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#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;
}
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