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test_index_composite.py
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test_index_composite.py
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" more elaborate that test_index.py """
from __future__ import absolute_import, division, print_function
import numpy as np
import unittest
import faiss
import os
import shutil
import tempfile
import platform
from common_faiss_tests import get_dataset_2, get_dataset
from faiss.contrib.datasets import SyntheticDataset
from faiss.contrib.inspect_tools import make_LinearTransform_matrix
from faiss.contrib.evaluation import check_ref_knn_with_draws
class TestRemoveFastScan(unittest.TestCase):
def do_test(self, ntotal, removed):
d = 20
xt, xb, _ = get_dataset_2(d, ntotal, ntotal, 0)
index = faiss.index_factory(20, 'IDMap2,PQ5x4fs')
index.train(xt)
index.add_with_ids(xb, np.arange(ntotal).astype("int64"))
before = index.reconstruct_n(0, ntotal)
index.remove_ids(np.array(removed))
for i in range(ntotal):
if i in removed:
# should throw RuntimeError as this vector should be removed
try:
after = index.reconstruct(i)
assert False
except RuntimeError:
pass
else:
after = index.reconstruct(i)
np.testing.assert_array_equal(before[i], after)
assert index.ntotal == ntotal - len(removed)
def test_remove_last_vector(self):
self.do_test(993, [992])
# test remove element from every address 0 -> 31
# [0, 32 + 1, 2 * 32 + 2, ....]
# [0, 33 , 66 , 99, 132, .....]
def test_remove_every_address(self):
removed = (33 * np.arange(32)).tolist()
self.do_test(1100, removed)
# test remove range of vectors and leave ntotal divisible by 32
def test_leave_complete_block(self):
self.do_test(1000, np.arange(8).tolist())
class TestRemove(unittest.TestCase):
def do_merge_then_remove(self, ondisk):
d = 10
nb = 1000
nq = 200
nt = 200
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
quantizer = faiss.IndexFlatL2(d)
index1 = faiss.IndexIVFFlat(quantizer, d, 20)
index1.train(xt)
filename = None
if ondisk:
filename = tempfile.mkstemp()[1]
invlists = faiss.OnDiskInvertedLists(
index1.nlist, index1.code_size,
filename)
index1.replace_invlists(invlists)
index1.add(xb[:int(nb / 2)])
index2 = faiss.IndexIVFFlat(quantizer, d, 20)
assert index2.is_trained
index2.add(xb[int(nb / 2):])
Dref, Iref = index1.search(xq, 10)
index1.merge_from(index2, int(nb / 2))
assert index1.ntotal == nb
index1.remove_ids(faiss.IDSelectorRange(int(nb / 2), nb))
assert index1.ntotal == int(nb / 2)
Dnew, Inew = index1.search(xq, 10)
assert np.all(Dnew == Dref)
assert np.all(Inew == Iref)
if filename is not None:
os.unlink(filename)
def test_remove_regular(self):
self.do_merge_then_remove(False)
@unittest.skipIf(platform.system() == 'Windows',
'OnDiskInvertedLists is unsupported on Windows.')
