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com.expleague.ml.methods.greedyRegion.GreedyTDLinearRegion Maven / Gradle / Ivy
package com.expleague.ml.methods.greedyRegion;
import com.expleague.commons.math.MathTools;
import com.expleague.commons.math.vectors.Mx;
import com.expleague.commons.math.vectors.MxTools;
import com.expleague.commons.math.vectors.impl.vectors.ArrayVec;
import com.expleague.ml.data.set.VecDataSet;
import com.expleague.commons.func.AdditiveStatistics;
import com.expleague.commons.math.vectors.Vec;
import com.expleague.commons.math.vectors.VecTools;
import com.expleague.commons.math.vectors.impl.mx.VecBasedMx;
import com.expleague.commons.random.FastRandom;
import com.expleague.commons.util.ArrayTools;
import com.expleague.ml.BFGrid;
import com.expleague.ml.Binarize;
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.models.LinearRegion;
import gnu.trove.list.array.TDoubleArrayList;
import java.util.ArrayList;
import java.util.List;
/**
* User: nooxoomo
*/
public class GreedyTDLinearRegion extends VecOptimization.Stub {
protected final BFGrid grid;
private final int depth;
private final double lambda;
private final FastRandom random = new FastRandom();
public GreedyTDLinearRegion(final BFGrid grid,
final int depth,
final double lambda) {
this.grid = grid;
this.depth = depth;
this.lambda = lambda;
}
@Override
public LinearRegion fit(final VecDataSet learn,
final Loss loss) {
final List conditions = new ArrayList<>(depth);
final boolean[] usedBF = new boolean[grid.size()];
final List mask = new ArrayList<>();
Vec bestSolution = new ArrayVec(0);
final BinarizedDataSet bds = learn.cache().cache(Binarize.class, VecDataSet.class).binarize(grid);
double currentScore = Double.POSITIVE_INFINITY;
final boolean[] isRight = new boolean[grid.size()];
final double[] scores = new double[grid.size()];
final Vec[] solutions = new Vec[grid.size()];
final int[] points = learnPoints(loss, learn);
TDoubleArrayList sums = new TDoubleArrayList(depth);
TDoubleArrayList weights = new TDoubleArrayList(depth);
final BFOptimizationRegion current = new BFOptimizationRegion(bds, loss, points);
{
AdditiveStatistics statistics = current.total();
sums.add(sum(statistics));
final double totalWeight = weight(statistics);
weights.add(totalWeight);
}
for (int level = 0; level < depth; ++level) {
current.visitAllSplits((bf, left, right) -> {
if (usedBF[bf.bfIndex]) {
scores[bf.bfIndex] = Double.POSITIVE_INFINITY;
solutions[bf.bfIndex] = null;
} else {
final double leftScore;
Vec leftBetas;
final double leftWeight = weight(left);
final double rightWeight = weight(right);
final double minExcluded = Math.min(leftWeight, rightWeight);
{
if (minExcluded > 3) {
final Vec regularizer = makeRegularizer(weights, leftWeight);
Mx invCov = makeInvMatrix(weights, leftWeight, regularizer);
Vec target = makeVector(sums, sum(left));
Vec adjustTarget = adjustTarget(target, weights, leftWeight);
leftBetas = MxTools.multiply(invCov, adjustTarget);
leftScore = calcScore(invCov, target, leftBetas);
} else {
leftBetas = null;
leftScore = Double.POSITIVE_INFINITY;
}
}
Vec rightBetas;
final double rightScore;
{
if (minExcluded > 3) {
final Vec regularizer = makeRegularizer(weights, rightWeight);
Mx invCov = makeInvMatrix(weights, rightWeight, regularizer);
Vec target = makeVector(sums, sum(right));
Vec adjustTarget = adjustTarget(target, weights, rightWeight);
rightBetas = MxTools.multiply(invCov, adjustTarget);
rightScore = calcScore(invCov, target, rightBetas);
} else {
rightBetas = null;
rightScore = Double.POSITIVE_INFINITY;
}
}
scores[bf.bfIndex] = leftScore > rightScore ? rightScore : leftScore;
isRight[bf.bfIndex] = leftScore > rightScore;
