edu.cmu.tetradapp.model.BayesPmWrapper 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.algcomparison.simulation.BayesNetSimulation;
import edu.cmu.tetrad.bayes.BayesIm;
import edu.cmu.tetrad.bayes.BayesPm;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DiscreteVariable;
import edu.cmu.tetrad.graph.Dag;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.GraphNode;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.session.SessionModel;
import edu.cmu.tetrad.util.Parameters;
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.HashMap;
import java.util.List;
import java.util.Map;
/**
* Wraps a Bayes Pm for use in the Tetrad application.
*
* @author josephramsey
*/
public class BayesPmWrapper implements SessionModel {
private static final long serialVersionUID = 23L;
private int numModels = 1;
private int modelIndex;
private String modelSourceName;
/**
* @serial Can be null.
*/
private String name;
private List bayesPms;
//==============================CONSTRUCTORS=========================//
/**
* Creates a new BayesPm from the given DAG and uses it to construct a new BayesPm.
*/
public BayesPmWrapper(Graph graph, Parameters params) {
if (graph == null) {
throw new NullPointerException("Graph must not be null.");
}
int lowerBound;
int upperBound;
if (params.getString("initializationMode", "trinary").equals("trinary")) {
lowerBound = upperBound = 3;
setBayesPm(graph, lowerBound, upperBound);
} else if (params.getString("initializationMode", "trinary").equals("range")) {
lowerBound = params.getInt("minCategories", 2);
upperBound = params.getInt("maxCategories", 4);
setBayesPm(graph, lowerBound, upperBound);
} else {
throw new IllegalStateException("Unrecognized type.");
}
}
public BayesPmWrapper(Simulation simulation) {
List bayesIms;
if (simulation == null) {
throw new NullPointerException("The Simulation box does not contain a simulation.");
}
edu.cmu.tetrad.algcomparison.simulation.Simulation _simulation = simulation.getSimulation();
if (_simulation == null) {
throw new NullPointerException("No data sets have been simulated.");
}
if (!(_simulation instanceof BayesNetSimulation)) {
throw new IllegalArgumentException("That was not a discrete Bayes net simulation.");
}
bayesIms = ((BayesNetSimulation) _simulation).getBayesIms();
if (bayesIms == null) {
throw new NullPointerException("It looks like you have not done a simulation.");
}
List bayesPms = new ArrayList<>();
for (BayesIm bayesIm : bayesIms) {
bayesPms.add(bayesIm.getBayesPm());
}
this.bayesPms = bayesPms;
this.numModels = simulation.getDataModelList().size();
this.modelIndex = 0;
this.modelSourceName = simulation.getName();
}
public BayesPmWrapper(Dag graph, BayesPm bayesPm, Parameters params) {
if (graph == null) {
throw new NullPointerException("Graph must not be null.");
}
if (bayesPm == null) {
throw new NullPointerException("BayesPm must not be null");
}
int lowerBound;
int upperBound;
if (params.getString("initializationMode", "trinary").equals("trinary")) {
lowerBound = upperBound = 3;
setBayesPm(new BayesPm(graph, bayesPm, lowerBound, upperBound));
} else if (params.getString("initializationMode", "trinary").equals("range")) {
lowerBound = params.getInt("minCategories", 2);
upperBound = params.getInt("maxCategories", 4);
setBayesPm(graph, lowerBound, upperBound);
} else {
throw new IllegalStateException("Unrecognized type.");
}
log(bayesPm);
}
/**
* Creates a new BayesPm from the given workbench and uses it to construct a new BayesPm.
*
* @throws RuntimeException If the parent graph cannot be converted into a DAG.
*/
public BayesPmWrapper(GraphWrapper graphWrapper, Parameters params) {
if (graphWrapper == null) {
throw new NullPointerException("Graph must not be null.");
}
Dag graph;
try {
graph = new Dag(graphWrapper.getGraph());
} catch (Exception e) {
throw new RuntimeException(
"The parent graph cannot be converted to " + "a DAG.");
}
int lowerBound;
int upperBound;
if (params.getString("bayesPmInitializationMode", "range").equals("trinary")) {
lowerBound = upperBound = 3;
setBayesPm(graph, lowerBound, upperBound);
} else if (params.getString("bayesPmInitializationMode", "range").equals("range")) {
lowerBound = params.getInt("minCategories", 2);
upperBound = params.getInt("maxCategories", 4);
setBayesPm(graph, lowerBound, upperBound);
} else {
throw new IllegalStateException("Unrecognized type.");
}
}
public BayesPmWrapper(BayesEstimatorWrapper wrapper) {
setBayesPm(new BayesPm(wrapper.getEstimatedBayesIm().getBayesPm()));
}
public BayesPmWrapper(BayesImWrapper wrapper) {
this.bayesPms = new ArrayList<>();
for (int i = 0; i < wrapper.getNumModels(); i++) {
wrapper.setModelIndex(i);
this.bayesPms.add(wrapper.getBayesIm().getBayesPm());
}
this.numModels = wrapper.getNumModels();
}
public BayesPmWrapper(GraphSource graphWrapper, DataWrapper dataWrapper) {
this(new Dag(graphWrapper.getGraph()), dataWrapper);
}
public BayesPmWrapper(Graph graph, DataWrapper dataWrapper) {
DataSet dataSet
= (DataSet) dataWrapper.getSelectedDataModel();
List vars = dataSet.getVariables();
Map nodesToVars
= new HashMap<>();
for (int i = 0; i < dataSet.getNumColumns(); i++) {
DiscreteVariable var = (DiscreteVariable) vars.get(i);
String name = var.getName();
Node node = new GraphNode(name);
nodesToVars.put(node.getName(), var);
}
BayesPm bayesPm = new BayesPm(graph);
List nodes = bayesPm.getDag().getNodes();
for (Node node : nodes) {
Node var = nodesToVars.get(node.getName());
if (var != null) {
DiscreteVariable var2 = nodesToVars.get(node.getName());
int numCategories = var2.getNumCategories();
List categories = new ArrayList<>();
for (int j = 0; j < numCategories; j++) {
categories.add(var2.getCategory(j));
}
bayesPm.setCategories(node, categories);
}
}
setBayesPm(bayesPm);
}
public BayesPmWrapper(GraphWrapper graphWrapper,
Simulation simulation) {
this(graphWrapper, (DataWrapper) simulation);
}
public BayesPmWrapper(AlgorithmRunner wrapper, Parameters params) {
this(new Dag(wrapper.getGraph()), params);
}
public BayesPmWrapper(AlgorithmRunner wrapper, DataWrapper dataWrapper) {
this(new Dag(wrapper.getGraph()), dataWrapper);
}
public BayesPmWrapper(AlgorithmRunner wrapper, Simulation simulation) {
this(new Dag(wrapper.getGraph()), simulation);
}
public BayesPmWrapper(BayesEstimatorWrapper wrapper, Simulation simulation) {
this(new Dag(wrapper.getGraph()), simulation);
}
public BayesPmWrapper(BayesEstimatorWrapper wrapper,
DataWrapper dataWrapper) {
this(new Dag(wrapper.getGraph()), dataWrapper);
}
/**
* Creates a new BayesPm from the given workbench and uses it to construct a new BayesPm.
*
* @throws RuntimeException If the parent graph cannot be converted into a DAG.
