Examples with AnomalyDescriptor com.amazon.randomcutforest.parkservices.AnomalyDescriptor used on opensource projects

Example 11 with AnomalyDescriptor

use of com.amazon.randomcutforest.parkservices.AnomalyDescriptor in project random-cut-forest-by-aws by aws.

the class ThresholdedRandomCutForestMapperTest method testRoundTripImpute.

@ParameterizedTest
@MethodSource("args")
public void testRoundTripImpute(TransformMethod transformMethod, ImputationMethod imputationMethod) {
    int sampleSize = 256;
    int baseDimensions = 1;
    int shingleSize = 8;
    int dimensions = baseDimensions * shingleSize;
    long seed = new Random().nextLong();
    ThresholdedRandomCutForest first = ThresholdedRandomCutForest.builder().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).forestMode(ForestMode.STREAMING_IMPUTE).internalShinglingEnabled(true).shingleSize(shingleSize).transformMethod(transformMethod).imputationMethod(imputationMethod).fillValues(new double[] { 1.0 }).anomalyRate(0.01).build();
    ThresholdedRandomCutForest second = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).forestMode(ForestMode.STREAMING_IMPUTE).internalShinglingEnabled(true).shingleSize(shingleSize).transformMethod(transformMethod).imputationMethod(imputationMethod).fillValues(new double[] { 1.0 }).anomalyRate(0.01).build();
    Random r = new Random();
    long count = 0;
    MultiDimDataWithKey dataWithKeys = ShingledMultiDimDataWithKeys.getMultiDimData(10 * sampleSize, 50, 100, 5, seed, baseDimensions);
    for (double[] point : dataWithKeys.data) {
        if (r.nextDouble() > 0.1) {
            long stamp = 1000 * count + r.nextInt(10) - 5;
            AnomalyDescriptor firstResult = first.process(point, stamp);
            AnomalyDescriptor secondResult = second.process(point, stamp);
            assertEquals(firstResult.getRCFScore(), secondResult.getRCFScore(), 1e-10);
        }
        ++count;
    }
    ;
    // serialize + deserialize
    ThresholdedRandomCutForestMapper mapper = new ThresholdedRandomCutForestMapper();
    ThresholdedRandomCutForest third = mapper.toModel(mapper.toState(second));
    MultiDimDataWithKey testData = ShingledMultiDimDataWithKeys.getMultiDimData(100, 50, 100, 5, seed, baseDimensions);
    // update re-instantiated forest
    for (double[] point : testData.data) {
        long stamp = 1000 * count + r.nextInt(10) - 5;
        AnomalyDescriptor firstResult = first.process(point, stamp);
        // AnomalyDescriptor secondResult = second.process(point, stamp);
        AnomalyDescriptor thirdResult = third.process(point, stamp);
        // assertEquals(firstResult.getRcfScore(), secondResult.getRcfScore(), 1e-10);
        assertEquals(firstResult.getRCFScore(), thirdResult.getRCFScore(), 1e-10);
        ++count;
    }
}

Example 12 with AnomalyDescriptor

use of com.amazon.randomcutforest.parkservices.AnomalyDescriptor in project random-cut-forest-by-aws by aws.

the class ThresholdedRandomCutForestMapperTest method testRoundTripStandardShingledInternal.

