@Override
protected void execImpl() {
Vec va = null, vp = null, avp = null;
try {
if (classification) {
// Create a new vectors - it is cheap since vector are only adaptation vectors
va = vactual.toEnum(); // always returns TransfVec
actual_domain = va._domain;
vp = vpredict.toEnum(); // always returns TransfVec
predicted_domain = vp._domain;
if (!Arrays.equals(actual_domain, predicted_domain)) {
domain = Utils.domainUnion(actual_domain, predicted_domain);
int[][] vamap = Model.getDomainMapping(domain, actual_domain, true);
va = TransfVec.compose((TransfVec) va, vamap, domain, false); // delete original va
int[][] vpmap = Model.getDomainMapping(domain, predicted_domain, true);
vp = TransfVec.compose((TransfVec) vp, vpmap, domain, false); // delete original vp
} else domain = actual_domain;
// The vectors are from different groups => align them, but properly delete it after
// computation
if (!va.group().equals(vp.group())) {
avp = vp;
vp = va.align(vp);
}
cm = new CM(domain.length).doAll(va, vp)._cm;
} else {
mse = new CM(1).doAll(vactual, vpredict).mse();
}
return;
} finally { // Delete adaptation vectors
if (va != null) UKV.remove(va._key);
if (vp != null) UKV.remove(vp._key);
if (avp != null) UKV.remove(avp._key);
}
}
@Override
protected void init() {
if (validation != null && n_folds != 0)
throw new UnsupportedOperationException(
"Cannot specify a validation dataset and non-zero number of cross-validation folds.");
if (n_folds < 0)
throw new UnsupportedOperationException(
"The number of cross-validation folds must be >= 0.");
super.init();
xval_models = new Key[n_folds];
for (int i = 0; i < xval_models.length; ++i)
xval_models[i] = Key.make(dest().toString() + "_xval" + i);
int rIndex = 0;
for (int i = 0; i < source.vecs().length; i++)
if (source.vecs()[i] == response) {
rIndex = i;
break;
}
_responseName = source._names != null && rIndex >= 0 ? source._names[rIndex] : "response";
_train = selectVecs(source);
_names = new String[cols.length];
for (int i = 0; i < cols.length; i++) _names[i] = source._names[cols[i]];
// Compute source response domain
if (classification) _sourceResponseDomain = getVectorDomain(response);
// Is validation specified?
if (validation != null) {
// Extract a validation response
int idx = validation.find(source.names()[rIndex]);
if (idx == -1)
throw new IllegalArgumentException(
"Validation set does not have a response column called " + _responseName);
_validResponse = validation.vecs()[idx];
// Compute output confusion matrix domain for classification:
// - if validation dataset is specified then CM domain is union of train and validation
// response domains
// else it is only domain of response column.
if (classification) {
_validResponseDomain = getVectorDomain(_validResponse);
if (_validResponseDomain != null) {
_cmDomain = Utils.domainUnion(_sourceResponseDomain, _validResponseDomain);
if (!Arrays.deepEquals(_sourceResponseDomain, _validResponseDomain)) {
_fromModel2CM =
Model.getDomainMapping(
_cmDomain,
_sourceResponseDomain,
false); // transformation from model produced response ~> cmDomain
_fromValid2CM =
Model.getDomainMapping(
_cmDomain,
_validResponseDomain,
false); // transformation from validation response domain ~> cmDomain
}
} else _cmDomain = _sourceResponseDomain;
} /* end of if classification */
} else if (classification) _cmDomain = _sourceResponseDomain;
}
@Override protected void init() {
super.init();
int rIndex = 0;
for( int i = 0; i < source.vecs().length; i++ )
if( source.vecs()[i] == response )
rIndex = i;
_responseName = source._names != null && rIndex >= 0 ? source._names[rIndex] : "response";
_train = selectVecs(source);
_names = new String[cols.length];
for( int i = 0; i < cols.length; i++ )
_names[i] = source._names[cols[i]];
// Compute source response domain
if (classification) _sourceResponseDomain = getVectorDomain(response);
// Is validation specified?
if( validation != null ) {
// Extract a validation response
int idx = validation.find(source.names()[rIndex]);
if( idx == -1 ) throw new IllegalArgumentException("Validation set does not have a response column called "+_responseName);
_validResponse = validation.vecs()[idx];
// Compute output confusion matrix domain for classification:
// - if validation dataset is specified then CM domain is union of train and validation response domains
// else it is only domain of response column.
if (classification) {
_validResponseDomain = getVectorDomain(_validResponse);
if (_validResponseDomain!=null) {
_cmDomain = Utils.domainUnion(_sourceResponseDomain, _validResponseDomain);
if (!Arrays.deepEquals(_sourceResponseDomain, _validResponseDomain)) {
_fromModel2CM = Model.getDomainMapping(_cmDomain, _sourceResponseDomain, false); // transformation from model produced response ~> cmDomain
_fromValid2CM = Model.getDomainMapping(_cmDomain, _validResponseDomain , false); // transformation from validation response domain ~> cmDomain
}
} else _cmDomain = _sourceResponseDomain;
} /* end of if classification */
} else if (classification) _cmDomain = _sourceResponseDomain;
}