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weka.classifiers.functions.SimpleLogistic 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 .
 */

/*
 *    SimpleLogistic.java
 *    Copyright (C) 2003-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.trees.lmt.LogisticBase;
import weka.core.*;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.NominalToBinary;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

/**
 
 * Classifier for building linear logistic regression
 * models. LogitBoost with simple regression functions as base learners is used
 * for fitting the logistic models. The optimal number of LogitBoost iterations
 * to perform is cross-validated, which leads to automatic attribute selection.
 * For more information see:

 * Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

 * 

 * Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree
 * Induction. In: 9th European Conference on Principles and Practice of
 * Knowledge Discovery in Databases, 675-683, 2005.
 * 

 
 *
 
 * BibTeX:
 * 
 * 
 * @article{Landwehr2005,
 *    author = {Niels Landwehr and Mark Hall and Eibe Frank},
 *    booktitle = {Machine Learning},
 *    number = {1-2},
 *    pages = {161-205},
 *    title = {Logistic Model Trees},
 *    volume = {95},
 *    year = {2005}
 * }
 * 
 * @inproceedings{Sumner2005,
 *    author = {Marc Sumner and Eibe Frank and Mark Hall},
 *    booktitle = {9th European Conference on Principles and Practice of Knowledge Discovery in Databases},
 *    pages = {675-683},
 *    publisher = {Springer},
 *    title = {Speeding up Logistic Model Tree Induction},
 *    year = {2005}
 * }
 * 
 * 

 * 
 *
 *  Valid options are:
 * 

 * 
 * 
 * -I <iterations>
 *  Set fixed number of iterations for LogitBoost
 * 
 * 
 * 
 * -S
 *  Use stopping criterion on training set (instead of
 *  cross-validation)
 * 
 * 
 * 
 * -P
 *  Use error on probabilities (rmse) instead of
 *  misclassification error for stopping criterion
 * 
 * 
 * 
 * -M <iterations>
 *  Set maximum number of boosting iterations
 * 
 * 
 * 
 * -H <iterations>
 *  Set parameter for heuristic for early stopping of
 *  LogitBoost.
 *  If enabled, the minimum is selected greedily, stopping
 *  if the current minimum has not changed for iter iterations.
 *  By default, heuristic is enabled with value 50. Set to
 *  zero to disable heuristic.
 * 
 * 
 * 
 * -W <beta>
 *  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.
 * 
 * 
 * 
 * -A
 *  The AIC is used to choose the best iteration (instead of CV or training error).
 * 
 * 
 
 *
 * @author Niels Landwehr
 * @author Marc Sumner
 * @version $Revision: 11568 $
 */
public class SimpleLogistic extends AbstractClassifier implements
  OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler,
  TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = 7397710626304705059L;

  /** The actual logistic regression model */
  protected LogisticBase m_boostedModel;

  /** Filter for converting nominal attributes to binary ones */
  protected NominalToBinary m_NominalToBinary = null;

  /** Filter for replacing missing values */
  protected ReplaceMissingValues m_ReplaceMissingValues = null;

  /** If non-negative, use this as fixed number of LogitBoost iterations */
  protected int m_numBoostingIterations;

  /** Maximum number of iterations for LogitBoost */
  protected int m_maxBoostingIterations = 500;

  /** Parameter for the heuristic for early stopping of LogitBoost */
  protected int m_heuristicStop = 50;

  /** If true, cross-validate number of LogitBoost iterations */
  protected boolean m_useCrossValidation;

  /**
   * If true, use minimize error on probabilities instead of misclassification
   * error
   */
  protected boolean m_errorOnProbabilities;

  /**
   * Threshold for trimming weights. Instances with a weight lower than this (as
   * a percentage of total weights) are not included in the regression fit.
   */
  protected double m_weightTrimBeta = 0;

  /** If true, the AIC is used to choose the best iteration */
  private boolean m_useAIC = false;

  /**
   * Constructor for creating SimpleLogistic object with standard options.
   */
  public SimpleLogistic() {
    m_numBoostingIterations = 0;
    m_useCrossValidation = true;
    m_errorOnProbabilities = false;
    m_weightTrimBeta = 0;
    m_useAIC = false;
  }

  /**
   * Constructor for creating SimpleLogistic object.
   * 
   * @param numBoostingIterations if non-negative, use this as fixed number of
   *          iterations for LogitBoost
   * @param useCrossValidation cross-validate number of LogitBoost iterations.
   * @param errorOnProbabilities minimize error on probabilities instead of
   *          misclassification error
   */
  public SimpleLogistic(int numBoostingIterations, boolean useCrossValidation,
    boolean errorOnProbabilities) {
    m_numBoostingIterations = numBoostingIterations;
    m_useCrossValidation = useCrossValidation;
    m_errorOnProbabilities = errorOnProbabilities;
    m_weightTrimBeta = 0;
    m_useAIC = false;
  }

