/*
* 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.multiclass;
import hivemall.model.FeatureValue;
import hivemall.model.IWeightValue;
import hivemall.model.Margin;
import hivemall.model.PredictionModel;
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;
/**
* Multi-class Adaptive Regularization of Weight Vectors (AROW) classifier.
*
* <pre>
* [1] K. Crammer, A. Kulesza, and M. Dredze, "Adaptive Regularization of Weight Vectors",
* In Proc. NIPS, 2009.
* </pre>
*/
@Description(
name = "train_multiclass_arow",
value = "_FUNC_(list<string|int|bigint> features, {int|string} label [, const string options])"
+ " - Returns a relation consists of <{int|string} label, {string|int|bigint} feature, float weight, float covar>",
extended = "Build a prediction model by Adaptive Regularization of Weight Vectors (AROW) multiclass classifier")
public class MulticlassAROWClassifierUDTF extends MulticlassOnlineClassifierUDTF {
/** 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|String} 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, @Nonnull Object actual_label) {
Margin margin = getMarginAndVariance(features, actual_label);
float m = margin.get();
if (m >= 1.f) {
return;
}
float var = margin.getVariance();
float beta = 1.f / (var + r);
float alpha = (1.f - m) * beta;
Object missed_label = margin.getMaxIncorrectLabel();
update(features, actual_label, missed_label, alpha, beta);
}
protected void update(@Nonnull final FeatureValue[] features, final Object actual_label,
final Object missed_label, final float alpha, final float beta) {
assert (actual_label != null);
if (actual_label.equals(missed_label)) {
throw new IllegalArgumentException("Actual label equals to missed label: "
+ actual_label);
}
PredictionModel model2add = label2model.get(actual_label);
if (model2add == null) {
model2add = createModel();
label2model.put(actual_label, model2add);
}
PredictionModel model2sub = null;
if (missed_label != null) {
model2sub = label2model.get(missed_label);
if (model2sub == null) {
model2sub = createModel();
label2model.put(missed_label, model2sub);
}
}
for (FeatureValue f : features) {// w[f] += y * x[f]
if (f == null) {
continue;
}
final Object k = f.getFeature();
final float v = f.getValueAsFloat();
IWeightValue old_correctclass_w = model2add.get(k);
IWeightValue new_correctclass_w = getNewWeight(old_correctclass_w, v, alpha, beta, true);
model2add.set(k, new_correctclass_w);
if (model2sub != null) {
IWeightValue old_wrongclass_w = model2sub.get(k);
IWeightValue new_wrongclass_w = getNewWeight(old_wrongclass_w, v, alpha, beta,
false);
model2sub.set(k, new_wrongclass_w);
}
}
}
private static IWeightValue getNewWeight(final IWeightValue old, final float v,
final float alpha, final float beta, final boolean positive) {
final float old_v;
final float old_cov;
if (old == null) {
old_v = 0.f;
old_cov = 1.f;
} else {
old_v = old.get();
old_cov = old.getCovariance();
}
float cv = old_cov * v;
float new_w = positive ? old_v + (alpha * cv) : old_v - (alpha * cv);
float new_cov = old_cov - (beta * cv * cv);
return new WeightValueWithCovar(new_w, new_cov);
}
@Description(
name = "train_multiclass_arowh",
value = "_FUNC_(list<string|int|bigint> features, int|string label [, const string options])"
+ " - Returns a relation consists of <int|string label, string|int|bigint feature, float weight, float covar>",
extended = "Build a prediction model by Adaptive Regularization of Weight Vectors (AROW) multiclass classifier using hinge loss")
public static final class AROWh extends MulticlassAROWClassifierUDTF {
/** 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, @Nonnull Object actual_label) {
Margin margin = getMarginAndVariance(features, actual_label);
float loss = loss(margin);
if (loss > 0.f) {
float var = margin.getVariance();
float beta = 1.f / (var + r);
float alpha = loss * beta;
Object missed_label = margin.getMaxIncorrectLabel();
update(features, actual_label, missed_label, alpha, beta);
}
}
/**
* @return C - m
*/
protected float loss(Margin margin) {
return c - margin.get();
}
}
}