edu.cmu.tetradapp.model.BuildPureClustersRunner 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.Graph;
import edu.cmu.tetrad.graph.LayoutUtil;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.NodeType;
import edu.cmu.tetrad.search.Bpc;
import edu.cmu.tetrad.search.utils.BpcAlgorithmType;
import edu.cmu.tetrad.search.utils.BpcTestType;
import edu.cmu.tetrad.search.utils.ClusterUtils;
import edu.cmu.tetrad.search.utils.MimUtils;
import edu.cmu.tetrad.search.work_in_progress.BpcTetradPurifyWashdown;
import edu.cmu.tetrad.search.work_in_progress.Washdown;
import edu.cmu.tetrad.sem.ReidentifyVariables;
import edu.cmu.tetrad.sem.SemIm;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.TetradSerializableUtils;
import edu.cmu.tetrad.util.Unmarshallable;
import java.rmi.MarshalledObject;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
/**
* Extends AbstractAlgorithmRunner to produce a wrapper for the BuildPureClusters algorithm.
*
* @author Ricardo Silva
*/
public class BuildPureClustersRunner extends AbstractMimRunner
implements GraphSource, Unmarshallable {
private static final long serialVersionUID = 23L;
/**
* To reidentify variables.
*/
private SemIm semIm;
private Graph trueGraph;
//============================CONSTRUCTORS============================//
/**
* Constructs a wrapper for the given DataWrapper.
*/
public BuildPureClustersRunner(DataWrapper dataWrapper,
Parameters pureClustersParams) {
super(dataWrapper, (Clusters) pureClustersParams.get("clusters", null), pureClustersParams);
}
public BuildPureClustersRunner(DataWrapper dataWrapper, SemImWrapper semImWrapper,
Parameters pureClustersParams) {
super(dataWrapper, (Clusters) pureClustersParams.get("clusters", null), pureClustersParams);
this.semIm = semImWrapper.getSemIm();
this.trueGraph = this.semIm.getSemPm().getGraph();
}
public BuildPureClustersRunner(DataWrapper dataWrapper, GraphWrapper graphWrapper,
Parameters pureClustersParams) {
super(dataWrapper, (Clusters) pureClustersParams.get("clusters", null), pureClustersParams);
this.trueGraph = graphWrapper.getGraph();
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @see TetradSerializableUtils
*/
public static PcRunner serializableInstance() {
return PcRunner.serializableInstance();
}
//===================PUBLIC METHODS OVERRIDING ABSTRACT================//
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the extending class.
*/
public void execute() {
boolean rKey = getParams().getBoolean("BPCrDown", false);
BpcAlgorithmType algorithm = (BpcAlgorithmType) getParams().get("bpcAlgorithmthmType", BpcAlgorithmType.FIND_ONE_FACTOR_CLUSTERS);
Graph searchGraph;
if (rKey) {
Washdown washdown;
Object source = getData();
if (source instanceof DataSet) {
washdown = new Washdown((DataSet) source, getParams().getDouble("alpha", 0.001));
} else {
washdown = new Washdown((CovarianceMatrix) source, getParams().getDouble("alpha", 0.001));
}
searchGraph = washdown.search();
} else {
BpcTestType tetradTestType = (BpcTestType) getParams().get("tetradTestType", BpcTestType.TETRAD_WISHART);
if (algorithm == BpcAlgorithmType.TETRAD_PURIFY_WASHDOWN) {
BpcTetradPurifyWashdown bpc;
Object source = getData();
if (source instanceof DataSet) {
bpc = new BpcTetradPurifyWashdown(
(DataSet) source,
tetradTestType,
getParams().getDouble("alpha", 0.001));
} else {
bpc = new BpcTetradPurifyWashdown((ICovarianceMatrix) source,
tetradTestType, getParams().getDouble("alpha", 0.001));
}
searchGraph = bpc.search();
} else if (algorithm == BpcAlgorithmType.BUILD_PURE_CLUSTERS) {
Bpc bpc;
DataModel source = getData();
BpcTestType testType = (BpcTestType) getParams().get("tetradTestType", BpcTestType.TETRAD_WISHART);
if (source instanceof ICovarianceMatrix) {
bpc = new Bpc((ICovarianceMatrix) source,
getParams().getDouble("alpha", 0.001),
testType
);
} else if (source instanceof DataSet) {
bpc = new Bpc(
(DataSet) source, getParams().getDouble("alpha", 0.001),
testType
);
} else {
throw new IllegalArgumentException();
