edu.cmu.tetradapp.model.CPDAGFitModel Maven / Gradle / Ivy
///////////////////////////////////////////////////////////////////////////////
// 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. //
// //
// 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., 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.DataModelList;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DataUtils;
import edu.cmu.tetrad.graph.*;
import edu.cmu.tetrad.sem.*;
import edu.cmu.tetrad.session.SessionModel;
import edu.cmu.tetrad.util.Parameters;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.util.ArrayList;
import java.util.List;
/**
* Compares a target workbench with a reference workbench by counting errors of omission and commission. (for edge
* presence only, not orientation).
*
* @author josephramsey
* @author Erin Korber (added remove latents functionality July 2004)
*/
public final class CPDAGFitModel implements SessionModel {
private static final long serialVersionUID = 23L;
private final Parameters parameters;
private final DataModelList dataModelList;
private String name;
private List bayesIms;
private List bayesPms;
private List referenceGraphs;
private List semPms;
//=============================CONSTRUCTORS==========================//
/**
* Compares the results of a PC to a reference workbench by counting errors of omission and commission. The counts
* can be retrieved using the methods
* countOmissionErrors and countCommissionErrors.
*/
public CPDAGFitModel(Simulation simulation, GeneralAlgorithmRunner algorithmRunner, Parameters params) {
if (params == null) {
throw new NullPointerException("Parameters must not be null");
}
this.parameters = params;
DataModelList dataModels = simulation.getDataModelList();
this.dataModelList = dataModels;
List graphs = algorithmRunner.getGraphs();
if (dataModels.size() != graphs.size()) {
throw new IllegalArgumentException("Sorry, I was expecting the same number of data sets as result graphs.");
}
if (dataModels.get(0).isDiscrete()) {
this.bayesPms = new ArrayList<>();
this.bayesIms = new ArrayList<>();
for (int i = 0; i < dataModels.size(); i++) {
DataSet dataSet = (DataSet) dataModels.get(0);
Graph dag = GraphTransforms.dagFromCpdag(graphs.get(0), null);
BayesPm pm = new BayesPmWrapper(dag, new DataWrapper(dataSet)).getBayesPm();
this.bayesPms.add(pm);
this.bayesIms.add(estimate(dataSet, pm));
}
} else if (dataModels.get(0).isContinuous()) {
this.semPms = new ArrayList<>();
List semIms = new ArrayList<>();
for (int i = 0; i < dataModels.size(); i++) {
DataSet dataSet = (DataSet) dataModels.get(0);
Graph dag = GraphTransforms.dagFromCpdag(graphs.get(0), null);
try {
SemPm pm = new SemPm(dag);
this.semPms.add(pm);
semIms.add(estimate(dataSet, pm));
} catch (Exception e) {
e.printStackTrace();
Graph mag = GraphTransforms.pagToMag(graphs.get(0));
// Ricf.RicfResult result = estimatePag(dataSet, mag);
SemGraph graph = new SemGraph(mag);
graph.setShowErrorTerms(false);
SemPm pm = new SemPm(graph);
this.semPms.add(pm);
semIms.add(estimatePag(dataSet, pm));
}
}
}
}
private BayesIm 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();
return estimator.estimate(bayesPm, dataSet);
} catch (ArrayIndexOutOfBoundsException e) {
e.printStackTrace();
throw new RuntimeException("Value assignments between Bayes PM " +
"and discrete data set do not match.");
}
}
private SemIm estimate(DataSet dataSet, SemPm semPm) {
Graph graph = semPm.getGraph();
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 {
SemEstimator estimator = new SemEstimator(dataSet, semPm);
return estimator.estimate();
} catch (ArrayIndexOutOfBoundsException e) {
e.printStackTrace();
throw new RuntimeException("Value assignments between Bayes PM " +
"and discrete data set do not match.");
}
}
private SemIm estimatePag(DataSet dataSet, SemPm pm) {
SemGraph graph = pm.getGraph();
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 {
SemOptimizer optimizer = new SemOptimizerRicf();
SemEstimator estimator = new SemEstimator(dataSet, pm, optimizer);
return estimator.estimate();
} catch (ArrayIndexOutOfBoundsException e) {
e.printStackTrace();
throw new RuntimeException("Value assignments between Bayes PM " +
"and discrete data set do not match.");
}
}
//==============================PUBLIC METHODS========================//
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
public BayesIm getBayesIm(int i) {
return this.bayesIms.get(i);
}
/**
* 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();
}
public List getReferenceGraphs() {
return this.referenceGraphs;
}
public List getBayesIms() {
return this.bayesIms;
}
public DataModelList getDataModelList() {
return this.dataModelList;
}
public List getBayesPms() {
return this.bayesPms;
}
public List getSemPms() {
return this.semPms;
}
public Parameters getParams() {
return this.parameters;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy