edu.cmu.tetradapp.model.RegressionRunner 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.EdgeListGraph;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.Triple;
import edu.cmu.tetrad.regression.Regression;
import edu.cmu.tetrad.regression.RegressionCovariance;
import edu.cmu.tetrad.regression.RegressionDataset;
import edu.cmu.tetrad.regression.RegressionResult;
import edu.cmu.tetrad.search.utils.MeekRules;
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.*;
/**
* Extends AbstractAlgorithmRunner to produce a wrapper for the Regression algorithm.
*
* @author Frank Wimberly after Joe Ramsey's PcRunner
*/
public class RegressionRunner implements AlgorithmRunner, RegressionModel {
private static final long serialVersionUID = 23L;
private final Parameters params;
private final DataModelList dataModels;
private final List variableNames;
private List regressorNames;
private String name;
private String targetName;
private Graph outGraph;
private RegressionResult result;
private Map allParamsSettings;
private int numModels = 1;
private int modelIndex;
private String modelSourceName;
//=========================CONSTRUCTORS===============================//
/**
* Constructs a wrapper for the given DataWrapper. The DataWrapper must contain a DataSet that is either a DataSet
* or a DataSet or a DataList containing either a DataSet or a DataSet as its selected model.
*/
public RegressionRunner(DataWrapper dataWrapper, Parameters params) {
if (dataWrapper == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
if (dataWrapper instanceof Simulation) {
Simulation simulation = (Simulation) dataWrapper;
this.numModels = dataWrapper.getDataModelList().size();
this.modelIndex = 0;
this.modelSourceName = simulation.getName();
}
this.params = params;
DataModel dataModel = dataWrapper.getSelectedDataModel();
if (dataModel instanceof DataSet) {
DataSet _dataSet = (DataSet) dataModel;
if (!_dataSet.isContinuous()) {
throw new IllegalArgumentException("Data set must be continuous.");
}
}
this.dataModels = dataWrapper.getDataModelList();
this.variableNames = dataModel.getVariableNames();
this.targetName = null;
this.regressorNames = new ArrayList<>();
TetradLogger.getInstance().log("info", "Linear Regression");
if (this.result == null) {
TetradLogger.getInstance().log("info", "Please double click this regression node to run the regession.");
} else {
TetradLogger.getInstance().log("result", "\n" + this.result.getResultsTable().toString());
}
}
/**
* Generates a simple exemplar of this class to test serialization.
*
* @see TetradSerializableUtils
*/
public static RegressionRunner serializableInstance() {
List variables = new LinkedList<>();
ContinuousVariable var1 = new ContinuousVariable("X");
ContinuousVariable var2 = new ContinuousVariable("Y");
variables.add(var1);
variables.add(var2);
DataSet _dataSet = new BoxDataSet(new DoubleDataBox(3, variables.size()), variables);
double[] col1data = {0.0, 1.0, 2.0};
double[] col2data = {2.3, 4.3, 2.5};
for (int i = 0; i < 3; i++) {
_dataSet.setDouble(i, 0, col1data[i]);
_dataSet.setDouble(i, 1, col2data[i]);
}
DataWrapper dataWrapper = new DataWrapper(_dataSet);
return new RegressionRunner(dataWrapper, new Parameters());
}
//===========================PUBLIC METHODS============================//
public DataModel getDataModel() {
//return (DataModel) this.dataWrapper.getDataModelList().get(0);
return this.dataModels.get(getModelIndex());
}
public Parameters getParams() {
return this.params;
}
public Graph getResultGraph() {
return this.outGraph;
}
private void setResultGraph(Graph graph) {
this.outGraph = graph;
}
public Graph getSourceGraph() {
return null;
}
//=================PUBLIC METHODS OVERRIDING ABSTRACT=================//
/**
* Executes the algorithm, producing (at least) a result workbench. Must be implemented in the extending class.
*/
public void execute() {
if (this.regressorNames.size() == 0 || this.targetName == null) {
this.outGraph = new EdgeListGraph();
return;
}
if (this.regressorNames.contains(this.targetName)) {
this.outGraph = new EdgeListGraph();
return;
}
Regression regression;
Node target;
List regressors;
if (getDataModel() instanceof DataSet) {
DataSet _dataSet = (DataSet) getDataModel();
regression = new RegressionDataset(_dataSet);
target = _dataSet.getVariable(this.targetName);
regressors = new LinkedList<>();
for (String regressorName : this.regressorNames) {
regressors.add(_dataSet.getVariable(regressorName));
}
double alpha = this.params.getDouble("alpha", 0.001);
regression.setAlpha(alpha);
this.result = regression.regress(target, regressors);
this.outGraph = regression.getGraph();
} else if (getDataModel() instanceof ICovarianceMatrix) {
ICovarianceMatrix covariances = (ICovarianceMatrix) getDataModel();
regression = new RegressionCovariance(covariances);
target = covariances.getVariable(this.targetName);
regressors = new LinkedList<>();
for (String regressorName : this.regressorNames) {
regressors.add(covariances.getVariable(regressorName));
}
double alpha = this.params.getDouble("alpha", 0.001);
regression.setAlpha(alpha);
this.result = regression.regress(target, regressors);
this.outGraph = regression.getGraph();
}
setResultGraph(this.outGraph);
}
public boolean supportsKnowledge() {
return false;
}
public MeekRules getMeekRules() {
throw new UnsupportedOperationException();
}
public Graph getExternalGraph() {
return null;
}
public void setExternalGraph(Graph graph) {
}
@Override
public String getAlgorithmName() {
return "Regression";
}
public RegressionResult getResult() {
return this.result;
}
public Graph getOutGraph() {
return this.outGraph;
}
@Override
public List getVariableNames() {
return this.variableNames;
}
@Override
public List getRegressorNames() {
return this.regressorNames;
}
@Override
public void setRegressorName(List predictors) {
this.regressorNames = predictors;
}
public String getTargetName() {
return this.targetName;
}
@Override
public void setTargetName(String target) {
this.targetName = target;
}
/**
* 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();
if (this.params == null) {
throw new NullPointerException();
}
}
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
public Graph getGraph() {
return this.outGraph;
}
/**
* @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<>();
}
@Override
public Map getParamSettings() {
Map paramSettings = new HashMap<>();
paramSettings.put("Algorithm", "Regression");
return paramSettings;
}
@Override
public Map getAllParamSettings() {
return this.allParamsSettings;
}
@Override
public void setAllParamSettings(Map paramSettings) {
this.allParamsSettings = paramSettings;
}
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;
}
@Override
public List getGraphs() {
return null;
}
}
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