/*
* 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/>.
*/
/*
* AveragingResultProducer.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Vector;
import weka.core.AdditionalMeasureProducer;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* Takes the results from a ResultProducer and submits the average to the result listener. Normally used with a CrossValidationResultProducer to perform n x m fold cross validation. For non-numeric result fields, the first value is used.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -F <field name>
* The name of the field to average over.
* (default "Fold")</pre>
*
* <pre> -X <num results>
* The number of results expected per average.
* (default 10)</pre>
*
* <pre> -S
* Calculate standard deviations.
* (default only averages)</pre>
*
* <pre> -W <class name>
* The full class name of a ResultProducer.
* eg: weka.experiment.CrossValidationResultProducer</pre>
*
* <pre>
* Options specific to result producer weka.experiment.CrossValidationResultProducer:
* </pre>
*
* <pre> -X <number of folds>
* The number of folds to use for the cross-validation.
* (default 10)</pre>
*
* <pre> -D
* Save raw split evaluator output.</pre>
*
* <pre> -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)</pre>
*
* <pre> -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator</pre>
*
* <pre>
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
* </pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -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).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* All options after -- will be passed to the result producer.
*
* @author Len Trigg (trigg@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class AveragingResultProducer
implements ResultListener, ResultProducer, OptionHandler,
AdditionalMeasureProducer, RevisionHandler {
/** for serialization */
static final long serialVersionUID = 2551284958501991352L;
/** 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 CrossValidationResultProducer();
/** The names of any additional measures to look for in SplitEvaluators */
protected String [] m_AdditionalMeasures = null;
/** The number of results expected to average over for each run */
protected int m_ExpectedResultsPerAverage = 10;
/** True if standard deviation fields should be produced */
protected boolean m_CalculateStdDevs;
/**
* The name of the field that will contain the number of results
* averaged over.
*/
protected String m_CountFieldName = "Num_" + CrossValidationResultProducer
.FOLD_FIELD_NAME;
/** The name of the key field to average over */
protected String m_KeyFieldName = CrossValidationResultProducer
.FOLD_FIELD_NAME;
/** The index of the field to average over in the resultproducers key */
protected int m_KeyIndex = -1;
/** Collects the keys from a single run */
protected FastVector m_Keys = new FastVector();
/** Collects the results from a single run */
protected FastVector m_Results = new FastVector();
/**
* 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 "Takes the results from a ResultProducer "
+"and submits the average to the result listener. Normally used with "
+"a CrossValidationResultProducer to perform n x m fold cross "
+"validation. For non-numeric result fields, the first value is used.";
}
/**
* Scans through the key field names of the result producer to find
* the index of the key field to average over. Sets the value of
* m_KeyIndex to the index, or -1 if no matching key field was found.
*
* @return the index of the key field to average over
*/
protected int findKeyIndex() {
m_KeyIndex = -1;
try {
if (m_ResultProducer != null) {
String [] keyNames = m_ResultProducer.getKeyNames();
for (int i = 0; i < keyNames.length; i++) {
if (keyNames[i].equals(m_KeyFieldName)) {
m_KeyIndex = i;
break;
}
}
}
} catch (Exception ex) {
}
return m_KeyIndex;
}
/**
* 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;
}
/**
* Simulates a run to collect the keys the sub-resultproducer could
* generate. Does some checking on the keys and determines the
* template key.
