#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; 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"; vector<weight_t> lambdas; Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas); 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<weight_t> means(num_feats, 0); if (conf.count("means")) { if (!gaussian_prior) { cerr << "Don't use --means without --gaussian_prior!\n"; exit(1); } Weights::InitFromFile(conf["means"].as<string>(), &means); } boost::shared_ptr<BatchOptimizer> o; const string omethod = conf["optimization_method"].as<string>(); 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"; } 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::WriteToFile(conf["output_weights"].as<string>(), lambdas, 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; }