Examples with IBoundingBoxView com.amazon.randomcutforest.tree.IBoundingBoxView used on opensource projects

Example 1 with IBoundingBoxView

use of com.amazon.randomcutforest.tree.IBoundingBoxView in project random-cut-forest-by-aws by aws.

the class AnomalyScoreVisitorTest method testAcceptEqualsLeafPoint.

@Test
public void testAcceptEqualsLeafPoint() {
    float[] pointToScore = { 0.0f, 0.0f };
    int sampleSize = 50;
    AnomalyScoreVisitor visitor = new AnomalyScoreVisitor(pointToScore, sampleSize);
    float[] point = Arrays.copyOf(pointToScore, pointToScore.length);
    INodeView node = mock(NodeView.class);
    when(node.getLeafPoint()).thenReturn(point);
    when(node.getBoundingBox()).thenReturn(new BoundingBox(point, point));
    int depth = 2;
    visitor.acceptLeaf(node, depth);
    double expectedScore = CommonUtils.defaultDampFunction(node.getMass(), sampleSize) / (depth + Math.log(node.getMass() + 1) / Math.log(2));
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
    depth--;
    IBoundingBoxView boundingBox = node.getBoundingBox().getMergedBox(new float[] { 1.0f, 1.0f });
    node = new NodeView(null, null, Null);
    visitor.accept(node, depth);
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
    depth--;
    boundingBox = boundingBox.getMergedBox(new float[] { -1.0f, -1.0f });
    node = new NodeView(null, null, Null);
    visitor.accept(node, depth);
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
}

Example 2 with IBoundingBoxView

use of com.amazon.randomcutforest.tree.IBoundingBoxView in project random-cut-forest-by-aws by aws.

the class AnomalyScoreVisitorTest method testAccept.

@Test
public void testAccept() {
    float[] pointToScore = new float[] { 0.0f, 0.0f };
    int sampleSize = 50;
    AnomalyScoreVisitor visitor = new AnomalyScoreVisitor(pointToScore, sampleSize);
    NodeView node = mock(NodeView.class);
    float[] otherPoint = new float[] { 1.0f, 1.0f };
    when(node.getLeafPoint()).thenReturn(otherPoint);
    when(node.getBoundingBox()).thenReturn(new BoundingBox(otherPoint, otherPoint));
    int depth = 4;
    visitor.acceptLeaf(node, depth);
    double expectedScore = 1.0 / (depth + 1);
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
    depth--;
    IBoundingBoxView boundingBox = node.getBoundingBox().getMergedBox(new float[] { 2.0f, 0.0f });
    when(node.getBoundingBox()).thenReturn(boundingBox);
    when(node.probailityOfSeparation(any())).thenReturn(1.0 / 3);
    visitor.accept(node, depth);
    double p = visitor.getProbabilityOfSeparation(boundingBox);
    expectedScore = p * (1.0 / (depth + 1)) + (1 - p) * expectedScore;
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
    depth--;
    boundingBox = boundingBox.getMergedBox(new float[] { -1.0f, 0.0f });
    when(node.getBoundingBox()).thenReturn(boundingBox);
    when(node.probailityOfSeparation(any())).thenReturn(0.0);
    visitor.accept(node, depth);
    p = visitor.getProbabilityOfSeparation(boundingBox);
    expectedScore = p * (1.0 / (depth + 1)) + (1 - p) * expectedScore;
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
    depth--;
    boundingBox = boundingBox.getMergedBox(new float[] { -1.0f, -1.0f });
    when(node.probailityOfSeparation(any())).thenReturn(0.0);
    visitor.accept(node, depth);
    p = visitor.getProbabilityOfSeparation(boundingBox);
    assertThat(visitor.getResult(), closeTo(CommonUtils.defaultScalarNormalizerFunction(expectedScore, sampleSize), EPSILON));
    assertTrue(visitor.pointInsideBox);
}

Example 3 with IBoundingBoxView

use of com.amazon.randomcutforest.tree.IBoundingBoxView in project random-cut-forest-by-aws by aws.

the class ImputeVisitorTest method testAccept.

@Test
public void testAccept() {
    float[] point = { queryPoint[0], 2.0f, -111.11f };
    INodeView node = mock(NodeView.class);
    when(node.getLeafPoint()).thenReturn(point);
    when(node.getLiftedLeafPoint()).thenReturn(point);
    when(node.getBoundingBox()).thenReturn(new BoundingBox(point, point));
    int depth = 100;
    int leafMass = 10;
    when(node.getMass()).thenReturn(leafMass);
    visitor.acceptLeaf(node, depth);
    float[] expected = new float[] { -1.0f, 2.0f, 3.0f };
    assertArrayEquals(expected, visitor.getResult().leafPoint);
    assertEquals(defaultScoreUnseenFunction(depth, leafMass), visitor.getAnomalyRank());
    depth--;
    IBoundingBoxView boundingBox = node.getBoundingBox().getMergedBox(new float[] { 99.0f, 4.0f, -19.0f });
    when(node.getBoundingBox()).thenReturn(boundingBox);
    when(node.probailityOfSeparation(any())).thenReturn(CommonUtils.getProbabilityOfSeparation(boundingBox, expected));
    when(node.getMass()).thenReturn(leafMass + 2);
    double oldRank = visitor.getAnomalyRank();
    visitor.accept(node, depth);
    assertArrayEquals(expected, visitor.getResult().leafPoint);
    double p = CommonUtils.getProbabilityOfSeparation(boundingBox, expected);
    double expectedRank = p * defaultScoreUnseenFunction(depth, node.getMass()) + (1 - p) * oldRank;
    assertEquals(expectedRank, visitor.getAnomalyRank(), EPSILON);
}

