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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* LearningRateResultProducer.java
* Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.core.AdditionalMeasureProducer;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
/**
* Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset. Normally used with an AveragingResultProducer and CrossValidationResultProducer combo to generate learning curve results. For non-numeric result fields, the first value is used.
*
*
* Valid options are:
*
* -X <num steps>
* The number of steps in the learning rate curve.
* (default 10)
*
* -W <class name>
* The full class name of a ResultProducer.
* eg: weka.experiment.CrossValidationResultProducer
*
*
* Options specific to result producer weka.experiment.AveragingResultProducer:
*
*
* -F <field name>
* The name of the field to average over.
* (default "Fold")
*
* -X <num results>
* The number of results expected per average.
* (default 10)
*
* -S
* Calculate standard deviations.
* (default only averages)
*
* -W <class name>
* The full class name of a ResultProducer.
* eg: weka.experiment.CrossValidationResultProducer
*
*
* Options specific to result producer weka.experiment.CrossValidationResultProducer:
*
*
* -X <number of folds>
* The number of folds to use for the cross-validation.
* (default 10)
*
* -D
* Save raw split evaluator output.
*
* -O <file/directory name/path>
* The filename where raw output will be stored.
* If a directory name is specified then then individual
* outputs will be gzipped, otherwise all output will be
* zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
*
* -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator
*
*
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
*
*
* -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes
*
* -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)
*
* -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).
*
* -P
* Add target and prediction columns to the result
* for each fold.
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
* All options after -- will be passed to the result producer.
*
* @author Len Trigg ([email protected])
* @version $Revision: 6425 $
*/
public class LearningRateResultProducer
implements ResultListener, ResultProducer, OptionHandler,
AdditionalMeasureProducer, RevisionHandler {
/** for serialization */
static final long serialVersionUID = -3841159673490861331L;
/** The dataset of interest */
protected Instances m_Instances;
/** The ResultListener to send results to */
protected ResultListener m_ResultListener = new CSVResultListener();
/** The ResultProducer used to generate results */
protected ResultProducer m_ResultProducer
= new AveragingResultProducer();
/** The names of any additional measures to look for in SplitEvaluators */
protected String [] m_AdditionalMeasures = null;
/**
* The minimum number of instances to use. If this is zero, the first
* step will contain m_StepSize instances
*/
protected int m_LowerSize = 0;
/**
* The maximum number of instances to use. -1 indicates no maximum
* (other than the total number of instances)
*/
protected int m_UpperSize = -1;
/** The number of instances to add at each step */
protected int m_StepSize = 10;
/** The current dataset size during stepping */
protected int m_CurrentSize = 0;
/** The name of the key field containing the learning rate step number */
public static String STEP_FIELD_NAME = "Total_instances";
/**
* Returns a string describing this result producer
* @return a description of the result producer suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "Tells a sub-ResultProducer to reproduce the current run for "
+"varying sized subsamples of the dataset. Normally used with "
+"an AveragingResultProducer and CrossValidationResultProducer "
+"combo to generate learning curve results. For non-numeric "
+"result fields, the first value is used.";
}
/**
* Determines if there are any constraints (imposed by the
* destination) on the result columns to be produced by
* resultProducers. Null should be returned if there are NO
* constraints, otherwise a list of column names should be
* returned as an array of Strings.
* @param rp the ResultProducer to which the constraints will apply
* @return an array of column names to which resutltProducer's
* results will be restricted.
* @throws Exception if constraints can't be determined
*/
public String [] determineColumnConstraints(ResultProducer rp)
throws Exception {
return null;
}
/**
* Gets the keys for a specified run number. Different run
* numbers correspond to different randomizations of the data. Keys
* produced should be sent to the current ResultListener
*
* @param run the run number to get keys for.
