Examples with DimensionTuple org.tribuo.regression.Regressor.DimensionTuple used on opensource projects

Example 1 with DimensionTuple

use of org.tribuo.regression.Regressor.DimensionTuple in project tribuo by oracle.

the class RegressionInfo method getDomain.

/**
 * Returns a set containing a Regressor for each dimension with the minimum value observed.
 * @return A set of Regressors, each with one active dimension.
 */
@Override
public Set<Regressor> getDomain() {
    TreeSet<DimensionTuple> outputs = new TreeSet<>(Comparator.comparing(DimensionTuple::getName));
    for (Map.Entry<String, MutableDouble> e : minMap.entrySet()) {
        outputs.add(new DimensionTuple(e.getKey(), e.getValue().doubleValue()));
    }
    // DimensionTuple is a subtype of Regressor, and this set is immutable.
    @SuppressWarnings("unchecked") SortedSet<Regressor> setOutputs = (SortedSet<Regressor>) (SortedSet) Collections.unmodifiableSortedSet(outputs);
    return setOutputs;
}

Example 2 with DimensionTuple

use of org.tribuo.regression.Regressor.DimensionTuple in project tribuo by oracle.

the class RegressorTrainingNode method createLeaf.

/**
 * Makes a {@link LeafNode}
 * @param impurityScore the impurity score for the node.
 * @param leafIndices the indices of the examples to be placed in the node.
 * @return A {@link LeafNode}
 */
private LeafNode<Regressor> createLeaf(double impurityScore, int[] leafIndices) {
    double mean = 0.0;
    double leafWeightSum = 0.0;
    double variance = 0.0;
    for (int i = 0; i < leafIndices.length; i++) {
        int idx = leafIndices[i];
        float value = targets[idx];
        float weight = weights[idx];
        leafWeightSum += weight;
        double oldMean = mean;
        mean += (weight / leafWeightSum) * (value - oldMean);
        variance += weight * (value - oldMean) * (value - mean);
    }
    variance = leafIndices.length > 1 ? variance / (leafWeightSum - 1) : 0;
    DimensionTuple leafPred = new DimensionTuple(dimName, mean, variance);
    return new LeafNode<>(impurityScore, leafPred, Collections.emptyMap(), false);
}

Example 3 with DimensionTuple

use of org.tribuo.regression.Regressor.DimensionTuple in project tribuo by oracle.

the class ElasticNetCDTrainer method train.

@Override
public SparseModel<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, generates provenance.
    TrainerProvenance trainerProvenance;
    SplittableRandom localRNG;
    synchronized (this) {
        if (invocationCount != INCREMENT_INVOCATION_COUNT) {
            setInvocationCount(invocationCount);
        }
        localRNG = rng.split();
        trainerProvenance = getProvenance();
        trainInvocationCounter++;
    }
    ImmutableFeatureMap featureIDMap = examples.getFeatureIDMap();
    ImmutableOutputInfo<Regressor> outputInfo = examples.getOutputIDInfo();
    int numFeatures = featureIDMap.size();
    int numOutputs = outputInfo.size();
    int numExamples = examples.size();
    SparseVector[] columns = SparseVector.transpose(examples, featureIDMap);
    String[] dimensionNames = new String[numOutputs];
    DenseVector[] regressionTargets = new DenseVector[numOutputs];
    for (int i = 0; i < numOutputs; i++) {
        dimensionNames[i] = outputInfo.getOutput(i).getNames()[0];
        regressionTargets[i] = new DenseVector(numExamples);
    }
    int i = 0;
    for (Example<Regressor> e : examples) {
        for (DimensionTuple d : e.getOutput()) {
            regressionTargets[outputInfo.getID(d)].set(i, d.getValue());
        }
        i++;
    }
    double l1Penalty = alpha * l1Ratio * numExamples;
    double l2Penalty = alpha * (1.0 - l1Ratio) * numExamples;
    double[] featureMeans = calculateMeans(columns);
    double[] featureVariances = new double[columns.length];
    Arrays.fill(featureVariances, 1.0);
    boolean center = false;
    for (i = 0; i < numFeatures; i++) {
        if (Math.abs(featureMeans[i]) > DELTA) {
            center = true;
            break;
        }
    }
    double[] columnNorms = new double[numFeatures];
    int[] featureIndices = new int[numFeatures];
    for (i = 0; i < numFeatures; i++) {
        featureIndices[i] = i;
        double variance = 0.0;
        for (VectorTuple v : columns[i]) {
            variance += (v.value - featureMeans[i]) * (v.value - featureMeans[i]);
        }
        columnNorms[i] = variance + (numExamples - columns[i].numActiveElements()) * featureMeans[i] * featureMeans[i];
    }
    ElasticNetState elState = new ElasticNetState(columns, featureIndices, featureMeans, columnNorms, l1Penalty, l2Penalty, center);
    SparseVector[] outputWeights = new SparseVector[numOutputs];
    double[] outputMeans = new double[numOutputs];
    for (int j = 0; j < dimensionNames.length; j++) {
        outputWeights[j] = trainSingleDimension(regressionTargets[j], elState, localRNG.split());
        outputMeans[j] = regressionTargets[j].sum() / numExamples;
    }
    // calculateVariances(regressionTargets,outputMeans);
    double[] outputVariances = new double[numOutputs];
    Arrays.fill(outputVariances, 1.0);
    ModelProvenance provenance = new ModelProvenance(SparseLinearModel.class.getName(), OffsetDateTime.now(), examples.getProvenance(), trainerProvenance, runProvenance);
    return new SparseLinearModel("elastic-net-model", dimensionNames, provenance, featureIDMap, outputInfo, outputWeights, DenseVector.createDenseVector(featureMeans), DenseVector.createDenseVector(featureVariances), outputMeans, outputVariances, false);
}

Example 4 with DimensionTuple

use of org.tribuo.regression.Regressor.DimensionTuple in project tribuo by oracle.

the class IndependentRegressionTreeModel method combine.

private Prediction<Regressor> combine(List<Prediction<Regressor>> predictions) {
    DimensionTuple[] tuples = new DimensionTuple[predictions.size()];
    int numUsed = 0;
    int i = 0;
    for (Prediction<Regressor> p : predictions) {
        if (numUsed < p.getNumActiveFeatures()) {
            numUsed = p.getNumActiveFeatures();
        }
        Regressor output = p.getOutput();
        if (output instanceof DimensionTuple) {
            tuples[i] = (DimensionTuple) output;
        } else {
            throw new IllegalStateException("All the leaves should contain DimensionTuple not Regressor");
        }
        i++;
    }
    Example<Regressor> example = predictions.get(0).getExample();
    return new Prediction<>(new Regressor(tuples), numUsed, example);
}