edu.cmu.tetradapp.model.DataWrapper 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.NodeType;
import edu.cmu.tetrad.regression.RegressionResult;
import edu.cmu.tetrad.session.DoNotAddOldModel;
import edu.cmu.tetrad.session.SimulationParamsSource;
import edu.cmu.tetrad.util.Parameters;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.Serial;
import java.util.*;
/**
* Wraps a DataModel as a model class for a Session, providing constructors for the parents of Tetrad that are specified
* by Tetrad.
*
* @author josephramsey
*/
public class DataWrapper implements KnowledgeEditable, KnowledgeBoxInput,
DoNotAddOldModel, SimulationParamsSource, MultipleDataSource {
private static final long serialVersionUID = 23L;
/**
* Maps columns to discretization specs so that user's work is not forgotten from one editing of the same data set
* to the next.
*
* @serial Cannot be null.
*/
private final Map discretizationSpecs = new HashMap();
/**
* @serial Can be null.
*/
private String name;
private DataModelList dataModelList;
/**
* Stores a reference to the source workbench, if there is one.
*
* @serial Can be null.
*/
private Graph sourceGraph;
/**
* The parameters being edited.
*/
private Parameters parameters;
private Map allParamSettings;
//==============================CONSTRUCTORS===========================//
protected DataWrapper() {
setDataModel(new BoxDataSet(new VerticalDoubleDataBox(0, 0), new LinkedList<>()));
this.parameters = new Parameters();
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*/
public DataWrapper(Parameters parameters) {
setDataModel(new BoxDataSet(new VerticalDoubleDataBox(0, 0), new LinkedList<>()));
this.parameters = parameters;
}
public DataWrapper(Simulation wrapper, Parameters parameters) {
this.name = wrapper.getName();
this.dataModelList = new DataModelList();
for (DataModel model : wrapper.getDataModels()) {
if (model instanceof DataSet) {
this.dataModelList.add(((DataSet) model).copy());
} else if (model instanceof CorrelationMatrix) {
this.dataModelList.add(new CorrelationMatrix((CorrelationMatrix) model));
} else if (model instanceof CovarianceMatrix) {
this.dataModelList.add(new CovarianceMatrix((CovarianceMatrix) model));
} else {
throw new IllegalArgumentException();
}
}
this.dataModelList = wrapper.getDataModelList();
this.parameters = parameters;
}
/**
* Copy constructor.
*
* @param wrapper the data wrapper to copy.
* @param parameters the parameters to use.
*/
public DataWrapper(DataWrapper wrapper, Parameters parameters) {
this.name = wrapper.name;
this.parameters = new Parameters(parameters);
DataModelList dataModelList = new DataModelList();
int selected = -1;
for (int i = 0; i < wrapper.getDataModelList().size(); i++) {
DataModel model = wrapper.getDataModelList().get(i);
if (model instanceof DataSet) {
dataModelList.add(((DataSet) model).copy());
} else if (model instanceof CorrelationMatrix) {
dataModelList.add(new CorrelationMatrix((CorrelationMatrix) model));
} else if (model instanceof CovarianceMatrix) {
dataModelList.add(new CovarianceMatrix((CovarianceMatrix) model));
} else {
throw new IllegalArgumentException();
}
if (model.equals(wrapper.getDataModelList().getSelectedModel())) {
selected = i;
}
}
if (selected > -1) {
dataModelList.setSelectedModel(dataModelList.get(selected));
}
if (wrapper.sourceGraph != null) {
this.sourceGraph = new EdgeListGraph(wrapper.sourceGraph);
}
this.dataModelList = dataModelList;
LogDataUtils.logDataModelList("Standalone data set.", getDataModelList());
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param dataSet the data set to use.
*/
public DataWrapper(DataSet dataSet) {
setDataModel(dataSet);
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param graph the graph to use.
* @param parameters the parameters to use.
*/
public DataWrapper(Graph graph, Parameters parameters) {
if (graph == null) {
throw new NullPointerException();
}
this.parameters = new Parameters(parameters);
List nodes = graph.getNodes();
List variables = new LinkedList<>();
for (Object node1 : nodes) {
Node node = (Node) node1;
String name = node.getName();
NodeType nodetype = node.getNodeType();
if (nodetype == NodeType.MEASURED) {
ContinuousVariable var = new ContinuousVariable(name);
variables.add(var);
}
}
DataSet dataSet = new BoxDataSet(new VerticalDoubleDataBox(0, variables.size()), variables);
DataModelList dataModelList = new DataModelList();
dataModelList.add(dataSet);
this.dataModelList = dataModelList;
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param dagWrapper the DAG to use.
