# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from __future__ import absolute_import, division, print_function

import unittest

import faiss

# noqa E741
# translation of test_knn.lua

import numpy as np
from common_faiss_tests import Randu10k, get_dataset_2, Randu10kUnbalanced

ev = Randu10k()

d = ev.d

# Parameters inverted indexes
ncentroids = int(4 * np.sqrt(ev.nb))
kprobe = int(np.sqrt(ncentroids))

# Parameters for LSH
nbits = d

# Parameters for indexes involving PQ
M = int(d / 8)  # for PQ: #subquantizers
nbits_per_index = 8  # for PQ


class IndexAccuracy(unittest.TestCase):
    def test_IndexFlatIP(self):
        q = faiss.IndexFlatIP(d)  # Ask inner product
        res = ev.launch("FLAT / IP", q)
        e = ev.evalres(res)
        assert e[1] == 1.0

    def test_IndexFlatL2(self):
        q = faiss.IndexFlatL2(d)
        res = ev.launch("FLAT / L2", q)
        e = ev.evalres(res)
        assert e[1] == 1.0

    def test_ivf_kmeans(self):
        ivfk = faiss.IndexIVFFlat(faiss.IndexFlatL2(d), d, ncentroids)
        ivfk.nprobe = kprobe
        res = ev.launch("IndexIVFFlat", ivfk)
        e = ev.evalres(res)
        # should give 0.260  0.260  0.260
        assert e[1] > 0.2

        # test parallel mode
        Dref, Iref = ivfk.search(ev.xq, 100)
        ivfk.parallel_mode = 1
        Dnew, Inew = ivfk.search(ev.xq, 100)
        assert (Iref != Inew).sum() < Iref.size / 5000.0
        assert np.all(Dref == Dnew)

    def test_indexLSH(self):
        q = faiss.IndexLSH(d, nbits)
        res = ev.launch("FLAT / LSH Cosine", q)
        e = ev.evalres(res)
        # should give 0.070  0.250  0.580
        assert e[10] > 0.2

    def test_IndexLSH_32_48(self):
        # CHECK: the difference between 32 and 48 does not make much sense
        for nbits2 in 32, 48:
            q = faiss.IndexLSH(d, nbits2)
            res = ev.launch("LSH half size", q)
            e = ev.evalres(res)
            # should give 0.003  0.019  0.108
            assert e[10] > 0.018

    def test_IndexPQ(self):
        q = faiss.IndexPQ(d, M, nbits_per_index)
        res = ev.launch("FLAT / PQ L2", q)
        e = ev.evalres(res)
        # should give 0.070  0.230  0.260
        assert e[10] > 0.2

    # Approximate search module: PQ with inner product distance
    def test_IndexPQ_ip(self):
        q = faiss.IndexPQ(d, M, nbits_per_index, faiss.METRIC_INNER_PRODUCT)
        res = ev.launch("FLAT / PQ IP", q)
        e = ev.evalres(res)
        # should give 0.070  0.230  0.260
        # (same result as regular PQ on normalized distances)
        assert e[10] > 0.2

    def test_IndexIVFPQ(self):
        ivfpq = faiss.IndexIVFPQ(faiss.IndexFlatL2(d), d, ncentroids, M, 8)
        ivfpq.nprobe = kprobe
        res = ev.launch("IVF PQ", ivfpq)
        e = ev.evalres(res)
        # should give 0.070  0.230  0.260
        assert e[10] > 0.2

