/*
* 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 hivemall.utils.math.StatsUtils;
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;
/**
* A multi-class confidence-weighted linear classification.
*
* <pre>
* [1] Mark Dredze, Koby Crammer and Fernando Pereira. "Confidence-weighted linear classification",
* In Proc. ICML, pp.264-271, 2008.
* [2] Koby Crammer, Mark Dredze and Alex Kulesza. "Multi-class confidence weighted algorithms",
* In Proc. EMNLP, Vol. 2, pp.496-504, 2008.
* </pre>
*
* @link http://dl.acm.org/citation.cfm?id=1390190
* @link http://dl.acm.org/citation.cfm?id=1699577
*/
@Description(
name = "train_multiclass_cw",
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 Confidence-Weighted (CW) multiclass classifier")
public class MulticlassConfidenceWeightedUDTF extends MulticlassOnlineClassifierUDTF {
/** confidence parameter phi */
protected float phi;
@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("phi", "confidence", true, "Confidence parameter [default 1.0]");
opts.addOption("eta", "hyper_c", true,
"Confidence hyperparameter eta in range (0.5, 1] [default 0.85]");
return opts;
}
@Override
protected CommandLine processOptions(ObjectInspector[] argOIs) throws UDFArgumentException {
final CommandLine cl = super.processOptions(argOIs);
float phi = 1.f;
if (cl != null) {
String phi_str = cl.getOptionValue("phi");
if (phi_str == null) {
String eta_str = cl.getOptionValue("eta");
if (eta_str != null) {
double eta = Double.parseDouble(eta_str);
if (eta <= 0.5 || eta > 1) {
throw new UDFArgumentException(
"Confidence hyperparameter eta must be in range (0.5, 1]: " + eta_str);
}
phi = (float) StatsUtils.probit(eta, 5d);
}
} else {
phi = Float.parseFloat(phi_str);
}
}
this.phi = phi;
return cl;
}
@Override
protected void train(@Nonnull final FeatureValue[] features, @Nonnull Object actual_label) {
Margin margin = getMarginAndVariance(features, actual_label, true);
float gamma = getGamma(margin);
if (gamma > 0.f) {// alpha = max(0, gamma)
Object missed_label = margin.getMaxIncorrectLabel();
update(features, gamma, actual_label, missed_label);
}
}
protected final float getGamma(Margin margin) {
float m = margin.get();
float var = margin.getVariance();
assert (var != 0);
float b = 1.f + 2.f * phi * m;
float gamma_numer = -b + (float) Math.sqrt(b * b - 8.f * phi * (m - phi * var));
float gamma_denom = 4.f * phi * var;
if (gamma_denom == 0.f) {// avoid divide-by-zero
return 0.f;
}
return gamma_numer / gamma_denom;
}
protected void update(@Nonnull final FeatureValue[] features, float alpha, Object actual_label,
Object missed_label) {
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, phi, 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, phi, false);
model2sub.set(k, new_wrongclass_w);
}
}
}
private static IWeightValue getNewWeight(final IWeightValue old, final float x,
final float alpha, final float phi, final boolean positive) {
final float old_w, old_cov;
if (old == null) {
old_w = 0.f;
old_cov = 1.f;
} else {
old_w = old.get();
old_cov = old.getCovariance();
}
float delta_w = alpha * old_cov * x;
float new_w = positive ? old_w + delta_w : old_w - delta_w;
float new_cov = 1.f / (1.f / old_cov + (2.f * alpha * phi * x * x));
return new WeightValueWithCovar(new_w, new_cov);
}
}