532 lines
16 KiB
Python
532 lines
16 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from __future__ import absolute_import, division, print_function
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import numpy as np
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import faiss
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import unittest
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from common_faiss_tests import get_dataset_2
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class TestPCA(unittest.TestCase):
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def test_pca(self):
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d = 64
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n = 1000
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np.random.seed(123)
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x = np.random.random(size=(n, d)).astype('float32')
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pca = faiss.PCAMatrix(d, 10)
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pca.train(x)
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y = pca.apply_py(x)
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# check that energy per component is decreasing
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column_norm2 = (y**2).sum(0)
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prev = 1e50
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for o in column_norm2:
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self.assertGreater(prev, o)
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prev = o
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def test_pca_epsilon(self):
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d = 64
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n = 1000
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np.random.seed(123)
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x = np.random.random(size=(n, d)).astype('float32')
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# make sure data is in a sub-space
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x[:, ::2] = 0
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# check division by 0 with default computation
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pca = faiss.PCAMatrix(d, 60, -0.5)
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pca.train(x)
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y = pca.apply(x)
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self.assertFalse(np.all(np.isfinite(y)))
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# check add epsilon
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pca = faiss.PCAMatrix(d, 60, -0.5)
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pca.epsilon = 1e-5
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pca.train(x)
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y = pca.apply(x)
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self.assertTrue(np.all(np.isfinite(y)))
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# check I/O
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index = faiss.index_factory(d, "PCAW60,Flat")
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index = faiss.deserialize_index(faiss.serialize_index(index))
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pca1 = faiss.downcast_VectorTransform(index.chain.at(0))
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pca1.epsilon = 1e-5
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index.train(x)
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pca = faiss.downcast_VectorTransform(index.chain.at(0))
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y = pca.apply(x)
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self.assertTrue(np.all(np.isfinite(y)))
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class TestRevSwigPtr(unittest.TestCase):
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def test_rev_swig_ptr(self):
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index = faiss.IndexFlatL2(4)
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xb0 = np.vstack([
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i * 10 + np.array([1, 2, 3, 4], dtype='float32')
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for i in range(5)])
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index.add(xb0)
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xb = faiss.rev_swig_ptr(index.get_xb(), 4 * 5).reshape(5, 4)
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self.assertEqual(np.abs(xb0 - xb).sum(), 0)
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class TestException(unittest.TestCase):
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def test_exception(self):
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index = faiss.IndexFlatL2(10)
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a = np.zeros((5, 10), dtype='float32')
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b = np.zeros(5, dtype='int64')
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# an unsupported operation for IndexFlat
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self.assertRaises(
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RuntimeError,
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index.add_with_ids, a, b
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)
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# assert 'add_with_ids not implemented' in str(e)
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def test_exception_2(self):
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self.assertRaises(
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RuntimeError,
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faiss.index_factory, 12, 'IVF256,Flat,PQ8'
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)
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# assert 'could not parse' in str(e)
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class TestMapLong2Long(unittest.TestCase):
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def test_maplong2long(self):
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keys = np.array([13, 45, 67], dtype=np.int64)
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vals = np.array([3, 8, 2], dtype=np.int64)
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m = faiss.MapLong2Long()
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m.add(keys, vals)
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assert np.all(m.search_multiple(keys) == vals)
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assert m.search(12343) == -1
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class TestOrthognalReconstruct(unittest.TestCase):
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def test_recons_orthonormal(self):
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lt = faiss.LinearTransform(20, 10, True)
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rs = np.random.RandomState(10)
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A, _ = np.linalg.qr(rs.randn(20, 20))
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A = A[:10].astype('float32')
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faiss.copy_array_to_vector(A.ravel(), lt.A)
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faiss.copy_array_to_vector(rs.randn(10).astype('float32'), lt.b)
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lt.set_is_orthonormal()
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lt.is_trained = True
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assert lt.is_orthonormal
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x = rs.rand(30, 20).astype('float32')
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xt = lt.apply_py(x)
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xtt = lt.reverse_transform(xt)
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xttt = lt.apply_py(xtt)
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err = ((xt - xttt)**2).sum()
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self.assertGreater(1e-5, err)
