/*
 *   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 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RandomForest.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees;

import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.meta.Bagging;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.PartitionGenerator;

/**
 * <!-- globalinfo-start -->
 * Class for constructing a forest of random trees.<br/>
 * <br/>
 * For more information see: <br/>
 * <br/>
 * Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
 * <p/>
 * <!-- globalinfo-end -->
 * <p>
 * <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{Breiman2001,
 *    author = {Leo Breiman},
 *    journal = {Machine Learning},
 *    number = {1},
 *    pages = {5-32},
 *    title = {Random Forests},
 *    volume = {45},
 *    year = {2001}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * <p>
 * <!-- options-start -->
 * Valid options are: <p/>
 *
 * <pre> -I &lt;number of trees&gt;
 *  Number of trees to build.</pre>
 *
 * <pre> -K &lt;number of features&gt;
 *  Number of features to consider (&lt;1=int(logM+1)).</pre>
 *
 * <pre> -S
 *  Seed for random number generator.
 *  (default 1)</pre>
 *
 * <pre> -depth &lt;num&gt;
 *  The maximum depth of the trees, 0 for unlimited.
 *  (default 0)</pre>
 *
 * <pre> -print
 *  Print the individual trees in the output</pre>
 *
 * <pre> -num-slots &lt;num&gt;
 *  Number of execution slots.
 *  (default 1 - i.e. no parallelism)</pre>
 *
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * <p>
 * <!-- options-end -->
 *
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 9186 $
 */
public class RandomForest
        extends AbstractClassifier
        implements OptionHandler, Randomizable, WeightedInstancesHandler,
        AdditionalMeasureProducer, TechnicalInformationHandler,
        PartitionGenerator {

    /**
     * for serialization
     */
    static final long serialVersionUID = 1116839470751428698L;

    /**
     * Number of trees in forest.
     */
    protected int m_numTrees = 10;

    /**
     * Number of features to consider in random feature selection.
     * If less than 1 will use int(logM+1) )
     */
    protected int m_numFeatures = 0;

    /**
     * The random seed.
     */
    protected int m_randomSeed = 1;

    /**
     * Final number of features that were considered in last build.
     */
    protected int m_KValue = 0;

    /**
     * The bagger.
     */
    protected Bagging m_bagger = null;

    /**
     * The maximum depth of the trees (0 = unlimited)
     */
    protected int m_MaxDepth = 0;

    /**
     * The number of threads to have executing at any one time
     */
    protected int m_numExecutionSlots = 1;

    /**
     * Print the individual trees in the output
     */
    protected boolean m_printTrees = false;

    /**
     * Returns a string describing classifier
     *
     * @return a description suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {

        return
                "Class for constructing a forest of random trees.\n\n"
                        + "For more information see: \n\n"
                        + getTechnicalInformation().toString();
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing
     * detailed information about the technical background of this class,
     * e.g., paper reference or book this class is based on.
     *
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;

        result = new TechnicalInformation(Type.ARTICLE);
        result.setValue(Field.AUTHOR, "Leo Breiman");
        result.setValue(Field.YEAR, "2001");
        result.setValue(Field.TITLE, "Random Forests");
        result.setValue(Field.JOURNAL, "Machine Learning");
        result.setValue(Field.VOLUME, "45");
        result.setValue(Field.NUMBER, "1");
        result.setValue(Field.PAGES, "5-32");

        return result;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numTreesTipText() {
        return "The number of trees to be generated.";
    }

    /**
     * Get the value of numTrees.
     *
     * @return Value of numTrees.
     */
    public int getNumTrees() {

        return m_numTrees;
    }

    /**
     * Set the value of numTrees.
     *
     * @param newNumTrees Value to assign to numTrees.
     */
    public void setNumTrees(int newNumTrees) {

        m_numTrees = newNumTrees;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numFeaturesTipText() {
        return "The number of attributes to be used in random selection (see RandomTree).";
    }

    /**
     * Get the number of features used in random selection.
     *
     * @return Value of numFeatures.
     */
    public int getNumFeatures() {

        return m_numFeatures;
    }

    /**
     * Set the number of features to use in random selection.
     *
     * @param newNumFeatures Value to assign to numFeatures.
     */
    public void setNumFeatures(int newNumFeatures) {

        m_numFeatures = newNumFeatures;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String seedTipText() {
        return "The random number seed to be used.";
    }

    /**
     * Set the seed for random number generation.
     *
     * @param seed the seed
     */
    public void setSeed(int seed) {

        m_randomSeed = seed;
    }

    /**
     * Gets the seed for the random number generations
     *
     * @return the seed for the random number generation
     */
    public int getSeed() {

        return m_randomSeed;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String maxDepthTipText() {
        return "The maximum depth of the trees, 0 for unlimited.";
    }

