com.expleague.ml.methods.trees.GreedyObliviousTreeWithWeakLearner Maven / Gradle / Ivy
package com.expleague.ml.methods.trees;
import com.expleague.commons.math.Trans;
import com.expleague.commons.math.vectors.Vec;
import com.expleague.commons.math.vectors.VecTools;
import com.expleague.commons.math.vectors.impl.vectors.ArrayVec;
import com.expleague.commons.random.FastRandom;
import com.expleague.ml.BFGrid;
import com.expleague.ml.data.set.VecDataSet;
import com.expleague.ml.data.tools.DataTools;
import com.expleague.ml.func.Ensemble;
import com.expleague.ml.loss.L2;
import com.expleague.ml.loss.StatBasedLoss;
import com.expleague.ml.loss.WeightedLoss;
import com.expleague.ml.methods.VecOptimization;
import com.expleague.ml.methods.linearRegressionExperiments.WeakLeastAngle;
import com.expleague.ml.models.ObliviousTree;
import java.util.List;
import java.util.Set;
import java.util.TreeSet;
/**
* User: noxoomo
*/
public class GreedyObliviousTreeWithWeakLearner extends VecOptimization.Stub {
private final GreedyObliviousTree> base;
private final FastRandom rand;
public GreedyObliviousTreeWithWeakLearner(
final GreedyObliviousTree> base,
final FastRandom rand) {
this.base = base;
this.rand = rand;
}
private int[] learnPoints(WeightedLoss loss) {
return loss.points();
}
@Override
public Trans fit(final VecDataSet ds, final Loss loss) {
final WeightedLoss bsLoss = DataTools.bootstrap(loss, rand);
final Trans[] result = new Trans[2];
result[0] = base.fit(ds, bsLoss);
final List conditions = ((ObliviousTree)result[0]).features();
//damn java 7 without unique, filters, etc and autoboxing overhead…
Set uniqueFeatures = new TreeSet<>();
for (BFGrid.Feature bf : conditions) {
if (!bf.row().empty()
)
uniqueFeatures.add(bf.findex());
}
// //prototype
while (uniqueFeatures.size() < 10) {
final int feature = rand.nextInt(ds.data().columns());
if (!base.grid.row(feature).empty())
uniqueFeatures.add(feature);
}
Vec newTarget = VecTools.copy(loss.target());
Vec predictions = result[0].transAll(ds.data()).col(0);
for (int i = 0; i < predictions.dim(); ++i)
newTarget.adjust(i, -predictions.get(i));
final int[] features = new int[uniqueFeatures.size()];
{
int j = 0;
for (Integer i : uniqueFeatures) {
features[j++] = i;
}
}
L2 localLoss = DataTools.newTarget(L2.class,newTarget,ds);
WeakLeastAngle regression = new WeakLeastAngle(learnPoints(bsLoss), features);
result[1] = regression.fit(ds,localLoss);
Vec weights = new ArrayVec(2);
VecTools.fill(weights,1.0);
return new Ensemble(result, weights);
}
}
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