Example usage for weka.core Utils partitionOptions

List of usage examples for weka.core Utils partitionOptions

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Introduction

In this page you can find the example usage for weka.core Utils partitionOptions.

Prototype

public static String[] partitionOptions(String[] options)

Source Link

Document

Returns the secondary set of options (if any) contained in the supplied options array.

Usage

From source file:WrapperSubset.java

License:Open Source License

/**
 * Parses a given list of options./* w  w w.j a  va2 s. c o  m*/
 * <p/>
 *
 * <!-- options-start --> Valid options are:
 * <p/>
 *
 * <pre>
 * -B &lt;base learner&gt;
 *  class name of base learner to use for  accuracy estimation.
 *  Place any classifier options LAST on the command line
 *  following a "--". eg.:
 *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
 *  (default: weka.classifiers.rules.ZeroR)
 * </pre>
 *
 * <pre>
 * -F &lt;num&gt;
 *  number of cross validation folds to use for estimating accuracy.
 *  (default=5)
 * </pre>
 *
 * <pre>
 * -R &lt;seed&gt;
 *  Seed for cross validation accuracy testimation.
 *  (default = 1)
 * </pre>
 *
 * <pre>
 * -T &lt;num&gt;
 *  threshold by which to execute another cross validation
 *  (standard deviation---expressed as a percentage of the mean).
 *  (default: 0.01 (1%))
 * </pre>
 *
 * <pre>
 * -E &lt;acc | rmse | mae | f-meas | auc | auprc&gt;
 *  Performance evaluation measure to use for selecting attributes.
 *  (Default = accuracy for discrete class and rmse for numeric class)
 * </pre>
 *
 * <pre>
 * -IRclass &lt;label | index&gt;
 *  Optional class value (label or 1-based index) to use in conjunction with
 *  IR statistics (f-meas, auc or auprc). Omitting this option will use
 *  the class-weighted average.
 * </pre>
 *
 * <pre>
 * Options specific to scheme 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 -->
 *
 * @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 optionString;
    resetOptions();
    optionString = Utils.getOption('B', options);

    if (optionString.length() == 0) {
        optionString = ZeroR.class.getName();
    }
    setClassifier(AbstractClassifier.forName(optionString, Utils.partitionOptions(options)));
    optionString = Utils.getOption('F', options);

    if (optionString.length() != 0) {
        setFolds(Integer.parseInt(optionString));
    }

    optionString = Utils.getOption('R', options);
    if (optionString.length() != 0) {
        setSeed(Integer.parseInt(optionString));
    }

    // optionString = Utils.getOption('S',options);
    // if (optionString.length() != 0)
    // {
    // seed = Integer.parseInt(optionString);
    // }
    optionString = Utils.getOption('T', options);

    if (optionString.length() != 0) {
        Double temp;
        temp = Double.valueOf(optionString);
        setThreshold(temp.doubleValue());
    }

    optionString = Utils.getOption('E', options);
    if (optionString.length() != 0) {
        if (optionString.equals("acc")) {
            setEvaluationMeasure(new SelectedTag(EVAL_ACCURACY, TAGS_EVALUATION));
        } else if (optionString.equals("rmse")) {
            setEvaluationMeasure(new SelectedTag(EVAL_RMSE, TAGS_EVALUATION));
        } else if (optionString.equals("mae")) {
            setEvaluationMeasure(new SelectedTag(EVAL_MAE, TAGS_EVALUATION));
        } else if (optionString.equals("f-meas")) {
            setEvaluationMeasure(new SelectedTag(EVAL_FMEASURE, TAGS_EVALUATION));
        } else if (optionString.equals("auc")) {
            setEvaluationMeasure(new SelectedTag(EVAL_AUC, TAGS_EVALUATION));
        } else if (optionString.equals("auprc")) {
            setEvaluationMeasure(new SelectedTag(EVAL_AUPRC, TAGS_EVALUATION));
        } else {
            throw new IllegalArgumentException("Invalid evaluation measure");
        }
    }

    optionString = Utils.getOption("IRClass", options);
    if (optionString.length() > 0) {
        setIRClassValue(optionString);
    }
}

From source file:com.spread.experiment.tempuntilofficialrelease.ClassificationViaClustering108.java