*
* @param run the run number
* @return a template key (null for the field being averaged)
* @throws Exception if an error occurs
*/
protected Object [] determineTemplate(int run) throws Exception {
if (m_Instances == null) {
throw new Exception("No Instances set");
}
m_ResultProducer.setInstances(m_Instances);
// Clear the collected results
m_Keys.removeAllElements();
m_Results.removeAllElements();
m_ResultProducer.doRunKeys(run);
checkForMultipleDifferences();
Object [] template = (Object [])((Object [])m_Keys.elementAt(0)).clone();
template[m_KeyIndex] = null;
// Check for duplicate keys
checkForDuplicateKeys(template);
return template;
}
/**
* 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 {
// Generate the template
Object [] template = determineTemplate(run);
String [] newKey = new String [template.length - 1];
System.arraycopy(template, 0, newKey, 0, m_KeyIndex);
System.arraycopy(template, m_KeyIndex + 1,
newKey, m_KeyIndex,
template.length - m_KeyIndex - 1);
m_ResultListener.acceptResult(this, newKey, null);
}
/**
* 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 {
// Generate the key and ask whether the result is required
Object [] template = determineTemplate(run);
String [] newKey = new String [template.length - 1];
System.arraycopy(template, 0, newKey, 0, m_KeyIndex);
System.arraycopy(template, m_KeyIndex + 1,
newKey, m_KeyIndex,
template.length - m_KeyIndex - 1);
if (m_ResultListener.isResultRequired(this, newKey)) {
// Clear the collected keys
m_Keys.removeAllElements();
m_Results.removeAllElements();
m_ResultProducer.doRun(run);
// Average the results collected
//System.err.println("Number of results collected: " + m_Keys.size());
// Check that the keys only differ on the selected key field
checkForMultipleDifferences();
template = (Object [])((Object [])m_Keys.elementAt(0)).clone();
template[m_KeyIndex] = null;
// Check for duplicate keys
checkForDuplicateKeys(template);
// Calculate the average and submit it if necessary
doAverageResult(template);
}
}
/**
* Compares a key to a template to see whether they match. Null
* fields in the template are ignored in the matching.
*
* @param template the template to match against
* @param test the key to test
* @return true if the test key matches the template on all non-null template
* fields
*/
protected boolean matchesTemplate(Object [] template, Object [] test) {
if (template.length != test.length) {
return false;
}
for (int i = 0; i < test.length; i++) {
if ((template[i] != null) && (!template[i].equals(test[i]))) {
return false;
}
}
return true;
}
/**
* Asks the resultlistener whether an average result is required, and
* if so, calculates it.
*
* @param template the template to match keys against when calculating the
* average
* @throws Exception if an error occurs
*/
protected void doAverageResult(Object [] template) throws Exception {
// Generate the key and ask whether the result is required
String [] newKey = new String [template.length - 1];
System.arraycopy(template, 0, newKey, 0, m_KeyIndex);
System.arraycopy(template, m_KeyIndex + 1,
newKey, m_KeyIndex,
template.length - m_KeyIndex - 1);
if (m_ResultListener.isResultRequired(this, newKey)) {
Object [] resultTypes = m_ResultProducer.getResultTypes();
Stats [] stats = new Stats [resultTypes.length];
for (int i = 0; i < stats.length; i++) {
stats[i] = new Stats();
}
Object [] result = getResultTypes();
int numMatches = 0;
for (int i = 0; i < m_Keys.size(); i++) {
Object [] currentKey = (Object [])m_Keys.elementAt(i);
// Skip non-matching keys
if (!matchesTemplate(template, currentKey)) {
continue;
}
// Add the results to the stats accumulator
Object [] currentResult = (Object [])m_Results.elementAt(i);
numMatches++;
for (int j = 0; j < resultTypes.length; j++) {
if (resultTypes[j] instanceof Double) {
if (currentResult[j] == null) {
// set the stats object for this result to null---
// more than likely this is an additional measure field
// not supported by the low level split evaluator
if (stats[j] != null) {
stats[j] = null;
}
/* throw new Exception("Null numeric result field found:\n"
+ DatabaseUtils.arrayToString(currentKey)
+ " -- "
+ DatabaseUtils
.arrayToString(currentResult)); */
}
if (stats[j] != null) {
double currentVal = ((Double)currentResult[j]).doubleValue();
stats[j].add(currentVal);
}
}
}
}
if (numMatches != m_ExpectedResultsPerAverage) {
throw new Exception("Expected " + m_ExpectedResultsPerAverage
+ " results matching key \""
+ DatabaseUtils.arrayToString(template)
+ "\" but got "
+ numMatches);
}
result[0] = new Double(numMatches);
Object [] currentResult = (Object [])m_Results.elementAt(0);
int k = 1;
for (int j = 0; j < resultTypes.length; j++) {
if (resultTypes[j] instanceof Double) {
if (stats[j] != null) {
stats[j].calculateDerived();
result[k++] = new Double(stats[j].mean);
} else {
result[k++] = null;
}
if (getCalculateStdDevs()) {
if (stats[j] != null) {
result[k++] = new Double(stats[j].stdDev);
} else {
result[k++] = null;
}
}
} else {
result[k++] = currentResult[j];
}
}
m_ResultListener.acceptResult(this, newKey, result);
}
}
/**
* Checks whether any duplicate results (with respect to a key template)
* were received.