* @throws Exception if a problem occurs while getting the keys
*/
public void doRunKeys(int run) throws Exception {
if (m_ResultProducer == null) {
throw new Exception("No ResultProducer set");
}
if (m_ResultListener == null) {
throw new Exception("No ResultListener set");
}
if (m_Instances == null) {
throw new Exception("No Instances set");
}
// Tell the resultproducer to send results to us
m_ResultProducer.setResultListener(this);
m_ResultProducer.setInstances(m_Instances);
// For each subsample size
if (m_LowerSize == 0) {
m_CurrentSize = m_StepSize;
} else {
m_CurrentSize = m_LowerSize;
}
while (m_CurrentSize <= m_Instances.numInstances() &&
((m_UpperSize == -1) ||
(m_CurrentSize <= m_UpperSize))) {
m_ResultProducer.doRunKeys(run);
m_CurrentSize += m_StepSize;
}
}
/**
* Gets the results for a specified run number. Different run
* numbers correspond to different randomizations of the data. Results
* produced should be sent to the current ResultListener
*
* @param run the run number to get results for.
* @throws Exception if a problem occurs while getting the results
*/
public void doRun(int run) throws Exception {
if (m_ResultProducer == null) {
throw new Exception("No ResultProducer set");
}
if (m_ResultListener == null) {
throw new Exception("No ResultListener set");
}
if (m_Instances == null) {
throw new Exception("No Instances set");
}
// Randomize on a copy of the original dataset
Instances runInstances = new Instances(m_Instances);
runInstances.randomize(new Random(run));
/*if (runInstances.classAttribute().isNominal()) {
runInstances.stratify(m_StepSize);
}*/
// Tell the resultproducer to send results to us
m_ResultProducer.setResultListener(this);
// For each subsample size
if (m_LowerSize == 0) {
m_CurrentSize = m_StepSize;
} else {
m_CurrentSize = m_LowerSize;
}
while (m_CurrentSize <= m_Instances.numInstances() &&
((m_UpperSize == -1) ||
(m_CurrentSize <= m_UpperSize))) {
m_ResultProducer.setInstances(new Instances(runInstances, 0,
m_CurrentSize));
m_ResultProducer.doRun(run);
m_CurrentSize += m_StepSize;
}
}
/**
* Prepare for the results to be received.
*
* @param rp the ResultProducer that will generate the results
* @throws Exception if an error occurs during preprocessing.
*/
public void preProcess(ResultProducer rp) throws Exception {
if (m_ResultListener == null) {
throw new Exception("No ResultListener set");
}
m_ResultListener.preProcess(this);
}
/**
* Prepare to generate results. The ResultProducer should call
* preProcess(this) on the ResultListener it is to send results to.
*
* @throws Exception if an error occurs during preprocessing.
*/
public void preProcess() throws Exception {
if (m_ResultProducer == null) {
throw new Exception("No ResultProducer set");
}
// Tell the resultproducer to send results to us
m_ResultProducer.setResultListener(this);
m_ResultProducer.preProcess();
}
/**
* When this method is called, it indicates that no more results
* will be sent that need to be grouped together in any way.
*
* @param rp the ResultProducer that generated the results
* @throws Exception if an error occurs
*/
public void postProcess(ResultProducer rp) throws Exception {
m_ResultListener.postProcess(this);
}
/**
* When this method is called, it indicates that no more requests to
* generate results for the current experiment will be sent. The
* ResultProducer should call preProcess(this) on the
* ResultListener it is to send results to.
*
* @throws Exception if an error occurs
*/
public void postProcess() throws Exception {
m_ResultProducer.postProcess();
}
/**
* Accepts results from a ResultProducer.
*
* @param rp the ResultProducer that generated the results
* @param key an array of Objects (Strings or Doubles) that uniquely
* identify a result for a given ResultProducer with given compatibilityState
* @param result the results stored in an array. The objects stored in
* the array may be Strings, Doubles, or null (for the missing value).
* @throws Exception if the result could not be accepted.
*/
public void acceptResult(ResultProducer rp, Object [] key, Object [] result)
throws Exception {
if (m_ResultProducer != rp) {
throw new Error("Unrecognized ResultProducer sending results!!");
}
// Add in current step as key field
Object [] newKey = new Object [key.length + 1];
System.arraycopy(key, 0, newKey, 0, key.length);
newKey[key.length] = new String("" + m_CurrentSize);
// Pass on to result listener
m_ResultListener.acceptResult(this, newKey, result);
}
/**
* Determines whether the results for a specified key must be
* generated.
