All Downloads are FREE. Search and download functionalities are using the official Maven repository.

weka.experiment.Experiment Maven / Gradle / Ivy

Go to download

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.

There is a newer version: 3.9.6
Show newest 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 .
 */

/*
 *    Experiment.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import java.beans.PropertyDescriptor;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serializable;
import java.lang.reflect.Array;
import java.lang.reflect.Method;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import javax.swing.DefaultListModel;

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 weka.core.converters.AbstractFileLoader;
import weka.core.converters.ConverterUtils;
import weka.core.xml.KOML;
import weka.core.xml.XMLOptions;
import weka.experiment.xml.XMLExperiment;

/**
 * Holds all the necessary configuration information for a standard type
 * experiment. This object is able to be serialized for storage on disk.
 * 
 *  Valid options are:
 * 

* *

 * -L <num>
 *  The lower run number to start the experiment from.
 *  (default 1)
 * 
* *
 * -U <num>
 *  The upper run number to end the experiment at (inclusive).
 *  (default 10)
 * 
* *
 * -T <arff file>
 *  The dataset to run the experiment on.
 *  (required, may be specified multiple times)
 * 
* *
 * -P <class name>
 *  The full class name of a ResultProducer (required).
 *  eg: weka.experiment.RandomSplitResultProducer
 * 
* *
 * -D <class name>
 *  The full class name of a ResultListener (required).
 *  eg: weka.experiment.CSVResultListener
 * 
* *
 * -N <string>
 *  A string containing any notes about the experiment.
 *  (default none)
 * 
* *
 * Options specific to result producer weka.experiment.RandomSplitResultProducer:
 * 
* *
 * -P <percent>
 *  The percentage of instances to use for training.
 *  (default 66)
 * 
* *
 * -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
 * 
* *
 * -R
 *  Set when data is not to be randomized and the data sets' size.
 *  Is not to be determined via probabilistic rounding.
 * 
* *
 * 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: 10376 $ */ public class Experiment implements Serializable, OptionHandler, RevisionHandler { /** for serialization */ static final long serialVersionUID = 44945596742646663L; /** The filename extension that should be used for experiment files */ public static String FILE_EXTENSION = ".exp"; /** Where results will be sent */ protected ResultListener m_ResultListener = new InstancesResultListener(); /** The result producer */ protected ResultProducer m_ResultProducer = new RandomSplitResultProducer(); /** Lower run number */ protected int m_RunLower = 1; /** Upper run number */ protected int m_RunUpper = 10; /** An array of dataset files */ protected DefaultListModel m_Datasets = new DefaultListModel(); /** True if the exp should also iterate over a property of the RP */ protected boolean m_UsePropertyIterator = false; /** The path to the iterator property */ protected PropertyNode[] m_PropertyPath; /** The array of values to set the property to */ protected Object m_PropertyArray; /** User notes about the experiment */ protected String m_Notes = ""; /** * Method names of additional measures of objects contained in the custom * property iterator. Only methods names beginning with "measure" and * returning doubles are recognised */ protected String[] m_AdditionalMeasures = null; /** * True if the class attribute is the first attribute for all datasets * involved in this experiment. */ protected boolean m_ClassFirst = false; /** * If true an experiment will advance the current data set befor any custom * itererator */ protected boolean m_AdvanceDataSetFirst = true; /** * Sets whether the first attribute is treated as the class for all datasets * involved in the experiment. This information is not output with the result * of the experiments! * * @param flag whether the class attribute is the first and not the last */ public void classFirst(boolean flag) { m_ClassFirst = flag; } /** * Get the value of m_DataSetFirstFirst. * * @return Value of m_DataSetFirstFirst. */ public boolean getAdvanceDataSetFirst() { return