solutions[bf.bfIndex] = leftScore > rightScore ? rightBetas : leftBetas;
}
});
final int bestSplit = ArrayTools.min(scores);
if (bestSplit < 0)
break;
if ((scores[bestSplit] >= currentScore))
break;
final BFGrid.BinaryFeature bestSplitBF = grid.bf(bestSplit);
final boolean bestSplitMask = isRight[bestSplitBF.bfIndex];
conditions.add(bestSplitBF);
usedBF[bestSplitBF.bfIndex] = true;
mask.add(bestSplitMask);
bestSolution = solutions[bestSplitBF.bfIndex];
currentScore = scores[bestSplit];
if (level < (depth - 1)) {
current.split(bestSplitBF, bestSplitMask);
final AdditiveStatistics total = current.total();
sums.add(sum(total));
final double weight = weight(total);
weights.add(weight);
}
}
final boolean[] masks = new boolean[conditions.size()];
for (int i = 0; i < masks.length; i++) {
masks[i] = mask.get(i);
}
//
final double bias = bestSolution.get(0);
final double[] values = new double[bestSolution.dim() - 1];
for (int i = 0; i < values.length; ++i) {
values[i] = bestSolution.get(i + 1);
}
return new LinearRegion(conditions, masks, bias, values);
}
private Vec adjustTarget(Vec target, TDoubleArrayList weights, double weight) {
final Vec adjusted = VecTools.copy(target);
for (int i = 0; i < target.dim(); ++i) {
final double w = i < weights.size() ? weights.get(i) : weight;
// adjusted.set(i, target.get(i) * w / (w + 1));
adjusted.set(i, target.get(i) * (w - 1) / w);
}
return adjusted;
}
private double calcScore(final Mx sigma, final Vec targetProj, final Vec betas) {
final double targetBetasProd = VecTools.multiply(targetProj, betas);
final Vec tmp = MxTools.multiply(sigma, targetProj);
final double targetThroughInvSigmaDot = VecTools.multiply(targetProj, tmp);
// final double rss = sum2 - 2 * targetThroughInvSigmaDot + targetBetasProd;
return (0.5 * targetBetasProd - targetThroughInvSigmaDot);
// return n * Math.log(rss / (n - targetProj.xdim())) + betas.xdim() * Math.log(n);
}
private Vec makeVector(TDoubleArrayList sums, double sum) {
Vec result = new ArrayVec(sums.size() + 1);
for (int i = 0; i < sums.size(); ++i) {
result.set(i, sums.get(i));
}
result.set(sums.size(), sum);
return result;
}
private Vec makeRegularizer(TDoubleArrayList weights, double weight) {
final Vec reg = new ArrayVec(weights.size() + 1);
VecTools.fill(reg, lambda);
// reg.set(weights.size(), lambda);
reg.set(0, 0);
return reg;
}
private Mx makeMatrix(TDoubleArrayList weights, double weight) {
final Mx cov = new VecBasedMx(weights.size() + 1, weights.size() + 1);
final int n = weights.size() + 1;
for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
int idx = j < i ? i : j;
cov.set(i, j, (idx < weights.size() ? weights.get(idx) : weight));
}
}
return cov;
}
private Mx makeInvMatrix(TDoubleArrayList weights, double weight, Vec regularizer) {
final Mx cov = new VecBasedMx(weights.size() + 1, weights.size() + 1);
final int n = weights.size() + 1;
for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
int idx = j < i ? i : j;
cov.set(i, j, (idx < weights.size() ? weights.get(idx) : weight));
}
}
if (regularizer != null) {
for (int i = 0; i < n; ++i) {
cov.adjust(i, i, regularizer.get(i));
}
}
return MxTools.inverseCholesky(cov);
}
class EmpericalBayesianLinearEstimator {
private final Mx empericalCov;
private final Vec targetProj;
private final double sum2;
private final double weight;
private Mx posteriorCov;
private Mx posteriorCovInv;
private Vec mu;
private Vec alphas;
private double tau;
private void updatePosteriors() {
for (int i = 0; i < empericalCov.rows(); ++i) {
for (int j = 0; j < empericalCov.columns(); ++j) {
posteriorCov.set(i, j, tau * empericalCov.get(i, j));
if (i == j) {
