Examples with FeatureNode de.bwaldvogel.liblinear.FeatureNode used on opensource projects

Example 1 with FeatureNode

use of de.bwaldvogel.liblinear.FeatureNode in project tribuo by oracle.

the class LibLinearRegressionModel method predict.

@Override
public Prediction<Regressor> predict(Example<Regressor> example) {
    FeatureNode[] features = LibLinearTrainer.exampleToNodes(example, featureIDMap, null);
    // Bias feature is always set
    if (features.length == 1) {
        throw new IllegalArgumentException("No features found in Example " + example.toString());
    }
    double[] scores = new double[models.get(0).getNrClass()];
    double[] regressedValues = new double[models.size()];
    // constructed correctly.
    for (int i = 0; i < regressedValues.length; i++) {
        regressedValues[mapping[i]] = Linear.predictValues(models.get(i), features, scores);
    }
    Regressor regressor = new Regressor(dimensionNames, regressedValues);
    return new Prediction<>(regressor, features.length - 1, example);
}

Example 2 with FeatureNode

use of de.bwaldvogel.liblinear.FeatureNode in project tribuo by oracle.

the class LibLinearTrainer method exampleToNodes.

/**
 * Converts a Tribuo {@link Example} into a liblinear {@code FeatureNode} array, including a bias feature.
 * <p>
 * If there is a collision between feature ids (i.e., if there is feature hashing or some other mechanism changing
 * the feature ids) then the feature values are summed.
 * @param example The input example.
 * @param featureIDMap The feature id map which contains the example's indices.
 * @param features A buffer. If null then an array list is created and used internally.
 * @param <T> The output type.
 * @return The features suitable for use in liblinear.
 */
public static <T extends Output<T>> FeatureNode[] exampleToNodes(Example<T> example, ImmutableFeatureMap featureIDMap, List<FeatureNode> features) {
    int biasIndex = featureIDMap.size() + 1;
    if (features == null) {
        features = new ArrayList<>();
    }
    features.clear();
    int prevIdx = -1;
    for (Feature f : example) {
        int id = featureIDMap.getID(f.getName());
        if (id > prevIdx) {
            prevIdx = id;
            features.add(new FeatureNode(id + 1, f.getValue()));
        } else if (id > -1) {
            // 
            // Collision, deal with it.
            int collisionIdx = Util.binarySearch(features, id + 1, FeatureNode::getIndex);
            if (collisionIdx < 0) {
                // 
                // Collision but not present in features
                // move data and bump i
                collisionIdx = -(collisionIdx + 1);
                features.add(collisionIdx, new FeatureNode(id + 1, f.getValue()));
            } else {
                // 
                // Collision present in features
                // add the values.
                FeatureNode n = features.get(collisionIdx);
                n.setValue(n.getValue() + f.getValue());
            }
        }
    }
    features.add(new FeatureNode(biasIndex, 1.0));
    return features.toArray(new FeatureNode[0]);
}

Example 3 with FeatureNode

use of de.bwaldvogel.liblinear.FeatureNode in project talismane by joliciel-informatique.

the class LinearSVMModelTrainer method addFeatureResult.

void addFeatureResult(String featureName, double value, Map<Integer, Feature> featureList, TObjectIntMap<String> featureIndexMap, TIntIntMap featureCountMap, CountingInfo countingInfo) {
    int featureIndex = featureIndexMap.get(featureName);
    if (featureIndex < 0) {
        featureIndex = countingInfo.currentFeatureIndex++;
        featureIndexMap.put(featureName, featureIndex);
    }
    if (cutoff > 1) {
        int featureCount = featureCountMap.get(featureIndex) + 1;
        if (featureCount == cutoff)
            countingInfo.featureCountOverCutoff++;
        featureCountMap.put(featureIndex, featureCount);
    }
    // if the same feature is added multiple times, we sum the values
    Feature feature = featureList.get(featureIndex);
    if (feature == null) {
        FeatureNode featureNode = new FeatureNode(featureIndex, value);
        featureList.put(featureIndex, featureNode);
    } else {
        FeatureNode featureNode = (FeatureNode) feature;
        featureNode.setValue(featureNode.getValue() + value);
    }
}

Example 4 with FeatureNode

use of de.bwaldvogel.liblinear.FeatureNode in project talismane by joliciel-informatique.

the class LinearSVMUtils method prepareData.

public static List<Feature> prepareData(List<FeatureResult<?>> featureResults, TObjectIntMap<String> featureIndexMap) {
    List<Feature> featureList = new ArrayList<Feature>(featureResults.size());
    for (FeatureResult<?> featureResult : featureResults) {
        if (featureResult.getOutcome() instanceof List) {
            @SuppressWarnings("unchecked") FeatureResult<List<WeightedOutcome<String>>> stringCollectionResult = (FeatureResult<List<WeightedOutcome<String>>>) featureResult;
            for (WeightedOutcome<String> stringOutcome : stringCollectionResult.getOutcome()) {
                int index = featureIndexMap.get(featureResult.getTrainingName() + "|" + featureResult.getTrainingOutcome(stringOutcome.getOutcome()));
                if (index >= 0) {
                    double value = stringOutcome.getWeight();
                    FeatureNode featureNode = new FeatureNode(index, value);
                    featureList.add(featureNode);
                }
            }
        } else {
            double value = 1.0;
            if (featureResult.getOutcome() instanceof Double) {
                @SuppressWarnings("unchecked") FeatureResult<Double> doubleResult = (FeatureResult<Double>) featureResult;
                value = doubleResult.getOutcome().doubleValue();
            }
            int index = featureIndexMap.get(featureResult.getTrainingName());
            if (index >= 0) {
                // we only need to bother adding features which existed in the
                // training set
                FeatureNode featureNode = new FeatureNode(index, value);
                featureList.add(featureNode);
            }
        }
    }
    return featureList;
}

Example 5 with FeatureNode

use of de.bwaldvogel.liblinear.FeatureNode in project palladian by palladian.

the class LibLinearLearner method makeInstance.

static de.bwaldvogel.liblinear.Feature[] makeInstance(Map<String, Integer> featureLabelIndices, FeatureVector featureVector, double bias) {
    List<de.bwaldvogel.liblinear.Feature> features = new ArrayList<>();
    for (VectorEntry<String, Value> vectorEntry : featureVector) {
        Value value = vectorEntry.value();
        Integer featureIndex = featureLabelIndices.get(vectorEntry.key());
        if (featureIndex != null && !value.isNull() && value instanceof NumericValue) {
            double floatValue = ((NumericValue) value).getDouble();
            if (Math.abs(floatValue) < 2 * Float.MIN_VALUE) {
                continue;
            }
            features.add(new FeatureNode(featureIndex + 1, /* 1-indexed */
            floatValue));
        }
    }
    if (bias >= 0) {
        // bias term
        features.add(new FeatureNode(featureLabelIndices.size() + 1, bias));
    }
    Collections.sort(features, new Comparator<de.bwaldvogel.liblinear.Feature>() {

        @Override
        public int compare(Feature o1, Feature o2) {
            return Integer.compare(o1.getIndex(), o2.getIndex());
        }
    });
    return features.toArray(new de.bwaldvogel.liblinear.Feature[features.size()]);
}