* @param parameters the parameters to use.
*/
public DataWrapper(DagWrapper dagWrapper, Parameters parameters) {
this(dagWrapper.getDag(), parameters);
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param wrapper the SEM graph to use.
* @param parameters the parameters to use.
*/
public DataWrapper(SemGraphWrapper wrapper, Parameters parameters) {
this(wrapper.getGraph(), parameters);
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param wrapper the SEM graph to use.
* @param parameters the parameters to use.
*/
public DataWrapper(GraphWrapper wrapper, Parameters parameters) {
this(wrapper.getGraph(), parameters);
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param regression the regression to use.
* @param wrapper the data model to use.
* @param parameters the parameters to use.
*/
public DataWrapper(RegressionRunner regression, DataWrapper wrapper, Parameters parameters) {
this(regression.getResult(), (DataSet) Objects.requireNonNull(wrapper.getDataModelList().getSelectedModel()),
parameters);
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param regression the regression to use.
* @param wrapper the data model to use.
* @param parameters the parameters to use.
*/
public DataWrapper(RegressionRunner regression, Simulation wrapper, Parameters parameters) {
this(regression.getResult(), (DataSet) Objects.requireNonNull(wrapper.getDataModelList().getSelectedModel()),
parameters);
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param result the regression result to use.
* @param data the data to use.
* @param parameters the parameters to use.
*/
public DataWrapper(RegressionResult result, DataSet data, Parameters parameters) {
this.parameters = new Parameters(parameters);
DataSet data2 = data.copy();
String predictedVariable = nextVariableName("Pred", data);
data2.addVariable(new ContinuousVariable(predictedVariable));
String[] regressorNames = result.getRegressorNames();
for (int i = 0; i < data.getNumRows(); i++) {
double[] x = new double[regressorNames.length];
for (int j = 0; j < regressorNames.length; j++) {
Node variable = data.getVariable(regressorNames[j]);
if (variable == null) {
throw new NullPointerException("Variable " + variable + " doesn't "
+ "exist in the input data.");
}
if (!(variable instanceof ContinuousVariable)) {
throw new IllegalArgumentException("Expecting a continuous variable: " + variable);
}
x[j] = data.getDouble(i, data.getColumn(variable));
}
double yHat = result.getPredictedValue(x);
data2.setDouble(i, data2.getColumn(data2.getVariable(predictedVariable)), yHat);
}
DataModelList dataModelList = new DataModelList();
dataModelList.add(data2);
this.dataModelList = dataModelList;
}
/**
* Constructs a data wrapper using a new DataSet as data model.
*
* @param mimBuild the mim build to use.
* @param parameters the parameters to use.
*/
public DataWrapper(MimBuildRunner mimBuild, Parameters parameters) {
this.parameters = new Parameters(parameters);
ICovarianceMatrix cov = mimBuild.getCovMatrix();
DataModelList dataModelList = new DataModelList();
dataModelList.add(cov);
this.dataModelList = dataModelList;
}
/**
* Generates a simple exemplar of this class to test serialization.
*/
public static PcRunner serializableInstance() {
return PcRunner.serializableInstance();
}
/**
* Given base b (a String), returns the first node in the sequence "b1", "b2", "b3", etc., which is not already the
* name of a node in the workbench.
*
* @param base the base string.
* @return the first string in the sequence not already being used.
*/
private String nextVariableName(String base, DataSet data) {
// Variable names should start with "1."
int i = -1;
String name = "?";
loop:
while (true) {
++i;
if (i == 0) {
name = base;
} else {
name = base + i;
}
for (Node node1 : data.getVariables()) {
if (node1.getName().equals(name)) {
continue loop;
}
}
break;
}
return name;
}
/**
* Stores a reference to the data model being wrapped.
*
* @return the list of models.
*/
public DataModelList getDataModelList() {
return this.dataModelList;
}
/**
* Set the data model list.
*
* @param dataModelList the data model list to set.
