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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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
* -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: 10203 $
*/
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 ArrayList m_Keys = new ArrayList();
/** Collects the results from a single run */
protected ArrayList m_Results = new ArrayList();
/**
* 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
*/
@Override
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.clear();
m_Results.clear();
m_ResultProducer.doRunKeys(run);
checkForMultipleDifferences();
Object[] template = m_Keys.get(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
*/
@Override
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
*/
@Override
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.clear();
m_Results.clear();
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 = m_Keys.get(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 = m_Keys.get(i);
// Skip non-matching keys
if (!matchesTemplate(template, currentKey)) {
continue;
}
// Add the results to the stats accumulator
Object[] currentResult = m_Results.get(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 = m_Results.get(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 = m_Keys.get(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 = m_Keys.get(0);
Object[] lastKey = m_Keys.get(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.
*/
@Override
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.
*/
@Override
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
*/
@Override
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
*/
@Override
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.
*/
@Override
public void acceptResult(ResultProducer rp, Object[] key, Object[] result)
throws Exception {
if (m_ResultProducer != rp) {
throw new Error("Unrecognized ResultProducer sending results!!");
}
m_Keys.add(key);
m_Results.add(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.
*/
@Override
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
*/
@Override
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)
*/
@Override
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)
*/
@Override
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 (Object resultType : resultTypes) {
if (resultType 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)
*/
@Override
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 (Object resultType : resultTypes) {
if (resultType instanceof Double) {
numNumeric++;
}
}
Object[] newResultTypes = new Object[resultTypes.length + 1 + numNumeric];
newResultTypes[0] = new Double(0);
int j = 1;
for (Object resultType : resultTypes) {
newResultTypes[j++] = resultType;
if (resultType 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
*/
@Override
public String getCompatibilityState() {
String result = // "-F " + Utils.quote(getKeyFieldName())
" -X " + getExpectedResultsPerAverage() + " ";
if (getCalculateStdDevs()) {
result += "-S ";
}
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.
*/
@Override
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