--- /dev/null
+
+///////////////////////////////////////////////////////////////////////////
+// This program is free software: you can redistribute it and/or modify //
+// it under the terms of the version 3 of the GNU General Public License //
+// as published by the Free Software Foundation. //
+// //
+// This program is distributed in the hope that it will be useful, but //
+// WITHOUT ANY WARRANTY; without even the implied warranty of //
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU //
+// General Public License for more details. //
+// //
+// You should have received a copy of the GNU General Public License //
+// along with this program. If not, see <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret, (C) IDIAP //
+// Contact <francois.fleuret@idiap.ch> for comments & bug reports //
+///////////////////////////////////////////////////////////////////////////
+
+#include "tools.h"
+#include "loss_machine.h"
+
+LossMachine::LossMachine(int loss_type) {
+ _loss_type = loss_type;
+}
+
+void LossMachine::get_loss_derivatives(SampleSet *samples,
+ scalar_t *responses,
+ scalar_t *derivatives) {
+
+ switch(_loss_type) {
+
+ case LOSS_EXPONENTIAL:
+ {
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ derivatives[n] =
+ - samples->label(n) * exp( - samples->label(n) * responses[n]);
+ }
+ }
+ break;
+
+ case LOSS_EV_REGULARIZED:
+ {
+ scalar_t sum_pos = 0, sum_sq_pos = 0, nb_pos = 0, m_pos, v_pos;
+ scalar_t sum_neg = 0, sum_sq_neg = 0, nb_neg = 0, m_neg, v_neg;
+
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) > 0) {
+ sum_pos += responses[n];
+ sum_sq_pos += sq(responses[n]);
+ nb_pos += 1.0;
+ }
+ else if(samples->label(n) < 0) {
+ sum_neg += responses[n];
+ sum_sq_neg += sq(responses[n]);
+ nb_neg += 1.0;
+ }
+ }
+
+ m_pos = sum_pos / nb_pos;
+ v_pos = sum_sq_pos/(nb_pos - 1) - sq(sum_pos)/(nb_pos * (nb_pos - 1));
+
+ scalar_t loss_pos = nb_pos * exp(v_pos/2 - m_pos);
+
+ m_neg = sum_neg / nb_neg;
+ v_neg = sum_sq_neg/(nb_neg - 1) - sq(sum_neg)/(nb_neg * (nb_neg - 1));
+
+ scalar_t loss_neg = nb_neg * exp(v_neg/2 + m_neg);
+
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) > 0) {
+ derivatives[n] =
+ ( - 1/nb_pos + (responses[n] - m_pos)/(nb_pos - 1)) * loss_pos;
+ } else if(samples->label(n) < 0) {
+ derivatives[n] =
+ ( 1/nb_neg + (responses[n] - m_neg)/(nb_neg - 1)) * loss_neg;
+ }
+ }
+ }
+
+ break;
+
+ case LOSS_HINGE:
+ {
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) != 0 && samples->label(n) * responses[n] < 1)
+ derivatives[n] = 1;
+ else
+ derivatives[n] = 0;
+ }
+ }
+ break;
+
+ case LOSS_LOGISTIC:
+ {
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) == 0)
+ derivatives[n] = 0.0;
+ else
+ derivatives[n] = samples->label(n) * 1/(1 + exp(samples->label(n) * responses[n]));
+ }
+ }
+ break;
+
+ default:
+ cerr << "Unknown loss type in BoostedClassifier::get_loss_derivatives."
