From f762dbbf10a8204d0d0b82e9acb29feacd3b3bb4 Mon Sep 17 00:00:00 2001 From: "Wu, Ke" Date: Tue, 7 Oct 2014 17:12:32 -0400 Subject: Import synutils --- utils/synutils/maxent-3.0/Makefile.am | 10 + utils/synutils/maxent-3.0/lbfgs.cpp | 110 ++++++ utils/synutils/maxent-3.0/lbfgs.h | 21 + utils/synutils/maxent-3.0/mathvec.h | 97 +++++ utils/synutils/maxent-3.0/maxent.cpp | 698 ++++++++++++++++++++++++++++++++++ utils/synutils/maxent-3.0/maxent.h | 395 +++++++++++++++++++ utils/synutils/maxent-3.0/owlqn.cpp | 137 +++++++ utils/synutils/maxent-3.0/sgd.cpp | 180 +++++++++ 8 files changed, 1648 insertions(+) create mode 100644 utils/synutils/maxent-3.0/Makefile.am create mode 100644 utils/synutils/maxent-3.0/lbfgs.cpp create mode 100644 utils/synutils/maxent-3.0/lbfgs.h create mode 100644 utils/synutils/maxent-3.0/mathvec.h create mode 100644 utils/synutils/maxent-3.0/maxent.cpp create mode 100644 utils/synutils/maxent-3.0/maxent.h create mode 100644 utils/synutils/maxent-3.0/owlqn.cpp create mode 100644 utils/synutils/maxent-3.0/sgd.cpp (limited to 'utils/synutils/maxent-3.0') diff --git a/utils/synutils/maxent-3.0/Makefile.am b/utils/synutils/maxent-3.0/Makefile.am new file mode 100644 index 00000000..64bb038c --- /dev/null +++ b/utils/synutils/maxent-3.0/Makefile.am @@ -0,0 +1,10 @@ +noinst_LIBRARIES = libtsuruoka_maxent.a + +libtsuruoka_maxent_a_SOURCES = \ + lbfgs.cpp \ + maxent.cpp \ + owlqn.cpp \ + sgd.cpp + +AM_CPPFLAGS = -W -Wall + diff --git a/utils/synutils/maxent-3.0/lbfgs.cpp b/utils/synutils/maxent-3.0/lbfgs.cpp new file mode 100644 index 00000000..9eb04bef --- /dev/null +++ b/utils/synutils/maxent-3.0/lbfgs.cpp @@ -0,0 +1,110 @@ +#include +#include +#include +#include +#include "mathvec.h" +#include "lbfgs.h" +#include "maxent.h" + +using namespace std; + +const static int M = LBFGS_M; +const static double LINE_SEARCH_ALPHA = 0.1; +const static double LINE_SEARCH_BETA = 0.5; + +// stopping criteria +int LBFGS_MAX_ITER = 300; +const static double MIN_GRAD_NORM = 0.0001; + + +double +ME_Model::backtracking_line_search( + const Vec & x0, const Vec & grad0, const double f0, + const Vec & dx, Vec & x, Vec & grad1) +{ + double t = 1.0 / LINE_SEARCH_BETA; + + double f; + do { + t *= LINE_SEARCH_BETA; + x = x0 + t * dx; + f = FunctionGradient(x.STLVec(), grad1.STLVec()); + // cout << "*"; + } while (f > f0 + LINE_SEARCH_ALPHA * t * dot_product(dx, grad0)); + + return f; +} + +// +// Jorge Nocedal, "Updating Quasi-Newton Matrices With Limited Storage", +// Mathematics of Computation, Vol. 35, No. 151, pp. 773-782, 1980. +// +Vec +approximate_Hg(const int iter, const Vec & grad, + const Vec s[], const Vec y[], const double z[]) +{ + int offset, bound; + if (iter <= M) { offset = 0; bound = iter; } + else { offset = iter - M; bound = M; } + + Vec q = grad; + double alpha[M], beta[M]; + for (int i = bound - 1; i >= 0; i--) { + const int j = (i + offset) % M; + alpha[i] = z[j] * dot_product(s[j], q); + q += -alpha[i] * y[j]; + } + if (iter > 0) { + const int j = (iter - 1) % M; + const double gamma = ((1.0 / z[j]) / dot_product(y[j], y[j])); + // static double gamma; + // if (gamma == 0) gamma = ((1.0 / z[j]) / dot_product(y[j], y[j])); + q *= gamma; + } + for (int i = 0; i <= bound - 1; i++) { + const int j = (i + offset) % M; + beta[i] = z[j] * dot_product(y[j], q); + q += s[j] * (alpha[i] - beta[i]); + } + + return q; +} + +vector +ME_Model::perform_LBFGS(const vector & x0) +{ + const size_t dim = x0.size(); + Vec x = x0; + + Vec grad(dim), dx(dim); + double f = FunctionGradient(x.STLVec(), grad.STLVec()); + + Vec s[M], y[M]; + double z[M]; // rho + + for (int iter = 0; iter < LBFGS_MAX_ITER; iter++) { + + fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter+1, -f, _train_error); + if (_nheldout > 