put creg in its own top-level folder

3 files changed, 1 insertions, 338 deletions

diff --git a/training/Makefile.am b/training/Makefile.am
index 4b69ea94..991ac210 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -1,6 +1,5 @@
 bin_PROGRAMS = \
   model1 \
-  creg \
   lbl_model \
   test_ngram \
   mr_em_map_adapter \
@@ -24,9 +23,6 @@ noinst_PROGRAMS = \
 
 TESTS = lbfgs_test optimize_test
 
-creg_SOURCES = creg.cc
-creg_LDADD = ./liblbfgs/liblbfgs.a $(top_srcdir)/utils/libutils.a -lz
-
 mpi_online_optimize_SOURCES = mpi_online_optimize.cc online_optimizer.cc
 mpi_online_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
diff --git a/training/creg.cc b/training/creg.cc
deleted file mode 100644
index 58adea00..00000000
--- a/training/creg.cc
+++ /dev/null
@@ -1,334 +0,0 @@
-#include <cstdlib>
-#include <iostream>
-#include <vector>
-#include <tr1/unordered_map>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "json_feature_map_lexer.h"
-#include "prob.h"
-#include "filelib.h"
-#include "weights.h"
-#include "sparse_vector.h"
-#include "liblbfgs/lbfgs++.h"
-
-using namespace std;
-using namespace std::tr1;
-namespace po = boost::program_options;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("training_features,x", po::value<string>(), "File containing training instance features (ARKRegression format)")
-        ("training_responses,y", po::value<string>(), "File containing training response features (ARKRegression format)")
-        ("linear,n", "Linear (rather than logistic) regression")
-        ("l1",po::value<double>()->default_value(0.0), "l_1 regularization strength")
-        ("l2",po::value<double>()->default_value(0.0), "l_2 regularization strength")
-        ("weights,w", po::value<string>(), "Initial weights")
-        ("epsilon,e", po::value<double>()->default_value(1e-4), "Epsilon for convergence test. Terminates when ||g|| < epsilon * max(1, ||x||)")
-        ("memory_buffers,m",po::value<unsigned>()->default_value(40), "Number of memory buffers for LBFGS")
-        ("help,h", "Help");
-  po::options_description dcmdline_options;
-  dcmdline_options.add(opts);
-  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
-  if (conf->count("help") || !conf->count("training_features") || !conf->count("training_responses")) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-}
-
-struct TrainingInstance {
-  SparseVector<float> x;
-  union {
-    unsigned label;  // for categorical predictions
-    float value;     // for continuous predictions
-  } y;
-};
-
-struct ReaderHelper {
-  explicit ReaderHelper(vector<TrainingInstance>* xyp) : xy_pairs(xyp), lc(), flag() {}
-  unordered_map<string, unsigned> id2ind;
-  vector<TrainingInstance>* xy_pairs;
-  int lc;
-  bool flag;
-};
-
-void ReaderCB(const string& id, const SparseVector<float>& fmap, void* extra) {
-  ReaderHelper& rh = *reinterpret_cast<ReaderHelper*>(extra);
-  ++rh.lc;
-  if (rh.lc % 1000 == 0) { cerr << '.'; rh.flag = true; }
-  if (rh.lc % 40000 == 0) { cerr << " [" << rh.lc << "]\n"; rh.flag = false; }
-  const unordered_map<string, unsigned>::iterator it = rh.id2ind.find(id);
-  if (it == rh.id2ind.end()) {
-    cerr << "Unlabeled example in line " << rh.lc << endl;
-    abort();
-  }
-  (*rh.xy_pairs)[it->second - 1].x = fmap;
-}
-
-void ReadLabeledInstances(const string& ffeats,
-                 const string& fresp,
-                 const bool is_continuous,
-                 vector<TrainingInstance>* xy_pairs,
-                 vector<string>* labels) {
-  bool flag = false;
-  xy_pairs->clear();
-  int lc = 0;
-  ReaderHelper rh(xy_pairs);
-  unordered_map<string, unsigned> label2id;
-  cerr << "Reading training responses from " << fresp << " ..." << endl;
-  ReadFile fr(fresp);
-  for (unsigned i = 0; i < labels->size(); ++i)
-    label2id[(*labels)[i]] = i;
-  istream& in = *fr.stream();
-  string line;
-  while(getline(in, line)) {
-    ++lc;
-    if (lc % 1000 == 0) { cerr << '.'; flag = true; }
-    if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; }
-    if (line.size() == 0) continue;
-    if (line[0] == '#') continue;
-    unsigned p = 0;
-    while (p < line.size() && line[p] != ' ' && line[p] != '\t') { ++p; }
-    unsigned& ind = rh.id2ind[line.substr(0, p)];
-    if (ind != 0) { cerr << "ID " << line.substr(0, p) << " duplicated in line " << lc << endl; abort(); }
-    while (p < line.size() && (line[p] == ' ' || line[p] == '\t')) { ++p; }
-    assert(p < line.size());
-    xy_pairs->push_back(TrainingInstance());
-    ind = xy_pairs->size();
-    if (is_continuous) {
