diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/Makefile.am | 5 | ||||
| -rw-r--r-- | utils/lbfgs.cpp | 108 | ||||
| -rw-r--r-- | utils/lbfgs.h | 20 | ||||
| -rw-r--r-- | utils/mathvec.h | 87 | ||||
| -rw-r--r-- | utils/maxent.cpp | 427 | ||||
| -rw-r--r-- | utils/maxent.h | 95 | ||||
| -rw-r--r-- | utils/owlqn.cpp | 127 | ||||
| -rw-r--r-- | utils/sgd.cpp | 193 |
8 files changed, 511 insertions, 551 deletions
diff --git a/utils/Makefile.am b/utils/Makefile.am index fabb4454..e0221e64 100644 --- a/utils/Makefile.am +++ b/utils/Makefile.am @@ -38,11 +38,8 @@ libutils_a_SOURCES = \ have_64_bits.h \ indices_after.h \ kernel_string_subseq.h \ - lbfgs.h \ - lbfgs.cpp \ logval.h \ m.h \ - mathvec.h \ maxent.h \ maxent.cpp \ murmur_hash3.h \ @@ -50,8 +47,6 @@ libutils_a_SOURCES = \ named_enum.h \ null_deleter.h \ null_traits.h \ - owlqn.cpp \ - sgd.cpp \ perfect_hash.h \ prob.h \ sampler.h \ diff --git a/utils/lbfgs.cpp b/utils/lbfgs.cpp deleted file mode 100644 index bd26f048..00000000 --- a/utils/lbfgs.cpp +++ /dev/null @@ -1,108 +0,0 @@ -#include <vector> -#include <iostream> -#include <cmath> -#include <stdio.h> -#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<double> ME_Model::perform_LBFGS(const vector<double>& 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/lbfgs.h b/utils/lbfgs.h deleted file mode 100644 index 4d706f7a..00000000 --- a/utils/lbfgs.h +++ /dev/null @@ -1,20 +0,0 @@ -#ifndef _LBFGS_H_ -#define _LBFGS_H_ - -#include <vector> - -// template<class FuncGrad> -// std::vector<double> -// perform_LBFGS(FuncGrad func_grad, const std::vector<double> & x0); - -std::vector<double> perform_LBFGS( - double (*func_grad)(const std::vector<double> &, std::vector<double> &), - const std::vector<double> &x0); - -std::vector<double> perform_OWLQN( - double (*func_grad)(const std::vector<double> &, std::vector<double> &), - const std::vector<double> &x0, const double C); - -const int LBFGS_M = 10; - -#endif diff --git a/utils/mathvec.h b/utils/mathvec.h deleted file mode 100644 index f8c60e5d..00000000 --- a/utils/mathvec.h +++ /dev/null @@ -1,87 +0,0 @@ -#ifndef _MATH_VECTOR_H_ -#define _MATH_VECTOR_H_ - -#include <vector> -#include <iostream> -#include <cassert> - -class Vec { - private: - std::vector<double> _v; - - public: - Vec(const size_t n = 0, const double val = 0) { _v.resize(n, val); } - Vec(const std::vector<double>& v) : _v(v) {} - const std::vector<double>& STLVec() const { return _v; } - std::vector<double>& 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/maxent.cpp b/utils/maxent.cpp index 0f49ee9d..fd772e08 100644 --- a/utils/maxent.cpp +++ b/utils/maxent.cpp @@ -3,12 +3,15 @@ */ #include "maxent.h" + +#include <vector> +#include <iostream> #include <cmath> #include <cstdio> -#include "lbfgs.h" using namespace std; +namespace maxent { double ME_Model::FunctionGradient(const vector<double>& x, vector<double>& grad) { assert((int)_fb.Size() == x.size()); @@ -601,6 +604,428 @@ vector<double> ME_Model::classify(ME_Sample& mes) const { return vp; } +// template<class FuncGrad> +// std::vector<double> +// perform_LBFGS(FuncGrad func_grad, const std::vector<double> & x0); + +std::vector<double> perform_LBFGS( + double (*func_grad)(const std::vector<double> &, std::vector<double> &), + const std::vector<double> &x0); + +std::vector<double> perform_OWLQN( + double (*func_grad)(const std::vector<double> &, std::vector<double> &), + const std::vector<double> &x0, const double C); + +const int LBFGS_M = 10; + +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; + +// LBFGS + +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<double> ME_Model::perform_LBFGS(const vector<double>& 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(); +} + +// OWLQN + +// stopping criteria +int OWLQN_MAX_ITER = 300; + +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<double> ME_Model::perform_OWLQN(const vector<double>& 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(); +} + +// SGD + +// 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<double>& _vl, + vector<double>& 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<double>& 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<double>& _vl, + const vector<double>& sum_eta, + vector<int>& 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<int> ri(_vs.size()); + for (size_t i = 0; i < ri.size(); i++) ri[i] = i; + + vector<double> 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<double> q(d, 0); + vector<int> last_updated(d, 0); + vector<double> 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<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++) { + update_folos_lazy(iter_sample, *k, _vl, sum_eta, last_updated); + } + } +#endif + + vector<double> 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<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++) { + 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<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++) { + 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; +} + +} // namespace maxent + /* * $Log: maxent.cpp,v $ * Revision 1.1.1.1 2007/05/15 08:30:35 kyoshida diff --git a/utils/maxent.h b/utils/maxent.h index b1efd88e..74d13a6f 100644 --- a/utils/maxent.h +++ b/utils/maxent.h @@ -5,21 +5,95 @@ #ifndef __MAXENT_H_ #define __MAXENT_H_ -#include <string> -#include <vector> -#include <list> -#include <map> #include <algorithm> #include <iostream> +#include <list> +#include <map> #include <string> +#include <unordered_map> +#include <vector> + #include <cassert> -#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 <unordered_map> -#endif +namespace maxent { +class Vec { + private: + std::vector<double> _v; + + public: + Vec(const size_t n = 0, const double val = 0) { _v.resize(n, val); } + Vec(const std::vector<double>& v) : _v(v) {} + const std::vector<double>& STLVec() const { return _v; } + std::vector<double>& 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; } // // data format for each sample for training/testing @@ -309,6 +383,7 @@ class ME_Model { static double FunctionGradientWrapper(const std::vector<double>& x, std::vector<double>& grad); }; +} // namespace maxent #endif diff --git a/utils/owlqn.cpp b/utils/owlqn.cpp deleted file mode 100644 index c3a0f0da..00000000 --- a/utils/owlqn.cpp +++ /dev/null @@ -1,127 +0,0 @@ -#include <vector> -#include <iostream> -#include <cmath> -#include <stdio.h> -#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<double> ME_Model::perform_OWLQN(const vector<double>& 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/sgd.cpp b/utils/sgd.cpp deleted file mode 100644 index 8613edca..00000000 --- a/utils/sgd.cpp +++ /dev/null @@ -1,193 +0,0 @@ -#include "maxent.h" -#include <cmath> -#include <stdio.h> - -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<double>& _vl, - vector<double>& 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<double>& 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<double>& _vl, - const vector<double>& sum_eta, - vector<int>& 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<int> ri(_vs.size()); - for (size_t i = 0; i < ri.size(); i++) ri[i] = i; - - vector<double> 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<double> q(d, 0); - vector<int> last_updated(d, 0); - vector<double> 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<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++) { - update_folos_lazy(iter_sample, *k, _vl, sum_eta, last_updated); - } - } -#endif - - vector<double> 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<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++) { - 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<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++) { - 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; -} |