    # TODO: translate evaluation of nested

    # Approximate search: PQ with full vector refinement
    def test_IndexPQ_refined(self):
        q = faiss.IndexPQ(d, M, nbits_per_index)
        res = ev.launch("PQ non-refined", q)
        e = ev.evalres(res)
        q.reset()

        rq = faiss.IndexRefineFlat(q)
        res = ev.launch("PQ refined", rq)
        e2 = ev.evalres(res)
        assert e2[10] >= e[10]
        rq.k_factor = 4

        res = ev.launch("PQ refined*4", rq)
        e3 = ev.evalres(res)
        assert e3[10] >= e2[10]

    def test_polysemous(self):
        index = faiss.IndexPQ(d, M, nbits_per_index)
        index.do_polysemous_training = True
        # reduce nb iterations to speed up training for the test
        index.polysemous_training.n_iter = 50000
        index.polysemous_training.n_redo = 1
        res = ev.launch("normal PQ", index)
        e_baseline = ev.evalres(res)
        index.search_type = faiss.IndexPQ.ST_polysemous

        index.polysemous_ht = int(M / 16.0 * 58)

        stats = faiss.cvar.indexPQ_stats
        stats.reset()

        res = ev.launch("Polysemous ht=%d" % index.polysemous_ht, index)
        e_polysemous = ev.evalres(res)
        # The randu dataset is difficult, so we are not too picky on
        # the results. Here we assert that we have < 10 % loss when
        # computing full PQ on fewer than 20% of the data.
        assert stats.n_hamming_pass < stats.ncode / 5
        # Test disabled because difference is 0.17 on aarch64
        # TODO check why???
        # assert e_polysemous[10] > e_baseline[10] - 0.1

    def test_ScalarQuantizer(self):
        quantizer = faiss.IndexFlatL2(d)
        ivfpq = faiss.IndexIVFScalarQuantizer(
            quantizer, d, ncentroids, faiss.ScalarQuantizer.QT_8bit
        )
        ivfpq.nprobe = kprobe
        res = ev.launch("IVF SQ", ivfpq)
        e = ev.evalres(res)
        # should give 0.234  0.236  0.236
        assert e[10] > 0.235

    def test_polysemous_OOM(self):
        """this used to cause OOM when training polysemous with large
        nb bits"""
        d = 32
        xt, xb, xq = get_dataset_2(d, 10000, 0, 0)
        index = faiss.IndexPQ(d, M, 13)
        index.do_polysemous_training = True
        index.pq.cp.niter = 0
        index.polysemous_training.max_memory = 128 * 1024 * 1024
        self.assertRaises(RuntimeError, index.train, xt)


class TestSQFlavors(unittest.TestCase):
    """tests IP in addition to L2, non multiple of 8 dimensions"""

    def add2columns(self, x):
        return np.hstack((x, np.zeros((x.shape[0], 2), dtype="float32")))

    def subtest_add2col(self, xb, xq, index, qname):
        """Test with 2 additional dimensions to take also the non-SIMD
        codepath. We don't retrain anything but add 2 dims to the
        queries, the centroids and the trained ScalarQuantizer.
        """
        nb, d = xb.shape

        d2 = d + 2
        xb2 = self.add2columns(xb)
        xq2 = self.add2columns(xq)

        nlist = index.nlist
        quantizer = faiss.downcast_index(index.quantizer)
        quantizer2 = faiss.IndexFlat(d2, index.metric_type)
        centroids = faiss.vector_to_array(quantizer.codes)
        centroids = centroids.view("float32").reshape(nlist, d)
        centroids2 = self.add2columns(centroids)
        quantizer2.add(centroids2)
        index2 = faiss.IndexIVFScalarQuantizer(
            quantizer2, d2, index.nlist, index.sq.qtype, index.metric_type
        )
        index2.nprobe = 4
        if qname in ("8bit", "4bit"):
            trained = faiss.vector_to_array(index.sq.trained).reshape(2, -1)
            nt = trained.shape[1]
            # 2 lines: vmins and vdiffs
            new_nt = int(nt * d2 / d)
            trained2 = np.hstack((trained, np.zeros((2, new_nt - nt),
                                  dtype="float32")))
            trained2[1, nt:] = 1.0  # set vdiff to 1 to avoid div by 0
            faiss.copy_array_to_vector(trained2.ravel(), index2.sq.trained)
        else:
            index2.sq.trained = index.sq.trained

        index2.is_trained = True
        index2.add(xb2)
        return index2.search(xq2, 10)