def test_remove_ondisk(self):
self.do_merge_then_remove(True)
def test_remove(self):
# only tests the python interface
index = faiss.IndexFlat(5)
xb = np.zeros((10, 5), dtype='float32')
xb[:, 0] = np.arange(10, dtype='int64') + 1000
index.add(xb)
index.remove_ids(np.arange(5, dtype='int64') * 2)
xb2 = faiss.vector_float_to_array(index.codes)
xb2 = xb2.view("float32").reshape(5, 5)
assert np.all(xb2[:, 0] == xb[np.arange(5) * 2 + 1, 0])
def test_remove_id_map(self):
sub_index = faiss.IndexFlat(5)
xb = np.zeros((10, 5), dtype='float32')
xb[:, 0] = np.arange(10) + 1000
index = faiss.IndexIDMap2(sub_index)
index.add_with_ids(xb, np.arange(10, dtype='int64') + 100)
assert index.reconstruct(104)[0] == 1004
index.remove_ids(np.array([103], dtype='int64'))
assert index.reconstruct(104)[0] == 1004
try:
index.reconstruct(103)
except RuntimeError:
pass
else:
assert False, 'should have raised an exception'
def test_factory_idmap2_suffix(self):
xb = np.zeros((10, 5), dtype='float32')
xb[:, 0] = np.arange(10) + 1000
index = faiss.index_factory(5, "Flat,IDMap2")
ids = np.arange(10, dtype='int64') + 100
index.add_with_ids(xb, ids)
assert index.reconstruct(104)[0] == 1004
index.remove_ids(np.array([103], dtype='int64'))
assert index.reconstruct(104)[0] == 1004
def test_factory_idmap2_prefix(self):
xb = np.zeros((10, 5), dtype='float32')
xb[:, 0] = np.arange(10) + 1000
index = faiss.index_factory(5, "IDMap2,Flat")
ids = np.arange(10, dtype='int64') + 100
index.add_with_ids(xb, ids)
assert index.reconstruct(109)[0] == 1009
index.remove_ids(np.array([100], dtype='int64'))
assert index.reconstruct(109)[0] == 1009
def test_remove_id_map_2(self):
# from https://github.com/facebookresearch/faiss/issues/255
rs = np.random.RandomState(1234)
X = rs.randn(10, 10).astype(np.float32)
idx = np.array([0, 10, 20, 30, 40, 5, 15, 25, 35, 45], np.int64)
remove_set = np.array([10, 30], dtype=np.int64)
index = faiss.index_factory(10, 'IDMap,Flat')
index.add_with_ids(X[:5, :], idx[:5])
index.remove_ids(remove_set)
index.add_with_ids(X[5:, :], idx[5:])
for i in range(10):
_, searchres = index.search(X[i:i + 1, :], 1)
if idx[i] in remove_set:
assert searchres[0] != idx[i]
else:
assert searchres[0] == idx[i]
def test_remove_id_map_binary(self):
sub_index = faiss.IndexBinaryFlat(40)
xb = np.zeros((10, 5), dtype='uint8')
xb[:, 0] = np.arange(10) + 100
index = faiss.IndexBinaryIDMap2(sub_index)
index.add_with_ids(xb, np.arange(10, dtype='int64') + 1000)
assert index.reconstruct(1004)[0] == 104
index.remove_ids(np.array([1003], dtype='int64'))
assert index.reconstruct(1004)[0] == 104
try:
index.reconstruct(1003)
except RuntimeError:
pass
else:
assert False, 'should have raised an exception'
# while we are there, let's test I/O as well...
fd, tmpnam = tempfile.mkstemp()
os.close(fd)
try:
faiss.write_index_binary(index, tmpnam)
index = faiss.read_index_binary(tmpnam)
finally:
os.remove(tmpnam)
assert index.reconstruct(1004)[0] == 104
try:
index.reconstruct(1003)
except RuntimeError:
pass
else:
assert False, 'should have raised an exception'
class TestRangeSearch(unittest.TestCase):
def test_range_search_id_map(self):
sub_index = faiss.IndexFlat(5, 1) # L2 search instead of inner product
xb = np.zeros((10, 5), dtype='float32')
xb[:, 0] = np.arange(10) + 1000
index = faiss.IndexIDMap2(sub_index)
index.add_with_ids(xb, np.arange(10, dtype=np.int64) + 100)