License:Open Source License

/**
 * Parses the options for this object.//from www .j av a2  s  .  c  o m
 * <p/>
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -output-debug-info
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 * -W &lt;clusterer&gt
 *  Full name of clusterer.
 *  (default: weka.clusterers.SimpleKMeans)
 * </pre>
 *
 * <pre>
 * -label-all-clusters
 *  If set, all clusters are labeled probabilistically instead of just those ones that best fit a class
 * </pre>
 *
 * <pre>
 * Options specific to clusterer weka.clusterers.SimpleKMeans:
 * </pre>
 * 
 * <pre>
 * -N &lt;num&gt;
 *  number of clusters.
 *  (default 2).
 * </pre>
 * 
 * <pre>
 * -V
 *  Display std. deviations for centroids.
 * </pre>
 * 
 * <pre>
 * -M
 *  Replace missing values with mean/mode.
 * </pre>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  Random number seed.
 *  (default 10)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @param options the options to use
 * @throws Exception if setting of options fails
 */
@Override
public void setOptions(String[] options) throws Exception {

    setLabelAllClusters(Utils.getFlag("label-all-clusters", options));
    String tmpStr = Utils.getOption('W', options);
    if (tmpStr.length() > 0) {
        setClusterer(AbstractClusterer.forName(tmpStr, null));
        setClusterer(AbstractClusterer.forName(tmpStr, Utils.partitionOptions(options)));
    } else {
        setClusterer(AbstractClusterer.forName(defaultClustererString(), null));
        setClusterer(AbstractClusterer.forName(defaultClustererString(), Utils.partitionOptions(options)));
    }

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
}

From source file:milk.classifiers.MIBoost.java

License:Open Source License

/**
  * Parses a given list of options. Valid options are:<p>
  *//from   w w  w . ja v a 2s.  c  o m
  * -D <br>
  * Turn on debugging output.<p>
  *
  *
  * @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 {
     setDebug(Utils.getFlag('D', options));

     String bin = Utils.getOption('B', options);
     if (bin.length() != 0) {
         setDiscretizeBin(Integer.parseInt(bin));
     } else {
         setDiscretizeBin(0);
     }

     String boostIterations = Utils.getOption('R', options);
     if (boostIterations.length() != 0) {
         setMaxIterations(Integer.parseInt(boostIterations));
     } else {
         setMaxIterations(10);
     }

     String classifierName = Utils.getOption('W', options);
     if (classifierName.length() != 0)
         m_Classifier = Classifier.forName(classifierName, Utils.partitionOptions(options));
     else {
         //throw new Exception("A classifier must be specified with"
         //         + " the -W option.");
         //m_Classifier = new weka.classifiers.trees.DecisionStump();    
     }
 }

From source file:milk.classifiers.MIWrapper.java

License:Open Source License

/**
  * Parses a given list of options. Valid options are:<p>
  *//w w  w.  ja  v a2 s. c  om
  * -D <br>
  * Turn on debugging output.<p>
  *
  * -W classname <br>
  * Specify the full class name of a classifier as the basis (required).<p>
  *
  * -P method_index <br>
  * Set which method to use in testing: 1.arithmatic average; 2.geometric average. (default: 1)<p>
  *
  * @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 {

     setDebug(Utils.getFlag('D', options));

     String methodString = Utils.getOption('P', options);
     if (methodString.length() != 0) {
         setMethod(Integer.parseInt(methodString));
     } else {
         setMethod(1);
     }

     String classifierName = Utils.getOption('W', options);
     if (classifierName.length() == 0) {
         throw new Exception("A classifier must be specified with" + " the -W option.");
     }
     setClassifier(Classifier.forName(classifierName, Utils.partitionOptions(options)));
 }

From source file:milk.classifiers.SimpleMI.java

License:Open Source License

/**
  * Parses a given list of options. Valid options are:<p>
  */*from w  ww .  j  av a 2  s  .c  o  m*/
  * -M method_index <br>
  * Set which method to use in transformation: 1.arithmatic average; 2.geometric centor
  * of a bag (default: 1)<p>
  *
  * @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 {

     String methodString = Utils.getOption('M', options);
     if (methodString.length() != 0) {
         setTransformMethod(Integer.parseInt(methodString));
     } else {
         setTransformMethod(1);
     }

     String classifierName = Utils.getOption('W', options);
     if (classifierName.length() == 0) {
         throw new Exception("A classifier must be specified with" + " the -W option.");
     }
     setClassifier(Classifier.forName(classifierName, Utils.partitionOptions(options)));
 }

