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Diffstat (limited to 'training/mr_optimize_reduce.cc')
| -rw-r--r-- | training/mr_optimize_reduce.cc | 243 |
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..56b73c30 --- /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,min(200ul, 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; +} |