posteriorCov.adjust(i, i, alphas.get(i));
}
}
}
posteriorCovInv = MxTools.inverseCholesky(posteriorCov);
mu = MxTools.multiply(posteriorCovInv, targetProj);
mu = VecTools.scale(mu, tau);
}
private void iterativeEstimate(int iterations) {
for (int k = 0; k < iterations; ++k) {
double N = weight;
for (int i = 0; i < alphas.dim(); ++i) {
final double gamma = 1.0 - alphas.get(i) * posteriorCovInv.get(i, i);
N -= gamma;
double val = gamma / MathTools.sqr(mu.get(i));
if (val > 1000 || Double.isInfinite(val)) {
val = 1000;
}
alphas.set(i, val);
}
// double sum = VecTools.sum(alphas);
// VecTools.scale(alphas, 1e-10 / sum);
double err = err();
tau = N / err;
updatePosteriors();
}
// updatePosteriors();
}
private void estimateAlpha() {
for (int k = 0; k < 2; ++k) {
double N = weight;
final double w = empericalCov.get(0, empericalCov.rows() - 1);
for (int i = 0; i < alphas.dim(); ++i) {
if (i == (alphas.dim() - 1)) {
final double q = tau * w - tau * tau * VecTools.sum(MxTools.multiply(posteriorCovInv, targetProj));
final double s = tau * w - tau * tau * w * w * VecTools.sum(posteriorCovInv);
if (q * q > s) {
alphas.set(i, MathTools.sqr(s) / (MathTools.sqr(q) - s));
} else {
alphas.set(i, 1e10);
}
}
final double gamma = 1.0 - alphas.get(i) * posteriorCovInv.get(i, i);
N -= gamma;
}
// double val = gamma / MathTools.sqr(mu.get(i));
double err = err();
tau = N / err;
updatePosteriors();
}
}
EmpericalBayesianLinearEstimator(final double sum, double sum2, double weight,
final Mx empericalCov,
final Vec targetCov) {
this.empericalCov = empericalCov;
this.targetProj = targetCov;
alphas = new ArrayVec(empericalCov.rows());
VecTools.fill(alphas, 1e-3);
final double var = (sum2 / weight) - MathTools.sqr(sum / weight);
tau = 1.0 / var;
this.sum2 = sum2;
this.weight = weight;
this.posteriorCov = new VecBasedMx(empericalCov);
updatePosteriors();
double err = err();
tau = weight / err;
// iterativeEstimate(5);
// estimateAlpha();
}
public Vec mu() {
return mu;
// Mx cov = new VecBasedMx(empericalCov);
// for (int i = 0; i < mu.xdim(); ++i) {
//// final double preWeight = weights.get(i - 1);
//// final double w = empericalCov.get(i, i);
// cov.adjust(i, i, alphas.get(i));
// }
// return MxTools.multiply(MxTools.inverseCholesky(cov), targetProj);
}
double score(Vec betas) {
return err();
// final double targetBetasProd = VecTools.multiply(targetProj, betas);
// final Vec tmp = MxTools.multiply(MxTools.inverseCholesky(empericalCov), targetProj);
// final double targetThroughInvSigmaDot = VecTools.multiply(targetProj, tmp);
// final double rss = sum2 - 2 * targetThroughInvSigmaDot + targetBetasProd;
// return 0.5 * targetBetasProd - targetThroughInvSigmaDot;
// return (0.5 * targetBetasProd - targetThroughInvSigmaDot);
}
double err() {
double err = sum2;
err -= 2 * tau * VecTools.multiply(targetProj, MxTools.multiply(posteriorCovInv, targetProj));
err += VecTools.multiply(mu, MxTools.multiply(empericalCov, mu));
assert (err > 0);
return err;
}
}
private int[] learnPoints(Loss loss, VecDataSet ds) {
if (loss instanceof WeightedLoss) {
return ((WeightedLoss) loss).points();
} else return ArrayTools.sequence(0, ds.length());
}
private double weight(final AdditiveStatistics stat) {
if (stat instanceof L2.Stat) {
return ((L2.Stat) stat).weight;
} else if (stat instanceof WeightedLoss.Stat) {
return weight(((WeightedLoss.Stat) stat).inside);
} else {
throw new RuntimeException("error");
}
}
private double sum(final AdditiveStatistics stat) {
if (stat instanceof L2.Stat) {
return ((L2.Stat) stat).sum;
} else if (stat instanceof WeightedLoss.Stat) {
return sum(((WeightedLoss.Stat) stat).inside);
} else {
throw new RuntimeException("error");
}
}
}
