use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.
the class PerformanceTestsDan method testIdaOutputForDan.
private void testIdaOutputForDan() {
int numRuns = 100;
for (int run = 0; run < numRuns; run++) {
double alphaGFci = 0.01;
double alphaPc = 0.01;
int penaltyDiscount = 1;
int depth = 3;
int maxPathLength = 3;
final int numVars = 15;
final double edgesPerNode = 1.0;
final int numCases = 1000;
// final int numLatents = RandomUtil.getInstance().nextInt(3) + 1;
final int numLatents = 6;
// writeToFile = false;
PrintStream out1;
PrintStream out2;
PrintStream out3;
PrintStream out4;
PrintStream out5;
PrintStream out6;
PrintStream out7;
PrintStream out8;
PrintStream out9;
PrintStream out10;
PrintStream out11;
PrintStream out12;
File dir0 = new File("gfci.output");
dir0.mkdirs();
File dir = new File(dir0, "" + (run + 1));
dir.mkdir();
try {
out1 = new PrintStream(new File(dir, "hyperparameters.txt"));
out2 = new PrintStream(new File(dir, "variables.txt"));
out3 = new PrintStream(new File(dir, "dag.long.txt"));
out4 = new PrintStream(new File(dir, "dag.matrix.txt"));
out5 = new PrintStream(new File(dir, "coef.matrix.txt"));
out6 = new PrintStream(new File(dir, "pag.long.txt"));
out7 = new PrintStream(new File(dir, "pag.matrix.txt"));
out8 = new PrintStream(new File(dir, "pattern.long.txt"));
out9 = new PrintStream(new File(dir, "pattern.matrix.txt"));
out10 = new PrintStream(new File(dir, "data.txt"));
out11 = new PrintStream(new File(dir, "true.pag.long.txt"));
out12 = new PrintStream(new File(dir, "true.pag.matrix.txt"));
} catch (FileNotFoundException e) {
e.printStackTrace();
throw new RuntimeException(e);
}
out1.println("Num _vars = " + numVars);
out1.println("Num edges = " + (int) (numVars * edgesPerNode));
out1.println("Num cases = " + numCases);
out1.println("Alpha for PC = " + alphaPc);
out1.println("Alpha for FFCI = " + alphaGFci);
out1.println("Penalty discount = " + penaltyDiscount);
out1.println("Depth = " + depth);
out1.println("Maximum reachable path length for dsep search and discriminating undirectedPaths = " + maxPathLength);
List<Node> vars = new ArrayList<>();
for (int i = 0; i < numVars; i++) vars.add(new GraphNode("X" + (i + 1)));
// Graph dag = DataGraphUtils.randomDagQuick2(varsWithLatents, 0, (int) (varsWithLatents.size() * edgesPerNode));
Graph dag = GraphUtils.randomGraph(vars, 0, (int) (vars.size() * edgesPerNode), 5, 5, 5, false);
GraphUtils.fixLatents1(numLatents, dag);
// List<Node> varsWithLatents = new ArrayList<Node>();
//
// Graph dag = getLatentGraph(_vars, varsWithLatents, edgesPerNode, numLatents);
out3.println(dag);
printDanMatrix(vars, dag, out4);
SemPm pm = new SemPm(dag);
SemIm im = new SemIm(pm);
NumberFormat nf = new DecimalFormat("0.0000");
for (int i = 0; i < vars.size(); i++) {
for (Node var : vars) {
if (im.existsEdgeCoef(var, vars.get(i))) {
double coef = im.getEdgeCoef(var, vars.get(i));
out5.print(nf.format(coef) + "\t");
} else {
out5.print(nf.format(0) + "\t");
}
}
out5.println();
}
out5.println();
String vars_temp = vars.toString();
vars_temp = vars_temp.replace("[", "");
vars_temp = vars_temp.replace("]", "");
vars_temp = vars_temp.replace("X", "");
out2.println(vars_temp);
List<Node> _vars = new ArrayList<>();
for (Node node : vars) {
if (node.getNodeType() == NodeType.MEASURED) {
_vars.add(node);
}
}
String _vars_temp = _vars.toString();
_vars_temp = _vars_temp.replace("[", "");
_vars_temp = _vars_temp.replace("]", "");
_vars_temp = _vars_temp.replace("X", "");
out2.println(_vars_temp);
DataSet fullData = im.simulateData(numCases, false);
DataSet data = DataUtils.restrictToMeasured(fullData);
ICovarianceMatrix cov = new CovarianceMatrix(data);
final IndTestFisherZ independenceTestGFci = new IndTestFisherZ(cov, alphaGFci);
final edu.cmu.tetrad.search.SemBicScore scoreGfci = new edu.cmu.tetrad.search.SemBicScore(cov);
out6.println("GFCI.PAG_of_the_true_DAG");
GFci gFci = new GFci(independenceTestGFci, scoreGfci);
gFci.setVerbose(false);
gFci.setMaxDegree(depth);
gFci.setMaxPathLength(maxPathLength);
// gFci.setPossibleDsepSearchDone(true);
gFci.setCompleteRuleSetUsed(true);
Graph pag = gFci.search();
out6.println(pag);
printDanMatrix(_vars, pag, out7);
out8.println("Pattern_of_the_true_DAG OVER MEASURED VARIABLES");
final IndTestFisherZ independencePc = new IndTestFisherZ(cov, alphaPc);
Pc pc = new Pc(independencePc);
pc.setVerbose(false);
pc.setDepth(depth);
Graph pattern = pc.search();
out8.println(pattern);
printDanMatrix(_vars, pattern, out9);
out10.println(data);
out11.println("True PAG_of_the_true_DAG");
final Graph truePag = new DagToPag(dag).convert();
out11.println(truePag);
printDanMatrix(_vars, truePag, out12);
out1.close();
out2.close();
out3.close();
out4.close();
out5.close();
out6.close();
out7.close();
out8.close();
out9.close();
out10.close();
out11.close();
out12.close();
}
}
use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.
