Beispiel #1
0
def main(args):
    """ Main entry.
    """

    data = Dataset(args.dataset)
    num, dim = data.base.shape

    # We are looking for the ten closest neighbours
    nearest = NearestFilter(args.topk)
    # We want unique candidates
    unique = UniqueFilter()

    # Create engines for all configurations
    for nbit, ntbl in itertools.product(args.nbits, args.ntbls):
        logging.info("Creating Engine ...")
        lshashes = [RandomBinaryProjections('rbp%d' % i, nbit)
                    for i in xrange(ntbl)]

        # Create engine with this configuration
        engine = Engine(dim, lshashes=lshashes,
                        vector_filters=[unique, nearest])
        logging.info("\tDone!")

        logging.info("Adding items ...")
        for i in xrange(num):
            engine.store_vector(data.base[i, :], i)
            if i % 100000 == 0:
                logging.info("\t%d/%d" % (i, data.nbae))
        logging.info("\tDone!")

        ids = np.zeros((data.nqry, args.topk), np.int)
        logging.info("Searching ...")
        tic()
        for i in xrange(data.nqry):
            reti = [y for x, y, z in
                    np.array(engine.neighbours(data.query[i]))]
            ids[i, :len(reti)] = reti
            if i % 100 == 0:
                logging.info("\t%d/%d" % (i, data.nqry))
        time_costs = toc()
        logging.info("\tDone!")

        report = os.path.join(args.exp_dir, "report.txt")
        with open(report, "a") as rptf:
            rptf.write("*" * 64 + "\n")
            rptf.write("* %s\n" % time.asctime())
            rptf.write("*" * 64 + "\n")

        r_at_k = compute_stats(data.groundtruth, ids, args.topk)[-1][-1]

        with open(report, "a") as rptf:
            rptf.write("=" * 64 + "\n")
            rptf.write("index_%s-nbit_%d-ntbl_%d\n" % ("NearPy", nbit, ntbl))
            rptf.write("-" * 64 + "\n")
            rptf.write("recall@%-8d%.4f\n" % (args.topk, r_at_k))
            rptf.write("time cost (ms): %.3f\n" %
                       (time_costs * 1000 / data.nqry))
Beispiel #2
0
def main(args):
    """ Main entry.
    """

    data = Dataset(args.dataset)
    f = data.base.shape[1]

    for ntrees in args.ntrees:
        t = AnnoyIndex(f)  # Length of item vector that will be indexed
        idxpath = os.path.join(args.exp_dir,
                               'sift_annoy_ntrees%d.idx' % ntrees)
        if not os.path.exists(idxpath):
            logging.info("Adding items ...")
            for i in xrange(data.nbae):
                t.add_item(i, data.base[i])
                if i % 100000 == 0:
                    logging.info("\t%d/%d" % (i, data.nbae))
            logging.info("\tDone!")
            logging.info("Building indexes ...")
            t.build(ntrees)
            logging.info("\tDone!")
            t.save(idxpath)
        else:
            logging.info("Loading indexes ...")
            t.load(idxpath)
            logging.info("\tDone!")

        ids = np.zeros((data.nqry, args.topk), np.int)
        logging.info("Searching ...")
        tic()
        for i in xrange(data.nqry):
            ids[i, :] = np.array(t.get_nns_by_vector(data.query[i], args.topk))
        time_costs = toc()
        logging.info("\tDone!")

        report = os.path.join(args.exp_dir, "report.txt")
        with open(report, "a") as rptf:
            rptf.write("*" * 64 + "\n")
            rptf.write("* %s\n" % time.asctime())
            rptf.write("*" * 64 + "\n")

        r_at_k = compute_stats(data.groundtruth, ids, args.topk)[-1][-1]

        with open(report, "a") as rptf:
            rptf.write("=" * 64 + "\n")
            rptf.write("index_%s-ntrees_%s\n" % ("Annoy", ntrees))
            rptf.write("-" * 64 + "\n")
            rptf.write("recall@%-8d%.4f\n" % (args.topk, r_at_k))
            rptf.write("time cost (ms): %.3f\n" %
                       (time_costs * 1000 / data.nqry))
Beispiel #3
0
def main(args):
    """ Main entry.
    """

    data = Dataset(args.dataset)
    f = data.base.shape[1]

    for ntrees in args.ntrees:
        t = AnnoyIndex(f)   # Length of item vector that will be indexed
        idxpath = os.path.join(args.exp_dir, 'sift_annoy_ntrees%d.idx' % ntrees)
        if not os.path.exists(idxpath):
            logging.info("Adding items ...")
            for i in xrange(data.nbae):
                t.add_item(i, data.base[i])
                if i % 100000 == 0:
                    logging.info("\t%d/%d" % (i, data.nbae))
            logging.info("\tDone!")
            logging.info("Building indexes ...")
            t.build(ntrees)
            logging.info("\tDone!")
            t.save(idxpath)
        else:
            logging.info("Loading indexes ...")
            t.load(idxpath)
            logging.info("\tDone!")

        ids = np.zeros((data.nqry, args.topk), np.int)
        logging.info("Searching ...")
        tic()
        for i in xrange(data.nqry):
            ids[i, :] = np.array(t.get_nns_by_vector(data.query[i], args.topk))
        time_costs = toc()
        logging.info("\tDone!")

        report = os.path.join(args.exp_dir, "report.txt")
        with open(report, "a") as rptf:
            rptf.write("*" * 64 + "\n")
            rptf.write("* %s\n" % time.asctime())
            rptf.write("*" * 64 + "\n")

        r_at_k = compute_stats(data.groundtruth, ids, args.topk)[-1][-1]

        with open(report, "a") as rptf:
            rptf.write("=" * 64 + "\n")
            rptf.write("index_%s-ntrees_%s\n" % ("Annoy", ntrees))
            rptf.write("-" * 64 + "\n")
            rptf.write("recall@%-8d%.4f\n" % (args.topk, r_at_k))
            rptf.write("time cost (ms): %.3f\n" %
                       (time_costs * 1000 / data.nqry))