@Test
public void testRoundTripStandardShingledInternal() {
    int sampleSize = 256;
    int baseDimensions = 2;
    int shingleSize = 8;
    int dimensions = baseDimensions * shingleSize;
    long seed = new Random().nextLong();
    RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).internalShinglingEnabled(true).shingleSize(shingleSize).randomSeed(seed).build();
    ThresholdedRandomCutForest first = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).internalShinglingEnabled(true).shingleSize(shingleSize).anomalyRate(0.01).adjustThreshold(true).boundingBoxCacheFraction(0).build();
    ThresholdedRandomCutForest second = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).internalShinglingEnabled(true).shingleSize(shingleSize).anomalyRate(0.01).adjustThreshold(true).build();
    double value = 0.75 + 0.5 * new Random().nextDouble();
    first.setLowerThreshold(value);
    second.setLowerThreshold(value);
    Random r = new Random();
    MultiDimDataWithKey dataWithKeys = ShingledMultiDimDataWithKeys.getMultiDimData(10 * sampleSize, 50, 100, 5, seed, baseDimensions);
    for (double[] point : dataWithKeys.data) {
        AnomalyDescriptor firstResult = first.process(point, 0L);
        AnomalyDescriptor secondResult = second.process(point, 0L);
        assertEquals(firstResult.getRCFScore(), secondResult.getRCFScore(), 1e-10);
        assertEquals(firstResult.getRCFScore(), forest.getAnomalyScore(point), 1e-4);
        if (firstResult.getAnomalyGrade() > 0) {
            assertEquals(secondResult.getAnomalyGrade(), firstResult.getAnomalyGrade(), 1e-10);
            assert (firstResult.getRCFScore() >= value);
        }
        forest.update(point);
    }
    // serialize + deserialize
    ThresholdedRandomCutForestMapper mapper = new ThresholdedRandomCutForestMapper();
    ThresholdedRandomCutForest third = mapper.toModel(mapper.toState(second));
    MultiDimDataWithKey testData = ShingledMultiDimDataWithKeys.getMultiDimData(100, 50, 100, 5, seed, baseDimensions);
    // update re-instantiated forest
    for (double[] point : testData.data) {
        AnomalyDescriptor firstResult = first.process(point, 0L);
        AnomalyDescriptor secondResult = second.process(point, 0L);
        AnomalyDescriptor thirdResult = third.process(point, 0L);
        double score = forest.getAnomalyScore(point);
        assertEquals(score, firstResult.getRCFScore(), 1e-4);
        assertEquals(firstResult.getRCFScore(), secondResult.getRCFScore(), 1e-10);
        assertEquals(firstResult.getRCFScore(), thirdResult.getRCFScore(), 1e-10);
        assertEquals(firstResult.getDataConfidence(), thirdResult.getDataConfidence(), 1e-10);
        forest.update(point);
    }
}

Example 13 with AnomalyDescriptor

use of com.amazon.randomcutforest.parkservices.AnomalyDescriptor in project random-cut-forest-by-aws by aws.

the class ThresholdedRandomCutForestMapperTest method testRoundTripAugmentedInitial.

@ParameterizedTest
@EnumSource(value = TransformMethod.class)
public void testRoundTripAugmentedInitial(TransformMethod method) {
    int sampleSize = 256;
    int baseDimensions = 2;
    int shingleSize = 8;
    int dimensions = baseDimensions * shingleSize;
    long seed = new Random().nextLong();
    // 0.25 * new Random().nextDouble();
    double value = 1.0;
    ThresholdedRandomCutForest first = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).forestMode(ForestMode.TIME_AUGMENTED).internalShinglingEnabled(true).shingleSize(shingleSize).transformMethod(method).anomalyRate(0.01).adjustThreshold(true).weights(new double[] { 1.0, 2.0 }).build();
    ThresholdedRandomCutForest second = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).forestMode(ForestMode.TIME_AUGMENTED).internalShinglingEnabled(true).shingleSize(shingleSize).transformMethod(method).anomalyRate(0.01).adjustThreshold(true).weights(new double[] { 1.0, 2.0 }).build();
    first.setLowerThreshold(value);
    second.setLowerThreshold(value);
    int count = 0;
    MultiDimDataWithKey dataWithKeys = ShingledMultiDimDataWithKeys.getMultiDimData(sampleSize, 50, 100, 5, seed, baseDimensions);
    for (double[] point : dataWithKeys.data) {
        AnomalyDescriptor firstResult = first.process(point, 0L);
        AnomalyDescriptor secondResult = second.process(point, 0L);
        assertEquals(firstResult.getRCFScore(), secondResult.getRCFScore(), 1e-10);
        assertEquals(secondResult.getAnomalyGrade(), firstResult.getAnomalyGrade(), 1e-10);
        // serialize + deserialize
        ThresholdedRandomCutForestMapper mapper = new ThresholdedRandomCutForestMapper();
        second = mapper.toModel(mapper.toState(second));
    }
}

Example 14 with AnomalyDescriptor

use of com.amazon.randomcutforest.parkservices.AnomalyDescriptor in project random-cut-forest-by-aws by aws.

the class ThresholdedRandomCutForestMapperTest method testRoundTripStandardInitial.