    # run on Sept 18, 2018 with nprobe=4 + 4 bit bugfix
    ref_results = {
        (0, "8bit"): 984,
        (0, "4bit"): 978,
        (0, "8bit_uniform"): 985,
        (0, "4bit_uniform"): 979,
        (0, "fp16"): 985,
        (1, "8bit"): 979,
        (1, "4bit"): 973,
        (1, "8bit_uniform"): 979,
        (1, "4bit_uniform"): 972,
        (1, "fp16"): 979,
        # added 2019-06-26
        (0, "6bit"): 985,
        (1, "6bit"): 987,
    }

    def subtest(self, mt):
        d = 32
        xt, xb, xq = get_dataset_2(d, 2000, 1000, 200)
        nlist = 64

        gt_index = faiss.IndexFlat(d, mt)
        gt_index.add(xb)
        gt_D, gt_I = gt_index.search(xq, 10)
        quantizer = faiss.IndexFlat(d, mt)
        for qname in "8bit 4bit 8bit_uniform 4bit_uniform fp16 6bit".split():
            qtype = getattr(faiss.ScalarQuantizer, "QT_" + qname)
            index = faiss.IndexIVFScalarQuantizer(quantizer, d, nlist, qtype,
                                                  mt)
            index.train(xt)
            index.add(xb)
            index.nprobe = 4  # hopefully more robust than 1
            D, I = index.search(xq, 10)
            ninter = faiss.eval_intersection(I, gt_I)
            assert abs(ninter - self.ref_results[(mt, qname)]) <= 10

            if qname == "6bit":
                # the test below fails triggers ASAN. TODO check what's wrong
                continue

            D2, I2 = self.subtest_add2col(xb, xq, index, qname)
            assert np.all(I2 == I)

            # also test range search

            if mt == faiss.METRIC_INNER_PRODUCT:
                radius = float(D[:, -1].max())
            else:
                radius = float(D[:, -1].min())

            lims, D3, I3 = index.range_search(xq, radius)
            ntot = ndiff = 0
            for i in range(len(xq)):
                l0, l1 = lims[i], lims[i + 1]
                Inew = set(I3[l0:l1])
                if mt == faiss.METRIC_INNER_PRODUCT:
                    mask = D2[i] > radius
                else:
                    mask = D2[i] < radius
                Iref = set(I2[i, mask])
                ndiff += len(Inew ^ Iref)
                ntot += len(Iref)
            assert ndiff < ntot * 0.01

            for pm in 1, 2:
                index.parallel_mode = pm
                lims4, D4, I4 = index.range_search(xq, radius)
                for qno in range(len(lims) - 1):
                    Iref = I3[lims[qno]: lims[qno + 1]]
                    Inew = I4[lims4[qno]: lims4[qno + 1]]
                    assert set(Iref) == set(Inew), "q %d ref %s new %s" % (
                        qno,
                        Iref,
                        Inew,
                    )

    def test_SQ_IP(self):
        self.subtest(faiss.METRIC_INNER_PRODUCT)

    def test_SQ_L2(self):
        self.subtest(faiss.METRIC_L2)

    def test_parallel_mode(self):
        d = 32
        xt, xb, xq = get_dataset_2(d, 2000, 1000, 200)

        index = faiss.index_factory(d, "IVF64,SQ8")
        index.train(xt)
        index.add(xb)
        index.nprobe = 4  # hopefully more robust than 1
        Dref, Iref = index.search(xq, 10)

        for pm in 1, 2, 3:
            index.parallel_mode = pm

            Dnew, Inew = index.search(xq, 10)
            np.testing.assert_array_equal(Iref, Inew)
            np.testing.assert_array_equal(Dref, Dnew)


class TestSQByte(unittest.TestCase):
    def subtest_8bit_direct(self, metric_type, d, quantizer_type):
        xt, xb, xq = get_dataset_2(d, 500, 1000, 30)

