phrasinator training initial

3 files changed, 305 insertions, 0 deletions

diff --git a/phrasinator/Makefile.am b/phrasinator/Makefile.am
new file mode 100644
index 00000000..c9b2a513
--- /dev/null
+++ b/phrasinator/Makefile.am
@@ -0,0 +1,6 @@
+bin_PROGRAMS = train_plm
+
+train_plm_SOURCES = train_plm.cc
+train_plm_LDADD = $(top_srcdir)/utils/libutils.a -lz
+
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
diff --git a/phrasinator/ccrp.h b/phrasinator/ccrp.h
new file mode 100644
index 00000000..9acf12ab
--- /dev/null
+++ b/phrasinator/ccrp.h
@@ -0,0 +1,294 @@
+#ifndef _CCRP_H_
+#define _CCRP_H_
+
+#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"
+
+// Chinese restaurant process (Pitman-Yor parameters) with table tracking.
+
+template <typename Dish, typename DishHash = boost::hash<Dish> >
+class CCRP {
+ public:
+  CCRP(double disc, double conc) :
+    num_tables_(),
+    num_customers_(),
+    discount_(disc),
+    concentration_(conc),
+    discount_prior_alpha_(std::numeric_limits<double>::quiet_NaN()),
+    discount_prior_beta_(std::numeric_limits<double>::quiet_NaN()),
+    concentration_prior_shape_(std::numeric_limits<double>::quiet_NaN()),
+    concentration_prior_rate_(std::numeric_limits<double>::quiet_NaN()) {}
+
+  CCRP(double d_alpha, double d_beta, double c_shape, double c_rate, double d = 0.1, double c = 10.0) :
+    num_tables_(),
+    num_customers_(),
+    discount_(d),
+    concentration_(c),
+    discount_prior_alpha_(d_alpha),
+    discount_prior_beta_(d_beta),
+    concentration_prior_shape_(c_shape),
+    concentration_prior_rate_(c_rate) {}
+
+  double discount() const { return discount_; }
+  double concentration() const { return concentration_; }
+
+  bool has_discount_prior() const {
+    return !std::isnan(discount_prior_alpha_);
+  }
+
+  bool has_concentration_prior() const {
+    return !std::isnan(concentration_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();
+  }
+
+  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 +1 or 0 indicating whether a new table was opened
+  int increment(const Dish& dish, const double& p0, MT19937* rng) {
+    DishLocations& loc = dish_locs_[dish];
+    bool share_table = false;
+    if (loc.total_dish_count_) {
+      const double p_empty = (concentration_ + num_tables_ * discount_) * p0;
+      const double p_share = (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
+      share_table = rng->SelectSample(p_empty, p_share);
+    }
+    if (share_table) {
+      double r = rng->next() * (loc.total_dish_count_ - loc.table_counts_.size() * discount_);
+      for (typename std::list<unsigned>::iterator ti = loc.table_counts_.begin();
+           ti != loc.table_counts_.end(); ++ti) {
+        r -= (*ti - discount_);
+        if (r <= 0.0) {
+          ++(*ti);
+          break;
+        }
+      }
+      if (r > 0.0) {
+        std::cerr << "Serious error: r=" << r << std::endl;
+        Print(&std::cerr);
+        assert(r <= 0.0);
+      }
+    } else {
+      loc.table_counts_.push_back(1u);
+      ++num_tables_;
+    }
+    ++loc.total_dish_count_;
+    ++num_customers_;
+    return (share_table ? 0 : 1);
+  }
+
+  // returns -1 or 0, indicating whether a table was closed
+  int decrement(const Dish& dish, MT19937* rng) {
+    DishLocations& loc = dish_locs_[dish];
+    assert(loc.total_dish_count_);
+    if (loc.total_dish_count_ == 1) {
+      dish_locs_.erase(dish);
+      --num_tables_;
+      --num_customers_;
+      return -1;
+    } else {
+      int delta = 0;
+      // sample customer to remove UNIFORMLY. that is, do NOT use the discount
+      // here. if you do, it will introduce (unwanted) bias!
