com.netease.news.classifier.naivebayes.AbstractNaiveBayesClassifier.java Source code

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Here is the source code for com.netease.news.classifier.naivebayes.AbstractNaiveBayesClassifier.java

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package com.netease.news.classifier.naivebayes;

/**
 * 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.
 */

import org.apache.mahout.classifier.AbstractVectorClassifier;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.Vector.Element;

/**
 * Class implementing the Naive Bayes Classifier Algorithm. Note that this class
 * supports {@link #classifyFull}, but not {@code classify} or
 * {@code classifyScalar}. The reason that these two methods are not
 * supported is because the scores computed by a NaiveBayesClassifier do not
 * represent probabilities.
 */
public abstract class AbstractNaiveBayesClassifier extends AbstractVectorClassifier {

    private final NaiveBayesModel model;

    protected AbstractNaiveBayesClassifier(NaiveBayesModel model) {
        this.model = model;
    }

    protected NaiveBayesModel getModel() {
        return model;
    }

    protected abstract double getScoreForLabelFeature(int label, int feature);

    protected double getScoreForLabelInstance(int label, Vector instance) {
        double result = 0.0;
        for (Element e : instance.nonZeroes()) {
            result += e.get() * getScoreForLabelFeature(label, e.index());
        }
        return result;
    }

    @Override
    public int numCategories() {
        return model.numLabels();
    }

    @Override
    public Vector classifyFull(Vector instance) {
        return classifyFull(model.createScoringVector(), instance);
    }

    @Override
    public Vector classifyFull(Vector r, Vector instance) {
        for (int label = 0; label < model.numLabels(); label++) {
            r.setQuick(label, getScoreForLabelInstance(label, instance));
        }
        return r;
    }

    /** Unsupported method. This implementation simply throws an {@link UnsupportedOperationException}. */
    @Override
    public double classifyScalar(Vector instance) {
        throw new UnsupportedOperationException("Not supported in Naive Bayes");
    }

    /** Unsupported method. This implementation simply throws an {@link UnsupportedOperationException}. */
    @Override
    public Vector classify(Vector instance) {
        throw new UnsupportedOperationException("probabilites not supported in Naive Bayes");
    }
}