/*
* Copyright (c) 2010 Pentaho Corporation. All rights reserved.
* This software was developed by Pentaho Corporation and is provided under the terms
* of the GNU Lesser General Public License, Version 2.1. You may not use
* this file except in compliance with the license. If you need a copy of the license,
* please go to http://www.gnu.org/licenses/lgpl-2.1.txt. The Original Code is Time Series
* Forecasting. The Initial Developer is Pentaho Corporation.
*
* Software distributed under the GNU Lesser Public License is distributed on an "AS IS"
* basis, WITHOUT WARRANTY OF ANY KIND, either express or implied. Please refer to
* the license for the specific language governing your rights and limitations.
*/
/*
* TimeSeriesTranslate.java
* Copyright (C) 2011 Pentaho Corporation
*/
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: 49983 $
*/
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());
return inst;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 49983 $");
}
/**
* 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);
}
}