edu.cmu.tetradapp.editor.LogisticRegressionEditor Maven / Gradle / Ivy
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// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// 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. //
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// 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. //
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// 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., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
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package edu.cmu.tetradapp.editor;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DiscreteVariable;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.LayoutUtil;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.regression.LogisticRegression;
import edu.cmu.tetrad.util.NumberFormatUtil;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.TetradLogger;
import edu.cmu.tetradapp.model.LogisticRegressionRunner;
import edu.cmu.tetradapp.workbench.GraphWorkbench;
import javax.swing.*;
import java.awt.*;
import java.text.NumberFormat;
/**
* Allows the user to execute a logistic regression in the GUI. Contains a panel that lets the user specify a target
* variable (which must be binary) and a list of continuous regressors, plus a tabbed pane that includes (a) a text area
* to show the report of the logistic regression and (b) a graph workbench to show the graph of the target with
* significant regressors from the regression as parents.
*
* @author josephramsey
* @author Frank Wimberly - adapted for EM Bayes estimator and Strucural EM Bayes estimator
*/
public class LogisticRegressionEditor extends JPanel {
private static final long serialVersionUID = 7779226528390174L;
/**
* Text area for display output.
*/
private final JTextArea modelParameters;
/**
* The number formatter used for printing reports.
*/
private final NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
public LogisticRegressionEditor(LogisticRegressionRunner regressionRunner) {
DataSet dataSet = (DataSet) regressionRunner.getDataModel();
for (Node node : dataSet.getVariables()) {
if (node instanceof DiscreteVariable) {
DiscreteVariable v = (DiscreteVariable) node;
if (v.getNumCategories() != 2) {
throw new IllegalArgumentException("Logistic regression requires a dataset in which all variables " +
"are either continuous or binary.");
}
}
}
GraphWorkbench workbench = new GraphWorkbench();
this.modelParameters = new JTextArea();
JButton executeButton = new JButton("Execute");
JTabbedPane tabbedPane = new JTabbedPane();
tabbedPane.setPreferredSize(new Dimension(600, 400));
tabbedPane.add("Model", new JScrollPane(this.modelParameters));
tabbedPane.add("Output Graph", new JScrollPane(workbench));
Parameters params = regressionRunner.getParams();
RegressionParamsEditorPanel paramsPanel
= new RegressionParamsEditorPanel(regressionRunner, params, regressionRunner.getDataModel(), true);
Box b = Box.createVerticalBox();
Box b1 = Box.createHorizontalBox();
b1.add(paramsPanel);
b1.add(Box.createHorizontalStrut(5));
b1.add(tabbedPane);
b.add(b1);
JPanel buttonPanel = new JPanel();
buttonPanel.add(executeButton);
b.add(buttonPanel);
setLayout(new BorderLayout());
add(b, BorderLayout.CENTER);
int numModels = regressionRunner.getNumModels();
if (numModels > 1) {
JComboBox comp = new JComboBox<>();
for (int i = 0; i < numModels; i++) {
comp.addItem(i + 1);
}
comp.addActionListener((e) -> {
Object selectedItem = comp.getSelectedItem();
if (selectedItem instanceof Integer) {
regressionRunner.setModelIndex(((Integer) selectedItem) - 1);
}
});
comp.setMaximumSize(comp.getPreferredSize());
Box c = Box.createHorizontalBox();
c.add(new JLabel("Using model"));
c.add(comp);
c.add(new JLabel("from "));
c.add(new JLabel(regressionRunner.getModelSourceName()));
c.add(Box.createHorizontalGlue());
add(c, BorderLayout.NORTH);
}
//this.modelParameters.setFont(new Font("Monospaced", Font.PLAIN, 12));
executeButton.addActionListener((e) -> {
regressionRunner.setAlpha(paramsPanel.getParams().getDouble("alpha", 0.001));
regressionRunner.execute();
// modelParameters.setText(regRunner.getReport());
print(regressionRunner.getResult(), regressionRunner.getAlpha());
Graph outGraph = regressionRunner.getOutGraph();
LayoutUtil.defaultLayout(outGraph);
LayoutUtil.fruchtermanReingoldLayout(outGraph);
workbench.setGraph(outGraph);
TetradLogger.getInstance().log("result", this.modelParameters.getText());
});
}
/**
* Sets the name of this editor.
*/
@Override
public void setName(String name) {
String oldName = getName();
super.setName(name);
this.firePropertyChange("name", oldName, getName());
}
//============================== Private Methods =====================================//
/**
* Prints the info in the result to the text area (doesn't use the results representation).
*/
private void print(LogisticRegression.Result result, double alpha) {
if (result == null) {
return;
}
// print cases
StringBuilder text = new StringBuilder(result.getNy0() + " cases have " + result.getTarget() + " = 0; ");
text.append(result.getNy1()).append(" cases have ").append(result.getTarget()).append(" = 1.\n\n");
// print avgs/SD
text.append("Var\tAvg\tSD\n");
for (int i = 1; i <= result.getNumRegressors(); i++) {
text.append(result.getRegressorNames().get(i - 1)).append("\t");
text.append(this.nf.format(result.getxMeans()[i])).append("\t");
text.append(this.nf.format(result.getxStdDevs()[i])).append("\n");
}
text.append("\nCoefficients and Standard Errors:\n");
text.append("Var\tCoeff.\tStdErr\tProb.\tSig.\n");
for (int i = 1; i <= result.getNumRegressors(); i++) {
text.append(result.getRegressorNames().get(i - 1)).append("\t");
text.append(this.nf.format(result.getCoefs()[i])).append("\t");
text.append(this.nf.format(result.getStdErrs()[i])).append("\t");
text.append(this.nf.format(result.getProbs()[i])).append("\t");
if (result.getProbs()[i] < alpha) {
text.append("*\n");
} else {
text.append("\n");
}
}
text.append("\n\nIntercept = ").append(this.nf.format(result.getIntercept())).append("\n");
this.modelParameters.setText(text.toString());
}
}
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