initial checkin

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@2 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 243 insertions, 0 deletions

diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
new file mode 100644
index 00000000..42727ecb
--- /dev/null
+++ b/training/mr_optimize_reduce.cc
@@ -0,0 +1,243 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "optimize.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+void SanityCheck(const vector<double>& w) {
+  for (int i = 0; i < w.size(); ++i) {
+    assert(!isnan(w[i]));
+    assert(!isinf(w[i]));
+  }
+}
+
+struct FComp {
+  const vector<double>& w_;
+  FComp(const vector<double>& w) : w_(w) {}
+  bool operator()(int a, int b) const {
+    return fabs(w_[a]) > fabs(w_[b]);
+  }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+  vector<int> fnums(w.size());
+  for (int i = 0; i < w.size(); ++i)
+    fnums[i] = i;
+  vector<int>::iterator mid = fnums.begin();
+  mid += (w.size() > 10 ? 10 : w.size());
+  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+  cerr << "TOP FEATURES:";
+  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+  }
+  cerr << endl;
+}
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("input_weights,i",po::value<string>(),"Input feature weights file")
+        ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
+        ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
+        ("state,s",po::value<string>(),"Read (and write if output_state is not set) optimizer state from this state file. In the first iteration, the file should not exist.")
+        ("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)")
+        ("output_state,S", po::value<string>(), "Output state file (optional override)")
+	("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
+        ("eta,e", po::value<double>()->default_value(0.1), "Learning rate for SGD (eta)")
+        ("gaussian_prior,p","Use a Gaussian prior on the weights")
+        ("means,u", po::value<string>(), "File containing the means for Gaussian prior")
+        ("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help,h", "Print this help message and exit");
+  po::options_description dconfig_options, dcmdline_options;
+  dconfig_options.add(opts);
+  dcmdline_options.add(opts).add(clo);
+  
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (conf->count("config")) {
+    ifstream config((*conf)["config"].as<string>().c_str());
+    po::store(po::parse_config_file(config, dconfig_options), *conf);
+  }
+  po::notify(*conf);
+
+  if (conf->count("help") || !conf->count("input_weights") || !conf->count("state")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+
+  const bool use_b64 = conf["input_format"].as<string>() == "b64";
+
+  Weights weights;
+  weights.InitFromFile(conf["input_weights"].as<string>());
+  const string s_obj = "**OBJ**";
+  int num_feats = FD::NumFeats();
+  cerr << "Number of features: " << num_feats << endl;
+  const bool gaussian_prior = conf.count("gaussian_prior");
+  vector<double> means(num_feats, 0);
+  if (conf.count("means")) {
+    if (!gaussian_prior) {
+      cerr << "Don't use --means without --gaussian_prior!\n";
+      exit(1);
+    }
+    Weights wm; 
+    wm.InitFromFile(conf["means"].as<string>());
+    if (num_feats != FD::NumFeats()) {
+      cerr << "[ERROR] Means file had unexpected features!\n";
+      exit(1);
+    }
+    wm.InitVector(&means);
+  }
+  shared_ptr<Optimizer> o;
+  const string omethod = conf["optimization_method"].as<string>();
+  if (omethod == "sgd")
+    o.reset(new SGDOptimizer(conf["eta"].as<double>()));
+  else if (omethod == "rprop")
+    o.reset(new RPropOptimizer(num_feats));  // TODO add configuration
+  else
+    o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
+  cerr << "Optimizer: " << o->Name() << endl;
+  string state_file = conf["state"].as<string>();
+  {
+    ifstream in(state_file.c_str(), ios::binary);
+    if (in)
+      o->Load(&in);
+    else
+      cerr << "No state file found, assuming ITERATION 1\n";
+  }
+
+  vector<double> lambdas(num_feats, 0);
+  weights.InitVector(&lambdas);
+  double objective = 0;
+  vector<double> gradient(num_feats, 0);
+  // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
+  // 0<TAB>**OBJ**=1.1;Feat1=1.0;
+  int total_lines = 0;  // TODO - this should be a count of the
+                        // training instances!!
+  while(cin) {
+    string line;
+    getline(cin, line);
+    if (line.empty()) continue;
+    ++total_lines;
+    int feat;
+    double val;
+    size_t i = line.find("\t");
+    assert(i != string::npos);
+    ++i;
+    if (use_b64) {
+      SparseVector<double> g;
+      double obj;
+      if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
+        cerr << "B64 decoder returned error, skipping gradient!\n";
+	cerr << "  START: " << line.substr(0,line.size() > 200 ? 200 : line.size()) << endl;
+	if (line.size() > 200)
+	  cerr << "    END: " << line.substr(line.size() - 200, 200) << endl;
+        cout << "-1\tRESTART\n";
+        exit(99);
+      }
+      objective += obj;
+      const SparseVector<double>& cg = g;
+      for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
+        if (it->first >= num_feats) {
+	  cerr << "Unexpected feature in gradient: " << FD::Convert(it->first) << endl;
+	  abort();
+        }
+        gradient[it->first] -= it->second;
+      }
+    } else {       // text encoding - your gradients will not be accurate!
+      while (i < line.size()) {
+        size_t start = i;
+        while (line[i] != '=' && i < line.size()) ++i;
+        if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
+        string fname = line.substr(start, i - start);
+        if (fname == s_obj) {
+          feat = -1;
+        } else {
+          feat = FD::Convert(line.substr(start, i - start));
+          if (feat >= num_feats) {
+	    cerr << "Unexpected feature in gradient: " << line.substr(start, i - start) << endl;
+	    abort();
+	  }
+        }
+        ++i;
+        start = i;
+        while (line[i] != ';' && i < line.size()) ++i;
+        if (i - start == 0) continue;
+        val = atof(line.substr(start, i - start).c_str());
+        ++i;
+        if (feat == -1) {
+          objective += val;
+        } else {
+          gradient[feat] -= val;
+        }
+      }
+    }
+  }
+
+  if (gaussian_prior) {
+    const double sigsq = conf["sigma_squared"].as<double>();
+    double norm = 0;
+    for (int k = 1; k < lambdas.size(); ++k) {
+      const double& lambda_k = lambdas[k];
+      if (lambda_k) {
+        const double param = (lambda_k - means[k]);
+        norm += param * param;
+        gradient[k] += param / sigsq;
+      }
+    }
+    const double reg = norm / (2.0 * sigsq);
+    cerr << "REGULARIZATION TERM: " << reg << endl;
+    objective += reg;
+  }
+  cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl;
+  double gnorm = 0;
+  for (int i = 0; i < gradient.size(); ++i)
+    gnorm += gradient[i] * gradient[i];
+  cerr << "  GNORM=" << sqrt(gnorm) << endl;
+  vector<double> old = lambdas;
+  int c = 0;
+  while (old == lambdas) {
+    ++c;
+    if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; }
+    o->Optimize(objective, gradient, &lambdas);
+    assert(c < 5);
+  }
+  old.clear();
+  SanityCheck(lambdas);
+  ShowLargestFeatures(lambdas);
+  weights.InitFromVector(lambdas);
+  weights.WriteToFile(conf["output_weights"].as<string>(), false);
+
+  const bool conv = o->HasConverged();
+  if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
+  
+  if (conf.count("output_state"))
+    state_file = conf["output_state"].as<string>();
+  ofstream out(state_file.c_str(), ios::binary);
+  cerr << "Writing state to: " << state_file << endl;
+  o->Save(&out);
+  out.close();
+
+  cout << o->EvaluationCount() << "\t" << conv << endl;
+  return 0;
+}