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

Example 21 with DiVector

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

the class Preprocessor method addRelevantAttribution.

/**
 * adds information of expected point to the result descriptor (provided it is
 * marked anomalous) Note that is uses relativeIndex; that is, it can determine
 * that the anomaly occurred in the past (but within the shingle) and not at the
 * current point -- even though the detection has triggered now While this may
 * appear to be improper, information theoretically we may have a situation
 * where an anomaly is only discoverable after the "horse has bolted" -- suppose
 * that we see a random mixture of the triples { 1, 2, 3} and {2, 4, 5}
 * corresponding to "slow weeks" and "busy weeks". For example 1, 2, 3, 1, 2, 3,
 * 2, 4, 5, 1, 2, 3, 2, 4, 5, ... etc. If we see { 2, 2, X } (at positions 0 and
 * 1 (mod 3)) and are yet to see X, then we can infer that the pattern is
 * anomalous -- but we cannot determine which of the 2's are to blame. If it
 * were the first 2, then the detection is late. If X = 3 then we know it is the
 * first 2 in that unfinished triple; and if X = 5 then it is the second 2. In a
 * sense we are only truly wiser once the bolted horse has returned! But if we
 * were to say that the anomaly was always at the second 2 then that appears to
 * be suboptimal -- one natural path can be based on the ratio of the triples {
 * 1, 2, 3} and {2, 4, 5} seen before. Even better, we can attempt to estimate a
 * dynamic time dependent ratio -- and that is what RCF would do.
 *
 * @param result the description of the current point
 */
protected void addRelevantAttribution(AnomalyDescriptor result) {
    int base = dimension / shingleSize;
    double[] reference = result.getCurrentInput();
    double[] point = result.getRCFPoint();
    double[] newPoint = result.getExpectedRCFPoint();
    int index = result.getRelativeIndex();
    if (newPoint != null) {
        if (index < 0 && result.isStartOfAnomaly()) {
            reference = getShingledInput(shingleSize + index);
            result.setPastValues(reference);
            result.setPastTimeStamp(getTimeStamp(shingleSize - 1 + index));
        }
        if (mode == ForestMode.TIME_AUGMENTED) {
            int endPosition = (shingleSize - 1 + index + 1) * dimension / shingleSize;
            double timeGap = (newPoint[endPosition - 1] - point[endPosition - 1]);
            long expectedTimestamp = (timeGap == 0) ? getTimeStamp(shingleSize - 1 + index) : inverseMapTime(timeGap, index);
            result.setExpectedTimeStamp(expectedTimestamp);
        }
        double[] values = getExpectedValue(index, reference, point, newPoint);
        result.setExpectedValues(0, values, 1.0);
    }
    int startPosition = (shingleSize - 1 + result.getRelativeIndex()) * base;
    DiVector attribution = result.getAttribution();
    if (mode == ForestMode.TIME_AUGMENTED) {
        --base;
    }
    double[] flattenedAttribution = new double[base];
    for (int i = 0; i < base; i++) {
        flattenedAttribution[i] = attribution.getHighLowSum(startPosition + i);
    }
    result.setRelevantAttribution(flattenedAttribution);
    if (mode == ForestMode.TIME_AUGMENTED) {
        result.setTimeAttribution(attribution.getHighLowSum(startPosition + base));
    }
}

Example 22 with DiVector

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

the class AnomalyAttributionRunnerTest method testAnomalyAttributionTransformer.

@Test
public void testAnomalyAttributionTransformer() {
    RandomCutForest forest = mock(RandomCutForest.class);
    when(forest.getDimensions()).thenReturn(2);
    AnomalyAttributionRunner.AnomalyAttributionTransformer transformer = new AnomalyAttributionRunner.AnomalyAttributionTransformer(forest);
    DiVector vector = new DiVector(2);
    vector.low[0] = 1.1;
    vector.high[1] = 2.2;
    when(forest.getAnomalyAttribution(new double[] { 1.0, 2.0 })).thenReturn(vector);
    assertEquals(Arrays.asList("1.1", "0.0", "0.0", "2.2"), transformer.getResultValues(1.0, 2.0));
    assertEquals(Arrays.asList("anomaly_low_0", "anomaly_high_0", "anomaly_low_1", "anomaly_high_1"), transformer.getResultColumnNames());
    assertEquals(Arrays.asList("NA", "NA", "NA", "NA"), transformer.getEmptyResultValue());
}

Example 23 with DiVector

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

the class AnomalyAttributionVisitorTest method testNew.

