static void Main(string[] args)
{
Dictionary <string, string> customBayesNetStructure = new Dictionary <string, string>
{
{ "class", "" },
{ "property_magnitude", "class" },
{ "housing", "property_magnitude,class" },
{ "purpose", "housing,class" },
{ "personal_status", "housing,class" },
{ "job", "property_magnitude,class" },
{ "employment", "job,class" },
{ "own_telephone", "job,class" },
{ "credit_history", "own_telephone,class" }
};
string trainPath = @"..\..\..\Datasets\example-train.csv";
string testPath = @"..\..\..\Datasets\example-test.csv";
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
// When there is no custom structure for model, it works like a classical bayes classifier
BayesNetClassifier model = new BayesNetClassifier(customBayesNetStructure);
model.Fit(trainPath, false);// Second boolean parameter is a choice of writing CPT's to file
List <string> predictions = model.Predict(testPath);
// Classification Report
EvaluationMetrics.GetModelReport(predictions, "bad", "good", stopWatch.ElapsedMilliseconds);
}
public static void GetModelReport(List <string> predictions, string positiveLabel, string negativeLabel, long elapsedTime)
{
Dictionary <string, int> conf = EvaluationMetrics.GetConfusionMatrix(BayesNetClassifier.testFeatures[BayesNetClassifier.columns.Count - 1], predictions, "bad", "good");
Dictionary <string, double> metrics = EvaluationMetrics.GetMetrics(conf);
Console.WriteLine(String.Format("Elapsed Time: {0} ms", elapsedTime));
Console.WriteLine("Confusion Matrix");
Console.WriteLine("--------------");
Console.WriteLine(String.Format(" {0,-3} {1,-3}\n {2,-3} {3,-3}", conf["TP"], conf["FN"], conf["FP"], conf["TN"]));
Console.WriteLine("--------------");
Console.WriteLine(String.Format("TP_Rate: {0}\nTN_Rate: {1}\nAccuracy: {2}", metrics["TP_RATE"], metrics["TN_RATE"], metrics["ACCURACY"]));
Console.Read();
}