        # rescale everything to get integer
        tmin, tmax = xt.min(), xt.max()

        def rescale(x):
            x = np.floor((x - tmin) * 256 / (tmax - tmin))
            x[x < 0] = 0
            x[x > 255] = 255
            return x

        def rescale_signed(x):
            x = np.floor((x - tmin) * 256 / (tmax - tmin))
            x[x < 0] = 0
            x[x > 255] = 255
            x -= 128
            return x

        if quantizer_type == faiss.ScalarQuantizer.QT_8bit_direct_signed:
            xt = rescale_signed(xt)
            xb = rescale_signed(xb)
            xq = rescale_signed(xq)
        else:
            xt = rescale(xt)
            xb = rescale(xb)
            xq = rescale(xq)

        gt_index = faiss.IndexFlat(d, metric_type)
        gt_index.add(xb)
        Dref, Iref = gt_index.search(xq, 10)

        index = faiss.IndexScalarQuantizer(
            d, quantizer_type, metric_type
        )
        index.add(xb)
        D, I = index.search(xq, 10)

        assert np.all(I == Iref)
        assert np.all(D == Dref)

        # same, with IVF

        nlist = 64
        quantizer = faiss.IndexFlat(d, metric_type)

        gt_index = faiss.IndexIVFFlat(quantizer, d, nlist, metric_type)
        gt_index.nprobe = 4
        gt_index.train(xt)
        gt_index.add(xb)
        Dref, Iref = gt_index.search(xq, 10)

        index = faiss.IndexIVFScalarQuantizer(
            quantizer, d, nlist, quantizer_type,
            metric_type
        )
        index.nprobe = 4
        index.by_residual = False
        index.train(xt)
        index.add(xb)
        D, I = index.search(xq, 10)

        assert np.all(I == Iref)
        assert np.all(D == Dref)

    def test_8bit_direct(self):
        for quantizer in faiss.ScalarQuantizer.QT_8bit_direct, faiss.ScalarQuantizer.QT_8bit_direct_signed:
            for d in 13, 16, 24:
                for metric_type in faiss.METRIC_L2, faiss.METRIC_INNER_PRODUCT:
                    self.subtest_8bit_direct(metric_type, d, quantizer)


class TestNNDescent(unittest.TestCase):
    def test_L1(self):
        search_Ls = [10, 20, 30]
        thresholds = [0.83, 0.92, 0.95]
        for search_L, threshold in zip(search_Ls, thresholds):
            self.subtest(32, faiss.METRIC_L1, 10, search_L, threshold)

    def test_L2(self):
        search_Ls = [10, 20, 30]
        thresholds = [0.83, 0.92, 0.95]
        for search_L, threshold in zip(search_Ls, thresholds):
            self.subtest(32, faiss.METRIC_L2, 10, search_L, threshold)

    def test_IP(self):
        search_Ls = [10, 20, 30]
        thresholds = [0.80, 0.90, 0.93]
        for search_L, threshold in zip(search_Ls, thresholds):
            self.subtest(32, faiss.METRIC_INNER_PRODUCT, 10, search_L, threshold)

    def subtest(self, d, metric, topk, search_L, threshold):
        metric_names = {
            faiss.METRIC_L1: "L1",
            faiss.METRIC_L2: "L2",
            faiss.METRIC_INNER_PRODUCT: "IP",
        }
        topk = 10
        nt, nb, nq = 2000, 1000, 200
        xt, xb, xq = get_dataset_2(d, nt, nb, nq)
        gt_index = faiss.IndexFlat(d, metric)
        gt_index.add(xb)
        gt_D, gt_I = gt_index.search(xq, topk)

        K = 16
        index = faiss.IndexNNDescentFlat(d, K, metric)
        index.nndescent.S = 10
        index.nndescent.R = 32
        index.nndescent.L = K + 20
        index.nndescent.iter = 5
        index.verbose = False

        index.nndescent.search_L = search_L

        index.add(xb)
        D, I = index.search(xq, topk)
        recalls = 0
        for i in range(nq):
            for j in range(topk):
                for k in range(topk):
                    if I[i, j] == gt_I[i, k]:
                        recalls += 1
                        break
        recall = 1.0 * recalls / (nq * topk)
        print(
            "Metric: {}, L: {}, Recall@{}: {}".format(
                metric_names[metric], search_L, topk, recall
            )
        )
        assert recall > threshold, "{} <= {}".format(recall, threshold)


class TestPQFlavors(unittest.TestCase):

