/*
* 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/>.
*/
/*
* PlainText.java
* Copyright (C) 2009-2012 University of Waikato, Hamilton, New Zealand
*/
package weka.classifiers.evaluation.output.prediction;
import weka.classifiers.Classifier;
import weka.core.Instance;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* Outputs the predictions in plain text.
* <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>
*
<!-- options-end -->
*
* @author fracpete (fracpete at waikato dot ac dot nz)
* @version $Revision: 8937 $
*/
public class PlainText
extends AbstractOutput {
/** for serialization. */
private static final long serialVersionUID = 2033389864898242735L;
/**
* Returns a string describing the output generator.
*
* @return a description suitable for
* displaying in the GUI
*/
public String globalInfo() {
return "Outputs the predictions in plain text.";
}
/**
* Returns a short display text, to be used in comboboxes.
*
* @return a short display text
*/
public String getDisplay() {
return "Plain text";
}
/**
* Performs the actual printing of the header.
*/
protected void doPrintHeader() {
if (m_Header.classAttribute().isNominal())
if (m_OutputDistribution)
append(" inst# actual predicted error distribution");
else
append(" inst# actual predicted error prediction");
else
append(" inst# actual predicted error");
if (m_Attributes != null) {
append(" (");
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(",");
append(m_Header.attribute(i).name());
first = false;
}
}
append(")");
}
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) {
boolean firstOutput = true;
m_Attributes.setUpper(instance.numAttributes() - 1);
for (int i=0; i<instance.numAttributes(); i++)
if (m_Attributes.isInRange(i) && i != instance.classIndex()) {
if (firstOutput) text.append("(");
else text.append(",");
text.append(instance.toString(i));
firstOutput = false;
}
if (!firstOutput) text.append(")");
}
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 width = 7 + m_NumDecimals;
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(Utils.padLeft("" + (index+1), 6));
if (inst.dataset().classAttribute().isNumeric()) {
// actual
if (inst.classIsMissing())
append(" " + Utils.padLeft("?", width));
else
append(" " + Utils.doubleToString(inst.classValue(), width, prec));
// predicted
if (Utils.isMissingValue(predValue))
append(" " + Utils.padLeft("?", width));
else
append(" " + Utils.doubleToString(predValue, width, prec));
// error
if (Utils.isMissingValue(predValue) || inst.classIsMissing())
append(" " + Utils.padLeft("?", width));
else
append(" " + Utils.doubleToString(predValue - inst.classValue(), width, prec));
} else {
// actual
append(" " + Utils.padLeft(((int) inst.classValue()+1) + ":" + inst.toString(inst.classIndex()), width));
// predicted
if (Utils.isMissingValue(predValue))
append(" " + Utils.padLeft("?", width));
else
append(" " + Utils.padLeft(((int) predValue+1) + ":" + inst.dataset().classAttribute().value((int)predValue), width));
// error?
if (!Utils.isMissingValue(predValue) && !inst.classIsMissing() && ((int) predValue+1 != (int) inst.classValue()+1))
append(" " + " + ");
else
append(" " + " ");
// prediction/distribution
if (m_OutputDistribution) {
if (Utils.isMissingValue(predValue)) {
append(" " + "?");
}
else {
append(" ");
for (int n = 0; n < dist.length; n++) {
if (n > 0)
append(",");
if (n == (int) predValue)
append("*");
append(Utils.doubleToString(dist[n], prec));
}
}
}
else {
if (Utils.isMissingValue(predValue))
append(" " + "?");
else
append(" " + Utils.doubleToString(dist[(int)predValue], prec));
}
}
// attributes
append(" " + attributeValuesString(withMissing) + "\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() {
}
}