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Diffstat (limited to 'utils/maxent.cpp')
| -rw-r--r-- | utils/maxent.cpp | 702 |
1 files changed, 702 insertions, 0 deletions
diff --git a/utils/maxent.cpp b/utils/maxent.cpp new file mode 100644 index 00000000..0f49ee9d --- /dev/null +++ b/utils/maxent.cpp @@ -0,0 +1,702 @@ +/* + * $Id: maxent.cpp,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $ + */ + +#include "maxent.h" +#include <cmath> +#include <cstdio> +#include "lbfgs.h" + +using namespace std; + +double ME_Model::FunctionGradient(const vector<double>& x, + vector<double>& grad) { + assert((int)_fb.Size() == x.size()); + for (size_t i = 0; i < x.size(); i++) { + _vl[i] = x[i]; + } + + double score = update_model_expectation(); + + if (_l2reg == 0) { + for (size_t i = 0; i < x.size(); i++) { + grad[i] = -(_vee[i] - _vme[i]); + } + } else { + const double c = _l2reg * 2; + for (size_t i = 0; i < x.size(); i++) { + grad[i] = -(_vee[i] - _vme[i] - c * _vl[i]); + } + } + + return -score; +} + +int ME_Model::perform_GIS(int C) { + cerr << "C = " << C << endl; + C = 1; + cerr << "performing AGIS" << endl; + vector<double> pre_v; + double pre_logl = -999999; + for (int iter = 0; iter < 200; iter++) { + + double logl = update_model_expectation(); + fprintf(stderr, "iter = %2d C = %d f = %10.7f train_err = %7.5f", iter, + C, logl, _train_error); + if (_heldout.size() > 0) { + double hlogl = heldout_likelihood(); + fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", hlogl, + _heldout_error); + } + cerr << endl; + + if (logl < pre_logl) { + C += 1; + _vl = pre_v; + iter--; + continue; + } + if (C > 1 && iter % 10 == 0) C--; + + pre_logl = logl; + pre_v = _vl; + for (int i = 0; i < _fb.Size(); i++) { + double coef = _vee[i] / _vme[i]; + _vl[i] += log(coef) / C; + } + } + cerr << endl; + + return 0; +} + +int ME_Model::perform_QUASI_NEWTON() { + const int dim = _fb.Size(); + vector<double> x0(dim); + + for (int i = 0; i < dim; i++) { + x0[i] = _vl[i]; + } + + vector<double> x; + if (_l1reg > 0) { + cerr << "performing OWLQN" << endl; + x = perform_OWLQN(x0, _l1reg); + } else { + cerr << "performing LBFGS" << endl; + x = perform_LBFGS(x0); + } + + for (int i = 0; i < dim; i++) { + _vl[i] = x[i]; + } + + return 0; +} + +int ME_Model::conditional_probability(const Sample& s, + std::vector<double>& membp) const { + // int num_classes = membp.size(); + double sum = 0; + int max_label = 0; + // double maxp = 0; + + vector<double> powv(_num_classes, 0.0); + for (vector<int>::const_iterator j = s.positive_features.begin(); + j != s.positive_features.end(); j++) { + for (vector<int>::const_iterator k = _feature2mef[*j].begin(); + k != _feature2mef[*j].end(); k++) { + powv[_fb.Feature(*k).label()] += _vl[*k]; + } + } + for (vector<pair<int, double> >::const_iterator j = s.rvfeatures.begin(); + j != s.rvfeatures.end(); j++) { + for (vector<int>::const_iterator k = _feature2mef[j->first].begin(); + k != _feature2mef[j->first].end(); k++) { + powv[_fb.Feature(*k).label()] += _vl[*k] * j->second; + } + } + + std::vector<double>::const_iterator pmax = + max_element(powv.begin(), powv.end()); + double offset = max(0.0, *pmax - 700); // to avoid overflow + for (int label = 0; label < _num_classes; label++) { + double pow = powv[label] - offset; + double prod = exp(pow); + // cout << pow << " " << prod << ", "; + // if (_ref_modelp != NULL) prod *= _train_refpd[n][label]; + if (_ref_modelp != NULL) prod *= s.ref_pd[label]; + assert(prod != 0); + membp[label] = prod; + sum += prod; + } + for (int label = 0; label < _num_classes; label++) { + membp[label] /= sum; + if (membp[label] > membp[max_label]) max_label = label; + } + return max_label; +} + +int ME_Model::make_feature_bag(const int cutoff) { + int max_num_features = 0; + +// count the occurrences of features +#ifdef USE_HASH_MAP + typedef