/*
* Hivemall: Hive scalable Machine Learning Library
*
* Copyright (C) 2013
* National Institute of Advanced Industrial Science and Technology (AIST)
* Registration Number: H25PRO-1520
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
package hivemall.regression;
import hivemall.common.LossFunctions;
import hivemall.common.OnlineVariance;
import hivemall.io.FeatureValue;
import hivemall.io.IWeightValue;
import hivemall.io.PredictionResult;
import hivemall.io.WeightValue.WeightValueWithCovar;
import java.util.Collection;
import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.Options;
import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
public class AROWRegressionUDTF extends OnlineRegressionUDTF {
/** Regularization parameter r */
protected float r;
@Override
public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
final int numArgs = argOIs.length;
if(numArgs != 2 && numArgs != 3) {
throw new UDFArgumentException(getClass().getSimpleName()
+ " takes arguments: List<Int|BigInt|Text> features, float target [, constant string options]");
}
return super.initialize(argOIs);
}
@Override
protected boolean useCovariance() {
return true;
}
@Override
protected Options getOptions() {
Options opts = super.getOptions();
opts.addOption("r", "regularization", true, "Regularization parameter for some r > 0 [default 0.1]");
return opts;
}
@Override
protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
final CommandLine cl = super.processOptions(argOIs);
float r = 0.1f;
if(cl != null) {
String r_str = cl.getOptionValue("r");
if(r_str != null) {
r = Float.parseFloat(r_str);
if(!(r > 0)) {
throw new UDFArgumentException("Regularization parameter must be greater than 0: "
+ r_str);
}
}
}
this.r = r;
return cl;
}
@Override
protected void train(Collection<?> features, float target) {
PredictionResult margin = calcScoreAndVariance(features);
float predicted = margin.getScore();
float loss = loss(target, predicted);
float var = margin.getVariance();
float beta = 1.f / (var + r);
update(features, loss, beta);
}
/**
* @return target - predicted
*/
protected float loss(float target, float predicted) {
return target - predicted; // y - m^Tx
}
@Override
protected void update(final Collection<?> features, final float coeff, final float beta) {
final ObjectInspector featureInspector = featureListOI.getListElementObjectInspector();
for(Object f : features) {
if(f == null) {
continue;
}
final Object k;
final float v;
if(parseFeature) {
FeatureValue fv = FeatureValue.parse(f);
k = fv.getFeature();
v = fv.getValue();
} else {
k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
v = 1.f;
}
IWeightValue old_w = model.get(k);
IWeightValue new_w = getNewWeight(old_w, v, coeff, beta);
model.set(k, new_w);
}
}
private static IWeightValue getNewWeight(final IWeightValue old, final float x, final float coeff, final float beta) {
final float old_w;
final float old_cov;
if(old == null) {
old_w = 0.f;
old_cov = 1.f;
} else {
old_w = old.get();
old_cov = old.getCovariance();
}
float cov_x = old_cov * x;
float new_w = old_w + coeff * cov_x * beta;
float new_cov = old_cov - (beta * cov_x * cov_x);
return new WeightValueWithCovar(new_w, new_cov);
}
public static class AROWe extends AROWRegressionUDTF {
/** Sensitivity to prediction mistakes */
protected float epsilon;
@Override
protected Options getOptions() {
Options opts = super.getOptions();
opts.addOption("e", "epsilon", true, "Sensitivity to prediction mistakes [default 0.1]");
return opts;
}
@Override
protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
CommandLine cl = super.processOptions(argOIs);
float epsilon = 0.1f;
if(cl != null) {
String opt_epsilon = cl.getOptionValue("epsilon");
if(opt_epsilon != null) {
epsilon = Float.parseFloat(opt_epsilon);
}
}
this.epsilon = epsilon;
return cl;
}
@Override
protected void train(Collection<?> features, float target) {
preTrain(target);
PredictionResult margin = calcScoreAndVariance(features);
float predicted = margin.getScore();
float loss = loss(target, predicted);
if(loss > 0.f) {
float coeff = (target - predicted) > 0.f ? loss : -loss;
float var = margin.getVariance();
float beta = 1.f / (var + r);
update(features, coeff, beta);
}
}
protected void preTrain(float target) {}
/**
* |w^t - y| - epsilon
*/
protected float loss(float target, float predicted) {
return LossFunctions.epsilonInsensitiveLoss(predicted, target, epsilon);
}
}
public static class AROWe2 extends AROWe {
private OnlineVariance targetStdDev;
@Override
public StructObjectInspector initialize(ObjectInspector[] argOIs)
throws UDFArgumentException {
this.targetStdDev = new OnlineVariance();
return super.initialize(argOIs);
}
@Override
protected void preTrain(float target) {
targetStdDev.handle(target);
}
@Override
protected float loss(float target, float predicted) {
float stddev = (float) targetStdDev.stddev();
float e = epsilon * stddev;
return LossFunctions.epsilonInsensitiveLoss(predicted, target, e);
}
}
}