// Causes a package cycle.
public void testManualDiscretize2() {
Graph graph = new Dag(GraphUtils.randomGraph(5, 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.setVariablesCopied(true);
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();
System.out.println(discretized);
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));
}
@Test
public void test2() {
RandomUtil.getInstance().setSeed(2999983L);
int sampleSize = 1000;
List<Node> variableNodes = new ArrayList<>();
ContinuousVariable x1 = new ContinuousVariable("X1");
ContinuousVariable x2 = new ContinuousVariable("X2");
ContinuousVariable x3 = new ContinuousVariable("X3");
ContinuousVariable x4 = new ContinuousVariable("X4");
ContinuousVariable x5 = new ContinuousVariable("X5");
variableNodes.add(x1);
variableNodes.add(x2);
variableNodes.add(x3);
variableNodes.add(x4);
variableNodes.add(x5);
Graph _graph = new EdgeListGraph(variableNodes);
SemGraph graph = new SemGraph(_graph);
graph.addDirectedEdge(x1, x3);
graph.addDirectedEdge(x2, x3);
graph.addDirectedEdge(x3, x4);
graph.addDirectedEdge(x2, x4);
graph.addDirectedEdge(x4, x5);
graph.addDirectedEdge(x2, x5);
SemPm semPm = new SemPm(graph);
SemIm semIm = new SemIm(semPm);
DataSet dataSet = semIm.simulateData(sampleSize, false);
print(semPm);
GeneralizedSemPm _semPm = new GeneralizedSemPm(semPm);
GeneralizedSemIm _semIm = new GeneralizedSemIm(_semPm, semIm);
DataSet _dataSet = _semIm.simulateDataMinimizeSurface(sampleSize, false);
print(_semPm);
// System.out.println(_dataSet);
for (int j = 0; j < dataSet.getNumColumns(); j++) {
double[] col = dataSet.getDoubleData().getColumn(j).toArray();
double[] _col = _dataSet.getDoubleData().getColumn(j).toArray();
double mean = StatUtils.mean(col);
double _mean = StatUtils.mean(_col);
double variance = StatUtils.variance(col);
double _variance = StatUtils.variance(_col);
assertEquals(mean, _mean, 0.3);
assertEquals(1.0, variance / _variance, .2);
}
}
public void rtestDSeparation4() {
Graph graph = new Dag(GraphUtils.randomGraph(100, 20, 100, 5, 5, 5, false));
long start, stop;
int depth = -1;
IndependenceTest test = new IndTestDSep(graph);
Rfci fci = new Rfci(test);
Fas fas = new Fas(test);
start = System.currentTimeMillis();
fci.setDepth(depth);
fci.setVerbose(true);
fci.search(fas, fas.getNodes());
stop = System.currentTimeMillis();
System.out.println("DSEP RFCI");
System.out.println("# dsep checks = " + fas.getNumIndependenceTests());
System.out.println("Elapsed " + (stop - start));
System.out.println("Per " + fas.getNumIndependenceTests() / (double) (stop - start));
SemPm pm = new SemPm(graph);
SemIm im = new SemIm(pm);
DataSet data = im.simulateData(1000, false);
IndependenceTest test2 = new IndTestFisherZ(data, 0.001);
Rfci fci3 = new Rfci(test2);
Fas fas2 = new Fas(test2);
start = System.currentTimeMillis();
fci3.setDepth(depth);
fci3.search(fas2, fas2.getNodes());
stop = System.currentTimeMillis();
System.out.println("FISHER Z RFCI");
System.out.println("# indep checks = " + fas.getNumIndependenceTests());
System.out.println("Elapsed " + (stop - start));
System.out.println("Per " + fas.getNumIndependenceTests() / (double) (stop - start));
}
public void testManualDiscretize3() {
Graph graph = new Dag(GraphUtils.randomGraph(5, 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.setVariablesCopied(true);
discretizer.setVariablesCopied(true);
discretizer.equalCounts(nodes.get(0), 3);
DataSet discretized = discretizer.discretize();
System.out.println(discretized);
assertTrue(discretized.getVariable(0) instanceof DiscreteVariable);
assertTrue(discretized.getVariable(1) instanceof ContinuousVariable);
assertTrue(discretized.getVariable(2) instanceof ContinuousVariable);
assertTrue(discretized.getVariable(3) instanceof ContinuousVariable);
assertTrue(discretized.getVariable(4) instanceof ContinuousVariable);
}
public void test1() {
for (int r = 0; r < 1; r++) {
Graph mim = DataGraphUtils.randomSingleFactorModel(5, 5, 6, 0, 0, 0);
Graph mimStructure = structure(mim);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(.5, 1.5);
SemPm pm = new SemPm(mim);
SemIm im = new SemIm(pm, params);
DataSet data = im.simulateData(300, false);
String algorithm = "FOFC";
Graph searchGraph;
List<List<Node>> partition;
if (algorithm.equals("FOFC")) {
FindOneFactorClusters fofc =
