edu.cmu.tetradapp.model.BayesEstimatorWrapper 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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// 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 //
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// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetradapp.model;
import edu.cmu.tetrad.bayes.BayesIm;
import edu.cmu.tetrad.bayes.BayesPm;
import edu.cmu.tetrad.bayes.MlBayesEstimator;
import edu.cmu.tetrad.data.DataModel;
import edu.cmu.tetrad.data.DataModelList;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DataUtils;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.NodeType;
import edu.cmu.tetrad.session.SessionModel;
import edu.cmu.tetrad.util.TetradLogger;
import edu.cmu.tetrad.util.TetradSerializableUtils;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.util.ArrayList;
import java.util.List;
/**
* Wraps a Bayes Pm for use in the Tetrad application.
*
* @author josephramsey
*/
public class BayesEstimatorWrapper implements SessionModel {
private static final long serialVersionUID = 23L;
private final DataWrapper dataWrapper;
private final List bayesIms = new ArrayList<>();
/**
* @serial Cannot be null.
*/
private String name;
/**
* @serial Cannot be null.
*/
private BayesIm bayesIm;
/**
* @serial Cannot be null.
*/
private DataSet dataSet;
private int numModels = 1;
private int modelIndex;
//=================================CONSTRUCTORS========================//
public BayesEstimatorWrapper(DataWrapper dataWrapper,
BayesPmWrapper bayesPmWrapper) {
if (dataWrapper == null) {
throw new NullPointerException(
"BayesDataWrapper must not be null.");
}
this.dataWrapper = dataWrapper;
if (bayesPmWrapper == null) {
throw new NullPointerException("BayesPmWrapper must not be null");
}
DataModelList dataModel = dataWrapper.getDataModelList();
if (dataModel != null) {
for (int i = 0; i < dataWrapper.getDataModelList().size(); i++) {
DataModel model = dataWrapper.getDataModelList().get(i);
DataSet dataSet = (DataSet) model;
bayesPmWrapper.setModelIndex(i);
BayesPm bayesPm = bayesPmWrapper.getBayesPm();
estimate(dataSet, bayesPm);
this.bayesIms.add(this.bayesIm);
}
this.bayesIm = this.bayesIms.get(0);
log(this.bayesIm);
} else {
throw new IllegalArgumentException("Data must consist of discrete data sets.");
}
this.name = bayesPmWrapper.getName();
this.numModels = this.bayesIms.size();
this.modelIndex = 0;
this.bayesIm = this.bayesIms.get(this.modelIndex);
DataModel model = dataModel.get(this.modelIndex);
this.dataSet = (DataSet) model;
}
public BayesEstimatorWrapper(DataWrapper dataWrapper,
BayesImWrapper bayesImWrapper) {
this(dataWrapper, new BayesPmWrapper(bayesImWrapper));
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @see TetradSerializableUtils
*/
public static PcRunner serializableInstance() {
return PcRunner.serializableInstance();
}
//==============================PUBLIC METHODS========================//
public BayesIm getEstimatedBayesIm() {
return this.bayesIm;
}
public void setBayesIm(BayesIm bayesIm) {
this.bayesIms.clear();
this.bayesIms.add(bayesIm);
}
public DataSet getDataSet() {
return this.dataSet;
}
public Graph getGraph() {
return this.bayesIm.getBayesPm().getDag();
}
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
public int getNumModels() {
return this.numModels;
}
public void setNumModels(int numModels) {
this.numModels = numModels;
}
public int getModelIndex() {
return this.modelIndex;
}
public void setModelIndex(int modelIndex) {
this.modelIndex = modelIndex;
this.bayesIm = this.bayesIms.get(modelIndex);
DataModel dataModel = this.dataWrapper.getDataModelList();
this.dataSet = (DataSet) ((DataModelList) dataModel).get(modelIndex);
}
//======================== Private Methods ======================//
/**
* Adds semantic checks to the default deserialization method. This method must have the standard signature for a
* readObject method, and the body of the method must begin with "s.defaultReadObject();". Other than that, any
* semantic checks can be specified and do not need to stay the same from version to version. A readObject method of
* this form may be added to any class, even if Tetrad sessions were previously saved out using a version of the
* class that didn't include it. (That's what the "s.defaultReadObject();" is for. See J. Bloch, Effective Java, for
* help.
*/
private void readObject(ObjectInputStream s)
throws IOException, ClassNotFoundException {
s.defaultReadObject();
if (this.bayesIm == null) {
throw new NullPointerException();
}
}
private void log(BayesIm im) {
TetradLogger.getInstance().log("info", "ML estimated Bayes IM.");
TetradLogger.getInstance().log("im", im.toString());
}
private void estimate(DataSet dataSet, BayesPm bayesPm) {
Graph graph = bayesPm.getDag();
for (Object o : graph.getNodes()) {
Node node = (Node) o;
if (node.getNodeType() == NodeType.LATENT) {
throw new IllegalArgumentException("Estimation of Bayes IM's "
+ "with latents is not supported.");
}
}
if (DataUtils.containsMissingValue(dataSet)) {
throw new IllegalArgumentException("Please remove or impute missing values.");
}
try {
MlBayesEstimator estimator = new MlBayesEstimator();
this.bayesIm = estimator.estimate(bayesPm, dataSet);
} catch (ArrayIndexOutOfBoundsException e) {
e.printStackTrace();
throw new RuntimeException("Value assignments between Bayes PM "
+ "and discrete data set do not match.");
}
}
}
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