From source file:milk.experiment.MIClassifierSplitEvaluator.java

License:Open Source License

/**
   * Parses a given list of options. Valid options are:<p>
   *//  w ww  . j a  va 2  s . c o  m
   * -W classname <br>
   * Specify the full class name of the classifier to evaluate. <p>
   *
   * -C class index <br>
   * The index of the class for which IR statistics are to
   * be output. (default 1) <p>
   *
   * All option after -- will be passed to the classifier.
   *
   * @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 {

      String cName = Utils.getOption('W', options);
      if (cName.length() == 0) {
          throw new Exception("A classifier 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
      // Classifier.
      setClassifier(MIClassifier.forName(cName, null));
      if (getClassifier() instanceof OptionHandler) {
          ((OptionHandler) getClassifier()).setOptions(Utils.partitionOptions(options));
          updateOptions();
      }

      String indexName = Utils.getOption('C', options);
      if (indexName.length() != 0) {
          m_IRclass = (new Integer(indexName)).intValue() - 1;
      } else {
          m_IRclass = 0;
      }
  }

From source file:milk.experiment.MICrossValidationResultProducer.java

License:Open Source License

/**
   * Parses a given list of options. Valid options are:<p>
   */*from  w w  w  . j a  v a2  s  .  co m*/
   * -X num_folds <br>
   * The number of folds to use for the cross-validation. <p>
   *
   * -D <br>
   * Specify that raw split evaluator output is to be saved. <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));

      String fName = Utils.getOption('O', options);
      if (fName.length() != 0) {
          setOutputFile(new File(fName));
      }

      String numFolds = Utils.getOption('X', options);
      if (numFolds.length() != 0) {
          setNumFolds(Integer.parseInt(numFolds));
      } else {
          setNumFolds(10);
      }

      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));
      }
  }

From source file:milk.experiment.MIExperiment.java

License:Open Source License

/**
   * Parses a given list of options. Valid options are:<p>
   */*from  ww  w  . j  a  v  a  2s  .c  o  m*/
   * -L num <br>
   * The lower run number to start the experiment from.
   * (default 1) <p>
   *
   * -U num <br>
   * The upper run number to end the experiment at (inclusive).
   * (default 10) <p>
   *
   * -T arff_file <br>
   * The dataset to run the experiment on.
   * (required, may be specified multiple times) <p>
   *
   * -P class_name <br>
   * The full class name of a ResultProducer (required).
   * eg: weka.experiment.RandomSplitResultProducer <p>
   *
   * -D "class_name" <br>
   * The full class name of a ResultListener followed by any options.
   * If options are supplied then full string must be enclosed in quotes.
   * (required).
   * eg: "weka.experiment.CSVResultListener -O outfile.csv" <p>
   *
   * All option after -- will be passed to the result producer. <p>
   *
   * @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 {

      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 <arff file name>");
      }

      String rlName = Utils.getOption('D', options);
      if (rlName.length() == 0) {
          throw new Exception("Required: -D <ResultListener class name>");
      }
      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((MIResultListener) Utils.forName(MIResultListener.class, clName, rlOptions));

      String rpName = Utils.getOption('P', options);
      if (rpName.length() == 0) {
          throw new Exception("Required: -P <ResultProducer class name>");
      }
      // 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(
              (MIResultProducer) Utils.forName(MIResultProducer.class, rpName, Utils.partitionOptions(options))); //GHF
      //GHF if (getResultProducer() instanceof OptionHandler) {
      //GHF  ((OptionHandler) getResultProducer())
      //GHF  .setOptions(Utils.partitionOptions(options));
      //GHF }
  }

From source file:milk.experiment.MIRandomSplitResultProducer.java

License:Open Source License

/**
   * Parses a given list of options. Valid options are:<p>
   */*  ww  w  . j  av  a 2  s .com*/
   * -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));
      }
  }

From source file:sg.edu.nus.comp.nlp.ims.classifiers.CMultiClassesSVM.java

License:Open Source License

@Override
public void setOptions(String[] p_Options) throws Exception {
    String iid = Utils.getOption('I', p_Options);
    if (iid != null) {
        this.m_IndexOfID = Integer.parseInt(iid);
    }/*w w w . j a v a 2  s  .  c  o m*/
    ((OptionHandler) this.m_Classifier).setOptions(Utils.partitionOptions(p_Options));
}