Examples with RandomCutForest com.amazon.randomcutforest.RandomCutForest used on opensource projects

Example 6 with RandomCutForest

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

the class JsonExample method run.

@Override
public void run() throws Exception {
    // Create and populate a random cut forest
    int dimensions = 4;
    int numberOfTrees = 50;
    int sampleSize = 256;
    Precision precision = Precision.FLOAT_64;
    RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(precision).build();
    int dataSize = 4 * sampleSize;
    NormalMixtureTestData testData = new NormalMixtureTestData();
    for (double[] point : testData.generateTestData(dataSize, dimensions)) {
        forest.update(point);
    }
    // Convert to JSON and print the number of bytes
    RandomCutForestMapper mapper = new RandomCutForestMapper();
    mapper.setSaveExecutorContextEnabled(true);
    ObjectMapper jsonMapper = new ObjectMapper();
    String json = jsonMapper.writeValueAsString(mapper.toState(forest));
    System.out.printf("dimensions = %d, numberOfTrees = %d, sampleSize = %d, precision = %s%n", dimensions, numberOfTrees, sampleSize, precision);
    System.out.printf("JSON size = %d bytes%n", json.getBytes().length);
    // Restore from JSON and compare anomaly scores produced by the two forests
    RandomCutForest forest2 = mapper.toModel(jsonMapper.readValue(json, RandomCutForestState.class));
    int testSize = 100;
    double delta = Math.log(sampleSize) / Math.log(2) * 0.05;
    int differences = 0;
    int anomalies = 0;
    for (double[] point : testData.generateTestData(testSize, dimensions)) {
        double score = forest.getAnomalyScore(point);
        double score2 = forest2.getAnomalyScore(point);
        // also scored as an anomaly by the other forest
        if (score > 1 || score2 > 1) {
            anomalies++;
            if (Math.abs(score - score2) > delta) {
                differences++;
            }
        }
        forest.update(point);
        forest2.update(point);
    }
    // first validate that this was a nontrivial test
    if (anomalies == 0) {
        throw new IllegalStateException("test data did not produce any anomalies");
    }
    // validate that the two forests agree on anomaly scores
    if (differences >= 0.01 * testSize) {
        throw new IllegalStateException("restored forest does not agree with original forest");
    }
    System.out.println("Looks good!");
}

Example 7 with RandomCutForest

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

the class ProtostuffExample method run.

@Override
public void run() throws Exception {
    // Create and populate a random cut forest
    int dimensions = 10;
    int numberOfTrees = 50;
    int sampleSize = 256;
    Precision precision = Precision.FLOAT_32;
    RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(precision).build();
    int dataSize = 1000 * sampleSize;
    NormalMixtureTestData testData = new NormalMixtureTestData();
    for (double[] point : testData.generateTestData(dataSize, dimensions)) {
        forest.update(point);
    }
    // Convert to an array of bytes and print the size
    RandomCutForestMapper mapper = new RandomCutForestMapper();
    mapper.setSaveExecutorContextEnabled(true);
    Schema<RandomCutForestState> schema = RuntimeSchema.getSchema(RandomCutForestState.class);
    LinkedBuffer buffer = LinkedBuffer.allocate(512);
    byte[] bytes;
    try {
        RandomCutForestState state = mapper.toState(forest);
        bytes = ProtostuffIOUtil.toByteArray(state, schema, buffer);
    } finally {
        buffer.clear();
    }
    System.out.printf("dimensions = %d, numberOfTrees = %d, sampleSize = %d, precision = %s%n", dimensions, numberOfTrees, sampleSize, precision);
    System.out.printf("protostuff size = %d bytes%n", bytes.length);
    // Restore from protostuff and compare anomaly scores produced by the two
    // forests
    RandomCutForestState state2 = schema.newMessage();
    ProtostuffIOUtil.mergeFrom(bytes, state2, schema);
    RandomCutForest forest2 = mapper.toModel(state2);
    int testSize = 100;
    double delta = Math.log(sampleSize) / Math.log(2) * 0.05;
    int differences = 0;
    int anomalies = 0;
    for (double[] point : testData.generateTestData(testSize, dimensions)) {
        double score = forest.getAnomalyScore(point);
        double score2 = forest2.getAnomalyScore(point);
        // also scored as an anomaly by the other forest
        if (score > 1 || score2 > 1) {
            anomalies++;
            if (Math.abs(score - score2) > delta) {
                differences++;
            }
        }
        forest.update(point);
        forest2.update(point);
    }
    // first validate that this was a nontrivial test
    if (anomalies == 0) {
        throw new IllegalStateException("test data did not produce any anomalies");
    }
    // validate that the two forests agree on anomaly scores
    if (differences >= 0.01 * testSize) {
        throw new IllegalStateException("restored forest does not agree with original forest");
    }
    System.out.println("Looks good!");
}

Example 8 with RandomCutForest

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

the class ProtostuffExampleWithShingles method run.