    /**
     * Get the maximum depth of trh tree, 0 for unlimited.
     *
     * @return the maximum depth.
     */
    public int getMaxDepth() {
        return m_MaxDepth;
    }

    /**
     * Set the maximum depth of the tree, 0 for unlimited.
     *
     * @param value the maximum depth.
     */
    public void setMaxDepth(int value) {
        m_MaxDepth = value;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String printTreesTipText() {
        return "Print the individual trees in the output";
    }

    /**
     * Set whether to print the individual ensemble trees in the output
     *
     * @param print true if the individual trees are to be printed
     */
    public void setPrintTrees(boolean print) {
        m_printTrees = print;
    }

    /**
     * Get whether to print the individual ensemble trees in the output
     *
     * @return true if the individual trees are to be printed
     */
    public boolean getPrintTrees() {
        return m_printTrees;
    }

    /**
     * Gets the out of bag error that was calculated as the classifier was built.
     *
     * @return the out of bag error
     */
    public double measureOutOfBagError() {

        if (m_bagger != null) {
            return m_bagger.measureOutOfBagError();
        } else return Double.NaN;
    }

    /**
     * Set the number of execution slots (threads) to use for building the
     * members of the ensemble.
     *
     * @param numSlots the number of slots to use.
     */
    public void setNumExecutionSlots(int numSlots) {
        m_numExecutionSlots = numSlots;
    }

    /**
     * Get the number of execution slots (threads) to use for building
     * the members of the ensemble.
     *
     * @return the number of slots to use
     */
    public int getNumExecutionSlots() {
        return m_numExecutionSlots;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numExecutionSlotsTipText() {
        return "The number of execution slots (threads) to use for " +
                "constructing the ensemble.";
    }

    /**
     * Returns an enumeration of the additional measure names.
     *
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {

        Vector newVector = new Vector(1);
        newVector.addElement("measureOutOfBagError");
        return newVector.elements();
    }

    /**
     * Returns the value of the named measure.
     *
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {

        if (additionalMeasureName.equalsIgnoreCase("measureOutOfBagError")) {
            return measureOutOfBagError();
        } else {
            throw new IllegalArgumentException(additionalMeasureName
                    + " not supported (RandomForest)");
        }
    }

    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options
     */
    public Enumeration listOptions() {

        Vector newVector = new Vector();

        newVector.addElement(new Option(
                "\tNumber of trees to build.",
                "I", 1, "-I <number of trees>"));

        newVector.addElement(new Option(
                "\tNumber of features to consider (<1=int(logM+1)).",
                "K", 1, "-K <number of features>"));

        newVector.addElement(new Option(
                "\tSeed for random number generator.\n"
                        + "\t(default 1)",
                "S", 1, "-S"));

        newVector.addElement(new Option(
                "\tThe maximum depth of the trees, 0 for unlimited.\n"
                        + "\t(default 0)",
                "depth", 1, "-depth <num>"));

        newVector.addElement(new Option(
                "\tPrint the individual trees in the output", "print", 0, "-print"));

        newVector.addElement(new Option(
                "\tNumber of execution slots.\n"
                        + "\t(default 1 - i.e. no parallelism)",
                "num-slots", 1, "-num-slots <num>"));

        Enumeration enu = super.listOptions();
        while (enu.hasMoreElements()) {
            newVector.addElement(enu.nextElement());
        }

        return newVector.elements();
    }

    /**
     * Gets the current settings of the forest.
     *
     * @return an array of strings suitable for passing to setOptions()
     */
    public String[] getOptions() {
        Vector result;
        String[] options;
        int i;

        result = new Vector();

        result.add("-I");
        result.add("" + getNumTrees());

        result.add("-K");
        result.add("" + getNumFeatures());

        result.add("-S");
        result.add("" + getSeed());

        if (getMaxDepth() > 0) {
            result.add("-depth");
            result.add("" + getMaxDepth());
        }

        if (m_printTrees) {
            result.add("-print");
        }

        result.add("-num-slots");
        result.add("" + getNumExecutionSlots());

        options = super.getOptions();
        for (i = 0; i < options.length; i++)
            result.add(options[i]);

        return (String[]) result.toArray(new String[result.size()]);
    }

    /**
     * Parses a given list of options. <p/>
     * <p>
     * <!-- options-start -->
     * Valid options are: <p/>
     *
     * <pre> -I &lt;number of trees&gt;
     *  Number of trees to build.</pre>
     *
     * <pre> -K &lt;number of features&gt;
     *  Number of features to consider (&lt;1=int(logM+1)).</pre>
     *
     * <pre> -S
     *  Seed for random number generator.
     *  (default 1)</pre>
     *
     * <pre> -depth &lt;num&gt;
     *  The maximum depth of the trees, 0 for unlimited.
     *  (default 0)</pre>
     *
     * <pre> -print
     *  Print the individual trees in the output</pre>
     *
     * <pre> -num-slots &lt;num&gt;
     *  Number of execution slots.
     *  (default 1 - i.e. no parallelism)</pre>
     *
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</pre>
     * <p>
     * <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {
        String tmpStr;

