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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. //
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// 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 //
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package edu.cmu.tetradapp.model;
import edu.cmu.tetrad.data.DataModel;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.Knowledge;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.GraphUtils;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.regression.RegressionDataset;
import edu.cmu.tetrad.regression.RegressionResult;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.TetradSerializableUtils;
import edu.cmu.tetradapp.session.SessionModel;
import java.io.Serial;
import java.util.*;
/**
* Implements a model for the linear adjustment regression. The linear adjustment regression model is used to calculate
* the total effect of a linear adjustment regression on a target node, given a source node and an adjustment set. The
* model also provides a method to retrieve the regression result string for a given source node, target node, and
* adjustment set.
*
* @author josephramsey
*/
public class LinearAdjustmentRegressionModel implements SessionModel, GraphSource, KnowledgeBoxInput {
@Serial
private static final long serialVersionUID = 23L;
/**
* The data model to check.
*/
private final DataModel dataModel;
/**
* The graph to check.
*/
private final Graph graph;
/**
* The parameters.
*/
private final Parameters parameters;
/**
* A private final List of nodes in a given variable.
*/
private final List nodes;
/**
* Private final field that holds a list of strings representing node names.
*/
private final List nodeNames;
/**
* The name of this model.
*/
private String name = "";
/**
* Represents a linear adjustment regression model.
*
* @param dataModel The data model used for regression.
* @param graphSource The source of the graph.
* @param parameters The parameters for the regression model.
*/
public LinearAdjustmentRegressionModel(DataWrapper dataModel, GraphSource graphSource, Parameters parameters) {
this.dataModel = dataModel.getSelectedDataModel();
this.nodes = dataModel.getVariables();
this.nodeNames = dataModel.getVarNames();
this.graph = GraphUtils.replaceNodes(graphSource.getGraph(), this.nodes);
this.parameters = parameters;
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @return a {@link Knowledge} object
* @see TetradSerializableUtils
*/
public static Knowledge serializableInstance() {
return new Knowledge();
}
/**
* Retrieves an adjustment set from the graph between the specified source and target nodes.
*
* @param source The source node.
* @param target The target node.
* @return A list of sets of nodes representing the adjustment sets.
* @throws IllegalArgumentException if there are no amenable paths.
*/
public List> getAdjustmentSets(Node source, Node target) {
int maxNumSets = parameters.getInt("pathsMaxNumSets");
int maxDistanceFromEndpoint = parameters.getInt("pathsMaxDistanceFromEndpoint");
int nearWhichEndpoint = parameters.getInt("pathsNearWhichEndpoint");
int maxPathLength = parameters.getInt("pathsMaxLength");
return graph.paths().adjustmentSets(source, target, maxNumSets, maxDistanceFromEndpoint, nearWhichEndpoint,
maxPathLength);
}
/**
* Calculates the total effect of a linear adjustment regression on a target node, given a source node
* and an adjustment set.
*
* @param source The source node.
* @param target The target node.
* @param adjustmentSet The adjustment set, which should not contain the source or target nodes.
* @return The total effect of the regression.
* @throws IllegalArgumentException if the adjustment set contains the source or target nodes.
*/
public double totalEffect(Node source, Node target, Set adjustmentSet) {
if (adjustmentSet.contains(source) || adjustmentSet.contains(target)) {
throw new IllegalArgumentException("Adjustment set cannot contain source or target nodes.");
}
RegressionDataset regressionDataset = new RegressionDataset((DataSet) dataModel);
List regressors = new ArrayList<>();
regressors.add(source);
regressors.addAll(adjustmentSet);
RegressionResult result = regressionDataset.regress(target, regressors);
return result.getCoef()[1];
}
/**
* Retrieves the regression result string for a given source node, target node, and adjustment set.
*
* @param source The source node.
* @param target The target node.
* @param adjustmentSet The adjustment set, which should not contain the source or target nodes.
* @return The regression result string.
* @throws IllegalArgumentException if the adjustment set contains the source or target nodes.
*/
public String getRegressionString(Node source, Node target, Set adjustmentSet) {
if (adjustmentSet.contains(source) || adjustmentSet.contains(target)) {
throw new IllegalArgumentException("Adjustment set cannot contain source or target nodes.");
}
RegressionDataset regressionDataset = new RegressionDataset((DataSet) dataModel);
List regressors = new ArrayList<>();
regressors.add(source);
regressors.addAll(adjustmentSet);
RegressionResult result = regressionDataset.regress(target, regressors);
return result.toString();
}
/**
* Retrieves the graph associated with this linear adjustment regression model.
*
* @return The graph.
*/
@Override
public Graph getGraph() {
return graph;
}
/**
* Retrieves the source graph associated with this linear adjustment regression model.
*
* @return The source graph.
*/
@Override
public Graph getSourceGraph() {
return graph;
}
/**
* Retrieves the result graph associated with this linear adjustment regression model.
*
* @return The result graph.
*/
@Override
public Graph getResultGraph() {
return graph;
}
/**
* Retrieves the list of variables associated with this method.
*
* @return the list of variables.
*/
@Override
public List getVariables() {
return new ArrayList<>(nodes);
}
/**
* Retrieves the list of variable names associated with this method.
*
* @return the list of variable names.
*/
@Override
public List getVariableNames() {
return new ArrayList<>(nodeNames);
}
/**
* Retrieves the name of the session model.
*
* @return the name of the session model.
*/
@Override
public String getName() {
return name;
}
/**
* Sets the name of the session model.
*
* @param name the name of the session model.
*/
@Override
public void setName(String name) {
this.name = name;
}
/**
* The parameters.
*
* @return the parameters.
*/
public Parameters getParameters() {
return parameters;
}
}
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