@Override
public void run() throws Exception {
    // Create and populate a random cut forest
    int dimensions = 10;
    int numberOfTrees = 50;
    int sampleSize = 256;
    Precision precision = Precision.FLOAT_64;
    RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(precision).shingleSize(dimensions).build();
    int count = 1;
    int dataSize = 1000 * sampleSize;
    for (double[] point : generateShingledData(dataSize, dimensions, 0)) {
        forest.update(point);
    }
    // Convert to an array of bytes and print the size
    RandomCutForestMapper mapper = new RandomCutForestMapper();
    mapper.setSaveExecutorContextEnabled(true);
    mapper.setSaveTreeStateEnabled(false);
    Schema<RandomCutForestState> schema = RuntimeSchema.getSchema(RandomCutForestState.class);
    LinkedBuffer buffer = LinkedBuffer.allocate(512);
    byte[] bytes;
    try {
        RandomCutForestState state = mapper.toState(forest);
        bytes = ProtostuffIOUtil.toByteArray(state, schema, buffer);
    } finally {
        buffer.clear();
    }
    System.out.printf("dimensions = %d, numberOfTrees = %d, sampleSize = %d, precision = %s%n", dimensions, numberOfTrees, sampleSize, precision);
    System.out.printf("protostuff size = %d bytes%n", bytes.length);
    // Restore from protostuff and compare anomaly scores produced by the two
    // forests
    RandomCutForestState state2 = schema.newMessage();
    ProtostuffIOUtil.mergeFrom(bytes, state2, schema);
    RandomCutForest forest2 = mapper.toModel(state2);
    int testSize = 10000;
    double delta = Math.log(sampleSize) / Math.log(2) * 0.05;
    int differences = 0;
    int anomalies = 0;
    for (double[] point : generateShingledData(testSize, dimensions, 2)) {
        double score = forest.getAnomalyScore(point);
        double score2 = forest2.getAnomalyScore(point);
        // also scored as an anomaly by the other forest
        if (score > 1 || score2 > 1) {
            anomalies++;
            if (Math.abs(score - score2) > delta) {
                differences++;
            }
        }
        forest.update(point);
        forest2.update(point);
    }
    // validate that the two forests agree on anomaly scores
    if (differences >= 0.01 * testSize) {
        throw new IllegalStateException("restored forest does not agree with original forest");
    }
    System.out.println("Looks good!");
}

Example 9 with RandomCutForest

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

the class DynamicThroughput method run.

@Override
public void run() throws Exception {
    // Create and populate a random cut forest
    int dimensions = 4;
    int numberOfTrees = 50;
    int sampleSize = 256;
    Precision precision = Precision.FLOAT_64;
    int dataSize = 10 * sampleSize;
    NormalMixtureTestData testData = new NormalMixtureTestData();
    // generate data once to eliminate caching issues
    testData.generateTestData(dataSize, dimensions);
    testData.generateTestData(sampleSize, dimensions);
    for (int i = 0; i < 5; i++) {
        RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).randomSeed(0).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(precision).build();
        RandomCutForest forest2 = RandomCutForest.builder().compact(true).dimensions(dimensions).randomSeed(0).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(precision).build();
        forest2.setBoundingBoxCacheFraction(i * 0.25);
        int anomalies = 0;
        for (double[] point : testData.generateTestData(dataSize, dimensions)) {
            double score = forest.getAnomalyScore(point);
            double score2 = forest2.getAnomalyScore(point);
            if (Math.abs(score - score2) > 1e-10) {
                anomalies++;
            }
            forest.update(point);
            forest2.update(point);
        }
        Instant start = Instant.now();
        for (double[] point : testData.generateTestData(sampleSize, dimensions)) {
            double score = forest.getAnomalyScore(point);
            double score2 = forest2.getAnomalyScore(point);
            if (Math.abs(score - score2) > 1e-10) {
                anomalies++;
            }
            forest.update(point);
            forest2.update(point);
        }
        Instant finish = Instant.now();
        // first validate that this was a nontrivial test
        if (anomalies > 0) {
            throw new IllegalStateException("score mismatch");
        }
        System.out.println("So far so good! Caching fraction = " + (i * 0.25) + ", Time =" + Duration.between(start, finish).toMillis() + " ms (note only one forest is changing)");
    }
}

Example 10 with RandomCutForest

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

the class BoxCacheTest method testChangingBoundingBoxFloat32.

@Test
public void testChangingBoundingBoxFloat32() {
    int dimensions = 4;
    int numberOfTrees = 1;
    int sampleSize = 64;
    int dataSize = 1000 * sampleSize;
    Random random = new Random();
    long seed = random.nextLong();
    double[][] big = RandomCutForestTest.generateShingledData(dataSize, dimensions, 2);
    RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(Precision.FLOAT_32).randomSeed(seed).boundingBoxCacheFraction(0).build();
    RandomCutForest otherForest = RandomCutForest.builder().compact(true).dimensions(dimensions).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(Precision.FLOAT_32).randomSeed(seed).boundingBoxCacheFraction(1).build();
    int num = 0;
    for (double[] point : big) {
        ++num;
        if (num % sampleSize == 0) {
            forest.setBoundingBoxCacheFraction(random.nextDouble());
        }
        assertEquals(forest.getAnomalyScore(point), otherForest.getAnomalyScore(point));
        forest.update(point);
        otherForest.update(point);
    }
}