        tmpStr = Utils.getOption('I', options);
        if (tmpStr.length() != 0) {
            m_numTrees = Integer.parseInt(tmpStr);
        } else {
            m_numTrees = 10;
        }

        tmpStr = Utils.getOption('K', options);
        if (tmpStr.length() != 0) {
            m_numFeatures = Integer.parseInt(tmpStr);
        } else {
            m_numFeatures = 0;
        }

        tmpStr = Utils.getOption('S', options);
        if (tmpStr.length() != 0) {
            setSeed(Integer.parseInt(tmpStr));
        } else {
            setSeed(1);
        }

        tmpStr = Utils.getOption("depth", options);
        if (tmpStr.length() != 0) {
            setMaxDepth(Integer.parseInt(tmpStr));
        } else {
            setMaxDepth(0);
        }

        setPrintTrees(Utils.getFlag("print", options));

        tmpStr = Utils.getOption("num-slots", options);
        if (tmpStr.length() > 0) {
            setNumExecutionSlots(Integer.parseInt(tmpStr));
        } else {
            setNumExecutionSlots(1);
        }

        super.setOptions(options);

        Utils.checkForRemainingOptions(options);
    }

    /**
     * Returns default capabilities of the classifier.
     *
     * @return the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
        return new RandomTree().getCapabilities();
    }

    /**
     * Builds a classifier for a set of instances.
     *
     * @param data the instances to train the classifier with
     * @throws Exception if something goes wrong
     */
    public void buildClassifier(Instances data) throws Exception {

        // can classifier handle the data?
        getCapabilities().testWithFail(data);

        // remove instances with missing class
        data = new Instances(data);
        data.deleteWithMissingClass();

        m_bagger = new Bagging();
        RandomTree rTree = new RandomTree();

        // set up the random tree options
        m_KValue = m_numFeatures;
        if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes()) + 1;
        rTree.setKValue(m_KValue);
        rTree.setMaxDepth(getMaxDepth());

        // set up the bagger and build the forest
        m_bagger.setClassifier(rTree);
        m_bagger.setSeed(m_randomSeed);
        m_bagger.setNumIterations(m_numTrees);
        m_bagger.setCalcOutOfBag(true);
        m_bagger.setNumExecutionSlots(m_numExecutionSlots);
        m_bagger.buildClassifier(data);
    }

    /**
     * Returns the class probability distribution for an instance.
     *
     * @param instance the instance to be classified
     * @return the distribution the forest generates for the instance
     * @throws Exception if computation fails
     */
    public double[] distributionForInstance(Instance instance) throws Exception {

        return m_bagger.distributionForInstance(instance);
    }

    /**
     * Outputs a description of this classifier.
     *
     * @return a string containing a description of the classifier
     */
    public String toString() {

        if (m_bagger == null) {
            return "Random forest not built yet";
        } else {
            StringBuffer temp = new StringBuffer();
            temp.append("Random forest of " + m_numTrees
                    + " trees, each constructed while considering "
                    + m_KValue + " random feature" + (m_KValue == 1 ? "" : "s") + ".\n"
                    + "Out of bag error: "
                    + Utils.doubleToString(m_bagger.measureOutOfBagError(), 4) + "\n"
                    + (getMaxDepth() > 0 ? ("Max. depth of trees: " + getMaxDepth() + "\n") : (""))
                    + "\n");
            if (m_printTrees) {
                temp.append(m_bagger.toString());
            }
            return temp.toString();
        }
    }

    /**
     * Builds the classifier to generate a partition.
     */
    public void generatePartition(Instances data) throws Exception {

        buildClassifier(data);
    }

    /**
     * Computes an array that indicates leaf membership
     */
    public double[] getMembershipValues(Instance inst) throws Exception {

        return m_bagger.getMembershipValues(inst);
    }

    /**
     * Returns the number of elements in the partition.
     */
    public int numElements() throws Exception {

        return m_bagger.numElements();
    }

    /**
     * Returns the revision string.
     *
     * @return the revision
     */
    public String getRevision() {
        return RevisionUtils.extract("$Revision: 9186 $");
    }

    /**
     * Main method for this class.
     *
     * @param argv the options
     */
    public static void main(String[] argv) {
        runClassifier(new RandomForest(), argv);
    }

}

