/*
* 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.regression;
import hivemall.common.LossFunctions;
import hivemall.common.OnlineVariance;
import hivemall.model.FeatureValue;
import hivemall.model.PredictionResult;
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;
@Description(
name = "train_pa1_regr",
value = "_FUNC_(array<int|bigint|string> features, float target [, constant string options])"
+ " - Returns a relation consists of <{int|bigint|string} feature, float weight>")
public class PassiveAggressiveRegressionUDTF extends RegressionBaseUDTF {
/** 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(
"_FUNC_ 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(@Nonnull final FeatureValue[] 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)) {
onlineUpdate(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);
}
@Description(
name = "train_pa1a_regr",
value = "_FUNC_(array<int|bigint|string> features, float target [, constant string options])"
+ " - Returns a relation consists of <{int|bigint|string} feature, float weight>")
public static final 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);
}
}
@Description(
name = "train_pa2_regr",
value = "_FUNC_(array<int|bigint|string> features, float target [, constant string options])"
+ " - Returns a relation consists of <{int|bigint|string} feature, float weight>")
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;
}
}
@Description(
name = "train_pa2a_regr",
value = "_FUNC_(array<int|bigint|string> features, float target [, constant string options])"
+ " - Returns a relation consists of <{int|bigint|string} feature, float weight>")
public static final 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);
}
}
}