it.unimi.dsi.fastutil.objects.Object2FloatMap Java Examples

/**
 * 个性化搜索
 * 
 * @param userIndex
 * @param searchKey
 * @return
 * @throws Exception
 */
@LockableMethod(strategy = HashLockableStrategy.class)
public Object2FloatMap<MovieItem> getSearchItems(@LockableParameter int userIndex, String searchKey) throws Exception {
    // 标识-得分映射
    Object2FloatMap<MovieItem> item2ScoreMap = new Object2FloatOpenHashMap<>();

    long current = System.currentTimeMillis();
    Query query = queryParser.parse(searchKey, MovieItem.TITLE);
    KeyValue<List<Document>, FloatList> search = engine.retrieveDocuments(query, null, 0, 1000);
    List<Document> documents = search.getKey();
    FloatList scores = search.getValue();
    for (int index = 0, size = documents.size(); index < size; index++) {
        Document document = documents.get(index);
        MovieItem item = items.get(document.getField(MovieItem.INDEX).numericValue().intValue());
        float score = scores.getFloat(index);
        item2ScoreMap.put(item, score);
    }
    String message = StringUtility.format("搜索数量:{},搜索耗时:{}", documents.size(), System.currentTimeMillis() - current);
    logger.info(message);

    return item2ScoreMap;
}
 

public static List<MovieItemOutput> instancesOf(Object2FloatMap<MovieItem> items) {
    List<MovieItemOutput> instances = new ArrayList<>(items.size());
    for (Object2FloatMap.Entry<MovieItem> term : items.object2FloatEntrySet()) {
        MovieItemOutput instance = new MovieItemOutput(term.getKey(), term.getFloatValue());
        instances.add(instance);
    }
    return instances;
}
 

/**
 * 获取推荐条目
 * 
 * @param userIndex
 * @param recommendKey
 * @return
 */
@ApiOperation(value = "获取推荐条目", notes = "获取推荐条目")
@GetMapping("/getRecommendItems")
public NormalOutput<List<MovieItemOutput>> getRecommendItems(@RequestParam int userIndex, @RequestParam String recommendKey) {
    Object2FloatMap<MovieItem> movies = movieService.getRecommendItems(userIndex, recommendKey);
    List<MovieItemOutput> instances = MovieItemOutput.instancesOf(movies);
    Collections.sort(instances, (left, right) -> {
        return Float.compare(right.getScore(), left.getScore());
    });
    return new NormalOutput<>(instances);
}
 

/**
 * 获取搜索条目
 * 
 * @param userIndex
 * @param searchKey
 * @return
 */
@ApiOperation(value = "获取搜索条目", notes = "获取搜索条目")
@GetMapping("/getSearchItems")
public NormalOutput<List<MovieItemOutput>> getSearchItems(@RequestParam int userIndex, @RequestParam String searchKey) throws Exception {
    Object2FloatMap<MovieItem> movies = movieService.getSearchItems(userIndex, searchKey);
    List<MovieItemOutput> instances = MovieItemOutput.instancesOf(movies);
    Collections.sort(instances, (left, right) -> {
        return Float.compare(right.getScore(), left.getScore());
    });
    return new NormalOutput<>(instances);
}
 

/**
 * 获取条目
 * 
 * @param userIndex
 * @param recommendKey
 * @param searchKey
 * @param filterClicked
 * @return
 * @throws Exception
 */
@ApiOperation(value = "获取条目", notes = "获取条目")
@GetMapping("/getItems")
public NormalOutput<List<MovieItemOutput>> getItems(@RequestParam int userIndex, @RequestParam String modelKey, @RequestParam String queryKey, @RequestParam boolean filterClicked) throws Exception {
    Object2FloatMap<MovieItem> movies = movieService.getItems(userIndex, modelKey, queryKey, filterClicked);
    List<MovieItemOutput> instances = MovieItemOutput.instancesOf(movies);
    Collections.sort(instances, (left, right) -> {
        return Float.compare(right.getScore(), left.getScore());
    });
    return new NormalOutput<>(instances);
}
 

/**
 * 个性化推荐
 * 
 * @param userIndex
 * @param recommendKey
 * @return
 */
@LockableMethod(strategy = HashLockableStrategy.class)
public Object2FloatMap<MovieItem> getRecommendItems(@LockableParameter int userIndex, String recommendKey) {
    // 标识-得分映射
    Object2FloatMap<MovieItem> item2ScoreMap = new Object2FloatOpenHashMap<>();

    long current = System.currentTimeMillis();
    Model model = models.get(recommendKey);
    ArrayInstance instance = new ArrayInstance(qualityOrder, quantityOrder);
    MovieUser user = users.get(userIndex);
    int itemSize = items.size();
    for (int itemIndex = 0; itemIndex < itemSize; itemIndex++) {
        // 过滤条目
        if (user.isClicked(itemIndex)) {
            continue;
        }
        instance.setQualityFeature(userDimension, userIndex);
        instance.setQualityFeature(itemDimension, itemIndex);
        model.predict(instance);
        MovieItem item = items.get(itemIndex);
        float score = instance.getQuantityMark();
        item2ScoreMap.put(item, score);
    }
    String message = StringUtility.format("推荐数量:{},推荐耗时:{}", itemSize, System.currentTimeMillis() - current);
    logger.info(message);

    return item2ScoreMap;
}
 

/**
 * 
 * @param userIndex
 * @param modelKey
 * @param queryKey
 * @param filterClicked
 * @return
 * @throws Exception
 */
@LockableMethod(strategy = HashLockableStrategy.class)
public Object2FloatMap<MovieItem> getItems(@LockableParameter int userIndex, String modelKey, String queryKey, boolean filterClicked) throws Exception {
    // 标识-得分映射
    Object2FloatMap<MovieItem> item2ScoreMap = new Object2FloatOpenHashMap<>();

    long current = System.currentTimeMillis();
    Model model = models.get(modelKey);
    ArrayInstance instance = new ArrayInstance(qualityOrder, quantityOrder);
    MovieUser user = users.get(userIndex);
    Query query = StringUtility.isBlank(queryKey) ? new MatchAllDocsQuery() : queryParser.parse(queryKey, MovieItem.TITLE);
    KeyValue<List<Document>, FloatList> retrieve = engine.retrieveDocuments(query, null, 0, 1000);
    List<Document> documents = retrieve.getKey();
    for (int index = 0, size = documents.size(); index < size; index++) {
        Document document = documents.get(index);
        MovieItem item = items.get(document.getField(MovieItem.INDEX).numericValue().intValue());
        int itemIndex = item.getIndex();
        // 过滤条目
        if (filterClicked && user.isClicked(itemIndex)) {
            continue;
        }
        instance.setQualityFeature(userDimension, userIndex);
        instance.setQualityFeature(itemDimension, itemIndex);
        model.predict(instance);
        float score = instance.getQuantityMark();
        item2ScoreMap.put(item, score);
    }
    String message = StringUtility.format("预测数量:{},预测耗时:{}", modelKey, documents.size(), System.currentTimeMillis() - current);
    logger.info(message);

    return item2ScoreMap;
}