*/
public void setDataModelList(DataModelList dataModelList) {
if (dataModelList == null) {
throw new NullPointerException("Data model list not provided.");
}
this.dataModelList = dataModelList;
}
/**
* @return the data model for this wrapper.
*/
public List getDataModels() {
return new ArrayList<>(this.dataModelList);
}
/**
* @return the selected data model for this wrapper.
*/
public DataModel getSelectedDataModel() {
DataModelList modelList = getDataModelList();
return modelList.getSelectedModel();
}
/**
* Sets the data model.
*
* @param dataModel the data model to set.
*/
public void setDataModel(DataModel dataModel) {
if (dataModel == null) {
dataModel = new BoxDataSet(new VerticalDoubleDataBox(0, 0), new LinkedList<>());
}
if (dataModel instanceof DataModelList) {
this.dataModelList = (DataModelList) dataModel;
} else {
DataModelList dataModelList = new DataModelList();
dataModelList.add(dataModel);
this.dataModelList = dataModelList;
}
}
/**
* @return the knowledge for this wrapper.
*/
public Knowledge getKnowledge() {
return getSelectedDataModel().getKnowledge().copy();
}
/**
* Sets knowledge to a copy of the given object.
*
* @param knowledge the knowledge to set.
*/
public void setKnowledge(Knowledge knowledge) {
getSelectedDataModel().setKnowledge(knowledge.copy());
}
/**
* @return the variable names of the selected data model.
*/
public List getVarNames() {
return getSelectedDataModel().getVariableNames();
}
/**
* @return the source graph.
*/
public Graph getSourceGraph() {
return this.sourceGraph;
}
/**
* Sets the source graph.
*
* @param sourceGraph the source graph to set.
*/
protected void setSourceGraph(Graph sourceGraph) {
this.sourceGraph = sourceGraph;
}
/**
* @return the result graph.
*/
public Graph getResultGraph() {
return getSourceGraph();
}
/**
* @return the variables, in order.
*/
public List getVariables() {
return this.getSelectedDataModel().getVariables();
}
/**
* 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.
*/
@Serial
private void readObject(ObjectInputStream s)
throws IOException, ClassNotFoundException {
s.defaultReadObject();
}
/**
* @return the name of the data wrapper.
*/
public String getName() {
return this.name;
}
/**
* Sets the name of the data wrapper.
*
* @param name the name to set.
*/
public void setName(String name) {
this.name = name;
}
/**
* Returns the parameters being edited.
*
* @return the parameters being edited.
*/
public Parameters getParams() {
return this.parameters;
}
/**
* Sets the parameters being edited.
*
* @param parameters the parameters to set.
*/
public void setParameters(Parameters parameters) {
this.parameters = parameters;
}
/**
* Returns the variable names.
*
* @return the variable names.
*/
public List getVariableNames() {
List variableNames = new ArrayList<>();
for (Node n : getVariables()) {
variableNames.add(n.getName());
}
return variableNames;
}
/**
* Returns the parameter setting map.
*
* @return the parameter setting map.
*/
@Override
public Map getParamSettings() {
Map paramSettings = new HashMap<>();
if (this.dataModelList == null) {
System.out.println();
}
if (this.dataModelList.size() > 1) {
paramSettings.put("# Datasets", Integer.toString(this.dataModelList.size()));
} else {
DataModel dataModel = this.dataModelList.get(0);
if (dataModel instanceof CovarianceMatrix) {
paramSettings.put("# Vars", Integer.toString(((CovarianceMatrix) dataModel).getDimension()));
paramSettings.put("N", Integer.toString(((CovarianceMatrix) dataModel).getSampleSize()));
} else {
paramSettings.put("# Vars", Integer.toString(((DataSet) dataModel).getNumColumns()));
paramSettings.put("N", Integer.toString(((DataSet) dataModel).getNumRows()));
}
}
return paramSettings;
}
/**
* Returns the parameter setting map.
*
* @return the parameter setting map.
*/
@Override
public Map getAllParamSettings() {
return this.allParamSettings;
}
/**
* Sets the parameter setting map.
*
* @param paramSettings the parameter setting map to set.
*/
@Override
public void setAllParamSettings(Map paramSettings) {
this.allParamSettings = paramSettings;
}
}
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