/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package hivemall.classifier;
import hivemall.common.LossFunctions;
import hivemall.model.FeatureValue;
import hivemall.model.IWeightValue;
import hivemall.model.PredictionResult;
import hivemall.model.WeightValue.WeightValueWithCovar;
import javax.annotation.Nonnull;
import org.apache.commons.cli.CommandLine;
import org.apache.commons.cli.Options;
import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
/**
* Adaptive Regularization of Weight Vectors (AROW) binary classifier.
*
* <pre>
* [1] K. Crammer, A. Kulesza, and M. Dredze, "Adaptive Regularization of Weight Vectors",
* In Proc. NIPS, 2009.
* </pre>
*/
@Description(
name = "train_arow",
value = "_FUNC_(list<string|int|bigint> features, int label [, const string options])"
+ " - Returns a relation consists of <string|int|bigint feature, float weight, float covar>",
extended = "Build a prediction model by Adaptive Regularization of Weight Vectors (AROW) binary classifier")
public class AROWClassifierUDTF extends BinaryOnlineClassifierUDTF {
/** 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(
"_FUNC_ takes 2 or 3 arguments: List<String|Int|BitInt> features, Int label [, 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(@Nonnull final FeatureValue[] features, int label) {
final float y = label > 0 ? 1.f : -1.f;
PredictionResult margin = calcScoreAndVariance(features);
float m = margin.getScore() * y;
if (m < 1.f) {
float var = margin.getVariance();
float beta = 1.f / (var + r);
float alpha = (1.f - m) * beta;
update(features, y, alpha, beta);
}
}
protected float loss(PredictionResult margin, float y) {
float m = margin.getScore() * y;
return m < 0.f ? 1.f : 0.f; // suffer loss = 1 if sign(t) != y
}
protected void update(@Nonnull final FeatureValue[] features, final float y, final float alpha,
final float beta) {
for (FeatureValue f : features) {
if (f == null) {
continue;
}
final Object k = f.getFeature();
final float v = f.getValueAsFloat();
IWeightValue old_w = model.get(k);
IWeightValue new_w = getNewWeight(old_w, v, y, alpha, beta);
model.set(k, new_w);
}
}
private static IWeightValue getNewWeight(final IWeightValue old, final float x, final float y,
final float alpha, 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 cv = old_cov * x;
float new_w = old_w + (y * alpha * cv);
float new_cov = old_cov - (beta * cv * cv);
return new WeightValueWithCovar(new_w, new_cov);
}
@Description(
name = "train_arowh",
value = "_FUNC_(list<string|int|bigint> features, int label [, const string options])"
+ " - Returns a relation consists of <string|int|bigint feature, float weight, float covar>",
extended = "Build a prediction model by AROW binary classifier using hinge loss")
public static class AROWh extends AROWClassifierUDTF {
/** 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 void train(@Nonnull final FeatureValue[] features, int label) {
final float y = label > 0 ? 1.f : -1.f;
PredictionResult margin = calcScoreAndVariance(features);
float p = margin.getScore();
float loss = loss(p, y); // C - m (m = y * p)
if (loss > 0.f) {// m < 1.0 || 1.0 - m > 0
float var = margin.getVariance();
float beta = 1.f / (var + r);
float alpha = loss * beta; // (1.f - m) * beta
update(features, y, alpha, beta);
}
}
/**
* @return C - y * p
*/
protected float loss(final float p, final float y) {
return LossFunctions.hingeLoss(p, y, c);
}
}
}