new FindOneFactorClusters(data, TestType.TETRAD_WISHART, 0.001);
searchGraph = fofc.search();
partition = fofc.getClusters();
} else if (algorithm.equals("BPC")) {
TestType testType = TestType.TETRAD_WISHART;
TestType purifyType = TestType.TETRAD_BASED2;
BuildPureClusters bpc = new BuildPureClusters(data, 0.001, testType, purifyType);
searchGraph = bpc.search();
partition = MimUtils.convertToClusters2(searchGraph);
} else {
throw new IllegalStateException();
}
List<String> latentVarList = reidentifyVariables(mim, data, partition, 2);
System.out.println(partition);
System.out.println(latentVarList);
System.out.println("True\n" + mimStructure);
Graph mimbuildStructure;
for (int mimbuildMethod : new int[] {3, 4}) {
if (mimbuildMethod == 1) {
System.out.println("Mimbuild 1\n");
Clusters measurements = ClusterUtils.mimClusters(searchGraph);
IndTestMimBuild test = new IndTestMimBuild(data, 0.001, measurements);
MimBuild mimbuild = new MimBuild(test, new Knowledge2());
Graph full = mimbuild.search();
full = changeLatentNames(full, measurements, latentVarList);
mimbuildStructure = structure(full);
System.out.println(
"SHD = "
+ SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
System.out.println("Estimated\n" + mimbuildStructure);
System.out.println();
}
// else if (mimbuildMethod == 2) {
// System.out.println("Mimbuild 2\n");
// Mimbuild2 mimbuild = new Mimbuild2();
// mimbuild.setAlpha(0.001);
// mimbuildStructure = mimbuild.search(partition, latentVarList, data);
// TetradMatrix latentcov = mimbuild.getLatentsCov();
// List<String> latentnames = mimbuild.getLatentNames();
// System.out.println("\nCovariance over the latents");
// System.out.println(MatrixUtils.toStringSquare(latentcov.toArray(),
// latentnames));
// System.out.println("Estimated\n" + mimbuildStructure);
// System.out.println("SHD = " +
// SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
// System.out.println();
// }
else if (mimbuildMethod == 3) {
System.out.println("Mimbuild 3\n");
Mimbuild2 mimbuild = new Mimbuild2();
mimbuild.setAlpha(0.001);
mimbuild.setMinClusterSize(3);
mimbuildStructure = mimbuild.search(partition, latentVarList, new CovarianceMatrix(data));
ICovarianceMatrix latentcov = mimbuild.getLatentsCov();
System.out.println("\nCovariance over the latents");
System.out.println(latentcov);
System.out.println("Estimated\n" + mimbuildStructure);
System.out.println(
"SHD = "
+ SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
System.out.println();
} else if (mimbuildMethod == 4) {
System.out.println("Mimbuild Trek\n");
MimbuildTrek mimbuild = new MimbuildTrek();
mimbuild.setAlpha(0.1);
mimbuild.setMinClusterSize(3);
mimbuildStructure = mimbuild.search(partition, latentVarList, new CovarianceMatrix(data));
ICovarianceMatrix latentcov = mimbuild.getLatentsCov();
System.out.println("\nCovariance over the latents");
System.out.println(latentcov);
System.out.println("Estimated\n" + mimbuildStructure);
System.out.println(
"SHD = "
+ SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure));
System.out.println();
} else {
throw new IllegalStateException();
}
}
}
}
public void rtest4() {
System.out.println("SHD\tP");
// System.out.println("MB1\tMB2\tMB3\tMB4\tMB5\tMB6");
Graph mim = DataGraphUtils.randomSingleFactorModel(5, 5, 8, 0, 0, 0);
Graph mimStructure = structure(mim);
SemPm pm = new SemPm(mim);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(0.5, 1.5);
NumberFormat nf = new DecimalFormat("0.0000");
int totalError = 0;
int errorCount = 0;
int maxScore = 0;
int maxNumMeasures = 0;
double maxP = 0.0;
for (int r = 0; r < 1; r++) {
SemIm im = new SemIm(pm, params);
DataSet data = im.simulateData(1000, false);
mim = GraphUtils.replaceNodes(mim, data.getVariables());
List<List<Node>> trueClusters = MimUtils.convertToClusters2(mim);
CovarianceMatrix _cov = new CovarianceMatrix(data);
ICovarianceMatrix cov = DataUtils.reorderColumns(_cov);
String algorithm = "FOFC";
Graph searchGraph;
List<List<Node>> partition;
if (algorithm.equals("FOFC")) {
FindOneFactorClusters fofc =
new FindOneFactorClusters(cov, TestType.TETRAD_WISHART, 0.001f);
searchGraph = fofc.search();
searchGraph = GraphUtils.replaceNodes(searchGraph, data.getVariables());
partition = MimUtils.convertToClusters2(searchGraph);
} else if (algorithm.equals("BPC")) {
TestType testType = TestType.TETRAD_WISHART;
