/*
* 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.classifier;
import hivemall.common.LossFunctions;
import hivemall.io.PredictionResult;
import java.util.List;
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 PassiveAggressiveUDTF extends BinaryOnlineClassifierUDTF {
@Override
public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
final int numArgs = argOIs.length;
if(numArgs != 2 && numArgs != 3) {
throw new UDFArgumentException("PassiveAggressiveUDTF takes 2 or 3 arguments: List<Text|Int|BitInt> features, int label [, constant string options]");
}
return super.initialize(argOIs);
}
@Override
protected void train(final List<?> features, final int label) {
final float y = label > 0 ? 1f : -1f;
PredictionResult margin = calcScoreAndNorm(features);
float p = margin.getScore();
float loss = LossFunctions.hingeLoss(p, y); // 1.0 - y * p
if(loss > 0.f) { // y * p < 1
float eta = eta(loss, margin);
float coeff = eta * y;
update(features, coeff);
}
}
/** returns learning rate */
protected float eta(float loss, PredictionResult margin) {
return loss / margin.getSquaredNorm();
}
public static class PA1 extends PassiveAggressiveUDTF {
/** Aggressiveness parameter */
protected float c;
@Override
protected Options getOptions() {
Options opts = super.getOptions();
opts.addOption("c", "aggressiveness", true, "Aggressiveness parameter C [default 1.0]");
return opts;
}
@Override
protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
final CommandLine cl = super.processOptions(argOIs);
float c = 1.f;
if(cl != null) {
String c_str = cl.getOptionValue("c");
if(c_str != null) {
c = Float.parseFloat(c_str);
if(!(c > 0.f)) {
throw new UDFArgumentException("Aggressiveness parameter C must be C > 0: "
+ c);
}
}
}
this.c = c;
return cl;
}
@Override
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 PA2 extends PA1 {
@Override
protected float eta(float loss, PredictionResult margin) {
float squared_norm = margin.getSquaredNorm();
float eta = loss / (squared_norm + (0.5f / c));
return eta;
}
}
}