*
* @param rp the ResultProducer wanting to generate the results
* @param key an array of Objects (Strings or Doubles) that uniquely
* identify a result for a given ResultProducer with given compatibilityState
* @return true if the result should be generated
* @throws Exception if it could not be determined if the result
* is needed.
*/
public boolean isResultRequired(ResultProducer rp, Object [] key)
throws Exception {
if (m_ResultProducer != rp) {
throw new Error("Unrecognized ResultProducer sending results!!");
}
// Add in current step as key field
Object [] newKey = new Object [key.length + 1];
System.arraycopy(key, 0, newKey, 0, key.length);
newKey[key.length] = new String("" + m_CurrentSize);
// Pass on request to result listener
return m_ResultListener.isResultRequired(this, newKey);
}
/**
* Gets the names of each of the columns produced for a single run.
*
* @return an array containing the name of each column
* @throws Exception if key names cannot be generated
*/
public String [] getKeyNames() throws Exception {
String [] keyNames = m_ResultProducer.getKeyNames();
String [] newKeyNames = new String [keyNames.length + 1];
System.arraycopy(keyNames, 0, newKeyNames, 0, keyNames.length);
// Think of a better name for this key field
newKeyNames[keyNames.length] = STEP_FIELD_NAME;
return newKeyNames;
}
/**
* Gets the data types of each of the columns produced for a single run.
* This method should really be static.
*
* @return an array containing objects of the type of each column. The
* objects should be Strings, or Doubles.
* @throws Exception if the key types could not be determined (perhaps
* because of a problem from a nested sub-resultproducer)
*/
public Object [] getKeyTypes() throws Exception {
Object [] keyTypes = m_ResultProducer.getKeyTypes();
Object [] newKeyTypes = new Object [keyTypes.length + 1];
System.arraycopy(keyTypes, 0, newKeyTypes, 0, keyTypes.length);
newKeyTypes[keyTypes.length] = "";
return newKeyTypes;
}
/**
* Gets the names of each of the columns produced for a single run.
* A new result field is added for the number of results used to
* produce each average.
* If only averages are being produced the names are not altered, if
* standard deviations are produced then "Dev_" and "Avg_" are prepended
* to each result deviation and average field respectively.
*
* @return an array containing the name of each column
* @throws Exception if the result names could not be determined (perhaps
* because of a problem from a nested sub-resultproducer)
*/
public String [] getResultNames() throws Exception {
return m_ResultProducer.getResultNames();
}
/**
* Gets the data types of each of the columns produced for a single run.
*
* @return an array containing objects of the type of each column. The
* objects should be Strings, or Doubles.
* @throws Exception if the result types could not be determined (perhaps
* because of a problem from a nested sub-resultproducer)
*/
public Object [] getResultTypes() throws Exception {
return m_ResultProducer.getResultTypes();
}
/**
* Gets a description of the internal settings of the result
* producer, sufficient for distinguishing a ResultProducer
* instance from another with different settings (ignoring
* those settings set through this interface). For example,
* a cross-validation ResultProducer may have a setting for the
* number of folds. For a given state, the results produced should
* be compatible. Typically if a ResultProducer is an OptionHandler,
* this string will represent the command line arguments required
* to set the ResultProducer to that state.
*
* @return the description of the ResultProducer state, or null
* if no state is defined
*/
public String getCompatibilityState() {
String result = " ";
// + "-F " + Utils.quote(getKeyFieldName())
// + " -X " + getStepSize() + " ";
if (m_ResultProducer == null) {
result += "";
} else {
result += "-W " + m_ResultProducer.getClass().getName();
result += " -- " + m_ResultProducer.getCompatibilityState();
}
return result.trim();
}
/**
* Returns an enumeration describing the available options..