dist = float(np.linalg.norm(xb[3] - xb[0])) * 0.99
res_subindex = sub_index.range_search(xb[[0], :], dist)
res_index = index.range_search(xb[[0], :], dist)
assert len(res_subindex[2]) == 2
np.testing.assert_array_equal(res_subindex[2] + 100, res_index[2])
class TestUpdate(unittest.TestCase):
def test_update(self):
d = 64
nb = 1000
nt = 1500
nq = 100
np.random.seed(123)
xb = np.random.random(size=(nb, d)).astype('float32')
xt = np.random.random(size=(nt, d)).astype('float32')
xq = np.random.random(size=(nq, d)).astype('float32')
index = faiss.index_factory(d, "IVF64,Flat")
index.train(xt)
index.add(xb)
index.nprobe = 32
D, I = index.search(xq, 5)
index.make_direct_map()
recons_before = np.vstack([index.reconstruct(i) for i in range(nb)])
# revert order of the 200 first vectors
nu = 200
index.update_vectors(np.arange(nu).astype('int64'),
xb[nu - 1::-1].copy())
recons_after = np.vstack([index.reconstruct(i) for i in range(nb)])
# make sure reconstructions remain the same
diff_recons = recons_before[:nu] - recons_after[nu - 1::-1]
assert np.abs(diff_recons).max() == 0
D2, I2 = index.search(xq, 5)
assert np.all(D == D2)
gt_map = np.arange(nb)
gt_map[:nu] = np.arange(nu, 0, -1) - 1
eqs = I.ravel() == gt_map[I2.ravel()]
assert np.all(eqs)
class TestPCAWhite(unittest.TestCase):
def test_white(self):
# generate data
d = 4
nt = 1000
nb = 200
nq = 200
# normal distribition
x = faiss.randn((nt + nb + nq) * d, 1234).reshape(nt + nb + nq, d)
index = faiss.index_factory(d, 'Flat')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
# NN search on normal distribution
index.add(xb)
Do, Io = index.search(xq, 5)
# make distribution very skewed
x *= [10, 4, 1, 0.5]
rr, _ = np.linalg.qr(faiss.randn(d * d).reshape(d, d))
x = np.dot(x, rr).astype('float32')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
# L2 search on skewed distribution
index = faiss.index_factory(d, 'Flat')
index.add(xb)
Dl2, Il2 = index.search(xq, 5)
# whiten + L2 search on L2 distribution
index = faiss.index_factory(d, 'PCAW%d,Flat' % d)
index.train(xt)
index.add(xb)
Dw, Iw = index.search(xq, 5)
# make sure correlation of whitened results with original
# results is much better than simple L2 distances
# should be 961 vs. 264
assert (faiss.eval_intersection(Io, Iw) >
2 * faiss.eval_intersection(Io, Il2))
class TestTransformChain(unittest.TestCase):
def test_chain(self):
# generate data
d = 4
nt = 1000
nb = 200
nq = 200
# normal distribition
x = faiss.randn((nt + nb + nq) * d, 1234).reshape(nt + nb + nq, d)
# make distribution very skewed
x *= [10, 4, 1, 0.5]
rr, _ = np.linalg.qr(faiss.randn(d * d).reshape(d, d))
x = np.dot(x, rr).astype('float32')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
index = faiss.index_factory(d, "L2norm,PCA2,L2norm,Flat")
assert index.chain.size() == 3
l2_1 = faiss.downcast_VectorTransform(index.chain.at(0))
assert l2_1.norm == 2
pca = faiss.downcast_VectorTransform(index.chain.at(1))
assert not pca.is_trained
index.train(xt)
assert pca.is_trained
index.add(xb)
D, I = index.search(xq, 5)
# do the computation manually and check if we get the same result
def manual_trans(x):
x = x.copy()
faiss.normalize_L2(x)
x = pca.apply_py(x)
faiss.normalize_L2(x)
return x
index2 = faiss.IndexFlatL2(2)
index2.add(manual_trans(xb))
D2, I2 = index2.search(manual_trans(xq), 5)
assert np.all(I == I2)
@unittest.skipIf(platform.system() == 'Windows', \
'Mmap not supported on Windows.')
