merge with upstream

1 files changed, 366 insertions, 0 deletions

diff --git a/utils/mfcr.h b/utils/mfcr.h
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+++ b/utils/mfcr.h
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+#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