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-rw-r--r--training/mr_optimize_reduce.cc243
1 files changed, 243 insertions, 0 deletions
diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
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+++ b/training/mr_optimize_reduce.cc
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+#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;
+}