Examples with ConvergingAccumulator com.amazon.randomcutforest.returntypes.ConvergingAccumulator used on opensource projects

Example 1 with ConvergingAccumulator

use of com.amazon.randomcutforest.returntypes.ConvergingAccumulator in project random-cut-forest-by-aws by aws.

the class RandomCutForest method getApproximateAnomalyAttribution.

public DiVector getApproximateAnomalyAttribution(float[] point) {
    if (!isOutputReady()) {
        return new DiVector(dimensions);
    }
    IVisitorFactory<DiVector> visitorFactory = new VisitorFactory<>((tree, y) -> new AnomalyAttributionVisitor(tree.projectToTree(y), tree.getMass()), (tree, x) -> x.lift(tree::liftFromTree));
    ConvergingAccumulator<DiVector> accumulator = new OneSidedConvergingDiVectorAccumulator(dimensions, DEFAULT_APPROXIMATE_ANOMALY_SCORE_HIGH_IS_CRITICAL, DEFAULT_APPROXIMATE_DYNAMIC_SCORE_PRECISION, DEFAULT_APPROXIMATE_DYNAMIC_SCORE_MIN_VALUES_ACCEPTED, numberOfTrees);
    Function<DiVector, DiVector> finisher = x -> x.scale(1.0 / accumulator.getValuesAccepted());
    return traverseForest(transformToShingledPoint(point), visitorFactory, accumulator, finisher);
}

Example 2 with ConvergingAccumulator

use of com.amazon.randomcutforest.returntypes.ConvergingAccumulator in project random-cut-forest-by-aws by aws.

the class RandomCutForest method getApproximateDynamicScore.

/**
 * Score a point using the given scoring functions. This method will
 * short-circuit before visiting all trees if the scores that are returned from
 * a subset of trees appears to be converging to a given value. See
 * {@link OneSidedConvergingDoubleAccumulator} for more about convergence.
 *
 * @param point                   input point
 * @param precision               controls early convergence
 * @param highIsCritical          this is true for the default scoring function.
 *                                If the user wishes to use a different scoring
 *                                function where anomaly scores are low values
 *                                (for example, height in tree) then this should
 *                                be set to false.
 * @param ignoreLeafMassThreshold said threshold
 * @param seen                    scoring function when the input matches some
 *                                tuple in the leaves
 * @param unseen                  scoring function when the input is not found
 * @param damp                    dampening function for duplicates which are
 *                                same as input (applies with seen)
 * @return the dynamic score under sequential early stopping
 */
public double getApproximateDynamicScore(float[] point, double precision, boolean highIsCritical, int ignoreLeafMassThreshold, BiFunction<Double, Double, Double> seen, BiFunction<Double, Double, Double> unseen, BiFunction<Double, Double, Double> damp) {
    checkArgument(ignoreLeafMassThreshold >= 0, "ignoreLeafMassThreshold should be greater than or equal to 0");
    if (!isOutputReady()) {
        return 0.0;
    }
    VisitorFactory<Double> visitorFactory = new VisitorFactory<>((tree, y) -> new DynamicScoreVisitor(tree.projectToTree(y), tree.getMass(), ignoreLeafMassThreshold, seen, unseen, damp));
    ConvergingAccumulator<Double> accumulator = new OneSidedConvergingDoubleAccumulator(highIsCritical, precision, DEFAULT_APPROXIMATE_DYNAMIC_SCORE_MIN_VALUES_ACCEPTED, numberOfTrees);
    Function<Double, Double> finisher = x -> x / accumulator.getValuesAccepted();
    return traverseForest(transformToShingledPoint(point), visitorFactory, accumulator, finisher);
}

Example 3 with ConvergingAccumulator

use of com.amazon.randomcutforest.returntypes.ConvergingAccumulator in project random-cut-forest-by-aws by aws.

the class RandomCutForest method getApproximateDynamicAttribution.

/**
 * Atrribution for dynamic sequential scoring; getL1Norm() should agree with
 * getDynamicScoringSequential
 *
 * @param point                   input
 * @param precision               parameter to stop early stopping
 * @param highIsCritical          are high values anomalous (otherwise low
 *                                values are anomalous)
 * @param ignoreLeafMassThreshold we ignore leaves with mass equal/below *
 *                                threshold
 * @param seen                    function for scoring points that have been
 *                                seen before
 * @param unseen                  function for scoring points not seen in tree
 * @param newDamp                 dampening function based on duplicates
 * @return attribution DiVector of the score
 */
public DiVector getApproximateDynamicAttribution(float[] point, double precision, boolean highIsCritical, int ignoreLeafMassThreshold, BiFunction<Double, Double, Double> seen, BiFunction<Double, Double, Double> unseen, BiFunction<Double, Double, Double> newDamp) {
    if (!isOutputReady()) {
        return new DiVector(dimensions);
    }
    VisitorFactory<DiVector> visitorFactory = new VisitorFactory<>((tree, y) -> new DynamicAttributionVisitor(y, tree.getMass(), ignoreLeafMassThreshold, seen, unseen, newDamp), (tree, x) -> x.lift(tree::liftFromTree));
    ConvergingAccumulator<DiVector> accumulator = new OneSidedConvergingDiVectorAccumulator(dimensions, highIsCritical, precision, DEFAULT_APPROXIMATE_DYNAMIC_SCORE_MIN_VALUES_ACCEPTED, numberOfTrees);
    Function<DiVector, DiVector> finisher = vector -> vector.scale(1.0 / accumulator.getValuesAccepted());
    return traverseForest(transformToShingledPoint(point), visitorFactory, accumulator, finisher);
}