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• AddCluster.java

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weka.filters.unsupervised.attribute.AddCluster Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 *    AddCluster.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import weka.clusterers.AbstractClusterer;
import weka.clusterers.Clusterer;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.WeightedAttributesHandler;
import weka.core.WeightedInstancesHandler;
import weka.core.WekaException;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;

import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.ObjectInputStream;
import java.util.ArrayList;
import java.util.Enumeration;
import java.util.Vector;

/**
 *  A filter that adds a new nominal attribute
 * representing the cluster assigned to each instance by the specified
 * clustering algorithm.

 * Either the clustering algorithm gets built with the first batch of data or
 * one specifies are serialized clusterer model file to use instead.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -W <clusterer specification>
 *  Full class name of clusterer to use, followed
 *  by scheme options. eg:
 *   "weka.clusterers.SimpleKMeans -N 3"
 *  (default: weka.clusterers.SimpleKMeans)
 * 
 * 
 * 
 * -serialized <file>
 *  Instead of building a clusterer on the data, one can also provide
 *  a serialized model and use that for adding the clusters.
 * 
 * 
 * 
 * -I <att1,att2-att4,...>
 *  The range of attributes the clusterer should ignore.
 * 
 * 
 * 
 * 
 * @author Richard Kirkby ([email protected])
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 15203 $
 */
public class AddCluster extends Filter implements UnsupervisedFilter,
  OptionHandler, WeightedAttributesHandler, WeightedInstancesHandler {

  /** for serialization. */
  static final long serialVersionUID = 7414280611943807337L;

  /** The clusterer used to do the cleansing. */
  protected Clusterer m_Clusterer = new weka.clusterers.SimpleKMeans();

  /** The file from which to load a serialized clusterer. */
  protected File m_SerializedClustererFile = new File(
    System.getProperty("user.dir"));

  /** The actual clusterer used to do the clustering. */
  protected Clusterer m_ActualClusterer = null;

  /** Range of attributes to ignore. */
  protected Range m_IgnoreAttributesRange = null;

  /** Filter for removing attributes. */
  protected Filter m_removeAttributes = new Remove();

  /**
   * Returns the Capabilities of this filter, makes sure that the class is never
   * set (for the clusterer).
   * 
   * @param data the data to use for customization
   * @return the capabilities of this object, based on the data
   * @see #getCapabilities()
   */
  @Override
  public Capabilities getCapabilities(Instances data) {
    Instances newData;

    newData = new Instances(data, 0);
    newData.setClassIndex(-1);

    return super.getCapabilities(newData);
  }

  /**
   * Returns the Capabilities of this filter.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result = m_Clusterer.getCapabilities();
    result.enableAllClasses();

    result.setMinimumNumberInstances(0);

    return result;
  }

  /**
   * tests the data whether the filter can actually handle it.
   * 
   * @param instanceInfo the data to test
   * @throws Exception if the test fails
   */
  @Override
  protected void testInputFormat(Instances instanceInfo) throws Exception {
    getCapabilities(instanceInfo).testWithFail(removeIgnored(instanceInfo));
  }

  /**
   * Sets the format of the input instances.
   * 
   * @param instanceInfo an Instances object containing the input instance
   *          structure (any instances contained in the object are ignored -
   *          only the structure is required).
   * @return true if the outputFormat may be collected immediately
   * @throws Exception if the inputFormat can't be set successfully
   */
  @Override
  public boolean setInputFormat(Instances instanceInfo) throws Exception {
    super.setInputFormat(instanceInfo);

    m_removeAttributes = null;

    return false;
  }

  /**
   * filters all attributes that should be ignored.
   * 
   * @param data the data to filter
   * @return the filtered data
   * @throws Exception if filtering fails
   */
  protected Instances removeIgnored(Instances data) throws Exception {
    Instances result = data;

    if (m_IgnoreAttributesRange != null || data.classIndex() >= 0) {
      m_removeAttributes = new Remove();
      String rangeString = "";
      if (m_IgnoreAttributesRange != null) {
        rangeString += m_IgnoreAttributesRange.getRanges();
      }
      if (data.classIndex() >= 0) {
        if (rangeString.length() > 0) {
          rangeString += "," + (data.classIndex() + 1);
        } else {
          rangeString = "" + (data.classIndex() + 1);
        }
      }
      ((Remove) m_removeAttributes).setAttributeIndices(rangeString);
      ((Remove) m_removeAttributes).setInvertSelection(false);
      m_removeAttributes.setInputFormat(data);
      result = Filter.useFilter(data, m_removeAttributes);
    }

    return result;
  }

  /**
   * Signify that this batch of input to the filter is finished.
   * 
   * @return true if there are instances pending output
   * @throws IllegalStateException if no input structure has been defined
   */
  @Override
  public boolean batchFinished() throws Exception {
    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }

