Examples with Node org.tribuo.common.tree.Node used on opensource projects

Example 1 with Node

use of org.tribuo.common.tree.Node in project tribuo by oracle.

the class CARTRegressionTrainer method train.

@Override
public TreeModel<Regressor> train(Dataset<Regressor> examples, Map<String, Provenance> runProvenance, int invocationCount) {
    if (examples.getOutputInfo().getUnknownCount() > 0) {
        throw new IllegalArgumentException("The supplied Dataset contained unknown Outputs, and this Trainer is supervised.");
    }
    // Creates a new RNG, adds one to the invocation count.
    SplittableRandom localRNG;
    TrainerProvenance trainerProvenance;
    synchronized (this) {
        if (invocationCount != INCREMENT_INVOCATION_COUNT) {
            setInvocationCount(invocationCount);
        }
        localRNG = rng.split();
        trainerProvenance = getProvenance();
        trainInvocationCounter++;
    }
    ImmutableFeatureMap featureIDMap = examples.getFeatureIDMap();
    ImmutableOutputInfo<Regressor> outputIDInfo = examples.getOutputIDInfo();
    Set<Regressor> domain = outputIDInfo.getDomain();
    int numFeaturesInSplit = Math.min(Math.round(fractionFeaturesInSplit * featureIDMap.size()), featureIDMap.size());
    int[] indices;
    int[] originalIndices = new int[featureIDMap.size()];
    for (int i = 0; i < originalIndices.length; i++) {
        originalIndices[i] = i;
    }
    if (numFeaturesInSplit != featureIDMap.size()) {
        indices = new int[numFeaturesInSplit];
    } else {
        indices = originalIndices;
    }
    float weightSum = 0.0f;
    for (Example<Regressor> e : examples) {
        weightSum += e.getWeight();
    }
    float scaledMinImpurityDecrease = getMinImpurityDecrease() * weightSum;
    AbstractTrainingNode.LeafDeterminer leafDeterminer = new AbstractTrainingNode.LeafDeterminer(maxDepth, minChildWeight, scaledMinImpurityDecrease);
    InvertedData data = RegressorTrainingNode.invertData(examples);
    Map<String, Node<Regressor>> nodeMap = new HashMap<>();
    for (Regressor r : domain) {
        String dimName = r.getNames()[0];
        int dimIdx = outputIDInfo.getID(r);
        AbstractTrainingNode<Regressor> root = new RegressorTrainingNode(impurity, data, dimIdx, dimName, examples.size(), featureIDMap, outputIDInfo, leafDeterminer);
        Deque<AbstractTrainingNode<Regressor>> queue = new ArrayDeque<>();
        queue.add(root);
        while (!queue.isEmpty()) {
            AbstractTrainingNode<Regressor> node = queue.poll();
            if ((node.getImpurity() > 0.0) && (node.getDepth() < maxDepth) && (node.getWeightSum() >= minChildWeight)) {
                if (numFeaturesInSplit != featureIDMap.size()) {
                    Util.randpermInPlace(originalIndices, localRNG);
                    System.arraycopy(originalIndices, 0, indices, 0, numFeaturesInSplit);
                }
                List<AbstractTrainingNode<Regressor>> nodes = node.buildTree(indices, localRNG, getUseRandomSplitPoints());
                // Use the queue as a stack to improve cache locality.
                for (AbstractTrainingNode<Regressor> newNode : nodes) {
                    queue.addFirst(newNode);
                }
            }
        }
        nodeMap.put(dimName, root.convertTree());
    }
    ModelProvenance provenance = new ModelProvenance(TreeModel.class.getName(), OffsetDateTime.now(), examples.getProvenance(), trainerProvenance, runProvenance);
    return new IndependentRegressionTreeModel("cart-tree", provenance, featureIDMap, outputIDInfo, false, nodeMap);
}

Example 2 with Node

use of org.tribuo.common.tree.Node in project tribuo by oracle.

the class IndependentRegressionTreeModel method getExcuse.

@Override
public Optional<Excuse<Regressor>> getExcuse(Example<Regressor> example) {
    SparseVector vec = SparseVector.createSparseVector(example, featureIDMap, false);
    if (vec.numActiveElements() == 0) {
        return Optional.empty();
    }
    List<String> list = new ArrayList<>();
    List<Prediction<Regressor>> predList = new ArrayList<>();
    Map<String, List<Pair<String, Double>>> map = new HashMap<>();
    for (Map.Entry<String, Node<Regressor>> e : roots.entrySet()) {
        list.clear();
        // 
        // Ensures we handle collisions correctly
        Node<Regressor> oldNode = e.getValue();
        Node<Regressor> curNode = e.getValue();
        while (curNode != null) {
            oldNode = curNode;
            if (oldNode instanceof SplitNode) {
                SplitNode<?> node = (SplitNode<?>) curNode;
                list.add(featureIDMap.get(node.getFeatureID()).getName());
            }
            curNode = oldNode.getNextNode(vec);
        }
        // 
        // oldNode must be a LeafNode.
        predList.add(((LeafNode<Regressor>) oldNode).getPrediction(vec.numActiveElements(), example));
        List<Pair<String, Double>> pairs = new ArrayList<>();
        int i = list.size() + 1;
        for (String s : list) {
            pairs.add(new Pair<>(s, i + 0.0));
            i--;
        }
        map.put(e.getKey(), pairs);
    }
    Prediction<Regressor> combinedPrediction = combine(predList);
    return Optional.of(new Excuse<>(example, combinedPrediction, map));
}

Example 3 with Node

use of org.tribuo.common.tree.Node in project tribuo by oracle.

the class IndependentRegressionTreeModel method predict.

@Override
public Prediction<Regressor> predict(Example<Regressor> example) {
    // 
    // Ensures we handle collisions correctly
    SparseVector vec = SparseVector.createSparseVector(example, featureIDMap, false);
    if (vec.numActiveElements() == 0) {
        throw new IllegalArgumentException("No features found in Example " + example.toString());
    }
    List<Prediction<Regressor>> predictionList = new ArrayList<>();
    for (Map.Entry<String, Node<Regressor>> e : roots.entrySet()) {
        Node<Regressor> oldNode = e.getValue();
        Node<Regressor> curNode = e.getValue();
        while (curNode != null) {
            oldNode = curNode;
            curNode = oldNode.getNextNode(vec);
        }
        // 
        // oldNode must be a LeafNode.
        predictionList.add(((LeafNode<Regressor>) oldNode).getPrediction(vec.numActiveElements(), example));
    }
    return combine(predictionList);
}