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def test_recons_orthogona_impossible(self):
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lt = faiss.LinearTransform(20, 10, True)
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rs = np.random.RandomState(10)
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A = rs.randn(10 * 20).astype('float32')
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faiss.copy_array_to_vector(A.ravel(), lt.A)
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faiss.copy_array_to_vector(rs.randn(10).astype('float32'), lt.b)
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lt.is_trained = True
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lt.set_is_orthonormal()
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assert not lt.is_orthonormal
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x = rs.rand(30, 20).astype('float32')
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xt = lt.apply_py(x)
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try:
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lt.reverse_transform(xt)
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except Exception:
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pass
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else:
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self.assertFalse('should do an exception')
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class TestMAdd(unittest.TestCase):
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def test_1(self):
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# try with dimensions that are multiples of 16 or not
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rs = np.random.RandomState(123)
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swig_ptr = faiss.swig_ptr
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for dim in 16, 32, 20, 25:
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for _repeat in 1, 2, 3, 4, 5:
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a = rs.rand(dim).astype('float32')
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b = rs.rand(dim).astype('float32')
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c = np.zeros(dim, dtype='float32')
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bf = rs.uniform(5.0) - 2.5
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idx = faiss.fvec_madd_and_argmin(
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dim, swig_ptr(a), bf, swig_ptr(b),
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swig_ptr(c))
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ref_c = a + b * bf
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assert np.abs(c - ref_c).max() < 1e-5
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assert idx == ref_c.argmin()
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class TestNyFuncs(unittest.TestCase):
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def test_l2(self):
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rs = np.random.RandomState(123)
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swig_ptr = faiss.swig_ptr
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for d in 1, 2, 4, 8, 12, 16:
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x = rs.rand(d).astype('float32')
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for ny in 128, 129, 130:
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print("d=%d ny=%d" % (d, ny))
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y = rs.rand(ny, d).astype('float32')
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ref = ((x - y) ** 2).sum(1)
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new = np.zeros(ny, dtype='float32')
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faiss.fvec_L2sqr_ny(swig_ptr(new), swig_ptr(x),
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swig_ptr(y), d, ny)
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assert np.abs(ref - new).max() < 1e-4
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def test_IP(self):
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# this one is not optimized with SIMD but just in case
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rs = np.random.RandomState(123)
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swig_ptr = faiss.swig_ptr
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for d in 1, 2, 4, 8, 12, 16:
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x = rs.rand(d).astype('float32')
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for ny in 128, 129, 130:
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print("d=%d ny=%d" % (d, ny))
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y = rs.rand(ny, d).astype('float32')
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ref = (x * y).sum(1)
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new = np.zeros(ny, dtype='float32')
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faiss.fvec_inner_products_ny(
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swig_ptr(new), swig_ptr(x), swig_ptr(y), d, ny)
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assert np.abs(ref - new).max() < 1e-4
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class TestMatrixStats(unittest.TestCase):
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def test_0s(self):
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rs = np.random.RandomState(123)
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m = rs.rand(40, 20).astype('float32')
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m[5:10] = 0
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comments = faiss.MatrixStats(m).comments
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print(comments)
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assert 'has 5 copies' in comments
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assert '5 null vectors' in comments
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def test_copies(self):
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rs = np.random.RandomState(123)
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m = rs.rand(40, 20).astype('float32')
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m[::2] = m[1::2]
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comments = faiss.MatrixStats(m).comments
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print(comments)
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assert '20 vectors are distinct' in comments
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def test_dead_dims(self):
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rs = np.random.RandomState(123)
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m = rs.rand(40, 20).astype('float32')
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m[:, 5:10] = 0
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comments = faiss.MatrixStats(m).comments
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print(comments)
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assert '5 dimensions are constant' in comments
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def test_rogue_means(self):
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rs = np.random.RandomState(123)
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m = rs.rand(40, 20).astype('float32')
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m[:, 5:10] += 12345
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comments = faiss.MatrixStats(m).comments
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print(comments)
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assert '5 dimensions are too large wrt. their variance' in comments
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def test_normalized(self):
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rs = np.random.RandomState(123)
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m = rs.rand(40, 20).astype('float32')
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faiss.normalize_L2(m)
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comments = faiss.MatrixStats(m).comments
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print(comments)