class TestRareIO(unittest.TestCase):
def compare_results(self, index1, index2, xq):
Dref, Iref = index1.search(xq, 5)
Dnew, Inew = index2.search(xq, 5)
assert np.all(Dref == Dnew)
assert np.all(Iref == Inew)
def do_mmappedIO(self, sparse, in_pretransform=False):
d = 10
nb = 1000
nq = 200
nt = 200
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
quantizer = faiss.IndexFlatL2(d)
index1 = faiss.IndexIVFFlat(quantizer, d, 20)
if sparse:
# makes the inverted lists sparse because all elements get
# assigned to the same invlist
xt += (np.ones(10) * 1000).astype('float32')
if in_pretransform:
# make sure it still works when wrapped in an IndexPreTransform
index1 = faiss.IndexPreTransform(index1)
index1.train(xt)
index1.add(xb)
_, fname = tempfile.mkstemp()
try:
faiss.write_index(index1, fname)
index2 = faiss.read_index(fname)
self.compare_results(index1, index2, xq)
index3 = faiss.read_index(fname, faiss.IO_FLAG_MMAP)
self.compare_results(index1, index3, xq)
finally:
if os.path.exists(fname):
os.unlink(fname)
def test_mmappedIO_sparse(self):
self.do_mmappedIO(True)
def test_mmappedIO_full(self):
self.do_mmappedIO(False)
def test_mmappedIO_pretrans(self):
self.do_mmappedIO(False, True)
class TestIVFFlatDedup(unittest.TestCase):
def test_dedup(self):
d = 10
nb = 1000
nq = 200
nt = 500
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
# introduce duplicates
xb[500:900:2] = xb[501:901:2]
xb[901::4] = xb[900::4]
xb[902::4] = xb[900::4]
xb[903::4] = xb[900::4]
# also in the train set
xt[201::2] = xt[200::2]
quantizer = faiss.IndexFlatL2(d)
index_new = faiss.IndexIVFFlatDedup(quantizer, d, 20)
index_new.verbose = True
# should display
# IndexIVFFlatDedup::train: train on 350 points after dedup (was 500 points)
index_new.train(xt)
index_ref = faiss.IndexIVFFlat(quantizer, d, 20)
assert index_ref.is_trained
index_ref.nprobe = 5
index_ref.add(xb)
index_new.nprobe = 5
index_new.add(xb)
Dref, Iref = index_ref.search(xq, 20)
Dnew, Inew = index_new.search(xq, 20)
check_ref_knn_with_draws(Dref, Iref, Dnew, Inew)
# test I/O
fd, tmpfile = tempfile.mkstemp()
os.close(fd)
try:
faiss.write_index(index_new, tmpfile)
index_st = faiss.read_index(tmpfile)
finally:
if os.path.exists(tmpfile):
os.unlink(tmpfile)
Dst, Ist = index_st.search(xq, 20)
check_ref_knn_with_draws(Dnew, Inew, Dst, Ist)
# test remove
toremove = np.hstack((np.arange(3, 1000, 5), np.arange(850, 950)))
toremove = toremove.astype(np.int64)
index_ref.remove_ids(toremove)
index_new.remove_ids(toremove)
Dref, Iref = index_ref.search(xq, 20)
Dnew, Inew = index_new.search(xq, 20)
check_ref_knn_with_draws(Dref, Iref, Dnew, Inew)
class TestSerialize(unittest.TestCase):
def test_serialize_to_vector(self):
d = 10
nb = 1000
nq = 200
nt = 500
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
index = faiss.IndexFlatL2(d)
index.add(xb)
Dref, Iref = index.search(xq, 5)
writer = faiss.VectorIOWriter()
faiss.write_index(index, writer)
ar_data = faiss.vector_to_array(writer.data)
# direct transfer of vector
reader = faiss.VectorIOReader()
reader.data.swap(writer.data)
index2 = faiss.read_index(reader)
Dnew, Inew = index2.search(xq, 5)
assert np.all(Dnew == Dref) and np.all(Inew == Iref)
# from intermediate numpy array
reader = faiss.VectorIOReader()
faiss.copy_array_to_vector(ar_data, reader.data)
index3 = faiss.read_index(reader)
Dnew, Inew = index3.search(xq, 5)
assert np.all(Dnew == Dref) and np.all(Inew == Iref)
@unittest.skipIf(platform.system() == 'Windows',
'OnDiskInvertedLists is unsupported on Windows.')
class TestRenameOndisk(unittest.TestCase):
def test_rename(self):
d = 10
nb = 500
nq = 100
nt = 100
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
quantizer = faiss.IndexFlatL2(d)
index1 = faiss.IndexIVFFlat(quantizer, d, 20)
index1.train(xt)
dirname = tempfile.mkdtemp()
try:
# make an index with ondisk invlists
invlists = faiss.OnDiskInvertedLists(
index1.nlist, index1.code_size,
dirname + '/aa.ondisk')
index1.replace_invlists(invlists)
index1.add(xb)
D1, I1 = index1.search(xq, 10)
faiss.write_index(index1, dirname + '/aa.ivf')
# move the index elsewhere
os.mkdir(dirname + '/1')
for fname in 'aa.ondisk', 'aa.ivf':
os.rename(dirname + '/' + fname,
dirname + '/1/' + fname)
