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/data/users/matthijs/github_faiss/faiss/Clustering.cpp
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 /* Copyright 2004-present Facebook. All Rights Reserved.
10 kmeans clustering routines
11 */
12
13 #include "Clustering.h"
14
15
16
17 #include <cmath>
18 #include <cstdio>
19 #include <cstring>
20
21 #include "utils.h"
22 #include "FaissAssert.h"
23 #include "IndexFlat.h"
24
25 namespace faiss {
26
27 ClusteringParameters::ClusteringParameters ():
28 niter(25),
29 nredo(1),
30 verbose(false), spherical(false),
31 update_index(false),
32 frozen_centroids(false),
33 min_points_per_centroid(39),
34 max_points_per_centroid(256),
35 seed(1234)
36 {}
37 // 39 corresponds to 10000 / 256 -> to avoid warnings on PQ tests with randu10k
38
39
40 Clustering::Clustering (int d, int k):
41 d(d), k(k) {}
42
43 Clustering::Clustering (int d, int k, const ClusteringParameters &cp):
44 ClusteringParameters (cp), d(d), k(k) {}
45
46
47
48 static double imbalance_factor (int n, int k, long *assign) {
49 std::vector<int> hist(k, 0);
50 for (int i = 0; i < n; i++)
51 hist[assign[i]]++;
52
53 double tot = 0, uf = 0;
54
55 for (int i = 0 ; i < k ; i++) {
56 tot += hist[i];
57 uf += hist[i] * (double) hist[i];
58 }
59 uf = uf * k / (tot * tot);
60
61 return uf;
62 }
63
64
65
66
67 void Clustering::train (idx_t nx, const float *x_in, Index & index) {
68 FAISS_THROW_IF_NOT_MSG (nx >= k,
69 "need at least as many training points as clusters");
70
71 double t0 = getmillisecs();
72
73 // yes it is the user's responsibility, but it may spare us some
74 // hard-to-debug reports.
75 for (size_t i = 0; i < nx * d; i++) {
76 FAISS_THROW_IF_NOT_MSG (finite (x_in[i]),
77 "input contains NaN's or Inf's");
78 }
79
80 const float *x = x_in;
81 ScopeDeleter<float> del1;
82
83 if (nx > k * max_points_per_centroid) {
84 if (verbose)
85 printf("Sampling a subset of %ld / %ld for training\n",
86 k * max_points_per_centroid, nx);
87 std::vector<int> perm (nx);
88 rand_perm (perm.data (), nx, seed);
89 nx = k * max_points_per_centroid;
90 float * x_new = new float [nx * d];
91 for (idx_t i = 0; i < nx; i++)
92 memcpy (x_new + i * d, x + perm[i] * d, sizeof(x_new[0]) * d);
93 x = x_new;
94 del1.set (x);
95 } else if (nx < k * min_points_per_centroid) {
96 fprintf (stderr,
97 "WARNING clustering %ld points to %ld centroids: "
98 "please provide at least %ld training points\n",
99 nx, k, idx_t(k) * min_points_per_centroid);
100 }
101
102
103 if (verbose)
104 printf("Clustering %d points in %ldD to %ld clusters, "
105 "redo %d times, %d iterations\n",
106 int(nx), d, k, nredo, niter);
107
108
109 idx_t * assign = new idx_t[nx];
110 ScopeDeleter<idx_t> del (assign);
111 float * dis = new float[nx];
112 ScopeDeleter<float> del2(dis);
113
114 // for redo
115 float best_err = 1e50;
116 std::vector<float> best_obj;
117 std::vector<float> best_centroids;
118
119 // support input centroids
120
121 FAISS_THROW_IF_NOT_MSG (
122 centroids.size() % d == 0,
123 "size of provided input centroids not a multiple of dimension");
124
125 size_t n_input_centroids = centroids.size() / d;
126
127 if (verbose && n_input_centroids > 0) {
128 printf (" Using %zd centroids provided as input (%sfrozen)\n",
129 n_input_centroids, frozen_centroids ? "" : "not ");
130 }
131
132 double t_search_tot = 0;
133 if (verbose) {
134 printf(" Preprocessing in %.2f s\n",
135 (getmillisecs() - t0)/1000.);
136 }
137 t0 = getmillisecs();
138
139 for (int redo = 0; redo < nredo; redo++) {
140
141 if (verbose && nredo > 1) {
142 printf("Outer iteration %d / %d\n", redo, nredo);
143 }
144
145
146 // initialize remaining centroids with random points from the dataset
147 centroids.resize (d * k);
148 std::vector<int> perm (nx);
149
150 rand_perm (perm.data(), nx, seed + 1 + redo * 15486557L);
151 for (int i = n_input_centroids; i < k ; i++)
152 memcpy (&centroids[i * d], x + perm[i] * d,
153 d * sizeof (float));
154
155 if (spherical)
156 fvec_renorm_L2 (d, k, centroids.data());
157
158 if (!index.is_trained)
159 index.train (k, centroids.data());
160
161 FAISS_THROW_IF_NOT (index.ntotal == 0);
162 index.add (k, centroids.data());
163 float err = 0;
164 for (int i = 0; i < niter; i++) {
165 double t0s = getmillisecs();
166 index.search (nx, x, 1, dis, assign);
167 t_search_tot += getmillisecs() - t0s;
168
169 err = 0;
170 for (int j = 0; j < nx; j++)
171 err += dis[j];
