@Override
protected Frame predictScoreImpl(Frame orig, Frame adaptedFr, String destination_key) {
Frame adaptFrm = new Frame(adaptedFr);
for (int i = 0; i < _parms._k; i++)
adaptFrm.add("PC" + String.valueOf(i + 1), adaptFrm.anyVec().makeZero());
new MRTask() {
@Override
public void map(Chunk chks[]) {
double tmp[] = new double[_output._names.length];
double preds[] = new double[_parms._k];
for (int row = 0; row < chks[0]._len; row++) {
double p[] = score0(chks, row, tmp, preds);
for (int c = 0; c < preds.length; c++) chks[_output._names.length + c].set(row, p[c]);
}
}
}.doAll(adaptFrm);
// Return the projection into principal component space
int x = _output._names.length, y = adaptFrm.numCols();
Frame f =
adaptFrm.extractFrame(
x, y); // this will call vec_impl() and we cannot call the delete() below just yet
f =
new Frame(
(null == destination_key ? Key.make() : Key.make(destination_key)),
f.names(),
f.vecs());
DKV.put(f);
makeMetricBuilder(null).makeModelMetrics(this, orig);
return f;
}
// GLRM scoring is data imputation based on feature domains using reconstructed XY (see Udell
// (2015), Section 5.3)
private Frame reconstruct(
Frame orig,
Frame adaptedFr,
Key destination_key,
boolean save_imputed,
boolean reverse_transform) {
final int ncols = _output._names.length;
assert ncols == adaptedFr.numCols();
String prefix = "reconstr_";
// Need [A,X,P] where A = adaptedFr, X = loading frame, P = imputed frame
// Note: A is adapted to original training frame, P has columns shuffled so cats come before
// nums!
Frame fullFrm = new Frame(adaptedFr);
Frame loadingFrm = DKV.get(_output._representation_key).get();
fullFrm.add(loadingFrm);
String[][] adaptedDomme = adaptedFr.domains();
for (int i = 0; i < ncols; i++) {
Vec v = fullFrm.anyVec().makeZero();
v.setDomain(adaptedDomme[i]);
fullFrm.add(prefix + _output._names[i], v);
}
GLRMScore gs = new GLRMScore(ncols, _parms._k, save_imputed, reverse_transform).doAll(fullFrm);
// Return the imputed training frame
int x = ncols + _parms._k, y = fullFrm.numCols();
Frame f =
fullFrm.extractFrame(
x, y); // this will call vec_impl() and we cannot call the delete() below just yet
f = new Frame((null == destination_key ? Key.make() : destination_key), f.names(), f.vecs());
DKV.put(f);
gs._mb.makeModelMetrics(
GLRMModel.this, orig, null, null); // save error metrics based on imputed data
return f;
}
