demo.DemoInference.java Source code

Introduction

Here is the source code for demo.DemoInference.java

Source

/*******************************************************************************
 * Copyright (C) 2014 Francois Petitjean
 * 
 * This file is part of Chordalysis.
 * 
 * Chordalysis 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, version 3 of the License.
 * 
 * Chordalysis is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS 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 Chordalysis.  If not, see <http://www.gnu.org/licenses/>.
 ******************************************************************************/
package demo;

import java.io.BufferedOutputStream;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.PrintWriter;
import java.net.URL;
import java.util.Arrays;

import model.DecomposableModel;
import model.Inference;
import weka.core.Instances;
import weka.core.converters.CSVLoader;
import explorer.ChordalysisModelling;

public class DemoInference {

    /**
     * @param args
     * @throws IOException 
     */
    public static void main(String[] args) throws IOException {

        CSVLoader loader = new CSVLoader();
        System.out.println("Downloading dataset...");
        URL oracle = new URL("http://repository.seasr.org/Datasets/UCI/csv/mushroom.csv");
        File csvFile = File.createTempFile("data-", ".csv");
        BufferedReader in = new BufferedReader(new InputStreamReader(oracle.openStream()));
        PrintWriter out = new PrintWriter(new BufferedOutputStream(new FileOutputStream(csvFile)));
        String inputLine;
        while ((inputLine = in.readLine()) != null) {
            out.println(inputLine);
        }
        in.close();
        out.close();
        System.out.println("Dataset written to: " + csvFile.getAbsolutePath());

        loader.setFile(csvFile);
        loader.setNominalAttributes("first-last");
        Instances instances = loader.getDataSet();
        String[] variablesNames = new String[instances.numAttributes()];
        String[][] outcomes = new String[instances.numAttributes()][];
        for (int i = 0; i < variablesNames.length; i++) {
            variablesNames[i] = instances.attribute(i).name();
            outcomes[i] = new String[instances.attribute(i).numValues() + 1];//+1 for missing
            for (int j = 0; j < outcomes[i].length - 1; j++) {
                outcomes[i][j] = instances.attribute(i).value(j);
            }
            outcomes[i][outcomes[i].length - 1] = "missing";
            System.out.println("Dom(" + variablesNames[i] + ") = " + Arrays.toString(outcomes[i]));

        }

        ChordalysisModelling modeller = new ChordalysisModelling(0.05);

        System.out.println("Learning...");
        modeller.buildModel(instances);
        DecomposableModel bestModel = modeller.getModel();
        //      bestModel.display(variablesNames);
        System.out.println("The model selected is:");
        System.out.println(bestModel.toString(variablesNames));

        Inference inference = new Inference(bestModel, variablesNames, outcomes);
        inference.setProbabilities(modeller.getLattice());
        String targetVariable = "population";
        System.out.println("initial beliefs on " + targetVariable + " "
                + Arrays.toString(inference.getBelief(targetVariable)));

        System.out.println("adding evidence poisonous and convex shape");
        inference.addEvidence("class", "e");
        inference.addEvidence("cap-shape", "x");
        inference.recordEvidence();

        System.out.println(
                "beliefs on " + targetVariable + " " + Arrays.toString(inference.getBelief(targetVariable)));

        inference.clearEvidences();
        System.out.println("reset beliefs");
        System.out.println(
                "reset beliefs on " + targetVariable + " " + Arrays.toString(inference.getBelief(targetVariable)));

    }
}