Examples with SparseVector org.tribuo.math.la.SparseVector used on opensource projects

Example 1 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class SparseLinearModel method getExcuse.

@Override
public Optional<Excuse<Regressor>> getExcuse(Example<Regressor> example) {
    Prediction<Regressor> prediction = predict(example);
    Map<String, List<Pair<String, Double>>> weightMap = new HashMap<>();
    SparseVector features = createFeatures(example);
    for (int i = 0; i < dimensions.length; i++) {
        List<Pair<String, Double>> classScores = new ArrayList<>();
        for (VectorTuple f : features) {
            double score = weights[i].get(f.index) * f.value;
            classScores.add(new Pair<>(featureIDMap.get(f.index).getName(), score));
        }
        classScores.sort((Pair<String, Double> o1, Pair<String, Double> o2) -> o2.getB().compareTo(o1.getB()));
        weightMap.put(dimensions[i], classScores);
    }
    return Optional.of(new Excuse<>(example, prediction, weightMap));
}

Example 2 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class TrainTest method main.

/**
 * Runs a TrainTest CLI.
 * @param args the command line arguments
 * @throws IOException if there is any error reading the examples.
 */
public static void main(String[] args) throws IOException {
    // 
    // Use the labs format logging.
    LabsLogFormatter.setAllLogFormatters();
    SLMOptions o = new SLMOptions();
    ConfigurationManager cm;
    try {
        cm = new ConfigurationManager(args, o);
    } catch (UsageException e) {
        logger.info(e.getMessage());
        return;
    }
    if (o.general.trainingPath == null || o.general.testingPath == null) {
        logger.info(cm.usage());
        return;
    }
    RegressionFactory factory = new RegressionFactory();
    Pair<Dataset<Regressor>, Dataset<Regressor>> data = o.general.load(factory);
    Dataset<Regressor> train = data.getA();
    Dataset<Regressor> test = data.getB();
    SparseTrainer<Regressor> trainer;
    switch(o.algorithm) {
        case SFS:
            trainer = new SLMTrainer(false, Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case LARS:
            trainer = new LARSTrainer(Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case LARSLASSO:
            trainer = new LARSLassoTrainer(Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case SFSN:
            trainer = new SLMTrainer(true, Math.min(train.getFeatureMap().size(), o.maxNumFeatures));
            break;
        case ELASTICNET:
            trainer = new ElasticNetCDTrainer(o.alpha, o.l1Ratio, 1e-4, o.iterations, false, o.general.seed);
            break;
        default:
            logger.warning("Unknown SLMType, found " + o.algorithm);
            return;
    }
    logger.info("Training using " + trainer.toString());
    final long trainStart = System.currentTimeMillis();
    SparseModel<Regressor> model = trainer.train(train);
    final long trainStop = System.currentTimeMillis();
    logger.info("Finished training regressor " + Util.formatDuration(trainStart, trainStop));
    logger.info("Selected features: " + model.getActiveFeatures());
    Map<String, SparseVector> weights = ((SparseLinearModel) model).getWeights();
    for (Map.Entry<String, SparseVector> e : weights.entrySet()) {
        logger.info("Target:" + e.getKey());
        logger.info("\tWeights: " + e.getValue());
        logger.info("\tWeights one norm: " + e.getValue().oneNorm());
        logger.info("\tWeights two norm: " + e.getValue().twoNorm());
    }
    final long testStart = System.currentTimeMillis();
    RegressionEvaluation evaluation = factory.getEvaluator().evaluate(model, test);
    final long testStop = System.currentTimeMillis();
    logger.info("Finished evaluating model " + Util.formatDuration(testStart, testStop));
    System.out.println(evaluation.toString());
    if (o.general.outputPath != null) {
        o.general.saveModel(model);
    }
}

Example 3 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class Util method shuffleInPlace.