+ << endl;
+ exit(1);
+ }
+
+}
+
+scalar_t LossMachine::loss(SampleSet *samples, scalar_t *responses) {
+ scalar_t l = 0;
+
+ switch(_loss_type) {
+
+ case LOSS_EXPONENTIAL:
+ {
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ l += exp( - samples->label(n) * responses[n]);
+ ASSERT(!isinf(l));
+ }
+ }
+ break;
+
+ case LOSS_EV_REGULARIZED:
+ {
+ scalar_t sum_pos = 0, sum_sq_pos = 0, nb_pos = 0, m_pos, v_pos;
+ scalar_t sum_neg = 0, sum_sq_neg = 0, nb_neg = 0, m_neg, v_neg;
+
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) > 0) {
+ sum_pos += responses[n];
+ sum_sq_pos += sq(responses[n]);
+ nb_pos += 1.0;
+ } else if(samples->label(n) < 0) {
+ sum_neg += responses[n];
+ sum_sq_neg += sq(responses[n]);
+ nb_neg += 1.0;
+ }
+ }
+
+ l = 0;
+
+ if(nb_pos > 0) {
+ m_pos = sum_pos / nb_pos;
+ v_pos = sum_sq_pos/(nb_pos - 1) - sq(sum_pos)/(nb_pos * (nb_pos - 1));
+ l += nb_pos * exp(v_pos/2 - m_pos);
+ }
+
+ if(nb_neg > 0) {
+ m_neg = sum_neg / nb_neg;
+ v_neg = sum_sq_neg/(nb_neg - 1) - sq(sum_neg)/(nb_neg * (nb_neg - 1));
+ l += nb_neg * exp(v_neg/2 + m_neg);
+ }
+
+ }
+ break;
+
+ case LOSS_HINGE:
+ {
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) != 0) {
+ if(samples->label(n) * responses[n] < 1)
+ l += (1 - samples->label(n) * responses[n]);
+ }
+ }
+ }
+ break;
+
+ case LOSS_LOGISTIC:
+ {
+ for(int n = 0; n < samples->nb_samples(); n++) {
+ if(samples->label(n) != 0) {
+ scalar_t u = - samples->label(n) * responses[n];
+ if(u > 20) {
+ l += u;
+ } if(u > -20) {
+ l += log(1 + exp(u));
+ }
+ }
+ }
+ }
+ break;
+
+ default:
+ cerr << "Unknown loss type in LossMachine::loss." << endl;
+ exit(1);
+ }
+
+ return l;
+}
+
+scalar_t LossMachine::optimal_weight(SampleSet *sample_set,
+ scalar_t *weak_learner_responses,
+ scalar_t *current_responses) {
+
+ switch(_loss_type) {
+
+ case LOSS_EXPONENTIAL:
+ {
+ scalar_t num = 0, den = 0, z;
+ for(int n = 0; n < sample_set->nb_samples(); n++) {
+ z = sample_set->label(n) * weak_learner_responses[n];
+ if(z > 0) {
+ num += exp( - sample_set->label(n) * current_responses[n]);
+ } else if(z < 0) {
+ den += exp( - sample_set->label(n) * current_responses[n]);
+ }
+ }
+
+ return 0.5 * log(num / den);
+ }
+ break;
+
+ case LOSS_EV_REGULARIZED:
+ {
+
+ scalar_t u = 0, du = -0.1;
+ scalar_t *responses = new scalar_t[sample_set->nb_samples()];
+
+ scalar_t l, prev_l = -1;
+
+ const scalar_t minimum_delta_for_optimization = 1e-5;
+
+ scalar_t shift = 0;
+
+ {
+ scalar_t sum_pos = 0, sum_sq_pos = 0, nb_pos = 0, m_pos, v_pos;
+ scalar_t sum_neg = 0, sum_sq_neg = 0, nb_neg = 0, m_neg, v_neg;
+
+ for(int n = 0; n < sample_set->nb_samples(); n++) {
+ if(sample_set->label(n) > 0) {
+ sum_pos += responses[n];
+ sum_sq_pos += sq(responses[n]);
+ nb_pos += 1.0;
+ } else if(sample_set->label(n) < 0) {
+ sum_neg += responses[n];
+ sum_sq_neg += sq(responses[n]);
+ nb_neg += 1.0;
+ }
+ }
+
+ if(nb_pos > 0) {
+ m_pos = sum_pos / nb_pos;
+ v_pos = sum_sq_pos/(nb_pos - 1) - sq(sum_pos)/(nb_pos * (nb_pos - 1));
+ shift = max(shift, v_pos/2 - m_pos);
+ }
+
+ if(nb_neg > 0) {
+ m_neg = sum_neg / nb_neg;
+ v_neg = sum_sq_neg/(nb_neg - 1) - sq(sum_neg)/(nb_neg * (nb_neg - 1));
+ shift = max(shift, v_neg/2 + m_neg);
+ }
+
+// (*global.log_stream) << "nb_pos = " << nb_pos << " nb_neg = " << nb_neg << endl;
+
+ }
+
+ int nb = 0;
+
+ while(nb < 100 && abs(du) > minimum_delta_for_optimization) {
+ nb++;
+
+// (*global.log_stream) << "l = " << l << " u = " << u << " du = " << du << endl;
+
+ u += du;
+ for(int s = 0; s < sample_set->nb_samples(); s++) {