  /**
   * Main method for testing this class
   *
   * @param argv commandline options
   */
  public static void main(String[] argv) {
    runClassifier(new SimpleLogistic(), argv);
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    return result;
  }

  /**
   * Builds the logistic regression using LogitBoost.
   *
   * @param data the training data
   * @throws Exception if something goes wrong
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();

    // replace missing values
    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(data);
    data = Filter.useFilter(data, m_ReplaceMissingValues);

    // convert nominal attributes
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(data);
    data = Filter.useFilter(data, m_NominalToBinary);

    // create actual logistic model
    m_boostedModel =
      new LogisticBase(m_numBoostingIterations, m_useCrossValidation,
        m_errorOnProbabilities);
    m_boostedModel.setMaxIterations(m_maxBoostingIterations);
    m_boostedModel.setHeuristicStop(m_heuristicStop);
    m_boostedModel.setWeightTrimBeta(m_weightTrimBeta);
    m_boostedModel.setUseAIC(m_useAIC);
    m_boostedModel.setNumDecimalPlaces(m_numDecimalPlaces);

    // build logistic model
    m_boostedModel.buildClassifier(data);
  }

  /**
   * Returns class probabilities for an instance.
   *
   * @param inst the instance to compute the probabilities for
   * @return the probabilities
   * @throws Exception if distribution can't be computed successfully
   */
  public double[] distributionForInstance(Instance inst) throws Exception {

    // replace missing values / convert nominal atts
    m_ReplaceMissingValues.input(inst);
    inst = m_ReplaceMissingValues.output();
    m_NominalToBinary.input(inst);
    inst = m_NominalToBinary.output();

    // obtain probs from logistic model
    return m_boostedModel.distributionForInstance(inst);
  }

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

    newVector.addElement(new Option(
      "\tSet fixed number of iterations for LogitBoost", "I", 1,
      "-I "));

    newVector.addElement(new Option(
      "\tUse stopping criterion on training set (instead of\n"
        + "\tcross-validation)", "S", 0, "-S"));

    newVector.addElement(new Option(
      "\tUse error on probabilities (rmse) instead of\n"
        + "\tmisclassification error for stopping criterion", "P", 0, "-P"));

    newVector
      .addElement(new Option("\tSet maximum number of boosting iterations",
        "M", 1, "-M "));

    newVector.addElement(new Option(
      "\tSet parameter for heuristic for early stopping of\n"
        + "\tLogitBoost.\n"
        + "\tIf enabled, the minimum is selected greedily, stopping\n"
        + "\tif the current minimum has not changed for iter iterations.\n"
        + "\tBy default, heuristic is enabled with value 50. Set to\n"
        + "\tzero to disable heuristic.", "H", 1, "-H "));

    newVector
      .addElement(new Option(
        "\tSet beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.\n",
        "W", 1, "-W "));

    newVector
      .addElement(new Option(
        "\tThe AIC is used to choose the best iteration (instead of CV or training error).\n",
        "A", 0, "-A"));

    newVector.addAll(Collections.list(super.listOptions()));

    return newVector.elements();
  }

  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    Vector options = new Vector();

    options.add("-I");
    options.add("" + getNumBoostingIterations());

    if (!getUseCrossValidation()) {
      options.add("-S");
    }

    if (getErrorOnProbabilities()) {
      options.add("-P");
    }

    options.add("-M");
    options.add("" + getMaxBoostingIterations());

    options.add("-H");
    options.add("" + getHeuristicStop());

    options.add("-W");
    options.add("" + getWeightTrimBeta());

    if (getUseAIC()) {
      options.add("-A");
    }

    Collections.addAll(options, super.getOptions());

    return options.toArray(new String[0]);
  }

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

   *
   
   *   Valid options are:
   * 

   *
   * 
   * -I <iterations>
   *  Set fixed number of iterations for LogitBoost
   * 
   *
   * 
   * -S
   *  Use stopping criterion on training set (instead of
   *  cross-validation)
   * 
   *
   * 
   * -P
   *  Use error on probabilities (rmse) instead of
   *  misclassification error for stopping criterion
   * 
   *
   * 
   * -M <iterations>
   *  Set maximum number of boosting iterations
   * 
   *
   * 
   * -H <iterations>
   *  Set parameter for heuristic for early stopping of
   *  LogitBoost.
   *  If enabled, the minimum is selected greedily, stopping
   *  if the current minimum has not changed for iter iterations.
   *  By default, heuristic is enabled with value 50. Set to
   *  zero to disable heuristic.
   * 
   *
   * 
   * -W <beta>
   *  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.
   * 
   *
   * 
   * -A
   *  The AIC is used to choose the best iteration (instead of CV or training error).
   * 
   *
   
