Examples with FeatureVector com.airbnb.aerosolve.core.FeatureVector used on opensource projects

Example 1 with FeatureVector

use of com.airbnb.aerosolve.core.FeatureVector in project aerosolve by airbnb.

the class FeatureVectorGen method toFeatureVector.

// TODO add a new function to consider dense feature.
public static FeatureVector toFeatureVector(Features features, List<StringFamily> stringFamilies, List<FloatFamily> floatFamilies) {
    FeatureVector featureVector = new FeatureVector();
    // Set string features.
    final Map<String, Set<String>> stringFeatures = new HashMap<>();
    featureVector.setStringFeatures(stringFeatures);
    setBIAS(stringFeatures);
    for (StringFamily featureFamily : stringFamilies) {
        stringFeatures.put(featureFamily.getFamilyName(), featureFamily.getFeatures());
    }
    final Map<String, Map<String, Double>> floatFeatures = new HashMap<>();
    featureVector.setFloatFeatures(floatFeatures);
    for (FloatFamily featureFamily : floatFamilies) {
        floatFeatures.put(featureFamily.getFamilyName(), featureFamily.getFeatures());
    }
    for (int i = 0; i < features.names.length; ++i) {
        Object feature = features.values[i];
        if (feature != null) {
            // Integer type = features.types[i];
            String name = features.names[i];
            if (feature instanceof Double || feature instanceof Float || feature instanceof Integer || feature instanceof Long) {
                for (FloatFamily featureFamily : floatFamilies) {
                    if (featureFamily.add(name, feature))
                        break;
                }
            } else if (feature instanceof String) {
                for (StringFamily featureFamily : stringFamilies) {
                    if (featureFamily.add(name, feature))
                        break;
                }
            } else if (feature instanceof Boolean) {
                for (StringFamily featureFamily : stringFamilies) {
                    if (featureFamily.add(name, (Boolean) feature))
                        break;
                }
            }
        }
    }
    return featureVector;
}

Example 2 with FeatureVector

use of com.airbnb.aerosolve.core.FeatureVector in project aerosolve by airbnb.

the class Transformer method addContextToItems.

/**
 * Adds the context's features to examples' features
 */
public void addContextToItems(Example examples) {
    Map<String, Set<String>> contextStringFeatures = null;
    Map<String, Map<String, Double>> contextFloatFeatures = null;
    Map<String, List<Double>> contextDenseFeatures = null;
    if (examples.context != null) {
        if (examples.context.stringFeatures != null) {
            contextStringFeatures = examples.context.getStringFeatures();
        }
        if (examples.context.floatFeatures != null) {
            contextFloatFeatures = examples.context.getFloatFeatures();
        }
        if (examples.context.denseFeatures != null) {
            contextDenseFeatures = examples.context.getDenseFeatures();
        }
    }
    for (FeatureVector item : examples.example) {
        addContextToItem(contextStringFeatures, contextFloatFeatures, contextDenseFeatures, item);
    }
}

Example 3 with FeatureVector

use of com.airbnb.aerosolve.core.FeatureVector in project aerosolve by airbnb.

the class MinKernelDenseFeatureDictionary method getKNearestNeighbors.

/**
 *  /**
 * Calculates the Min Kernel distance to each dictionary element.
 * Returns the top K elements as a new sparse feature.
 */
@Override
public FeatureVector getKNearestNeighbors(KNearestNeighborsOptions options, FeatureVector featureVector) {
    FeatureVector result = new FeatureVector();
    Map<String, List<Double>> denseFeatures = featureVector.getDenseFeatures();
    if (denseFeatures == null) {
        return result;
    }
    PriorityQueue<SimpleEntry<String, Double>> pq = new PriorityQueue<>(options.getNumNearest() + 1, new EntryComparator());
    String idKey = options.getIdKey();
    Map<String, Map<String, Double>> floatFeatures = new HashMap<>();
    String myId = featureVector.getStringFeatures().get(idKey).iterator().next();
    for (FeatureVector supportVector : dictionaryList) {
        Double minKernel = FeatureVectorUtil.featureVectorMinKernel(featureVector, supportVector);
        Set<String> idSet = supportVector.getStringFeatures().get(idKey);
        String id = idSet.iterator().next();
        if (id == myId)
            continue;
        SimpleEntry<String, Double> entry = new SimpleEntry<String, Double>(id, minKernel);
        pq.add(entry);
        if (pq.size() > options.getNumNearest()) {
            pq.poll();
        }
    }
    HashMap<String, Double> newFeature = new HashMap<>();
    while (pq.peek() != null) {
        SimpleEntry<String, Double> entry = pq.poll();
        newFeature.put(entry.getKey(), entry.getValue());
    }
    floatFeatures.put(options.getOutputKey(), newFeature);
    result.setFloatFeatures(floatFeatures);
    return result;
}

Example 4 with FeatureVector

use of com.airbnb.aerosolve.core.FeatureVector in project aerosolve by airbnb.

the class LocalitySensitiveHashSparseFeatureDictionary method getKNearestNeighbors.