-      xy_pairs->back().y.value = strtof(&line[p], 0);
-    } else { // categorical predictions
-      unordered_map<string, unsigned>::iterator it = label2id.find(line.substr(p));
-      if (it == label2id.end()) {
-        const string label = line.substr(p);
-        it = label2id.insert(make_pair(label, labels->size())).first;
-        labels->push_back(label);
-      }
-      xy_pairs->back().y.label = it->second;  // label id
-    }
-  }
-  if (flag) cerr << endl;
-  if (!is_continuous) {
-    cerr << "LABELS:";
-    for (unsigned j = 0; j < labels->size(); ++j)
-      cerr << " " << (*labels)[j];
-    cerr << endl;
-  }
-  cerr << "Reading training features from " << ffeats << " ..." << endl;
-  ReadFile ff(ffeats);
-  JSONFeatureMapLexer::ReadRules(ff.stream(), ReaderCB, &rh);
-  if (rh.flag) cerr << endl;
-}
-
-// helper base class (not polymorphic- just a container and some helper functions) for loss functions
-// real loss functions should implement double operator()(const vector<double>& x, double* g),
-// which should evaluate f(x) and g = f'(x)
-struct BaseLoss {
-  // dimp1 = number of categorial outputs possible for logistic regression
-  // for linear regression, it should be 1 more than the dimension of the response variable
-  BaseLoss(
-      const vector<TrainingInstance>& tr,
-      unsigned dimp1,
-      unsigned numfeats,
-      unsigned ll2) : training(tr), K(dimp1), p(numfeats), l2(ll2) {}
-
-  // weight vector layout for K classes, with p features
-  //   w[0 : K-1] = bias weights
-  //   w[y*p + K : y*p + K + p - 1] = feature weights for y^th class
-  // this representation is used in ComputeDotProducts and GradAdd
-  void ComputeDotProducts(const SparseVector<float>& fx,  // feature vector of x
-                          const vector<double>& w,         // full weight vector
-                          vector<double>* pdotprods) const {
-    vector<double>& dotprods = *pdotprods;
-    const unsigned km1 = K - 1;
-    dotprods.resize(km1);
-    for (unsigned y = 0; y < km1; ++y)
-      dotprods[y] = w[y];  // bias terms
-    for (SparseVector<float>::const_iterator it = fx.begin(); it != fx.end(); ++it) {
-      const float fval = it->second;
-      const unsigned fid = it->first;
-      for (unsigned y = 0; y < km1; ++y)
-        dotprods[y] += w[fid + y * p + km1] * fval;
-    }
-  }
-
-  double ApplyRegularizationTerms(const vector<double>& weights,
-                                  double* g) const {
-    double reg = 0;
-    for (size_t i = K - 1; i < weights.size(); ++i) {
-      const double& w_i = weights[i];
-      reg += l2 * w_i * w_i;
-      g[i] += 2 * l2 * w_i;
-    }
-    return reg;
-  }
-
-  void GradAdd(const SparseVector<float>& fx,
-               const unsigned y,
-               const double scale,
-               double* acc) const {
-    acc[y] += scale; // class bias
-    for (SparseVector<float>::const_iterator it = fx.begin();
-         it != fx.end(); ++it)
-      acc[it->first + y * p + K - 1] += it->second * scale;
-  }
-
-  const vector<TrainingInstance>& training;
-  const unsigned K, p;
-  const double l2;
-};
-
-struct UnivariateSquaredLoss : public BaseLoss {
-  UnivariateSquaredLoss(
-          const vector<TrainingInstance>& tr,
-          unsigned numfeats,
-          const double l2) : BaseLoss(tr, 2, numfeats, l2) {}
-
-  // evaluate squared loss and gradient
-  double operator()(const vector<double>& x, double* g) const {
-    fill(g, g + x.size(), 0.0);
-    double cll = 0;
-    vector<double> dotprods(1);  // univariate prediction
-    for (int i = 0; i < training.size(); ++i) {
-      const SparseVector<float>& fmapx = training[i].x;
-      const double refy = training[i].y.value;
-      ComputeDotProducts(fmapx, x, &dotprods);
-      double diff = dotprods[0] - refy;
-      cll += diff * diff;
-
-      double scale = 2 * diff;
-      GradAdd(fmapx, 0, scale, g);
-    }
-    double reg = ApplyRegularizationTerms(x, g);
-    return cll + reg;
-  }
-};
-
-struct MulticlassLogLoss : public BaseLoss {
-  MulticlassLogLoss(
-          const vector<TrainingInstance>& tr,
-          unsigned k,
-          unsigned numfeats,
-          const double l2) : BaseLoss(tr, k, numfeats, l2) {}
-
-  // evaluate log loss and gradient
-  double operator()(const vector<double>& x, double* g) const {
-    fill(g, g + x.size(), 0.0);
-    vector<double> dotprods(K - 1);  // K-1 degrees of freedom
-    vector<prob_t> probs(K);
-    double cll = 0;
-    for (int i = 0; i < training.size(); ++i) {
-      const SparseVector<float>& fmapx = training[i].x;