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {

    String optionString = Utils.getOption('I', options);
    if (optionString.length() != 0) {
      setNumBoostingIterations((new Integer(optionString)).intValue());
    }

    setUseCrossValidation(!Utils.getFlag('S', options));
    setErrorOnProbabilities(Utils.getFlag('P', options));

    optionString = Utils.getOption('M', options);
    if (optionString.length() != 0) {
      setMaxBoostingIterations((new Integer(optionString)).intValue());
    }

    optionString = Utils.getOption('H', options);
    if (optionString.length() != 0) {
      setHeuristicStop((new Integer(optionString)).intValue());
    }

    optionString = Utils.getOption('W', options);
    if (optionString.length() != 0) {
      setWeightTrimBeta((new Double(optionString)).doubleValue());
    }

    setUseAIC(Utils.getFlag('A', options));

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
  }

  /**
   * Get the value of numBoostingIterations.
   *
   * @return the number of boosting iterations
   */
  public int getNumBoostingIterations() {
    return m_numBoostingIterations;
  }

  /**
   * Set the value of numBoostingIterations.
   *
   * @param n the number of boosting iterations
   */
  public void setNumBoostingIterations(int n) {
    m_numBoostingIterations = n;
  }

  /**
   * Get the value of useCrossValidation.
   *
   * @return true if cross-validation is used
   */
  public boolean getUseCrossValidation() {
    return m_useCrossValidation;
  }

  /**
   * Set the value of useCrossValidation.
   *
   * @param l whether to use cross-validation
   */
  public void setUseCrossValidation(boolean l) {
    m_useCrossValidation = l;
  }

  /**
   * Get the value of errorOnProbabilities.
   *
   * @return If true, use minimize error on probabilities instead of
   *         misclassification error
   */
  public boolean getErrorOnProbabilities() {
    return m_errorOnProbabilities;
  }

  /**
   * Set the value of errorOnProbabilities.
   *
   * @param l If true, use minimize error on probabilities instead of
   *          misclassification error
   */
  public void setErrorOnProbabilities(boolean l) {
    m_errorOnProbabilities = l;
  }

  /**
   * Get the value of maxBoostingIterations.
   *
   * @return the maximum number of boosting iterations
   */
  public int getMaxBoostingIterations() {
    return m_maxBoostingIterations;
  }

  /**
   * Set the value of maxBoostingIterations.
   *
   * @param n the maximum number of boosting iterations
   */
  public void setMaxBoostingIterations(int n) {
    m_maxBoostingIterations = n;
  }

  /**
   * Get the value of heuristicStop.
   *
   * @return the value of heuristicStop
   */
  public int getHeuristicStop() {
    return m_heuristicStop;
  }

  /**
   * Set the value of heuristicStop.
   *
   * @param n the value of heuristicStop
   */
  public void setHeuristicStop(int n) {
    if (n == 0)
      m_heuristicStop = m_maxBoostingIterations;
    else
      m_heuristicStop = n;
  }

  /**
   * Get the value of weightTrimBeta.
   */
  public double getWeightTrimBeta() {
    return m_weightTrimBeta;
  }

  /**
   * Set the value of weightTrimBeta.
   */
  public void setWeightTrimBeta(double n) {
    m_weightTrimBeta = n;
  }

  /**
   * Get the value of useAIC.
   *
   * @return Value of useAIC.
   */
  public boolean getUseAIC() {
    return m_useAIC;
  }

  /**
   * Set the value of useAIC.
   *
   * @param c Value to assign to useAIC.
   */
  public void setUseAIC(boolean c) {
    m_useAIC = c;
  }

  /**
   * Get the number of LogitBoost iterations performed (= the number of
   * regression functions fit by LogitBoost).
   *
   * @return the number of LogitBoost iterations performed
   */
  public int getNumRegressions() {
    return m_boostedModel.getNumRegressions();
  }

  /**
   * Returns a description of the logistic model (attributes/coefficients).
   *
   * @return the model as string
   */
  public String toString() {
    if (m_boostedModel == null)
      return "No model built";
    return "SimpleLogistic:\n" + m_boostedModel.toString();
  }