}
searchGraph = bpc.search();
}
// else if (algorithm == BpcAlgorithmType.FIND_ONE_FACTOR_CLUSTERS) {
//// FindOneFactorClusters bpc;
//// Object source = getContinuousData();
////
//// if (source instanceof DataSet) {
//// bpc = new FindOneFactorClusters(
//// (DataSet) source,
//// tetradTestType,
//// getParameters().getAlternativePenalty());
//// } else {
//// bpc = new FindOneFactorClusters((ICovarianceMatrix) source,
//// tetradTestType, getParameters().getAlternativePenalty());
//// }
////
//// searchGraph = bpc.search();
//
// FindOneFactorClusters2 bpc;
// Object source = getContinuousData();
// FindOneFactorClusters2.Algorithm sag = FindOneFactorClusters2.Algorithm.SAG;
//
// if (source instanceof DataSet) {
// bpc = new FindOneFactorClusters2(
// (DataSet) source,
// tetradTestType, sag,
// getParameters().getAlternativePenalty());
//
//// bpc = new FindTwoFactorClusters4(
//// (DataSet) source,
//// getParameters().getAlternativePenalty());
// } else {
// bpc = new FindOneFactorClusters2((ICovarianceMatrix) source,
// tetradTestType, sag, getParameters().getAlternativePenalty());
////
//// bpc = new FindTwoFactorClusters4((ICovarianceMatrix) source,
//// getParameters().getAlternativePenalty());
// }
//
// searchGraph = bpc.search();
//
// }
// else if (algorithm == BpcAlgorithmType.FIND_TWO_FACTOR_CLUSTERS) {
// FindTwoFactorClusters2 bpc;
// Object source = getContinuousData();
//
// if (source instanceof DataSet) {
// bpc = new FindTwoFactorClusters2(
// (DataSet) source,
// tetradTestType,
// getParameters().getAlternativePenalty());
//
//// bpc = new FindTwoFactorClusters4(
//// (DataSet) source,
//// getParameters().getAlternativePenalty());
// } else {
// bpc = new FindTwoFactorClusters2((ICovarianceMatrix) source,
// tetradTestType, getParameters().getAlternativePenalty());
////
//// bpc = new FindTwoFactorClusters4((ICovarianceMatrix) source,
//// getParameters().getAlternativePenalty());
// }
//
// searchGraph = bpc.search();
// }
else {
throw new IllegalStateException();
}
}
if (this.semIm != null) {
List> partition = MimUtils.convertToClusters2(searchGraph);
List variableNames = ReidentifyVariables.reidentifyVariables2(partition, this.trueGraph, (DataSet) getData());
rename(searchGraph, partition, variableNames);
// searchGraph = reidentifyVariables2(searchGraph, semIm);
} else if (this.trueGraph != null) {
List> partition = MimUtils.convertToClusters2(searchGraph);
List variableNames = ReidentifyVariables.reidentifyVariables1(partition, this.trueGraph);
rename(searchGraph, partition, variableNames);
// searchGraph = reidentifyVariables(searchGraph, trueGraph);
}
System.out.println("Search Graph " + searchGraph);
try {
Graph graph = new MarshalledObject<>(searchGraph).get();
LayoutUtil.defaultLayout(graph);
LayoutUtil.fruchtermanReingoldLayout(graph);
setResultGraph(graph);
setClusters(MimUtils.convertToClusters(graph, getData().getVariables()));
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException(e);
}
}
private void rename(Graph searchGraph, List> partition, List variableNames) {
for (Node node : searchGraph.getNodes()) {
if (!(node.getNodeType() == NodeType.LATENT)) {
continue;
}
List children = searchGraph.getChildren(node);
ReidentifyVariables.getLatents(searchGraph).forEach(children::remove);
for (int i = 0; i < partition.size(); i++) {
if (new HashSet<>(partition.get(i)).equals(new HashSet<>(children))) {
node.setName(variableNames.get(i));
}
}
}
}
public Graph getGraph() {
return getResultGraph();
}
public java.util.List getVariables() {
List latents = new ArrayList<>();
for (String name : getVariableNames()) {
Node node = new ContinuousVariable(name);
node.setNodeType(NodeType.LATENT);
latents.add(node);
}
return latents;
}
public List getVariableNames() {
List> partition = ClusterUtils.clustersToPartition(getClusters(),
getData().getVariables());
return ClusterUtils.generateLatentNames(partition.size());
}
}
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