    # run on Dec 14, 2018
    ref_results = {
        (1, True): 800,
        (1, True, 20): 794,
        (1, False): 769,
        (0, True): 831,
        (0, True, 20): 828,
        (0, False): 829,
    }

    def test_IVFPQ_IP(self):
        self.subtest(faiss.METRIC_INNER_PRODUCT)

    def test_IVFPQ_L2(self):
        self.subtest(faiss.METRIC_L2)

    def subtest(self, mt):
        d = 32
        xt, xb, xq = get_dataset_2(d, 2000, 1000, 200)
        nlist = 64

        gt_index = faiss.IndexFlat(d, mt)
        gt_index.add(xb)
        gt_D, gt_I = gt_index.search(xq, 10)
        quantizer = faiss.IndexFlat(d, mt)
        for by_residual in True, False:

            index = faiss.IndexIVFPQ(quantizer, d, nlist, 4, 8)
            index.metric_type = mt
            index.by_residual = by_residual
            if by_residual:
                # perform cheap polysemous training
                index.do_polysemous_training = True
                pt = faiss.PolysemousTraining()
                pt.n_iter = 50000
                pt.n_redo = 1
                index.polysemous_training = pt

            index.train(xt)
            index.add(xb)
            index.nprobe = 4
            D, I = index.search(xq, 10)

            ninter = faiss.eval_intersection(I, gt_I)

            assert abs(ninter - self.ref_results[mt, by_residual]) <= 3

            index.use_precomputed_table = 0
            D2, I2 = index.search(xq, 10)
            assert np.all(I == I2)

            if by_residual:

                index.use_precomputed_table = 1
                index.polysemous_ht = 20
                D, I = index.search(xq, 10)
                ninter = faiss.eval_intersection(I, gt_I)

                # polysemous behaves bizarrely on ARM
                assert (
                    ninter >= self.ref_results[mt, by_residual,
                                               index.polysemous_ht] - 4
                )

            # also test range search

            if mt == faiss.METRIC_INNER_PRODUCT:
                radius = float(D[:, -1].max())
            else:
                radius = float(D[:, -1].min())

            lims, D3, I3 = index.range_search(xq, radius)
            ntot = ndiff = 0
            for i in range(len(xq)):
                l0, l1 = lims[i], lims[i + 1]
                Inew = set(I3[l0:l1])
                if mt == faiss.METRIC_INNER_PRODUCT:
                    mask = D2[i] > radius
                else:
                    mask = D2[i] < radius
                Iref = set(I2[i, mask])
                ndiff += len(Inew ^ Iref)
                ntot += len(Iref)
            assert ndiff < ntot * 0.02

    def test_IVFPQ_non8bit(self):
        d = 16
        xt, xb, xq = get_dataset_2(d, 10000, 2000, 200)
        nlist = 64

        gt_index = faiss.IndexFlat(d)
        gt_index.add(xb)
        gt_D, gt_I = gt_index.search(xq, 10)

        quantizer = faiss.IndexFlat(d)
        ninter = {}
        for v in "2x8", "8x2":
            if v == "8x2":
                index = faiss.IndexIVFPQ(quantizer, d, nlist, 2, 8)
            else:
                index = faiss.IndexIVFPQ(quantizer, d, nlist, 8, 2)
            index.train(xt)
            index.add(xb)
            index.nprobe = 16