*
* @param template the template key.
* @throws Exception if duplicate results are detected
*/
protected void checkForDuplicateKeys(Object [] template) throws Exception {
Hashtable hash = new Hashtable();
int numMatches = 0;
for (int i = 0; i < m_Keys.size(); i++) {
Object [] current = (Object [])m_Keys.elementAt(i);
// Skip non-matching keys
if (!matchesTemplate(template, current)) {
continue;
}
if (hash.containsKey(current[m_KeyIndex])) {
throw new Exception("Duplicate result received:"
+ DatabaseUtils.arrayToString(current));
}
numMatches++;
hash.put(current[m_KeyIndex], current[m_KeyIndex]);
}
if (numMatches != m_ExpectedResultsPerAverage) {
throw new Exception("Expected " + m_ExpectedResultsPerAverage
+ " results matching key \""
+ DatabaseUtils.arrayToString(template)
+ "\" but got "
+ numMatches);
}
}
/**
* Checks that the keys for a run only differ in one key field. If they
* differ in more than one field, a more sophisticated averager will submit
* multiple results - for now an exception is thrown. Currently assumes that
* the most differences will be shown between the first and last
* result received.
*
* @throws Exception if the keys differ on fields other than the
* key averaging field
*/
protected void checkForMultipleDifferences() throws Exception {
Object [] firstKey = (Object [])m_Keys.elementAt(0);
Object [] lastKey = (Object [])m_Keys.elementAt(m_Keys.size() - 1);
/*
System.err.println("First key:" + DatabaseUtils.arrayToString(firstKey));
System.err.println("Last key :" + DatabaseUtils.arrayToString(lastKey));
*/
for (int i = 0; i < firstKey.length; i++) {
if ((i != m_KeyIndex) && !firstKey[i].equals(lastKey[i])) {
throw new Exception("Keys differ on fields other than \""
+ m_KeyFieldName
+ "\" -- time to implement multiple averaging");
}
}
}
/**
* 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);
findKeyIndex();
if (m_KeyIndex == -1) {
throw new Exception("No key field called " + m_KeyFieldName
+ " produced by "
+ m_ResultProducer.getClass().getName());
}
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!!");
}
m_Keys.addElement(key);
m_Results.addElement(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!!");
}
return true;
}
/**
* 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 {
if (m_KeyIndex == -1) {
throw new Exception("No key field called " + m_KeyFieldName
+ " produced by "
+ m_ResultProducer.getClass().getName());
}
String [] keyNames = m_ResultProducer.getKeyNames();
String [] newKeyNames = new String [keyNames.length - 1];
System.arraycopy(keyNames, 0, newKeyNames, 0, m_KeyIndex);
System.arraycopy(keyNames, m_KeyIndex + 1,
newKeyNames, m_KeyIndex,
keyNames.length - m_KeyIndex - 1);
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 {
if (m_KeyIndex == -1) {
throw new Exception("No key field called " + m_KeyFieldName
+ " produced by "
+ m_ResultProducer.getClass().getName());
}
Object [] keyTypes = m_ResultProducer.getKeyTypes();
// Find and remove the key field that is being averaged over
Object [] newKeyTypes = new String [keyTypes.length - 1];
System.arraycopy(keyTypes, 0, newKeyTypes, 0, m_KeyIndex);
System.arraycopy(keyTypes, m_KeyIndex + 1,
newKeyTypes, m_KeyIndex,
keyTypes.length - m_KeyIndex - 1);
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 {
String [] resultNames = m_ResultProducer.getResultNames();
