edu.cmu.tetrad.algcomparison.algorithm.other.FactorAnalysis Maven / Gradle / Ivy
package edu.cmu.tetrad.algcomparison.algorithm.other;
import edu.cmu.tetrad.algcomparison.algorithm.Algorithm;
import edu.cmu.tetrad.annotation.Bootstrapping;
import edu.cmu.tetrad.data.ContinuousVariable;
import edu.cmu.tetrad.data.DataModel;
import edu.cmu.tetrad.data.DataSet;
import edu.cmu.tetrad.data.DataType;
import edu.cmu.tetrad.graph.*;
import edu.cmu.tetrad.util.*;
import edu.pitt.dbmi.algo.resampling.GeneralResamplingTest;
import org.apache.commons.math3.util.FastMath;
import java.text.NumberFormat;
import java.util.ArrayList;
import java.util.List;
import java.util.Vector;
/**
* Factor analysis.
*
* @author josephramsey
*/
@Bootstrapping
public class FactorAnalysis implements Algorithm {
private static final long serialVersionUID = 23L;
public Graph search(DataModel ds, Parameters parameters) {
if (parameters.getInt(Params.NUMBER_RESAMPLING) < 1) {
DataSet selectedModel = (DataSet) ds;
if (selectedModel == null) {
throw new NullPointerException("Data not specified.");
}
edu.cmu.tetrad.search.FactorAnalysis analysis = new edu.cmu.tetrad.search.FactorAnalysis(selectedModel);
analysis.setThreshold(parameters.getDouble("convergenceThreshold"));
analysis.setNumFactors(parameters.getInt("numFactors"));
double threshold = parameters.getDouble("fa_threshold");
Matrix unrotatedSolution = analysis.successiveResidual();
Matrix rotatedSolution = analysis.successiveFactorVarimax(unrotatedSolution);
SemGraph graph = new SemGraph();
Vector observedVariables = new Vector<>();
for (Node a : selectedModel.getVariables()) {
graph.addNode(a);
observedVariables.add(a);
}
Vector factors = new Vector<>();
if (parameters.getBoolean("useVarimax")) {
for (int i = 0; i < rotatedSolution.getNumColumns(); i++) {
ContinuousVariable factor = new ContinuousVariable("Factor" + (i + 1));
factor.setNodeType(NodeType.LATENT);
graph.addNode(factor);
factors.add(factor);
}
for (int i = 0; i < rotatedSolution.getNumRows(); i++) {
for (int j = 0; j < rotatedSolution.getNumColumns(); j++) {
if (FastMath.abs(rotatedSolution.get(i, j)) > threshold) {
graph.addDirectedEdge(factors.get(j), observedVariables.get(i));
}
}
}
} else {
for (int i = 0; i < unrotatedSolution.getNumColumns(); i++) {
ContinuousVariable factor = new ContinuousVariable("Factor" + (i + 1));
factor.setNodeType(NodeType.LATENT);
graph.addNode(factor);
factors.add(factor);
}
for (int i = 0; i < unrotatedSolution.getNumRows(); i++) {
for (int j = 0; j < unrotatedSolution.getNumColumns(); j++) {
if (FastMath.abs(unrotatedSolution.get(i, j)) > threshold) {
graph.addDirectedEdge(factors.get(j), observedVariables.get(i));
}
}
}
}
if (parameters.getBoolean(Params.VERBOSE)) {
NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
String output = "Unrotated Factor Loading Matrix:\n";
output += tableString(unrotatedSolution, nf, Double.POSITIVE_INFINITY);
if (unrotatedSolution.getNumColumns() != 1) {
output += "\n\nRotated Matrix (using sequential varimax):\n";
output += tableString(rotatedSolution, nf, threshold);
}
System.out.println(output);
TetradLogger.getInstance().forceLogMessage(output);
}
return graph;
} else {
FactorAnalysis algorithm = new FactorAnalysis();
DataSet data = (DataSet) ds;
GeneralResamplingTest search = new GeneralResamplingTest(data, algorithm, parameters.getInt(Params.NUMBER_RESAMPLING), parameters.getDouble(Params.PERCENT_RESAMPLE_SIZE), parameters.getBoolean(Params.RESAMPLING_WITH_REPLACEMENT), parameters.getInt(Params.RESAMPLING_ENSEMBLE), parameters.getBoolean(Params.ADD_ORIGINAL_DATASET));
search.setParameters(parameters);
search.setVerbose(parameters.getBoolean(Params.VERBOSE));
return search.search();
}
}
private String tableString(Matrix matrix, NumberFormat nf, double threshold) {
TextTable table = new TextTable(matrix.getNumRows() + 1, matrix.getNumColumns() + 1);
for (int i = 0; i < matrix.getNumRows() + 1; i++) {
for (int j = 0; j < matrix.getNumColumns() + 1; j++) {
if (i > 0 && j == 0) {
table.setToken(i, 0, "X" + i);
} else if (i == 0 && j > 0) {
table.setToken(0, j, "Factor " + j);
} else if (i > 0) {
double coefficient = matrix.get(i - 1, j - 1);
String token = !Double.isNaN(coefficient) ? nf.format(coefficient) : "Undefined";
token += FastMath.abs(coefficient) > threshold ? "*" : " ";
table.setToken(i, j, token);
}
}
}
return "\n" + table;
}
public Graph getComparisonGraph(Graph graph) {
return GraphUtils.undirectedGraph(graph);
}
public String getDescription() {
return "GLASSO (Graphical LASSO)";
}
@Override
public DataType getDataType() {
return DataType.Mixed;
}
@Override
public List getParameters() {
List params = new ArrayList<>();
params.add("fa_threshold");
params.add("numFactors");
params.add("useVarimax");
params.add("convergenceThreshold");
params.add(Params.VERBOSE);
return params;
}
}