m_AdvanceDataSetFirst; } /** * Set the value of m_AdvanceDataSetFirst. * * @param newAdvanceDataSetFirst Value to assign to m_AdvanceRunFirst. */ public void setAdvanceDataSetFirst(boolean newAdvanceDataSetFirst) { m_AdvanceDataSetFirst = newAdvanceDataSetFirst; } /** * Gets whether the custom property iterator should be used. * * @return true if so */ public boolean getUsePropertyIterator() { return m_UsePropertyIterator; } /** * Sets whether the custom property iterator should be used. * * @param newUsePropertyIterator true if so */ public void setUsePropertyIterator(boolean newUsePropertyIterator) { m_UsePropertyIterator = newUsePropertyIterator; } /** * Gets the path of properties taken to get to the custom property to iterate * over. * * @return an array of PropertyNodes */ public PropertyNode[] getPropertyPath() { return m_PropertyPath; } /** * Sets the path of properties taken to get to the custom property to iterate * over. * * @param newPropertyPath an array of PropertyNodes */ public void setPropertyPath(PropertyNode[] newPropertyPath) { m_PropertyPath = newPropertyPath; } /** * Sets the array of values to set the custom property to. * * @param newPropArray a value of type Object which should be an array of the * appropriate values. */ public void setPropertyArray(Object newPropArray) { m_PropertyArray = newPropArray; } /** * Gets the array of values to set the custom property to. * * @return a value of type Object which should be an array of the appropriate * values. */ public Object getPropertyArray() { return m_PropertyArray; } /** * Gets the number of custom iterator values that have been defined for the * experiment. * * @return the number of custom property iterator values. */ public int getPropertyArrayLength() { return Array.getLength(m_PropertyArray); } /** * Gets a specified value from the custom property iterator array. * * @param index the index of the value wanted * @return the property array value */ public Object getPropertyArrayValue(int index) { return Array.get(m_PropertyArray, index); } /* * These may potentially want to be made un-transient if it is decided that * experiments may be saved mid-run and later resumed */ /** The current run number when the experiment is running */ protected transient int m_RunNumber; /** The current dataset number when the experiment is running */ protected transient int m_DatasetNumber; /** The current custom property value index when the experiment is running */ protected transient int m_PropertyNumber; /** True if the experiment has finished running */ protected transient boolean m_Finished = true; /** The dataset currently being used */ protected transient Instances m_CurrentInstances; /** The custom property value that has actually been set */ protected transient int m_CurrentProperty; /** * When an experiment is running, this returns the current run number. * * @return the current run number. */ public int getCurrentRunNumber() { return m_RunNumber; } /** * When an experiment is running, this returns the current dataset number. * * @return the current dataset number. */ public int getCurrentDatasetNumber() { return m_DatasetNumber; } /** * When an experiment is running, this returns the index of the current custom * property value. * * @return the index of the current custom property value. */ public int getCurrentPropertyNumber() { return m_PropertyNumber; } /** * Prepares an experiment for running, initializing current iterator settings. * * @throws Exception if an error occurs */ public void initialize() throws Exception { m_RunNumber = getRunLower(); m_DatasetNumber = 0; m_PropertyNumber = 0; m_CurrentProperty = -1; m_CurrentInstances = null; m_Finished = false; if (m_UsePropertyIterator && (m_PropertyArray == null)) { throw new Exception("Null array for property iterator"); } if (getRunLower() > getRunUpper()) { throw new Exception("Lower run number is greater than upper run number"); } if (getDatasets().size() == 0) { throw new Exception("No datasets have been specified"); } if (m_ResultProducer == null) { throw new Exception("No ResultProducer set"); } if (m_ResultListener == null) { throw new Exception("No ResultListener set"); } // if (m_UsePropertyIterator && (m_PropertyArray != null)) { determineAdditionalResultMeasures(); // } m_ResultProducer.setResultListener(m_ResultListener); m_ResultProducer.setAdditionalMeasures(m_AdditionalMeasures); m_ResultProducer.preProcess(); // constrain the additional measures to be only those allowable // by the ResultListener String[] columnConstraints = m_ResultListener .determineColumnConstraints(m_ResultProducer); if (columnConstraints != null) { m_ResultProducer.setAdditionalMeasures(columnConstraints); } } /** * Iterate over the objects in the property array to determine what (if any) * additional measures they support * * @throws Exception if additional measures don't comply to the naming * convention (starting with "measure") */ private void determineAdditionalResultMeasures() throws Exception { m_AdditionalMeasures = null; ArrayList measureNames = new ArrayList(); // first try the result producer, then property array if applicable if (m_ResultProducer instanceof AdditionalMeasureProducer) { Enumeration am = ((AdditionalMeasureProducer) m_ResultProducer) .enumerateMeasures(); while (am.hasMoreElements()) { String mname = am.nextElement(); if (mname.startsWith("measure")) { if (measureNames.indexOf(mname) == -1) { measureNames.add(mname); } } else { throw new Exception("Additional measures in " + m_ResultProducer.getClass().getName() + " must obey the naming convention" + " of starting with \"measure\""); } } } if (m_UsePropertyIterator && (m_PropertyArray != null)) { for (int i = 0; i < Array.getLength(m_PropertyArray); i++) { Object current = Array.get(m_PropertyArray, i); if (current instanceof AdditionalMeasureProducer) { Enumeration am = ((AdditionalMeasureProducer) current) .enumerateMeasures(); while (am.hasMoreElements()) { String mname = am.nextElement(); if (mname.startsWith("measure")) { if (measureNames.indexOf(mname) == -1) { measureNames.add(mname); } } else { throw new Exception("Additional measures in " + current.getClass().getName() + " must obey the naming convention" + " of starting with \"measure\""); } } } } } if (measureNames.size() > 0) { m_AdditionalMeasures = new String[measureNames.size()]; for (int i = 0; i < measureNames.size(); i++) { m_AdditionalMeasures[i] = measureNames.get(i); } } } /** * Recursively sets the custom property value, by setting all values along the * property path. * * @param propertyDepth the current position along the property path * @param origValue the value to set the property to * @throws Exception if an error occurs */ protected void setProperty(int propertyDepth, Object origValue) throws Exception { PropertyDescriptor current = m_PropertyPath[propertyDepth].property; Object subVal = null; if (propertyDepth < m_PropertyPath.length - 1) { Method getter = current.getReadMethod(); Object getArgs[] = {}; subVal = getter.invoke(origValue, getArgs); setProperty(propertyDepth + 1, subVal); } else { subVal = Array.get(m_PropertyArray, m_PropertyNumber); } Method setter = current.getWriteMethod(); Object[] args = { subVal }; setter.invoke(origValue, args); } /** * Returns true if there are more iterations to carry out in the experiment. * * @return true if so */ public boolean hasMoreIterations() { return !m_Finished; } /** * Carries out the next iteration of the experiment. * * @throws Exception if an error occurs */ public void nextIteration() throws Exception { if (m_UsePropertyIterator) { if (m_CurrentProperty != m_PropertyNumber) { setProperty(0, m_ResultProducer); m_CurrentProperty = m_PropertyNumber; } } if (m_CurrentInstances == null) { File currentFile = (File) getDatasets().elementAt(m_DatasetNumber); AbstractFileLoader loader = ConverterUtils.getLoaderForFile(currentFile); loader.setFile(currentFile); Instances data = new Instances(loader.getDataSet()); // only set class attribute if not already done by loader if (data.classIndex() == -1) { if (m_ClassFirst) { data.setClassIndex(0); } else { data.setClassIndex(data.numAttributes() - 1); } } m_CurrentInstances = data; m_ResultProducer.setInstances(m_CurrentInstances); } m_ResultProducer.doRun(m_RunNumber); advanceCounters(); } /** * Increments iteration counters appropriately. */ public void advanceCounters() { if (m_AdvanceDataSetFirst) { m_RunNumber++; if (m_RunNumber > getRunUpper()) { m_RunNumber = getRunLower(); m_DatasetNumber++; m_CurrentInstances = null; if (m_DatasetNumber >= getDatasets().size()) { m_DatasetNumber = 0; if (m_UsePropertyIterator) { m_PropertyNumber++; if (m_PropertyNumber >= Array.getLength(m_PropertyArray)) { m_Finished = true; } } else { m_Finished = true; } } } } else { // advance by custom iterator before data set m_RunNumber++; if (m_RunNumber > getRunUpper()) { m_RunNumber = getRunLower(); if (m_UsePropertyIterator) { m_PropertyNumber++; if (m_PropertyNumber >= Array.getLength(m_PropertyArray)) { m_PropertyNumber = 0; m_DatasetNumber++; m_CurrentInstances = null; if (m_DatasetNumber >= getDatasets().size()) { m_Finished = true; } } } else { m_DatasetNumber++; m_CurrentInstances = null; if (m_DatasetNumber >= getDatasets().size()) { m_Finished = true; } } } } } public void runExperiment(boolean verbose) { while (hasMoreIterations()) { try { if (verbose) { String current = "Iteration:"; if (getUsePropertyIterator()) { int cnum = getCurrentPropertyNumber(); String ctype = getPropertyArray().getClass().getComponentType() .getName(); int lastDot = ctype.lastIndexOf('.'); if (lastDot != -1) { ctype = ctype.substring(lastDot + 1); } String cname = " " + ctype + "=" + (cnum + 1) + ":" + getPropertyArrayValue(cnum).getClass().getName(); current += cname; } String dname = ((File) getDatasets().elementAt( getCurrentDatasetNumber())).getName(); current += " Dataset=" + dname + " Run=" + (getCurrentRunNumber()); System.out.println(current); } nextIteration(); } catch (Exception ex) { ex.printStackTrace(); System.err.println(ex.getMessage()); advanceCounters(); // Try to keep plowing through } } } /** * Runs all iterations of the experiment, continuing past errors. */ public void runExperiment() { runExperiment(false); } /** * Signals that the experiment is finished running, so that cleanup can be * done. * * @throws Exception if an error occurs */ public void postProcess() throws Exception { m_ResultProducer.postProcess(); } /** * Gets the datasets in the experiment. * * @return the datasets in the experiment. */ public DefaultListModel getDatasets() { return m_Datasets; } /** * Set the datasets to use in the experiment * * @param ds the list of datasets to use */ public void setDatasets(DefaultListModel ds) { m_Datasets = ds; } /** * Gets the result listener where results will be sent. * * @return the result listener where results will be sent. */ public ResultListener getResultListener() { return m_ResultListener; } /** * Sets the result listener where results will be sent. * * @param newResultListener the result listener where results will be sent. */ public void setResultListener(ResultListener newResultListener) { m_ResultListener = newResultListener; } /** * Get the result producer used for the current experiment. * * @return the result producer used for the current experiment. */ public ResultProducer getResultProducer() { return m_ResultProducer; } /** * Set the result producer used for the current experiment. * * @param newResultProducer result producer to use for the current experiment. */ public void setResultProducer(ResultProducer newResultProducer) { m_ResultProducer = newResultProducer; } /** * Get the upper run number for the experiment. * * @return the upper run number for the experiment. */ public int getRunUpper() { return m_RunUpper; } /** * Set the upper run number for the experiment. * * @param newRunUpper the upper run number for the experiment. */ public void setRunUpper(int newRunUpper) { m_RunUpper = newRunUpper; } /** * Get the lower run number for the experiment. * * @return the lower run number for the experiment. */ public int getRunLower() { return m_RunLower; } /** * Set the lower run number for the experiment. * * @param newRunLower the lower run number for the experiment. */ public void setRunLower(int newRunLower) { m_RunLower = newRunLower; } /** * Get the user notes. * * @return User notes associated with the experiment. */ public String getNotes() { return m_Notes; } /** * Set the user notes. * * @param newNotes New user notes. */ public void setNotes(String newNotes) { m_Notes = newNotes; } /** * Returns an enumeration describing the available options.. * * @return an enumeration of all the available options. */ @Override public Enumeration

* * Valid options are: *

* *

   * -L <num>
   *  The lower run number to start the experiment from.
   *  (default 1)
   * 
* *
   * -U <num>
   *  The upper run number to end the experiment at (inclusive).
   *  (default 10)
   * 
* *
   * -T <arff file>
   *  The dataset to run the experiment on.
   *  (required, may be specified multiple times)
   * 
* *
   * -P <class name>
   *  The full class name of a ResultProducer (required).
   *  eg: weka.experiment.RandomSplitResultProducer
   * 
* *
   * -D <class name>
   *  The full class name of a ResultListener (required).
   *  eg: weka.experiment.CSVResultListener
   * 
* *
   * -N <string>
   *  A string containing any notes about the experiment.
   *  (default none)
   * 
* *
   * Options specific to result producer weka.experiment.RandomSplitResultProducer:
   * 
* *
   * -P <percent>
   *  The percentage of instances to use for training.
   *  (default 66)
   * 
* *
   * -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
   * 
* *
   * -R
   *  Set when data is not to be randomized and the data sets' size.
   *  Is not to be determined via probabilistic rounding.
   * 
* *
   * 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 */ @Override public void setOptions(String[] options) throws Exception { String lowerString = Utils.getOption('L', options); if (lowerString.length() != 0) { setRunLower(Integer.parseInt(lowerString)); } else { setRunLower(1); } String upperString = Utils.getOption('U', options); if (upperString.length() != 0) { setRunUpper(Integer.parseInt(upperString)); } else { setRunUpper(10); } if (getRunLower() > getRunUpper()) { throw new Exception("Lower (" + getRunLower() + ") is greater than upper (" + getRunUpper() + ")"); } setNotes(Utils.getOption('N', options)); getDatasets().removeAllElements(); String dataName; do { dataName = Utils.getOption('T', options); if (dataName.length() != 0) { File dataset = new File(dataName); getDatasets().addElement(dataset); } } while (dataName.length() != 0); if (getDatasets().size() == 0) { throw new Exception("Required: -T "); } String rlName = Utils.getOption('D', options); if (rlName.length() == 0) { throw new Exception("Required: -D "); } rlName = rlName.trim(); // split off any options int breakLoc = rlName.indexOf(' '); String clName = rlName; String rlOptionsString = ""; String[] rlOptions = null; if (breakLoc != -1) { clName = rlName.substring(0, breakLoc); rlOptionsString = rlName.substring(breakLoc).trim(); rlOptions = Utils.splitOptions(rlOptionsString); } setResultListener((ResultListener) Utils.forName(ResultListener.class, clName, rlOptions)); String rpName = Utils.getOption('P', options); if (rpName.length() == 0) { throw new Exception("Required: -P "); } // Do it first without options, so if an exception is thrown during // the option setting, listOptions will contain options for the actual // RP. // GHF -- nice idea, but it prevents you from using result producers that // have *required* parameters setResultProducer((ResultProducer) Utils.forName(ResultProducer.class, rpName, Utils.partitionOptions(options))); // GHF // GHF if (getResultProducer() instanceof OptionHandler) { // GHF ((OptionHandler) getResultProducer()) // GHF .setOptions(Utils.partitionOptions(options)); // GHF } } /** * Gets the current settings of the experiment iterator. * * @return an array of strings suitable for passing to setOptions */ @Override public String[] getOptions() { // Currently no way to set custompropertyiterators from the command line m_UsePropertyIterator = false; m_PropertyPath = null; m_PropertyArray = null; String[] rpOptions = new String[0]; if ((m_ResultProducer != null) && (m_ResultProducer instanceof OptionHandler)) { rpOptions = ((OptionHandler) m_ResultProducer).getOptions(); } String[] options = new String[rpOptions.length + getDatasets().size() * 2 + 11]; int current = 0; options[current++] = "-L"; options[current++] = "" + getRunLower(); options[current++] = "-U"; options[current++] = "" + getRunUpper(); if (getDatasets().size() != 0) { for (int i = 0; i < getDatasets().size(); i++) { options[current++] = "-T"; options[current++] = getDatasets().elementAt(i).toString(); } } if (getResultListener() != null) { options[current++] = "-D"; options[current++] = getResultListener().getClass().getName(); } if (getResultProducer() != null) { options[current++] = "-P"; options[current++] = getResultProducer().getClass().getName(); } if (!getNotes().equals("")) { options[current++] = "-N"; options[current++] = getNotes(); } options[current++] = "--"; System.arraycopy(rpOptions, 0, options, current, rpOptions.length); current += rpOptions.length; while (current < options.length) { options[current++] = ""; } return options; } /** * Gets a string representation of the experiment configuration. * * @return a value of type 'String' */ @Override public String toString() { String result = "Runs from: " + m_RunLower + " to: " + m_RunUpper + '\n'; result += "Datasets:"; for (int i = 0; i < m_Datasets.size(); i++) { result += " " + m_Datasets.elementAt(i); } result += '\n'; result += "Custom property iterator: " + (m_UsePropertyIterator ? "on" : "off") + "\n"; if (m_UsePropertyIterator) { if (m_PropertyPath == null) { throw new Error("*** null propertyPath ***"); } if (m_PropertyArray == null) { throw new Error("*** null propertyArray ***"); } if (m_PropertyPath.length > 1) { result += "Custom property path:\n"; for (int i = 0; i < m_PropertyPath.length - 1; i++) { PropertyNode pn = m_PropertyPath[i]; result += "" + (i + 1) + " " + pn.parentClass.getName() + "::" + pn.toString() + ' ' + pn.value.toString() + '\n'; } } result += "Custom property name:" + m_PropertyPath[m_PropertyPath.length - 1].toString() + '\n'; result += "Custom property values:\n"; for (int i = 0; i < Array.getLength(m_PropertyArray); i++) { Object current = Array.get(m_PropertyArray, i); result += " " + (i + 1) + " " + current.getClass().getName() + " " + current.toString() + '\n'; } } result += "ResultProducer: " + m_ResultProducer + '\n'; result += "ResultListener: " + m_ResultListener + '\n'; if (!getNotes().equals("")) { result += "Notes: " + getNotes(); } return result; } /** * Loads an experiment from a file. * * @param filename the file to load the experiment from * @return the experiment * @throws Exception if loading fails */ public static Experiment read(String filename) throws Exception { Experiment result; // KOML? if ((KOML.isPresent()) && (filename.toLowerCase().endsWith(KOML.FILE_EXTENSION))) { result = (Experiment) KOML.read(filename); } // XML? else if (filename.toLowerCase().endsWith(".xml")) { XMLExperiment xml = new XMLExperiment(); result = (Experiment) xml.read(filename); } // binary else { FileInputStream fi = new FileInputStream(filename); ObjectInputStream oi = new ObjectInputStream(new BufferedInputStream(fi)); result = (Experiment) oi.readObject(); oi.close(); } return result; } /** * Writes the experiment to disk. * * @param filename the file to write to * @param exp the experiment to save * @throws Exception if writing fails */ public static void write(String filename, Experiment exp) throws Exception { // KOML? if ((KOML.isPresent()) && (filename.toLowerCase().endsWith(KOML.FILE_EXTENSION))) { KOML.write(filename, exp); } // XML? else if (filename.toLowerCase().endsWith(".xml")) { XMLExperiment xml = new XMLExperiment(); xml.write(filename, exp); } // binary else { FileOutputStream fo = new FileOutputStream(filename); ObjectOutputStream oo = new ObjectOutputStream(new BufferedOutputStream( fo)); oo.writeObject(exp); oo.close(); } } /** * Configures/Runs the Experiment from the command line. * * @param args command line arguments to the Experiment. */ public static void main(String[] args) { try { weka.core.WekaPackageManager.loadPackages(false, true, false); Experiment exp = null; // get options from XML? String xmlOption = Utils.getOption("xml", args); if (!xmlOption.equals("")) { args = new XMLOptions(xmlOption).toArray(); } String expFile = Utils.getOption('l', args); String saveFile = Utils.getOption('s', args); boolean runExp = Utils.getFlag('r', args); boolean verbose = Utils.getFlag("verbose", args); if (expFile.length() == 0) { exp = new Experiment(); try { exp.setOptions(args); Utils.checkForRemainingOptions(args); } catch (Exception ex) { ex.printStackTrace(); String result = "Usage:\n\n" + "-l \n" + "\tLoad experiment from file (default use cli options).\n" + "\tThe type is determined, based on the extension (" + FILE_EXTENSION + " or .xml)\n" + "-s \n" + "\tSave experiment to file after setting other options.\n" + "\tThe type is determined, based on the extension (" + FILE_EXTENSION + " or .xml)\n" + "\t(default don't save)\n" + "-r\n" + "\tRun experiment (default don't run)\n" + "-xml \n" + "\tget options from XML-Data instead from parameters.\n" + "-verbose\n" + "\toutput progress information to std out." + "\n"; Enumeration





© 2015 - 2024 Weber Informatics LLC | Privacy Policy