*/
public BayesPmWrapper(DagWrapper dagWrapper, Parameters params) {
if (dagWrapper == null) {
throw new NullPointerException("Graph must not be null.");
}
Dag graph;
try {
graph = new Dag(dagWrapper.getDag());
} catch (Exception e) {
throw new RuntimeException(
"The parent graph cannot be converted to " + "a DAG.");
}
int lowerBound;
int upperBound;
if (params.getString("bayesPmInitializationMode", "trinary").equals("trinary")) {
lowerBound = upperBound = 3;
} else if (params.getString("bayesPmInitializationMode", "trinary").equals("range")) {
lowerBound = params.getInt("minCategories", 2);
upperBound = params.getInt("maxCategories", 4);
} else {
throw new IllegalStateException("Unrecognized type.");
}
setBayesPm(graph, lowerBound, upperBound);
}
public BayesPmWrapper(DagWrapper dagWrapper,
BayesPmWrapper oldBayesPmWrapper, Parameters params) {
try {
if (dagWrapper == null) {
throw new NullPointerException("Graph must not be null.");
}
if (oldBayesPmWrapper == null) {
throw new NullPointerException("BayesPm must not be null");
}
Graph graph = dagWrapper.getDag();
int lowerBound;
int upperBound;
String string = params.getString("bayesPmInitializationMode", "trinary");
if (string.equals("trinary")) {
lowerBound = upperBound = 3;
setBayesPm(new BayesPm(graph,
oldBayesPmWrapper.getBayesPm(), lowerBound, upperBound));
} else if (string.equals("range")) {
lowerBound = params.getInt("minCategories", 2);
upperBound = params.getInt("maxCategories", 4);
setBayesPm(graph, lowerBound, upperBound);
} else {
throw new IllegalStateException("Unrecognized type.");
}
} catch (Exception e) {
throw new RuntimeException(
"The parent graph cannot be converted to " + "a DAG.");
}
}
public BayesPmWrapper(DagWrapper dagWrapper, DataWrapper dataWrapper) {
DataSet dataSet
= (DataSet) dataWrapper.getSelectedDataModel();
List vars = dataSet.getVariables();
Map nodesToVars
= new HashMap<>();
for (int i = 0; i < dataSet.getNumColumns(); i++) {
DiscreteVariable var = (DiscreteVariable) vars.get(i);
String name = var.getName();
Node node = new GraphNode(name);
nodesToVars.put(node.getName(), var);
}
Dag graph = new Dag(dagWrapper.getDag());
BayesPm bayesPm = new BayesPm(graph);
List nodes = bayesPm.getDag().getNodes();
for (Node node : nodes) {
Node var = nodesToVars.get(node.getName());
if (var != null) {
DiscreteVariable var2 = nodesToVars.get(node.getName());
int numCategories = var2.getNumCategories();
List categories = new ArrayList<>();
for (int j = 0; j < numCategories; j++) {
categories.add(var2.getCategory(j));
}
bayesPm.setCategories(node, categories);
}
}
setBayesPm(bayesPm);
}
public BayesPmWrapper(DagWrapper dagWrapper, Simulation dataWrapper) {
this(dagWrapper, (DataWrapper) dataWrapper);
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @see TetradSerializableUtils
*/
public static BayesPmWrapper serializableInstance() {
return new BayesPmWrapper(Dag.serializableInstance(), new Parameters());
}
private void setBayesPm(Graph graph, int lowerBound, int upperBound) {
BayesPm b = new BayesPm(graph, lowerBound, upperBound);
setBayesPm(b);
}
//=============================PUBLIC METHODS========================//
public BayesPm getBayesPm() {
return this.bayesPms.get(getModelIndex());
}
private void setBayesPm(BayesPm b) {
this.bayesPms = new ArrayList<>();
this.bayesPms.add(b);
}
/**
* 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 Graph getGraph() {
return getBayesPm().getDag();
}
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
//================================= Private Methods ==================================//
private void log(BayesPm pm) {
TetradLogger.getInstance().log("info", "Bayes Parametric Model (Bayes PM)");
TetradLogger.getInstance().log("pm", pm.toString());
}
public Graph getSourceGraph() {
return getGraph();
}
public Graph getResultGraph() {
return getGraph();
}
public List getVariableNames() {
return getGraph().getNodeNames();
}
public List getVariables() {
return getGraph().getNodes();
}
public int getNumModels() {
return this.numModels;
}
public int getModelIndex() {
return this.modelIndex;
}
public void setModelIndex(int modelIndex) {
this.modelIndex = modelIndex;
}
public String getModelSourceName() {
return this.modelSourceName;
}
}
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