the class Washdown method gof.
/**
* @return the p value of the given model.
*/
private double gof(List<List<Node>> clusters) {
clusters = removeEmpty(clusters);
Graph graph = pureMeasurementModel(clusters);
SemPm pm = new SemPm(graph);
SemEstimator estimator;
if (cov != null) {
estimator = new SemEstimator(cov, pm);
} else {
estimator = new SemEstimator(dataSet, pm);
}
SemIm est = estimator.estimate();
return est.getBicScore();
}
use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.
the class FactorAnalysisAction method main.
public static void main(String[] args) {
java.util.List<Node> nodes = new ArrayList<>();
for (int i = 0; i < 9; i++) {
nodes.add(new ContinuousVariable("X" + (i + 1)));
}
Graph graph = new Dag(GraphUtils.randomGraph(nodes, 0, 9, 30, 15, 15, false));
SemPm pm = new SemPm(graph);
SemIm im = new SemIm(pm);
DataSet data = im.simulateData(500, false);
ICovarianceMatrix cov = new CovarianceMatrix(data);
FactorAnalysis factorAnalysis = new FactorAnalysis(cov);
// factorAnalysis.centroidUnity();
factorAnalysis.successiveResidual();
}
use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.
the class SemEstimatorWrapper method setParams.
private boolean setParams(SemPmWrapper semPmWrapper, DataModelList dataModel) {
for (DataModel model : dataModel) {
if (model instanceof DataSet) {
DataSet dataSet = (DataSet) model;
SemPm semPm = semPmWrapper.getSemPm();
this.semPm = semPm;
SemEstimator estimator = new SemEstimator(dataSet, semPm, getOptimizer());
estimator.setNumRestarts(getParams().getInt("numRestarts", 1));
estimator.setScoreType((ScoreType) getParams().get("scoreType", ScoreType.Fgls));
if (!degreesOfFreedomCheck(semPm)) {
return true;
}
estimator.estimate();
getMultipleResultList().add(estimator);
} else if (model instanceof ICovarianceMatrix) {
ICovarianceMatrix covMatrix = new CovarianceMatrix((ICovarianceMatrix) model);
SemPm semPm = semPmWrapper.getSemPm();
this.semPm = semPm;
SemEstimator estimator = new SemEstimator(covMatrix, semPm, getOptimizer());
estimator.setNumRestarts(getParams().getInt("numRestarts", 1));
estimator.setScoreType((ScoreType) getParams().get("scoreType", ScoreType.Fgls));
if (!degreesOfFreedomCheck(semPm)) {
return true;
}
estimator.estimate();
getMultipleResultList().add(estimator);
} else {
throw new IllegalArgumentException("Data must consist of continuous data sets or covariance matrices.");
}
}
return false;
}
use of edu.cmu.tetrad.sem.SemPm in project tetrad by cmu-phil.
the class TestDiscretizer method testManualDiscretize2.
@Test
public void testManualDiscretize2() {
List<Node> nodes1 = new ArrayList<>();
for (int i = 0; i < 5; i++) {
nodes1.add(new ContinuousVariable("X" + (i + 1)));
}
Graph graph = new Dag(GraphUtils.randomGraph(nodes1, 0, 5, 3, 3, 3, false));
SemPm pm = new SemPm(graph);
SemIm im = new SemIm(pm);
DataSet data = im.simulateData(100, false);
List<Node> nodes = data.getVariables();
Discretizer discretizer = new Discretizer(data);
discretizer.equalCounts(nodes.get(0), 3);
discretizer.equalIntervals(nodes.get(1), 2);
discretizer.equalCounts(nodes.get(2), 5);
discretizer.equalIntervals(nodes.get(3), 8);
discretizer.equalCounts(nodes.get(4), 4);
DataSet discretized = discretizer.discretize();
assertEquals(2, maxInColumn(discretized, 0));
assertEquals(1, maxInColumn(discretized, 1));
assertEquals(4, maxInColumn(discretized, 2));
assertEquals(7, maxInColumn(discretized, 3));
assertEquals(3, maxInColumn(discretized, 4));
}
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