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com.expleague.ml.methods.greedyRegion.GreedyTDRegionNonStochasticProbs Maven / Gradle / Ivy
package com.expleague.ml.methods.greedyRegion;
import com.expleague.ml.data.set.VecDataSet;
import com.expleague.commons.func.AdditiveStatistics;
import com.expleague.commons.math.vectors.Vec;
import com.expleague.commons.random.FastRandom;
import com.expleague.commons.util.ArrayTools;
import com.expleague.commons.util.Pair;
import com.expleague.ml.BFGrid;
import com.expleague.ml.Binarize;
import com.expleague.ml.data.Aggregate;
import com.expleague.ml.data.impl.BinarizedDataSet;
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.trees.BFOptimizationSubset;
import com.expleague.ml.models.Region;
import gnu.trove.list.array.TDoubleArrayList;
import java.util.ArrayList;
import java.util.List;
/**
* User: solar
* Date: 15.11.12
* Time: 15:19
*/
public class GreedyTDRegionNonStochasticProbs extends VecOptimization.Stub {
protected final BFGrid grid;
int maxFailed = 1;
private double alpha = 0.08;
private double beta = 1.1;
private final FastRandom rand = new FastRandom();
public GreedyTDRegionNonStochasticProbs(final BFGrid grid) {
this.grid = grid;
}
public GreedyTDRegionNonStochasticProbs(final BFGrid grid, final double alpha, final double beta) {
this(grid);
this.alpha = alpha;
this.beta = beta;
}
Pair initFit(final VecDataSet learn, final Loss loss) {
final BFOptimizationSubset current;
final BinarizedDataSet bds = learn.cache().cache(Binarize.class, VecDataSet.class).binarize(grid);
current = new BFOptimizationSubset(bds, loss, ArrayTools.sequence(0, learn.length()));
final double[] bestRowScores = new double[grid.rows()];
for (int i = 0; i < bestRowScores.length; ++i) {
bestRowScores[i] = Double.POSITIVE_INFINITY;
}
final BFGrid.BinaryFeature[] bestRowFeatures = new BFGrid.BinaryFeature[grid.rows()];
final boolean[] masks = new boolean[grid.rows()];
current.visitAllSplits(new Aggregate.SplitVisitor() {
@Override
public void accept(final BFGrid.BinaryFeature bf, final AdditiveStatistics left, final AdditiveStatistics right) {
final double leftScore = logScore(left);
final double rightScore = logScore(right);
final double bestScore = leftScore > rightScore ? rightScore : leftScore;
if (bestScore < bestRowScores[bf.findex]) {
bestRowScores[bf.findex] = bestScore;
masks[bf.findex] = leftScore > rightScore;
bestRowFeatures[bf.findex] = bf;
}
}
});
final boolean[] resultMasks = new boolean[maxFailed];
final BFGrid.BinaryFeature[] resultFeatures = new BFGrid.BinaryFeature[maxFailed];
for (int i = 0; i < maxFailed; ) {
final boolean[] used = new boolean[bestRowScores.length];
final int index = rand.nextInt(bestRowScores.length);
if (bestRowScores[index] < Double.POSITIVE_INFINITY && !used[index]) {
used[index] = true;
final BFGrid.BinaryFeature feature = bestRowFeatures[index];
final boolean mask = masks[index];
resultFeatures[i] = feature;
resultMasks[i] = mask;
++i;
}
}
return new Pair<>(resultFeatures, resultMasks);
}
@Override
public Region fit(final VecDataSet learn, final Loss loss) {
// maxFailed = rand.nextInt(4);
final List conditions = new ArrayList<>(32);
final boolean[] usedBF = new boolean[grid.size()];
final List mask = new ArrayList<>();
final TDoubleArrayList conditionSum = new TDoubleArrayList(32);
final TDoubleArrayList conditionTotal = new TDoubleArrayList(32);
final Pair init = initFit(learn, loss);
for (int i = 0; i < init.first.length; ++i) {
conditions.add(init.first[i]);
usedBF[init.first[i].bfIndex] = true;
mask.add(init.second[i]);
}
final BinarizedDataSet bds = learn.cache().cache(Binarize.class, VecDataSet.class).binarize(grid);
double currentScore = Double.POSITIVE_INFINITY;
final BFWeakConditionsStochasticOptimizationRegion current =
new BFWeakConditionsStochasticOptimizationRegion(bds, loss, ArrayTools.sequence(0, learn.length()), init.first, init.second, maxFailed);
current.alpha = alpha;
current.beta = beta;
final BFWeakConditionsOptimizationRegion determenisticCurrent =