0) { + const double heldout_logl = heldout_likelihood(); + fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl, _heldout_error); + } + fprintf(stderr, "\n"); + + if (sqrt(dot_product(grad, grad)) < MIN_GRAD_NORM) break; + + dx = -1 * approximate_Hg(iter, grad, s, y, z); + + Vec x1(dim), grad1(dim); + f = backtracking_line_search(x, grad, f, dx, x1, grad1); + + s[iter % M] = x1 - x; + y[iter % M] = grad1 - grad; + z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]); + x = x1; + grad = grad1; + } + + return x.STLVec(); +} + diff --git a/utils/synutils/maxent-3.0/lbfgs.h b/utils/synutils/maxent-3.0/lbfgs.h new file mode 100644 index 00000000..113919a3 --- /dev/null +++ b/utils/synutils/maxent-3.0/lbfgs.h @@ -0,0 +1,21 @@ +#ifndef _LBFGS_H_ +#define _LBFGS_H_ + +//template +//std::vector +//perform_LBFGS(FuncGrad func_grad, const std::vector & x0); + +std::vector +perform_LBFGS(double (*func_grad)(const std::vector &, std::vector &), + const std::vector & x0); + + +std::vector +perform_OWLQN(double (*func_grad)(const std::vector &, std::vector &), + const std::vector & x0, + const double C); + +//const int LBFGS_M = 7; +const int LBFGS_M = 10; + +#endif diff --git a/utils/synutils/maxent-3.0/mathvec.h b/utils/synutils/maxent-3.0/mathvec.h new file mode 100644 index 00000000..4ec82797 --- /dev/null +++ b/utils/synutils/maxent-3.0/mathvec.h @@ -0,0 +1,97 @@ +#ifndef _MATH_VECTOR_H_ +#define _MATH_VECTOR_H_ + +#include +#include +#include + +class Vec +{ +private: + std::vector _v; +public: + Vec(const size_t n = 0, const double val = 0) { _v.resize(n, val); } + Vec(const std::vector & v) : _v(v) {} + const std::vector & STLVec() const { return _v; } + std::vector & STLVec() { return _v; } + size_t Size() const { return _v.size(); } + double & operator[](int i) { return _v[i]; } + const double & operator[](int i) const { return _v[i]; } + Vec & operator+=(const Vec & b) { + assert(b.Size() == _v.size()); + for (size_t i = 0; i < _v.size(); i++) { + _v[i] += b[i]; + } + return *this; + } + Vec & operator*=(const double c) { + for (size_t i = 0; i < _v.size(); i++) { + _v[i] *= c; + } + return *this; + } + void Project(const Vec & y) { + for (size_t i = 0; i < _v.size(); i++) { + // if (sign(_v[i]) != sign(y[i])) _v[i] = 0; + if (_v[i] * y[i] <=0) _v[i] = 0; + } + } +}; + +inline double dot_product(const Vec & a, const Vec & b) +{ + double sum = 0; + for (size_t i = 0; i < a.Size(); i++) { + sum += a[i] * b[i]; + } + return sum; +} + +inline std::ostream & operator<<(std::ostream & s, const Vec & a) +{ + s << "("; + for (size_t i = 0; i < a.Size(); i++) { + if (i != 0) s << ", "; + s << a[i]; + } + s << ")"; + return s; +} + +inline const Vec operator+(const Vec & a, const Vec & b) +{ + Vec v(a.Size()); + assert(a.Size() == b.Size()); + for (size_t i = 0; i < a.Size(); i++) { + v[i] = a[i] + b[i]; + } + return v; +} + +inline const Vec operator-(const Vec & a, const Vec & b) +{ + Vec v(a.Size()); + assert(a.Size() == b.Size()); + for (size_t i = 0; i < a.Size(); i++) { + v[i] = a[i] - b[i]; + } + return v; +} + +inline const Vec operator*(const Vec & a, const double c) +{ + Vec v(a.Size()); + for (size_t i = 0; i < a.Size(); i++) { + v[i] = a[i] * c; + } + return v; +} + +inline const Vec operator*(const double c, const Vec & a) +{ + return a * c; +} + + + +#endif diff --git a/utils/synutils/maxent-3.0/maxent.cpp b/utils/synutils/maxent-3.0/maxent.cpp new file mode 100644 index 00000000..feb0efdc --- /dev/null +++ b/utils/synutils/maxent-3.0/maxent.cpp @@ -0,0 +1,698 @@ +/* + * $Id: maxent.cpp,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $ + */ + +#include "maxent.h" +#include +#include +#include "lbfgs.h" + +using namespace std; + +double +ME_Model::FunctionGradient(const vector & x, vector & 