Tutorials > Tutorial 9: Structure learning

// tutorial9.cpp

// 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

#include "smile.h"

#include <cstdio>

#include <utility>

using namespace std;

int Tutorial9()

{

printf("Starting Tutorial9...\n");

DSL_errorH().RedirectToFile(stdout);

DSL_dataset ds;

int res = ds.ReadFile("Credit10k.csv");

if (DSL_OKAY != res)

{

printf("Dataset load failed\n");

return res;

}

printf("Dataset has %d variables (columns) and %d records (rows)\n",

ds.GetNumberOfVariables(), ds.GetNumberOfRecords());

double bestScore;

DSL_bs bayesSearch;

bayesSearch.nrIteration = 50;

DSL_network net1;

bayesSearch.seed = 9876543;

res = bayesSearch.Learn(ds, net1, NULL, NULL, &bestScore);

if (DSL_OKAY != res)

{

printf("Bayesian Search failed (%d)\n", res);

return res;

}

net1.SimpleGraphLayout();

printf("1st Bayesian Search finished, structure score: %g\n", bestScore);

net1.WriteFile("tutorial9-bs1.xdsl");

DSL_network net2;

bayesSearch.seed = 3456789;

res = bayesSearch.Learn(ds, net2, NULL, NULL, &bestScore);

if (DSL_OKAY != res)

{

printf("Bayesian Search failed (%d)\n", res);

return res;

}

net2.SimpleGraphLayout();

printf("2nd Bayesian Search finished, structure score: %g\n", bestScore);

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)

{

printf("Can't find dataset variables for background knowledge\n");

printf("The loaded file may not be Credit10k.csv\n");

return DSL_OUT_OF_RANGE;

}

DSL_network net3;

bayesSearch.bkk.forbiddenArcs.push_back(make_pair(idxAge, idxCreditWorthiness));

bayesSearch.bkk.forcedArcs.push_back(make_pair(idxAge, idxProfession));

res = bayesSearch.Learn(ds, net3, NULL, NULL, &bestScore);

if (DSL_OKAY != res)

{

printf("Bayesian Search finished (%d)\n", res);

return res;

}

net3.SimpleGraphLayout();

printf("3rd Bayesian Search complete, structure score: %g\n", bestScore);

net3.WriteFile("tutorial9-bs3.xdsl");

DSL_network net4;

DSL_tan tan;

tan.seed = 777999;

tan.classvar = "CreditWorthiness";

res = tan.Learn(ds, net4);

if (DSL_OKAY != res)

{

printf("TAN failed (%d)\n", res);

return res;

}

net4.SimpleGraphLayout();

printf("Tree-augmented Naive Bayes finished\n");

net4.WriteFile("tutorial9-tan.xdsl");

DSL_pc pc;

DSL_pattern pattern;

res = pc.Learn(ds, pattern);

if (DSL_OKAY != res)

{

printf("PC failed (%d)\n", res);

return res;

}

DSL_network net5;

pattern.ToNetwork(ds, net5);

net5.SimpleGraphLayout();

printf("PC finished, proceeding to parameter learning\n");

net5.WriteFile("tutorial9-pc.xdsl");

DSL_em em;

string errMsg;

vector<DSL_datasetMatch> matching;

res = ds.MatchNetwork(net5, matching, errMsg);

if (DSL_OKAY != res)

{

printf("Can't automatically match network with dataset: %s\n", errMsg.c_str());

return DSL_OUT_OF_RANGE;

}

em.SetUniformizeParameters(false);

em.SetRandomizeParameters(false);

em.SetEquivalentSampleSize(0);

res = em.Learn(ds, net5, matching);

if (DSL_OKAY != res)

{

printf("EM failed (%d)\n", res);

return res;

}

printf("EM finished\n");

net5.WriteFile("tutorial9-pc-em.xdsl");

printf("Tutorial9 complete\n");

return DSL_OKAY;

}