    Instances toFilter = getInputFormat();

    if (!isFirstBatchDone()) {
      // serialized model or build clusterer from scratch?
      File file = getSerializedClustererFile();
      if (!file.isDirectory()) {
        int[] attsToIgnore = null;
        ObjectInputStream ois = new ObjectInputStream(new FileInputStream(file));
        m_ActualClusterer = (Clusterer) ois.readObject();
        Instances header = null;
        // let's see whether there's an Instances header stored as well
        try {
          header = (Instances) ois.readObject();
          // ignored atts
          attsToIgnore = (int[]) ois.readObject();
        } catch (Exception e) {
          // ignored
        }
        ois.close();

        if (attsToIgnore != null && attsToIgnore.length > 0) {
          m_removeAttributes = new Remove();
          ((Remove) m_removeAttributes).setAttributeIndicesArray(attsToIgnore);
          ((Remove) m_removeAttributes).setInvertSelection(false);
          m_removeAttributes.setInputFormat(toFilter);
        }

        // same dataset format?
        if ((header != null)
          && (!header.equalHeaders(toFilter))) {
          throw new WekaException(
            "Training header of clusterer and filter dataset don't match:\n"
              + header.equalHeadersMsg(toFilter));
        }
      } else {
        // filter out attributes if necessary
        Instances toFilterIgnoringAttributes = removeIgnored(toFilter);

        m_ActualClusterer = AbstractClusterer.makeCopy(m_Clusterer);
        m_ActualClusterer.buildClusterer(toFilterIgnoringAttributes);
      }

      // create output dataset with new attribute
      Instances filtered = new Instances(toFilter, 0);
      ArrayList nominal_values = new ArrayList(
        m_ActualClusterer.numberOfClusters());
      for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) {
        nominal_values.add("cluster" + (i + 1));
      }
      filtered.insertAttributeAt(new Attribute("cluster", nominal_values),
        filtered.numAttributes());

      setOutputFormat(filtered);
    }

    // build new dataset
    for (int i = 0; i < toFilter.numInstances(); i++) {
      convertInstance(toFilter.instance(i));
    }

    flushInput();
    m_NewBatch = true;
    m_FirstBatchDone = true;

    return (numPendingOutput() != 0);
  }

  /**
   * Input an instance for filtering. Ordinarily the instance is processed and
   * made available for output immediately. Some filters require all instances
   * be read before producing output.
   *
   * @param instance the input instance
   * @return true if the filtered instance may now be collected with output().
   * @throws IllegalStateException if no input format has been defined.
   */
  @Override
  public boolean input(Instance instance) throws Exception {
    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }

    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }

    if (outputFormatPeek() != null) {
      convertInstance(instance);
      return true;
    }

    bufferInput(instance);
    return false;
  }

  /**
   * Convert a single instance over. The converted instance is added to the end
   * of the output queue.
   *
   * @param instance the instance to convert
   * @throws Exception if something goes wrong
   */
  protected void convertInstance(Instance instance) throws Exception {
    Instance original, processed;
    original = instance;

    // copy values
    double[] instanceVals = new double[instance.numAttributes() + 1];
    for (int j = 0; j < instance.numAttributes(); j++) {
      instanceVals[j] = original.value(j);
    }
    Instance filteredI = null;
    if (m_removeAttributes != null) {
      m_removeAttributes.input(instance);
      filteredI = m_removeAttributes.output();
    } else {
      filteredI = instance;
    }

    // add cluster to end
    try {
      instanceVals[instance.numAttributes()] = m_ActualClusterer
        .clusterInstance(filteredI);
    } catch (Exception e) {
      // clusterer couldn't cluster instance -> missing
      instanceVals[instance.numAttributes()] = Utils.missingValue();
    }

    // create new instance
    if (original instanceof SparseInstance) {
      processed = new SparseInstance(original.weight(), instanceVals);
    } else {
      processed = new DenseInstance(original.weight(), instanceVals);
    }

    copyValues(processed, false, instance.dataset(), outputFormatPeek());

    push(processed); // No need to copy instance
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration listOptions() {
    Vector result = new Vector(3);

    result.addElement(new Option(
      "\tFull class name of clusterer to use, followed\n"
        + "\tby scheme options. eg:\n"
        + "\t\t\"weka.clusterers.SimpleKMeans -N 3\"\n"
        + "\t(default: weka.clusterers.SimpleKMeans)", "W", 1,
      "-W "));

    result.addElement(new Option(
      "\tInstead of building a clusterer on the data, one can also provide\n"
        + "\ta serialized model and use that for adding the clusters.",
      "serialized", 1, "-serialized "));

    result.addElement(new Option(
      "\tThe range of attributes the clusterer should ignore.\n", "I", 1,
      "-I "));

    return result.elements();
  }

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -W <clusterer specification>
   *  Full class name of clusterer to use, followed
   *  by scheme options. eg:
   *   "weka.clusterers.SimpleKMeans -N 3"
   *  (default: weka.clusterers.SimpleKMeans)
   * 
   * 
   * 
   * -serialized <file>
   *  Instead of building a clusterer on the data, one can also provide
   *  a serialized model and use that for adding the clusters.
   * 
   * 
   * 
   * -I <att1,att2-att4,...>
   *  The range of attributes the clusterer should ignore.
   * 
   * 
   * 
   * 
   * @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 tmpStr;
    String[] tmpOptions;
    File file;
    boolean serializedModel;

