100 lines
2.7 KiB
Python
100 lines
2.7 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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#! /usr/bin/env python2
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# a few common functions for the tests
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import numpy as np
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import faiss
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# reduce number of threads to avoid excessive nb of threads in opt
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# mode (recuces runtime from 100s to 4s!)
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faiss.omp_set_num_threads(4)
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def random_unitary(n, d, seed):
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x = faiss.randn(n * d, seed).reshape(n, d)
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faiss.normalize_L2(x)
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return x
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class Randu10k:
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def __init__(self):
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self.nb = 10000
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self.nq = 1000
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self.nt = 10000
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self.d = 128
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self.xb = random_unitary(self.nb, self.d, 1)
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self.xt = random_unitary(self.nt, self.d, 2)
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self.xq = random_unitary(self.nq, self.d, 3)
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dotprods = np.dot(self.xq, self.xb.T)
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self.gt = dotprods.argmax(1)
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self.k = 100
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def launch(self, name, index):
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if not index.is_trained:
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index.train(self.xt)
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index.add(self.xb)
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return index.search(self.xq, self.k)
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def evalres(self, DI):
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D, I = DI
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e = {}
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for rank in 1, 10, 100:
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e[rank] = ((I[:, :rank] == self.gt.reshape(-1, 1)).sum() /
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float(self.nq))
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print("1-recalls: %s" % e)
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return e
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class Randu10kUnbalanced(Randu10k):
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def __init__(self):
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Randu10k.__init__(self)
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weights = 0.95 ** np.arange(self.d)
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rs = np.random.RandomState(123)
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weights = weights[rs.permutation(self.d)]
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self.xb *= weights
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self.xb /= np.linalg.norm(self.xb, axis=1)[:, np.newaxis]
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self.xq *= weights
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self.xq /= np.linalg.norm(self.xq, axis=1)[:, np.newaxis]
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self.xt *= weights
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self.xt /= np.linalg.norm(self.xt, axis=1)[:, np.newaxis]
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dotprods = np.dot(self.xq, self.xb.T)
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self.gt = dotprods.argmax(1)
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self.k = 100
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def get_dataset(d, nb, nt, nq):
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rs = np.random.RandomState(123)
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xb = rs.rand(nb, d).astype('float32')
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xt = rs.rand(nt, d).astype('float32')
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xq = rs.rand(nq, d).astype('float32')
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return (xt, xb, xq)
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def get_dataset_2(d, nt, nb, nq):
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"""A dataset that is not completely random but still challenging to
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index
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"""
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d1 = 10 # intrinsic dimension (more or less)
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n = nb + nt + nq
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rs = np.random.RandomState(1338)
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x = rs.normal(size=(n, d1))
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x = np.dot(x, rs.rand(d1, d))
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# now we have a d1-dim ellipsoid in d-dimensional space
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# higher factor (>4) -> higher frequency -> less linear
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x = x * (rs.rand(d) * 4 + 0.1)
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x = np.sin(x)
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x = x.astype('float32')
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return x[:nt], x[nt:nt + nb], x[nt + nb:]
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