-      const unsigned refy = training[i].y.label;
-      //cerr << "FMAP: " << fmapx << endl;
-      ComputeDotProducts(fmapx, x, &dotprods);
-      prob_t z;
-      for (unsigned j = 0; j < dotprods.size(); ++j)
-        z += (probs[j] = prob_t(dotprods[j], init_lnx()));
-      z += (probs.back() = prob_t::One());
-      for (unsigned y = 0; y < probs.size(); ++y) {
-        probs[y] /= z;
-        //cerr << "  p(y=" << y << ")=" << probs[y].as_float() << "\tz=" << z << endl;
-      }
-      cll -= log(probs[refy]);  // log p(y | x)
-
-      for (unsigned y = 0; y < dotprods.size(); ++y) {
-        double scale = probs[y].as_float();
-        if (y == refy) { scale -= 1.0; }
-        GradAdd(fmapx, y, scale, g);
-      }
-    }
-    double reg = ApplyRegularizationTerms(x, g);
-    return cll + reg;
-  }
-};
-
-template <class LossFunction>
-double LearnParameters(LossFunction& loss,
-                       const double l1,
-                       const unsigned l1_start,
-                       const unsigned memory_buffers,
-                       const double eps,
-                       vector<double>* px) {
-  LBFGS<LossFunction> lbfgs(px, loss, memory_buffers, l1, l1_start, eps);
-  lbfgs.MinimizeFunction();
-  return 0;
-}
-
-int main(int argc, char** argv) {
-  po::variables_map conf;
-  InitCommandLine(argc, argv, &conf);
-  string line;
-  vector<TrainingInstance> training;
-  const string xfile = conf["training_features"].as<string>();
-  const string yfile = conf["training_responses"].as<string>();
-  double l1 = conf["l1"].as<double>();
-  double l2 = conf["l2"].as<double>();
-  const unsigned memory_buffers = conf["memory_buffers"].as<unsigned>();
-  const double epsilon = conf["epsilon"].as<double>();
-  if (l1 < 0.0) {
-    cerr << "L1 strength must be >= 0\n";
-    return 1;
-  }
-  if (l2 < 0.0) {
-    cerr << "L2 strength must be >= 0\n";
-    return 2;
-  }
-
-  const bool is_continuous = conf.count("linear");
-  vector<string> labels; // only populated for non-continuous models
-  ReadLabeledInstances(xfile, yfile, is_continuous, &training, &labels);
-
-  if (conf.count("weights")) {
-    cerr << "Initial weights are not implemented, please implement." << endl;
-    // TODO read weights for categorical and continuous predictions
-    // can't use normal cdec weight framework
-    abort();
-  }
-
-  cerr << "         Number of features: " << FD::NumFeats() << endl;
-  cerr << "Number of training examples: " << training.size() << endl;
-  const unsigned p = FD::NumFeats();
-  cout.precision(15);
-
-  if (conf.count("linear")) {  // linear regression
-    vector<double> weights(1 + FD::NumFeats(), 0.0);
-    cerr << "       Number of parameters: " << weights.size() << endl;
-    UnivariateSquaredLoss loss(training, p, l2);
-    LearnParameters(loss, l1, 1, memory_buffers, epsilon, &weights);
-    cout << p << "\t***CONTINUOUS***" << endl;
-    cout << "***BIAS***\t" << weights[0] << endl;
-    for (unsigned f = 0; f < p; ++f) {
-      const double w = weights[1 + f];
-      if (w)
-        cout << FD::Convert(f) << "\t" << w << endl;
-    }
-  } else {                     // logistic regression
-    vector<double> weights((1 + FD::NumFeats()) * (labels.size() - 1), 0.0);
-    cerr << "       Number of parameters: " << weights.size() << endl;
-    cerr << "           Number of labels: " << labels.size() << endl;
-    const unsigned K = labels.size();
-    const unsigned km1 = K - 1;
-    MulticlassLogLoss loss(training, K, p, l2);
-    LearnParameters(loss, l1, km1, memory_buffers, epsilon, &weights);
-
-    cout << p << "\t***CATEGORICAL***";
-    for (unsigned y = 0; y < K; ++y)
-      cout << '\t' << labels[y];
-    cout << endl;
-    for (unsigned y = 0; y < km1; ++y)
-      cout << labels[y] << "\t***BIAS***\t" << weights[y] << endl;
-    for (unsigned y = 0; y < km1; ++y) {
-      for (unsigned f = 0; f < p; ++f) {
-        const double w = weights[km1 + y * p + f];
-        if (w)
-          cout << labels[y] << "\t" << FD::Convert(f) << "\t" << w << endl;
-      }
-    }
-  }
-
-  return 0;
-}
-
diff --git a/training/liblbfgs/lbfgs++.h b/training/liblbfgs/lbfgs++.h
index 92ead955..2b40c19b 100644
--- a/training/liblbfgs/lbfgs++.h
+++ b/training/liblbfgs/lbfgs++.h
@@ -90,6 +90,7 @@ class LBFGS {
                              lbfgsfloatval_t *g,
                              const int n,
                              const lbfgsfloatval_t step) {
+      (void) x;
       (void) n;
       (void) step;
       if (!silence) { ec++; std::cerr << '.'; }