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Diffstat (limited to 'pro-train/mr_pro_reduce.cc')
| -rw-r--r-- | pro-train/mr_pro_reduce.cc | 279 |
1 files changed, 279 insertions, 0 deletions
diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc new file mode 100644 index 00000000..aff410a0 --- /dev/null +++ b/pro-train/mr_pro_reduce.cc @@ -0,0 +1,279 @@ +#include <cstdlib> +#include <sstream> +#include <iostream> +#include <fstream> +#include <vector> + +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "filelib.h" +#include "weights.h" +#include "sparse_vector.h" +#include "optimize.h" + +using namespace std; +namespace po = boost::program_options; + +// since this is a ranking model, there should be equal numbers of +// positive and negative examples, so the bias should be 0 +static const double MAX_BIAS = 1e-10; + +void InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation") + ("interpolation,p",po::value<double>()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev") + ("memory_buffers,m",po::value<unsigned>()->default_value(200), "Number of memory buffers (LBFGS)") + ("sigma_squared,s",po::value<double>()->default_value(0.1), "Sigma squared for Gaussian prior") + ("min_reg,r",po::value<double>()->default_value(1e-8), "When tuning (-T) regularization strength, minimum regularization strenght") + ("max_reg,R",po::value<double>()->default_value(10.0), "When tuning (-T) regularization strength, maximum regularization strenght") + ("testset,t",po::value<string>(), "Optional held-out test set") + ("tune_regularizer,T", "Use the held out test set (-t) to tune the regularization strength") + ("help,h", "Help"); + po::options_description dcmdline_options; + dcmdline_options.add(opts); + po::store(parse_command_line(argc, argv, dcmdline_options), *conf); + if (conf->count("help")) { + cerr << dcmdline_options << endl; + exit(1); + } +} + +void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) { + SparseVector<weight_t>& x = *out; + size_t last_start = cur; + size_t last_comma = string::npos; + while(cur <= line.size()) { + if (line[cur] == ' ' || cur == line.size()) { + if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) { + cerr << "[ERROR] " << line << endl << " position = " << cur << endl; + exit(1); + } + const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); + if (cur < line.size()) line[cur] = 0; + const weight_t val = strtod(&line[last_comma + 1], NULL); + x.set_value(fid, val); + + last_comma = string::npos; + last_start = cur+1; + } else { + if (line[cur] == '=') + last_comma = cur; + } + ++cur; + } +} + +void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<weight_t> > >* corpus) { + istream& in = *pin; + corpus->clear(); + bool flag = false; + int lc = 0; + string line; + SparseVector<weight_t> x; + while(getline(in, line)) { + ++lc; + if (lc % 1000 == 0) { cerr << '.'; flag = true; } + if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; } + if (line.empty()) continue; + const size_t ks = line.find("\t"); + assert(string::npos != ks); + assert(ks == 1); + const bool y = line[0] == '1'; + x.clear(); + ParseSparseVector(line, ks + 1, &x); + corpus->push_back(make_pair(y, x)); + } + if (flag) cerr << endl; +} + +void GradAdd(const SparseVector<weight_t>& v, const double scale, vector<weight_t>* acc) { + for (SparseVector<weight_t>::const_iterator it = v.begin(); + it != v.end(); ++it) { + (*acc)[it->first] += it->second * scale; + } +} + +double TrainingInference(const vector<weight_t>& x, + const vector<pair<bool, SparseVector<weight_t> > >& corpus, + vector<weight_t>* g = NULL) { + double cll = 0; + for (int i = 0; i < corpus.size(); ++i) { + const double dotprod = corpus[i].second.dot(x) + (x.size() ? x[0] : weight_t()); // x[0] is bias + double lp_false = dotprod; + double lp_true = -dotprod; + if (0 < lp_true) { + lp_true += log1p(exp(-lp_true)); + lp_false = log1p(exp(lp_false)); + } else { + lp_true = log1p(exp(lp_true)); + lp_false += log1p(exp(-lp_false)); + } + lp_true*=-1; + lp_false*=-1; + if (corpus[i].first) { // true label + cll -= lp_true; + if (g) { + // g -= corpus[i].second * exp(lp_false); + GradAdd(corpus[i].second, -exp(lp_false), g); + (*g)[0] -= exp(lp_false); // bias + } + } else { // false label + cll -= lp_false; + if (g) { + // g += corpus[i].second * exp(lp_true); + GradAdd(corpus[i].second, exp(lp_true), g); + (*g)[0] += exp(lp_true); // bias + } + } + } + return cll; +} + +// return held-out log likelihood +double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training, + const vector<pair<bool, SparseVector<weight_t> > >& testing, + const double sigsq, + const unsigned memory_buffers, + vector<weight_t>* px) { + vector<weight_t>& x = *px; + vector<weight_t> vg(FD::NumFeats(), 0.0); + bool converged = false; + LBFGSOptimizer opt(FD::NumFeats(), memory_buffers); + double tppl = 0.0; + while(!converged) { + fill(vg.begin(), vg.end(), 0.0); + double cll = TrainingInference(x, training, &vg); + double ppl = cll / log(2); + ppl /= training.size(); + ppl = pow(2.0, ppl); + + // evaluate optional held-out test set + if (testing.size()) { + tppl = TrainingInference(x, testing) / log(2); + tppl /= testing.size(); + tppl = pow(2.0, tppl); + } + + // handle regularizer +#if 1 + double norm = 0; + for (int i = 1; i < x.size(); ++i) { + const double mean_i = 0.0; + const double param = (x[i] - mean_i); + norm += param * param; + vg[i] += param / sigsq; + } + const double reg = norm / (2.0 * sigsq); +#else + double reg = 0; +#endif + cll += reg; + cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t"; + try { + vector<weight_t> old_x = x; + do { + opt.Optimize(cll, vg, &x); + converged = opt.HasConverged(); + } while (!converged && x == old_x); + } catch (...) { + cerr << "Exception caught, assuming convergence is close enough...\n"; + converged = true; + } + if (fabs(x[0]) > MAX_BIAS) { + cerr << "Biased model learned. Are your training instances wrong?\n"; + cerr << " BIAS: " << x[0] << endl; + } + } + return tppl; +} + +int main(int argc, char** argv) { + po::variables_map conf; + InitCommandLine(argc, argv, &conf); + string line; + vector<pair<bool, SparseVector<weight_t> > > training, testing; + const bool tune_regularizer = conf.count("tune_regularizer"); + if (tune_regularizer && !conf.count("testset")) { + cerr << "--tune_regularizer requires --testset to be set\n"; + return 1; + } + const double min_reg = conf["min_reg"].as<double>(); + const double max_reg = conf["max_reg"].as<double>(); + double sigsq = conf["sigma_squared"].as<double>(); // will be overridden if parameter is tuned + assert(sigsq > 0.0); + assert(min_reg > 0.0); + assert(max_reg > 0.0); + assert(max_reg > min_reg); + const double psi = conf["interpolation"].as<double>(); + if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; } + ReadCorpus(&cin, &training); + if (conf.count("testset")) { + ReadFile rf(conf["testset"].as<string>()); + ReadCorpus(rf.stream(), &testing); + } + cerr << "Number of features: " << FD::NumFeats() << endl; + + vector<weight_t> x, prev_x; // x[0] is bias + if (conf.count("weights")) { + Weights::InitFromFile(conf["weights"].as<string>(), &x); + x.resize(FD::NumFeats()); + prev_x = x; + } else { + x.resize(FD::NumFeats()); + prev_x = x; + } + cerr << " Number of features: " << x.size() << endl; + cerr << "Number of training examples: " << training.size() << endl; + cerr << "Number of testing examples: " << testing.size() << endl; + double tppl = 0.0; + vector<pair<double,double> > sp; + vector<double> smoothed; + if (tune_regularizer) { + sigsq = min_reg; + const double steps = 18; + double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps); + cerr << "SWEEP FACTOR: " << sweep_factor << endl; + while(sigsq < max_reg) { + tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x); + sp.push_back(make_pair(sigsq, tppl)); + sigsq *= sweep_factor; + } + smoothed.resize(sp.size(), 0); + smoothed[0] = sp[0].second; + smoothed.back() = sp.back().second; + for (int i = 1; i < sp.size()-1; ++i) { + double prev = sp[i-1].second; + double next = sp[i+1].second; + double cur = sp[i].second; + smoothed[i] = (prev*0.2) + cur * 0.6 + (0.2*next); + } + double best_ppl = 9999999; + unsigned best_i = 0; + for (unsigned i = 0; i < sp.size(); ++i) { + if (smoothed[i] < best_ppl) { + best_ppl = smoothed[i]; + best_i = i; + } + } + sigsq = sp[best_i].first; + } // tune regularizer + tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x); + if (conf.count("weights")) { + for (int i = 1; i < x.size(); ++i) { + x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi); + } + } + cout.precision(15); + cout << "# sigma^2=" << sigsq << "\theld out perplexity="; + if (tppl) { cout << tppl << endl; } else { cout << "N/A\n"; } + if (sp.size()) { + cout << "# Parameter sweep:\n"; + for (int i = 0; i < sp.size(); ++i) { + cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl; + } + } + Weights::WriteToFile("-", x); + return 0; +} |