*
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(2);
newVector.addElement(new Option(
"\tThe number of steps in the learning rate curve.\n"
+"\t(default 10)",
"X", 1,
"-X "));
newVector.addElement(new Option(
"\tThe full class name of a ResultProducer.\n"
+"\teg: weka.experiment.CrossValidationResultProducer",
"W", 1,
"-W "));
if ((m_ResultProducer != null) &&
(m_ResultProducer instanceof OptionHandler)) {
newVector.addElement(new Option(
"",
"", 0, "\nOptions specific to result producer "
+ m_ResultProducer.getClass().getName() + ":"));
Enumeration enu = ((OptionHandler)m_ResultProducer).listOptions();
while (enu.hasMoreElements()) {
newVector.addElement(enu.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:
*
* -X <num steps>
* The number of steps in the learning rate curve.
* (default 10)
*
* -W <class name>
* The full class name of a ResultProducer.
* eg: weka.experiment.CrossValidationResultProducer
*
*
* Options specific to result producer weka.experiment.AveragingResultProducer:
*
*
* -F <field name>
* The name of the field to average over.
* (default "Fold")
*
* -X <num results>
* The number of results expected per average.
* (default 10)
*
* -S
* Calculate standard deviations.
* (default only averages)
*
* -W <class name>
* The full class name of a ResultProducer.
* eg: weka.experiment.CrossValidationResultProducer
*
*
* Options specific to result producer weka.experiment.CrossValidationResultProducer:
*
*
* -X <number of folds>
* The number of folds to use for the cross-validation.
* (default 10)
*
* -D
* Save raw split evaluator output.
*
* -O <file/directory name/path>
* The filename where raw output will be stored.
* If a directory name is specified then then individual
* outputs will be gzipped, otherwise all output will be
* zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
*
* -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator
*
*
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
*
*
* -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes
*
* -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)
*
* -I <index>
* The index of an attribute to output in the
* results. This attribute should identify an
* instance in order to know which instances are
* in the test set of a cross validation. if 0
* no output (default 0).
*
* -P
* Add target and prediction columns to the result
* for each fold.
*
*
* Options specific to classifier weka.classifiers.rules.ZeroR:
*
*
* -D
* If set, classifier is run in debug mode and
* may output additional info to the console
*
*
* All options after -- will be passed to the result producer.
*
* @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 stepSize = Utils.getOption('S', options);
if (stepSize.length() != 0) {
setStepSize(Integer.parseInt(stepSize));
} else {
setStepSize(10);
}
String lowerSize = Utils.getOption('L', options);
if (lowerSize.length() != 0) {
setLowerSize(Integer.parseInt(lowerSize));
} else {
setLowerSize(0);
}
String upperSize = Utils.getOption('U', options);
if (upperSize.length() != 0) {
setUpperSize(Integer.parseInt(upperSize));
} else {
setUpperSize(-1);
}
String rpName = Utils.getOption('W', options);
if (rpName.length() == 0) {
throw new Exception("A ResultProducer must be specified with"
+ " the -W option.");
}
// Do it first without options, so if an exception is thrown during
// the option setting, listOptions will contain options for the actual
// RP.
setResultProducer((ResultProducer)Utils.forName(
ResultProducer.class,
rpName,
null));
if (getResultProducer() instanceof OptionHandler) {
((OptionHandler) getResultProducer())
.setOptions(Utils.partitionOptions(options));
}
}
/**
* Gets the current settings of the result producer.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
String [] seOptions = new String [0];
if ((m_ResultProducer != null) &&
(m_ResultProducer instanceof OptionHandler)) {
seOptions = ((OptionHandler)m_ResultProducer).getOptions();
}
String [] options = new String [seOptions.length + 9];
int current = 0;
options[current++] = "-S";
options[current++] = "" + getStepSize();
options[current++] = "-L";
options[current++] = "" + getLowerSize();
options[current++] = "-U";
options[current++] = "" + getUpperSize();
if (getResultProducer() != null) {
options[current++] = "-W";
options[current++] = getResultProducer().getClass().getName();
}
options[current++] = "--";
System.arraycopy(seOptions, 0, options, current,
seOptions.length);
current += seOptions.length;
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Set a list of method names for additional measures to look for
* in SplitEvaluators. This could contain many measures (of which only a
* subset may be produceable by the current resultProducer) if an experiment
* is the type that iterates over a set of properties.
