Java tutorial
/* * 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. */ /* * MIRandomSplitResultProducer.java * Copyright (C) 1999 University of Waikato * */ package milk.experiment; import milk.core.*; import milk.classifiers.*; import weka.core.Instance; import weka.core.Instances; import weka.core.OptionHandler; import weka.core.Option; import weka.core.Utils; import weka.core.AdditionalMeasureProducer; import weka.experiment.OutputZipper; import java.util.Enumeration; import java.util.Calendar; import java.util.TimeZone; import java.util.Random; import java.util.Vector; import java.io.File; /** * Generates a single train/test split and calls the appropriate * SplitEvaluator to generate some results. * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.15 $ */ public class MIRandomSplitResultProducer implements MIResultProducer, OptionHandler, AdditionalMeasureProducer { /** The dataset of interest */ protected Exemplars m_Instances; /** The ResultListener to send results to */ protected MIResultListener m_ResultListener = new MICSVResultListener(); /** The percentage of instances to use for training */ protected double m_TrainPercent = 66; /** Whether dataset is to be randomized */ protected boolean m_randomize = true; /** The SplitEvaluator used to generate results */ protected MISplitEvaluator m_SplitEvaluator = new MIClassifierSplitEvaluator(); /** The names of any additional measures to look for in SplitEvaluators */ protected String[] m_AdditionalMeasures = null; /** Save raw output of split evaluators --- for debugging purposes */ protected boolean m_debugOutput = false; /** The output zipper to use for saving raw splitEvaluator output */ protected OutputZipper m_ZipDest = null; /** The destination output file/directory for raw output */ protected File m_OutputFile = new File(new File(System.getProperty("user.dir")), "splitEvalutorOut.zip"); /* The name of the key field containing the dataset name */ public static String DATASET_FIELD_NAME = "Dataset"; /* The name of the key field containing the run number */ public static String RUN_FIELD_NAME = "Run"; /* The name of the result field containing the timestamp */ public static String TIMESTAMP_FIELD_NAME = "Date_time"; /** * 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 "Performs a random train and test using a supplied " + "evaluator."; } /** * Sets the dataset that results will be obtained for. * * @param instances a value of type 'Instances'. */ public void setInstances(Exemplars instances) { m_Instances = instances; } /** * 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 SplitEvaluator) 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_SplitEvaluator != null) { System.err.println( "MIRandomSplitResultProducer: setting additional " + "measures for " + "split evaluator"); m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures); } } /** * Returns an enumeration of any additional measure names that might be * in the SplitEvaluator * @return an enumeration of the measure names */ public Enumeration enumerateMeasures() { Vector newVector = new Vector(); if (m_SplitEvaluator instanceof AdditionalMeasureProducer) { Enumeration en = ((AdditionalMeasureProducer) m_SplitEvaluator).enumerateMeasures(); while (en.hasMoreElements()) { String mname = (String) en.nextElement(); newVector.addElement(mname); } } return newVector.elements(); } /** * Returns the value of the named measure * @param measureName the name of the measure to query for its value * @return the value of the named measure * @exception IllegalArgumentException if the named measure is not supported */ public double getMeasure(String additionalMeasureName) { if (m_SplitEvaluator instanceof AdditionalMeasureProducer) { return ((AdditionalMeasureProducer) m_SplitEvaluator).getMeasure(additionalMeasureName); } else { throw new IllegalArgumentException( "MIRandomSplitResultProducer: " + "Can't return value for : " + additionalMeasureName + ". " + m_SplitEvaluator.getClass().getName() + " " + "is not an AdditionalMeasureProducer"); } } /** * Sets the object to send results of each run to. * * @param listener a value of type 'ResultListener' */ public void setResultListener(MIResultListener listener) { m_ResultListener = listener; } /** * Gets a Double representing the current date and time. * eg: 1:46pm on 20/5/1999 -> 19990520.1346 * * @return a value of type Double */ public static Double getTimestamp() { Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC")); double timestamp = now.get(Calendar.YEAR) * 10000 + (now.get(Calendar.MONTH) + 1) * 100 + now.get(Calendar.DAY_OF_MONTH) + now.get(Calendar.HOUR_OF_DAY) / 100.0 + now.get(Calendar.MINUTE) / 10000.0; return new Double(timestamp); } /** * Prepare to generate results. * * @exception Exception if an error occurs during preprocessing. */ public void preProcess() throws Exception { if (m_SplitEvaluator == null) { throw new Exception("No SplitEvalutor set"); } if (m_ResultListener == null) { throw new Exception("No ResultListener set"); } m_ResultListener.preProcess(this); } /** * Perform any postprocessing. When this method is called, it indicates * that no more requests to generate results for the current experiment * will be sent. * * @exception Exception if an error occurs */ public void postProcess() throws Exception { m_ResultListener.postProcess(this); if (m_debugOutput) { if (m_ZipDest != null) { m_ZipDest.finished(); m_ZipDest = 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. * @exception Exception if a problem occurs while getting the keys */ public void doRunKeys(int run) throws Exception { if (m_Instances == null) { throw new Exception("No Instances set"); } // Add in some fields to the key like run number, dataset name Object[] seKey = m_SplitEvaluator.getKey(); Object[] key = new Object[seKey.length + 2]; key[0] = Utils.backQuoteChars(m_Instances.relationName()); key[1] = "" + run; System.arraycopy(seKey, 0, key, 2, seKey.length); if (m_ResultListener.isResultRequired(this, key)) { try { m_ResultListener.acceptResult(this, key, null); } catch (Exception ex) { // Save the train and test datasets for debugging purposes? throw ex; } } } /** * 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. * @exception Exception if a problem occurs while getting the results */ public void doRun(int run) throws Exception { if (getRawOutput()) { if (m_ZipDest == null) { m_ZipDest = new OutputZipper(m_OutputFile); } } if (m_Instances == null) { throw new Exception("No Instances set"); } // Add in some fields to the key like run number, dataset name Object[] seKey = m_SplitEvaluator.getKey(); Object[] key = new Object[seKey.length + 2]; key[0] = Utils.backQuoteChars(m_Instances.relationName()); key[1] = "" + run; System.arraycopy(seKey, 0, key, 2, seKey.length); if (m_ResultListener.isResultRequired(this, key)) { // Randomize on a copy of the original dataset Exemplars runInstances = new Exemplars(m_Instances); Exemplars train; Exemplars test; if (!m_randomize) { // Don't do any randomization int trainSize = Utils.round(runInstances.numExemplars() * m_TrainPercent / 100); int testSize = runInstances.numExemplars() - trainSize; train = new Exemplars(runInstances, 0, trainSize); test = new Exemplars(runInstances, trainSize, testSize); } else { Random rand = new Random(run); runInstances.randomize(rand); // Nominal class if (runInstances.classAttribute().isNominal()) { // create the subset for each classs int numClasses = runInstances.numClasses(); Exemplars[] subsets = new Exemplars[numClasses + 1]; for (int i = 0; i < numClasses + 1; i++) { subsets[i] = new Exemplars(runInstances, 10); } // divide instances into subsets for (int i = 0; i < runInstances.numExemplars(); i++) { Exemplar inst = runInstances.exemplar(i); if (inst.getInstances().instance(0).classIsMissing()) { subsets[numClasses].add(inst); } else { subsets[(int) inst.classValue()].add(inst); } } // Compactify them for (int i = 0; i < numClasses + 1; i++) { subsets[i].compactify(); } // merge into train and test sets train = new Exemplars(runInstances, runInstances.numExemplars()); test = new Exemplars(runInstances, runInstances.numExemplars()); for (int i = 0; i < numClasses + 1; i++) { int trainSize = Utils.probRound(subsets[i].numExemplars() * m_TrainPercent / 100, rand); for (int j = 0; j < trainSize; j++) { train.add(subsets[i].exemplar(j)); } for (int j = trainSize; j < subsets[i].numExemplars(); j++) { test.add(subsets[i].exemplar(j)); } // free memory subsets[i] = null; } train.compactify(); test.compactify(); // randomize the final sets train.randomize(rand); test.randomize(rand); } else { // Numeric target int trainSize = Utils.probRound(runInstances.numExemplars() * m_TrainPercent / 100, rand); int testSize = runInstances.numExemplars() - trainSize; train = new Exemplars(runInstances, 0, trainSize); test = new Exemplars(runInstances, trainSize, testSize); } } try { Object[] seResults = m_SplitEvaluator.getResult(train, test); Object[] results = new Object[seResults.length + 1]; results[0] = getTimestamp(); System.arraycopy(seResults, 0, results, 1, seResults.length); if (m_debugOutput) { String resultName = ("" + run + "." + Utils.backQuoteChars(runInstances.relationName()) + "." + m_SplitEvaluator.toString()).replace(' ', '_'); resultName = Utils.removeSubstring(resultName, "weka.classifiers."); resultName = Utils.removeSubstring(resultName, "weka.filters."); resultName = Utils.removeSubstring(resultName, "weka.attributeSelection."); m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(), resultName); } m_ResultListener.acceptResult(this, key, results); } catch (Exception ex) { // Save the train and test datasets for debugging purposes? throw ex; } } } /** * Gets the names of each of the columns produced for a single run. * This method should really be static. * * @return an array containing the name of each column */ public String[] getKeyNames() { String[] keyNames = m_SplitEvaluator.getKeyNames(); // Add in the names of our extra key fields String[] newKeyNames = new String[keyNames.length + 2]; newKeyNames[0] = DATASET_FIELD_NAME; newKeyNames[1] = RUN_FIELD_NAME; System.arraycopy(keyNames, 0, newKeyNames, 2, keyNames.length); 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. */ public Object[] getKeyTypes() { Object[] keyTypes = m_SplitEvaluator.getKeyTypes(); // Add in the types of our extra fields Object[] newKeyTypes = new String[keyTypes.length + 2]; newKeyTypes[0] = new String(); newKeyTypes[1] = new String(); System.arraycopy(keyTypes, 0, newKeyTypes, 2, keyTypes.length); return newKeyTypes; } /** * Gets the names of each of the columns produced for a single run. * This method should really be static. * * @return an array containing the name of each column */ public String[] getResultNames() { String[] resultNames = m_SplitEvaluator.getResultNames(); // Add in the names of our extra Result fields String[] newResultNames = new String[resultNames.length + 1]; newResultNames[0] = TIMESTAMP_FIELD_NAME; System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length); return newResultNames; } /** * 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. */ public Object[] getResultTypes() { Object[] resultTypes = m_SplitEvaluator.getResultTypes(); // Add in the types of our extra Result fields 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 = "-P " + m_TrainPercent; if (!getRandomizeData()) { result += " -R"; } if (m_SplitEvaluator == null) { result += " <null SplitEvaluator>"; } else { result += " -W " + m_SplitEvaluator.getClass().getName(); } 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 outputFileTipText() { return "Set the destination for saving raw output. If the rawOutput " + "option is selected, then output from the splitEvaluator for " + "individual train-test splits is saved. If the destination is a " + "directory, " + "then each output is saved to an individual gzip file; if the " + "destination is a file, then each output is saved as an entry " + "in a zip file."; } /** * Get the value of OutputFile. * * @return Value of OutputFile. */ public File getOutputFile() { return m_OutputFile; } /** * Set the value of OutputFile. * * @param newOutputFile Value to assign to OutputFile. */ public void setOutputFile(File newOutputFile) { m_OutputFile = newOutputFile; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String randomizeDataTipText() { return "Do not randomize dataset if true"; } /** * Get if dataset is to be randomized * @return true if dataset is to be randomized */ public boolean getRandomizeData() { return m_randomize; } /** * Set to true if dataset is to be randomized * @param d true if dataset is to be randomized */ public void setRandomizeData(boolean d) { m_randomize = d; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String rawOutputTipText() { return "Save raw output (useful for debugging). If set, then output is " + "sent to the destination specified by outputFile"; } /** * Get if raw split evaluator output is to be saved * @return true if raw split evalutor output is to be saved */ public boolean getRawOutput() { return m_debugOutput; } /** * Set to true if raw split evaluator output is to be saved * @param d true if output is to be saved */ public void setRawOutput(boolean d) { m_debugOutput = d; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String trainPercentTipText() { return "Set the percentage of data to use for training."; } /** * Get the value of TrainPercent. * * @return Value of TrainPercent. */ public double getTrainPercent() { return m_TrainPercent; } /** * Set the value of TrainPercent. * * @param newTrainPercent Value to assign to TrainPercent. */ public void setTrainPercent(double newTrainPercent) { m_TrainPercent = newTrainPercent; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String splitEvaluatorTipText() { return "The evaluator to apply to the cross validation folds. " + "This may be a classifier, regression scheme etc."; } /** * Get the SplitEvaluator. * * @return the SplitEvaluator. */ public MISplitEvaluator getSplitEvaluator() { return m_SplitEvaluator; } /** * Set the SplitEvaluator. * * @param newSplitEvaluator new SplitEvaluator to use. */ public void setSplitEvaluator(MISplitEvaluator newSplitEvaluator) { m_SplitEvaluator = newSplitEvaluator; m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures); } /** * Returns an enumeration describing the available options.. