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authorChris Dyer <cdyer@cs.cmu.edu>2012-01-03 16:59:11 -0500
committerChris Dyer <cdyer@cs.cmu.edu>2012-01-03 16:59:11 -0500
commite069f9ad0c7aa54eb38bb6c68f5d624193c0bef7 (patch)
tree182155711d0d236d8334cc57e7dfbd3a6d94399c /utils
parent16994c379cf9944968c778671ecd39a5ee95d43c (diff)
multi-floor chinese restaurant described by wood&teh (2009)
Diffstat (limited to 'utils')
-rw-r--r--utils/Makefile.am6
-rw-r--r--utils/mfcr.h354
-rw-r--r--utils/mfcr_test.cc72
3 files changed, 430 insertions, 2 deletions
diff --git a/utils/Makefile.am b/utils/Makefile.am
index df667655..3e559c75 100644
--- a/utils/Makefile.am
+++ b/utils/Makefile.am
@@ -1,8 +1,8 @@
bin_PROGRAMS = reconstruct_weights
-noinst_PROGRAMS = ts phmt
-TESTS = ts phmt
+noinst_PROGRAMS = ts phmt mfcr_test
+TESTS = ts phmt mfcr_test
if HAVE_GTEST
noinst_PROGRAMS += \
@@ -40,6 +40,8 @@ phmt_SOURCES = phmt.cc
ts_SOURCES = ts.cc
dict_test_SOURCES = dict_test.cc
dict_test_LDADD = $(GTEST_LDFLAGS) $(GTEST_LIBS)
+mfcr_test_SOURCES = mfcr_test.cc
+mfcr_test_LDADD = $(GTEST_LDFLAGS) $(GTEST_LIBS)
weights_test_SOURCES = weights_test.cc
weights_test_LDADD = $(GTEST_LDFLAGS) $(GTEST_LIBS)
logval_test_SOURCES = logval_test.cc
diff --git a/utils/mfcr.h b/utils/mfcr.h
new file mode 100644
index 00000000..3eb133fc
--- /dev/null
+++ b/utils/mfcr.h
@@ -0,0 +1,354 @@
+#ifndef _MFCR_H_
+#define _MFCR_H_
+
+#include <algorithm>
+#include <numeric>
+#include <cassert>
+#include <cmath>
+#include <list>
+#include <iostream>
+#include <vector>
+#include <tr1/unordered_map>
+#include <boost/functional/hash.hpp>
+#include "sampler.h"
+#include "slice_sampler.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 <typename Dish, typename DishHash = boost::hash<Dish> >
+class MFCR {
+ public:
+
+ MFCR(unsigned num_floors, double d, double alpha) :
+ num_floors_(num_floors),
+ num_tables_(),
+ num_customers_(),
+ d_(d),
+ alpha_(alpha),
+ d_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
+ d_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
+ alpha_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+ alpha_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
+
+ MFCR(unsigned num_floors, double d_alpha, double d_beta, double alpha_shape, double alpha_rate, double d = 0.9, double alpha = 10.0) :
+ num_floors_(num_floors),
+ num_tables_(),
+ num_customers_(),
+ d_(d),
+ alpha_(alpha),
+ d_prior_alpha_(d_alpha),
+ d_prior_beta_(d_beta),
+ alpha_prior_shape_(alpha_shape),
+ alpha_prior_rate_(alpha_rate) {}
+
+ double d() const { return d_; }
+ double alpha() const { return alpha_; }
+
+ bool has_d_prior() const {
+ return !std::isnan(d_prior_alpha_);
+ }
+
+ bool has_alpha_prior() const {
+ return !std::isnan(alpha_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
+ TableCount increment(const Dish& dish, const std::vector<double>& p0s, const std::vector<double>& lambdas, MT19937* rng) {
+ assert(p0s.size() == num_floors_);
+ assert(lambdas.size() == num_floors_);
+
+ DishLocations& loc = dish_locs_[dish];
+ // marg_p0 = marginal probability of opening a new table on any floor with label dish
+ const double marg_p0 = std::inner_product(p0s.begin(), p0s.end(), lambdas.begin(), 0.0);
+ assert(marg_p0 <= 1.0);
+ int floor = -1;
