/*
* 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.PredictionResult;
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.StructObjectInspector;
public class PassiveAggressiveRegressionUDTF extends OnlineRegressionUDTF {
/** Aggressiveness parameter */
protected float c;
/** Sensitivity to prediction mistakes */
protected float epsilon;
@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 Options getOptions() {
Options opts = super.getOptions();
opts.addOption("c", "aggressiveness", true, "Aggressiveness paramete [default Float.MAX_VALUE]");
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 c = aggressiveness();
float epsilon = 0.1f;
if(cl != null) {
String opt_c = cl.getOptionValue("c");
if(opt_c != null) {
c = Float.parseFloat(opt_c);
if(!(c > 0.f)) {
throw new UDFArgumentException("Aggressiveness parameter C must be C > 0: " + c);
}
}
String opt_epsilon = cl.getOptionValue("epsilon");
if(opt_epsilon != null) {
epsilon = Float.parseFloat(opt_epsilon);
}
}
this.c = c;
this.epsilon = epsilon;
return cl;
}
protected float aggressiveness() {
return Float.MAX_VALUE;
}
@Override
protected void train(Collection<?> features, float target) {
preTrain(target);
PredictionResult margin = calcScoreAndNorm(features);
float predicted = margin.getScore();
float loss = loss(target, predicted);
if(loss > 0.f) {
int sign = (target - predicted) > 0.f ? 1 : -1; // sign(y - (W^t)x)
float eta = eta(loss, margin); // min(C, loss / |x|^2)
float coeff = sign * eta;
if(!Float.isInfinite(coeff)) {
update(features, coeff);
}
}
}
protected void preTrain(float target) {}
/**
* |w^t - y| - epsilon
*/
protected float loss(float target, float predicted) {
return LossFunctions.epsilonInsensitiveLoss(predicted, target, epsilon);
}
/**
* min(C, loss / |x|^2)
*/
protected float eta(float loss, PredictionResult margin) {
float squared_norm = margin.getSquaredNorm();
float eta = loss / squared_norm;
return Math.min(c, eta);
}
public static class PA1a extends PassiveAggressiveRegressionUDTF {
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);
}
}
public static class PA2 extends PassiveAggressiveRegressionUDTF {
@Override
protected float aggressiveness() {
return 1.f;
}
@Override
protected float eta(float loss, PredictionResult margin) {
float squared_norm = margin.getSquaredNorm();
float eta = loss / (squared_norm + (0.5f / c));
return eta;
}
}
public static class PA2a extends PA2 {
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);
}
}
}