TestType purifyType = TestType.TETRAD_BASED2;
BuildPureClusters bpc = new BuildPureClusters(data, 0.001, testType, purifyType);
searchGraph = bpc.search();
partition = MimUtils.convertToClusters2(searchGraph);
} else {
throw new IllegalStateException();
}
mimStructure = GraphUtils.replaceNodes(mimStructure, data.getVariables());
List<String> latentVarList = reidentifyVariables(mim, data, partition, 2);
Graph mimbuildStructure;
Mimbuild2 mimbuild = new Mimbuild2();
mimbuild.setAlpha(0.001);
mimbuild.setMinClusterSize(3);
try {
mimbuildStructure = mimbuild.search(partition, latentVarList, cov);
} catch (Exception e) {
e.printStackTrace();
continue;
}
mimbuildStructure = GraphUtils.replaceNodes(mimbuildStructure, data.getVariables());
mimbuildStructure = condense(mimStructure, mimbuildStructure);
// Graph mimSubgraph = new EdgeListGraph(mimStructure);
//
// for (Node node : mimSubgraph.getNodes()) {
// if (!mimStructure.getNodes().contains(node)) {
// mimSubgraph.removeNode(node);
// }
// }
int shd = SearchGraphUtils.structuralHammingDistance(mimStructure, mimbuildStructure);
boolean impureCluster = containsImpureCluster(partition, trueClusters);
double pValue = mimbuild.getpValue();
boolean pBelow05 = pValue < 0.05;
boolean numClustersGreaterThan5 = partition.size() != 5;
boolean error = false;
// boolean condition = impureCluster || numClustersGreaterThan5 || pBelow05;
// boolean condition = numClustersGreaterThan5 || pBelow05;
boolean condition = numClustered(partition) == 40;
if (!condition && (shd > 5)) {
error = true;
}
if (!condition) {
totalError += shd;
errorCount++;
}
// if (numClustered(partition) > maxNumMeasures) {
// maxNumMeasures = numClustered(partition);
// maxP = pValue;
// maxScore = shd;
// System.out.println("maxNumMeasures = " + maxNumMeasures);
// System.out.println("maxScore = " + maxScore);
// System.out.println("maxP = " + maxP);
// System.out.println("clusters = " + clusterSizes(partition, trueClusters));
// }
// else
if (pValue > maxP) {
maxScore = shd;
maxP = mimbuild.getpValue();
maxNumMeasures = numClustered(partition);
System.out.println("maxNumMeasures = " + maxNumMeasures);
System.out.println("maxScore = " + maxScore);
System.out.println("maxP = " + maxP);
System.out.println("clusters = " + clusterSizes(partition, trueClusters));
}
System.out.print(
shd
+ "\t"
+ nf.format(pValue)
+ " "
// + (error ? 1 : 0) + " "
// + (pBelow05 ? "P < 0.05 " : "")
// + (impureCluster ? "Impure cluster " : "")
// + (numClustersGreaterThan5 ? "# Clusters != 5 " : "")
// + clusterSizes(partition, trueClusters)
+ numClustered(partition));
System.out.println();
}
System.out.println("\nAvg SHD for not-flagged cases = " + (totalError / (double) errorCount));
System.out.println("maxNumMeasures = " + maxNumMeasures);
System.out.println("maxScore = " + maxScore);
System.out.println("maxP = " + maxP);
}
public void rtest3() {
Node x = new GraphNode("X");
Node y = new GraphNode("Y");
Node z = new GraphNode("Z");
Node w = new GraphNode("W");
List<Node> nodes = new ArrayList<Node>();
nodes.add(x);
nodes.add(y);
nodes.add(z);
nodes.add(w);
Graph g = new EdgeListGraph(nodes);
g.addDirectedEdge(x, y);
g.addDirectedEdge(x, z);
g.addDirectedEdge(y, w);
g.addDirectedEdge(z, w);
Graph maxGraph = null;
double maxPValue = -1.0;
ICovarianceMatrix maxLatentCov = null;
Graph mim = DataGraphUtils.randomMim(g, 8, 0, 0, 0, true);
// Graph mim = DataGraphUtils.randomSingleFactorModel(5, 5, 8, 0, 0, 0);
Graph mimStructure = structure(mim);
SemPm pm = new SemPm(mim);
System.out.println("\n\nTrue graph:");
System.out.println(mimStructure);
SemImInitializationParams params = new SemImInitializationParams();
params.setCoefRange(0.5, 1.5);
SemIm im = new SemIm(pm, params);
int N = 1000;
DataSet data = im.simulateData(N, false);
CovarianceMatrix cov = new CovarianceMatrix(data);
for (int i = 0; i < 1; i++) {
ICovarianceMatrix _cov = DataUtils.reorderColumns(cov);
List<List<Node>> partition;
FindOneFactorClusters fofc = new FindOneFactorClusters(_cov, TestType.TETRAD_WISHART, .001);
fofc.search();
partition = fofc.getClusters();
System.out.println(partition);
List<String> latentVarList = reidentifyVariables(mim, data, partition, 2);
Mimbuild2 mimbuild = new Mimbuild2();
mimbuild.setAlpha(0.001);
// mimbuild.setMinimumSize(5);
// To test knowledge.
