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-rw-r--r--training/mr_optimize_reduce.cc231
1 files changed, 0 insertions, 231 deletions
diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
deleted file mode 100644
index 461e6b5f..00000000
--- a/training/mr_optimize_reduce.cc
+++ /dev/null
@@ -1,231 +0,0 @@
-#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;
-}