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 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 x0(dim); + + for (int i = 0; i < dim; i++) { x0[i] = _vl[i]; } + + vector 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 & membp) const +{ + //int num_classes = membp.size(); + double sum = 0; + int max_label = -1; + // double maxp = 0; + + vector powv(_num_classes, 0.0); + for (vector::const_iterator j = s.positive_features.begin(); j != s.positive_features.end(); j++){ + for (vector::const_iterator k = _feature2mef[*j].begin(); k != _feature2mef[*j].end(); k++) { + powv[_fb.Feature(*k).label()] += _vl[*k]; + } + } + for (vector >::const_iterator j = s.rvfeatures.begin(); j != s.rvfeatures.end(); j++) { + for (vector::const_iterator k = _feature2mef[j->first].begin(); k != _feature2mef[j->first].end(); k++) { + powv[_fb.Feature(*k).label()] += _vl[*k] * j->second; + } + } + + std::vector::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; + } + assert(max_label >= 0); + 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 __gnu_cxx::hash_map map_type; +#else + typedef std::map map_type; +#endif + map_type count; + if (cutoff > 0) { + for (std::vector::const_iterator i = _vs.begin(); i != _vs.end(); i++) { + for (std::vector::const_iterator j = i->positive_features.begin(); j != i->positive_features.end(); j++) { + count[ME_Feature(i->label, *j).body()]++; + } + for (std::vector >::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::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::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 >::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::const_iterator i = _heldout.begin(); i != _heldout.end(); i++) { + vector 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::const_iterator i = _vs.begin(); i != _vs.end(); i++, n++) { + vector 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::const_iterator j = i->positive_features.begin(); j != i->positive_features.end(); j++){ + for (vector::const_iterator k = _feature2mef[*j].begin(); k != _feature2mef[*j].end(); k++) { + _vme[*k] += membp[_fb.Feature(*k).label()]; + } + } + for (vector >::const_iterator j = i->rvfeatures.begin(); j != i->rvfeatures.end(); j++) { + for (vector::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 & vms) +{ + _vs.clear(); + for (vector::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::const_iterator j = mes.features.begin(); j != mes.features.end(); j++) { + s.positive_features.push_back(_featurename_bag.Put(*j)); + } + for (vector >::const_iterator j = mes.rvfeatures.begin(); j != mes.rvfeatures.end(); j++) { + s.rvfeatures.push_back(pair(_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::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::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::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::const_iterator j = i->positive_features.begin(); j != i->positive_features.end(); j++){ + for (vector::const_iterator k = _feature2mef[*j].begin(); k != _feature2mef[*j].end(); k++) { + if (_fb.Feature(*k).label() == i->label) _vee[*k] += 1.0; + } + } + + for (vector >::const_iterator j = i->rvfeatures.begin(); j != i->rvfeatures.end(); j++) { + for (vector::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, 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 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 v0 = s.ref_pd; + vector 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 & membp) const +{ + // vector 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 +ME_Model::classify(ME_Sample & mes) const +{ + Sample s; + for (vector::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 >::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(id, j->second)); + } + } + if (_ref_modelp != NULL) { + s.ref_pd = _ref_modelp->classify(mes); + set_ref_dist(s); + } + + vector 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 + * + */ + diff --git a/utils/synutils/maxent-3.0/maxent.h b/utils/synutils/maxent-3.0/maxent.h new file mode 100644 index 00000000..a4391ead --- /dev/null +++ b/utils/synutils/maxent-3.0/maxent.h @@ -0,0 +1,395 @@ +/* + * $Id: maxent.h,v 1.1.1.1 2007/05/15 08:30:35 kyoshida Exp $ + */ + +#ifndef __MAXENT_H_ +#define __MAXENT_H_ + +#include +#include +#include +#include +#include +#include +#include +#include +#include "mathvec.h" + +#define USE_HASH_MAP // if you encounter errors with hash, try commenting out this line. (the program will be a bit slower, though) +#ifdef USE_HASH_MAP +#include +#endif + +// +// data format for each sample for training/testing +// +struct ME_Sample +{ +public: + ME_Sample() : label("") {}; + ME_Sample(const std::string & l) : label(l) {}; + void set_label(const std::string & l) { label = l; } + + // to add a binary feature + void add_feature(const std::string & f) { + features.push_back(f); + } + + // to add a real-valued feature + void add_feature(const std::string & s, const double d) { + rvfeatures.push_back(std::pair(s, d)); + } + +public: + std::string label; + std::vector features; + std::vector > rvfeatures; + + // obsolete + void add_feature(const std::pair & f) { + rvfeatures.push_back(f); // real-valued features + } +}; + + +// +// for those who want to use load_from_array() +// +typedef struct ME_Model_Data +{ + char * label; + char * feature; + double weight; +} ME_Model_Data; + + +class ME_Model +{ +public: + + void add_training_sample(const ME_Sample & s); + int train(); + std::vector classify(ME_Sample & s) const; + bool load_from_file(const std::string & filename); + bool save_to_file(const std::string & filename, const double th = 0) const; + int num_classes() const { return _num_classes; } + std::string get_class_label(int i) const { return _label_bag.Str(i); } + int get_class_id(const std::string & s) const { return _label_bag.Id(s); } + void get_features(std::list< std::pair< std::pair, double> > & fl); + void set_heldout(const int h, const int n = 0) { _nheldout = h; _early_stopping_n = n; }; + void use_l1_regularizer(const double v) { _l1reg = v; } + void use_l2_regularizer(const double v) { _l2reg = v; } + void use_SGD(int iter = 30, double eta0 = 1, double alpha = 0.85) { + _optimization_method = SGD; + SGD_ITER = iter; SGD_ETA0 = eta0; SGD_ALPHA = alpha; + } + bool load_from_array(const ME_Model_Data data[]); + void set_reference_model(const ME_Model & ref_model) { _ref_modelp = &ref_model; }; + void clear(); + + ME_Model() { + _l1reg = _l2reg = 0; + _nheldout = 0; + _early_stopping_n = 0; + _ref_modelp = NULL; + _optimization_method = LBFGS; + } + +public: + // obsolete. just for downward compatibility + int train(const std::vector & train); + +private: + + enum OPTIMIZATION_METHOD { LBFGS, OWLQN, SGD } _optimization_method; + // OWLQN and SGD are available only for L1-regularization + + int SGD_ITER; + double SGD_ETA0; + double SGD_ALPHA; + + double _l1reg, _l2reg; + + struct Sample { + int label; + std::vector positive_features; + std::vector > rvfeatures; + std::vector ref_pd; // reference probability distribution + bool operator<(const Sample & x) const { + for (unsigned int i = 0; i < positive_features.size(); i++) { + if (i >= x.positive_features.size()) return false; + int v0 = positive_features[i]; + int v1 = x.positive_features[i]; + if (v0 < v1) return true; + if (v0 > v1) return false; + } + return false; + } + }; + + struct ME_Feature + { + enum { MAX_LABEL_TYPES = 255 }; + + // ME_Feature(const int l, const int f) : _body((l << 24) + f) { + // assert(l >= 0 && l < 256); + // assert(f >= 0 && f <= 0xffffff); + // }; + // int label() const { return _body >> 24; } + // int feature() const { return _body & 0xffffff; } + ME_Feature(const int l, const int f) : _body((f << 8) + l) { + assert(l >= 0 && l <= MAX_LABEL_TYPES); + assert(f >= 0 && f <= 0xffffff); + }; + int label() const { return _body & 0xff; } + int feature() const { return _body >> 8; } + unsigned int body() const { return _body; } + private: + unsigned int _body; + }; + + struct ME_FeatureBag + { +#ifdef USE_HASH_MAP + typedef __gnu_cxx::hash_map map_type; +#else + typedef std::map map_type; +#endif + map_type mef2id; + std::vector id2mef; + int Put(const ME_Feature & i) { + map_type::const_iterator j = mef2id.find(i.body()); + if (j == mef2id.end()) { + int id = id2mef.size(); + id2mef.push_back(i); + mef2id[i.body()] = id; + return id; + } + return j->second; + } + int Id(const ME_Feature & i) const { + map_type::const_iterator j = mef2id.find(i.body()); + if (j == mef2id.end()) { + return -1; + } + return j->second; + } + ME_Feature Feature(int id) const { + assert(id >= 0 && id < (int)id2mef.size()); + return id2mef[id]; + } + int Size() const { + return id2mef.size(); + } + void Clear() { + mef2id.clear(); + id2mef.clear(); + } + }; + + struct hashfun_str + { + size_t operator()(const std::string& s) const { + assert(sizeof(int) == 4 && sizeof(char) == 1); + const int* p = reinterpret_cast(s.c_str()); + size_t v = 0; + int n = s.size() / 4; + for (int i = 0; i < n; i++, p++) { + // v ^= *p; + v ^= *p << (4 * (i % 2)); // note) 0 <= char < 128 + } + int m = s.size() % 4; + for (int i = 0; i < m; i++) { + v ^= s[4 * n + i] << (i * 8); + } + return v; + } + }; + + struct MiniStringBag + { +#ifdef USE_HASH_MAP + typedef __gnu_cxx::hash_map map_type; +#else + typedef std::map map_type; +#endif + int _size; + map_type str2id; + MiniStringBag() : _size(0) {} + int Put(const std::string & i) { + map_type::const_iterator j = str2id.find(i); + if (j == str2id.end()) { + int id = _size; + _size++; + str2id[i] = id; + return id; + } + return j->second; + } + int Id(const std::string & i) const { + map_type::const_iterator j = str2id.find(i); + if (j == str2id.end()) return -1; + return j->second; + } + int Size() const { return _size; } + void Clear() { str2id.clear(); _size = 0; } + map_type::const_iterator begin() const { return str2id.begin(); } + map_type::const_iterator end() const { return str2id.end(); } + }; + + struct StringBag : public MiniStringBag + { + std::vector id2str; + int Put(const std::string & i) { + map_type::const_iterator j = str2id.find(i); + if (j == str2id.end()) { + int id = id2str.size(); + id2str.push_back(i); + str2id[i] = id; + return id; + } + return j->second; + } + std::string Str(const int id) const { + assert(id >= 0 && id < (int)id2str.size()); + return id2str[id]; + } + int Size() const { return id2str.size(); } + void Clear() { + str2id.clear(); + id2str.clear(); + } + }; + + std::vector _vs; // vector of training_samples + StringBag _label_bag; + MiniStringBag _featurename_bag; + std::vector _vl; // vector of lambda + ME_FeatureBag _fb; + int _num_classes; + std::vector _vee; // empirical expectation + std::vector _vme; // empirical expectation + std::vector< std::vector< int > > _feature2mef; + std::vector< Sample > _heldout; + double _train_error; // current error rate on the training data + double _heldout_error; // current error rate on the heldout data + int _nheldout; + int _early_stopping_n; + std::vector _vhlogl; + const ME_Model * _ref_modelp; + + double heldout_likelihood(); + int conditional_probability(const Sample & nbs, std::vector & membp) const; + int make_feature_bag(const int cutoff); + int classify(const Sample & nbs, std::vector & membp) const; + double update_model_expectation(); + int