Java tutorial
/* * RapidMiner * * Copyright (C) 2001-2008 by Rapid-I and the contributors * * Complete list of developers available at our web site: * * http://rapid-i.com * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU Affero 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 Affero General Public License for more details. * * You should have received a copy of the GNU Affero General Public License * along with this program. If not, see http://www.gnu.org/licenses/. */ package com.rapidminer.gui.plotter; import java.awt.BasicStroke; import java.awt.Color; import java.awt.Graphics; import java.awt.Graphics2D; import java.awt.Stroke; import java.awt.geom.Rectangle2D; import javax.swing.JComponent; import javax.swing.JLabel; import javax.swing.JSlider; import javax.swing.event.ChangeEvent; import javax.swing.event.ChangeListener; import org.jfree.chart.ChartFactory; import org.jfree.chart.JFreeChart; import org.jfree.chart.axis.NumberAxis; import org.jfree.chart.block.BlockBorder; import org.jfree.chart.plot.CategoryPlot; import org.jfree.chart.plot.PlotOrientation; import org.jfree.chart.plot.XYPlot; import org.jfree.chart.renderer.category.BarRenderer; import org.jfree.chart.renderer.xy.DeviationRenderer; import org.jfree.chart.title.LegendTitle; import org.jfree.data.category.CategoryDataset; import org.jfree.data.category.DefaultCategoryDataset; import org.jfree.data.xy.XYDataset; import org.jfree.data.xy.XYSeries; import org.jfree.data.xy.XYSeriesCollection; import org.jfree.ui.HorizontalAlignment; import org.jfree.ui.RectangleEdge; import org.jfree.ui.RectangleInsets; import com.rapidminer.datatable.DataTable; import com.rapidminer.datatable.DataTableExampleSetAdapter; import com.rapidminer.example.Attribute; import com.rapidminer.example.ExampleSet; import com.rapidminer.example.Tools; import com.rapidminer.operator.MissingIOObjectException; import com.rapidminer.operator.OperatorCreationException; import com.rapidminer.operator.OperatorException; import com.rapidminer.operator.learner.bayes.Distribution; import com.rapidminer.operator.learner.bayes.DistributionModel; import com.rapidminer.operator.learner.bayes.NaiveBayes; import com.rapidminer.tools.LogService; import com.rapidminer.tools.OperatorService; /** * This plotter can be used in order to plot a distribution model * like the one which can be delivered by NaiveBayes. * * @author Sebastian Land, Ingo Mierswa, Tobias Malbrecht * @version $Id: DistributionPlotter.java,v 1.7 2008/05/09 19:22:51 ingomierswa Exp $ */ public class DistributionPlotter extends PlotterAdapter { private static final long serialVersionUID = 2923008541302883925L; private static final int NUMBER_OF_STEPS = 300; private boolean plot = true; private int plotColumn = -1; private int groupColumn = -1; private transient DistributionModel model; private transient DataTable dataTable; private boolean createFromDataTable = false; private JSlider kernelSlider = new JSlider(1, 200, 1); public DistributionPlotter() { kernelSlider.addChangeListener(new ChangeListener() { public void stateChanged(ChangeEvent e) { if (!kernelSlider.getValueIsAdjusting()) { repaint(); } } }); } public DistributionPlotter(DistributionModel model) { this(); this.model = model; createFromDataTable = false; this.plotColumn = 0; } public DistributionPlotter(DataTable dataTable) { this(); setDataTable(dataTable); } public void setDataTable(DataTable dataTable) { super.setDataTable(dataTable); this.dataTable = dataTable; this.createFromDataTable = true; } public void preparePlots() { if (createFromDataTable) { plot = false; if ((groupColumn >= 0) && (plotColumn >= 0) && (groupColumn != plotColumn)) { ExampleSet wrappedExampleSet = DataTableExampleSetAdapter .createExampleSetFromDataTable(this.dataTable); Attribute[] attributes = Tools.createRegularAttributeArray(wrappedExampleSet); Attribute label = attributes[groupColumn]; if (label.isNominal()) { wrappedExampleSet.getAttributes().setLabel(label); try { NaiveBayes modelLearner = (NaiveBayes) OperatorService.createOperator(NaiveBayes.class); modelLearner.setParameter(NaiveBayes.PARAMETER_USE_KERNEL, "true"); modelLearner.setParameter(NaiveBayes.PARAMETER_NUMBER_OF_KERNELS, kernelSlider.getValue() + ""); this.model = (DistributionModel) modelLearner.learn(wrappedExampleSet); } catch (OperatorCreationException e) { LogService.getGlobal() .logWarning("Cannot create distribution model generator. Skip plot..."); } catch (MissingIOObjectException e) { LogService.getGlobal() .logWarning("No distribution model was created from data. Skip plot..."); } catch (OperatorException e) { LogService.getGlobal() .logWarning("Error during creation of distribution model. Skip plot..."); } plot = true; } } } } public void paintComponent(Graphics graphics) { super.paintComponent(graphics); paintComponent(graphics, getWidth(), getHeight()); } public void paintComponent(Graphics graphics, int width, int height) { preparePlots(); if (plot) { JFreeChart chart = null; try { if (!Double.isNaN(model.getUpperBound(plotColumn))) { chart = createNumericalChart(); } else { chart = createNominalChart(); } } catch (Exception e) { // do nothing - just do not draw the chart } if (chart != null) { // set the background color for the chart... chart.setBackgroundPaint(Color.white); // legend settings LegendTitle legend = chart.getLegend(); if (legend != null) { legend.setPosition(RectangleEdge.TOP); legend.setFrame(BlockBorder.NONE); legend.setHorizontalAlignment(HorizontalAlignment.LEFT); } Rectangle2D drawRect = new Rectangle2D.Double(0, 0, width, height); chart.draw((Graphics2D) graphics, drawRect); } } } private XYDataset createNumericalDataSet() { //NormalDistribution normal = new NormalDistribution(1.464d, 0.174d); //normal.getProbability(1.5d); XYSeriesCollection dataSet = new XYSeriesCollection(); double start = model.getLowerBound(plotColumn); double end = model.getUpperBound(plotColumn); double stepSize = (end - start) / (NUMBER_OF_STEPS - 1); for (int classIndex : model.getClasses()) { XYSeries series = new XYSeries(model.getLabelName(classIndex)); for (double currentValue = start; currentValue <= end; currentValue += stepSize) { series.add(currentValue, model.getProbabilityForAttribute(classIndex, plotColumn, currentValue)); } dataSet.addSeries(series); } return dataSet; } private JFreeChart createNumericalChart() { JFreeChart chart; XYDataset dataset = createNumericalDataSet(); // create the chart... chart = ChartFactory.createXYLineChart(null, // chart title "value", // x axis label "density", // y axis label dataset, // data PlotOrientation.VERTICAL, true, // include legend true, // tooltips false // urls ); chart.setBackgroundPaint(Color.white); // get a reference to the plot for further customisation... XYPlot plot = (XYPlot) chart.getPlot(); plot.setBackgroundPaint(Color.WHITE); plot.setAxisOffset(new RectangleInsets(5.0, 5.0, 5.0, 5.0)); plot.setDomainGridlinePaint(Color.LIGHT_GRAY); plot.setRangeGridlinePaint(Color.LIGHT_GRAY); DeviationRenderer renderer = new DeviationRenderer(true, false); Stroke stroke = new BasicStroke(2.0f, BasicStroke.CAP_ROUND, BasicStroke.JOIN_ROUND); if (dataset.getSeriesCount() == 1) { renderer.setSeriesStroke(0, stroke); renderer.setSeriesPaint(0, Color.RED); renderer.setSeriesFillPaint(0, Color.RED); } else { for (int i = 0; i < dataset.getSeriesCount(); i++) { renderer.setSeriesStroke(i, stroke); Color color = getPointColor((double) i / (double) (dataset.getSeriesCount() - 1)); renderer.setSeriesPaint(i, color); renderer.setSeriesFillPaint(i, color); } } renderer.setAlpha(0.12f); plot.setRenderer(renderer); NumberAxis xAxis = (NumberAxis) plot.getDomainAxis(); xAxis.setStandardTickUnits(NumberAxis.createIntegerTickUnits()); return chart; } private JFreeChart createNominalChart() { JFreeChart chart; CategoryDataset dataset = createNominalDataSet(); // create the chart... chart = ChartFactory.createBarChart(null, // chart title "value", // x axis label "density", // y axis label dataset, // data PlotOrientation.VERTICAL, true, // include legend true, // tooltips false // urls ); CategoryPlot plot = (CategoryPlot) chart.getPlot(); BarRenderer renderer = new BarRenderer(); if (dataset.getRowCount() == 1) { renderer.setSeriesPaint(0, Color.RED); renderer.setSeriesFillPaint(0, Color.RED); } else { for (int i = 0; i < dataset.getRowCount(); i++) { Color color = getPointColor((double) i / (double) (dataset.getRowCount() - 1)); renderer.setSeriesPaint(i, color); renderer.setSeriesFillPaint(i, color); } } plot.setRenderer(renderer); return chart; } private CategoryDataset createNominalDataSet() { DefaultCategoryDataset dataset = new DefaultCategoryDataset(); for (Integer classIndex : model.getClasses()) { Distribution distribution = model.getDistribution(classIndex, plotColumn); String labelName = model.getLabelName(classIndex); for (Double value : model.getValues(plotColumn)) { String valueName; if (Double.isNaN(value)) valueName = "unkown"; else valueName = distribution.mapValue(value); dataset.addValue(model.getProbabilityForAttribute(classIndex, plotColumn, value), labelName, valueName); } } return dataset; } public void setPlotColumn(int column, boolean plot) { this.plotColumn = column; this.plot = plot; repaint(); } public boolean getPlotColumn(int column) { return (column == this.plotColumn && plot); } public String getPlotName() { return "Plot Column:"; } public int getNumberOfAxes() { return 1; } public void setAxis(int index, int dimension) { if (groupColumn != dimension) { groupColumn = dimension; repaint(); } } public int getAxis(int index) { return groupColumn; } public String getAxisName(int axis) { return "Class Column:"; } public JComponent getOptionsComponent(int index) { switch (index) { case 0: JLabel label = new JLabel("Number of Kernels:"); label.setToolTipText( "Select the number of kernels used for the estimation of the distribution of numerical attributes."); return label; case 1: return kernelSlider; default: return null; } } }