+      double r = rng->next() * loc.total_dish_count_;
+      --loc.total_dish_count_;
+      for (typename std::list<unsigned>::iterator ti = loc.table_counts_.begin();
+           ti != loc.table_counts_.end(); ++ti) {
+        r -= *ti;
+        if (r <= 0.0) {
+          if ((--(*ti)) == 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);
+      }
+      --num_customers_;
+      return delta;
+    }
+  }
+
+  double prob(const Dish& dish, const double& p0) const {
+    const typename std::tr1::unordered_map<Dish, DishLocations, DishHash>::const_iterator it = dish_locs_.find(dish);
+    const double r = num_tables_ * discount_ + concentration_;
+    if (it == dish_locs_.end()) {
+      return r * p0 / (num_customers_ + concentration_);
+    } else {
+      return (it->second.total_dish_count_ - discount_ * it->second.table_counts_.size() + r * p0) /
+               (num_customers_ + concentration_);
+    }
+  }
+
+  double log_crp_prob() const {
+    return log_crp_prob(discount_, concentration_);
+  }
+
+  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 P_0's
+  double log_crp_prob(const double& discount, const double& concentration) const {
+    double lp = 0.0;
+    if (has_discount_prior())
+      lp = log_beta_density(discount, discount_prior_alpha_, discount_prior_beta_);
+    if (has_concentration_prior())
+      lp += log_gamma_density(concentration, concentration_prior_shape_, concentration_prior_rate_);
+    assert(lp <= 0.0);
+    if (num_customers_) {
+      if (discount > 0.0) {
+        const double r = lgamma(1.0 - discount);
+        lp += lgamma(concentration) - lgamma(concentration + num_customers_)
+             + num_tables_ * log(discount) + lgamma(concentration / discount + num_tables_)
+             - lgamma(concentration / discount);
+        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<unsigned>::const_iterator ti = cur.table_counts_.begin(); ti != cur.table_counts_.end(); ++ti) {
+            lp += lgamma(*ti - discount) - 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_discount_prior() || has_concentration_prior());
+    DiscountResampler dr(*this);
+    ConcentrationResampler cr(*this);
+    for (int iter = 0; iter < nloop; ++iter) {
+      if (has_concentration_prior()) {
+        concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+                               std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
+      }
+      if (has_discount_prior()) {
+        discount_ = slice_sampler1d(dr, discount_, *rng, std::numeric_limits<double>::min(),
+                               1.0, 0.0, niterations, 100*niterations);
+      }
+    }
+    concentration_ = slice_sampler1d(cr, concentration_, *rng, 0.0,
+                             std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations);
+  }
+
+  struct DiscountResampler {
+    DiscountResampler(const CCRP& crp) : crp_(crp) {}
+    const CCRP& crp_;
+    double operator()(const double& proposed_discount) const {
+      return crp_.log_crp_prob(proposed_discount, crp_.concentration_);
+    }
+  };
+
+  struct ConcentrationResampler {
+    ConcentrationResampler(const CCRP& crp) : crp_(crp) {}
+    const CCRP& crp_;
+    double operator()(const double& proposed_concentration) const {
+      return crp_.log_crp_prob(crp_.discount_, proposed_concentration);
+    }
+  };
+
+  struct DishLocations {
+    DishLocations() : total_dish_count_() {}
+    unsigned total_dish_count_;        // customers at all tables with this dish
+    std::list<unsigned> 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 {
+    std::cerr << "PYP(d=" << discount_ << ",c=" << concentration_ << ") 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<unsigned>::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 concentration_;
+
+  // optional beta prior on discount_ (NaN if no prior)
+  double discount_prior_alpha_;
+  double discount_prior_beta_;
+
+  // optional gamma prior on concentration_ (NaN if no prior)
+  double concentration_prior_shape_;
+  double concentration_prior_rate_;
+};
+
+template <typename T,typename H>
+std::ostream& operator<<(std::ostream& o, const CCRP<T,H>& c) {
+  c.Print(&o);
+  return o;
+}
+
+#endif
diff --git a/phrasinator/train_plm.cc b/phrasinator/train_plm.cc
new file mode 100644
index 00000000..bb41ad26
--- /dev/null
+++ b/phrasinator/train_plm.cc
@@ -0,0 +1,5 @@
+#include <iostream>
+
+int main(int argc, char** argv) {
+  return 0;
+}