  /**
   * Returns the fraction of all attributes in the data that are used in the
   * logistic model (in percent). An attribute is used in the model if it is
   * used in any of the models for the different classes.
   *
   * @return percentage of attributes used in the model
   */
  public double measureAttributesUsed() {
    return m_boostedModel.percentAttributesUsed();
  }

  /**
   * Returns an enumeration of the additional measure names
   *
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector(3);
    newVector.addElement("measureAttributesUsed");
    newVector.addElement("measureNumIterations");
    return newVector.elements();
  }

  /**
   * Returns the value of the named measure
   *
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @throws IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    if (additionalMeasureName.compareToIgnoreCase("measureAttributesUsed") == 0) {
      return measureAttributesUsed();
    } else if (additionalMeasureName
      .compareToIgnoreCase("measureNumIterations") == 0) {
      return getNumRegressions();
    } else {
      throw new IllegalArgumentException(additionalMeasureName
        + " not supported (SimpleLogistic)");
    }
  }

  /**
   * Returns a string describing classifier
   *
   * @return a description suitable for displaying in the explorer/experimenter
   *         gui
   */
  public String globalInfo() {
    return "Classifier for building linear logistic regression models. LogitBoost with simple regression "
      + "functions as base learners is used for fitting the logistic models. The optimal number of LogitBoost "
      + "iterations to perform is cross-validated, which leads to automatic attribute selection. "
      + "For more information see:\n" + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing detailed
   * information about the technical background of this class, e.g., paper
   * reference or book this class is based on.
   *
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation result;
    TechnicalInformation additional;

    result = new TechnicalInformation(Type.ARTICLE);
    result
      .setValue(Field.AUTHOR, "Niels Landwehr and Mark Hall and Eibe Frank");
    result.setValue(Field.TITLE, "Logistic Model Trees");
    result.setValue(Field.BOOKTITLE, "Machine Learning");
    result.setValue(Field.YEAR, "2005");
    result.setValue(Field.VOLUME, "95");
    result.setValue(Field.PAGES, "161-205");
    result.setValue(Field.NUMBER, "1-2");

    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR,
      "Marc Sumner and Eibe Frank and Mark Hall");
    additional.setValue(Field.TITLE,
      "Speeding up Logistic Model Tree Induction");
    additional
      .setValue(Field.BOOKTITLE,
        "9th European Conference on Principles and Practice of Knowledge Discovery in Databases");
    additional.setValue(Field.YEAR, "2005");
    additional.setValue(Field.PAGES, "675-683");
    additional.setValue(Field.PUBLISHER, "Springer");

    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 numBoostingIterationsTipText() {
    return "Set fixed number of iterations for LogitBoost. If >= 0, this sets the number of LogitBoost iterations "
      + "to perform. If < 0, the number is cross-validated or a stopping criterion on the training set is used "
      + "(depending on the value of useCrossValidation).";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String useCrossValidationTipText() {
    return "Sets whether the number of LogitBoost iterations is to be cross-validated or the stopping criterion "
      + "on the training set should be used. If not set (and no fixed number of iterations was given), "
      + "the number of LogitBoost iterations is used that minimizes the error on the training set "
      + "(misclassification error or error on probabilities depending on errorOnProbabilities).";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String errorOnProbabilitiesTipText() {
    return "Use error on the probabilties as error measure when determining the best number of LogitBoost iterations. "
      + "If set, the number of LogitBoost iterations is chosen that minimizes the root mean squared error "
      + "(either on the training set or in the cross-validation, depending on useCrossValidation).";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String maxBoostingIterationsTipText() {
    return "Sets the maximum number of iterations for LogitBoost. Default value is 500, for very small/large "
      + "datasets a lower/higher value might be preferable.";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String heuristicStopTipText() {
    return "If heuristicStop > 0, the heuristic for greedy stopping while cross-validating the number of "
      + "LogitBoost iterations is enabled. This means LogitBoost is stopped if no new error minimum "
      + "has been reached in the last heuristicStop iterations. It is recommended to use this heuristic, "
      + "it gives a large speed-up especially on small datasets. The default value is 50.";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String weightTrimBetaTipText() {
    return "Set the beta value used for weight trimming in LogitBoost. "
      + "Only instances carrying (1 - beta)% of the weight from previous iteration "
      + "are used in the next iteration. Set to 0 for no weight trimming. "
      + "The default value is 0.";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String useAICTipText() {
    return "The AIC is used to determine when to stop LogitBoost iterations "
      + "(instead of cross-validation or training error).";
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numDecimalPlacesTipText() {
    return "The number of decimal places to be used for the output of coefficients.";
  }

  /**
   * Returns the revision string.
   *
   * @return the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 11568 $");
  }
}