+ bool share_table = false;
+ if (loc.total_dish_count_) {
+ const double p_empty = (alpha_ + num_tables_ * d_) * marg_p0;
+ const double p_share = (loc.total_dish_count_ - loc.table_counts_.size() * d_);
+ share_table = rng->SelectSample(p_empty, p_share);
+ }
+ if (share_table) {
+ double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * d_);
+ for (typename std::list<TableCount>::iterator ti = loc.table_counts_.begin();
+ ti != loc.table_counts_.end(); ++ti) {
+ r -= ti->count - d_;
+ 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
+ double r = rng->next() * marg_p0;
+ for (unsigned i = 0; i < p0s.size(); ++i) {
+ r -= p0s[i] * lambdas[i];
+ if (r <= 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);
+ }
+
+ double prob(const Dish& dish, const std::vector<double>& p0s, const std::vector<double>& lambdas) const {
+ assert(p0s.size() == num_floors_);
+ assert(lambdas.size() == num_floors_);
+ const double marg_p0 = std::inner_product(p0s.begin(), p0s.end(), lambdas.begin(), 0.0);
+ assert(marg_p0 <= 1.0);
+ const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
+ const double r = num_tables_ * d_ + alpha_;
+ if (it == dish_locs_.end()) {
+ return r * marg_p0 / (num_customers_ + alpha_);
+ } else {
+ return (it->second.total_dish_count_ - d_ * it->second.table_counts_.size() + r * marg_p0) /
+ (num_customers_ + alpha_);
+ }
+ }
+
+ double log_crp_prob() const {
+ return log_crp_prob(d_, alpha_);
+ }
+
+ static double log_beta_density(const double& x, const double& alpha, const double& beta) {
+ assert(x > 0.0);
+ assert(x < 1.0);
+ assert(alpha > 0.0);
+ assert(beta > 0.0);
+ const double lp = (alpha-1)*log(x)+(beta-1)*log(1-x)+lgamma(alpha+beta)-lgamma(alpha)-lgamma(beta);
+ return lp;
+ }
+
+ static double log_gamma_density(const double& x, const double& shape, const double& rate) {
+ assert(x >= 0.0);
+ assert(shape > 0.0);
+ assert(rate > 0.0);
+ const double lp = (shape-1)*log(x) - shape*log(rate) - x/rate - lgamma(shape);
+ return lp;
+ }
+
+ // taken from http://en.wikipedia.org/wiki/Chinese_restaurant_process
+ // does not include draws from G_w's
+ double log_crp_prob(const double& d, const double& alpha) const {
+ double lp = 0.0;
+ if (has_d_prior())
+ lp = log_beta_density(d, d_prior_alpha_, d_prior_beta_);
+ if (has_alpha_prior())
+ lp += log_gamma_density(alpha, alpha_prior_shape_, alpha_prior_rate_);
+ assert(lp <= 0.0);
+ if (num_customers_) {
+ if (d > 0.0) {
+ const double r = lgamma(1.0 - d);
+ lp += lgamma(alpha) - lgamma(alpha + num_customers_)
+ + num_tables_ * log(d) + lgamma(alpha / d + num_tables_)
+ - lgamma(alpha / d);
+ 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 - d) - r;
+ }
+ }
+ } else {
+ assert(!"not implemented yet");
+ }
+ }
+ assert(std::isfinite(lp));
+ return lp;
+ }
+
+ void resample_hyperparameters(MT19937* rng, const unsigned nloop = 5, const unsigned niterations = 10) {
+ assert(has_d_prior() || has_alpha_prior());
+ DiscountResampler dr(*this);
+ ConcentrationResampler cr(*this);
+ for (int iter = 0; iter < nloop; ++iter) {
+ if (has_alpha_prior()) {
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
+ std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
+ }
+ if (has_d_prior()) {
+ d_ = slice_sampler1d(dr, d_, *rng, std::numeric_limits<double>::min(),