    serializedModel = false;
    tmpStr = Utils.getOption("serialized", options);
    if (tmpStr.length() != 0) {
      file = new File(tmpStr);
      if (!file.exists()) {
        throw new FileNotFoundException("File '" + file.getAbsolutePath()
          + "' not found!");
      }
      if (file.isDirectory()) {
        throw new FileNotFoundException("'" + file.getAbsolutePath()
          + "' points to a directory not a file!");
      }
      setSerializedClustererFile(file);
      serializedModel = true;
    } else {
      setSerializedClustererFile(null);
    }

    if (!serializedModel) {
      tmpStr = Utils.getOption('W', options);
      if (tmpStr.length() == 0) {
        tmpStr = weka.clusterers.SimpleKMeans.class.getName();
      }
      tmpOptions = Utils.splitOptions(tmpStr);
      if (tmpOptions.length == 0) {
        throw new Exception("Invalid clusterer specification string");
      }
      tmpStr = tmpOptions[0];
      tmpOptions[0] = "";
      setClusterer(AbstractClusterer.forName(tmpStr, tmpOptions));
    }

    setIgnoredAttributeIndices(Utils.getOption('I', options));

    Utils.checkForRemainingOptions(options);
  }

  /**
   * Gets the current settings of the filter.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {
    Vector result;
    File file;

    result = new Vector();

    file = getSerializedClustererFile();
    if ((file != null) && (!file.isDirectory())) {
      result.add("-serialized");
      result.add(file.getAbsolutePath());
    } else {
      result.add("-W");
      result.add(getClustererSpec());
    }

    if (!getIgnoredAttributeIndices().equals("")) {
      result.add("-I");
      result.add(getIgnoredAttributeIndices());
    }

    return result.toArray(new String[result.size()]);
  }

  /**
   * Returns a string describing this filter.
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "A filter that adds a new nominal attribute representing the cluster "
      + "assigned to each instance by the specified clustering algorithm.\n"
      + "Either the clustering algorithm gets built with the first batch of "
      + "data or one specifies are serialized clusterer model file to use "
      + "instead.";
  }

  /**
   * Returns the tip text for this property.
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String clustererTipText() {
    return "The clusterer to assign clusters with.";
  }

  /**
   * Sets the clusterer to assign clusters with.
   * 
   * @param clusterer The clusterer to be used (with its options set).
   */
  public void setClusterer(Clusterer clusterer) {
    m_Clusterer = clusterer;
  }

  /**
   * Gets the clusterer used by the filter.
   * 
   * @return The clusterer being used.
   */
  public Clusterer getClusterer() {
    return m_Clusterer;
  }

  /**
   * Gets the clusterer specification string, which contains the class name of
   * the clusterer and any options to the clusterer.
   * 
   * @return the clusterer string.
   */
  protected String getClustererSpec() {
    Clusterer c = getClusterer();
    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
        + Utils.joinOptions(((OptionHandler) c).getOptions());
    }
    return c.getClass().getName();
  }

  /**
   * Returns the tip text for this property.
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String ignoredAttributeIndicesTipText() {
    return "The range of attributes to be ignored by the clusterer. eg: first-3,5,9-last";
  }

  /**
   * Gets ranges of attributes to be ignored.
   * 
   * @return a string containing a comma-separated list of ranges
   */
  public String getIgnoredAttributeIndices() {
    if (m_IgnoreAttributesRange == null) {
      return "";
    } else {
      return m_IgnoreAttributesRange.getRanges();
    }
  }

  /**
   * Sets the ranges of attributes to be ignored. If provided string is null, no
   * attributes will be ignored.
   * 
   * @param rangeList a string representing the list of attributes. eg:
   *          first-3,5,6-last
   * @throws IllegalArgumentException if an invalid range list is supplied
   */
  public void setIgnoredAttributeIndices(String rangeList) {
    if ((rangeList == null) || (rangeList.length() == 0)) {
      m_IgnoreAttributesRange = null;
    } else {
      m_IgnoreAttributesRange = new Range();
      m_IgnoreAttributesRange.setRanges(rangeList);
    }
  }

  /**
   * Gets the file pointing to a serialized, built clusterer. If it is null or
   * pointing to a directory it will not be used.
   * 
   * @return the file the serialized, built clusterer is located in
   */
  public File getSerializedClustererFile() {
    return m_SerializedClustererFile;
  }

  /**
   * Sets the file pointing to a serialized, built clusterer. If the argument is
   * null, doesn't exist or pointing to a directory, then the value is ignored.
   * 
   * @param value the file pointing to the serialized, built clusterer
   */
  public void setSerializedClustererFile(File value) {
    if ((value == null) || (!value.exists())) {
      value = new File(System.getProperty("user.dir"));
    }

    m_SerializedClustererFile = value;
  }

  /**
   * Returns the tip text for this property.
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String serializedClustererFileTipText() {
    return "A file containing the serialized model of a built clusterer.";
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 15203 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain arguments to the filter: use -h for help
   */
  public static void main(String[] argv) {
    runFilter(new AddCluster(), argv);
  }
}