@ParameterizedTest
@EnumSource(value = TransformMethod.class)
public void testRoundTripStandardInitial(TransformMethod method) {
    int sampleSize = 256;
    int baseDimensions = 2;
    int shingleSize = 8;
    int dimensions = baseDimensions * shingleSize;
    long seed = new Random().nextLong();
    ThresholdedRandomCutForest first = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).internalShinglingEnabled(true).shingleSize(shingleSize).anomalyRate(0.01).adjustThreshold(true).build();
    ThresholdedRandomCutForest second = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).precision(Precision.FLOAT_32).randomSeed(seed).internalShinglingEnabled(true).shingleSize(shingleSize).anomalyRate(0.01).adjustThreshold(true).build();
    MultiDimDataWithKey dataWithKeys = ShingledMultiDimDataWithKeys.getMultiDimData(sampleSize, 50, 100, 5, seed, baseDimensions);
    for (double[] point : dataWithKeys.data) {
        AnomalyDescriptor firstResult = first.process(point, 0L);
        AnomalyDescriptor secondResult = second.process(point, 0L);
        assertEquals(firstResult.getRCFScore(), secondResult.getRCFScore(), 1e-10);
        assertEquals(secondResult.getAnomalyGrade(), firstResult.getAnomalyGrade(), 1e-10);
        // serialize + deserialize
        ThresholdedRandomCutForestMapper mapper = new ThresholdedRandomCutForestMapper();
        second = mapper.toModel(mapper.toState(second));
    }
}

Example 15 with AnomalyDescriptor

use of com.amazon.randomcutforest.parkservices.AnomalyDescriptor in project random-cut-forest-by-aws by aws.

the class Thresholded1DGaussianMix method run.

@Override
public void run() throws Exception {
    // Create and populate a random cut forest
    int shingleSize = 4;
    int numberOfTrees = 50;
    int sampleSize = 256;
    Precision precision = Precision.FLOAT_32;
    int dataSize = 4 * sampleSize;
    // change this to try different number of attributes,
    // this parameter is not expected to be larger than 5 for this example
    int baseDimensions = 1;
    int count = 0;
    int dimensions = baseDimensions * shingleSize;
    ThresholdedRandomCutForest forest = new ThresholdedRandomCutForest.Builder<>().compact(true).dimensions(dimensions).randomSeed(0).numberOfTrees(numberOfTrees).shingleSize(shingleSize).sampleSize(sampleSize).precision(precision).anomalyRate(0.01).forestMode(ForestMode.TIME_AUGMENTED).build();
    long seed = new Random().nextLong();
    System.out.println("Anomalies would correspond to a run, based on a change of state.");
    System.out.println("Each change is normal <-> anomaly;  so after the second change the data is normal");
    System.out.println("seed = " + seed);
    NormalMixtureTestData normalMixtureTestData = new NormalMixtureTestData(10, 1.0, 50, 2.0, 0.01, 0.1);
    MultiDimDataWithKey dataWithKeys = normalMixtureTestData.generateTestDataWithKey(dataSize, 1, 0);
    int keyCounter = 0;
    for (double[] point : dataWithKeys.data) {
        AnomalyDescriptor result = forest.process(point, count);
        if (keyCounter < dataWithKeys.changeIndices.length && result.getInternalTimeStamp() == dataWithKeys.changeIndices[keyCounter]) {
            System.out.println("timestamp " + (result.getInputTimestamp()) + " CHANGE");
            ++keyCounter;
        }
        if (keyCounter < dataWithKeys.changeIndices.length && count == dataWithKeys.changeIndices[keyCounter]) {
            System.out.println("timestamp " + (count) + " CHANGE ");
            ++keyCounter;
        }
        if (result.getAnomalyGrade() != 0) {
            System.out.print("timestamp " + (count) + " RESULT value ");
            for (int i = 0; i < baseDimensions; i++) {
                System.out.print(result.getCurrentInput()[i] + ", ");
            }
            System.out.print("score " + result.getRCFScore() + ", grade " + result.getAnomalyGrade() + ", ");
            if (result.isExpectedValuesPresent()) {
                if (result.getRelativeIndex() != 0 && result.isStartOfAnomaly()) {
                    System.out.print(-result.getRelativeIndex() + " steps ago, instead of ");
                    for (int i = 0; i < baseDimensions; i++) {
                        System.out.print(result.getPastValues()[i] + ", ");
                    }
                    System.out.print("expected ");
                    for (int i = 0; i < baseDimensions; i++) {
                        System.out.print(result.getExpectedValuesList()[0][i] + ", ");
                        if (result.getPastValues()[i] != result.getExpectedValuesList()[0][i]) {
                            System.out.print("( " + (result.getPastValues()[i] - result.getExpectedValuesList()[0][i]) + " ) ");
                        }
                    }
                } else {
                    System.out.print("expected ");
                    for (int i = 0; i < baseDimensions; i++) {
                        System.out.print(result.getExpectedValuesList()[0][i] + ", ");
                        if (result.getCurrentInput()[i] != result.getExpectedValuesList()[0][i]) {
                            System.out.print("( " + (result.getCurrentInput()[i] - result.getExpectedValuesList()[0][i]) + " ) ");
                        }
                    }
                }
            }
            System.out.println();
        }
        ++count;
    }
}