// training
double drwnMultiClassLogisticBase::train(const drwnClassifierDataset& dataset)
{
if (dataset.hasWeights()) {
return train(dataset.features, dataset.targets, dataset.weights);
} else {
return train(dataset.features, dataset.targets);
}
}
// training
double drwnDecisionTree::train(const drwnClassifierDataset& dataset)
{
if (dataset.hasWeights()) {
return train(dataset.features, dataset.targets, dataset.weights);
} else {
return train(dataset.features, dataset.targets);
}
}
// training
double drwnCompositeClassifier::train(const drwnClassifierDataset& dataset)
{
DRWN_FCN_TIC;
// compute label weights
vector<int> classCounts(_nClasses, 0);
int totalCount = 0;
for (int i = 0; i < dataset.size(); i++) {
if (dataset.targets[i] >= 0) {
classCounts[dataset.targets[i]] += 1;
totalCount += 1;
}
}
// learn binary classifiers
vector<double> weights(dataset.size(), 1.0);
vector<int> targets(dataset.size(), -1);
for (unsigned i = 0; i < _binaryClassifiers.size(); i++) {
delete _binaryClassifiers[i];
}
_binaryClassifiers.clear();
switch (_method) {
case DRWN_ONE_VS_ALL:
for (int k = 0; k < _nClasses; k++) {
if (classCounts[k] == 0) continue;
DRWN_LOG_VERBOSE("...training <class " << (k + 1) << ">-vs-all");
const double w1 = 1.0 / (double)classCounts[k];
const double w0 = 1.0 / (double)(totalCount - classCounts[k]);
for (int i = 0; i < dataset.size(); i++) {
targets[i] = (dataset.targets[i] == k ? 1 : 0);
weights[i] = (dataset.targets[i] == k ? w1 : w0);
}
drwnClassifier *c = drwnClassifierFactory::get().create(_baseClassifier.c_str());
c->initialize(_nFeatures, 2);
c->train(dataset.features, targets, weights);
_binaryClassifiers.push_back(c);
}
break;
case DRWN_ONE_VS_ONE:
for (int k = 0; k < _nClasses; k++) {
if (classCounts[k] == 0) continue;
for (int l = 0; l < k; l++) {
if (classCounts[l] == 0) continue;
DRWN_LOG_VERBOSE("...training <class " << (k + 1) << ">-vs-<" << " class " << (l + 1) << ">");
const double w1 = 1.0 / (double)classCounts[k];
const double w0 = 1.0 / (double)classCounts[l];
for (int i = 0; i < dataset.size(); i++) {
if ((dataset.targets[i] != k) && (dataset.targets[i] != l)) {
targets[i] = -1;
} else {
targets[i] = (dataset.targets[i] == k ? 1 : 0);
weights[i] = (dataset.targets[i] == k ? w1 : w0);
}
}
drwnClassifier *c = drwnClassifierFactory::get().create(_baseClassifier.c_str());
c->initialize(_nFeatures, 2);
c->train(dataset.features, targets, weights);
_binaryClassifiers.push_back(c);
}
}
break;
default:
DRWN_LOG_FATAL("unknown method in drwnCompositeClassifier::train");
}
// clear some memory
targets.clear();
weights.clear();
// learn calibration weights
vector<double> f(2);
vector<vector<double> > features(dataset.size(), vector<double>(_binaryClassifiers.size(), 0));
for (int i = 0; i < dataset.size(); i++) {
for (unsigned j = 0; j < _binaryClassifiers.size(); j++) {
_binaryClassifiers[j]->getClassScores(dataset.features[i], f);
features[i][j] = f[0] - f[1];
}
}
DRWN_LOG_VERBOSE("whitening feature vectors...");
_featureWhitener.train(features);
_featureWhitener.transform(features);
// train multi-class logistic model
DRWN_LOG_VERBOSE("learning calibration weights...");
_calibrationWeights.initialize(_binaryClassifiers.size(), _nClasses);
if (dataset.hasWeights()) {
