merge upstream/master

1 files changed, 279 insertions, 0 deletions

diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
new file mode 100644
index 00000000..aff410a0
--- /dev/null
+++ b/pro-train/mr_pro_reduce.cc
@@ -0,0 +1,279 @@
+#include <cstdlib>
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "filelib.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "optimize.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+// since this is a ranking model, there should be equal numbers of
+// positive and negative examples, so the bias should be 0
+static const double MAX_BIAS = 1e-10;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation")
+        ("interpolation,p",po::value<double>()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev")
+        ("memory_buffers,m",po::value<unsigned>()->default_value(200), "Number of memory buffers (LBFGS)")
+        ("sigma_squared,s",po::value<double>()->default_value(0.1), "Sigma squared for Gaussian prior")
+        ("min_reg,r",po::value<double>()->default_value(1e-8), "When tuning (-T) regularization strength, minimum regularization strenght")
+        ("max_reg,R",po::value<double>()->default_value(10.0), "When tuning (-T) regularization strength, maximum regularization strenght")
+        ("testset,t",po::value<string>(), "Optional held-out test set")
+        ("tune_regularizer,T", "Use the held out test set (-t) to tune the regularization strength")
+        ("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")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) {
+  SparseVector<weight_t>& x = *out;
+  size_t last_start = cur;
+  size_t last_comma = string::npos;
+  while(cur <= line.size()) {
+    if (line[cur] == ' ' || cur == line.size()) {
+      if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
+        cerr << "[ERROR] " << line << endl << "  position = " << cur << endl;
+        exit(1);
+      }
+      const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
+      if (cur < line.size()) line[cur] = 0;
+      const weight_t val = strtod(&line[last_comma + 1], NULL);
+      x.set_value(fid, val);
+
+      last_comma = string::npos;
+      last_start = cur+1;
+    } else {
+      if (line[cur] == '=')
+        last_comma = cur;
+    }
+    ++cur;
+  }
+}
+
+void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<weight_t> > >* corpus) {
+  istream& in = *pin;
+  corpus->clear();
+  bool flag = false;
+  int lc = 0;
+  string line;
+  SparseVector<weight_t> x;
+  while(getline(in, line)) {
+    ++lc;
+    if (lc % 1000 == 0) { cerr << '.'; flag = true; }
+    if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; }
+    if (line.empty()) continue;
+    const size_t ks = line.find("\t");
+    assert(string::npos != ks);
+    assert(ks == 1);
+    const bool y = line[0] == '1';
+    x.clear();
+    ParseSparseVector(line, ks + 1, &x);
+    corpus->push_back(make_pair(y, x));
+  }
+  if (flag) cerr << endl;
+}
+
+void GradAdd(const SparseVector<weight_t>& v, const double scale, vector<weight_t>* acc) {
+  for (SparseVector<weight_t>::const_iterator it = v.begin();
+       it != v.end(); ++it) {
+    (*acc)[it->first] += it->second * scale;
+  }
+}
+
+double TrainingInference(const vector<weight_t>& x,
+                         const vector<pair<bool, SparseVector<weight_t> > >& corpus,
+                         vector<weight_t>* g = NULL) {
+  double cll = 0;
+  for (int i = 0; i < corpus.size(); ++i) {
+    const double dotprod = corpus[i].second.dot(x) + (x.size() ? x[0] : weight_t()); // x[0] is bias
+    double lp_false = dotprod;
+    double lp_true = -dotprod;
+    if (0 < lp_true) {
+      lp_true += log1p(exp(-lp_true));
+      lp_false = log1p(exp(lp_false));
+    } else {
+      lp_true = log1p(exp(lp_true));
+      lp_false += log1p(exp(-lp_false));
+    }
+    lp_true*=-1;
+    lp_false*=-1;
+    if (corpus[i].first) {  // true label
+      cll -= lp_true;
+      if (g) {
+        // g -= corpus[i].second * exp(lp_false);
+        GradAdd(corpus[i].second, -exp(lp_false), g);
+        (*g)[0] -= exp(lp_false); // bias
+      }
+    } else {                  // false label
+      cll -= lp_false;
+      if (g) {
+        // g += corpus[i].second * exp(lp_true);
+        GradAdd(corpus[i].second, exp(lp_true), g);
+        (*g)[0] += exp(lp_true); // bias
+      }
+    }
+  }
+  return cll;
+}
+
+// return held-out log likelihood
+double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training,
+                       const vector<pair<bool, SparseVector<weight_t> > >& testing,
+                       const double sigsq,
+                       const unsigned memory_buffers,
