com.jstarcraft.ai.data.DataInstance#getQualityFeature

Source File: BUCMRankingModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    for (int topicIndex = 0; topicIndex < factorSize; ++topicIndex) {
        float sum = 0F;
        for (Entry<Float, Integer> term : scoreIndexes.entrySet()) {
            double score = term.getKey();
            if (score > meanScore) {
                sum += topicItemScoreProbabilities[topicIndex][itemIndex][term.getValue()];
            }
        }
        value += userTopicProbabilities.getValue(userIndex, topicIndex) * topicItemProbabilities.getValue(topicIndex, itemIndex) * sum;
    }
    instance.setQuantityMark(value);
}

Source File: BHFreeRankingModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    for (Entry<Float, Integer> entry : scoreIndexes.entrySet()) {
        float score = entry.getKey();
        float probability = 0F;
        for (int userTopic = 0; userTopic < userTopicSize; userTopic++) {
            for (int itemTopic = 0; itemTopic < itemTopicSize; itemTopic++) {
                probability += user2TopicProbabilities.getValue(userIndex, userTopic) * userTopic2ItemTopicProbabilities.getValue(userTopic, itemTopic) * userTopic2ItemTopicItemSums[userTopic][itemTopic][itemIndex] * userTopic2ItemTopicScoreProbabilities[userTopic][itemTopic][entry.getValue()];
            }
        }
        value += score * probability;
    }
    instance.setQuantityMark(value);
}

Source File: DataSorterTestCase.java From jstarcraft-ai with Apache License 2.0

6 votes

@Test
public void testSort() {
    DataModule module = getDataModule();
    DataSorter sorter = new DataSorter() {

        @Override
        public int sort(DataInstance left, DataInstance right) {
            return right.getQualityFeature(0) - left.getQualityFeature(0);
        }

    };
    module = sorter.sort(module);
    for (int index = 0; index < instanceCapacity; index++) {
        Assert.assertEquals(instanceCapacity - 1 - index, module.getInstance(index).getQualityFeature(0));
    }
}

Source File: UserClusterModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = 0F;
    for (int topicIndex = 0; topicIndex < factorSize; topicIndex++) {
        float topicProbability = userTopicProbabilities.getValue(userIndex, topicIndex);
        float topicValue = 0F;
        for (Entry<Float, Integer> entry : scoreIndexes.entrySet()) {
            float score = entry.getKey();
            float probability = topicScoreMatrix.getValue(topicIndex, entry.getValue());
            topicValue += score * probability;
        }
        value += topicProbability * topicValue;
    }
    instance.setQuantityMark(value);
}

Source File: FISMRMSEModel.java From jstarcraft-rns with Apache License 2.0

6 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    DefaultScalar scalar = DefaultScalar.getInstance();
    float bias = userBiases.getValue(userIndex) + itemBiases.getValue(itemIndex);
    float sum = 0F;
    int count = 0;
    for (VectorScalar term : scoreMatrix.getRowVector(userIndex)) {
        int index = term.getIndex();
        // for test, i and j will be always unequal as j is unrated
        if (index != itemIndex) {
            DenseVector userVector = userFactors.getRowVector(index);
            DenseVector itemVector = itemFactors.getRowVector(itemIndex);
            sum += scalar.dotProduct(userVector, itemVector).getValue();
            count++;
        }
    }
    sum *= (float) (count > 0 ? Math.pow(count, -alpha) : 0F);
    instance.setQuantityMark(bias + sum);
}

Source File: RankGeoFMModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    DefaultScalar scalar = DefaultScalar.getInstance();
    float value = scalar.dotProduct(explicitUserFactors.getRowVector(userIndex), itemFactors.getRowVector(itemIndex)).getValue();
    value += scalar.dotProduct(implicitUserFactors.getRowVector(userIndex), geoInfluences.getRowVector(itemIndex)).getValue();
    instance.setQuantityMark(value);
}

