/*
* 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 <http://www.gnu.org/licenses/>.
*/
/*
* CSV.java
* Copyright (C) 2009-2012 University of Waikato, Hamilton, New Zealand
*/
package weka.classifiers.evaluation.output.prediction;
import java.util.Enumeration;
import java.util.Vector;
import weka.classifiers.Classifier;
import weka.core.Instance;
import weka.core.Option;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* Outputs the predictions as CSV.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -p <range>
* The range of attributes to print in addition to the classification.
* (default: none)</pre>
*
* <pre> -distribution
* Whether to turn on the output of the class distribution.
* Only for nominal class attributes.
* (default: off)</pre>
*
* <pre> -decimals <num>
* The number of digits after the decimal point.
* (default: 3)</pre>
*
* <pre> -file <path>
* The file to store the output in, instead of outputting it on stdout.
* Gets ignored if the supplied path is a directory.
* (default: .)</pre>
*
* <pre> -suppress
* In case the data gets stored in a file, then this flag can be used
* to suppress the regular output.
* (default: not suppressed)</pre>
*
* <pre> -use-tab
* Whether to use TAB as separator instead of comma.
* (default: comma)</pre>
*
<!-- options-end -->
*
* @author fracpete (fracpete at waikato dot ac dot nz)
* @version $Revision: 8937 $
*/
public class CSV
extends AbstractOutput {
/** for serialization. */
private static final long serialVersionUID = 3401604538169573720L;
/** the delimiter. */
protected String m_Delimiter = ",";
/**
* Returns a string describing the output generator.
*
* @return a description suitable for
* displaying in the GUI
*/
public String globalInfo() {
return "Outputs the predictions as CSV.";
}
/**
* Returns a short display text, to be used in comboboxes.
*
* @return a short display text
*/
public String getDisplay() {
return "CSV";
}
/**
* Returns an enumeration of all the available options..
*
* @return an enumeration of all available options.
*/
public Enumeration listOptions() {
Vector result;
Enumeration enm;
result = new Vector();
enm = super.listOptions();
while (enm.hasMoreElements())
result.add(enm.nextElement());
result.addElement(new Option(
"\tWhether to use TAB as separator instead of comma.\n"
+ "\t(default: comma)",
"use-tab", 0, "-use-tab"));
return result.elements();
}
/**
* Sets the OptionHandler's options using the given list. All options
* will be set (or reset) during this call (i.e. incremental setting
* of options is not possible).
*
* @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 {
setUseTab(Utils.getFlag("use-tab", options));
super.setOptions(options);
}
/**
* Gets the current option settings for the OptionHandler.
*
* @return the list of current option settings as an array of strings
*/
public String[] getOptions() {
Vector<String> result;
String[] options;
int i;
result = new Vector<String>();
options = super.getOptions();
for (i = 0; i < options.length; i++)
result.add(options[i]);
if (getUseTab())
result.add("-use-tab");
return result.toArray(new String[result.size()]);
}
/**
* Sets whether to use tab instead of comma as separator.
*
* @param value true if tab is to be used
*/
public void setUseTab(boolean value) {
if (value)
m_Delimiter = "\t";
else
m_Delimiter = ",";
}
/**
* Returns whether tab is used as separator.
*
* @return true if tab is used instead of comma
*/
public boolean getUseTab() {
return m_Delimiter.equals("\t");
}
/**
* Returns the tip text for this property.
*
* @return tip text for this property suitable for
* displaying in the GUI
*/
public String useTabTipText() {
return "Whether to use TAB instead of COMMA as column separator.";
}
/**
* Performs the actual printing of the header.
*/
protected void doPrintHeader() {
if (m_Header.classAttribute().isNominal()) {
if (m_OutputDistribution) {
append("inst#" + m_Delimiter + "actual" + m_Delimiter + "predicted" + m_Delimiter + "error" + m_Delimiter + "distribution");
for (int i = 1; i < m_Header.classAttribute().numValues(); i++)
append(m_Delimiter);
}
else {
append("inst#" + m_Delimiter + "actual" + m_Delimiter + "predicted" + m_Delimiter + "error" + m_Delimiter + "prediction");
}
}
else {
append("inst#" + m_Delimiter + "actual" + m_Delimiter + "predicted" + m_Delimiter + "error");
}
if (m_Attributes != null) {
append(m_Delimiter);
boolean first = true;
for (int i = 0; i < m_Header.numAttributes(); i++) {
if (i == m_Header.classIndex())
continue;
if (m_Attributes.isInRange(i)) {
if (!first)
append(m_Delimiter);
append(m_Header.attribute(i).name());
first = false;
}
}
}
append("\n");
}
/**
* Builds a string listing the attribute values in a specified range of indices,
* separated by commas and enclosed in brackets.
