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/data/users/matthijs/github_faiss/faiss/PolysemousTraining.h
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the BSD+Patents license found in the
* LICENSE file in the root directory of this source tree.
*/
// Copyright 2004-present Facebook. All Rights Reserved.
// -*- c++ -*-
#ifndef FAISS_POLYSEMOUS_TRAINING_INCLUDED
#define FAISS_POLYSEMOUS_TRAINING_INCLUDED
#include "ProductQuantizer.h"
namespace faiss {
/// parameters used for the simulated annealing method
struct SimulatedAnnealingParameters {
// optimization parameters
double init_temperature; // init probaility of accepting a bad swap
double temperature_decay; // at each iteration the temp is multiplied by this
int n_iter; // nb of iterations
int n_redo; // nb of runs of the simulation
int seed; // random seed
int verbose;
bool only_bit_flips; // restrict permutation changes to bit flips
bool init_random; // intialize with a random permutation (not identity)
// set reasonable defaults
SimulatedAnnealingParameters ();
};
/// abstract class for the loss function
struct PermutationObjective {
int n;
virtual double compute_cost (const int *perm) const = 0;
// what would the cost update be if iw and jw were swapped?
// default implementation just computes both and computes the difference
virtual double cost_update (const int *perm, int iw, int jw) const;
virtual ~PermutationObjective () {}
};
struct ReproduceDistancesObjective : PermutationObjective {
double dis_weight_factor;
static double sqr (double x) { return x * x; }
// weihgting of distances: it is more important to reproduce small
// distances well
double dis_weight (double x) const;
std::vector<double> source_dis; ///< "real" corrected distances (size n^2)
const double * target_dis; ///< wanted distances (size n^2)
std::vector<double> weights; ///< weights for each distance (size n^2)
double get_source_dis (int i, int j) const;
// cost = quadratic difference between actual distance and Hamming distance
double compute_cost(const int* perm) const override;
// what would the cost update be if iw and jw were swapped?
// computed in O(n) instead of O(n^2) for the full re-computation
double cost_update(const int* perm, int iw, int jw) const override;
ReproduceDistancesObjective (
int n,
const double *source_dis_in,
const double *target_dis_in,
double dis_weight_factor);
static void compute_mean_stdev (const double *tab, size_t n2,
double *mean_out, double *stddev_out);
void set_affine_target_dis (const double *source_dis_in);
~ReproduceDistancesObjective() override {}
};
struct RandomGenerator;
/// Simulated annealing optimization algorithm for permutations.
struct SimulatedAnnealingOptimizer: SimulatedAnnealingParameters {
PermutationObjective *obj;
int n; ///< size of the permutation
FILE *logfile; /// logs values of the cost function
SimulatedAnnealingOptimizer (PermutationObjective *obj,
const SimulatedAnnealingParameters &p);
RandomGenerator *rnd;
/// remember intial cost of optimization
double init_cost;
// main entry point. Perform the optimization loop, starting from
// and modifying permutation in-place
double optimize (int *perm);
// run the optimization and return the best result in best_perm
double run_optimization (int * best_perm);
virtual ~SimulatedAnnealingOptimizer ();
};
/// optimizes the order of indices in a ProductQuantizer
struct PolysemousTraining: SimulatedAnnealingParameters {
enum Optimization_type_t {
OT_None,
OT_ReproduceDistances_affine, ///< default
OT_Ranking_weighted_diff /// same as _2, but use rank of y+ - rank of y-
};
Optimization_type_t optimization_type;
// use 1/4 of the training points for the optimization, with
// max. ntrain_permutation. If ntrain_permutation == 0: train on
// centroids
int ntrain_permutation;
double dis_weight_factor; // decay of exp that weights distance loss
// filename pattern for the logging of iterations
std::string log_pattern;
// sets default values
PolysemousTraining ();
/// reorder the centroids so that the Hamming distace becomes a
/// good approximation of the SDC distance (called by train)
void optimize_pq_for_hamming (ProductQuantizer & pq,
size_t n, const float *x) const;
/// called by optimize_pq_for_hamming
void optimize_ranking (ProductQuantizer &pq, size_t n, const float *x) const;
/// called by optimize_pq_for_hamming
void optimize_reproduce_distances (ProductQuantizer &pq) const;
};
} // namespace faiss
#endif
faiss::RandomGenerator
random generator that can be used in multithreaded contexts
Definition: utils.h:48
faiss::PolysemousTraining::OT_Ranking_weighted_diff
same as _2, but use rank of y+ - rank of y-
Definition: PolysemousTraining.h:130
faiss::SimulatedAnnealingOptimizer::SimulatedAnnealingOptimizer
SimulatedAnnealingOptimizer(PermutationObjective *obj, const SimulatedAnnealingParameters &p)
logs values of the cost function
Definition: PolysemousTraining.cpp:67
faiss::ReproduceDistancesObjective
Definition: PolysemousTraining.h:58
faiss::SimulatedAnnealingOptimizer::n
int n
size of the permutation
Definition: PolysemousTraining.h:101
faiss::ReproduceDistancesObjective::target_dis
const double * target_dis
wanted distances (size n^2)
Definition: PolysemousTraining.h:69
faiss::SimulatedAnnealingOptimizer::init_cost
double init_cost
remember intial cost of optimization
Definition: PolysemousTraining.h:109
faiss::PolysemousTraining::optimize_ranking
void optimize_ranking(ProductQuantizer &pq, size_t n, const float *x) const
called by optimize_pq_for_hamming
Definition: PolysemousTraining.cpp:826
faiss::PolysemousTraining
optimizes the order of indices in a ProductQuantizer
Definition: PolysemousTraining.h:125
faiss::ReproduceDistancesObjective::weights
std::vector< double > weights
weights for each distance (size n^2)
Definition: PolysemousTraining.h:70
faiss::SimulatedAnnealingParameters
parameters used for the simulated annealing method
Definition: PolysemousTraining.h:25
faiss::PolysemousTraining::OT_ReproduceDistances_affine
default
Definition: PolysemousTraining.h:129
faiss::PermutationObjective
abstract class for the loss function
Definition: PolysemousTraining.h:44
faiss::PolysemousTraining::Optimization_type_t
Optimization_type_t
Definition: PolysemousTraining.h:127
faiss::ProductQuantizer
Definition: ProductQuantizer.h:25
faiss::ReproduceDistancesObjective::source_dis
std::vector< double > source_dis
&quot;real&quot; corrected distances (size n^2)
Definition: PolysemousTraining.h:68
faiss::PolysemousTraining::optimize_reproduce_distances
void optimize_reproduce_distances(ProductQuantizer &pq) const
called by optimize_pq_for_hamming
Definition: PolysemousTraining.cpp:763
faiss::PolysemousTraining::optimize_pq_for_hamming
void optimize_pq_for_hamming(ProductQuantizer &pq, size_t n, const float *x) const
Definition: PolysemousTraining.cpp:933
faiss::SimulatedAnnealingOptimizer
Simulated annealing optimization algorithm for permutations.
Definition: PolysemousTraining.h:98