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package tutorials;
import smile.*;
import smile.learning.*;
// Tutorial9 loads Credit10k.csv file
// and runs multiple structure learning algorithms
// using the loaded dataset.
// Use the link below to download the Credit10k.csv file:
// https://support.bayesfusion.com/docs/Examples/Learning/Credit10K.csv
public class Tutorial10 {
public static void run() {
System.out.println("Starting Tutorial9...");
DataSet ds = new DataSet();
try
{
ds.readFile("Credit10k.csv");
} catch (SMILEException ex)
{
System.out.println("Dataset load failed");
return;
}
System.out.printf("Dataset has %d variables (columns) and %d records (rows)\n",
ds.getVariableCount(), ds.getRecordCount());
BayesianSearch bayesSearch = new BayesianSearch();
bayesSearch.setIterationCount(50);
bayesSearch.setRandSeed(9876543);
Network net1;
try
{
net1 = bayesSearch.learn(ds);
} catch (SMILEException ex)
{
System.out.println("Bayesian Search failed");
return;
}
System.out.printf("1st Bayesian Search finished, structure score: %f\n",
bayesSearch.getLastScore());
net1.writeFile("tutorial9-bs1.xdsl");
Network net2;
bayesSearch.setRandSeed(3456789);
try
{
net2 = bayesSearch.learn(ds);
}
catch (SMILEException ex)
{
System.out.println("Bayesian Search failed");
return;
}
System.out.printf("2nd Bayesian Search finished, structure score: %f\n",
bayesSearch.getLastScore());
net2.writeFile("tutorial9-bs2.xdsl");
int idxAge = ds.findVariable("Age");
int idxProfession = ds.findVariable("Profession");
int idxCreditWorthiness = ds.findVariable("CreditWorthiness");
if (idxAge < 0 || idxProfession < 0 || idxCreditWorthiness < 0)
{
System.out.println("Can't find dataset variables for background knowledge");
System.out.println("The loaded file may not be Credit10k.csv");
return;
}
BkKnowledge backgroundKnowledge = new BkKnowledge();
backgroundKnowledge.matchData(ds);
backgroundKnowledge.addForbiddenArc(idxAge, idxCreditWorthiness);
backgroundKnowledge.addForcedArc(idxAge, idxProfession);
bayesSearch.setBkKnowledge(backgroundKnowledge);
Network net3;
try
{
net3 = bayesSearch.learn(ds);
}
catch (SMILEException ex)
{
System.out.println("Bayesian Search failed");
return;
}
System.out.printf("3rd Bayesian Search finished, structure score: %f\n",
bayesSearch.getLastScore());
net3.writeFile("tutorial9-bs3.xdsl");
Network net4;
TAN tan = new TAN();
tan.setRandSeed(777999);
tan.setClassVariableId("CreditWorthiness");
try
{
net4 = tan.learn(ds);
}
catch (SMILEException ex)
{
System.out.println("TAN failed");
return;
}
System.out.println("Tree-augmented Naive Bayes finished");
net4.writeFile("tutorial9-tan.xdsl");
PC pc = new PC();
Pattern pattern;
try
{
pattern = pc.learn(ds);
} catch (SMILEException ex)
{
System.out.println("PC failed");
return;
}
Network net5 = pattern.makeNetwork(ds);
System.out.println("PC finished, proceeding to parameter learning");
net5.writeFile("tutorial9-pc.xdsl");
EM em = new EM();
DataMatch[] matching;
try
{
matching = ds.matchNetwork(net5);
}
catch (SMILEException ex)
{
System.out.println("Can't automatically match network with dataset");
return;
}
em.setUniformizeParameters(false);
em.setRandomizeParameters(false);
em.setEqSampleSize(0);
try
{
em.learn(ds, net5, matching);
}
catch (SMILEException ex)
{
System.out.println("EM failed");
return;
}
System.out.println("EM finished");
net5.writeFile("tutorial9-pc-em.xdsl");
System.out.println("Tutorial10 complete");
}
}