@Override
public FeatureVector getKNearestNeighbors(KNearestNeighborsOptions options, FeatureVector featureVector) {
    FeatureVector result = new FeatureVector();
    Map<String, Set<String>> stringFeatures = featureVector.getStringFeatures();
    if (stringFeatures == null) {
        return result;
    }
    String featureKey = options.getFeatureKey();
    Set<String> keys = stringFeatures.get(featureKey);
    if (keys == null) {
        return result;
    }
    if (!haveLSH) {
        buildHashTable(featureKey);
    }
    String idKey = options.getIdKey();
    PriorityQueue<SimpleEntry<String, Double>> pq = new PriorityQueue<>(options.getNumNearest() + 1, new EntryComparator());
    Map<String, Map<String, Double>> floatFeatures = new HashMap<>();
    String myId = featureVector.getStringFeatures().get(idKey).iterator().next();
    Set<Integer> candidates = getCandidates(keys);
    for (Integer candidate : candidates) {
        FeatureVector supportVector = dictionaryList.get(candidate);
        double sim = similarity(featureVector, supportVector, featureKey);
        Set<String> idSet = supportVector.getStringFeatures().get(idKey);
        String id = idSet.iterator().next();
        if (id == myId) {
            continue;
        }
        SimpleEntry<String, Double> entry = new SimpleEntry<String, Double>(id, sim);
        pq.add(entry);
        if (pq.size() > options.getNumNearest()) {
            pq.poll();
        }
    }
    HashMap<String, Double> newFeature = new HashMap<>();
    while (pq.peek() != null) {
        SimpleEntry<String, Double> entry = pq.poll();
        newFeature.put(entry.getKey(), entry.getValue());
    }
    floatFeatures.put(options.getOutputKey(), newFeature);
    result.setFloatFeatures(floatFeatures);
    return result;
}

Example 5 with FeatureVector

use of com.airbnb.aerosolve.core.FeatureVector in project aerosolve by airbnb.

the class LowRankLinearModelTest method testLoad.

@Test
public void testLoad() {
    CharArrayWriter charWriter = new CharArrayWriter();
    BufferedWriter writer = new BufferedWriter(charWriter);
    ModelHeader header = new ModelHeader();
    header.setModelType("low_rank_linear");
    header.setLabelDictionary(makeLabelDictionary());
    Map<String, FloatVector> labelWeightVector = makeLabelWeightVector();
    Map<String, java.util.List<Double>> labelEmbedding = new HashMap<>();
    for (Map.Entry<String, FloatVector> labelRepresentation : labelWeightVector.entrySet()) {
        float[] values = labelRepresentation.getValue().getValues();
        ArrayList<Double> arrayList = new ArrayList<>();
        for (int i = 0; i < 3; i++) {
            arrayList.add((double) values[i]);
        }
        labelEmbedding.put(labelRepresentation.getKey(), arrayList);
    }
    header.setLabelEmbedding(labelEmbedding);
    header.setNumRecords(4);
    ArrayList<Double> ws = new ArrayList<>();
    ws.add(1.0);
    ws.add(0.0);
    ws.add(0.0);
    ModelRecord record1 = new ModelRecord();
    record1.setModelHeader(header);
    ModelRecord record2 = new ModelRecord();
    record2.setFeatureFamily("a");
    record2.setFeatureName("cat");
    record2.setWeightVector(ws);
    ModelRecord record3 = new ModelRecord();
    record3.setFeatureFamily("a");
    record3.setFeatureName("dog");
    record3.setWeightVector(ws);
    ModelRecord record4 = new ModelRecord();
    record4.setFeatureFamily("a");
    record4.setFeatureName("fish");
    record4.setWeightVector(ws);
    ModelRecord record5 = new ModelRecord();
    record5.setFeatureFamily("a");
    record5.setFeatureName("horse");
    record5.setWeightVector(ws);
    try {
        writer.write(Util.encode(record1) + "\n");
        writer.write(Util.encode(record2) + "\n");
        writer.write(Util.encode(record3) + "\n");
        writer.write(Util.encode(record4) + "\n");
        writer.write(Util.encode(record5) + "\n");
        writer.close();
    } catch (IOException e) {
        assertTrue("Could not write", false);
    }
    String serialized = charWriter.toString();
    assertTrue(serialized.length() > 0);
    StringReader strReader = new StringReader(serialized);
    BufferedReader reader = new BufferedReader(strReader);
    FeatureVector animalFv = makeFeatureVector("animal");
    FeatureVector colorFv = makeFeatureVector("color");
    try {
        Optional<AbstractModel> model = ModelFactory.createFromReader(reader);
        assertTrue(model.isPresent());
        ArrayList<MulticlassScoringResult> s1 = model.get().scoreItemMulticlass(animalFv);
        assertEquals(s1.size(), 3);
        assertEquals(0.0f, s1.get(0).score, 3.0f);
        assertEquals(0.0f, s1.get(1).score, 1e-10f);
        assertEquals(0.0f, s1.get(2).score, 1e-10f);
        ArrayList<MulticlassScoringResult> s2 = model.get().scoreItemMulticlass(colorFv);
        assertEquals(s2.size(), 3);
        assertEquals(0.0f, s2.get(0).score, 1e-10f);
        assertEquals(0.0f, s2.get(1).score, 1e-10f);
        assertEquals(0.0f, s2.get(2).score, 1e-10f);
    } catch (IOException e) {
        assertTrue("Could not read", false);
    }
}