            D, I = index.search(xq, 10)
            ninter[v] = faiss.eval_intersection(I, gt_I)
        # this should be the case but we don't observe
        # that... Probavly too few test points
        #  assert ninter['2x8'] > ninter['8x2']
        # ref numbers updated on 2025-01-19 after fixing nprobe typo
        # (was 'npobe')
        # Old values were 458/465 based on broken test with default nprobe
        # New values reflect proper nprobe=16 configuration with much better
        # search quality
        assert abs(ninter["2x8"] - 929) < 4
        assert abs(ninter["8x2"] - 960) < 4


class TestFlat1D(unittest.TestCase):
    def test_flat_1d(self):
        rs = np.random.RandomState(123545)
        k = 10
        xb = rs.uniform(size=(100, 1)).astype("float32")
        # make sure to test below and above
        xq = rs.uniform(size=(1000, 1)).astype("float32") * 1.1 - 0.05

        ref = faiss.IndexFlatL2(1)
        ref.add(xb)
        ref_D, ref_I = ref.search(xq, k)

        new = faiss.IndexFlat1D()
        new.add(xb)

        new_D, new_I = new.search(xq, 10)

        ndiff = (np.abs(ref_I - new_I) != 0).sum()

        assert ndiff < 100
        new_D = new_D ** 2
        max_diff_D = np.abs(ref_D - new_D).max()
        assert max_diff_D < 1e-5

    def test_size_0(self):
        # just make sure it does not crash on small nb
        index = faiss.IndexFlat1D()
        rs = np.random.RandomState(123)
        for i in range(3):
            x = np.array([[rs.rand()]])
            D, I = index.search(x, 10)
            self.assertEqual((I == -1).sum(), 10 - i)
            index.add(x)


class OPQRelativeAccuracy(unittest.TestCase):
    # translated from test_opq.lua

    def test_OPQ(self):

        M = 4

        ev = Randu10kUnbalanced()
        d = ev.d
        index = faiss.IndexPQ(d, M, 8)

        res = ev.launch("PQ", index)
        e_pq = ev.evalres(res)

        index_pq = faiss.IndexPQ(d, M, 8)
        opq_matrix = faiss.OPQMatrix(d, M)
        # opq_matrix.verbose = true
        opq_matrix.niter = 10
        opq_matrix.niter_pq = 4
        index = faiss.IndexPreTransform(opq_matrix, index_pq)

        res = ev.launch("OPQ", index)
        e_opq = ev.evalres(res)

        # verify that OPQ better than PQ
        for r in 1, 10, 100:
            assert e_opq[r] > e_pq[r]

    def test_OIVFPQ(self):
        # Parameters inverted indexes
        ncentroids = 50
        M = 4

        ev = Randu10kUnbalanced()
        d = ev.d
        quantizer = faiss.IndexFlatL2(d)
        index = faiss.IndexIVFPQ(quantizer, d, ncentroids, M, 8)
        index.nprobe = 20

        res = ev.launch("IVFPQ", index)
        e_ivfpq = ev.evalres(res)

        quantizer = faiss.IndexFlatL2(d)
        index_ivfpq = faiss.IndexIVFPQ(quantizer, d, ncentroids, M, 8)
        index_ivfpq.nprobe = 20
        opq_matrix = faiss.OPQMatrix(d, M)
        opq_matrix.niter = 12
        index = faiss.IndexPreTransform(opq_matrix, index_ivfpq)

        res = ev.launch("O+IVFPQ", index)
        e_oivfpq = ev.evalres(res)

        # verify same on OIVFPQ
        for r in 1, 10, 100:
            assert e_oivfpq[r] >= e_ivfpq[r]


class TestRoundoff(unittest.TestCase):
    def test_roundoff(self):
        # params that force use of BLAS implementation
        nb = 100
        nq = 25
        d = 4
        xb = np.zeros((nb, d), dtype="float32")

        xb[:, 0] = np.arange(nb) + 12345
        xq = xb[:nq] + 0.3

        index = faiss.IndexFlat(d)
        index.add(xb)

        D, I = index.search(xq, 1)

        # this does not work
        assert not np.all(I.ravel() == np.arange(nq))

        index = faiss.IndexPreTransform(faiss.CenteringTransform(d),
                                        faiss.IndexFlat(d))

        index.train(xb)
        index.add(xb)