std::unordered_map<unsigned int, int> map_type; +#else + typedef std::map<unsigned int, int> map_type; +#endif + map_type count; + if (cutoff > 0) { + for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end(); + i++) { + for (std::vector<int>::const_iterator j = i->positive_features.begin(); + j != i->positive_features.end(); j++) { + count[ME_Feature(i->label, *j).body()]++; + } + for (std::vector<pair<int, double> >::const_iterator j = + i->rvfeatures.begin(); + j != i->rvfeatures.end(); j++) { + count[ME_Feature(i->label, j->first).body()]++; + } + } + } + + int n = 0; + for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end(); + i++, n++) { + max_num_features = + max(max_num_features, (int)(i->positive_features.size())); + for (std::vector<int>::const_iterator j = i->positive_features.begin(); + j != i->positive_features.end(); j++) { + const ME_Feature feature(i->label, *j); + // if (cutoff > 0 && count[feature.body()] < cutoff) continue; + if (cutoff > 0 && count[feature.body()] <= cutoff) continue; + _fb.Put(feature); + // cout << i->label << "\t" << *j << "\t" << id << endl; + // feature2sample[id].push_back(n); + } + for (std::vector<pair<int, double> >::const_iterator j = + i->rvfeatures.begin(); + j != i->rvfeatures.end(); j++) { + const ME_Feature feature(i->label, j->first); + // if (cutoff > 0 && count[feature.body()] < cutoff) continue; + if (cutoff > 0 && count[feature.body()] <= cutoff) continue; + _fb.Put(feature); + } + } + count.clear(); + + // cerr << "num_classes = " << _num_classes << endl; + // cerr << "max_num_features = " << max_num_features << endl; + + init_feature2mef(); + + return max_num_features; +} + +double ME_Model::heldout_likelihood() { + double logl = 0; + int ncorrect = 0; + for (std::vector<Sample>::const_iterator i = _heldout.begin(); + i != _heldout.end(); i++) { + vector<double> membp(_num_classes); + int l = classify(*i, membp); + logl += log(membp[i->label]); + if (l == i->label) ncorrect++; + } + _heldout_error = 1 - (double)ncorrect / _heldout.size(); + + return logl /= _heldout.size(); +} + +double ME_Model::update_model_expectation() { + double logl = 0; + int ncorrect = 0; + + _vme.resize(_fb.Size()); + for (int i = 0; i < _fb.Size(); i++) _vme[i] = 0; + + int n = 0; + for (vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end(); + i++, n++) { + vector<double> membp(_num_classes); + int max_label = conditional_probability(*i, membp); + + logl += log(membp[i->label]); + // cout << membp[*i] << " " << logl << " "; + if (max_label == i->label) ncorrect++; + + // model_expectation + for (vector<int>::const_iterator j = i->positive_features.begin(); + j != i->positive_features.end(); j++) { + for (vector<int>::const_iterator k = _feature2mef[*j].begin(); + k != _feature2mef[*j].end(); k++) { + _vme[*k] += membp[_fb.Feature(*k).label()]; + } + } + for (vector<pair<int, double> >::const_iterator j = i->rvfeatures.begin(); + j != i->rvfeatures.end(); j++) { + for (vector<int>::const_iterator k = _feature2mef[j->first].begin(); + k != _feature2mef[j->first].end(); k++) { + _vme[*k] += membp[_fb.Feature(*k).label()] * j->second; + } + } + } + + for (int i = 0; i < _fb.Size(); i++) { + _vme[i] /= _vs.size(); + } + + _train_error = 1 - (double)ncorrect / _vs.size(); + + logl /= _vs.size(); + + if (_l2reg > 0) { + const double c = _l2reg; + for (int i = 0; i < _fb.Size(); i++) { + logl -= _vl[i] * _vl[i] * c; + } + } + + // logl /= _vs.size(); + + // fprintf(stderr, "iter =%3d logl = %10.7f train_acc = %7.5f\n", iter, + // logl, (double)ncorrect/train.size()); + // fprintf(stderr, "logl = %10.7f train_acc = %7.5f\n", logl, + // (double)ncorrect/_train.size()); + + return logl; +} + +int ME_Model::train(const vector<ME_Sample>& vms) { + _vs.clear(); + for (vector<ME_Sample>::const_iterator i = vms.begin(); i != vms.end(); i++) { + add_training_sample(*i); + } + + return train(); +} + +void ME_Model::add_training_sample(const ME_Sample& mes) { + Sample s; + s.label = _label_bag.Put(mes.label); + if (s.label > ME_Feature::MAX_LABEL_TYPES) { + cerr << "error: too many types of labels." << endl; + exit(1); + } + for (vector<string>::const_iterator j = mes.features.begin(); + j != mes.features.end(); j++) { + s.positive_features.push_back(_featurename_bag.Put(*j)); + } + for (vector<pair<string, double> >::const_iterator j = mes.rvfeatures.begin(); + j != mes.rvfeatures.end(); j++) { + s.rvfeatures.push_back( + pair<int, double>(_featurename_bag.Put(j->first), j->second)); + } + if (_ref_modelp != NULL) { + ME_Sample tmp = mes; + ; + s.ref_pd = _ref_modelp->classify(tmp); + } + // cout << s.label << "\t"; + // for (vector<int>::const_iterator j = s.positive_features.begin(); j != + // s.positive_features.end(); j++){ + // cout << *j << " "; + // } + // cout << endl; + + _vs.push_back(s); +} + +int ME_Model::train() { + if (_l1reg > 0 && _l2reg > 0) { + cerr << "error: L1 and L2 regularizers cannot be used simultaneously." + << endl; + return 0; + } + if (_vs.size() == 0) { + cerr << "error: no training data." << endl; + return 0; + } + if (_nheldout >= (int)_vs.size()) { + cerr << "error: too much heldout data. no training data is available." + << endl; + return 0; + } + // if (_nheldout > 0) random_shuffle(_vs.begin(), _vs.end()); + + int max_label = 0; + for (std::vector<Sample>::const_iterator i = _vs.begin(); i != _vs.end(); + i++) { + max_label = max(max_label, i->label); + } + _num_classes = max_label + 1; + if (_num_classes != _label_bag.Size()) { + cerr << "warning: _num_class != _label_bag.Size()" << endl; + } + + if (_ref_modelp != NULL) { + cerr << "setting reference distribution..."; + for (int i = 0; i < _ref_modelp->num_classes(); i++) { + _label_bag.Put(_ref_modelp->get_class_label(i)); + } + _num_classes = _label_bag.Size(); + for (vector<Sample>::iterator i = _vs.begin(); i != _vs.end(); i++) { + set_ref_dist(*i); + } + cerr << "done" << endl; + } + + for (int i = 0; i < _nheldout; i++) { + _heldout.push_back(_vs.back()); + _vs.pop_back(); + } + + sort(_vs.begin(), _vs.end()); + + int cutoff = 0; + if (cutoff > 0) cerr << "cutoff threshold = " << cutoff << endl; + if (_l1reg > 0) cerr << "L1 regularizer = " << _l1reg << endl; + if (_l2reg > 0) cerr << "L2 regularizer = " << _l2reg << endl; + + // normalize + _l1reg /= _vs.size(); + _l2reg /= _vs.size(); + + cerr << "preparing for estimation..."; + make_feature_bag(cutoff); + // _vs.clear(); + cerr << "done" << endl; + cerr << "number of samples = " << _vs.size() << endl; + cerr << "number of features = " << _fb.Size() << endl; + + cerr << "calculating empirical expectation..."; + _vee.resize(_fb.Size()); + for (int i = 0; i < _fb.Size(); i++) { + _vee[i] = 0; + } + for (int n = 0; n < (int)_vs.size(); n++) { + const Sample* i = &_vs[n]; + for (vector<int>::const_iterator j = i->positive_features.begin(); + j != i->positive_features.end(); j++) { + for (vector<int>::const_iterator k = _feature2mef[*j].begin(); + k != _feature2mef[*j].end(); k++) { + if (_fb.Feature(*k).label() == i->label) _vee[*k] += 1.0; + } + } + + for (vector<pair<int, double> >::const_iterator j = i->rvfeatures.begin(); + j != i->rvfeatures.end(); j++) { + for (vector<int>::const_iterator k = _feature2mef[j->first].begin(); + k != _feature2mef[j->first].end(); k++) { + if (_fb.Feature(*k).label() == i->label) _vee[*k] += j->second; + } + } + } + for (int i = 0; i < _fb.Size(); i++) { + _vee[i] /= _vs.size(); + } + cerr << "done" << endl; + + _vl.resize(_fb.Size()); + for (int i = 0; i < _fb.Size(); i++) _vl[i] = 0.0; + + if (_optimization_method == SGD) { + perform_SGD(); + } else { + perform_QUASI_NEWTON(); + } + + int num_active = 0; + for (int i = 0; i < _fb.Size(); i++) { + if (_vl[i] != 0) num_active++; + } + cerr << "number of active features = " << num_active << endl; + + return 0; +} + +void ME_Model::get_features(list<pair<pair<string, string>, double> >& fl) { + fl.clear(); + // for (int i = 0; i < _fb.Size(); i++) { + // ME_Feature f = _fb.Feature(i); + // fl.push_back( make_pair(make_pair(_label_bag.Str(f.label()), + // _featurename_bag.Str(f.feature())), _vl[i])); + // } + for (MiniStringBag::map_type::const_iterator i = _featurename_bag.begin(); + i != _featurename_bag.end(); i++) { + for (int j = 0; j < _label_bag.Size(); j++) { + string label = _label_bag.Str(j); + string history = i->first; + int id = _fb.Id(ME_Feature(j, i->second)); + if (id < 0) continue; + fl.push_back(make_pair(make_pair(label, history), _vl[id])); + } + } +} + +void ME_Model::clear() { + _vl.clear(); + _label_bag.Clear(); + _featurename_bag.Clear(); + _fb.Clear(); + _feature2mef.clear(); + _vee.clear(); + _vme.clear(); + _vs.clear(); + _heldout.clear(); +} + +bool ME_Model::load_from_file(const string& filename) { + FILE* fp = fopen(filename.c_str(), "r"); + if (!fp) { + cerr << "error: cannot open " << filename << "!" << endl; + return false; + } + + _vl.clear(); + _label_bag.Clear(); + _featurename_bag.Clear(); + _fb.Clear(); + char buf[1024]; + while (fgets(buf, 1024, fp)) { + string line(buf); + string::size_type t1 = line.find_first_of('\t'); + string::size_type t2 = line.find_last_of('\t'); + string classname = line.substr(0, t1); + string featurename = line.substr(t1 + 1, t2 - (t1 + 1)); + float lambda; + string w = line.substr(t2 + 1); + sscanf(w.c_str(), "%f", &lambda); + + int label = _label_bag.Put(classname); + int feature = _featurename_bag.Put(featurename); + _fb.Put(ME_Feature(label, feature)); + _vl.push_back(lambda); + } + + _num_classes = _label_bag.Size(); + + init_feature2mef(); + + fclose(fp); + + return true; +} + +void ME_Model::init_feature2mef() { + _feature2mef.clear(); + for (int i = 0; i < _featurename_bag.Size(); i++) { + vector<int> vi; + for (int k = 0; k < _num_classes; k++) { + int id = _fb.Id(ME_Feature(k, i)); + if (id >= 0) vi.push_back(id); + } + _feature2mef.push_back(vi); + } +} + +bool ME_Model::load_from_array(const ME_Model_Data data[]) { + _vl.clear(); + for (int i = 0;; i++) { + if (string(data[i].label) == "///") break; + int label = _label_bag.Put(data[i].label); + int feature = _featurename_bag.Put(data[i].feature); + _fb.Put(ME_Feature(label, feature)); + _vl.push_back(data[i].weight); + } + _num_classes = _label_bag.Size(); + + init_feature2mef(); + + return true; +} + +bool ME_Model::save_to_file(const string& filename, const double th) const { + FILE* fp = fopen(filename.c_str(), "w"); + if (!fp) { + cerr << "error: cannot open " << filename << "!" << endl; + return false; + } + + // for (int i = 0; i < _fb.Size(); i++) { + // if (_vl[i] == 0) continue; // ignore zero-weight features + // ME_Feature f = _fb.Feature(i); + // fprintf(fp, "%s\t%s\t%f\n", _label_bag.Str(f.label()).c_str(), + // _featurename_bag.Str(f.feature()).c_str(), _vl[i]); + // } + for (MiniStringBag::map_type::const_iterator i = _featurename_bag.begin(); + i != _featurename_bag.end(); i++) { + for (int j = 0; j < _label_bag.Size(); j++) { + string label = _label_bag.Str(j); + string history = i->first; + int id = _fb.Id(ME_Feature(j, i->second)); + if (id < 0) continue; + if (_vl[id] == 0) continue; // ignore zero-weight features + if (fabs(_vl[id]) < th) continue; // cut off low-weight features + fprintf(fp, "%s\t%s\t%f\n", label.c_str(), history.c_str(), _vl[id]); + } + } + + fclose(fp); + + return true; +} + +void ME_Model::set_ref_dist(Sample& s) const { + vector<double> v0 = s.ref_pd; + vector<double> v(_num_classes); + for (unsigned int i = 0; i < v.size(); i++) { + v[i] = 0; + string label = get_class_label(i); + int id_ref = _ref_modelp->get_class_id(label); + if (id_ref != -1) { + v[i] = v0[id_ref]; + } + if (v[i] == 0) v[i] = 0.001; // to avoid -inf logl + } + s.ref_pd = v; +} + +int ME_Model::classify(const Sample& nbs, vector<double>& membp) const { + // vector<double> membp(_num_classes); + assert(_num_classes == (int)membp.size()); + conditional_probability(nbs, membp); + int max_label = 0; + double max = 0.0; + for (int i = 0; i < (int)membp.size(); i++) { + // cout << membp[i] << " "; + if (membp[i] > max) { + max_label = i; + max = membp[i]; + } + } + // cout << endl; + return max_label; +} + +vector<double> ME_Model::classify(ME_Sample& mes) const { + Sample s; + for (vector<string>::const_iterator j = mes.features.begin(); + j != mes.features.end(); j++) { + int id = _featurename_bag.Id(*j); + if (id >= 0) s.positive_features.push_back(id); + } + for (vector<pair<string, double> >::const_iterator j = mes.rvfeatures.begin(); + j != mes.rvfeatures.end(); j++) { + int id = _featurename_bag.Id(j->first); + if (id >= 0) { + s.rvfeatures.push_back(pair<int, double>(id, j->second)); + } + } + if (_ref_modelp != NULL) { + s.ref_pd = _ref_modelp->classify(mes); + set_ref_dist(s); + } + + vector<double> vp(_num_classes); + int label = classify(s, vp); + mes.label = get_class_label(label); + return vp; +} + +/* + * $Log: maxent.cpp,v $ + * Revision 1.1.1.1 2007/05/15 08:30:35 kyoshida + * stepp tagger, by Okanohara and Tsuruoka + * + * Revision 1.28 2006/08/21 17:30:38 tsuruoka + * use MAX_LABEL_TYPES + * + * Revision 1.27 2006/07/25 13:19:53 tsuruoka + * sort _vs[] + * + * Revision 1.26 2006/07/18 11:13:15 tsuruoka + * modify comments + * + * Revision 1.25 2006/07/18 10:02:15 tsuruoka + * remove sample2feature[] + * speed up conditional_probability() + * + * Revision 1.24 2006/07/18 05:10:51 tsuruoka + * add ref_dist + * + * Revision 1.23 2005/12/24 07:05:32 tsuruoka + * modify conditional_probability() to avoid overflow + * + * Revision 1.22 2005/12/24 07:01:25 tsuruoka + * add cutoff for real-valued features + * + * Revision 1.21 2005/12/23 10:33:02 tsuruoka + * support real-valued features + * + * Revision 1.20 2005/12/23 09:15:29 tsuruoka + * modify _train to reduce memory consumption + * + * Revision 1.19 2005/10/28 13:10:14 tsuruoka + * fix for overflow (thanks to Ming Li) + * + * Revision 1.18 2005/10/28 13:03:07 tsuruoka + * add progress_bar + * + * Revision 1.17 2005/09/12 13:51:16 tsuruoka + * Sample: list -> vector + * + * Revision 1.16 2005/09/12 13:27:10 tsuruoka + * add add_training_sample() + * + * Revision 1.15 2005/04/27 11:22:27 tsuruoka + * bugfix + * ME_Sample: list -> vector + * + * Revision 1.14 2005/04/27 10:00:42 tsuruoka + * remove tmpfb + * + * Revision 1.13 2005/04/26 14:25:53 tsuruoka + * add MiniStringBag, USE_HASH_MAP + * + * Revision 1.12 2005/02/11 10:20:08 tsuruoka + * modify cutoff + * + * Revision 1.11 2004/10/04 05:50:25 tsuruoka + * add Clear() + * + * Revision 1.10 2004/08/26 16:52:26 tsuruoka + * fix load_from_file() + * + * Revision 1.9 2004/08/09 12:27:21 tsuruoka + * change messages + * + * Revision 1.8 2004/08/04 13:55:18 tsuruoka + * modify _sample2feature + * + * Revision 1.7 2004/07/28 13:42:58 tsuruoka + * add AGIS + * + * Revision 1.6 2004/07/28 05:54:13 tsuruoka + * get_class_name() -> get_class_label() + * ME_Feature: bugfix + * + * Revision 1.5 2004/07/27 16:58:47 tsuruoka + * modify the interface of classify() + * + * Revision 1.4 2004/07/26 17:23:46 tsuruoka + * _sample2feature: list -> vector + * + * Revision 1.3 2004/07/26 15:49:23 tsuruoka + * modify ME_Feature + * + * Revision 1.2 2004/07/26 13:52:18 tsuruoka + * modify cutoff + * + * Revision 1.1 2004/07/26 13:10:55 tsuruoka + * add files + * + * Revision 1.20 2004/07/22 08:34:45 tsuruoka + * modify _sample2feature[] + * + * Revision 1.19 2004/07/21 16:33:01 tsuruoka + * remove some comments + * + */ |