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assert 'vectors are normalized' in comments
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class TestScalarQuantizer(unittest.TestCase):
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def test_8bit_equiv(self):
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rs = np.random.RandomState(123)
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for _it in range(20):
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for d in 13, 16, 24:
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x = np.floor(rs.rand(5, d) * 256).astype('float32')
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x[0] = 0
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x[1] = 255
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# make sure to test extreme cases
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x[2, 0] = 0
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x[3, 0] = 255
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x[2, 1] = 255
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x[3, 1] = 0
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ref_index = faiss.IndexScalarQuantizer(
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d, faiss.ScalarQuantizer.QT_8bit)
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ref_index.train(x[:2])
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ref_index.add(x[2:3])
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index = faiss.IndexScalarQuantizer(
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d, faiss.ScalarQuantizer.QT_8bit_direct)
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assert index.is_trained
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index.add(x[2:3])
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assert np.all(
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faiss.vector_to_array(ref_index.codes) ==
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faiss.vector_to_array(index.codes))
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# Note that distances are not the same because ref_index
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# reconstructs x as x + 0.5
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D, I = index.search(x[3:], 1)
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# assert D[0, 0] == Dref[0, 0]
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# print(D[0, 0], ((x[3] - x[2]) ** 2).sum())
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assert D[0, 0] == ((x[3] - x[2]) ** 2).sum()
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def test_6bit_equiv(self):
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rs = np.random.RandomState(123)
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for d in 3, 6, 8, 16, 36:
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trainset = np.zeros((2, d), dtype='float32')
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trainset[0, :] = 0
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trainset[0, :] = 63
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index = faiss.IndexScalarQuantizer(
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d, faiss.ScalarQuantizer.QT_6bit)
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index.train(trainset)
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print('cs=', index.code_size)
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x = rs.randint(64, size=(100, d)).astype('float32')
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# verify encoder / decoder
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index.add(x)
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x2 = index.reconstruct_n(0, x.shape[0])
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assert np.all(x == x2 - 0.5)
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# verify AVX decoder (used only for search)
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y = 63 * rs.rand(20, d).astype('float32')
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D, I = index.search(y, 10)
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for i in range(20):
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for j in range(10):
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dis = ((y[i] - x2[I[i, j]]) ** 2).sum()
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# print(dis, D[i, j])
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assert abs(D[i, j] - dis) / dis < 1e-5
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class TestRandom(unittest.TestCase):
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def test_rand(self):
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x = faiss.rand(2000)
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assert np.all(x >= 0) and np.all(x < 1)
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h, _ = np.histogram(x, np.arange(0, 1, 0.1))
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assert h.min() > 160 and h.max() < 240
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def test_randint(self):
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x = faiss.randint(20000, vmax=100)
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assert np.all(x >= 0) and np.all(x < 100)
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c = np.bincount(x, minlength=100)
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print(c)
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assert c.max() - c.min() < 50 * 2
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class TestPairwiseDis(unittest.TestCase):
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def test_L2(self):
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swig_ptr = faiss.swig_ptr
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x = faiss.rand((100, 10), seed=1)
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y = faiss.rand((200, 10), seed=2)
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ix = faiss.randint(50, vmax=100)
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iy = faiss.randint(50, vmax=200)
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dis = np.empty(50, dtype='float32')
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faiss.pairwise_indexed_L2sqr(
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10, 50,
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swig_ptr(x), swig_ptr(ix),
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swig_ptr(y), swig_ptr(iy),
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swig_ptr(dis))
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for i in range(50):
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assert np.allclose(
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dis[i], ((x[ix[i]] - y[iy[i]]) ** 2).sum())
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def test_IP(self):
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swig_ptr = faiss.swig_ptr
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x = faiss.rand((100, 10), seed=1)
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y = faiss.rand((200, 10), seed=2)
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ix = faiss.randint(50, vmax=100)
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iy = faiss.randint(50, vmax=200)
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dis = np.empty(50, dtype='float32')
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faiss.pairwise_indexed_inner_product(
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10, 50,
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swig_ptr(x), swig_ptr(ix),
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swig_ptr(y), swig_ptr(iy),
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swig_ptr(dis))
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for i in range(50):
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assert np.allclose(
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dis[i], np.dot(x[ix[i]], y[iy[i]]))
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class TestSWIGWrap(unittest.TestCase):
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""" various regressions with the SWIG wrapper """