172 obj.push_back (err);
173
174 int nsplit = km_update_centroids (
175 x, centroids.data(),
176 assign, d, k, nx, frozen_centroids ? n_input_centroids : 0);
177
178 if (verbose) {
179 printf (" Iteration %d (%.2f s, search %.2f s): "
180 "objective=%g imbalance=%.3f nsplit=%d \r",
181 i, (getmillisecs() - t0) / 1000.0,
182 t_search_tot / 1000,
183 err, imbalance_factor (nx, k, assign),
184 nsplit);
185 fflush (stdout);
186 }
187
188 if (spherical)
189 fvec_renorm_L2 (d, k, centroids.data());
190
191 index.reset ();
192 if (update_index)
193 index.train (k, centroids.data());
194
195 assert (index.ntotal == 0);
196 index.add (k, centroids.data());
197 }
198 if (verbose) printf("\n");
199 if (nredo > 1) {
200 if (err < best_err) {
201 if (verbose)
202 printf ("Objective improved: keep new clusters\n");
203 best_centroids = centroids;
204 best_obj = obj;
205 best_err = err;
206 }
207 index.reset ();
208 }
209 }
210 if (nredo > 1) {
211 centroids = best_centroids;
212 obj = best_obj;
213 }
214
215 }
216
217 float kmeans_clustering (size_t d, size_t n, size_t k,
218 const float *x,
219 float *centroids)
220 {
221 Clustering clus (d, k);
222 clus.verbose = d * n * k > (1L << 30);
223 // display logs if > 1Gflop per iteration
224 IndexFlatL2 index (d);
225 clus.train (n, x, index);
226 memcpy(centroids, clus.centroids.data(), sizeof(*centroids) * d * k);
227 return clus.obj.back();
228 }
229
230 } // namespace faiss
faiss::km_update_centroids
int km_update_centroids(const float *x, float *centroids, long *assign, size_t d, size_t k, size_t n, size_t k_frozen)
Definition: utils.cpp:1399
faiss::ClusteringParameters::niter
int niter
clustering iterations
Definition: Clustering.h:25
faiss::ClusteringParameters::nredo
int nredo
redo clustering this many times and keep best
Definition: Clustering.h:26
faiss::ClusteringParameters::ClusteringParameters
ClusteringParameters()
sets reasonable defaults
Definition: Clustering.cpp:27
faiss::Index::reset
virtual void reset()=0
removes all elements from the database.
faiss::Index::train
virtual void train(idx_t, const float *)
Definition: Index.h:89
faiss::IndexFlatL2
Definition: IndexFlat.h:79
faiss::Clustering::Clustering
Clustering(int d, int k)
the only mandatory parameters are k and d
Definition: Clustering.cpp:40
faiss::Clustering::k
size_t k
nb of centroids
Definition: Clustering.h:60
faiss::ScopeDeleter
Definition: FaissException.h:39
faiss::ClusteringParameters::seed
int seed
seed for the random number generator
Definition: Clustering.h:36
faiss::ClusteringParameters::frozen_centroids
bool frozen_centroids
use the centroids provided as input and do not change them during iterations
Definition: Clustering.h:31
faiss::ClusteringParameters::min_points_per_centroid
int min_points_per_centroid
otherwise you get a warning
Definition: Clustering.h:33
faiss::Clustering
Definition: Clustering.h:57
faiss::Index::add
virtual void add(idx_t n, const float *x)=0
faiss::Clustering::obj
std::vector< float > obj
Definition: Clustering.h:67
faiss::kmeans_clustering
float kmeans_clustering(size_t d, size_t n, size_t k, const float *x, float *centroids)
Definition: Clustering.cpp:217
faiss::Index::ntotal
idx_t ntotal
total nb of indexed vectors
Definition: Index.h:65
faiss::getmillisecs
double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:74
faiss::Clustering::centroids
std::vector< float > centroids
centroids (k * d)
Definition: Clustering.h:63
faiss::Clustering::d
size_t d
dimension of the vectors
Definition: Clustering.h:59
faiss::Index::search
virtual void search(idx_t n, const float *x, idx_t k, float *distances, idx_t *labels) const =0
faiss::ClusteringParameters::update_index
bool update_index
update index after each iteration?
Definition: Clustering.h:30
faiss::ClusteringParameters
Definition: Clustering.h:24
faiss::Index
Definition: Index.h:60
faiss::Clustering::train
virtual void train(idx_t n, const float *x, faiss::Index &index)
Index is used during the assignment stage.
Definition: Clustering.cpp:67
faiss::Index::is_trained
bool is_trained
set if the Index does not require training, or if training is done already
Definition: Index.h:69
faiss::ClusteringParameters::spherical
bool spherical
do we want normalized centroids?
Definition: Clustering.h:29
faiss::ClusteringParameters::max_points_per_centroid
int max_points_per_centroid
to limit size of dataset
Definition: Clustering.h:34