// Add in the names of our extra Result fields
if (getCalculateStdDevs()) {
Object [] resultTypes = m_ResultProducer.getResultTypes();
int numNumeric = 0;
for (int i = 0; i < resultTypes.length; i++) {
if (resultTypes[i] instanceof Double) {
numNumeric++;
}
}
String [] newResultNames = new String [resultNames.length +
1 + numNumeric];
newResultNames[0] = m_CountFieldName;
int j = 1;
for (int i = 0; i < resultNames.length; i++) {
newResultNames[j++] = "Avg_" + resultNames[i];
if (resultTypes[i] instanceof Double) {
newResultNames[j++] = "Dev_" + resultNames[i];
}
}
return newResultNames;
} else {
String [] newResultNames = new String [resultNames.length + 1];
newResultNames[0] = m_CountFieldName;
System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
return newResultNames;
}
}
/**
* 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 {
Object [] resultTypes = m_ResultProducer.getResultTypes();
// Add in the types of our extra Result fields
if (getCalculateStdDevs()) {
int numNumeric = 0;
for (int i = 0; i < resultTypes.length; i++) {
if (resultTypes[i] instanceof Double) {
numNumeric++;
}
}
Object [] newResultTypes = new Object [resultTypes.length +
1 + numNumeric];
newResultTypes[0] = new Double(0);
int j = 1;
for (int i = 0; i < resultTypes.length; i++) {
newResultTypes[j++] = resultTypes[i];
if (resultTypes[i] instanceof Double) {
newResultTypes[j++] = new Double(0);
}
}
return newResultTypes;
} else {
Object [] newResultTypes = new Object [resultTypes.length + 1];
newResultTypes[0] = new Double(0);
System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
return newResultTypes;
}
}
/**
* 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 " + getExpectedResultsPerAverage() + " ";
if (getCalculateStdDevs()) {
result += "-S ";
}
if (m_ResultProducer == null) {
result += "<null ResultProducer>";
} 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 name of the field to average over.\n"
+"\t(default \"Fold\")",
"F", 1,
"-F <field name>"));
newVector.addElement(new Option(
"\tThe number of results expected per average.\n"
+"\t(default 10)",
"X", 1,
"-X <num results>"));
newVector.addElement(new Option(
"\tCalculate standard deviations.\n"
+"\t(default only averages)",
"S", 0,
"-S"));
newVector.addElement(new Option(
"\tThe full class name of a ResultProducer.\n"
+"\teg: weka.experiment.CrossValidationResultProducer",
"W", 1,
"-W <class name>"));
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. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -F <field name>
* The name of the field to average over.
* (default "Fold")</pre>
*
* <pre> -X <num results>
* The number of results expected per average.
* (default 10)</pre>
*
* <pre> -S
* Calculate standard deviations.
* (default only averages)</pre>
*
* <pre> -W <class name>
* The full class name of a ResultProducer.
* eg: weka.experiment.CrossValidationResultProducer</pre>
*
* <pre>
* Options specific to result producer weka.experiment.CrossValidationResultProducer:
* </pre>
*
* <pre> -X <number of folds>
* The number of folds to use for the cross-validation.
* (default 10)</pre>
*
* <pre> -D
* Save raw split evaluator output.</pre>
*
* <pre> -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)</pre>
*
* <pre> -W <class name>
* The full class name of a SplitEvaluator.
