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/*
 * 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.
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package org.apache.mahout.classifier.sgd;

import org.apache.mahout.classifier.AbstractVectorClassifier;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.Vector;

import java.util.Random;

/**
 * 

Provides a stochastic mixture of ranking updates and normal logistic updates. This uses a * combination of AUC driven learning to improve ranking performance and traditional log-loss driven * learning to improve log-likelihood.

* *

See www.eecs.tufts.edu/~dsculley/papers/combined-ranking-and-regression.pdf

* *

This implementation only makes sense for the binomial case.

*/ public class MixedGradient implements Gradient { private final double alpha; private final RankingGradient rank; private final Gradient basic; private final Random random = RandomUtils.getRandom(); private boolean hasZero; private boolean hasOne; public MixedGradient(double alpha, int window) { this.alpha = alpha; this.rank = new RankingGradient(window); this.basic = this.rank.getBaseGradient(); } @Override public Vector apply(String groupKey, int actual, Vector instance, AbstractVectorClassifier classifier) { if (random.nextDouble() < alpha) { // one option is to apply a ranking update relative to our recent history if (!hasZero || !hasOne) { throw new IllegalStateException(); } return rank.apply(groupKey, actual, instance, classifier); } else { hasZero |= actual == 0; hasOne |= actual == 1; // the other option is a normal update, but we have to update our history on the way rank.addToHistory(actual, instance); return basic.apply(groupKey, actual, instance, classifier); } } }




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