perform_QUASI_NEWTON(); + int perform_SGD(); + int perform_GIS(int C); + std::vector perform_LBFGS(const std::vector & x0); + std::vector perform_OWLQN(const std::vector & x0, const double C); + double backtracking_line_search(const Vec & x0, const Vec & grad0, const double f0, const Vec & dx, Vec & x, Vec & grad1); + double regularized_func_grad(const double C, const Vec & x, Vec & grad); + double constrained_line_search(double C, const Vec & x0, const Vec & grad0, const double f0, const Vec & dx, Vec & x, Vec & grad1); + + + void set_ref_dist(Sample & s) const; + void init_feature2mef(); + + double FunctionGradient(const std::vector & x, std::vector & grad); + static double FunctionGradientWrapper(const std::vector & x, std::vector & grad); + +}; + + +#endif + + +/* + * $Log: maxent.h,v $ + * Revision 1.1.1.1 2007/05/15 08:30:35 kyoshida + * stepp tagger, by Okanohara and Tsuruoka + * + * Revision 1.24 2006/08/21 17:30:38 tsuruoka + * use MAX_LABEL_TYPES + * + * Revision 1.23 2006/07/25 13:19:53 tsuruoka + * sort _vs[] + * + * Revision 1.22 2006/07/18 11:13:15 tsuruoka + * modify comments + * + * Revision 1.21 2006/07/18 10:02:15 tsuruoka + * remove sample2feature[] + * speed up conditional_probability() + * + * Revision 1.20 2006/07/18 05:10:51 tsuruoka + * add ref_dist + * + * Revision 1.19 2005/12/23 10:33:02 tsuruoka + * support real-valued features + * + * Revision 1.18 2005/12/23 09:15:29 tsuruoka + * modify _train to reduce memory consumption + * + * Revision 1.17 2005/10/28 13:02:34 tsuruoka + * set_heldout(): add default value + * Feature() + * + * Revision 1.16 2005/09/12 13:51:16 tsuruoka + * Sample: list -> vector + * + * Revision 1.15 2005/09/12 13:27:10 tsuruoka + * add add_training_sample() + * + * Revision 1.14 2005/04/27 11:22:27 tsuruoka + * bugfix + * ME_Sample: list -> vector + * + * Revision 1.13 2005/04/27 10:20:19 tsuruoka + * MiniStringBag -> StringBag + * + * Revision 1.12 2005/04/27 10:00:42 tsuruoka + * remove tmpfb + * + * Revision 1.11 2005/04/26 14:25:53 tsuruoka + * add MiniStringBag, USE_HASH_MAP + * + * Revision 1.10 2004/10/04 05:50:25 tsuruoka + * add Clear() + * + * Revision 1.9 2004/08/09 12:27:21 tsuruoka + * change messages + * + * Revision 1.8 2004/08/04 13:55:19 tsuruoka + * modify _sample2feature + * + * Revision 1.7 2004/07/29 05:51:13 tsuruoka + * remove modeldata.h + * + * Revision 1.6 2004/07/28 13:42:58 tsuruoka + * add AGIS + * + * Revision 1.5 2004/07/28 05:54:14 tsuruoka + * get_class_name() -> get_class_label() + * ME_Feature: bugfix + * + * Revision 1.4 2004/07/27 16:58:47 tsuruoka + * modify the interface of classify() + * + * Revision 1.3 2004/07/26 17:23:46 tsuruoka + * _sample2feature: list -> vector + * + * Revision 1.2 2004/07/26 15:49:23 tsuruoka + * modify ME_Feature + * + * Revision 1.1 2004/07/26 13:10:55 tsuruoka + * add files + * + * Revision 1.18 2004/07/22 08:34:45 tsuruoka + * modify _sample2feature[] + * + * Revision 1.17 2004/07/21 16:33:01 tsuruoka + * remove some comments + * + */ diff --git a/utils/synutils/maxent-3.0/owlqn.cpp b/utils/synutils/maxent-3.0/owlqn.cpp new file mode 100644 index 00000000..7b2cea7d --- /dev/null +++ b/utils/synutils/maxent-3.0/owlqn.cpp @@ -0,0 +1,137 @@ +#include +#include +#include +#include +#include "mathvec.h" +#include "lbfgs.h" +#include "maxent.h" + +using namespace std; + +const static int M = LBFGS_M; +const static double LINE_SEARCH_ALPHA = 0.1; +const static double LINE_SEARCH_BETA = 0.5; + +// stopping criteria +int OWLQN_MAX_ITER = 300; +const static double MIN_GRAD_NORM = 0.0001; + + +Vec approximate_Hg(const int iter, const Vec & grad, + const Vec s[], const Vec y[], const double z[]); + + +inline int sign(double x) +{ + if (x > 0) return 1; + if (x < 0) return -1; + return 