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Diffstat (limited to 'utils/mfcr.h')
| -rw-r--r-- | utils/mfcr.h | 366 |
1 files changed, 366 insertions, 0 deletions
diff --git a/utils/mfcr.h b/utils/mfcr.h new file mode 100644 index 00000000..886f01ef --- /dev/null +++ b/utils/mfcr.h @@ -0,0 +1,366 @@ +#ifndef _MFCR_H_ +#define _MFCR_H_ + +#include <algorithm> +#include <numeric> +#include <cassert> +#include <cmath> +#include <list> +#include <iostream> +#include <vector> +#include <iterator> +#include <tr1/unordered_map> +#include <boost/functional/hash.hpp> +#include "sampler.h" +#include "slice_sampler.h" +#include "m.h" + +struct TableCount { + TableCount() : count(), floor() {} + TableCount(int c, int f) : count(c), floor(f) { + assert(f >= 0); + } + int count; // count or delta (may be 0, <0, or >0) + unsigned char floor; // from which floor? +}; + +std::ostream& operator<<(std::ostream& o, const TableCount& tc) { + return o << "[c=" << tc.count << " floor=" << static_cast<unsigned int>(tc.floor) << ']'; +} + +// Multi-Floor Chinese Restaurant as proposed by Wood & Teh (AISTATS, 2009) to simulate +// graphical Pitman-Yor processes. +// http://jmlr.csail.mit.edu/proceedings/papers/v5/wood09a/wood09a.pdf +// +// Implementation is based on Blunsom, Cohn, Goldwater, & Johnson (ACL 2009) and code +// referenced therein. +// http://www.aclweb.org/anthology/P/P09/P09-2085.pdf +// +template <unsigned Floors, typename Dish, typename DishHash = boost::hash<Dish> > +class MFCR { + public: + + MFCR(double d, double strength) : + num_tables_(), + num_customers_(), + discount_(d), + strength_(strength), + discount_prior_strength_(std::numeric_limits<double>::quiet_NaN()), + discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()), + strength_prior_shape_(std::numeric_limits<double>::quiet_NaN()), + strength_prior_rate_(std::numeric_limits<double>::quiet_NaN()) { check_hyperparameters(); } + + MFCR(double discount_strength, double discount_beta, double strength_shape, double strength_rate, double d = 0.9, double strength = 10.0) : + num_tables_(), + num_customers_(), + discount_(d), + strength_(strength), + discount_prior_strength_(discount_strength), + discount_prior_beta_(discount_beta), + strength_prior_shape_(strength_shape), + strength_prior_rate_(strength_rate) { check_hyperparameters(); } + + void check_hyperparameters() { + if (discount_ < 0.0 || discount_ >= 1.0) { + std::cerr << "Bad discount: " << discount_ << std::endl; + abort(); + } + if (strength_ <= -discount_) { + std::cerr << "Bad strength: " << strength_ << " (discount=" << discount_ << ")" << std::endl; + abort(); + } + } + + double discount() const { return discount_; } + double strength() const { return strength_; } + void set_discount(double d) { discount_ = d; check_hyperparameters(); } + void set_strength(double a) { strength_ = a; check_hyperparameters(); } + + bool has_discount_prior() const { + return !std::isnan(discount_prior_strength_); + } + + bool has_strength_prior() const { + return !std::isnan(strength_prior_shape_); + } + + void clear() { + num_tables_ = 0; + num_customers_ = 0; + dish_locs_.clear(); + } + + unsigned num_tables() const { + return num_tables_; + } + + unsigned num_tables(const Dish& dish) const { + const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); + if (it == dish_locs_.end()) return 0; + return it->second.table_counts_.size(); + } + + // this is not terribly efficient but it should not typically be necessary to execute this query + unsigned num_tables(const Dish& dish, const unsigned floor) const { + const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); + if (it == dish_locs_.end()) return 0; + unsigned c = 0; + for (typename std::list<TableCount>::const_iterator i = it->second.table_counts_.begin(); + i != it->second.table_counts_.end(); ++i) { + if (i->floor == floor) ++c; + } + return c; + } + + unsigned num_customers() const { + return num_customers_; + } + + unsigned num_customers(const Dish& dish) const { + const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); + if (it == dish_locs_.end()) return 0; + return