new compatible rhmm

papers/21why/examples/PearlExample.java

@@ -0,0 +1,179 @@
+package examples;
+
+import ch.idsia.credici.IO;
+import ch.idsia.credici.factor.EquationBuilder;
+import ch.idsia.credici.inference.CausalMultiVE;
+import ch.idsia.credici.inference.CredalCausalVE;
+import ch.idsia.credici.model.StructuralCausalModel;
+import ch.idsia.credici.model.builder.CausalBuilder;
+import ch.idsia.credici.model.builder.EMCredalBuilder;
+import ch.idsia.credici.utility.DataUtil;
+import ch.idsia.credici.utility.FactorUtil;
+import ch.idsia.crema.factor.bayesian.BayesianFactor;
+import ch.idsia.crema.factor.credal.vertex.VertexFactor;
+import ch.idsia.crema.model.graphical.SparseModel;
+import ch.idsia.crema.model.graphical.specialized.BayesianNetwork;
+import gnu.trove.map.TIntIntMap;
+import jdk.jshell.spi.ExecutionControl;
+import org.apache.commons.lang3.ArrayUtils;
+import org.junit.Assert;
+
+import java.io.IOException;
+import java.nio.file.Path;
+import java.util.HashMap;
+import java.util.stream.IntStream;
+
+public class PearlExample {
+	public static void main(String[] args) throws InterruptedException, ExecutionControl.NotImplementedException, IOException {
+
+		String wdir = ".";
+		String folder = Path.of(wdir, "papers/21why/examples/").toString();
+
+		BayesianNetwork bnet = new BayesianNetwork();
+
+		int X = bnet.addVariable(2);
+		int Y = bnet.addVariable(2);
+		int Z = bnet.addVariable(2);
+
+		bnet.addParents(X, Z);
+		bnet.addParents(Y, X, Z);
+
+
+		// states x, y, z = True = 1
+		int x=1, y=1, z=1;
+
+		// states x', y', z' = False = 0
+		int x_=0, y_=0, z_=0;
+
+
+		BayesianFactor counts = new BayesianFactor(bnet.getDomain(Z,X,Y));
+		//new double[]{1,	13,	313,114, 109, 107, 41, 2});
+
+
+		counts.setValue(2, z_, x_, y_);
+		counts.setValue(114, z_, x_, y);
+		counts.setValue(41, z_, x, y_);
+		counts.setValue(313, z_, x, y);
+
+		counts.setValue(107, z, x_, y_);
+		counts.setValue(13, z, x_, y);
+		counts.setValue(109, z, x, y_);
+		counts.setValue(1, z, x, y);
+
+		FactorUtil.print(counts);
+
+
+		int N = (int) counts.marginalize(X, Y, Z).getValueAt(0);
+		Assert.assertEquals(700, N, 0);
+
+
+		BayesianFactor nz = counts.marginalize(X, Y);
+		BayesianFactor nxz =  counts.marginalize(Y);
+		BayesianFactor nxy = counts.marginalize(Z);
+
+
+		BayesianFactor px_z = nxz.divide(nz);
+		BayesianFactor py_xz = counts.reorderDomain(0,1,2).divide(nxz.reorderDomain(X,Z));
+		BayesianFactor pz = nz.scalarMultiply(1.0/N);
+		BayesianFactor py_x = nxy.divide(nxy.marginalize(Y));
+		BayesianFactor pxy = nxy.scalarMultiply(1.0/N);
+
+
+		//BayesianFactor
+
+		TIntIntMap[] data = DataUtil.dataFromCounts(counts);
+
+
+		// Empirical network
+		bnet.setFactor(Z, pz);
+		bnet.setFactor(Y, py_xz);
+		bnet.setFactor(X, px_z);
+
+
+		int[] exoVarSizes = IntStream.of(bnet.getVariables())
+				.mapToObj(v -> bnet.getDomain(ArrayUtils.add(bnet.getParents(v), v)))
+				.mapToInt(dom -> dom.getCombinations()+1)
+				.toArray();
+
+
+
+
+
+		// Equationless
+		StructuralCausalModel m_eqless = CausalBuilder.of(bnet).build();
+
+		BayesianFactor fx = EquationBuilder.of(m_eqless).fromVector(X, 1,0, 0,0, 0,1, 1,1);
+		m_eqless.setFactor(X, fx);
+
+
+		m_eqless.fillExogenousWithRandomFactors(3);
+
+
+		m_eqless.getFactor(X).filter(X, x_).filter(Z, z_);
+		m_eqless.getFactor(X).filter(X, x).filter(Z, z_);
+		m_eqless.getFactor(X).filter(X, x_).filter(Z, z);
+		m_eqless.getFactor(X).filter(X, x).filter(Z, z);
+
+
+
+
+
+		HashMap empData = DataUtil.getEmpiricalMap(m_eqless, data);
+		empData = FactorUtil.fixEmpiricalMap(empData,6);
+
+		System.out.println(empData);
+
+		SparseModel vmodelPGM =  m_eqless.toVCredal(empData.values());
+		System.out.println(m_eqless);
+		System.out.println("vmodel PGM:");
+		System.out.println(vmodelPGM);
+
+
+		SparseModel hmodelPGM =  m_eqless.toHCredal(empData.values());
+		System.out.println("hmodel PGM:");
+		System.out.println(hmodelPGM);
+
+
+		CredalCausalVE inf = new CredalCausalVE(m_eqless, empData.values());
+
+		IO.write(m_eqless, folder+"consPearl.uai");
+		DataUtil.toCSV(folder+"dataPearl.csv", data);
+
+
+		VertexFactor pn = (VertexFactor) inf.probNecessity(X,Y,1,0);
+		VertexFactor ps = (VertexFactor) inf.probSufficiency(X,Y,1,0);
+		VertexFactor pns = (VertexFactor) inf.probNecessityAndSufficiency(X,Y,1,0);
+
+
+		System.out.println("PGM exact method");
+		System.out.println("====================");
+		System.out.println("pn="+pn);
+		System.out.println("ps="+ps);
+		System.out.println("pns="+pns);
+
+
+
+		// EM
+		for(int u: m_eqless.getExogenousVars())
+			System.out.println(u+": "+m_eqless.getFactor(u));
+
+		EMCredalBuilder builder = EMCredalBuilder.of(m_eqless, data)
+				.setMaxEMIter(200)
+				.setNumTrajectories(20)
+				//.setNumDecimalsRound(-1)
+				.build();
+
+
+		CausalMultiVE inf2 = new CausalMultiVE(builder.getSelectedPoints());
+
+		VertexFactor pns2 = (VertexFactor) inf2.probNecessityAndSufficiency(X,Y,1,0);
+
+		//System.out.println("pn2="+pn2);
+		//System.out.println("ps2="+ps2);
+		System.out.println("pns2="+pns2);
+
+
+
+
+	}
+}

papers/21why/examplesconsPearl.uai

@@ -0,0 +1,17 @@
+CAUSAL 
+6 
+2 2 2 4 16 2 
+6 
+3	3 2 0 
+4	4 2 0 1 
+2	5 2 
+1	3 
+1	4 
+1	5 
+
+8	1 0 0 0 0 1 1 1 
+64	0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 1 1 0 0 0 1 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 
+2	0 1 
+4	0.842 0.071 0.054 0.033 
+16	0.01 0.001 0.31 0.003 0.005 0.077 0.001 0.007 0.001 0.048 0.054 0.152 0.002 0.314 0.003 0.012 
+2	0.7 0.3