*
* @param instance the instance to print the values from
* @return a string listing values of the attributes in the range
*/
protected String attributeValuesString(Instance instance) {
StringBuffer text = new StringBuffer();
if (m_Attributes != null) {
m_Attributes.setUpper(instance.numAttributes() - 1);
boolean first = true;
for (int i=0; i<instance.numAttributes(); i++)
if (m_Attributes.isInRange(i) && i != instance.classIndex()) {
if (!first)
text.append(m_Delimiter);
text.append(instance.toString(i));
first = false;
}
}
return text.toString();
}
/**
* Store the prediction made by the classifier as a string.
*
* @param dist the distribution to use
* @param inst the instance to generate text from
* @param index the index in the dataset
* @throws Exception if something goes wrong
*/
protected void doPrintClassification(double[] dist, Instance inst, int index) throws Exception {
int prec = m_NumDecimals;
Instance withMissing = (Instance)inst.copy();
withMissing.setDataset(inst.dataset());
double predValue = 0;
if (Utils.sum(dist) == 0) {
predValue = Utils.missingValue();
} else {
if (inst.classAttribute().isNominal()) {
predValue = Utils.maxIndex(dist);
} else {
predValue = dist[0];
}
}
// index
append("" + (index+1));
if (inst.dataset().classAttribute().isNumeric()) {
// actual
if (inst.classIsMissing())
append(m_Delimiter + "?");
else
append(m_Delimiter + Utils.doubleToString(inst.classValue(), prec));
// predicted
if (Utils.isMissingValue(predValue))
append(m_Delimiter + "?");
else
append(m_Delimiter + Utils.doubleToString(predValue, prec));
// error
if (Utils.isMissingValue(predValue) || inst.classIsMissing())
append(m_Delimiter + "?");
else
append(m_Delimiter + Utils.doubleToString(predValue - inst.classValue(), prec));
} else {
// actual
append(m_Delimiter + ((int) inst.classValue()+1) + ":" + inst.toString(inst.classIndex()));
// predicted
if (Utils.isMissingValue(predValue))
append(m_Delimiter + "?");
else
append(m_Delimiter + ((int) predValue+1) + ":" + inst.dataset().classAttribute().value((int)predValue));
// error?
if (!Utils.isMissingValue(predValue) && !inst.classIsMissing() && ((int) predValue+1 != (int) inst.classValue()+1))
append(m_Delimiter + "+");
else
append(m_Delimiter + "");
// prediction/distribution
if (m_OutputDistribution) {
if (Utils.isMissingValue(predValue)) {
append(m_Delimiter + "?");
}
else {
append(m_Delimiter);
for (int n = 0; n < dist.length; n++) {
if (n > 0)
append(m_Delimiter);
if (n == (int) predValue)
append("*");
append(Utils.doubleToString(dist[n], prec));
}
}
}
else {
if (Utils.isMissingValue(predValue))
append(m_Delimiter + "?");
else
append(m_Delimiter + Utils.doubleToString(dist[(int)predValue], prec));
}
}
// attributes
if (m_Attributes != null)
append(m_Delimiter + attributeValuesString(withMissing));
append("\n");
}
/**
* Store the prediction made by the classifier as a string.
*
* @param classifier the classifier to use
* @param inst the instance to generate text from
* @param index the index in the dataset
* @throws Exception if something goes wrong
*/
protected void doPrintClassification(Classifier classifier, Instance inst, int index) throws Exception {
double[] d = classifier.distributionForInstance(inst);
doPrintClassification(d, inst, index);
}
/**
* Does nothing.
*/
protected void doPrintFooter() {
}
}