Examples with SamplerPlusTree com.amazon.randomcutforest.executor.SamplerPlusTree used on opensource projects

Example 1 with SamplerPlusTree

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

the class RandomCutForestTest method testGetApproximateAnomalyAttribution.

@Test
public void testGetApproximateAnomalyAttribution() {
    float[] point = { 1.2f, -3.4f };
    DiVector zero = new DiVector(dimensions);
    DiVector result = forest.getApproximateAnomalyAttribution(point);
    assertFalse(forest.isOutputReady());
    assertArrayEquals(zero.high, result.high, EPSILON);
    assertArrayEquals(zero.low, result.low, EPSILON);
    doReturn(true).when(forest).isOutputReady();
    ConvergingAccumulator<DiVector> accumulator = new OneSidedConvergingDiVectorAccumulator(dimensions, RandomCutForest.DEFAULT_APPROXIMATE_ANOMALY_SCORE_HIGH_IS_CRITICAL, RandomCutForest.DEFAULT_APPROXIMATE_DYNAMIC_SCORE_PRECISION, RandomCutForest.DEFAULT_APPROXIMATE_DYNAMIC_SCORE_MIN_VALUES_ACCEPTED, numberOfTrees);
    for (int i = 0; i < numberOfTrees; i++) {
        SamplerPlusTree<Integer, float[]> component = (SamplerPlusTree<Integer, float[]>) components.get(i);
        ITree<Integer, float[]> tree = component.getTree();
        DiVector treeResult = new DiVector(dimensions);
        for (int j = 0; j < dimensions; j++) {
            treeResult.high[j] = Math.random();
            treeResult.low[j] = Math.random();
        }
        when(tree.traverse(aryEq(point), any(VisitorFactory.class))).thenReturn(treeResult);
        when(tree.getMass()).thenReturn(256);
        if (!accumulator.isConverged()) {
            accumulator.accept(treeResult);
        }
    }
    DiVector expectedResult = accumulator.getAccumulatedValue().scale(1.0 / accumulator.getValuesAccepted());
    result = forest.getApproximateAnomalyAttribution(point);
    assertArrayEquals(expectedResult.high, result.high, EPSILON);
    assertArrayEquals(expectedResult.low, result.low, EPSILON);
}

Example 2 with SamplerPlusTree

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

the class RandomCutForestTest method testGetSimpleDensity.

@Test
public void testGetSimpleDensity() {
    float[] point = { 12.3f, -45.6f };
    DensityOutput zero = new DensityOutput(dimensions, sampleSize);
    assertFalse(forest.samplersFull());
    DensityOutput result = forest.getSimpleDensity(point);
    assertEquals(zero.getDensity(), result.getDensity(), EPSILON);
    doReturn(true).when(forest).samplersFull();
    List<InterpolationMeasure> intermediateResults = new ArrayList<>();
    for (int i = 0; i < numberOfTrees; i++) {
        InterpolationMeasure treeResult = new InterpolationMeasure(dimensions, sampleSize);
        for (int j = 0; j < dimensions; j++) {
            treeResult.measure.high[j] = Math.random();
            treeResult.measure.low[j] = Math.random();
            treeResult.distances.high[j] = Math.random();
            treeResult.distances.low[j] = Math.random();
            treeResult.probMass.high[j] = Math.random();
            treeResult.probMass.low[j] = Math.random();
        }
        SamplerPlusTree<Integer, float[]> component = (SamplerPlusTree<Integer, float[]>) components.get(i);
        ITree<Integer, float[]> tree = component.getTree();
        when(tree.traverse(aryEq(point), any(VisitorFactory.class))).thenReturn(treeResult);
        intermediateResults.add(treeResult);
    }
    Collector<InterpolationMeasure, ?, InterpolationMeasure> collector = InterpolationMeasure.collector(dimensions, sampleSize, numberOfTrees);
    DensityOutput expectedResult = new DensityOutput(intermediateResults.stream().collect(collector));
    result = forest.getSimpleDensity(point);
    assertEquals(expectedResult.getDensity(), result.getDensity(), EPSILON);
}

Example 3 with SamplerPlusTree

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

the class RandomCutForestTest method testGetAnomalyAttribution.