Source File: RankVFCDModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    DefaultScalar scalar = DefaultScalar.getInstance();
    float score = 0F;
    if (scoreMatrix.getColumnVector(itemIndex).getElementSize() == 0) {
        score = scalar.dotProduct(userFactors.getRowVector(userIndex), factorMatrix.getColumnVector(itemIndex)).getValue();
    } else {
        score = scalar.dotProduct(userFactors.getRowVector(userIndex), explicitItemFactors.getRowVector(itemIndex)).getValue();
    }
    instance.setQuantityMark(score);
}

Source File: SLIMModel.java From jstarcraft-rns with Apache License 2.0

5 votes

/**
 * predict a specific ranking score for user userIdx on item itemIdx.
 *
 * @param userIndex user index
 * @param itemIndex item index
 * @return predictive ranking score for user userIdx on item itemIdx
 * @throws ModelException if error occurs
 */
@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    int[] neighbors = itemNeighbors[itemIndex];
    if (neighbors == null) {
        instance.setQuantityMark(0F);
        return;
    }
    instance.setQuantityMark(predict(userIndex, itemIndex, neighbors, -1));
}

Source File: CCDModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    DefaultScalar scalar = DefaultScalar.getInstance();
    float score = scalar.dotProduct(userFactors.getRowVector(userIndex), itemFactors.getRowVector(itemIndex)).getValue();
    if (score == 0F) {
        score = meanScore;
    }
    instance.setQuantityMark(score);
}

Source File: ItemBigramModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    List<Integer> items = userItemMap.get(userIndex);
    int scoreIndex = items == null ? itemSize : items.get(items.size() - 1); // last
    // rated
    // item
    float value = 0F;
    for (int topicIndex = 0; topicIndex < factorSize; topicIndex++) {
        value += userTopicProbabilities.getValue(userIndex, topicIndex) * topicItemBigramProbabilities[topicIndex][scoreIndex][itemIndex];
    }

    instance.setQuantityMark(value);
}

Source File: DeepCrossModel.java From jstarcraft-rns with Apache License 2.0

5 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    float value = outputData.getValue(userIndex, itemIndex);
    instance.setQuantityMark(value);
}

Source File: VBPRModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex, featureVector.getValue(itemIndex), factorMatrix.getColumnVector(itemIndex)));
}

Source File: TopicMFATModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void prepare(Configurator configuration, DataModule model, DataSpace space) {
    super.prepare(configuration, model, space);

    commentField = configuration.getString("data.model.fields.comment");
    commentDimension = model.getQualityInner(commentField);
    MemoryQualityAttribute attribute = (MemoryQualityAttribute) space.getQualityAttribute(commentField);
    Object[] documentValues = attribute.getDatas();

    // init hyper-parameters
    lambda = configuration.getFloat("recommender.regularization.lambda", 0.001F);
    lambdaU = configuration.getFloat("recommender.regularization.lambdaU", 0.001F);
    lambdaV = configuration.getFloat("recommender.regularization.lambdaV", 0.001F);
    lambdaB = configuration.getFloat("recommender.regularization.lambdaB", 0.001F);
    numberOfTopics = configuration.getInteger("recommender.topic.number", 10);
    learnRatio = configuration.getFloat("recommender.iterator.learnrate", 0.01F);
    epocheSize = configuration.getInteger("recommender.iterator.maximum", 10);

    numberOfDocuments = scoreMatrix.getElementSize();

    // count the number of words, build the word dictionary and
    // userItemToDoc dictionary
    Map<String, Integer> wordDictionaries = new HashMap<>();
    Table<Integer, Integer, Float> documentTable = HashBasedTable.create();
    // TODO rowCount改为documentIndex?
    int rowCount = 0;
    userItemToDocument = HashBasedTable.create();
    for (DataInstance sample : model) {
        int userIndex = sample.getQualityFeature(userDimension);
        int itemIndex = sample.getQualityFeature(itemDimension);
        int documentIndex = sample.getQualityFeature(commentDimension);
        userItemToDocument.put(userIndex, itemIndex, rowCount);
        // convert wordIds to wordIndices
        String data = (String) documentValues[documentIndex];
        String[] words = data.isEmpty() ? new String[0] : data.split(":");
        for (String word : words) {
            Integer wordIndex = wordDictionaries.get(word);
            if (wordIndex == null) {
                wordIndex = numberOfWords++;
                wordDictionaries.put(word, wordIndex);
            }
            Float oldValue = documentTable.get(rowCount, wordIndex);
            if (oldValue == null) {
                oldValue = 0F;
            }
            float newValue = oldValue + 1F / words.length;
            documentTable.put(rowCount, wordIndex, newValue);
        }
        rowCount++;
    }
    // build W
    W = SparseMatrix.valueOf(numberOfDocuments, numberOfWords, documentTable);