Example 4 with IBoundingBoxView

use of com.amazon.randomcutforest.tree.IBoundingBoxView in project random-cut-forest-by-aws by aws.

the class RandomCutForest method getDynamicSimulatedScore.

/**
 * Similar to above but now the scoring takes in a function of Bounding Box to
 * probabilities (vector over the dimensions); and produces a score af-if the
 * tree were built using that function (when in reality the tree is an RCF).
 * Changing the defaultRCFgVec function to some other function f() will provide
 * a mechanism of dynamic scoring for trees that are built using f() which is
 * the purpose of TransductiveScalarScore visitor. Note that the answer is an
 * MCMC simulation and is not normalized (because the scoring functions are
 * flexible and unknown) and over a small number of trees the errors can be
 * large specially if vecSep is very far from defaultRCFgVec
 *
 * Given the large number of possible sources of distortion, ignoreLeafThreshold
 * is not supported.
 *
 * @param point  point to be scored
 * @param seen   the score function for seen point
 * @param unseen score function for unseen points
 * @param damp   dampening the score for duplicates
 * @param vecSep the function of (BoundingBox) -> array of probabilities
 * @return the simuated score
 */
public double getDynamicSimulatedScore(float[] point, BiFunction<Double, Double, Double> seen, BiFunction<Double, Double, Double> unseen, BiFunction<Double, Double, Double> damp, Function<IBoundingBoxView, double[]> vecSep) {
    if (!isOutputReady()) {
        return 0.0;
    }
    VisitorFactory<Double> visitorFactory = new VisitorFactory<>((tree, y) -> new SimulatedTransductiveScalarScoreVisitor(tree.projectToTree(y), tree.getMass(), seen, unseen, damp, CommonUtils::defaultRCFgVecFunction, vecSep));
    BinaryOperator<Double> accumulator = Double::sum;
    Function<Double, Double> finisher = sum -> sum / numberOfTrees;
    return traverseForest(transformToShingledPoint(point), visitorFactory, accumulator, finisher);
}

Example 5 with IBoundingBoxView

use of com.amazon.randomcutforest.tree.IBoundingBoxView in project random-cut-forest-by-aws by aws.

the class SimpleInterpolationVisitor method accept.

@Override
public void accept(INodeView node, int depthOfNode) {
    if (pointInsideBox) {
        return;
    }
    IBoundingBoxView largeBox;
    IBoundingBoxView smallBox;
    if (pointEqualsLeaf) {
        largeBox = node.getBoundingBox();
        theShadowBox = theShadowBox == null ? node.getSiblingBoundingBox(pointToScore) : theShadowBox.getMergedBox(node.getSiblingBoundingBox(pointToScore));
        smallBox = theShadowBox;
    } else {
        smallBox = node.getBoundingBox();
        largeBox = smallBox.getMergedBox(pointToScore);
    }
    updateForCompute(smallBox, largeBox);
    double probOfCut = sumOfDifferenceInRange / sumOfNewRange;
    if (probOfCut <= 0) {
        pointInsideBox = true;
    } else {
        double fieldVal = fieldExt(node, centerOfMass, savedMass, pointToScore);
        double influenceVal = influenceExt(node, centerOfMass, savedMass, pointToScore);
        // otherwise the center of mass is the 0 vector
        for (int i = 0; i < pointToScore.length; i++) {
            double prob = differenceInRangeVector[2 * i] / sumOfNewRange;
            stored.probMass.high[i] = prob * influenceVal + (1 - probOfCut) * stored.probMass.high[i];
            stored.measure.high[i] = prob * fieldVal + (1 - probOfCut) * stored.measure.high[i];
            stored.distances.high[i] = prob * directionalDistanceVector[2 * i] * influenceVal + (1 - probOfCut) * stored.distances.high[i];
        }
        for (int i = 0; i < pointToScore.length; i++) {
            double prob = differenceInRangeVector[2 * i + 1] / sumOfNewRange;
            stored.probMass.low[i] = prob * influenceVal + (1 - probOfCut) * stored.probMass.low[i];
            stored.measure.low[i] = prob * fieldVal + (1 - probOfCut) * stored.measure.low[i];
            stored.distances.low[i] = prob * directionalDistanceVector[2 * i + 1] * influenceVal + (1 - probOfCut) * stored.distances.low[i];
        }
    }
}