@Test
public void testGetAnomalyAttribution() {
    float[] point = { 1.2f, -3.4f };
    assertFalse(forest.isOutputReady());
    DiVector zero = new DiVector(dimensions);
    DiVector result = forest.getAnomalyAttribution(point);
    assertArrayEquals(zero.high, result.high);
    assertArrayEquals(zero.low, result.low);
    doReturn(true).when(forest).isOutputReady();
    DiVector expectedResult = new DiVector(dimensions);
    for (int i = 0; i < numberOfTrees; i++) {
        DiVector treeResult = new DiVector(dimensions);
        for (int j = 0; j < dimensions; j++) {
            treeResult.high[j] = Math.random();
            treeResult.low[j] = Math.random();
        }
        SamplerPlusTree<Integer, float[]> component = (SamplerPlusTree<Integer, float[]>) components.get(i);
        ITree<Integer, float[]> tree = component.getTree();
        when(tree.traverse(aryEq(point), any(VisitorFactory.class))).thenReturn(treeResult);
        when(tree.getMass()).thenReturn(256);
        DiVector.addToLeft(expectedResult, treeResult);
    }
    expectedResult = expectedResult.scale(1.0 / numberOfTrees);
    result = forest.getAnomalyAttribution(point);
    assertArrayEquals(expectedResult.high, result.high, EPSILON);
    assertArrayEquals(expectedResult.low, result.low, EPSILON);
}

Example 4 with SamplerPlusTree

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

the class RandomCutForestMapper method singlePrecisionForest.

public RandomCutForest singlePrecisionForest(RandomCutForest.Builder<?> builder, RandomCutForestState state, IPointStore<float[]> extPointStore, List<ITree<Integer, float[]>> extTrees, List<IStreamSampler<Integer>> extSamplers) {
    checkArgument(builder != null, "builder cannot be null");
    checkArgument(extTrees == null || extTrees.size() == state.getNumberOfTrees(), "incorrect number of trees");
    checkArgument(extSamplers == null || extSamplers.size() == state.getNumberOfTrees(), "incorrect number of samplers");
    checkArgument(extSamplers != null | state.isSaveSamplerStateEnabled(), " need samplers ");
    checkArgument(extPointStore != null || state.isSaveCoordinatorStateEnabled(), " need coordinator state ");
    Random random = builder.getRandom();
    ComponentList<Integer, float[]> components = new ComponentList<>();
    CompactRandomCutTreeContext context = new CompactRandomCutTreeContext();
    IPointStore<float[]> pointStore = (extPointStore == null) ? new PointStoreMapper().toModel(state.getPointStoreState()) : extPointStore;
    PointStoreCoordinator<float[]> coordinator = new PointStoreCoordinator<>(pointStore);
    coordinator.setTotalUpdates(state.getTotalUpdates());
    context.setPointStore(pointStore);
    context.setMaxSize(state.getSampleSize());
    RandomCutTreeMapper treeMapper = new RandomCutTreeMapper();
    List<CompactRandomCutTreeState> treeStates = state.isSaveTreeStateEnabled() ? state.getCompactRandomCutTreeStates() : null;
    CompactSamplerMapper samplerMapper = new CompactSamplerMapper();
    List<CompactSamplerState> samplerStates = state.isSaveSamplerStateEnabled() ? state.getCompactSamplerStates() : null;
    for (int i = 0; i < state.getNumberOfTrees(); i++) {
        IStreamSampler<Integer> sampler = (extSamplers != null) ? extSamplers.get(i) : samplerMapper.toModel(samplerStates.get(i), random.nextLong());
        ITree<Integer, float[]> tree;
        if (extTrees != null) {
            tree = extTrees.get(i);
        } else if (treeStates != null) {
            tree = treeMapper.toModel(treeStates.get(i), context, random.nextLong());
            sampler.getSample().forEach(s -> tree.addPoint(s.getValue(), s.getSequenceIndex()));
            tree.setConfig(Config.BOUNDING_BOX_CACHE_FRACTION, treeStates.get(i).getBoundingBoxCacheFraction());
        } else {
            // using boundingBoxCahce for the new tree
            tree = new RandomCutTree.Builder().capacity(state.getSampleSize()).randomSeed(random.nextLong()).pointStoreView(pointStore).boundingBoxCacheFraction(state.getBoundingBoxCacheFraction()).centerOfMassEnabled(state.isCenterOfMassEnabled()).storeSequenceIndexesEnabled(state.isStoreSequenceIndexesEnabled()).build();
            sampler.getSample().forEach(s -> tree.addPoint(s.getValue(), s.getSequenceIndex()));
        }
        components.add(new SamplerPlusTree<>(sampler, tree));
    }
    builder.precision(Precision.FLOAT_32);
    return new RandomCutForest(builder, coordinator, components, random);
}

Example 5 with SamplerPlusTree

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

the class RandomCutForestMapper method toState.