# try to read it: fails!
try:
index2 = faiss.read_index(dirname + '/1/aa.ivf')
except RuntimeError:
pass # normal
else:
assert False
# read it with magic flag
index2 = faiss.read_index(dirname + '/1/aa.ivf',
faiss.IO_FLAG_ONDISK_SAME_DIR)
D2, I2 = index2.search(xq, 10)
assert np.all(I1 == I2)
finally:
shutil.rmtree(dirname)
class TestInvlistMeta(unittest.TestCase):
def test_slice_vstack(self):
d = 10
nb = 1000
nq = 100
nt = 200
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
quantizer = faiss.IndexFlatL2(d)
index = faiss.IndexIVFFlat(quantizer, d, 30)
index.train(xt)
index.add(xb)
Dref, Iref = index.search(xq, 10)
# faiss.wait()
il0 = index.invlists
ils = []
ilv = faiss.InvertedListsPtrVector()
for sl in 0, 1, 2:
il = faiss.SliceInvertedLists(il0, sl * 10, sl * 10 + 10)
ils.append(il)
ilv.push_back(il)
il2 = faiss.VStackInvertedLists(ilv.size(), ilv.data())
index2 = faiss.IndexIVFFlat(quantizer, d, 30)
index2.replace_invlists(il2)
index2.ntotal = index.ntotal
D, I = index2.search(xq, 10)
assert np.all(D == Dref)
assert np.all(I == Iref)
def test_stop_words(self):
d = 10
nb = 1000
nq = 1
nt = 200
xt, xb, xq = get_dataset_2(d, nt, nb, nq)
index = faiss.index_factory(d, "IVF32,Flat")
index.nprobe = 4
index.train(xt)
index.add(xb)
Dref, Iref = index.search(xq, 10)
il = index.invlists
maxsz = max(il.list_size(i) for i in range(il.nlist))
il2 = faiss.StopWordsInvertedLists(il, maxsz + 1)
index.own_invlists
index.own_invlists = False
index.replace_invlists(il2, False)
D1, I1 = index.search(xq, 10)
np.testing.assert_array_equal(Dref, D1)
np.testing.assert_array_equal(Iref, I1)
# cleanup to avoid segfault on exit
index.replace_invlists(il, False)
# voluntarily unbalance one invlist
i = int(I1[0, 0])
index.add(np.vstack([xb[i]] * (maxsz + 10)))
# introduce stopwords again
index.replace_invlists(il2, False)
D2, I2 = index.search(xq, 10)
self.assertFalse(i in list(I2.ravel()))
# avoid mem leak
index.replace_invlists(il, True)
class TestSplitMerge(unittest.TestCase):
def do_test(self, index_key, subset_type):
xt, xb, xq = get_dataset_2(32, 1000, 100, 10)
index = faiss.index_factory(32, index_key)
index.train(xt)
nsplit = 3
sub_indexes = [faiss.clone_index(index) for i in range(nsplit)]
index.add(xb)
Dref, Iref = index.search(xq, 10)
nlist = index.nlist
for i in range(nsplit):
if subset_type in (1, 3):
index.copy_subset_to(sub_indexes[i], subset_type, nsplit, i)
elif subset_type in (0, 2):
j0 = index.ntotal * i // nsplit
j1 = index.ntotal * (i + 1) // nsplit
index.copy_subset_to(sub_indexes[i], subset_type, j0, j1)
elif subset_type == 4:
index.copy_subset_to(
sub_indexes[i], subset_type,
i * nlist // nsplit, (i + 1) * nlist // nsplit)
index_shards = faiss.IndexShards(False, False)
for i in range(nsplit):
index_shards.add_shard(sub_indexes[i])
Dnew, Inew = index_shards.search(xq, 10)
np.testing.assert_array_equal(Iref, Inew)
np.testing.assert_array_equal(Dref, Dnew)
def test_Flat_subset_type_0(self):
self.do_test("IVF30,Flat", subset_type=0)
def test_Flat_subset_type_1(self):
self.do_test("IVF30,Flat", subset_type=1)
def test_Flat_subset_type_2(self):
self.do_test("IVF30,PQ4np", subset_type=2)
def test_Flat_subset_type_3(self):
self.do_test("IVF30,Flat", subset_type=3)
def test_Flat_subset_type_4(self):
self.do_test("IVF30,Flat", subset_type=4)
class TestIndependentQuantizer(unittest.TestCase):
def test_sidebyside(self):
""" provide double-sized vectors to the index, where each vector