+ 1.0, 0.0, niterations, 100*niterations);
+ }
+ }
+ alpha_ = slice_sampler1d(cr, alpha_, *rng, 0.0,
+ 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_.alpha_);
+ }
+ };
+
+ struct ConcentrationResampler {
+ ConcentrationResampler(const MFCR& crp) : crp_(crp) {}
+ const MFCR& crp_;
+ double operator()(const double& proposed_alpha) const {
+ return crp_.log_crp_prob(crp_.d_, proposed_alpha);
+ }
+ };
+
+ 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(d=" << d_ << ",alpha=" << alpha_ << ") 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_floors_;
+ unsigned num_tables_;
+ unsigned num_customers_;
+ std::tr1::unordered_map<Dish, DishLocations, DishHash> dish_locs_;
+
+ double d_;
+ double alpha_;
+
+ // optional beta prior on d_ (NaN if no prior)
+ double d_prior_alpha_;
+ double d_prior_beta_;
+
+ // optional gamma prior on alpha_ (NaN if no prior)
+ double alpha_prior_shape_;
+ double alpha_prior_rate_;
+};
+
+template <typename T,typename H>
+std::ostream& operator<<(std::ostream& o, const MFCR<T,H>& c) {
+ c.Print(&o);
+ return o;
+}
+
+#endif
diff --git a/utils/mfcr_test.cc b/utils/mfcr_test.cc
new file mode 100644
index 00000000..7c45a37c
--- /dev/null
+++ b/utils/mfcr_test.cc
@@ -0,0 +1,72 @@
+#include "mfcr.h"
+
+#include <iostream>
+#include <cassert>
+#include <cmath>
+
+#include "sampler.h"
+
+using namespace std;
+
+void test_exch(MT19937* rng) {
+ MFCR<int> crp(2, 0.5, 3.0);
+ vector<double> lambdas(2);
+ vector<double> p0s(2);
+ lambdas[0] = 0.2;
+ lambdas[1] = 0.8;
+ p0s[0] = 1.0;
+ p0s[1] = 1.0;
+
+ double tot = 0;
+ double tot2 = 0;
+ double xt = 0;
+ int cust = 10;
+ vector<int> hist(cust + 1, 0), hist2(cust + 1, 0);
+ for (int i = 0; i < cust; ++i) { crp.increment(1, p0s, lambdas, rng); }
+ const int samples = 100000;
+ const bool simulate = true;
+ for (int k = 0; k < samples; ++k) {
+ if (!simulate) {
+ crp.clear();
+ for (int i = 0; i < cust; ++i) { crp.increment(1, p0s, lambdas, rng); }
+ } else {
+ int da = rng->next() * cust;
+ bool a = rng->next() < 0.45;
+ if (a) {
+ for (int i = 0; i < da; ++i) { crp.increment(1, p0s, lambdas, rng); }
+ for (int i = 0; i < da; ++i) { crp.decrement(1, rng); }
+ xt += 1.0;
+ } else {
+ for (int i = 0; i < da; ++i) { crp.decrement(1, rng); }
+ for (int i = 0; i < da; ++i) { crp.increment(1, p0s, lambdas, rng); }
+ }
+ }
+ int c = crp.num_tables(1);
+ ++hist[c];
+ tot += c;
+ int c2 = crp.num_tables(1,0); // tables on floor 0 with dish 1
+ ++hist2[c2];
+ tot2 += c2;
+ }
+ cerr << cust << " = " << crp.num_customers() << endl;
+ cerr << "P(a) = " << (xt / samples) << endl;
+ cerr << "E[num tables] = " << (tot / samples) << endl;
+ double error = fabs((tot / samples) - 6.894);
+ cerr << " error = " << error << endl;
+ for (int i = 1; i <= cust; ++i)
+ cerr << i << ' ' << (hist[i]) << endl;
+ cerr << "E[num tables on floor 0] = " << (tot2 / samples) << endl;
+ double error2 = fabs((tot2 / samples) - 1.379);
+ cerr << " error2 = " << error2 << endl;
+ for (int i = 1; i <= cust; ++i)
+ cerr << i << ' ' << (hist2[i]) << endl;
+ assert(error < 0.05); // these can fail with very low probability
+ assert(error2 < 0.05);
+};
+
+int main(int argc, char** argv) {
+ MT19937 rng;
+ test_exch(&rng);
+ return 0;
+}
+