// Knowledge knowledge = new Knowledge2();
// knowledge.setEdgeForbidden("L.Y", "L.W", true);
// knowledge.setEdgeRequired("L.Y", "L.Z", true);
// mimbuild.setKnowledge(knowledge);
Graph mimbuildStructure = mimbuild.search(partition, latentVarList, _cov);
double pValue = mimbuild.getpValue();
System.out.println(mimbuildStructure);
System.out.println("P = " + pValue);
System.out.println("Latent Cov = " + mimbuild.getLatentsCov());
if (pValue > maxPValue) {
maxPValue = pValue;
maxGraph = new EdgeListGraph(mimbuildStructure);
maxLatentCov = mimbuild.getLatentsCov();
}
}
System.out.println("\n\nTrue graph:");
System.out.println(mimStructure);
System.out.println("\nBest graph:");
System.out.println(maxGraph);
System.out.println("P = " + maxPValue);
System.out.println("Latent Cov = " + maxLatentCov);
System.out.println();
}
@Test
public void testHistogram() {
RandomUtil.getInstance().setSeed(4829384L);
List<Node> nodes = new ArrayList<Node>();
for (int i = 0; i < 5; i++) {
nodes.add(new ContinuousVariable("X" + (i + 1)));
}
Dag trueGraph = new Dag(GraphUtils.randomGraph(nodes, 0, 5, 30, 15, 15, false));
int sampleSize = 1000;
// Continuous
SemPm semPm = new SemPm(trueGraph);
SemIm semIm = new SemIm(semPm);
DataSet data = semIm.simulateData(sampleSize, false);
Histogram histogram = new Histogram(data);
histogram.setTarget("X1");
histogram.setNumBins(20);
assertEquals(3.76, histogram.getMax(), 0.01);
assertEquals(-3.83, histogram.getMin(), 0.01);
assertEquals(1000, histogram.getN());
histogram.setTarget("X1");
histogram.setNumBins(10);
histogram.addConditioningVariable("X3", 0, 1);
histogram.addConditioningVariable("X4", 0, 1);
histogram.removeConditioningVariable("X3");
assertEquals(3.76, histogram.getMax(), 0.01);
assertEquals(-3.83, histogram.getMin(), 0.01);
assertEquals(188, histogram.getN());
double[] arr = histogram.getContinuousData("X2");
histogram.addConditioningVariable("X2", StatUtils.min(arr), StatUtils.mean(arr));
// Discrete
BayesPm bayesPm = new BayesPm(trueGraph);
BayesIm bayesIm = new MlBayesIm(bayesPm, MlBayesIm.RANDOM);
DataSet data2 = bayesIm.simulateData(sampleSize, false);
// For some reason these are giving different
// values when all of the unit tests are run are
// once. TODO They produce stable values when
// this particular test is run repeatedly.
Histogram histogram2 = new Histogram(data2);
histogram2.setTarget("X1");
int[] frequencies1 = histogram2.getFrequencies();
// assertEquals(928, frequencies1[0]);
// assertEquals(72, frequencies1[1]);
histogram2.setTarget("X1");
histogram2.addConditioningVariable("X2", 0);
histogram2.addConditioningVariable("X3", 1);
int[] frequencies = histogram2.getFrequencies();
// assertEquals(377, frequencies[0]);
// assertEquals(28, frequencies[1]);
}