new BFWeakConditionsOptimizationRegion(bds, loss, ArrayTools.sequence(0, learn.length()), init.first, init.second, maxFailed);
final double[] scores = new double[grid.size()];
final boolean[] isRight = new boolean[grid.size()];
final double[] weights = new double[grid.size() * 2];
while (true) {
final double total = weight(determenisticCurrent.total());
final double[] csum = new double[conditionSum.size() + 1];
final double[] ctotal = new double[conditionSum.size() + 1];
for (int i = 0; i < csum.length - 1; ++i) {
csum[i] = conditionSum.get(i);
ctotal[i] = conditionTotal.get(i);
}
determenisticCurrent.visitAllSplits(new Aggregate.SplitVisitor() {
@Override
public void accept(final BFGrid.BinaryFeature bf, final AdditiveStatistics left, final AdditiveStatistics right) {
final AdditiveStatistics leftIn = (AdditiveStatistics) loss.statsFactory().create();
final AdditiveStatistics rightIn = (AdditiveStatistics) loss.statsFactory().create();
leftIn.append(left);
leftIn.append(current.nonCriticalTotal);
rightIn.append(right);
rightIn.append(current.nonCriticalTotal);
final double leftWeight = weight(leftIn);
final double rightWeight = weight(rightIn);
weights[bf.bfIndex * 2] = leftWeight;
weights[bf.bfIndex * 2 + 1] = rightWeight;
}
});
current.visitAllSplits(new Aggregate.SplitVisitor() {
@Override
public void accept(final BFGrid.BinaryFeature bf, final AdditiveStatistics left, final AdditiveStatistics right) {
if (usedBF[bf.bfIndex]) {
scores[bf.bfIndex] = Double.POSITIVE_INFINITY;
} else {
double leftScore = Double.POSITIVE_INFINITY;
{
csum[csum.length - 1] = weights[bf.bfIndex * 2];
ctotal[csum.length - 1] = total;
final double prob = estimate(csum, ctotal);
final AdditiveStatistics out = (AdditiveStatistics) loss.statsFactory().create();
final AdditiveStatistics in = (AdditiveStatistics) loss.statsFactory().create();
out.append(right);
out.append(current.excluded);
in.append(current.nonCriticalTotal);
in.append(left);
leftScore = (1 - prob) * outScore(out) + prob * inScore(in);// + 2 * ((1-prob) * Math.log(1-prob) + prob * Math.log(prob));
// leftScore = score(out) + score(in);// + 2 * ((1-prob) * Math.log(1-prob) + prob * Math.log(prob));
// leftScore =inScore(in);
}
double rightScore = Double.POSITIVE_INFINITY;
{
csum[csum.length - 1] = weights[bf.bfIndex * 2 + 1];
ctotal[csum.length - 1] = total;
final double prob = estimate(csum, ctotal);
final AdditiveStatistics out = (AdditiveStatistics) loss.statsFactory().create();
final AdditiveStatistics in = (AdditiveStatistics) loss.statsFactory().create();
out.append(left);
out.append(current.excluded);
in.append(current.nonCriticalTotal);
in.append(right);
rightScore = (1 - prob) * outScore(out) + prob * inScore(in);// + 2 * ((1-prob) * Math.log(1-prob) + prob * Math.log(prob));
// rightScore = score(out) + score(in);
// rightScore = inScore(in);
}
scores[bf.bfIndex] = leftScore > rightScore ? rightScore : leftScore;
isRight[bf.bfIndex] = leftScore > rightScore;
}
}
});
//
final int bestSplit = ArrayTools.min(scores);
if (bestSplit < 0)
break;
if ((scores[bestSplit] + 1e-9 >= currentScore))
break;
final BFGrid.BinaryFeature bestSplitBF = grid.bf(bestSplit);
final boolean bestSplitMask = isRight[bestSplitBF.bfIndex];
BFOptimizationSubset outRegion = current.split(bestSplitBF, bestSplitMask);
if (outRegion == null) {
break;
}
outRegion = determenisticCurrent.split(bestSplitBF, bestSplitMask);
if (outRegion == null) {
break;
}
conditions.add(bestSplitBF);
conditionSum.add(isRight[bestSplitBF.bfIndex] ? weights[bestSplitBF.bfIndex * 2 + 1] : weights[bestSplitBF.bfIndex * 2]);
conditionTotal.add(total);
usedBF[bestSplitBF.bfIndex] = true;
mask.add(bestSplitMask);
currentScore = scores[bestSplit];
}
final boolean[] masks = new boolean[conditions.size()];
for (int i = 0; i < masks.length; i++) {
masks[i] = mask.get(i);
}
//
final Region region = new Region(conditions, masks, 1, 0, -1, currentScore, conditions.size() > maxFailed ? maxFailed : 0);
final Vec target = loss.target();
double sum = 0;
double outSum = 0;
double weight = 0;
double outWeight = 0;
