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• WekaAlgorithmAncestor.java

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weka.classifiers.neural.common.WekaAlgorithmAncestor 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 .
 */

package weka.classifiers.neural.common;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.neural.common.learning.LearningKernelFactory;
import weka.classifiers.neural.common.training.NeuralTrainer;
import weka.classifiers.neural.common.training.TrainerFactory;
import weka.classifiers.neural.common.transfer.TransferFunctionFactory;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.WeightedInstancesHandler;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Enumeration;
import java.util.LinkedList;
import java.util.List;
import java.util.Vector;

/**
 * 
Title: Weka Neural Implementation
 * 
Description: ...
 * 
Copyright: Copyright (c) 2003
 * 
Company: N/A
 *
 * @author Jason Brownlee
 * @version 1.0
 */

public abstract class WekaAlgorithmAncestor extends AbstractClassifier
  implements WeightedInstancesHandler {

  private final static int PARAM_TRAINING_ITERATIONS = 0;

  private final static int PARAM_LEARNING_RATE = 1;

  private final static int PARAM_BIAS_CONSTANT = 2;

  private final static int PARAM_RANDOM_SEED = 3;

  // param flags
  private final static String[] PARAMETERS =
    {
      "I", // iterations
      "L", // learning rate
      "B",  // bias constant
      "R"  // random seed
    };

  // param flags
  private final static String[] PARAMETER_NOTES =
    {
      "", // iterations
      "", // learning rate
      "",  // bias constant
      ""  // random seed
    };

  // descriptions for all parameters
  private final static String[] PARAM_DESCRIPTIONS =
    {
      "Number of training iterations (anywhere from a few hundred to a few thousand)",
      "Learning Rate - between 0.05 and 0.75 (recommend 0.1 for most cases)",
      "Bias constant input, (recommend 1.0, use 0.0 for no bias constant input)",
      Constants.DESCRIPTION_RANDOM_SEED
    };

  // the model
  protected NeuralModel model;

  protected RandomWrapper rand;

  // random number seed
  protected long randomNumberSeed = 0;

  // learning rate
  protected double learningRate = 0.0;

  // learning rate function
  protected int learningRateFunction = 0;

  // bias input constant
  protected double biasInput = 0.0;

  // transfer function
  protected int transferFunction = 0;

  // training mode
  protected int trainingMode = 0;

  // number of training iterations
  protected int trainingIterations = 0;

  // stats on the dataset used to build the model
  protected int numInstances = 0;

  protected int numClasses = 0;

  protected int numAttributes = 0;

  protected boolean classIsNominal = false;


  public abstract String globalInfo();

  protected abstract void validateArguments() throws Exception;

  protected abstract NeuralModel prepareAlgorithm(Instances instances) throws Exception;

  protected abstract Collection getListOptions();

  protected abstract void setArguments(String[] options) throws Exception;

  protected abstract Collection getAlgorithmOptions();


  public double[] getAllWeights() {
    return model.getAllWeights();
  }


  public void buildClassifier(Instances instances)
    throws Exception {
    // prepare the random number seed
    rand = new RandomWrapper(randomNumberSeed);

    // prepare the dataset for use
    Instances trainingInstances = prepareTrainingDataset(instances);

    // whether or not the class is nominal
    if (trainingInstances.classAttribute().isNominal()) {
      classIsNominal = true;
    }
    else {
      classIsNominal = false;
    }

    // validate user provided arguments
    validateAlgorithmArguments();

    // initialise the model
    model = prepareAlgorithm(trainingInstances);

    // build the model
    NeuralTrainer trainer = TrainerFactory.factory(trainingMode, rand);
    trainer.trainModel(model, trainingInstances, trainingIterations);
  }


  protected void validateAlgorithmArguments() throws Exception {
    // num training iterations
    if (trainingIterations <= 0) {
      throw new Exception("The number of training iterations must be > 0");
    }

    // validate child arguments
    validateArguments();
  }


  public double[] distributionForInstance(Instance instance)
    throws Exception {
    if (model == null) {
      throw new Exception("Model has not been constructed");
    }

    // verify number of classes
    if (instance.numClasses() != numClasses) {
      throw new Exception("Number of classes in instance (" + instance.numClasses() + ") does not match expected (" + numClasses + ").");
    }

    // verify the number of attributes
    if (instance.numAttributes() != numAttributes) {
      throw new Exception("Number of attributes in instance (" + instance.numAttributes() + ") does not match expected (" + numAttributes + ").");
    }

    // get the network output
    double[] output = model.getDistributionForInstance(instance);

    // return the class distribution
    return output;
  }

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

    result.disableAll();