is the concatenation of twice the same vector """
ds = SyntheticDataset(32, 1000, 500, 50)
index = faiss.index_factory(ds.d, "IVF32,SQ8")
index.train(ds.get_train())
index.add(ds.get_database())
index.nprobe = 4
Dref, Iref = index.search(ds.get_queries(), 10)
select32first = make_LinearTransform_matrix(
np.eye(64, dtype='float32')[:32])
select32last = make_LinearTransform_matrix(
np.eye(64, dtype='float32')[32:])
quantizer = faiss.IndexPreTransform(
select32first,
index.quantizer
)
index2 = faiss.IndexIVFIndependentQuantizer(
quantizer,
index, select32last
)
xq2 = np.hstack([ds.get_queries()] * 2)
quantizer.search(xq2, 30)
Dnew, Inew = index2.search(xq2, 10)
np.testing.assert_array_equal(Dref, Dnew)
np.testing.assert_array_equal(Iref, Inew)
# test add
index2.reset()
xb2 = np.hstack([ds.get_database()] * 2)
index2.add(xb2)
Dnew, Inew = index2.search(xq2, 10)
np.testing.assert_array_equal(Dref, Dnew)
np.testing.assert_array_equal(Iref, Inew)
def test_half_store(self):
""" the index stores only the first half of each vector
but the coarse quantizer sees them entirely """
ds = SyntheticDataset(32, 1000, 500, 50)
gt = ds.get_groundtruth(10)
select32first = make_LinearTransform_matrix(
np.eye(32, dtype='float32')[:16])
index_ivf = faiss.index_factory(ds.d // 2, "IVF32,Flat")
index_ivf.nprobe = 4
index = faiss.IndexPreTransform(select32first, index_ivf)
index.train(ds.get_train())
index.add(ds.get_database())
Dref, Iref = index.search(ds.get_queries(), 10)
perf_ref = faiss.eval_intersection(Iref, gt)
index_ivf = faiss.index_factory(ds.d // 2, "IVF32,Flat")
index_ivf.nprobe = 4
index = faiss.IndexIVFIndependentQuantizer(
faiss.IndexFlatL2(ds.d),
index_ivf, select32first
)
index.train(ds.get_train())
index.add(ds.get_database())
Dnew, Inew = index.search(ds.get_queries(), 10)
perf_new = faiss.eval_intersection(Inew, gt)
self.assertLess(perf_ref, perf_new)
def test_precomputed_tables(self):
""" see how precomputed tables behave with centroid distance estimates from a mismatching
coarse quantizer """
ds = SyntheticDataset(48, 2000, 500, 250)
gt = ds.get_groundtruth(10)
index = faiss.IndexIVFIndependentQuantizer(
faiss.IndexFlatL2(48),
faiss.index_factory(16, "IVF64,PQ4np"),
faiss.PCAMatrix(48, 16)
)
index.train(ds.get_train())
index.add(ds.get_database())
index_ivf = faiss.downcast_index(faiss.extract_index_ivf(index))
index_ivf.nprobe = 4
Dref, Iref = index.search(ds.get_queries(), 10)
perf_ref = faiss.eval_intersection(Iref, gt)
index_ivf.use_precomputed_table = 1
index_ivf.precompute_table()
Dnew, Inew = index.search(ds.get_queries(), 10)
perf_new = faiss.eval_intersection(Inew, gt)
# to be honest, it is not clear which one is better...
self.assertNotEqual(perf_ref, perf_new)
# check IO while we are at it
index2 = faiss.deserialize_index(faiss.serialize_index(index))
D2, I2 = index2.search(ds.get_queries(), 10)
np.testing.assert_array_equal(Dnew, D2)
np.testing.assert_array_equal(Inew, I2)
class TestSearchAndReconstruct(unittest.TestCase):
def run_search_and_reconstruct(self, index, xb, xq, k=10, eps=None):
n, d = xb.shape
assert xq.shape[1] == d
assert index.d == d
D_ref, I_ref = index.search(xq, k)
R_ref = index.reconstruct_n(0, n)
D, I, R = index.search_and_reconstruct(xq, k)
np.testing.assert_almost_equal(D, D_ref, decimal=5)
self.assertTrue((I == I_ref).all())
self.assertEqual(R.shape[:2], I.shape)
self.assertEqual(R.shape[2], d)
