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Diffstat (limited to 'utils/sgd.cpp')
| -rw-r--r-- | utils/sgd.cpp | 193 |
1 files changed, 193 insertions, 0 deletions
diff --git a/utils/sgd.cpp b/utils/sgd.cpp new file mode 100644 index 00000000..8613edca --- /dev/null +++ b/utils/sgd.cpp @@ -0,0 +1,193 @@ +#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; +} |