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def test_size_t_ptr(self):
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# issue 1064
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index = faiss.IndexHNSWFlat(10, 32)
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hnsw = index.hnsw
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index.add(np.random.rand(100, 10).astype('float32'))
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be = np.empty(2, 'uint64')
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hnsw.neighbor_range(23, 0, faiss.swig_ptr(be), faiss.swig_ptr(be[1:]))
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def test_id_map_at(self):
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# issue 1020
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n_features = 100
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feature_dims = 10
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features = np.random.random((n_features, feature_dims)).astype(np.float32)
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idx = np.arange(n_features).astype(np.int64)
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index = faiss.IndexFlatL2(feature_dims)
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index = faiss.IndexIDMap2(index)
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index.add_with_ids(features, idx)
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[index.id_map.at(int(i)) for i in range(index.ntotal)]
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def test_downcast_Refine(self):
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index = faiss.IndexRefineFlat(
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faiss.IndexScalarQuantizer(10, faiss.ScalarQuantizer.QT_8bit)
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)
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# serialize and deserialize
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index2 = faiss.deserialize_index(
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faiss.serialize_index(index)
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)
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assert isinstance(index2, faiss.IndexRefineFlat)
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def do_test_array_type(self, dtype):
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""" tests swig_ptr and rev_swig_ptr for this type of array """
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a = np.arange(12).astype(dtype)
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ptr = faiss.swig_ptr(a)
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print(ptr)
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a2 = faiss.rev_swig_ptr(ptr, 12)
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np.testing.assert_array_equal(a, a2)
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def test_all_array_types(self):
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self.do_test_array_type('float32')
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self.do_test_array_type('float64')
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self.do_test_array_type('int8')
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self.do_test_array_type('uint8')
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self.do_test_array_type('int16')
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self.do_test_array_type('uint16')
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self.do_test_array_type('int32')
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self.do_test_array_type('uint32')
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self.do_test_array_type('int64')
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self.do_test_array_type('uint64')
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def test_int64(self):
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# see https://github.com/facebookresearch/faiss/issues/1529
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v = faiss.Int64Vector()
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for i in range(10):
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v.push_back(i)
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a = faiss.vector_to_array(v)
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assert a.dtype == 'int64'
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np.testing.assert_array_equal(a, np.arange(10, dtype='int64'))
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# check if it works in an IDMap
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idx = faiss.IndexIDMap(faiss.IndexFlatL2(32))
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idx.add_with_ids(
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np.random.rand(10, 32).astype('float32'),
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np.random.randint(1000, size=10, dtype='int64')
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)
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faiss.vector_to_array(idx.id_map)
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class TestNNDescentKNNG(unittest.TestCase):
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def test_knng_L2(self):
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self.subtest(32, 10, faiss.METRIC_L2)
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def test_knng_IP(self):
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self.subtest(32, 10, faiss.METRIC_INNER_PRODUCT)
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def subtest(self, d, K, metric):
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metric_names = {faiss.METRIC_L1: 'L1',
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faiss.METRIC_L2: 'L2',
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faiss.METRIC_INNER_PRODUCT: 'IP'}
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nb = 1000
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_, xb, _ = get_dataset_2(d, 0, nb, 0)
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_, knn = faiss.knn(xb, xb, K + 1, metric)
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knn = knn[:, 1:]
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index = faiss.IndexNNDescentFlat(d, K, metric)
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index.nndescent.S = 10
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index.nndescent.R = 32
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index.nndescent.L = K + 20
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index.nndescent.iter = 5
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index.verbose = True
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index.add(xb)
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graph = index.nndescent.final_graph
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graph = faiss.vector_to_array(graph)
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graph = graph.reshape(nb, K)
|
|
|
|
recalls = 0
|
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for i in range(nb):
|
|
for j in range(K):
|
|
for k in range(K):
|
|
if graph[i, j] == knn[i, k]:
|
|
recalls += 1
|
|
break
|
|
recall = 1.0 * recalls / (nb * K)
|
|
print('Metric: {}, knng accuracy: {}'.format(metric_names[metric], recall))
|
|
assert recall > 0.99
|
|
|
|
|
|
class TestResultHeap(unittest.TestCase):
|
|
|
|
def test_keep_min(self):
|
|
self.run_test(False)
|
|
|
|
def test_keep_max(self):
|
|
self.run_test(True)
|
|
|
|
def run_test(self, keep_max):
|
|
nq = 100
|
|
nb = 1000
|
|
restab = faiss.rand((nq, nb), 123)
|
|
ids = faiss.randint((nq, nb), 1324, 10000)
|
|
all_rh = {}
|
|
for nstep in 1, 3:
|
|
rh = faiss.ResultHeap(nq, 10, keep_max=keep_max)
|
|
for i in range(nstep):
|
|
i0, i1 = i * nb // nstep, (i + 1) * nb // nstep
|
|
D = restab[:, i0:i1].copy()
|
|
I = ids[:, i0:i1].copy()
|
|
rh.add_result(D, I)
|
|
rh.finalize()
|
|
if keep_max:
|
|
assert np.all(rh.D[:, :-1] >= rh.D[:, 1:])
|
|
else:
|
|
assert np.all(rh.D[:, :-1] <= rh.D[:, 1:])
|
|
all_rh[nstep] = rh
|
|
|
|
np.testing.assert_equal(all_rh[1].D, all_rh[3].D)
|
|
np.testing.assert_equal(all_rh[1].I, all_rh[3].I)
|