# (n, k, ..) -> (n * k, ..)
I_flat = I.reshape(-1)
R_flat = R.reshape(-1, d)
# Filter out -1s when not enough results
R_flat = R_flat[I_flat >= 0]
I_flat = I_flat[I_flat >= 0]
recons_ref_err = np.mean(np.linalg.norm(R_flat - R_ref[I_flat]))
self.assertLessEqual(recons_ref_err, 1e-6)
def norm1(x):
return np.sqrt((x ** 2).sum(axis=1))
recons_err = np.mean(norm1(R_flat - xb[I_flat]))
print('Reconstruction error = %.3f' % recons_err)
if eps is not None:
self.assertLessEqual(recons_err, eps)
return D, I, R
def test_IndexFlat(self):
d = 32
nb = 1000
nt = 1500
nq = 200
(xt, xb, xq) = get_dataset(d, nb, nt, nq)
index = faiss.IndexFlatL2(d)
index.add(xb)
self.run_search_and_reconstruct(index, xb, xq, eps=0.0)
def test_IndexIVFFlat(self):
d = 32
nb = 1000
nt = 1500
nq = 200
(xt, xb, xq) = get_dataset(d, nb, nt, nq)
quantizer = faiss.IndexFlatL2(d)
index = faiss.IndexIVFFlat(quantizer, d, 32, faiss.METRIC_L2)
index.cp.min_points_per_centroid = 5 # quiet warning
index.nprobe = 4
index.train(xt)
index.add(xb)
self.run_search_and_reconstruct(index, xb, xq, eps=0.0)
def test_IndexIVFPQ(self):
d = 32
nb = 1000
nt = 1500
nq = 200
(xt, xb, xq) = get_dataset(d, nb, nt, nq)
quantizer = faiss.IndexFlatL2(d)
index = faiss.IndexIVFPQ(quantizer, d, 32, 8, 8)
index.cp.min_points_per_centroid = 5 # quiet warning
index.nprobe = 4
index.train(xt)
index.add(xb)
self.run_search_and_reconstruct(index, xb, xq, eps=1.0)
def test_MultiIndex(self):
d = 32
nb = 1000
nt = 1500
nq = 200
(xt, xb, xq) = get_dataset(d, nb, nt, nq)
index = faiss.index_factory(d, "IMI2x5,PQ8np")
faiss.ParameterSpace().set_index_parameter(index, "nprobe", 4)
index.train(xt)
index.add(xb)
self.run_search_and_reconstruct(index, xb, xq, eps=1.0)
def test_IndexTransform(self):
d = 32
nb = 1000
nt = 1500
nq = 200
(xt, xb, xq) = get_dataset(d, nb, nt, nq)
index = faiss.index_factory(d, "L2norm,PCA8,IVF32,PQ8np")
faiss.ParameterSpace().set_index_parameter(index, "nprobe", 4)
index.train(xt)
index.add(xb)
self.run_search_and_reconstruct(index, xb, xq)
class TestSearchAndGetCodes(unittest.TestCase):
def do_test(self, factory_string):
ds = SyntheticDataset(32, 1000, 100, 10)
index = faiss.index_factory(ds.d, factory_string)
index.train(ds.get_train())
index.add(ds.get_database())
index.nprobe
index.nprobe = 10
Dref, Iref = index.search(ds.get_queries(), 10)
D, I, codes = index.search_and_return_codes(
ds.get_queries(), 10, include_listnos=True)
np.testing.assert_array_equal(I, Iref)
np.testing.assert_array_equal(D, Dref)
# verify that we get the same distances when decompressing from
# returned codes (the codes are compatible with sa_decode)
for qi in range(ds.nq):
q = ds.get_queries()[qi]
xbi = index.sa_decode(codes[qi])
D2 = ((q - xbi) ** 2).sum(1)
np.testing.assert_allclose(D2, D[qi], rtol=1e-5)
def test_ivfpq(self):
self.do_test("IVF20,PQ4x4np")
def test_ivfsq(self):
self.do_test("IVF20,SQ8")
def test_ivfrq(self):
self.do_test("IVF20,RQ3x4")