weka.classifiers.timeseries.core.TimeSeriesTranslate Maven / Gradle / Ivy

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Provides a time series forecasting environment for Weka. Includes a wrapper for Weka regression schemes that automates the process of creating lagged variables and date-derived periodic variables and provides the ability to do closed-loop forecasting. New evaluation routines are provided by a special evaluation module and graphing of predictions/forecasts are provided via the JFreeChart library. Includes both command-line and GUI user interfaces. Sample time series data can be found in ${WEKA_HOME}/packages/timeseriesForecasting/sample-data.

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/*
 *   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 .
 */


/*
 *    TimeSeriesTranslate.java
 *    Copyright (C) 2010-2016 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.timeseries.core;

import weka.core.Capabilities;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
import weka.core.UnsupportedAttributeTypeException;
import weka.core.Utils;
import weka.core.Capabilities.Capability;

/**
 * Re-written version of weka.filters.unsupervised.attribute.TimeSeriesTranslate. 
 * Uses java.utils collection classes.
 * 
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision: 45163 $
 */
public class TimeSeriesTranslate extends AbstractTimeSeriesFilter {
  
  
  /**
   * For serialization
   */
  private static final long serialVersionUID = -4799796255517698151L;

  /**
   * Returns a string describing this classifier
   * @return a description of the classifier suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return
        "An instance filter that assumes instances form time-series data and "
      + "replaces attribute values in the current instance with the equivalent "
      + "attribute values of some previous (or future) instance. For "
      + "instances where the desired value is unknown either the instance may "
      + "be dropped, or missing values used. Skips the class attribute if it is set.";
  }

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

    // attributes
    result.enableAllAttributes();
    result.enable(Capability.MISSING_VALUES);
    
    // class
    result.enableAllClasses();
    result.enable(Capability.MISSING_CLASS_VALUES);
    result.enable(Capability.NO_CLASS);
    
    return result;
  }

  /**
   * 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 UnsupportedAttributeTypeException if selected
   * attributes are not numeric or nominal.
   */
  public boolean setInputFormat(Instances instanceInfo) throws Exception {

    if ((instanceInfo.classIndex() > 0) && (!getFillWithMissing())) {
      throw new IllegalArgumentException("TimeSeriesTranslate: Need to fill in missing values " +
                                         "using appropriate option when class index is set.");
    }
    super.setInputFormat(instanceInfo);
    // Create the output buffer
    Instances outputFormat = new Instances(instanceInfo, 0); 
    for(int i = 0; i < instanceInfo.numAttributes(); i++) {
      if (i != instanceInfo.classIndex()) {
        if (m_SelectedCols.isInRange(i)) {
          if (outputFormat.attribute(i).isNominal()
              || outputFormat.attribute(i).isNumeric()) {
            outputFormat.renameAttribute(i, outputFormat.attribute(i).name()
                                         + (m_InstanceRange < 0 ? '-' : '+')
                                         + Math.abs(m_InstanceRange));
          } else {
            throw new UnsupportedAttributeTypeException("Only numeric and nominal attributes may be "
                                                        + " manipulated in time series.");
          }
        }
      }
    }
    outputFormat.setClassIndex(instanceInfo.classIndex());
    setOutputFormat(outputFormat);
    return true;
  }
  
  /**
   * Creates a new instance the same as one instance (the "destination")
   * but with some attribute values copied from another instance
   * (the "source")
   *
   * @param source the source instance
   * @param dest the destination instance
   * @return the new merged instance
   */
  protected Instance mergeInstances(Instance source, Instance dest) {

    Instances outputFormat = outputFormatPeek();
    double[] vals = new double[outputFormat.numAttributes()];
    for(int i = 0; i < vals.length; i++) {
      if ((i != outputFormat.classIndex()) && (m_SelectedCols.isInRange(i))) {
        if (source != null) {
          vals[i] = source.value(i);
        } else {
          vals[i] = Utils.missingValue();
        }
      } else {
        vals[i] = dest.value(i);
      }
    }
    Instance inst = null;
    if (dest instanceof SparseInstance) {
      inst = new SparseInstance(dest.weight(), vals);
    } else {
      inst = new DenseInstance(dest.weight(), vals);
    }
    // inst.setDataset(dest.dataset());
    // push() sets the dataset to the output format, however, if
    // a preview transformation is being done then push() does not
    // get called, so set the output format correctly here.
    inst.setDataset(outputFormat);
    return inst;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return            the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 45163 $");
  }
  
  /**
   * 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 TimeSeriesTranslate(), argv);
  }
}