+                       vector<weight_t>* px) {
+  vector<weight_t>& x = *px;
+  vector<weight_t> vg(FD::NumFeats(), 0.0);
+  bool converged = false;
+  LBFGSOptimizer opt(FD::NumFeats(), memory_buffers);
+  double tppl = 0.0;
+  while(!converged) {
+    fill(vg.begin(), vg.end(), 0.0);
+    double cll = TrainingInference(x, training, &vg);
+    double ppl = cll / log(2);
+    ppl /= training.size();
+    ppl = pow(2.0, ppl);
+
+    // evaluate optional held-out test set
+    if (testing.size()) {
+      tppl = TrainingInference(x, testing) / log(2);
+      tppl /= testing.size();
+      tppl = pow(2.0, tppl);
+    }
+
+    // handle regularizer
+#if 1
+    double norm = 0;
+    for (int i = 1; i < x.size(); ++i) {
+      const double mean_i = 0.0;
+      const double param = (x[i] - mean_i);
+      norm += param * param;
+      vg[i] += param / sigsq;
+    } 
+    const double reg = norm / (2.0 * sigsq);
+#else
+    double reg = 0;
+#endif
+    cll += reg;
+    cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t";
+    try {
+      vector<weight_t> old_x = x;
+      do {
+        opt.Optimize(cll, vg, &x);
+        converged = opt.HasConverged();
+      } while (!converged && x == old_x);
+    } catch (...) {
+      cerr << "Exception caught, assuming convergence is close enough...\n";
+      converged = true;
+    }
+    if (fabs(x[0]) > MAX_BIAS) {
+      cerr << "Biased model learned. Are your training instances wrong?\n";
+      cerr << "  BIAS: " << x[0] << endl;
+    }
+  }
+  return tppl;
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  string line;
+  vector<pair<bool, SparseVector<weight_t> > > training, testing;
+  const bool tune_regularizer = conf.count("tune_regularizer");
+  if (tune_regularizer && !conf.count("testset")) {
+    cerr << "--tune_regularizer requires --testset to be set\n";
+    return 1;
+  }
+  const double min_reg = conf["min_reg"].as<double>();
+  const double max_reg = conf["max_reg"].as<double>();
+  double sigsq = conf["sigma_squared"].as<double>(); // will be overridden if parameter is tuned
+  assert(sigsq > 0.0);
+  assert(min_reg > 0.0);
+  assert(max_reg > 0.0);
+  assert(max_reg > min_reg);
+  const double psi = conf["interpolation"].as<double>();
+  if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; }
+  ReadCorpus(&cin, &training);
+  if (conf.count("testset")) {
+    ReadFile rf(conf["testset"].as<string>());
+    ReadCorpus(rf.stream(), &testing);
+  }
+  cerr << "Number of features: " << FD::NumFeats() << endl;
+
+  vector<weight_t> x, prev_x;  // x[0] is bias
+  if (conf.count("weights")) {
+    Weights::InitFromFile(conf["weights"].as<string>(), &x);
+    x.resize(FD::NumFeats());
+    prev_x = x;
+  } else {
+    x.resize(FD::NumFeats());
+    prev_x = x;
+  }
+  cerr << "         Number of features: " << x.size() << endl;
+  cerr << "Number of training examples: " << training.size() << endl;
+  cerr << "Number of  testing examples: " << testing.size() << endl;
+  double tppl = 0.0;
+  vector<pair<double,double> > sp;
+  vector<double> smoothed;
+  if (tune_regularizer) {
+    sigsq = min_reg;
+    const double steps = 18;
+    double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps);
+    cerr << "SWEEP FACTOR: " << sweep_factor << endl;
+    while(sigsq < max_reg) {
+      tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+      sp.push_back(make_pair(sigsq, tppl));
+      sigsq *= sweep_factor;
+    }
+    smoothed.resize(sp.size(), 0);
+    smoothed[0] = sp[0].second;
+    smoothed.back() = sp.back().second; 
+    for (int i = 1; i < sp.size()-1; ++i) {
+      double prev = sp[i-1].second;
+      double next = sp[i+1].second;
+      double cur = sp[i].second;
+      smoothed[i] = (prev*0.2) + cur * 0.6 + (0.2*next);
+    }
+    double best_ppl = 9999999;
+    unsigned best_i = 0;
+    for (unsigned i = 0; i < sp.size(); ++i) {
+      if (smoothed[i] < best_ppl) {
+        best_ppl = smoothed[i];
+        best_i = i;
+      }
+    }
+    sigsq = sp[best_i].first;
+  }  // tune regularizer
+  tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+  if (conf.count("weights")) {
+    for (int i = 1; i < x.size(); ++i) {
+      x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi);
+    }
+  }
+  cout.precision(15);
+  cout << "# sigma^2=" << sigsq << "\theld out perplexity=";
+  if (tppl) { cout << tppl << endl; } else { cout << "N/A\n"; }
+  if (sp.size()) {
+    cout << "# Parameter sweep:\n";
+    for (int i = 0; i < sp.size(); ++i) {
+      cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl;
+    }
+  }
+  Weights::WriteToFile("-", x);
+  return 0;
+}