_calibrationWeights.train(features, dataset.targets, dataset.weights);
} else {
_calibrationWeights.train(features, dataset.targets);
}
_bValid = true;
DRWN_FCN_TOC;
return 0.0;
}
// training
double drwnBoostedClassifier::train(const drwnClassifierDataset& dataset)
{
DRWN_FCN_TIC;
// pre-compute sorted feature index
vector<vector<int> > sortIndex;
drwnDecisionTree::computeSortedFeatureIndex(dataset.features, sortIndex);
// allocate weights
vector<double> weights;
if (dataset.hasWeights()) {
weights = dataset.weights;
} else {
weights.resize(dataset.size(), 1.0);
}
// mark unknown samples
drwnBitArray sampleIndex(dataset.size());
sampleIndex.ones();
for (int i = 0; i < dataset.size(); i++) {
if (dataset.targets[i] < 0) {
sampleIndex.clear(i);
weights[i] = 0.0;
}
}
vector<int> predicted(dataset.size(), -1);
// iterate over rounds
DRWN_START_PROGRESS("training", _numRounds);
for (int i = 0; i < _numRounds; i++) {
DRWN_LOG_STATUS("training boosted classifier round " << i << " of " << _numRounds << "...");
DRWN_INC_PROGRESS;
// normalize the sample weights
Eigen::Map<VectorXd>(&weights[0], weights.size()) /=
Eigen::Map<VectorXd>(&weights[0], weights.size()).sum();
// learn a weak-learner with current weights
drwnDecisionTree *tree = new drwnDecisionTree(_nFeatures, _nClasses);
tree->setProperty(tree->findProperty("maxDepth"), _maxDepth);
tree->learnDecisionTree(dataset.features, dataset.targets,
weights, sortIndex, sampleIndex);
// predict classes and calculate eplison
double epsilon = 0.0;
for (int j = 0; j < dataset.size(); j++) {
if (dataset.targets[j] < 0) continue;
predicted[j] = tree->getClassification(dataset.features[j]);
if (predicted[j] != dataset.targets[j]) {
epsilon += weights[j];
}
}
if (epsilon >= 1.0 - 1.0 / _nClasses) {
DRWN_LOG_WARNING("boosting terminated at round "
<< (i + 1) << " of " << _numRounds);
delete tree;
break;
}
// check for perfect classification
if ((i == 0) && (epsilon == 0.0)) {
DRWN_LOG_WARNING("boosting found a perfect classifier in first round");
_alphas.push_back(1.0);
_weakLearners.push_back(tree);
break;
}
// calculate boosting coefficient
double alpha;
if (_method == DRWN_BOOST_GENTLE) {
alpha = 1.0;
} else {
alpha = log((1.0 - epsilon) / epsilon) + log(_nClasses - 1.0);
if (!isfinite(alpha)) {
DRWN_LOG_WARNING("boosting terminated at round "
<< (i + 1) << " of " << _numRounds << " (non-finite alpha)");
delete tree;
break;
}
}
_alphas.push_back(alpha);
_weakLearners.push_back(tree);
// update the sample weights
const double nu = exp(alpha);
for (unsigned j = 0; j < weights.size(); j++) {
if (predicted[j] != dataset.targets[j]) {
weights[j] *= nu;
}
}
}
DRWN_END_PROGRESS;
#if 0
// normalize boosting coefficients
Eigen::Map<VectorXd>(&_alphas[0], _alphas.size()) /=
Eigen::Map<VectorXd>(&_alphas[0], _alphas.size()).sum();
#endif
_bValid = true;
// return classification accuracy
double totalCorrect = 0.0;
double totalWeight = 0.0;
for (int j = 0; j < dataset.size(); j++) {
if (dataset.targets[j] < 0) continue;
predicted[j] = this->getClassification(dataset.features[j]);
if (predicted[j] == dataset.targets[j]) {
totalCorrect += dataset.hasWeights() ? dataset.weights[j] : 1.0;
}
totalWeight += dataset.hasWeights() ? dataset.weights[j] : 1.0;
}
DRWN_FCN_TOC;
return (totalWeight > 0.0) ? totalCorrect / totalWeight : 1.0;
}