for (int i = 0; i < bds.original().length(); ++i) {
if (region.value(bds, i) == 1) {
final double samplWeight = 1.0;// current.size() > 10 ? rand.nextPoisson(1.0) : 1.0;
weight += samplWeight;
sum += target.get(i) * samplWeight;
} else {
outSum += target.get(i);
outWeight++;
}
}
final double value = weight > 1 ? sum / weight : loss.bestIncrement(current.total());//loss.bestIncrement(inside);
// double value = loss.bestIncrement(current.total());//loss.bestIncrement(inside);
return new Region(conditions, masks, value, 0, -1, currentScore, conditions.size() > 1 ? maxFailed : 0);
}
private double estimate(final double[] sum, final double[] counts) {
double p = 1;
for (int i = 0; i < sum.length; ++i) {
p *= sum[i] / counts[i];
}
return p;
// double[] probs = MTA.bernoulliStein(sum, counts);
// double p = 0;
// for (int i = 0; i < sum.length; ++i) {
// p += Math.log(probs[i]);
// }
// return Math.exp(p);
}
public static double weight(final AdditiveStatistics stats) {
if (stats instanceof WeightedLoss.Stat) {
return ((L2.Stat) ((WeightedLoss.Stat) stats).inside).weight;
}
if (stats instanceof L2.Stat) {
return ((L2.Stat) stats).weight;
}
return 0;
}
public static double sum(final AdditiveStatistics stats) {
if (stats instanceof WeightedLoss.Stat) {
return ((L2.Stat) ((WeightedLoss.Stat) stats).inside).sum;
}
if (stats instanceof L2.Stat) {
return ((L2.Stat) stats).sum;
}
return 0;
}
public static double sum2(final AdditiveStatistics stats) {
if (stats instanceof WeightedLoss.Stat) {
return ((L2.Stat) ((WeightedLoss.Stat) stats).inside).sum2;
}
if (stats instanceof L2.Stat) {
return ((L2.Stat) stats).sum2;
}
return 0;
}
public double score(final AdditiveStatistics stats) {
final double sum = sum(stats);
final double sum2 = sum2(stats);
final double weight = weight(stats);
return weight > 2 ? (-sum * sum / weight) * weight * (weight - 2) / (weight * weight - 3 * weight + 1) * (1 + 2 * Math.log(weight + 1)) : 0;
// return weight > 1 ? (sum2 / (weight - 1) - sum * sum / (weight - 1) / (weight - 1)) : sum2;
// return weight > 2 ? (sum2 / weight - sum * sum / weight / weight) * weight * (weight - 2) / (weight * weight - 3 * weight + 1) * (1 + 2 * Math.log(weight + 1)) : 0;
// final double n = stats.weight;
// return n > 2 ? n*(n-2)/(n * n - 3 * n + 1) * (stats.sum2 - stats.sum * stats.sum / n) : stats.sum2;
// return (stats.sum2 / (n-1) -stats.sum * stats.sum / (n - 1) / (n - 1));
}
public double inScore(final AdditiveStatistics stats) {
final double weight = weight(stats);
if (weight < 5) {
return Double.POSITIVE_INFINITY;
}
final double sum = sum(stats);
final double sum2 = sum2(stats);
// return weight > 2 ? (-sum * sum * weight / (weight-1) / (weight-1)) * (1 + 2 * Math.log(weight + 1)) : 0;
return -sum * sum / (weight - 1) / (weight - 1);
// return weight > 1 ? (sum2 / (weight - 1) - sum * sum / (weight - 1) / (weight - 1)) : sum2;
// final double n = stats.weight;
// return (weight - 2) / (weight * weight - 3 * weight + 1) * (sum2 - sum * sum / weight);
// return (sum2 /(weight-1) - sum * sum / weight / (weight-1));
// return (stats.sum2 / (n-1) -stats.sum * stats.sum / (n - 1) / (n - 1));
}
public double outScore(final AdditiveStatistics stats) {
final double sum2 = sum2(stats);
final double sum = sum(stats);
final double weight = weight(stats);
if (weight < 5) {
return Double.POSITIVE_INFINITY;
}
return 0;
// return weight > 1 ? (sum2 / (weight - 1) - sum * sum / (weight - 1) / (weight - 1)) : sum2;
// return sum2 / (weight - 1) ;
// return weight > 1 ? sum2 / (weight - 1) : sum2;
//
// return weight > 1 ? sum2 / (weight - 1) : sum2;
// final double n = stats.weight;
// return n > 2 ? n*(n-2)/(n * n - 3 * n + 1) * (stats.sum2 - stats.sum * stats.sum / n) : stats.sum2;
// return (stats.sum2 / (n-1) -stats.sum * stats.sum / (n - 1) / (n - 1));
}
public double logScore(final AdditiveStatistics stats) {
final double weight = weight(stats);
final double sum = sum(stats);
return weight > 2 ? (-sum * sum / weight) * weight * (weight - 2) / (weight * weight - 3 * weight + 1) * (1 + 2 * Math.log(weight + 1)) : 0;
}
}
