edu.cmu.tetradapp.model.PValueImproverWrapper 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.data.*;
import edu.cmu.tetrad.graph.*;
import edu.cmu.tetrad.search.utils.GraphSearchUtils;
import edu.cmu.tetrad.search.utils.MeekRules;
import edu.cmu.tetrad.search.work_in_progress.Hbsms;
import edu.cmu.tetrad.search.work_in_progress.HbsmsBeam;
import edu.cmu.tetrad.search.work_in_progress.HbsmsGes;
import edu.cmu.tetrad.sem.SemIm;
import edu.cmu.tetrad.util.*;
import java.beans.PropertyChangeEvent;
import java.beans.PropertyChangeListener;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.Serial;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
/**
* Extends AbstractAlgorithmRunner to produce a wrapper for the GES algorithm.
*
* @author Ricardo Silva
*/
public class PValueImproverWrapper extends AbstractAlgorithmRunner {
@Serial
private static final long serialVersionUID = 23L;
private final DataWrapper dataWrapper;
private final Parameters params = new Parameters();
private AlgorithmType algorithmType = AlgorithmType.BEAM;
private String name;
private Graph externalGraph;
private Graph graph;
private transient List listeners;
private Parameters params2;
private SemIm originalSemIm;
private SemIm newSemIm;
public PValueImproverWrapper(DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
super(dataWrapper, params, knowledgeBoxModel);
this.dataWrapper = dataWrapper;
this.params2 = params;
this.graph = new EdgeListGraph(dataWrapper.getSelectedDataModel().getVariables());
}
//============================CONSTRUCTORS============================//
public PValueImproverWrapper(DataWrapper dataWrapper,
Parameters params) {
super(dataWrapper, params, null);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(dataWrapper.getSelectedDataModel().getVariables()));
}
public PValueImproverWrapper(GraphWrapper graphWrapper,
DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
super(dataWrapper, params, knowledgeBoxModel);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(GraphWrapper graphWrapper,
DataWrapper dataWrapper,
Parameters params) {
super(dataWrapper, params);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(DagWrapper graphWrapper,
DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
super(dataWrapper, params, knowledgeBoxModel);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(DagWrapper graphWrapper,
DataWrapper dataWrapper,
Parameters params) {
super(dataWrapper, params);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(SemGraphWrapper graphWrapper,
DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
super(dataWrapper, params, knowledgeBoxModel);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(SemGraphWrapper graphWrapper,
DataWrapper dataWrapper,
Parameters params) {
super(dataWrapper, params);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(AbstractAlgorithmRunner graphWrapper,
DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
super(dataWrapper, params, knowledgeBoxModel);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
public PValueImproverWrapper(AbstractAlgorithmRunner graphWrapper,
DataWrapper dataWrapper,
Parameters params) {
super(dataWrapper, params);
this.dataWrapper = dataWrapper;
this.params2 = params;
setGraph(new EdgeListGraph(graphWrapper.getGraph()));
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @see TetradSerializableUtils
*/
public static PcRunner serializableInstance() {
return PcRunner.serializableInstance();
}
public AlgorithmType getAlgorithmType() {
return this.algorithmType;
}
public void setAlgorithmType(AlgorithmType algorithmType) {
this.algorithmType = algorithmType;
}
//============================PUBLIC METHODS==========================//
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
public boolean isShuffleMoves() {
return false;
}
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the extending class.
*/
public void execute() {
DataModel dataModel = getDataModel();
Knowledge knowledge = (Knowledge) this.params2.get("knowledge", new Knowledge());
if (this.externalGraph == null) {
this.externalGraph = new EdgeListGraph(dataModel.getVariables());
}
Graph graph2 = new EdgeListGraph(this.externalGraph);
graph2 = GraphUtils.replaceNodes(graph2, dataModel.getVariables());
Hbsms search;
if (dataModel instanceof DataSet) {
DataSet dataSet = (DataSet) dataModel;
if (getAlgorithmType() == AlgorithmType.BEAM) {
search = new HbsmsBeam(graph2, dataSet, knowledge);
} else if (getAlgorithmType() == AlgorithmType.FGES) {
search = new HbsmsGes(graph2, dataSet);
search.setKnowledge(knowledge);
} else {
throw new IllegalStateException();
}
} else if (dataModel instanceof CovarianceMatrix) {
CovarianceMatrix covarianceMatrix = (CovarianceMatrix) dataModel;
if (getAlgorithmType() == AlgorithmType.BEAM) {
