boa.aggregators.NaiveBayesAggregator.java Source code

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Here is the source code for boa.aggregators.NaiveBayesAggregator.java

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/*
 * Copyright 2014, Hridesh Rajan, Robert Dyer,
 *                 and Iowa State University of Science and Technology
 *
 * Licensed 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 boa.aggregators;

import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import weka.classifiers.bayes.NaiveBayes;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Utils;

/**
 * A Boa aggregator for training the model using Naive Bayes.
 * 
 * @author ankuraga
 *
 * Modified by @abshakiba
 *
 */
@AggregatorSpec(name = "NaiveBayes", formalParameters = { "string", "string" })
public class NaiveBayesAggregator extends MLAggregator {
    private Map<String, List<String>> vectors = new HashMap<String, List<String>>();
    private ArrayList<Double> vector = new ArrayList<Double>();
    private String[] options;
    private String[] attributeList;
    private int count = 0;
    private int inc = 0;
    private NaiveBayes model;

    FastVector fvAttributes = new FastVector();

    // 1, "", [], ...

    public NaiveBayesAggregator(final String s, final String a) {
        super(s);
        try {
            options = Utils.splitOptions(s);
            getAttributes(a, fvAttributes);

        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public void aggregate(final String data, final String metadata) throws IOException, InterruptedException {

        if (this.count != this.fvAttributes.length) {
            try {
                if (Integer.parseInt(data.substring(0, 1)) >= 0 && Integer.parseInt(token.substring(0, 1)) <= 9) {
                    this.vector.add(Double.parseDouble(data));
                }

            } catch (Exception e) {

            }

            this.vector.add(Double.parseDouble(data));
            this.count++;
        }

        if (this.count == this.getVectorSize()) {
            this.vectors.put("Vector " + this.inc, this.vector);
            this.inc++;
            this.vector = new ArrayList<Double>();
            this.count = 0;
        }
    }

    /** {@inheritDoc} */
    @Override
    public void finish() throws IOException, InterruptedException {

        Instances trainingSet = new Instances("NaiveBayes", fvAttributes, 1);
        trainingSet.setClassIndex(NumOfAttributes - 1);

        for (List<Double> vector : this.vectors.values()) {
            Instance instance = new Instance(NumOfAttributes);
            for (int i = 0; i < vector.size(); i++) {
                instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
            }
            trainingSet.add(instance);
        }

        try {
            this.model = new NaiveBayes();
            this.model.setOptions(options);
            this.model.buildClassifier(trainingSet);
        } catch (Exception ex) {
        }

        this.saveModel(this.model);
    }

}