edu.cmu.tetradapp.model.IndTestChooser Maven / Gradle / Ivy
The newest version!
///////////////////////////////////////////////////////////////////////////////
// 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.search.IndependenceTest;
import edu.cmu.tetrad.search.score.ImagesScore;
import edu.cmu.tetrad.search.score.Score;
import edu.cmu.tetrad.search.score.SemBicScore;
import edu.cmu.tetrad.search.test.*;
import edu.cmu.tetrad.search.utils.ResolveSepsets;
import edu.cmu.tetrad.search.work_in_progress.IndTestFisherZPercentIndependent;
import edu.cmu.tetrad.search.work_in_progress.IndTestMultinomialLogisticRegression;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.Params;
import edu.cmu.tetradapp.util.IndTestType;
import edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald;
import java.util.ArrayList;
import java.util.List;
/**
* Chooses an independence test for a particular data source.
*
* @author josephramsey
*/
final class IndTestChooser {
private boolean precomputeCovariances = true;
/**
* getTest.
*
* @param dataSource a {@link java.lang.Object} object
* @param params a {@link edu.cmu.tetrad.util.Parameters} object
* @return a {@link edu.cmu.tetrad.search.IndependenceTest} object
*/
public IndependenceTest getTest(Object dataSource, Parameters params) {
return getTest(dataSource, params, IndTestType.DEFAULT);
}
/**
* getTest.
*
* @param dataSource a {@link java.lang.Object} object
* @param params a {@link edu.cmu.tetrad.util.Parameters} object
* @param testType a {@link edu.cmu.tetradapp.util.IndTestType} object
* @return an independence checker appropriate to the given data source. Also sets the Parameters on the params to
* an appropriate type object (using the existing one if it's of the right type).
*/
public IndependenceTest getTest(Object dataSource, Parameters params,
IndTestType testType) {
if (dataSource == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
if (dataSource instanceof DataModelList datasets) {
List _dataSets = new ArrayList<>();
for (DataModel dataModel : datasets) {
_dataSets.add((DataSet) dataModel);
}
return getMultiContinuousTest(_dataSets, params, testType);
}
if (dataSource instanceof DataSet dataSet) {
if (dataSet.isContinuous() || dataSet.getNumColumns() == 0) {
DataSet dataContinuous =
(DataSet) dataSource;
return getContinuousTest(dataContinuous, params, testType);
} else if (dataSet.isDiscrete()) {
DataSet dataDiscrete =
(DataSet) dataSource;
return getDiscreteTest(dataDiscrete, params, testType);
}
if (dataSet.isMixed()) {
DataSet dataMixed = (DataSet) dataSource;
return getMixedTest(dataMixed, params, testType);
}
}
if (dataSource instanceof Graph) {
return getGraphTest((Graph) dataSource, params,
IndTestType.M_SEPARATION);
}
if (dataSource instanceof ICovarianceMatrix) {
return getCovMatrixTest((ICovarianceMatrix) dataSource, params);
}
if (dataSource instanceof IndependenceFacts) {
return new IndTestIndependenceFacts((IndependenceFacts) dataSource);
}
throw new IllegalStateException(
"Unrecognized data source type: " + dataSource.getClass());
}
private IndependenceTest getMixedTest(DataSet dataSet,
Parameters params, IndTestType testType) {
if (IndTestType.MIXED_MLR == testType) {
return new IndTestMultinomialLogisticRegressionWald(dataSet, params.getDouble("alpha", 0.001), false);
} else if (IndTestType.LINEAR_REGRESSION == testType) {
return new IndTestRegression(dataSet,
params.getDouble("alpha", 0.001));
} else {
params.set("indTestType", IndTestType.MIXED_MLR);
return new IndTestMultinomialLogisticRegression(dataSet, params.getDouble("alpha", 0.001));
}
}
private IndependenceTest getContinuousTest(DataSet dataSet,
Parameters params, IndTestType testType) {
if (IndTestType.CONDITIONAL_CORRELATION == testType) {
return new IndTestConditionalCorrelation(dataSet, params.getDouble(Params.ALPHA),
params.getDouble(Params.SCALING_FACTOR), params.getInt(Params.BASIS_TYPE),
params.getInt(Params.NUM_BASIS_FUNCTIONS), params.getDouble(Params.BASIS_SCALE));
}
if (IndTestType.FISHER_Z == testType) {
return new IndTestFisherZ(dataSet, params.getDouble("alpha", 0.001));
}
// if (IndTestType.FISHER_ZD == testType) {
// IndTestFisherZ test = new IndTestFisherZ(dataSet, params.getDouble("alpha", 0.001));
//// test.setUsePseudoinverse(true);
// return test;
// }
if (IndTestType.SEM_BIC == testType) {
return new ScoreIndTest(new SemBicScore(new CovarianceMatrix(dataSet)));
}
{
params.set("indTestType", IndTestType.FISHER_Z);
return new IndTestFisherZ(dataSet, params.getDouble("alpha", 0.001));
}
}
private IndependenceTest getMultiContinuousTest(List dataSets,
Parameters params, IndTestType testType) {
if (IndTestType.POOL_RESIDUALS_FISHER_Z == testType) {
return new IndTestFisherZPercentIndependent(dataSets, params.getDouble("alpha", 0.001));
}
if (IndTestType.TIPPETT == testType) {
List independenceTests = new ArrayList<>();
for (DataSet dataModel : dataSets) {
independenceTests.add(new IndTestFisherZ(dataModel, params.getDouble("alpha",
0.001)));
}
return new IndTestMulti(independenceTests, ResolveSepsets.Method.tippett);
}
if (IndTestType.FISHER == testType) {
return new IndTestFisherZFisherPValue(dataSets, params.getDouble("alpha", 0.001));
}
if (IndTestType.SEM_BIC == testType) {
List scores = new ArrayList<>();
for (DataSet dataSet : dataSets) {
SemBicScore _score = new SemBicScore(dataSet, precomputeCovariances);
scores.add(_score);
}
ImagesScore imagesScore = new ImagesScore(scores);
return new ScoreIndTest(imagesScore);
}
{
return new IndTestFisherZConcatenateResiduals(dataSets, params.getDouble("alpha", 0.001));
}
}
private IndependenceTest getDiscreteTest(DataSet dataDiscrete, Parameters params, IndTestType testType) {
if (IndTestType.G_SQUARE == testType) {
return new IndTestGSquare(dataDiscrete, params.getDouble("alpha", 0.001));
}
if (IndTestType.CHI_SQUARE == testType) {
return new IndTestChiSquare(dataDiscrete, params.getDouble("alpha", 0.001));
}
if (IndTestType.MIXED_MLR == testType) {
return new IndTestMultinomialLogisticRegression(dataDiscrete, params.getDouble("alpha", 0.001));
} else {
params.set("indTestType", IndTestType.CHI_SQUARE);
return new IndTestChiSquare(dataDiscrete, params.getDouble("alpha", 0.001));
}
}
private IndependenceTest getGraphTest(Graph graph, Parameters params,
IndTestType testType) {
if (IndTestType.M_SEPARATION != testType) {
params.set("indTestType", IndTestType.M_SEPARATION);
}
return new MsepTest(graph);
}
private IndependenceTest getCovMatrixTest(ICovarianceMatrix covMatrix,
Parameters params) {
return new IndTestFisherZ(covMatrix,
params.getDouble("alpha", 0.001));
}
/**
* Setter for the field precomputeCovariances.
*
* @param precomputeCovariances a boolean
*/
public void setPrecomputeCovariances(boolean precomputeCovariances) {
this.precomputeCovariances = precomputeCovariances;
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy