(state.getLastAnomalyPoint());
    descriptor.setExpectedRCFPoint(state.getLastExpectedPoint());
    descriptor.setRelativeIndex(state.getLastRelativeIndex());
    descriptor.setForestMode(forestMode);
    descriptor.setTransformMethod(transformMethod);
    descriptor.setImputationMethod(ImputationMethod.valueOf(state.getPreprocessorStates()[0].getImputationMethod()));
    PredictorCorrector predictorCorrector = new PredictorCorrector(thresholder);
    predictorCorrector.setIgnoreSimilar(state.isIgnoreSimilar());
    predictorCorrector.setIgnoreSimilarFactor(state.getIgnoreSimilarFactor());
    predictorCorrector.setTriggerFactor(state.getTriggerFactor());
    predictorCorrector.setNumberOfAttributors(state.getNumberOfAttributors());
    return new ThresholdedRandomCutForest(forestMode, transformMethod, forest, predictorCorrector, preprocessor, descriptor);
}