Examples with FeatureExtractors io.anserini.ltr.feature.FeatureExtractors used on opensource projects

Example 1 with FeatureExtractors

use of io.anserini.ltr.feature.FeatureExtractors in project Anserini by castorini.

the class BaseFeatureExtractor method printFeatures.

/**
     * Prints feature vectors wrt to the qrels, one vector per qrel
     * @param out
     * @throws IOException
     */
public void printFeatures(PrintStream out) throws IOException {
    Map<String, RerankerContext> queryContextMap = buildRerankerContextMap();
    FeatureExtractors extractors = getExtractors();
    Bits liveDocs = MultiFields.getLiveDocs(reader);
    Set<String> fieldsToLoad = getFieldsToLoad();
    // We need to open a searcher
    IndexSearcher searcher = new IndexSearcher(reader);
    this.printHeader(out, extractors);
    // Iterate through all the qrels and for each document id we have for them
    LOG.debug("Processing queries");
    for (String qid : this.qrels.getQids()) {
        LOG.debug(String.format("Processing qid: %s", qid));
        // Get the map of documents
        RerankerContext context = queryContextMap.get(qid);
        for (Map.Entry<String, Integer> entry : this.qrels.getDocMap(qid).entrySet()) {
            String docId = entry.getKey();
            int qrelScore = entry.getValue();
            // We issue a specific query
            TopDocs topDocs = searcher.search(docIdQuery(docId), 1);
            if (topDocs.totalHits == 0) {
                LOG.warn(String.format("Document Id %s expected but not found in index, skipping...", docId));
                continue;
            }
            ScoreDoc hit = topDocs.scoreDocs[0];
            Document doc = reader.document(hit.doc, fieldsToLoad);
            //TODO factor for test
            Terms terms = reader.getTermVector(hit.doc, getTermVectorField());
            if (terms == null) {
                LOG.debug(String.format("No term vectors found for doc %s, qid %s", docId, qid));
                continue;
            }
            float[] featureValues = extractors.extractAll(doc, terms, context);
            writeFeatureVector(out, qid, qrelScore, docId, featureValues);
        }
        LOG.debug(String.format("Finished processing for qid: %s", qid));
        out.flush();
    }
}

Example 2 with FeatureExtractors

use of io.anserini.ltr.feature.FeatureExtractors in project Anserini by castorini.

the class BaseFeatureExtractor method printFeatureForAllDocs.

/**
   * Iterates through all the documents and print the features for each of the queries
   * This way we are not iterating over the entire index for each query to save disk access
   * @param out
   * @throws IOException
   */
public void printFeatureForAllDocs(PrintStream out) throws IOException {
    Map<String, RerankerContext> queryContextMap = buildRerankerContextMap();
    FeatureExtractors extractors = getExtractors();
    Bits liveDocs = MultiFields.getLiveDocs(reader);
    Set<String> fieldsToLoad = getFieldsToLoad();
    this.printHeader(out, extractors);
    for (int docId = 0; docId < reader.maxDoc(); docId++) {
        // Only check live docs if we have some
        if (reader.hasDeletions() && (liveDocs == null || !liveDocs.get(docId))) {
            LOG.warn(String.format("Document %d not in live docs", docId));
            continue;
        }
        Document doc = reader.document(docId, fieldsToLoad);
        String docIdString = doc.get(getIdField());
        // NOTE doc frequencies should not be retrieved from here, term vector returned is as if on single document
        // index
        //reader.getTermVector(docId, getTermVectorField());
        Terms terms = MultiFields.getTerms(reader, getTermVectorField());
        if (terms == null) {
            continue;
        }
        for (Map.Entry<String, RerankerContext> entry : queryContextMap.entrySet()) {
            float[] featureValues = extractors.extractAll(doc, terms, entry.getValue());
            writeFeatureVector(out, entry.getKey(), qrels.getRelevanceGrade(entry.getKey(), docIdString), docIdString, featureValues);
        }
        out.flush();
        LOG.debug(String.format("Completed computing feature vectors for doc %d", docId));
    }
}

Example 3 with FeatureExtractors

use of io.anserini.ltr.feature.FeatureExtractors in project Anserini by castorini.

the class FeatureExtractorCli method main.