@Test
public void testNew() {
    float[] point = new float[] { 1.1f, -2.2f, 3.3f };
    int treeMass = 99;
    AnomalyAttributionVisitor visitor = new AnomalyAttributionVisitor(point, treeMass);
    assertFalse(visitor.pointInsideBox);
    for (int i = 0; i < point.length; i++) {
        assertFalse(visitor.coordInsideBox[i]);
    }
    assertFalse(visitor.ignoreLeaf);
    assertEquals(0, visitor.ignoreLeafMassThreshold);
    DiVector result = visitor.getResult();
    double[] zero = new double[point.length];
    assertArrayEquals(zero, result.high);
    assertArrayEquals(zero, result.low);
}

Example 24 with DiVector

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

the class AnomalyAttributionVisitorTest method testAcceptLeafNotEquals.

@Test
public void testAcceptLeafNotEquals() {
    float[] point = new float[] { 1.1f, -2.2f, 3.3f };
    float[] anotherPoint = new float[] { -4.0f, 5.0f, 6.0f };
    INodeView leafNode = mock(NodeView.class);
    when(leafNode.getLeafPoint()).thenReturn(anotherPoint);
    when(leafNode.getBoundingBox()).thenReturn(new BoundingBox(anotherPoint, anotherPoint));
    int leafDepth = 100;
    int leafMass = 4;
    when(leafNode.getMass()).thenReturn(leafMass);
    int treeMass = 21;
    AnomalyAttributionVisitor visitor = new AnomalyAttributionVisitor(point, treeMass, 0);
    visitor.acceptLeaf(leafNode, leafDepth);
    double expectedScoreSum = defaultScoreUnseenFunction(leafDepth, leafMass);
    double sumOfNewRange = (1.1 - (-4.0)) + (5.0 - (-2.2)) + (6.0 - 3.3);
    DiVector result = visitor.getResult();
    assertEquals(defaultScalarNormalizerFunction(expectedScoreSum * (1.1 - (-4.0)) / sumOfNewRange, treeMass), result.high[0], EPSILON);
    assertEquals(0.0, result.low[0]);
    assertEquals(0.0, result.high[1]);
    assertEquals(defaultScalarNormalizerFunction(expectedScoreSum * (5.0 - (-2.2)) / sumOfNewRange, treeMass), result.low[1], EPSILON);
    assertEquals(0.0, result.high[2]);
    assertEquals(defaultScalarNormalizerFunction(expectedScoreSum * (6.0 - 3.3) / sumOfNewRange, treeMass), result.low[2], EPSILON);
    visitor = new AnomalyAttributionVisitor(point, treeMass, 3);
    visitor.acceptLeaf(leafNode, leafDepth);
    result = visitor.getResult();
    assertEquals(defaultScalarNormalizerFunction(expectedScoreSum * (1.1 - (-4.0)) / sumOfNewRange, treeMass), result.high[0], EPSILON);
    assertEquals(0.0, result.low[0]);
    assertEquals(0.0, result.high[1]);
    assertEquals(defaultScalarNormalizerFunction(expectedScoreSum * (5.0 - (-2.2)) / sumOfNewRange, treeMass), result.low[1], EPSILON);
    assertEquals(0.0, result.high[2]);
    assertEquals(defaultScalarNormalizerFunction(expectedScoreSum * (6.0 - 3.3) / sumOfNewRange, treeMass), result.low[2], EPSILON);
    visitor = new AnomalyAttributionVisitor(point, treeMass, 4);
    visitor.acceptLeaf(leafNode, leafDepth);
    double expectedScore = expectedScoreSum / (2 * point.length);
    result = visitor.getResult();
    for (int i = 0; i < point.length; i++) {
        assertEquals(defaultScalarNormalizerFunction(expectedScore, treeMass), result.low[i], EPSILON);
        assertEquals(defaultScalarNormalizerFunction(expectedScore, treeMass), result.high[i], EPSILON);
    }
}