search = new HbsmsBeam(graph2, covarianceMatrix, knowledge);
} else if (getAlgorithmType() == AlgorithmType.FGES) {
throw new IllegalArgumentException("GES method requires a dataset; a covariance matrix was provided.");
} else {
throw new IllegalStateException();
}
} else {
throw new IllegalStateException();
}
Parameters params = getParams();
search.setAlpha(params.getDouble("alpha", 0.001));
search.setBeamWidth(params.getInt("beamWidth", 5));
search.setHighPValueAlpha(params.getDouble("zeroEdgeP", 0.05));
this.graph = search.search();
// this.graph = search.getNewSemIm().getSemPm().getGraph();
setOriginalSemIm(search.getOriginalSemIm());
this.newSemIm = search.getNewSemIm();
fireGraphChange(this.graph);
if (getSourceGraph() != null) {
LayoutUtil.arrangeBySourceGraph(this.graph, getSourceGraph());
} else if (knowledge.isDefaultToKnowledgeLayout()) {
GraphSearchUtils.arrangeByKnowledgeTiers(this.graph, knowledge);
} else {
LayoutUtil.defaultLayout(this.graph);
}
setResultGraph(GraphTransforms.cpdagForDag(this.graph));
}
public boolean supportsKnowledge() {
return true;
}
public MeekRules getMeekRules() {
MeekRules rules = new MeekRules();
rules.setKnowledge((Knowledge) this.params.get("knowledge", new Knowledge()));
return rules;
}
@Override
public String getAlgorithmName() {
return "BFF";
}
private boolean isMeekPreventCycles() {
return this.params.getBoolean("MeekPreventCycles", false);
}
public void addPropertyChangeListener(PropertyChangeListener l) {
if (!getListeners().contains(l)) getListeners().add(l);
}
private void fireGraphChange(Graph graph) {
for (PropertyChangeListener l : getListeners()) {
l.propertyChange(new PropertyChangeEvent(this, "graph", null, graph));
}
}
public Graph getGraph() {
return getResultGraph();
}
private void setGraph(EdgeListGraph graph) {
this.graph = new EdgeListGraph(graph);
this.externalGraph = new EdgeListGraph(graph);
}
/**
* @return the names of the triple classifications. Coordinates with
*/
public List getTriplesClassificationTypes() {
return new LinkedList<>();
}
/**
* @param node The node that the classifications are for. All triple from adjacencies to this node to adjacencies to
* this node through the given node will be considered.
* @return the list of triples corresponding to getTripleClassificationNames for the given node.
*/
public List> getTriplesLists(Node node) {
return new LinkedList<>();
}
private List getListeners() {
if (this.listeners == null) {
this.listeners = new ArrayList<>();
}
return this.listeners;
}
public DataSet simulateDataCholesky(int sampleSize, Matrix covar, List variableNodes) {
List variables = new LinkedList<>(variableNodes);
List newVariables = new ArrayList<>();
for (Node node : variables) {
ContinuousVariable continuousVariable = new ContinuousVariable(node.getName());
continuousVariable.setNodeType(node.getNodeType());
newVariables.add(continuousVariable);
}
DataSet fullDataSet = new BoxDataSet(new VerticalDoubleDataBox(sampleSize, newVariables.size()), newVariables);
Matrix cholesky = MatrixUtils.cholesky(covar);
// Simulate the data by repeatedly calling the Cholesky.exogenousData
// method. Store only the data for the measured variables.
for (int row = 0; row < sampleSize; row++) {
// Step 1. Generate normal samples.
double[] exoData = new double[cholesky.getNumRows()];
for (int i = 0; i < exoData.length; i++) {
exoData[i] = RandomUtil.getInstance().nextNormal(0, 1);
// exoData[i] = randomUtil.nextUniform(-1, 1);
}
// Step 2. Multiply by cholesky to get correct covariance.
double[] point = new double[exoData.length];
for (int i = 0; i < exoData.length; i++) {
double sum = 0.0;
for (int j = 0; j <= i; j++) {
sum += cholesky.get(i, j) * exoData[j];
}
point[i] = sum;
}
for (int col = 0; col < variables.size(); col++) {
int index = variableNodes.indexOf(variables.get(col));
double value = point[index];
if (Double.isNaN(value) || Double.isInfinite(value)) {
throw new IllegalArgumentException("Value out of range: " + value);
}
fullDataSet.setDouble(row, col, value);
}
}
return DataTransforms.restrictToMeasured(fullDataSet);
}
/**
* 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.
*
* @throws java.io.IOException
* @throws ClassNotFoundException
*/
private void readObject(ObjectInputStream s)
throws IOException, ClassNotFoundException {
s.defaultReadObject();
if (this.params2 == null) {
this.params2 = new Parameters();
}
}
public SemIm getOriginalSemIm() {
return this.originalSemIm;
}
private void setOriginalSemIm(SemIm originalSemIm) {
if (this.originalSemIm == null) {
this.originalSemIm = originalSemIm;
}
}
public SemIm getNewSemIm() {
return this.newSemIm;
}
public void setNewSemIm(SemIm newSemIm) {
this.newSemIm = newSemIm;
}
public enum AlgorithmType {
BEAM, FGES
}
}
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