/**
   * requires the user to supply the index directory and also the directory containing the qrels and topics
   * @param args  indexDir, qrelFile, topicFile, outputFile
   */
public static void main(String[] args) throws Exception {
    long curTime = System.nanoTime();
    FeatureExtractionArgs parsedArgs = new FeatureExtractionArgs();
    CmdLineParser parser = new CmdLineParser(parsedArgs, ParserProperties.defaults().withUsageWidth(90));
    try {
        parser.parseArgument(args);
    } catch (CmdLineException e) {
        System.err.println(e.getMessage());
        parser.printUsage(System.err);
        return;
    }
    Directory indexDirectory = FSDirectory.open(Paths.get(parsedArgs.indexDir));
    IndexReader reader = DirectoryReader.open(indexDirectory);
    Qrels qrels = new Qrels(parsedArgs.qrelFile);
    FeatureExtractors extractors = null;
    if (parsedArgs.extractors != null) {
        extractors = FeatureExtractors.loadExtractor(parsedArgs.extractors);
    }
    // Query parser needed to construct the query object for feature extraction in the loop
    PrintStream out = new PrintStream(new FileOutputStream(new File(parsedArgs.outputFile)));
    if (parsedArgs.collection.equals("Trec") || parsedArgs.collection.equals("Webxml")) {
        // Open the topics file and read it
        String className = parsedArgs.collection.equals("gov2") ? "Trec" : "Webxml";
        TopicReader tr = (TopicReader) Class.forName("io.anserini.search.query." + className + "TopicReader").getConstructor(Path.class).newInstance(Paths.get(parsedArgs.topicsFile));
        SortedMap<Integer, String> topics = tr.read();
        LOG.debug(String.format("%d topics found", topics.size()));
        WebFeatureExtractor extractor = new WebFeatureExtractor(reader, qrels, convertTopicsFormat(topics), extractors);
        extractor.printFeatures(out);
    } else if (parsedArgs.collection.equals("twitter")) {
        Map<String, String> topics = MicroblogTopicSet.fromFile(new File(parsedArgs.topicsFile)).toMap();
        LOG.debug(String.format("%d topics found", topics.size()));
        TwitterFeatureExtractor extractor = new TwitterFeatureExtractor(reader, qrels, topics, extractors);
        extractor.printFeatures(out);
    } else {
        System.err.println("Unrecognized collection " + parsedArgs.collection);
    }
}

Example 4 with FeatureExtractors

use of io.anserini.ltr.feature.FeatureExtractors in project Anserini by castorini.

the class SearchTweets method main.

public static void main(String[] args) throws Exception {
    long curTime = System.nanoTime();
    SearchArgs searchArgs = new SearchArgs();
    CmdLineParser parser = new CmdLineParser(searchArgs, ParserProperties.defaults().withUsageWidth(90));
    try {
        parser.parseArgument(args);
    } catch (CmdLineException e) {
        System.err.println(e.getMessage());
        parser.printUsage(System.err);
        System.err.println("Example: SearchTweets" + parser.printExample(OptionHandlerFilter.REQUIRED));
        return;
    }
    LOG.info("Reading index at " + searchArgs.index);
    Directory dir;
    if (searchArgs.inmem) {
        LOG.info("Using MMapDirectory with preload");
        dir = new MMapDirectory(Paths.get(searchArgs.index));
        ((MMapDirectory) dir).setPreload(true);
    } else {
        LOG.info("Using default FSDirectory");
        dir = FSDirectory.open(Paths.get(searchArgs.index));
    }
    IndexReader reader = DirectoryReader.open(dir);
    IndexSearcher searcher = new IndexSearcher(reader);
    if (searchArgs.ql) {
        LOG.info("Using QL scoring model");
        searcher.setSimilarity(new LMDirichletSimilarity(searchArgs.mu));
    } else if (searchArgs.bm25) {
        LOG.info("Using BM25 scoring model");
        searcher.setSimilarity(new BM25Similarity(searchArgs.k1, searchArgs.b));
    } else {
        LOG.error("Error: Must specify scoring model!");
        System.exit(-1);
    }
    RerankerCascade cascade = new RerankerCascade();
    if (searchArgs.rm3) {
        cascade.add(new Rm3Reranker(IndexTweets.ANALYZER, StatusField.TEXT.name, "src/main/resources/io/anserini/rerank/rm3/rm3-stoplist.twitter.txt"));
        cascade.add(new RemoveRetweetsTemporalTiebreakReranker());
    } else {
        cascade.add(new RemoveRetweetsTemporalTiebreakReranker());
    }
    if (!searchArgs.model.isEmpty() && searchArgs.extractors != null) {
        LOG.debug(String.format("Ranklib model used, modeled loaded from %s", searchArgs.model));
        cascade.add(new RankLibReranker(searchArgs.model, StatusField.TEXT.name, searchArgs.extractors));
    }
    FeatureExtractors extractorChain = null;
    if (searchArgs.extractors != null) {
        extractorChain = FeatureExtractors.loadExtractor(searchArgs.extractors);
    }
    if (searchArgs.dumpFeatures) {
        PrintStream out = new PrintStream(searchArgs.featureFile);
        Qrels qrels = new Qrels(searchArgs.qrels);
        cascade.add(new TweetsLtrDataGenerator(out, qrels, extractorChain));
    }
    MicroblogTopicSet topics = MicroblogTopicSet.fromFile(new File(searchArgs.topics));
    PrintStream out = new PrintStream(new FileOutputStream(new File(searchArgs.output)));
    LOG.info("Writing output to " + searchArgs.output);
    LOG.info("Initialized complete! (elapsed time = " + (System.nanoTime() - curTime) / 1000000 + "ms)");
    long totalTime = 0;
    int cnt = 0;
    for (MicroblogTopic topic : topics) {
        long curQueryTime = System.nanoTime();
        Query filter = LongPoint.newRangeQuery(StatusField.ID.name, 0L, topic.getQueryTweetTime());
        Query query = AnalyzerUtils.buildBagOfWordsQuery(StatusField.TEXT.name, IndexTweets.ANALYZER, topic.getQuery());
        BooleanQuery.Builder builder = new BooleanQuery.Builder();
        builder.add(filter, BooleanClause.Occur.FILTER);
        builder.add(query, BooleanClause.Occur.MUST);
        Query q = builder.build();
        TopDocs rs = searcher.search(q, searchArgs.hits);
        List<String> queryTokens = AnalyzerUtils.tokenize(IndexTweets.ANALYZER, topic.getQuery());
        RerankerContext context = new RerankerContext(searcher, query, topic.getId(), topic.getQuery(), queryTokens, StatusField.TEXT.name, filter);
        ScoredDocuments docs = cascade.run(ScoredDocuments.fromTopDocs(rs, searcher), context);
        for (int i = 0; i < docs.documents.length; i++) {
            String qid = topic.getId().replaceFirst("^MB0*", "");
            out.println(String.format("%s Q0 %s %d %f %s", qid, docs.documents[i].getField(StatusField.ID.name).numericValue(), (i + 1), docs.scores[i], searchArgs.runtag));
        }
        long qtime = (System.nanoTime() - curQueryTime) / 1000000;
        LOG.info("Query " + topic.getId() + " (elapsed time = " + qtime + "ms)");
        totalTime += qtime;
        cnt++;
    }
    LOG.info("All queries completed!");
    LOG.info("Total elapsed time = " + totalTime + "ms");
    LOG.info("Average query latency = " + (totalTime / cnt) + "ms");
    reader.close();
    out.close();
}