0; +}; + +static Vec +pseudo_gradient(const Vec & x, const Vec & grad0, const double C) +{ + Vec grad = grad0; + for (size_t i = 0; i < x.Size(); i++) { + if (x[i] != 0) { + grad[i] += C * sign(x[i]); + continue; + } + const double gm = grad0[i] - C; + if (gm > 0) { + grad[i] = gm; + continue; + } + const double gp = grad0[i] + C; + if (gp < 0) { + grad[i] = gp; + continue; + } + grad[i] = 0; + } + + return grad; +} + +double +ME_Model::regularized_func_grad(const double C, const Vec & x, Vec & grad) +{ + double f = FunctionGradient(x.STLVec(), grad.STLVec()); + for (size_t i = 0; i < x.Size(); i++) { + f += C * fabs(x[i]); + } + + return f; +} + +double +ME_Model::constrained_line_search(double C, + const Vec & x0, const Vec & grad0, const double f0, + const Vec & dx, Vec & x, Vec & grad1) +{ + // compute the orthant to explore + Vec orthant = x0; + for (size_t i = 0; i < orthant.Size(); i++) { + if (orthant[i] == 0) orthant[i] = -grad0[i]; + } + + double t = 1.0 / LINE_SEARCH_BETA; + + double f; + do { + t *= LINE_SEARCH_BETA; + x = x0 + t * dx; + x.Project(orthant); + // for (size_t i = 0; i < x.Size(); i++) { + // if (x0[i] != 0 && sign(x[i]) != sign(x0[i])) x[i] = 0; + // } + + f = regularized_func_grad(C, x, grad1); + // cout << "*"; + } while (f > f0 + LINE_SEARCH_ALPHA * dot_product(x - x0, grad0)); + + return f; +} + +vector +ME_Model::perform_OWLQN(const vector & x0, const double C) +{ + const size_t dim = x0.size(); + Vec x = x0; + + Vec grad(dim), dx(dim); + double f = regularized_func_grad(C, x, grad); + + Vec s[M], y[M]; + double z[M]; // rho + + for (int iter = 0; iter < OWLQN_MAX_ITER; iter++) { + Vec pg = pseudo_gradient(x, grad, C); + + fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter+1, -f, _train_error); + if (_nheldout > 0) { + const double heldout_logl = heldout_likelihood(); + fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl, _heldout_error); + } + fprintf(stderr, "\n"); + + if (sqrt(dot_product(pg, pg)) < MIN_GRAD_NORM) break; + + dx = -1 * approximate_Hg(iter, pg, s, y, z); + if (dot_product(dx, pg) >= 0) + dx.Project(-1 * pg); + + Vec x1(dim), grad1(dim); + f = constrained_line_search(C, x, pg, f, dx, x1, grad1); + + s[iter % M] = x1 - x; + y[iter % M] = grad1 - grad; + z[iter % M] = 1.0 / dot_product(y[iter % M], s[iter % M]); + + x = x1; + grad = grad1; + } + + return x.STLVec(); +} + diff --git a/utils/synutils/maxent-3.0/sgd.cpp b/utils/synutils/maxent-3.0/sgd.cpp new file mode 100644 index 00000000..6d28c23f --- /dev/null +++ b/utils/synutils/maxent-3.0/sgd.cpp @@ -0,0 +1,180 @@ +#include "maxent.h" +#include +#include + +using namespace std; + +//const double SGD_ETA0 = 1; +//const double SGD_ITER = 30; +//const double SGD_ALPHA = 0.85; + + +//#define FOLOS_NAIVE +//#define FOLOS_LAZY +#define SGD_CP + +inline void +apply_l1_penalty(const int i, const double u, + vector & _vl, vector & q) +{ + double & w = _vl[i]; + const double z = w; + double & qi = q[i]; + if (w > 0) { + w = max(0.0, w - (u + qi)); + } else if (w < 0) { + w = min(0.0, w + (u - qi)); + } + qi += w - z; +} + +static double +l1norm(const vector& v) +{ + double sum = 0; + for (size_t i = 0; i < v.size(); i++) sum += abs(v[i]); + return sum; +} + +inline void +update_folos_lazy(const int iter_sample, + const int k, vector & _vl, const vector & sum_eta, + vector & last_updated) +{ + const double penalty = sum_eta[iter_sample] - sum_eta[last_updated[k]]; + double & x = _vl[k]; + if (x > 0) x = max(0.0, x - penalty); + else x = min(0.0, x + penalty); + last_updated[k] = iter_sample; +} + +int +ME_Model::perform_SGD() +{ + if (_l2reg > 0) { + cerr << "error: L2 regularization is currently not supported in SGD mode." << endl; + exit(1); + } + + cerr << "performing SGD" << endl; + + const double l1param = _l1reg; + + const int d = _fb.Size(); + + vector ri(_vs.size()); + for (size_t i = 0; i < ri.size(); i++) ri[i] = i; + + vector grad(d); + int iter_sample = 0; + const double eta0 = SGD_ETA0; + + // cerr << "l1param = " << l1param << endl; + cerr << "eta0 = " << eta0 << " alpha = " << SGD_ALPHA << endl; + + double u = 0; + vector q(d, 0); + vector last_updated(d, 0); + vector sum_eta; + sum_eta.push_back(0); + + for (int iter = 0; iter < SGD_ITER; iter++) { + + random_shuffle(ri.begin(), ri.end()); + + double logl = 0; + int ncorrect = 0, ntotal = 0; + for (size_t i = 0; i < _vs.size(); i++, ntotal++, iter_sample++) { + const Sample & s = _vs[ri[i]]; + +#ifdef FOLOS_LAZY + for (vector::const_iterator j = s.positive_features.begin(); j != s.positive_features.end(); j++){ + for (vector::const_iterator k = _feature2mef[*j].begin(); k != _feature2mef[*j].end(); k++) { + update_folos_lazy(iter_sample, *k, _vl, sum_eta, last_updated); + } + } +#endif + + vector membp(_num_classes); + const int max_label = conditional_probability(s, membp); + + const double eta = eta0 * pow(SGD_ALPHA, (double)iter_sample / _vs.size()); // exponential decay + // const double eta = eta0 / (1.0 + (double)iter_sample / _vs.size()); + + // if (iter_sample % _vs.size() == 0) cerr << "eta = " << eta << endl; + u += eta * l1param; + + sum_eta.push_back(sum_eta.back() + eta * l1param); + + logl += log(membp[s.label]); + if (max_label == s.label) ncorrect++; + + // binary features + for (vector::const_iterator j = s.positive_features.begin(); j != s.positive_features.end(); j++){ + for (vector::const_iterator k = _feature2mef[*j].begin(); k != _feature2mef[*j].end(); k++) { + const double me = membp[_fb.Feature(*k).label()]; + const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0); + const double grad = (me - ee); + _vl[*k] -= eta * grad; +#ifdef SGD_CP + apply_l1_penalty(*k, u, _vl, q); +#endif + } + } + // real-valued features + for (vector >::const_iterator j = s.rvfeatures.begin(); j != s.rvfeatures.end(); j++) { + for (vector::const_iterator k = _feature2mef[j->first].begin(); k != _feature2mef[j->first].end(); k++) { + const double me = membp[_fb.Feature(*k).label()]; + const double ee = (_fb.Feature(*k).label() == s.label ? 1.0 : 0); + const double grad = (me - ee) * j->second; + _vl[*k] -= eta * grad; +#ifdef SGD_CP + apply_l1_penalty(*k, u, _vl, q); +#endif + } + } + +#ifdef FOLOS_NAIVE + for (size_t j = 0; j < d; j++) { + double & x = _vl[j]; + if (x > 0) x = max(0.0, x - eta * l1param); + else x = min(0.0, x + eta * l1param); + } +#endif + + } + logl /= _vs.size(); + // fprintf(stderr, "%4d logl = %8.3f acc = %6.4f ", iter, logl, (double)ncorrect / ntotal); + +#ifdef FOLOS_LAZY + if (l1param > 0) { + for (size_t j = 0; j < d; j++) + update_folos_lazy(iter_sample, j, _vl, sum_eta, last_updated); + } +#endif + + double f = logl; + if (l1param > 0) { + const double l1 = l1norm(_vl); // this is not accurate when lazy update is used + // cerr << "f0 = " << update_model_expectation() - l1param * l1 << " "; + f -= l1param * l1; + int nonzero = 0; + for (int j = 0; j < d; j++) if (_vl[j] != 0) nonzero++; + // cerr << " f = " << f << " l1 = " << l1 << " nonzero_features = " << nonzero << endl; + } + // fprintf(stderr, "%4d obj = %7.3f acc = %6.4f", iter+1, f, (double)ncorrect/ntotal); + // fprintf(stderr, "%4d obj = %f", iter+1, f); + fprintf(stderr, "%3d obj(err) = %f (%6.4f)", iter+1, f, 1 - (double)ncorrect/ntotal); + + if (_nheldout > 0) { + double heldout_logl = heldout_likelihood(); + // fprintf(stderr, " heldout_logl = %f acc = %6.4f\n", heldout_logl, 1 - _heldout_error); + fprintf(stderr, " heldout_logl(err) = %f (%6.4f)", heldout_logl, _heldout_error); + } + fprintf(stderr, "\n"); + + + } + + return 0; +} -- cgit v1.2.3