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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. //
// //
// 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.BoxDataSet;
import edu.cmu.tetrad.data.DataModel;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DoubleDataBox;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.search.IndependenceTest;
import edu.cmu.tetrad.search.test.IndTestChiSquare;
import edu.cmu.tetrad.search.test.IndTestFisherZ;
import edu.cmu.tetrad.search.test.IndTestGSquare;
import edu.cmu.tetrad.search.test.IndTestRegression;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.TetradLogger;
import edu.cmu.tetradapp.util.IndTestType;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.io.Serial;
import java.util.ArrayList;
import java.util.List;
/**
* Abstract subclass for Markov Blanket searches. This should be used so that the markov blanket search can also be used
* as input for a search box.
*
* @author Tyler Gibson
* @version $Id: $Id
*/
public abstract class AbstractMBSearchRunner extends DataWrapper implements MarkovBlanketSearchRunner {
@Serial
private static final long serialVersionUID = 23L;
/**
* The source data model.
*/
private final DataSet source;
/**
* The search params.
*/
private final Parameters params;
/**
* Data model.
*/
private DataSet dataModel;
/**
* The variables in the markov blanket.
*/
private List variables;
/**
* The name of the search algorithm
*/
private String searchName;
/**
* Conctructs the abstract search runner.
*
* @param source - The source data the search is acting on.
* @param params - The params for the search.
* @serial may be null.
*/
AbstractMBSearchRunner(DataModel source, Parameters params) {
super(AbstractMBSearchRunner.castData(source));
if (source == null) {
throw new NullPointerException("The source data was null.");
}
if (params == null) {
throw new NullPointerException("Search params were null.");
}
this.params = params;
this.source = (DataSet) source;
}
private static DataSet castData(DataModel model) {
if (model instanceof DataSet) {
return (DataSet) model;
}
throw new IllegalStateException("The data model must be a rectangular data set.");
}
/**
* Getter for the field params.
*
* @return the parameters for the search.
*/
public Parameters getParams() {
return this.params;
}
/**
* getDataModelForMarkovBlanket.
*
* @return the data model for the variables in the Markov blanket or null if the runner has not executed yet.
*/
public DataSet getDataModelForMarkovBlanket() {
return this.dataModel;
}
/**
* getMarkovBlanket.
*
* @return the variables in the MB searhc.
*/
public List getMarkovBlanket() {
return this.variables;
}
/**
* Getter for the field source.
*
* @return the source of the search.
*/
public DataSet getSource() {
return this.source;
}
/**
* Getter for the field searchName.
*
* @return the search name, or "Markov Blanket Search" by default.
*/
public String getSearchName() {
if (this.searchName == null) {
return "Markov Blanket Search";
}
return this.searchName;
}
//============== Protected methods ===============================//
/**
* {@inheritDoc}
*/
public void setSearchName(String n) {
this.searchName = n;
}
/**
* Makes sure the data is not empty.
*/
void validate() {
if (this.source.getNumColumns() == 0 || this.source.getNumRows() == 0) {
throw new IllegalStateException("Cannot run algorithm on an empty data set.");
}
}
/**
* Sets the results of the search.
*/
void setSearchResults(List nodes) {
if (nodes == null) {
throw new NullPointerException("nodes were null.");
}
this.variables = new ArrayList<>(nodes);
if (nodes.isEmpty()) {
this.dataModel = new BoxDataSet(new DoubleDataBox(this.source.getNumRows(), nodes.size()), nodes);
} else {
this.dataModel = this.source.subsetColumns(nodes);
}
this.setDataModel(this.dataModel);
}
//==================== Private Methods ===========================//
/**
* @return an appropriate independence test given the type of data set and values in the params.
*/
IndependenceTest getIndependenceTest() {
IndTestType type = (IndTestType) this.params.get("indTestType", IndTestType.FISHER_Z);
if (this.source.isContinuous() || this.source.getNumColumns() == 0) {
if (IndTestType.FISHER_Z == type) {
return new IndTestFisherZ(this.source, this.params.getDouble("alpha", 0.001));
}
// if (IndTestType.FISHER_ZD == type) {
// IndTestFisherZ test = new IndTestFisherZ(this.source, this.params.getDouble("alpha", 0.001));
//// test.setUsePseudoinverse(true);
// return test;
// }
if (IndTestType.LINEAR_REGRESSION == type) {
return new IndTestRegression(this.source, this.params.getDouble("alpha", 0.001));
} else {
this.params.set("indTestType", IndTestType.FISHER_Z);
return new IndTestFisherZ(this.source, this.params.getDouble("alpha", 0.001));
}
}
if (this.source.isDiscrete()) {
if (IndTestType.G_SQUARE == type) {
return new IndTestGSquare(this.source, this.params.getDouble("alpha", 0.001));
}
if (IndTestType.CHI_SQUARE != type) {
this.params.set("indTestType", IndTestType.CHI_SQUARE);
}
return new IndTestChiSquare(this.source, this.params.getDouble("alpha", 0.001));
}
throw new IllegalStateException("Cannot find Independence for Data source.");
}
/**
* Writes the object to the specified ObjectOutputStream.
*
* @param out The ObjectOutputStream to write the object to.
* @throws IOException If an I/O error occurs.
*/
/**
* Writes the object to the specified ObjectOutputStream.
*
* @param out The ObjectOutputStream to write the object to.
* @throws IOException If an I/O error occurs.
*/
@Serial
private void writeObject(ObjectOutputStream out) throws IOException {
try {
out.defaultWriteObject();
} catch (IOException e) {
TetradLogger.getInstance().log("Failed to serialize object: " + getClass().getCanonicalName()
+ ", " + e.getMessage());
throw e;
}
}
/**
* Reads the object from the specified ObjectInputStream. This method is used during deserialization
* to restore the state of the object.
*
* @param in The ObjectInputStream to read the object from.
* @throws IOException If an I/O error occurs.
* @throws ClassNotFoundException If the class of the serialized object cannot be found.
*/
@Serial
private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException {
try {
in.defaultReadObject();
} catch (IOException e) {
TetradLogger.getInstance().log("Failed to deserialize object: " + getClass().getCanonicalName()
+ ", " + e.getMessage());
throw e;
}
}
}
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