bool Rank::train (ISamples &samples,
ssi_size_t stream_index) {
if (!_model) {
ssi_wrn ("a model has not been set yet");
return false;
}
release ();
_n_scores = samples.getStream (stream_index).dim;
_scores = new score[_n_scores];
Evaluation eval;
Trainer trainer (_model, stream_index);
SSI_DBG (SSI_LOG_LEVEL_DEBUG, "evaluate dimensions:");
for (ssi_size_t ndim = 0; ndim < _n_scores; ndim++) {
ISSelectDim samples_s (&samples);
samples_s.setSelection (stream_index, 1, &ndim);
if (_options.loo) {
eval.evalLOO (&trainer, samples_s);
} else if (_options.louo) {
eval.evalLOUO (&trainer, samples_s);
} else {
eval.evalKFold (&trainer, samples_s, _options.kfold);
}
_scores[ndim].index = ndim;
_scores[ndim].value = eval.get_classwise_prob ();
SSI_DBG (SSI_LOG_LEVEL_DEBUG, " #%02u -> %.2f", _scores[ndim].index, _scores[ndim].value);
}
trainer.release ();
return true;
}
bool FeatureFusion::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_streams = samples.getStreamSize ();
_n_classes = samples.getClassSize ();
_n_models = n_models;
//initialize weights
ssi_real_t **weights = new ssi_real_t*[n_models];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
weights[n_model] = new ssi_real_t[_n_classes+1];
}
if (samples.hasMissingData ()) {
_handle_md = true;
ISMissingData samples_h (&samples);
Evaluation eval;
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nMissing data detected.\n");
}
//models[0] is featfuse_model, followed by singlechannel_models
ISMergeDim ffusionSamples (&samples);
ISMissingData ffusionSamples_h (&ffusionSamples);
ffusionSamples_h.setStream(0);
if (!models[0]->isTrained ()) { models[0]->train (ffusionSamples_h, 0); }
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
eval.eval (*models[0], ffusionSamples_h, 0);
eval.print();
}
//dummy weights for fused model
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
weights[0][n_class] = 0.0f;
}
weights[0][_n_classes] = 0.0f;
for (ssi_size_t n_model = 1; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
samples_h.setStream (n_model - 1);
models[n_model]->train (samples_h, n_model - 1);
}
eval.eval (*models[n_model], samples_h, n_model - 1);
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
eval.print();
}
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
weights[n_model][n_class] = eval.get_class_prob (n_class);
}
weights[n_model][_n_classes] = eval.get_classwise_prob ();
}
//calculate fillers
_filler = new ssi_size_t[_n_streams];
for (ssi_size_t n_fill = 0; n_fill < _n_streams; n_fill++) {
_filler[n_fill] = 1;
ssi_real_t filler_weight = weights[1][_n_classes];
for (ssi_size_t n_model = 2; n_model < n_models; n_model++) {
if (filler_weight < weights[n_model][_n_classes]) {
_filler[n_fill] = n_model;
filler_weight = weights[n_model][_n_classes];
}
}
weights[_filler[n_fill]][_n_classes] = 0.0f;
}
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nfiller:\n");
for (ssi_size_t n_model = 0; n_model < _n_streams; n_model++) {
ssi_print("%d ", _filler[n_model]);
}ssi_print("\n");
}
}
else{
_handle_md = false;
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nNo missing data detected.\n");
}
ISMergeDim ffusionSamples (&samples);
if (!models[0]->isTrained ()) { models[0]->train (ffusionSamples, 0); }
//dummy
_filler = new ssi_size_t[_n_streams];
for (ssi_size_t n_fill = 0; n_fill < _n_streams; n_fill++) {
_filler[n_fill] = 0;
}
}
if (weights) {
for (ssi_size_t n_model = 0; n_model < _n_models; n_model++) {
delete[] weights[n_model];
}
delete[] weights;
weights = 0;
}
return true;
}
bool WeightedMajorityVoting::train (ssi_size_t n_models,
IModel **models,
ISamples &samples) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (samples.getStreamSize () != n_models) {
ssi_wrn ("#models (%u) differs from #streams (%u)", n_models, samples.getStreamSize ());
return false;
}
if (isTrained ()) {
ssi_wrn ("already trained");
return false;
}
_n_streams = samples.getStreamSize ();
_n_classes = samples.getClassSize ();
_n_models = n_models;
_weights = new ssi_real_t*[n_models];
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
_weights[n_model] = new ssi_real_t[_n_classes+1];
}
if (samples.hasMissingData ()) {
ISMissingData samples_h (&samples);
Evaluation eval;
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) {
samples_h.setStream (n_model);
models[n_model]->train (samples_h, n_model);
}
eval.eval (*models[n_model], samples_h, n_model);
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
_weights[n_model][n_class] = eval.get_class_prob (n_class);
}
_weights[n_model][_n_classes] = eval.get_classwise_prob ();
}
}
else{
Evaluation eval;
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
if (!models[n_model]->isTrained ()) { models[n_model]->train (samples, n_model); }
eval.eval (*models[n_model], samples, n_model);
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
_weights[n_model][n_class] = eval.get_class_prob (n_class);
}
_weights[n_model][_n_classes] = eval.get_classwise_prob ();
}
}
if (ssi_log_level >= SSI_LOG_LEVEL_DEBUG) {
ssi_print("\nClassifier Weights: \n");
for (ssi_size_t n_model = 0; n_model < n_models; n_model++) {
for (ssi_size_t n_class = 0; n_class < _n_classes; n_class++) {
ssi_print ("%f ", _weights[n_model][n_class]);
}
ssi_print ("%f\n", _weights[n_model][_n_classes]);
}
}
return true;
}
