weka.experiment.ResultMatrixGnuPlot Maven / Gradle / Ivy
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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* ResultMatrixCSV.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.experiment;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.Version;
/**
* This matrix is a container for the datasets and classifier setups and
* their statistics. It outputs the data in CSV format.
*
*
* @author FracPete (fracpete at waikato dot ac dot nz)
* @version $Revision: 1.5 $
*/
public class ResultMatrixGnuPlot
extends ResultMatrix {
/** for serialization */
private static final long serialVersionUID = -234648254944790097L;
/**
* initializes the matrix as 1x1 matrix
*/
public ResultMatrixGnuPlot() {
this(1, 1);
}
/**
* initializes the matrix with the given dimensions
*/
public ResultMatrixGnuPlot(int cols, int rows) {
super(cols, rows);
}
/**
* initializes the matrix with the values from the given matrix
* @param matrix the matrix to get the values from
*/
public ResultMatrixGnuPlot(ResultMatrix matrix) {
super(matrix);
}
/**
* returns the name of the output format
*/
public String getDisplayName() {
return "GNUPlot";
}
/**
* removes the stored data but retains the dimensions of the matrix
*/
public void clear() {
super.clear();
setRowNameWidth(50);
setColNameWidth(50);
setEnumerateRowNames(false);
setEnumerateColNames(false);
LEFT_PARENTHESES = "";
RIGHT_PARENTHESES = "";
}
/**
* returns the header of the matrix as a string
* @see #m_HeaderKeys
* @see #m_HeaderValues
*/
public String toStringHeader() {
return new ResultMatrixPlainText(this).toStringHeader();
}
/**
* returns the matrix in CSV format
*/
public String toStringMatrix() {
StringBuffer result;
String[][] cells;
int i;
int n;
String line;
String title;
String generated;
result = new StringBuffer();
cells = toArray();
// generation comment
generated = "# generated by WEKA " + Version.VERSION + "\n";
// data
result.append("\n");
result.append("##################\n");
result.append("# file: plot.dat #\n");
result.append("##################\n");
result.append(generated);
result.append("# contains the data for the plot\n");
// key for x-axis
result.append("\n");
result.append("# key for the x-axis\n");
for (i = 1; i < cells.length - 1; i++)
result.append("# " + i + " - " + cells[i][0] + "\n");
// the data itself
result.append("\n");
result.append("# data for the plot\n");
for (i = 1; i < cells.length - 1; i++) {
result.append(Integer.toString(i));
for (n = 1; n < cells[i].length; n++) {
if (isSignificance(n))
continue;
result.append(" ");
result.append(Utils.quote(cells[i][n]));
}
result.append("\n");
}
result.append("#######\n");
result.append("# end #\n");
result.append("#######\n");
// script
result.append("\n");
result.append("##################\n");
result.append("# file: plot.scr #\n");
result.append("##################\n");
result.append(generated);
result.append("# script to plot the data\n");
result.append("\n");
result.append("# display it in a window:\n");
result.append("set terminal x11\n");
result.append("set output\n");
result.append("\n");
result.append("# to display all data rows:\n");
result.append("set xrange [0:" + ((cells.length - 2) + 1) + "]\n");
result.append("\n");
result.append("# axis labels, e.g.:\n");
result.append("#set xlabel \"Datasets\"\n");
result.append("#set ylabel \"Accuracy in %\"\n");
result.append("\n");
result.append("# the plot commands\n");
n = 1;
i = 0;
while (i < cells[0].length - 1) {
i++;
if (isSignificance(i))
continue;
n++;
// plot
if (i == 1)
line = "plot";
else
line = "replot";
line += " \"plot.dat\"";
// title
title = "title \"" + cells[0][i] + "\"";
// columns
line += " using 1:" + n;
if (getShowStdDev()) {
n++;
i++;
// errorbars
line += ":" + n;
}
// options
line += " with";
if (getShowStdDev())
line += " yerrorbars";
else
line += " lines";
line += " " + title;
result.append(line + "\n");
}
result.append("\n");
result.append("# generate ps:\n");
result.append("#set terminal postscript\n");
result.append("#set output \"plot.ps\"\n");
result.append("#replot\n");
result.append("\n");
result.append("# generate png:\n");
result.append("#set terminal png size 800,600\n");
result.append("#set output \"plot.png\"\n");
result.append("#replot\n");
result.append("\n");
result.append("# wait for user to hit \n");
result.append("pause -1\n");
result.append("#######\n");
result.append("# end #\n");
result.append("#######\n");
return result.toString();
}
/**
* returns returns a key for all the col names, for better readability if
* the names got cut off
*/
public String toStringKey() {
return new ResultMatrixPlainText(this).toStringKey();
}
/**
* returns the summary as string
*/
public String toStringSummary() {
return new ResultMatrixPlainText(this).toStringSummary();
}
/**
* returns the ranking in a string representation
*/
public String toStringRanking() {
return new ResultMatrixPlainText(this).toStringRanking();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.5 $");
}
/**
* for testing only
*/
public static void main(String[] args) {
ResultMatrix matrix;
int i;
int n;
matrix = new ResultMatrixGnuPlot(3, 3);
// set header
matrix.addHeader("header1", "value1");
matrix.addHeader("header2", "value2");
matrix.addHeader("header2", "value3");
// set values
for (i = 0; i < matrix.getRowCount(); i++) {
for (n = 0; n < matrix.getColCount(); n++) {
matrix.setMean(n, i, (i+1)*n);
matrix.setStdDev(n, i, ((double) (i+1)*n) / 100);
if (i == n) {
if (i % 2 == 1)
matrix.setSignificance(n, i, SIGNIFICANCE_WIN);
else
matrix.setSignificance(n, i, SIGNIFICANCE_LOSS);
}
}
}
System.out.println("\n\n--> " + matrix.getDisplayName());
System.out.println("\n1. complete\n");
System.out.println(matrix.toStringHeader() + "\n");
System.out.println(matrix.toStringMatrix() + "\n");
System.out.println(matrix.toStringKey());
System.out.println("\n2. complete with std deviations\n");
matrix.setShowStdDev(true);
System.out.println(matrix.toStringMatrix());
System.out.println("\n3. cols numbered\n");
matrix.setPrintColNames(false);
System.out.println(matrix.toStringMatrix());
System.out.println("\n4. second col missing\n");
matrix.setColHidden(1, true);
System.out.println(matrix.toStringMatrix());
System.out.println("\n5. last row missing, rows numbered too\n");
matrix.setRowHidden(2, true);
matrix.setPrintRowNames(false);
System.out.println(matrix.toStringMatrix());
System.out.println("\n6. mean prec to 3\n");
matrix.setMeanPrec(3);
matrix.setPrintRowNames(false);
System.out.println(matrix.toStringMatrix());
}
}
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