jjj.asap.sas.models1.job.BuildRegressionModels.java Source code

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Here is the source code for jjj.asap.sas.models1.job.BuildRegressionModels.java

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/*
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or GITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 * Copyright (C) 2012 James Jesensky
 */

package jjj.asap.sas.models1.job;

import java.io.FileNotFoundException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.LinkedList;
import java.util.List;
import java.util.Queue;
import java.util.concurrent.Future;

import jjj.asap.sas.util.Bucket;
import jjj.asap.sas.util.Job;
import jjj.asap.sas.util.Progress;
import jjj.asap.sas.weka.RegressionModelBuilder;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.SingleClassifierEnhancer;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.meta.AdditiveRegression;
import weka.classifiers.meta.RandomSubSpace;
import weka.classifiers.trees.DecisionStump;
import weka.classifiers.trees.REPTree;
import weka.core.SelectedTag;

/**
 * Builds a bucket of basic models
 */
public class BuildRegressionModels extends Job {

    private static final SelectedTag M5 = new SelectedTag(LinearRegression.SELECTION_M5,
            LinearRegression.TAGS_SELECTION);
    private static final SelectedTag NONE = new SelectedTag(LinearRegression.SELECTION_NONE,
            LinearRegression.TAGS_SELECTION);

    /**
     * args[0] - input bucket
     * args[1] - output bucket
     */
    public static void main(String[] args) throws Exception {
        Job job = new BuildRegressionModels(args[0], args[1]);
        Job.log("ARGS", Arrays.toString(args));
        job.start();
    }

    private String inputBucket;
    private String outputBucket;

    public BuildRegressionModels(String inputBucket, String outputBucket) {
        super();
        this.inputBucket = inputBucket;
        this.outputBucket = outputBucket;
    }

    @Override
    protected void run() throws Exception {

        // validate args
        if (!Bucket.isBucket("datasets", inputBucket)) {
            throw new FileNotFoundException(inputBucket);
        }
        if (!Bucket.isBucket("models", outputBucket)) {
            throw new FileNotFoundException(outputBucket);
        }

        // create prototype classifiers
        List<Classifier> models = new ArrayList<Classifier>();

        LinearRegression m5 = new LinearRegression();
        m5.setAttributeSelectionMethod(M5);

        LinearRegression lr = new LinearRegression();
        lr.setAttributeSelectionMethod(NONE);

        RandomSubSpace rss = new RandomSubSpace();
        rss.setClassifier(lr);
        rss.setNumIterations(30);

        AdditiveRegression boostedStumps = new AdditiveRegression();
        boostedStumps.setClassifier(new DecisionStump());
        boostedStumps.setNumIterations(1000);

        AdditiveRegression boostedTrees = new AdditiveRegression();
        boostedTrees.setClassifier(new REPTree());
        boostedTrees.setNumIterations(100);

        models.add(m5);
        models.add(boostedStumps);
        models.add(boostedTrees);
        models.add(rss);

        // init multi-threading
        Job.startService();
        final Queue<Future<Object>> queue = new LinkedList<Future<Object>>();

        // get the input from the bucket
        List<String> names = Bucket.getBucketItems("datasets", this.inputBucket);
        for (String dsn : names) {

            for (Classifier model : models) {

                String tag = null;
                if (model instanceof SingleClassifierEnhancer) {
                    tag = model.getClass().getSimpleName() + "-"
                            + ((SingleClassifierEnhancer) model).getClassifier().getClass().getSimpleName();
                } else {
                    tag = model.getClass().getSimpleName();
                }

                queue.add(Job.submit(new RegressionModelBuilder(dsn, tag, AbstractClassifier.makeCopy(model),
                        this.outputBucket)));
            }
        }

        // wait on complete
        Progress progress = new Progress(queue.size(), this.getClass().getSimpleName());
        while (!queue.isEmpty()) {
            try {
                queue.remove().get();
            } catch (Exception e) {
                Job.log("ERROR", e.toString());
            }
            progress.tick();
        }
        progress.done();
        Job.stopService();

    }

}