Examples with RandomCutForestState com.amazon.randomcutforest.state.RandomCutForestState used on opensource projects

Example 11 with RandomCutForestState

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

the class StateMapperShingledBenchmark method roundTripFromJson.

@Benchmark
@OperationsPerInvocation(NUM_TEST_SAMPLES)
public String roundTripFromJson(BenchmarkState state, Blackhole blackhole) throws JsonProcessingException {
    String json = state.json;
    double[][] testData = state.testData;
    for (int i = 0; i < NUM_TEST_SAMPLES; i++) {
        ObjectMapper jsonMapper = new ObjectMapper();
        RandomCutForestState forestState = jsonMapper.readValue(json, RandomCutForestState.class);
        RandomCutForestMapper mapper = new RandomCutForestMapper();
        mapper.setSaveExecutorContextEnabled(true);
        mapper.setSaveTreeStateEnabled(state.saveTreeState);
        RandomCutForest forest = mapper.toModel(forestState);
        double score = forest.getAnomalyScore(testData[i]);
        blackhole.consume(score);
        forest.update(testData[i]);
        json = jsonMapper.writeValueAsString(mapper.toState(forest));
    }
    bytes = json.getBytes();
    return json;
}

Example 12 with RandomCutForestState

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

the class StateMapperBenchmark method roundTripFromJson.

@Benchmark
@OperationsPerInvocation(NUM_TEST_SAMPLES)
public String roundTripFromJson(BenchmarkState state, Blackhole blackhole) throws JsonProcessingException {
    String json = state.json;
    double[][] testData = state.testData;
    for (int i = 0; i < NUM_TEST_SAMPLES; i++) {
        ObjectMapper jsonMapper = new ObjectMapper();
        RandomCutForestState forestState = jsonMapper.readValue(json, RandomCutForestState.class);
        RandomCutForestMapper mapper = new RandomCutForestMapper();
        mapper.setSaveExecutorContextEnabled(true);
        mapper.setSaveTreeStateEnabled(state.saveTreeState);
        forest = mapper.toModel(forestState);
        double score = forest.getAnomalyScore(testData[i]);
        blackhole.consume(score);
        forest.update(testData[i]);
        json = jsonMapper.writeValueAsString(mapper.toState(forest));
    }
    bytes = json.getBytes();
    return json;
}

Example 13 with RandomCutForestState

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

the class StateMapperBenchmark method roundTripFromProtostuff.

@Benchmark
@OperationsPerInvocation(NUM_TEST_SAMPLES)
public byte[] roundTripFromProtostuff(BenchmarkState state, Blackhole blackhole) {
    bytes = state.protostuff;
    double[][] testData = state.testData;
    for (int i = 0; i < NUM_TEST_SAMPLES; i++) {
        Schema<RandomCutForestState> schema = RuntimeSchema.getSchema(RandomCutForestState.class);
        RandomCutForestState forestState = schema.newMessage();
        ProtostuffIOUtil.mergeFrom(bytes, forestState, schema);
        RandomCutForestMapper mapper = new RandomCutForestMapper();
        mapper.setSaveExecutorContextEnabled(true);
        mapper.setSaveTreeStateEnabled(state.saveTreeState);
        forest = mapper.toModel(forestState);
        double score = forest.getAnomalyScore(testData[i]);
        blackhole.consume(score);
        forest.update(testData[i]);
        forestState = mapper.toState(forest);
        LinkedBuffer buffer = LinkedBuffer.allocate(512);
        try {
            bytes = ProtostuffIOUtil.toByteArray(forestState, schema, buffer);
        } finally {
            buffer.clear();
        }
    }
    return bytes;
}

Example 14 with RandomCutForestState

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

the class StateMapperBenchmark method roundTripFromState.

@Benchmark
@OperationsPerInvocation(NUM_TEST_SAMPLES)
public RandomCutForestState roundTripFromState(BenchmarkState state, Blackhole blackhole) {
    RandomCutForestState forestState = state.forestState;
    double[][] testData = state.testData;
    for (int i = 0; i < NUM_TEST_SAMPLES; i++) {
        RandomCutForestMapper mapper = new RandomCutForestMapper();
        mapper.setSaveExecutorContextEnabled(true);
        mapper.setSaveTreeStateEnabled(state.saveTreeState);
        forest = mapper.toModel(forestState);
        double score = forest.getAnomalyScore(testData[i]);
        blackhole.consume(score);
        forest.update(testData[i]);
        forestState = mapper.toState(forest);
    }
    return forestState;
}

Example 15 with RandomCutForestState

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

the class ObjectStreamExample 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);
    System.out.printf("dimensions = %d, numberOfTrees = %d, sampleSize = %d, precision = %s%n", dimensions, numberOfTrees, sampleSize, precision);
    byte[] bytes = serialize(mapper.toState(forest));
    System.out.printf("Object output stream size = %d bytes%n", bytes.length);
    // Restore from object stream and compare anomaly scores produced by the two
    // forests
    RandomCutForestState state2 = (RandomCutForestState) deserialize(bytes);
    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!");
}