Examples with MutableDouble com.oracle.labs.mlrg.olcut.util.MutableDouble used on opensource projects

Example 1 with MutableDouble

use of com.oracle.labs.mlrg.olcut.util.MutableDouble in project tribuo by oracle.

the class MutableRegressionInfo method observe.

@Override
public void observe(Regressor output) {
    if (output == RegressionFactory.UNKNOWN_REGRESSOR) {
        unknownCount++;
    } else {
        if (overallCount != 0) {
            // Validate that the dimensions in this regressor are the same as the ones already observed.
            String[] names = output.getNames();
            if (names.length != countMap.size()) {
                throw new IllegalArgumentException("Expected this Regressor to contain " + countMap.size() + " dimensions, found " + names.length);
            }
            for (String name : names) {
                if (!countMap.containsKey(name)) {
                    throw new IllegalArgumentException("Regressor contains unexpected dimension named '" + name + "'");
                }
            }
        }
        for (Regressor.DimensionTuple r : output) {
            String name = r.getName();
            double value = r.getValue();
            // Update max and min
            minMap.merge(name, new MutableDouble(value), (a, b) -> a.doubleValue() < b.doubleValue() ? a : b);
            maxMap.merge(name, new MutableDouble(value), (a, b) -> a.doubleValue() > b.doubleValue() ? a : b);
            // Update count
            MutableLong countValue = countMap.computeIfAbsent(name, k -> new MutableLong());
            countValue.increment();
            // Update mean
            MutableDouble meanValue = meanMap.computeIfAbsent(name, k -> new MutableDouble());
            double delta = value - meanValue.doubleValue();
            meanValue.increment(delta / countValue.longValue());
            // Update running sum of squares
            double delta2 = value - meanValue.doubleValue();
            MutableDouble sumSquaresValue = sumSquaresMap.computeIfAbsent(name, k -> new MutableDouble());
            sumSquaresValue.increment(delta * delta2);
        }
        overallCount++;
    }
}

Example 2 with MutableDouble

use of com.oracle.labs.mlrg.olcut.util.MutableDouble 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 3 with MutableDouble

use of com.oracle.labs.mlrg.olcut.util.MutableDouble in project tribuo by oracle.

the class XGBoostModel method getTopFeatures.

@Override
public Map<String, List<Pair<String, Double>>> getTopFeatures(int n) {
    try {
        int maxFeatures = n < 0 ? featureIDMap.size() : n;
        Map<String, List<Pair<String, Double>>> map = new HashMap<>();
        for (int i = 0; i < models.size(); i++) {
            Booster model = models.get(i);
            Map<String, MutableDouble> outputMap = new HashMap<>();
            Map<String, Integer> xgboostMap = model.getFeatureScore("");
            for (Map.Entry<String, Integer> f : xgboostMap.entrySet()) {
                int id = Integer.parseInt(f.getKey().substring(1));
                String name = featureIDMap.get(id).getName();
                MutableDouble curVal = outputMap.computeIfAbsent(name, (k) -> new MutableDouble());
                curVal.increment(f.getValue());
            }
            Comparator<Pair<String, Double>> comparator = Comparator.comparingDouble(p -> Math.abs(p.getB()));
            PriorityQueue<Pair<String, Double>> q = new PriorityQueue<>(maxFeatures, comparator);
            for (Map.Entry<String, MutableDouble> e : outputMap.entrySet()) {
                Pair<String, Double> cur = new Pair<>(e.getKey(), e.getValue().doubleValue());
                if (q.size() < maxFeatures) {
                    q.offer(cur);
                } else if (comparator.compare(cur, q.peek()) > 0) {
                    q.poll();
                    q.offer(cur);
                }
            }
            List<Pair<String, Double>> list = new ArrayList<>();
            while (q.size() > 0) {
                list.add(q.poll());
            }
            Collections.reverse(list);
            if (models.size() == 1) {
                map.put(Model.ALL_OUTPUTS, list);
            } else {
                String dimensionName = outputIDInfo.getOutput(i).toString();
                map.put(dimensionName, list);
            }
        }
        return map;
    } catch (XGBoostError e) {
        logger.log(Level.SEVERE, "XGBoost threw an error", e);
        return Collections.emptyMap();
    }
}