* @param additionalMeasures an array of measure names, null if none
*/
public void setAdditionalMeasures(String [] additionalMeasures) {
m_AdditionalMeasures = additionalMeasures;
if (m_ResultProducer != null) {
System.err.println("LearningRateResultProducer: setting additional "
+"measures for "
+"ResultProducer");
m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures);
}
}
/**
* Returns an enumeration of any additional measure names that might be
* in the result producer
* @return an enumeration of the measure names
*/
public Enumeration enumerateMeasures() {
Vector newVector = new Vector();
if (m_ResultProducer instanceof AdditionalMeasureProducer) {
Enumeration en = ((AdditionalMeasureProducer)m_ResultProducer).
enumerateMeasures();
while (en.hasMoreElements()) {
String mname = (String)en.nextElement();
newVector.addElement(mname);
}
}
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 (m_ResultProducer instanceof AdditionalMeasureProducer) {
return ((AdditionalMeasureProducer)m_ResultProducer).
getMeasure(additionalMeasureName);
} else {
throw new IllegalArgumentException("LearningRateResultProducer: "
+"Can't return value for : "+additionalMeasureName
+". "+m_ResultProducer.getClass().getName()+" "
+"is not an AdditionalMeasureProducer");
}
}
/**
* Sets the dataset that results will be obtained for.
*
* @param instances a value of type 'Instances'.
*/
public void setInstances(Instances instances) {
m_Instances = instances;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String lowerSizeTipText() {
return "Set the minmum number of instances in a dataset. Setting zero "
+ "here will actually use number of instances at the first "
+ "step (since it makes no sense to use zero instances :-))";
}
/**
* Get the value of LowerSize.
*
* @return Value of LowerSize.
*/
public int getLowerSize() {
return m_LowerSize;
}
/**
* Set the value of LowerSize.
*
* @param newLowerSize Value to assign to
* LowerSize.
*/
public void setLowerSize(int newLowerSize) {
m_LowerSize = newLowerSize;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String upperSizeTipText() {
return "Set the maximum number of instances in a dataset. Setting -1 "
+ "sets no upper limit (other than the total number of instances "
+ "in the full dataset)";
}
/**
* Get the value of UpperSize.
*
* @return Value of UpperSize.
*/
public int getUpperSize() {
return m_UpperSize;
}
/**
* Set the value of UpperSize.
*
* @param newUpperSize Value to assign to
* UpperSize.
*/
public void setUpperSize(int newUpperSize) {
m_UpperSize = newUpperSize;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String stepSizeTipText() {
return "Set the number of instances to add at each step.";
}
/**
* Get the value of StepSize.
*
* @return Value of StepSize.
*/
public int getStepSize() {
return m_StepSize;
}
/**
* Set the value of StepSize.
*
* @param newStepSize Value to assign to
* StepSize.
*/
public void setStepSize(int newStepSize) {
m_StepSize = newStepSize;
}
/**
* Sets the object to send results of each run to.
*
* @param listener a value of type 'ResultListener'
*/
public void setResultListener(ResultListener listener) {
m_ResultListener = listener;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String resultProducerTipText() {
return "Set the resultProducer for which learning rate results should be "
+ "generated.";
}
/**
* Get the ResultProducer.
*
* @return the ResultProducer.
*/
public ResultProducer getResultProducer() {
return m_ResultProducer;
}
/**
* Set the ResultProducer.
*
* @param newResultProducer new ResultProducer to use.
*/
public void setResultProducer(ResultProducer newResultProducer) {
m_ResultProducer = newResultProducer;
m_ResultProducer.setResultListener(this);
}
/**
* Gets a text descrption of the result producer.
*
* @return a text description of the result producer.
*/
public String toString() {
String result = "LearningRateResultProducer: ";
result += getCompatibilityState();
if (m_Instances == null) {
result += ": ";
} else {
result += ": " + Utils.backQuoteChars(m_Instances.relationName());
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 6425 $");
}
} // LearningRateResultProducer
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