/*
* 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 -->
*
<!-- technical-bibtex-start -->
* BibTeX:
* <pre>
* @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 -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -I <number of trees>
* Number of trees to build.</pre>
*
* <pre> -K <number of features>
* Number of features to consider (<1=int(logM+1)).</pre>
*
* <pre> -S
* Seed for random number generator.
* (default 1)</pre>
*
* <pre> -depth <num>
* 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 <num>
* 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>
*
<!-- 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/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -I <number of trees>
* Number of trees to build.</pre>
*
* <pre> -K <number of features>
* Number of features to consider (<1=int(logM+1)).</pre>
*
* <pre> -S
* Seed for random number generator.
* (default 1)</pre>
*
* <pre> -depth <num>
* 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 <num>
* 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>
*
<!-- 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);
}
}