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(5); newVector.addElement(new Option( "\tThe percentage of instances to use for training.\n" +"\t(default 66)", "P", 1, "-P <percent>")); newVector.addElement(new Option( "Save raw split evaluator output.", "D",0,"-D")); newVector.addElement(new Option( "\tThe filename where raw output will be stored.\n" +"\tIf a directory name is specified then then individual\n" +"\toutputs will be gzipped, otherwise all output will be\n" +"\tzipped to the named file. Use in conjuction with -D." +"\t(default splitEvalutorOut.zip)", "O", 1, "-O <file/directory name/path>")); newVector.addElement(new Option( "\tThe full class name of a SplitEvaluator.\n" +"\teg: weka.experiment.ClassifierSplitEvaluator", "W", 1, "-W <class name>")); newVector.addElement(new Option( "Set when data is not to be randomized.", "R",0,"-R")); if ((m_SplitEvaluator != null) && (m_SplitEvaluator instanceof OptionHandler)) { newVector.addElement(new Option( "", "", 0, "\nOptions specific to split evaluator " + m_SplitEvaluator.getClass().getName() + ":")); Enumeration enum = ((OptionHandler)m_SplitEvaluator).listOptions(); while (enum.hasMoreElements()) { newVector.addElement(enum.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options. Valid options are:<p> * * -P num <br> * The percent of instances used for training. <p> * * -D <br> * Specify that raw split evaluator output is to be saved. <p> * * -R <br> * Do not randomize the dataset. <p> * * -O file/directory name <br> * Specify the file or directory to which raw split evaluator output * is to be saved. If a directory is specified, then each output string * is saved as an individual gzip file. If a file is specified, then * each output string is saved as an entry in a zip file. <p> * * -W classname <br> * Specify the full class name of the split evaluator. <p> * * All option after -- will be passed to the split evaluator. * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { setRawOutput(Utils.getFlag('D', options)); setRandomizeData(!Utils.getFlag('R', options)); String fName = Utils.getOption('O', options); if (fName.length() != 0) { setOutputFile(new File(fName)); } String trainPct = Utils.getOption('P', options); if (trainPct.length() != 0) { setTrainPercent((new Double(trainPct)).doubleValue()); } else { setTrainPercent(66); } String seName = Utils.getOption('W', options); if (seName.length() == 0) { throw new Exception("A SplitEvaluator 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 // SE. setSplitEvaluator((MISplitEvaluator) Utils.forName(MISplitEvaluator.class, seName, null)); if (getSplitEvaluator() instanceof OptionHandler) { ((OptionHandler) getSplitEvaluator()).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_SplitEvaluator != null) && (m_SplitEvaluator instanceof OptionHandler)) { seOptions = ((OptionHandler) m_SplitEvaluator).getOptions(); } String[] options = new String[seOptions.length + 9]; int current = 0; options[current++] = "-P"; options[current++] = "" + getTrainPercent(); if (getRawOutput()) { options[current++] = "-D"; } if (!getRandomizeData()) { options[current++] = "-R"; } options[current++] = "-O"; options[current++] = getOutputFile().getName(); if (getSplitEvaluator() != null) { options[current++] = "-W"; options[current++] = getSplitEvaluator().getClass().getName(); } options[current++] = "--"; System.arraycopy(seOptions, 0, options, current, seOptions.length); current += seOptions.length; while (current < options.length) { options[current++] = ""; } return options; } /** * Gets a text descrption of the result producer. * * @return a text description of the result producer. */ public String toString() { String result = "MIRandomSplitResultProducer: "; result += getCompatibilityState(); if (m_Instances == null) { result += ": <null Instances>"; } else { result += ": " + Utils.backQuoteChars(m_Instances.relationName()); } return result; } } // MIRandomSplitResultProducer