it->total_dish_count_; + } + + // returns (delta, floor) indicating whether a new table (delta) was opened and on which floor + template <class InputIterator, class InputIterator2> + TableCount increment(const Dish& dish, InputIterator p0s, InputIterator2 lambdas, MT19937* rng) { + DishLocations& loc = dish_locs_[dish]; + // marg_p0 = marginal probability of opening a new table on any floor with label dish + typedef typename std::iterator_traits<InputIterator>::value_type F; + const F marg_p0 = std::inner_product(p0s, p0s + Floors, lambdas, F(0.0)); + assert(marg_p0 <= F(1.0001)); + int floor = -1; + bool share_table = false; + if (loc.total_dish_count_) { + const F p_empty = F(strength_ + num_tables_ * discount_) * marg_p0; + const F p_share = F(loc.total_dish_count_ - loc.table_counts_.size() * discount_); + share_table = rng->SelectSample(p_empty, p_share); + } + if (share_table) { + // this can be done with doubles since P0 (which may be tiny) is not involved + double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * discount_); + for (typename std::list<TableCount>::iterator ti = loc.table_counts_.begin(); + ti != loc.table_counts_.end(); ++ti) { + r -= ti->count - discount_; + if (r <= 0.0) { + ++ti->count; + floor = ti->floor; + break; + } + } + if (r > 0.0) { + std::cerr << "Serious error: r=" << r << std::endl; + Print(&std::cerr); + assert(r <= 0.0); + } + } else { // sit at currently empty table -- must sample what floor + if (Floors == 1) { + floor = 0; + } else { + F r = F(rng->next()) * marg_p0; + for (unsigned i = 0; i < Floors; ++i) { + r -= (*p0s) * (*lambdas); + ++p0s; + ++lambdas; + if (r <= F(0.0)) { + floor = i; + break; + } + } + } + assert(floor >= 0); + loc.table_counts_.push_back(TableCount(1, floor)); + ++num_tables_; + } + ++loc.total_dish_count_; + ++num_customers_; + return (share_table ? TableCount(0, floor) : TableCount(1, floor)); + } + + // returns first = -1 or 0, indicating whether a table was closed, and on what floor (second) + TableCount decrement(const Dish& dish, MT19937* rng) { + DishLocations& loc = dish_locs_[dish]; + assert(loc.total_dish_count_); + int floor = -1; + int delta = 0; + if (loc.total_dish_count_ == 1) { + floor = loc.table_counts_.front().floor; + dish_locs_.erase(dish); + --num_tables_; + --num_customers_; + delta = -1; + } else { + // sample customer to remove UNIFORMLY. that is, do NOT use the d + // here. if you do, it will introduce (unwanted) bias! + double r = rng->next() * loc.total_dish_count_; + --loc.total_dish_count_; + --num_customers_; + for (typename std::list<TableCount>::iterator ti = loc.table_counts_.begin(); + ti != loc.table_counts_.end(); ++ti) { + r -= ti->count; + if (r <= 0.0) { + floor = ti->floor; + if ((--ti->count) == 0) { + --num_tables_; + delta = -1; + loc.table_counts_.erase(ti); + } + break; + } + } + if (r > 0.0) { + std::cerr << "Serious error: r=" << r << std::endl; + Print(&std::cerr); + assert(r <= 0.0); + } + } + return TableCount(delta, floor); + } + + template <class InputIterator, class InputIterator2> + typename std::iterator_traits<InputIterator>::value_type prob(const Dish& dish, InputIterator p0s, InputIterator2 lambdas) const { + typedef typename std::iterator_traits<InputIterator>::value_type F; + const F marg_p0 = std::inner_product(p0s, p0s + Floors, lambdas, F(0.0)); + assert(marg_p0 <= F(1.0001)); + const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish); + const F r = F(num_tables_ * discount_ + strength_); + if (it == dish_locs_.end()) { + return r * marg_p0 / F(num_customers_ + strength_); + } else { + return (F(it->second.total_dish_count_ - discount_ * it->second.table_counts_.size()) + F(r * marg_p0)) / + F(num_customers_ + strength_); + } + } + + double log_crp_prob() const { + return log_crp_prob(discount_, strength_); + } + + // taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process + // does not include draws from G_w's + double log_crp_prob(const double& discount, const double& strength) const { + double lp = 0.0; + if (has_discount_prior()) + lp = Md::log_beta_density(discount, discount_prior_strength_, discount_prior_beta_); + if (has_strength_prior()) + lp += Md::log_gamma_density(strength + discount, strength_prior_shape_, strength_prior_rate_); + assert(lp <= 0.0); + if (num_customers_) { + if (discount > 0.0) { + const double r = lgamma(1.0 - discount); + if (strength) + lp += lgamma(strength) - lgamma(strength / discount); + lp += - lgamma(strength + num_customers_) + + num_tables_ * log(discount) + lgamma(strength / discount + num_tables_); + assert(std::isfinite(lp)); + for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin(); + it != dish_locs_.end(); ++it) { + const DishLocations& cur = it->second; + for (std::list<TableCount>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) { + lp += lgamma(ti->count - discount) - r; + } + } + } else if (!discount) { // discount == 0.0 + lp += lgamma(strength) + num_tables_ * log(strength) - lgamma(strength + num_tables_); + assert(std::isfinite(lp)); + for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin(); + it != dish_locs_.end(); ++it) { + const DishLocations& cur = it->second; + lp += lgamma(cur.table_counts_.size()); + } + } else { + assert(!"discount less than 0 detected!"); + } + } + assert(std::isfinite(lp)); + return lp; + } + + void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) { + assert(has_discount_prior() || has_strength_prior()); + DiscountResampler dr(*this); + StrengthResampler sr(*this); + for (int iter = 0; iter < nloop; ++iter) { + if (has_strength_prior()) { + strength_ = slice_sampler1d(sr, strength_, *rng, -discount_, + std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); + } + if (has_discount_prior()) { + double min_discount = std::numeric_limits<double>::min(); + if (strength_ < 0.0) min_discount -= strength_; + discount_ = slice_sampler1d(dr, discount_, *rng, min_discount, + 1.0, 0.0, niterations, 100*niterations); + } + } + strength_ = slice_sampler1d(sr, strength_, *rng, -discount_, + std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); + } + + struct DiscountResampler { + DiscountResampler(const MFCR& crp) : crp_(crp) {} + const MFCR& crp_; + double operator()(const double& proposed_d) const { + return crp_.log_crp_prob(proposed_d, crp_.strength_); + } + }; + + struct StrengthResampler { + StrengthResampler(const MFCR& crp) : crp_(crp) {} + const MFCR& crp_; + double operator()(const double& proposediscount_strength) const { + return crp_.log_crp_prob(crp_.discount_, proposediscount_strength); + } + }; + + struct DishLocations { + DishLocations() : total_dish_count_() {} + unsigned total_dish_count_; // customers at all tables with this dish + std::list<TableCount> table_counts_; // list<> gives O(1) deletion and insertion, which we want + // .size() is the number of tables for this dish + }; + + void Print(std::ostream* out) const { + (*out) << "MFCR<" << Floors << ">(d=" << discount_ << ",strength=" << strength_ << ") customers=" << num_customers_ << std::endl; + for (typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.begin(); + it != dish_locs_.end(); ++it) { + (*out) << it->first << " (" << it->second.total_dish_count_ << " on " << it->second.table_counts_.size() << " tables): "; + for (typename std::list<TableCount>::const_iterator i = it->second.table_counts_.begin(); + i != it->second.table_counts_.end(); ++i) { + (*out) << " " << *i; + } + (*out) << std::endl; + } + } + + typedef typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator const_iterator; + const_iterator begin() const { + return dish_locs_.begin(); + } + const_iterator end() const { + return dish_locs_.end(); + } + + unsigned num_tables_; + unsigned num_customers_; + std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_; + + double discount_; + double strength_; + + // optional beta prior on discount_ (NaN if no prior) + double discount_prior_strength_; + double discount_prior_beta_; + + // optional gamma prior on strength_ (NaN if no prior) + double strength_prior_shape_; + double strength_prior_rate_; +}; + +template <unsigned N,typename T,typename H> +std::ostream& operator<<(std::ostream& o, const MFCR<N,T,H>& c) { + c.Print(&o); + return o; +} + +#endif |