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

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

    result.setMinimumNumberInstances(1);

    return result;
  }

  protected Instances prepareTrainingDataset(Instances aInstances) throws Exception {
    Instances trainingInstances = new Instances(aInstances);
    trainingInstances.deleteWithMissingClass();

    getCapabilities().testWithFail(trainingInstances);

    numInstances = trainingInstances.numInstances();
    numClasses = trainingInstances.numClasses();
    numAttributes = trainingInstances.numAttributes();

    return trainingInstances;
  }


  public String toString() {
    StringBuilder buffer = new StringBuilder();

    buffer.append("--------------------------------------------");

    buffer.append("\n");

    // algorithm name
    buffer.append(globalInfo() + "\n");

    // check if the model has been constructed
    if (model == null) {
      buffer.append("The model has not been constructed");
    }
    else {
      buffer.append("Random Number Seed:     " + randomNumberSeed + "\n");
      buffer.append("Learning Rate:          " + learningRate + "\n");
      buffer.append("Learning Rate Function: " + LearningKernelFactory.getDescription(learningRateFunction) + "\n");
      buffer.append("Constant Bias Input:    " + biasInput + "\n");
      buffer.append("Training Iterations:    " + trainingIterations + "\n");
      buffer.append("Training Mode:          " + TrainerFactory.getDescriptionForMode(trainingMode) + "\n");
      buffer.append("Transfer Function       " + TransferFunctionFactory.getDescriptionForFunction(transferFunction) + "\n");
      buffer.append("\n");
      buffer.append(model.getModelInformation());
    }

    buffer.append("--------------------------------------------");

    return buffer.toString();
  }


  public Enumeration listOptions() {
    Vector list = new Vector(PARAMETERS.length);

    for (int i = 0; i < PARAMETERS.length; i++) {
      String param = "-" + PARAMETERS[i] + " " + PARAMETER_NOTES[i];
      list.add(new Option("\t" + PARAM_DESCRIPTIONS[i], PARAMETERS[i], 1, param));
    }

    Collection c = getListOptions();
    if (c != null) {
      list.addAll(c);
    }

    return list.elements();
  }

  public void setOptions(String[] options)
    throws Exception {
    String[] values = new String[PARAMETERS.length];
    for (int i = 0; i < values.length; i++) {
      values[i] = weka.core.Utils.getOption(PARAMETERS[i].charAt(0), options);
    }

    for (int i = 0; i < values.length; i++) {
      String data = values[i];

      if (data == null || data.length() == 0) {
	continue;
      }

      switch (i) {
	case PARAM_TRAINING_ITERATIONS: {
	  trainingIterations = Integer.parseInt(data);
	  break;
	}
	case PARAM_LEARNING_RATE: {
	  learningRate = Double.parseDouble(data);
	  break;
	}
	case PARAM_BIAS_CONSTANT: {
	  biasInput = Double.parseDouble(data);
	  break;
	}
	case PARAM_RANDOM_SEED: {
	  randomNumberSeed = Long.parseLong(data);
	  break;
	}
	default: {
	  throw new Exception("Invalid option offset: " + i);
	}
      }
    }

    // pass of options to decendents
    setArguments(options);
  }


  protected boolean hasValue(String aString) {
    return (aString != null && aString.length() != 0);
  }


  public String[] getOptions() {
    List list = new ArrayList();

    list.add("-" + PARAMETERS[PARAM_TRAINING_ITERATIONS]);
    list.add(Integer.toString(trainingIterations));

    list.add("-" + PARAMETERS[PARAM_LEARNING_RATE]);
    list.add(Double.toString(learningRate));

    list.add("-" + PARAMETERS[PARAM_BIAS_CONSTANT]);
    list.add(Double.toString(biasInput));

    list.add("-" + PARAMETERS[PARAM_RANDOM_SEED]);
    list.add(Long.toString(randomNumberSeed));

    Collection c = getAlgorithmOptions();
    if (c != null) {
      list.addAll(c);
    }

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

  public String trainingIterationsTipText() {
    return PARAM_DESCRIPTIONS[PARAM_TRAINING_ITERATIONS];
  }

  public String learningRateTipText() {
    return PARAM_DESCRIPTIONS[PARAM_LEARNING_RATE];
  }

  public String biasInputTipText() {
    return PARAM_DESCRIPTIONS[PARAM_BIAS_CONSTANT];
  }

  public String randomNumberSeedTipText() {
    return PARAM_DESCRIPTIONS[PARAM_RANDOM_SEED];
  }


  // accessor and mutator for algorithm parameters
  public int getTrainingIterations() {
    return trainingIterations;
  }

  public void setTrainingIterations(int i) {
    trainingIterations = i;
  }

  public double getLearningRate() {
    return learningRate;
  }

  public void setLearningRate(double l) {
    learningRate = l;
  }

  public double getBiasInput() {
    return biasInput;
  }

  public void setBiasInput(double l) {
    biasInput = l;
  }

  public long getRandomNumberSeed() {
    return randomNumberSeed;
  }

  public void setRandomNumberSeed(long l) {
    randomNumberSeed = l;
  }
}