diff options
Diffstat (limited to 'dtrain/dtrain.cc')
| -rw-r--r-- | dtrain/dtrain.cc | 26 |
1 files changed, 18 insertions, 8 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc index 5c95c7f1..79047fd9 100644 --- a/dtrain/dtrain.cc +++ b/dtrain/dtrain.cc @@ -19,12 +19,13 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_*, smooth_*, approx_*") ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences") ("print_weights", po::value<string>(), "weights to print on each iteration") - ("hstreaming", po::value<string>()->default_value("N/A"), "run in hadoop streaming mode, arg is a task id") + ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id") ("learning_rate", po::value<weight_t>()->default_value(0.0005), "learning rate") ("gamma", po::value<weight_t>()->default_value(0), "gamma for SVM (0 for perceptron)") ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use") ("select_weights", po::value<string>()->default_value("last"), "output 'best' or 'last' weights ('VOID' to throw away)") ("keep_w", po::value<bool>()->zero_tokens(), "protocol weights for each iteration") + ("unit_weight_vector", po::value<bool>()->zero_tokens(), "Rescale weight vector after each input") #ifdef DTRAIN_LOCAL ("refs,r", po::value<string>(), "references in local mode") #endif @@ -46,7 +47,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) return false; } if (cfg->count("hstreaming") && (*cfg)["output"].as<string>() != "-") { - cerr << "When using 'hstreaming' the 'output' param should be '-'."; + cerr << "When using 'hstreaming' the 'output' param should be '-'." << endl; return false; } #ifdef DTRAIN_LOCAL @@ -98,6 +99,8 @@ main(int argc, char** argv) task_id = cfg["hstreaming"].as<string>(); cerr.precision(17); } + bool unit_weight_vector = false; + if (cfg.count("unit_weight_vector")) unit_weight_vector = true; HSReporter rep(task_id); bool keep_w = false; if (cfg.count("keep_w")) keep_w = true; @@ -226,7 +229,7 @@ main(int argc, char** argv) #endif score_t score_sum = 0.; score_t model_sum(0); - unsigned ii = 0, nup = 0, npairs = 0; + unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0; if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl; while(true) @@ -369,21 +372,25 @@ main(int argc, char** argv) if (rank_error > 0) { SparseVector<weight_t> diff_vec = it->second.f - it->first.f; lambdas.plus_eq_v_times_s(diff_vec, eta); - nup++; + rank_errors++; } + if (margin < 1) margin_violations++; } else { // SVM score_t margin = it->first.model - it->second.model; if (rank_error > 0 || margin < 1) { SparseVector<weight_t> diff_vec = it->second.f - it->first.f; lambdas.plus_eq_v_times_s(diff_vec, eta); - nup++; + if (rank_error > 0) rank_errors++; + if (margin < 1) margin_violations++; } // regularization lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs)); } } } + + if (unit_weight_vector && sample_from == "forest") lambdas /= lambdas.l2norm(); ++ii; @@ -437,15 +444,18 @@ main(int argc, char** argv) cerr << _p << " (" << model_diff << ")" << endl; cerr << " avg #pairs: "; cerr << _np << npairs/(float)in_sz << endl; - cerr << " avg #up: "; - cerr << nup/(float)in_sz << endl; + cerr << " avg #rank err: "; + cerr << rank_errors/(float)in_sz << endl; + cerr << " avg #margin viol: "; + cerr << margin_violations/float(in_sz) << endl; } if (hstreaming) { rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), score_avg); rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), model_avg); rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), npairs/(weight_t)in_sz); - rep.update_counter("Updates avg #"+boost::lexical_cast<string>(t+1), nup/(weight_t)in_sz); + rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), rank_errors/(weight_t)in_sz); + rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), margin_violations/(weight_t)in_sz); unsigned nonz = (unsigned)lambdas.size_nonzero(); rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz); rep.update_gcounter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz); |