    userBiases = DenseVector.valueOf(userSize);
    userBiases.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    itemBiases = DenseVector.valueOf(itemSize);
    itemBiases.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    userFactors = DenseMatrix.valueOf(userSize, numberOfTopics);
    userFactors.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    itemFactors = DenseMatrix.valueOf(itemSize, numberOfTopics);
    itemFactors.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });

    K1 = initStd;
    K2 = initStd;

    topicVector = DenseVector.valueOf(numberOfTopics);
    function = new SoftMaxActivationFunction();

    // init theta and phi
    // TODO theta实际是documentFactors
    documentFactors = DenseMatrix.valueOf(numberOfDocuments, numberOfTopics);
    calculateTheta();
    // TODO phi实际是wordFactors
    wordFactors = DenseMatrix.valueOf(numberOfTopics, numberOfWords);
    wordFactors.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(RandomUtility.randomFloat(0.01F));
    });

    logger.info("number of users : " + userSize);
    logger.info("number of Items : " + itemSize);
    logger.info("number of words : " + wordDictionaries.size());
}

Source File: TFIDFModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Source File: URPModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Source File: TopicMFMTModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void prepare(Configurator configuration, DataModule model, DataSpace space) {
    super.prepare(configuration, model, space);

    commentField = configuration.getString("data.model.fields.comment");
    commentDimension = model.getQualityInner(commentField);
    MemoryQualityAttribute attribute = (MemoryQualityAttribute) space.getQualityAttribute(commentField);
    Object[] documentValues = attribute.getDatas();

    // init hyper-parameters
    lambda = configuration.getFloat("recommender.regularization.lambda", 0.001F);
    lambdaU = configuration.getFloat("recommender.regularization.lambdaU", 0.001F);
    lambdaV = configuration.getFloat("recommender.regularization.lambdaV", 0.001F);
    lambdaB = configuration.getFloat("recommender.regularization.lambdaB", 0.001F);
    numberOfTopics = configuration.getInteger("recommender.topic.number", 10);
    learnRatio = configuration.getFloat("recommender.iterator.learnrate", 0.01F);
    epocheSize = configuration.getInteger("recommender.iterator.maximum", 10);

    numberOfDocuments = scoreMatrix.getElementSize();

    // count the number of words, build the word dictionary and
    // userItemToDoc dictionary
    Map<String, Integer> wordDictionaries = new HashMap<>();
    Table<Integer, Integer, Float> documentTable = HashBasedTable.create();
    int rowCount = 0;
    userItemToDocument = HashBasedTable.create();
    for (DataInstance sample : model) {
        int userIndex = sample.getQualityFeature(userDimension);
        int itemIndex = sample.getQualityFeature(itemDimension);
        int documentIndex = sample.getQualityFeature(commentDimension);
        userItemToDocument.put(userIndex, itemIndex, rowCount);
        // convert wordIds to wordIndices
        String data = (String) documentValues[documentIndex];
        String[] words = data.isEmpty() ? new String[0] : data.split(":");
        for (String word : words) {
            Integer wordIndex = wordDictionaries.get(word);
            if (wordIndex == null) {
                wordIndex = numberOfWords++;
                wordDictionaries.put(word, wordIndex);
            }
            Float oldValue = documentTable.get(rowCount, wordIndex);
            if (oldValue == null) {
                oldValue = 0F;
            }
            float newValue = oldValue + 1F / words.length;
            documentTable.put(rowCount, wordIndex, newValue);
        }
        rowCount++;
    }
    // build W
    W = SparseMatrix.valueOf(numberOfDocuments, numberOfWords, documentTable);