/**
 * Create a {@link RandomCutForestState} object representing the state of the
 * given forest. If the forest is compact and the {@code saveTreeState} flag is
 * set to true, then structure of the trees in the forest will be included in
 * the state object. If the flag is set to false, then the state object will
 * only contain the sampler data for each tree. If the
 * {@code saveExecutorContext} is true, then the executor context will be
 * included in the state object.
 *
 * @param forest A Random Cut Forest whose state we want to capture.
 * @return a {@link RandomCutForestState} object representing the state of the
 *         given forest.
 * @throws IllegalArgumentException if the {@code saveTreeState} flag is true
 *                                  and the forest is not compact.
 */
@Override
public RandomCutForestState toState(RandomCutForest forest) {
    if (saveTreeStateEnabled) {
        checkArgument(forest.isCompact(), "tree state cannot be saved for noncompact forests");
    }
    RandomCutForestState state = new RandomCutForestState();
    state.setNumberOfTrees(forest.getNumberOfTrees());
    state.setDimensions(forest.getDimensions());
    state.setTimeDecay(forest.getTimeDecay());
    state.setSampleSize(forest.getSampleSize());
    state.setShingleSize(forest.getShingleSize());
    state.setCenterOfMassEnabled(forest.isCenterOfMassEnabled());
    state.setOutputAfter(forest.getOutputAfter());
    state.setStoreSequenceIndexesEnabled(forest.isStoreSequenceIndexesEnabled());
    state.setTotalUpdates(forest.getTotalUpdates());
    state.setCompact(forest.isCompact());
    state.setInternalShinglingEnabled(forest.isInternalShinglingEnabled());
    state.setBoundingBoxCacheFraction(forest.getBoundingBoxCacheFraction());
    state.setSaveSamplerStateEnabled(saveSamplerStateEnabled);
    state.setSaveTreeStateEnabled(saveTreeStateEnabled);
    state.setSaveCoordinatorStateEnabled(saveCoordinatorStateEnabled);
    state.setPrecision(forest.getPrecision().name());
    state.setCompressed(compressionEnabled);
    state.setPartialTreeState(partialTreeStateEnabled);
    if (saveExecutorContextEnabled) {
        ExecutionContext executionContext = new ExecutionContext();
        executionContext.setParallelExecutionEnabled(forest.isParallelExecutionEnabled());
        executionContext.setThreadPoolSize(forest.getThreadPoolSize());
        state.setExecutionContext(executionContext);
    }
    if (saveCoordinatorStateEnabled) {
        PointStoreCoordinator<?> pointStoreCoordinator = (PointStoreCoordinator<?>) forest.getUpdateCoordinator();
        PointStoreMapper mapper = new PointStoreMapper();
        mapper.setCompressionEnabled(compressionEnabled);
        mapper.setNumberOfTrees(forest.getNumberOfTrees());
        PointStoreState pointStoreState = mapper.toState((PointStore) pointStoreCoordinator.getStore());
        state.setPointStoreState(pointStoreState);
    }
    List<CompactSamplerState> samplerStates = null;
    if (saveSamplerStateEnabled) {
        samplerStates = new ArrayList<>();
    }
    List<ITree<Integer, ?>> trees = null;
    if (saveTreeStateEnabled) {
        trees = new ArrayList<>();
    }
    CompactSamplerMapper samplerMapper = new CompactSamplerMapper();
    samplerMapper.setCompressionEnabled(compressionEnabled);
    for (IComponentModel<?, ?> component : forest.getComponents()) {
        SamplerPlusTree<Integer, ?> samplerPlusTree = (SamplerPlusTree<Integer, ?>) component;
        CompactSampler sampler = (CompactSampler) samplerPlusTree.getSampler();
        if (samplerStates != null) {
            samplerStates.add(samplerMapper.toState(sampler));
        }
        if (trees != null) {
            trees.add(samplerPlusTree.getTree());
        }
    }
    state.setCompactSamplerStates(samplerStates);
    if (trees != null) {
        RandomCutTreeMapper treeMapper = new RandomCutTreeMapper();
        List<CompactRandomCutTreeState> treeStates = trees.stream().map(t -> treeMapper.toState((RandomCutTree) t)).collect(Collectors.toList());
        state.setCompactRandomCutTreeStates(treeStates);
    }
    return state;
}