/**
 * In place shuffle of the features, labels and weights.
 * @param features Input features.
 * @param regressors Input regressors.
 * @param weights Input weights.
 * @param rng SplittableRandom number generator.
 */
public static void shuffleInPlace(SparseVector[] features, DenseVector[] regressors, double[] weights, SplittableRandom rng) {
    int size = features.length;
    // Shuffle array
    for (int i = size; i > 1; i--) {
        int j = rng.nextInt(i);
        // swap features
        SparseVector tmpFeature = features[i - 1];
        features[i - 1] = features[j];
        features[j] = tmpFeature;
        // swap regressors
        DenseVector tmpRegressors = regressors[i - 1];
        regressors[i - 1] = regressors[j];
        regressors[j] = tmpRegressors;
        // swap weights
        double tmpWeight = weights[i - 1];
        weights[i - 1] = weights[j];
        weights[j] = tmpWeight;
    }
}

Example 4 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class TensorFlowModel method predictBatch.

private List<Prediction<T>> predictBatch(List<Example<T>> batchExamples) {
    if (closed) {
        throw new IllegalStateException("Can't use a closed model, the state has gone.");
    }
    // Convert the batch
    List<SparseVector> vectors = new ArrayList<>(batchExamples.size());
    int[] numActiveElements = new int[batchExamples.size()];
    for (int i = 0; i < batchExamples.size(); i++) {
        SparseVector vec = SparseVector.createSparseVector(batchExamples.get(i), featureIDMap, false);
        numActiveElements[i] = vec.numActiveElements();
        vectors.add(vec);
    }
    // Send a batch to Tensorflow
    try (TensorMap transformedInput = featureConverter.convert(vectors);
        Tensor outputTensor = transformedInput.feedInto(session.runner()).fetch(outputName).run().get(0)) {
        // Transform the returned tensor into a list of Predictions.
        return outputConverter.convertToBatchPrediction(outputTensor, outputIDInfo, numActiveElements, batchExamples);
    }
}

Example 5 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class MultinomialNaiveBayesModel method predict.

@Override
public Prediction<Label> predict(Example<Label> example) {
    SparseVector exVector = SparseVector.createSparseVector(example, featureIDMap, false);
    if (exVector.minValue() < 0.0) {
        throw new IllegalArgumentException("Example has negative feature values, example = " + example.toString());
    }
    if (exVector.numActiveElements() == 0) {
        throw new IllegalArgumentException("No features found in Example " + example.toString());
    }
    /* Since we keep the label by feature matrix sparse, we need to manually
         * add the weights contributed by smoothing unobserved features. We need to
         * add in the portion of the inner product for the indices that are active
         * in the example but are not active in the labelWordProbs matrix (but are
         * still non-zero due to smoothing).
         */
    double[] alphaOffsets = new double[outputIDInfo.size()];
    int vocabSize = labelWordProbs.getDimension2Size();
    if (alpha > 0.0) {
        for (int i = 0; i < outputIDInfo.size(); i++) {
            double unobservedProb = Math.log(alpha / (labelWordProbs.getRow(i).oneNorm() + (vocabSize * alpha)));
            int[] mismatchedIndices = exVector.difference(labelWordProbs.getRow(i));
            double inExampleFactor = 0.0;
            for (int idx = 0; idx < mismatchedIndices.length; idx++) {
                // TODO - exVector.get is slow as it does a binary search into the vector.
                inExampleFactor += exVector.get(mismatchedIndices[idx]) * unobservedProb;
            }
            alphaOffsets[i] = inExampleFactor;
        }
    }
    DenseVector prediction = labelWordProbs.leftMultiply(exVector);
    prediction.intersectAndAddInPlace(DenseVector.createDenseVector(alphaOffsets));
    prediction.normalize(normalizer);
    Map<String, Label> distribution = new LinkedHashMap<>();
    Label maxLabel = null;
    double maxScore = Double.NEGATIVE_INFINITY;
    for (VectorTuple vt : prediction) {
        String name = outputIDInfo.getOutput(vt.index).getLabel();
        Label label = new Label(name, vt.value);
        if (vt.value > maxScore) {
            maxScore = vt.value;
            maxLabel = label;
        }
        distribution.put(name, label);
    }
    Prediction<Label> p = new Prediction<>(maxLabel, distribution, exVector.numActiveElements(), example, true);
    return p;
}