use of com.amazon.randomcutforest.IComponentModel 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;
}
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