        D, I = index.search(xq, 1)

        # this works
        assert np.all(I.ravel() == np.arange(nq))


class TestSpectralHash(unittest.TestCase):

    # run on 2019-04-02
    ref_results = {
        (32, "global", 10): 505,
        (32, "centroid", 10): 524,
        (32, "centroid_half", 10): 21,
        (32, "median", 10): 510,
        (32, "global", 1): 8,
        (32, "centroid", 1): 20,
        (32, "centroid_half", 1): 26,
        (32, "median", 1): 14,
        (64, "global", 10): 768,
        (64, "centroid", 10): 767,
        (64, "centroid_half", 10): 21,
        (64, "median", 10): 765,
        (64, "global", 1): 28,
        (64, "centroid", 1): 21,
        (64, "centroid_half", 1): 20,
        (64, "median", 1): 29,
        (128, "global", 10): 968,
        (128, "centroid", 10): 945,
        (128, "centroid_half", 10): 21,
        (128, "median", 10): 958,
        (128, "global", 1): 271,
        (128, "centroid", 1): 279,
        (128, "centroid_half", 1): 171,
        (128, "median", 1): 253,
    }

    def test_sh(self):
        d = 32
        xt, xb, xq = get_dataset_2(d, 2000, 1000, 200)
        nlist, nprobe = 1, 1

        gt_index = faiss.IndexFlatL2(d)
        gt_index.add(xb)
        gt_D, gt_I = gt_index.search(xq, 10)

        for nbit in 32, 64, 128:
            quantizer = faiss.IndexFlatL2(d)

            index_lsh = faiss.IndexLSH(d, nbit, True)
            index_lsh.add(xb)
            D, I = index_lsh.search(xq, 10)
            ninter = faiss.eval_intersection(I, gt_I)

            print("LSH baseline: %d" % ninter)

            for period in 10.0, 1.0:

                for tt in "global centroid centroid_half median".split():
                    index = faiss.IndexIVFSpectralHash(
                        quantizer, d, nlist, nbit, period
                    )
                    index.nprobe = nprobe
                    index.threshold_type = getattr(
                        faiss.IndexIVFSpectralHash, "Thresh_" + tt
                    )

                    index.train(xt)
                    index.add(xb)
                    D, I = index.search(xq, 10)

                    ninter = faiss.eval_intersection(I, gt_I)
                    key = (nbit, tt, period)

                    assert abs(ninter - self.ref_results[key]) <= 14


class TestRefine(unittest.TestCase):
    def do_test(self, metric):
        d = 32
        xt, xb, xq = get_dataset_2(d, 2000, 1000, 200)
        index1 = faiss.index_factory(d, "PQ4x4np", metric)
        Dref, Iref = faiss.knn(xq, xb, 10, metric)

        index1.train(xt)
        index1.add(xb)

        D1, I1 = index1.search(xq, 100)

        recall1 = (I1 == Iref[:, :1]).sum()

        # add refine index on top
        index_flat = faiss.IndexFlat(d, metric)
        index_flat.add(xb)

        index2 = faiss.IndexRefine(index1, index_flat)
        index2.k_factor = 10.0
        D2, I2 = index2.search(xq, 10)

        # check distance is computed properly
        for i in range(len(xq)):
            x1 = xq[i]
            x2 = xb[I2[i, 5]]
            if metric == faiss.METRIC_L2:
                dref = ((x1 - x2) ** 2).sum()
            else:
                dref = np.dot(x1, x2)
            np.testing.assert_almost_equal(dref, D2[i, 5], decimal=5)

        # check that with refinement, the recall@10 is the same as
        # the original recall@100
        recall2 = (I2 == Iref[:, :1]).sum()
        self.assertEqual(recall1, recall2)

    def test_IP(self):
        self.do_test(faiss.METRIC_INNER_PRODUCT)

    def test_L2(self):
        self.do_test(faiss.METRIC_L2)