    userBiases = DenseVector.valueOf(userSize);
    userBiases.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    itemBiases = DenseVector.valueOf(itemSize);
    itemBiases.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    userFactors = DenseMatrix.valueOf(userSize, numberOfTopics);
    userFactors.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    itemFactors = DenseMatrix.valueOf(itemSize, numberOfTopics);
    itemFactors.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(distribution.sample().floatValue());
    });
    K = initStd;

    topicVector = DenseVector.valueOf(numberOfTopics);
    function = new SoftMaxActivationFunction();

    // init theta and phi
    // TODO theta实际是documentFactors
    documentFactors = DenseMatrix.valueOf(numberOfDocuments, numberOfTopics);
    calculateTheta();
    // TODO phi实际是wordFactors
    wordFactors = DenseMatrix.valueOf(numberOfTopics, numberOfWords);
    wordFactors.iterateElement(MathCalculator.SERIAL, (scalar) -> {
        scalar.setValue(RandomUtility.randomFloat(0.01F));
    });

    logger.info("number of users : " + userSize);
    logger.info("number of Items : " + itemSize);
    logger.info("number of words : " + wordDictionaries.size());
}

Source File: CDAEModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(outputData.getFloat(userIndex, itemIndex));
}

Source File: ItemKNNRatingModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    SparseVector userVector = userVectors[userIndex];
    MathVector neighbors = itemNeighbors[itemIndex];
    if (userVector.getElementSize() == 0 || neighbors.getElementSize() == 0) {
        instance.setQuantityMark(meanScore);
        return;
    }

    float sum = 0F, absolute = 0F;
    int count = 0;
    int leftCursor = 0, rightCursor = 0, leftSize = userVector.getElementSize(), rightSize = neighbors.getElementSize();
    Iterator<VectorScalar> leftIterator = userVector.iterator();
    VectorScalar leftTerm = leftIterator.next();
    Iterator<VectorScalar> rightIterator = neighbors.iterator();
    VectorScalar rightTerm = rightIterator.next();
    // 判断两个有序数组中是否存在相同的数字
    while (leftCursor < leftSize && rightCursor < rightSize) {
        if (leftTerm.getIndex() == rightTerm.getIndex()) {
            count++;
            float correlation = rightTerm.getValue();
            float score = leftTerm.getValue();
            sum += correlation * (score - itemMeans.getValue(rightTerm.getIndex()));
            absolute += Math.abs(correlation);
            if (leftIterator.hasNext()) {
                leftTerm = leftIterator.next();
            }
            if (rightIterator.hasNext()) {
                rightTerm = rightIterator.next();
            }
            leftCursor++;
            rightCursor++;
        } else if (leftTerm.getIndex() > rightTerm.getIndex()) {
            if (rightIterator.hasNext()) {
                rightTerm = rightIterator.next();
            }
            rightCursor++;
        } else if (leftTerm.getIndex() < rightTerm.getIndex()) {
            if (leftIterator.hasNext()) {
                leftTerm = leftIterator.next();
            }
            leftCursor++;
        }
    }

    if (count == 0) {
        instance.setQuantityMark(meanScore);
        return;
    }

    instance.setQuantityMark(absolute > 0 ? itemMeans.getValue(itemIndex) + sum / absolute : meanScore);
}

Source File: BiasedMFModel.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public void predict(DataInstance instance) {
    int userIndex = instance.getQualityFeature(userDimension);
    int itemIndex = instance.getQualityFeature(itemDimension);
    instance.setQuantityMark(predict(userIndex, itemIndex));
}

Source File: QualityFeatureDataSplitter.java From jstarcraft-rns with Apache License 2.0

4 votes

@Override
public int split(DataInstance instance) {
    return instance.getQualityFeature(dimension);
}

Java Code Examples for com.jstarcraft.ai.data.DataInstance#getQualityFeature()