* eg: weka.experiment.ClassifierSplitEvaluator</pre>
*
* <pre>
* Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
* </pre>
*
* <pre> -W <class name>
* The full class name of the classifier.
* eg: weka.classifiers.bayes.NaiveBayes</pre>
*
* <pre> -C <index>
* The index of the class for which IR statistics
* are to be output. (default 1)</pre>
*
* <pre> -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).</pre>
*
* <pre> -P
* Add target and prediction columns to the result
* for each fold.</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.rules.ZeroR:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
<!-- options-end -->
*
* 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 keyFieldName = Utils.getOption('F', options);
if (keyFieldName.length() != 0) {
setKeyFieldName(keyFieldName);
} else {
setKeyFieldName(CrossValidationResultProducer.FOLD_FIELD_NAME);
}
String numResults = Utils.getOption('X', options);
if (numResults.length() != 0) {
setExpectedResultsPerAverage(Integer.parseInt(numResults));
} else {
setExpectedResultsPerAverage(10);
}
setCalculateStdDevs(Utils.getFlag('S', options));
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 + 8];
int current = 0;
options[current++] = "-F";
options[current++] = "" + getKeyFieldName();
options[current++] = "-X";
options[current++] = "" + getExpectedResultsPerAverage();
if (getCalculateStdDevs()) {
options[current++] = "-S";
}
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("AveragingResultProducer: 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("AveragingResultProducer: "
+"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 calculateStdDevsTipText() {
return "Record standard deviations for each run.";
}
/**
* Get the value of CalculateStdDevs.
*
* @return Value of CalculateStdDevs.
*/
public boolean getCalculateStdDevs() {
return m_CalculateStdDevs;
}
/**
* Set the value of CalculateStdDevs.
*
* @param newCalculateStdDevs Value to assign to CalculateStdDevs.
*/
public void setCalculateStdDevs(boolean newCalculateStdDevs) {
m_CalculateStdDevs = newCalculateStdDevs;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String expectedResultsPerAverageTipText() {
return "Set the expected number of results to average per run. "
+"For example if a CrossValidationResultProducer is being used "
+"(with the number of folds set to 10), then the expected number "
+"of results per run is 10.";
}
/**
* Get the value of ExpectedResultsPerAverage.
*
* @return Value of ExpectedResultsPerAverage.
*/
public int getExpectedResultsPerAverage() {
return m_ExpectedResultsPerAverage;
}
/**
* Set the value of ExpectedResultsPerAverage.
*
* @param newExpectedResultsPerAverage Value to assign to
* ExpectedResultsPerAverage.
*/
public void setExpectedResultsPerAverage(int newExpectedResultsPerAverage) {
m_ExpectedResultsPerAverage = newExpectedResultsPerAverage;
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String keyFieldNameTipText() {
return "Set the field name that will be unique for a run.";
}
/**
* Get the value of KeyFieldName.
*
* @return Value of KeyFieldName.
*/
public String getKeyFieldName() {
return m_KeyFieldName;
}
/**
* Set the value of KeyFieldName.
*
* @param newKeyFieldName Value to assign to KeyFieldName.
*/
public void setKeyFieldName(String newKeyFieldName) {
m_KeyFieldName = newKeyFieldName;
m_CountFieldName = "Num_" + m_KeyFieldName;
findKeyIndex();
}
/**
* 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 results are to be averaged.";
}
/**
* 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);
findKeyIndex();
}
/**
* Gets a text descrption of the result producer.
*
* @return a text description of the result producer.
*/
public String toString() {
String result = "AveragingResultProducer: ";
result += getCompatibilityState();
if (m_Instances == null) {
result += ": <null Instances>";
} else {
result += ": " + Utils.backQuoteChars(m_Instances.relationName());
}
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
} // AveragingResultProducer