Example 5 with FeatureExtractors

use of io.anserini.ltr.feature.FeatureExtractors in project Anserini by castorini.

the class SearchWebCollection method main.

public static void main(String[] args) throws Exception {
    SearchArgs searchArgs = new SearchArgs();
    CmdLineParser parser = new CmdLineParser(searchArgs, ParserProperties.defaults().withUsageWidth(90));
    try {
        parser.parseArgument(args);
    } catch (CmdLineException e) {
        System.err.println(e.getMessage());
        parser.printUsage(System.err);
        System.err.println("Example: SearchWebCollection" + parser.printExample(OptionHandlerFilter.REQUIRED));
        return;
    }
    LOG.info("Reading index at " + searchArgs.index);
    Directory dir;
    if (searchArgs.inmem) {
        LOG.info("Using MMapDirectory with preload");
        dir = new MMapDirectory(Paths.get(searchArgs.index));
        ((MMapDirectory) dir).setPreload(true);
    } else {
        LOG.info("Using default FSDirectory");
        dir = FSDirectory.open(Paths.get(searchArgs.index));
    }
    Similarity similarity = null;
    if (searchArgs.ql) {
        LOG.info("Using QL scoring model");
        similarity = new LMDirichletSimilarity(searchArgs.mu);
    } else if (searchArgs.bm25) {
        LOG.info("Using BM25 scoring model");
        similarity = new BM25Similarity(searchArgs.k1, searchArgs.b);
    } else {
        LOG.error("Error: Must specify scoring model!");
        System.exit(-1);
    }
    RerankerCascade cascade = new RerankerCascade();
    boolean useQueryParser = false;
    if (searchArgs.rm3) {
        cascade.add(new Rm3Reranker(new EnglishAnalyzer(), FIELD_BODY, "src/main/resources/io/anserini/rerank/rm3/rm3-stoplist.gov2.txt"));
        useQueryParser = true;
    } else {
        cascade.add(new IdentityReranker());
    }
    FeatureExtractors extractors = null;
    if (searchArgs.extractors != null) {
        extractors = FeatureExtractors.loadExtractor(searchArgs.extractors);
    }
    if (searchArgs.dumpFeatures) {
        PrintStream out = new PrintStream(searchArgs.featureFile);
        Qrels qrels = new Qrels(searchArgs.qrels);
        cascade.add(new WebCollectionLtrDataGenerator(out, qrels, extractors));
    }
    Path topicsFile = Paths.get(searchArgs.topics);
    if (!Files.exists(topicsFile) || !Files.isRegularFile(topicsFile) || !Files.isReadable(topicsFile)) {
        throw new IllegalArgumentException("Topics file : " + topicsFile + " does not exist or is not a (readable) file.");
    }
    TopicReader tr = (TopicReader) Class.forName("io.anserini.search.query." + searchArgs.topicReader + "TopicReader").getConstructor(Path.class).newInstance(topicsFile);
    SortedMap<Integer, String> topics = tr.read();
    final long start = System.nanoTime();
    SearchWebCollection searcher = new SearchWebCollection(searchArgs.index);
    searcher.search(topics, searchArgs.output, similarity, searchArgs.hits, cascade, useQueryParser, searchArgs.keepstop);
    searcher.close();
    final long durationMillis = TimeUnit.MILLISECONDS.convert(System.nanoTime() - start, TimeUnit.NANOSECONDS);
    LOG.info("Total " + topics.size() + " topics searched in " + DurationFormatUtils.formatDuration(durationMillis, "HH:mm:ss"));
}