com.expleague.ml.methods.multiclass.gradfac.GradFacMulticlass Maven / Gradle / Ivy
package com.expleague.ml.methods.multiclass.gradfac;
import com.expleague.commons.math.Func;
import com.expleague.commons.math.vectors.Mx;
import com.expleague.commons.math.vectors.Vec;
import com.expleague.commons.math.vectors.VecTools;
import com.expleague.ml.data.set.VecDataSet;
import com.expleague.ml.factorization.Factorization;
import com.expleague.ml.methods.VecOptimization;
import com.expleague.ml.methods.multiclass.MultiClassOneVsRest;
import com.expleague.commons.math.vectors.impl.mx.VecBasedMx;
import com.expleague.commons.util.Pair;
import com.expleague.ml.data.tools.DataTools;
import com.expleague.ml.func.ScaledVectorFunc;
import com.expleague.ml.loss.L2;
/**
* User: qdeee
* Date: 25.12.14
*/
public class GradFacMulticlass implements VecOptimization {
private final VecOptimization inner;
private final Factorization matrixDecomposition;
private final Class extends L2> local;
private final boolean printErrors;
public GradFacMulticlass(final VecOptimization inner, final Factorization matrixDecomposition, final Class extends L2> local) {
this(inner, matrixDecomposition, local, false);
}
public GradFacMulticlass(final VecOptimization inner, final Factorization matrixDecomposition, final Class extends L2> local, final boolean printErrors) {
this.inner = inner;
this.matrixDecomposition = matrixDecomposition;
this.local = local;
this.printErrors = printErrors;
}
@Override
public ScaledVectorFunc fit(VecDataSet learn, L2 mllLogitGradient) {
final Mx gradient = mllLogitGradient.target instanceof Mx
? (Mx)mllLogitGradient.target
: new VecBasedMx(mllLogitGradient.target.dim() / learn.length(), mllLogitGradient.target);
final Pair pair = matrixDecomposition.factorize(gradient);
final Vec h = pair.getFirst();
final Vec b = pair.getSecond();
final double normB = VecTools.norm(b);
VecTools.scale(b, 1 / normB);
VecTools.scale(h, normB);
final L2 loss = DataTools.newTarget(local, h, learn);
final Func model = MultiClassOneVsRest.extractFunc(inner.fit(learn, loss));
final ScaledVectorFunc resultModel = new ScaledVectorFunc(model, b);
if (printErrors) {
final Mx mxAfterFactor = VecTools.outer(h, b);
final Mx mxAfterFit = resultModel.transAll(learn.data());
final double gradNorm = VecTools.norm(gradient);
final double error1 = VecTools.distance(gradient, mxAfterFactor);
final double error2 = VecTools.distance(mxAfterFactor, mxAfterFit);
final double totalError = VecTools.distance(gradient, mxAfterFit);
System.out.println(String.format("grad_norm = %f, err1 = %f, err2 = %f, absErr = %f", gradNorm, error1, error2, totalError));
}
return resultModel; //not MultiClassModel, for boosting compatibility
}
}
// cn \t gradnorm \t rel_fact_err
/*
if (printErrors) {
final RealMatrix realMatrix = new Array2DRowRealMatrix(gradient.rows(), gradient.columns());
final int rows = gradient.rows();
final int columns = gradient.columns();
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
realMatrix.setEntry(i, j, gradient.get(i, j));
}
}
final SingularValueDecomposition singularValueDecomposition = new SingularValueDecomposition(realMatrix);
System.out.print(singularValueDecomposition.getConditionNumber() + "\t");
final Mx mxAfterFactor = VecTools.outer(h, b);
// final Mx mxAfterFit = resultModel.transAll(learn.data());
final double gradNorm = VecTools.norm(gradient);
final double error1 = VecTools.distance(gradient, mxAfterFactor);
// final double error2 = VecTools.distance(mxAfterFactor, mxAfterFit);
// final double totalError = VecTools.distance(gradient, mxAfterFit);
// System.out.println(String.format("%f\t%f\t%f\t%f", gradNorm, error1, error2, totalError));
System.out.print(gradNorm + "\t");
System.out.print(error1 / gradNorm + "\n");
}
*/
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