+ responses[s] = current_responses[s] + u * weak_learner_responses[s] ;
+ }
+
+ {
+ scalar_t sum_pos = 0, sum_sq_pos = 0, nb_pos = 0, m_pos, v_pos;
+ scalar_t sum_neg = 0, sum_sq_neg = 0, nb_neg = 0, m_neg, v_neg;
+
+ for(int n = 0; n < sample_set->nb_samples(); n++) {
+ if(sample_set->label(n) > 0) {
+ sum_pos += responses[n];
+ sum_sq_pos += sq(responses[n]);
+ nb_pos += 1.0;
+ } else if(sample_set->label(n) < 0) {
+ sum_neg += responses[n];
+ sum_sq_neg += sq(responses[n]);
+ nb_neg += 1.0;
+ }
+ }
+
+ l = 0;
+
+ if(nb_pos > 0) {
+ m_pos = sum_pos / nb_pos;
+ v_pos = sum_sq_pos/(nb_pos - 1) - sq(sum_pos)/(nb_pos * (nb_pos - 1));
+ l += nb_pos * exp(v_pos/2 - m_pos - shift);
+ }
+
+ if(nb_neg > 0) {
+ m_neg = sum_neg / nb_neg;
+ v_neg = sum_sq_neg/(nb_neg - 1) - sq(sum_neg)/(nb_neg * (nb_neg - 1));
+ l += nb_neg * exp(v_neg/2 + m_neg - shift);
+ }
+
+ }
+
+ if(l > prev_l) du = du * -0.25;
+ prev_l = l;
+ }
+
+ delete[] responses;
+
+ return u;
+ }
+
+ case LOSS_HINGE:
+ case LOSS_LOGISTIC:
+ {
+
+ scalar_t u = 0, du = -0.1;
+ scalar_t *responses = new scalar_t[sample_set->nb_samples()];
+
+ scalar_t l, prev_l = -1;
+
+ const scalar_t minimum_delta_for_optimization = 1e-5;
+
+ int n = 0;
+ while(n < 100 && abs(du) > minimum_delta_for_optimization) {
+ n++;
+ u += du;
+ for(int s = 0; s < sample_set->nb_samples(); s++) {
+ responses[s] = current_responses[s] + u * weak_learner_responses[s] ;
+ }
+ l = loss(sample_set, responses);
+ if(l > prev_l) du = du * -0.25;
+ prev_l = l;
+ }
+
+ (*global.log_stream) << "END l = " << l << " du = " << du << endl;
+
+ delete[] responses;
+
+ return u;
+ }
+
+ default:
+ cerr << "Unknown loss type in LossMachine::optimal_weight." << endl;
+ exit(1);
+ }
+
+}
+
+void LossMachine::subsample(int nb, scalar_t *labels, scalar_t *responses,
+ int nb_to_sample, int *sample_nb_occurences, scalar_t *sample_responses,
+ int allow_duplicates) {
+
+ switch(_loss_type) {
+
+ case LOSS_EXPONENTIAL:
+ {
+ scalar_t *weights = new scalar_t[nb];
+
+ for(int n = 0; n < nb; n++) {
+ if(labels[n] == 0) {
+ weights[n] = 0;
+ } else {
+ weights[n] = exp( - labels[n] * responses[n]);
+ }
+ sample_nb_occurences[n] = 0;
+ sample_responses[n] = 0.0;
+ }
+
+ scalar_t total_weight;
+ int nb_sampled = 0, sum_sample_nb_occurences = 0;
+
+ int *sampled_indexes = new int[nb_to_sample];
+
+ (*global.log_stream) << "Sampling " << nb_to_sample << " samples." << endl;
+
+ do {
+ total_weight = robust_sampling(nb,
+ weights,
+ nb_to_sample,
+ sampled_indexes);
+
+ for(int k = 0; nb_sampled < nb_to_sample && k < nb_to_sample; k++) {
+ int i = sampled_indexes[k];
+ if(allow_duplicates || sample_nb_occurences[i] == 0) nb_sampled++;
+ sample_nb_occurences[i]++;
+ sum_sample_nb_occurences++;
+ }
+ } while(nb_sampled < nb_to_sample);
+
+ (*global.log_stream) << "nb_sampled = " << nb_sampled << " nb_to_sample = " << nb_to_sample << endl;
+
+ (*global.log_stream) << "Done." << endl;
+
+ delete[] sampled_indexes;
+
+ scalar_t unit_weight = log(total_weight / scalar_t(sum_sample_nb_occurences));
+
+ for(int n = 0; n < nb; n++) {
+ if(sample_nb_occurences[n] > 0) {
+ if(allow_duplicates) {
+ sample_responses[n] = - labels[n] * unit_weight;
+ } else {
+ sample_responses[n] = - labels[n] * (unit_weight + log(scalar_t(sample_nb_occurences[n])));
+ sample_nb_occurences[n] = 1;
+ }
+ }
+ }
+
+ delete[] weights;
+
+ }
+ break;
+
+ default:
+ cerr << "Unknown loss type in LossMachine::resample." << endl;
+ exit(1);
+ }
+
+
+}
projects / folded-ctf.git / blobdiff