public static void main(String[] args) throws IOException, Exception {
// break into cases for each data set
// for congressional, partition into training and test sets
int whichDataSet = -1;
Scanner in = new Scanner(System.in);
String whichMonk = "";
boolean IG = false;
int tempIG = -1;
// check input parameters
try {
if (args.length > 3) throw new NumberFormatException();
tempIG = Integer.parseInt(args[0]);
whichDataSet = Integer.parseInt(args[1]);
// System.out.println(tempIG + " " + whichDataSet);
if (args.length < 2 || whichDataSet < 0 || whichDataSet > 2 || tempIG < 0 || tempIG > 1) {
throw new NumberFormatException();
}
// System.out.println(tempIG + " " + whichDataSet);
if (args.length < 3 && whichDataSet == 1) {
throw new NumberFormatException();
}
// System.out.println(tempIG + "* " + whichDataSet);
if (args.length == 3) {
int whichMonkInt = Integer.parseInt(args[2]);
if (whichMonkInt == 1) whichMonk = "monks-1";
else if (whichMonkInt == 2) whichMonk = "monks-2";
else if (whichMonkInt == 3) whichMonk = "monks-3";
else throw new NumberFormatException();
}
if (tempIG == 0) IG = false;
else IG = true;
} catch (NumberFormatException e) {
System.out.println(
"ERROR: \n\nUsage: ./run_traditional <IG or GR> <whichDataSet> <whichMonkDataSet> \n"
+ "IG or GR: (0) for information gain, (1) for gain ratio"
+ "\nwhichDataSet can be: (0=Congressional, 1=MONK, 2=Mushroom)"
+ "\nwhichMonkDataSet is only relevant if Monk chosen can be: (1 2 or 3)");
System.exit(1);
}
// cases for each data set
switch (whichDataSet) {
case 0:
{
fileName = "house-votes-84.data";
System.out.println("\n\nRunning data set: " + fileName);
parser = new CongressionalInputParser();
outputClasses = parser.initializeOutputClasses();
masterAttributes = parser.initializeMasterAttributesAndValues();
attributes = parser.getAttributesSet();
examples = parser.readExamples(fileName);
partitionIntoTestAndTraining(examples);
traditionalDecisionTreeBuilder =
new GreedyInformationGainDecisionTree(
trainingExamples, masterAttributes, attributes, outputClasses, IG);
System.out.println("\n\nnumber of congressional Examples: " + examples.size());
break;
}
case 1:
{
String fileName1 = whichMonk + ".train";
String fileName2 = whichMonk + ".test";
System.out.println("\n\nRunning data set: " + fileName1);
parser = new MonkInputParser();
outputClasses = parser.initializeOutputClasses();
masterAttributes = parser.initializeMasterAttributesAndValues();
attributes = parser.getAttributesSet();
trainingExamples = parser.readExamples(fileName1);
testExamples = parser.readExamples(fileName2);
traditionalDecisionTreeBuilder =
new GreedyInformationGainDecisionTree(
trainingExamples, masterAttributes, attributes, outputClasses, IG);
System.out.println(
"\n\nnumber of monk " + whichMonk + " Examples: " + trainingExamples.size());
break;
}
case 2:
{
String fileName1 = "agaricus-lepiota.data";
System.out.println("\n\nRunning data set: " + fileName1);
parser = new MushroomInputParser();
outputClasses = parser.initializeOutputClasses();
masterAttributes = parser.initializeMasterAttributesAndValues();
attributes = parser.getAttributesSet();
examples = parser.readExamples(fileName1);
// testExamples = parser.readExamples(fileName2);
partitionIntoTestAndTraining(examples);
traditionalDecisionTreeBuilder =
new GreedyInformationGainDecisionTree(
trainingExamples, masterAttributes, attributes, outputClasses, IG);
System.out.println("\n\nnumber of mushroom Examples: " + examples.size());
break;
}
}
// System.out.println(trainingExamples.get(1).getAttributeList());
finalDecisionTree =
traditionalDecisionTreeBuilder.makeDecisionTree(trainingExamples, attributes, null);
// System.out.println(finalDecisionTree.toString());
System.out.println("\nTesting the tree on the TRAINING examples...");
tester = new DecisionTreeTester(trainingExamples, masterAttributes, attributes, outputClasses);
tester.test(finalDecisionTree);
String out = tester.printPerformanceMetrics();
System.out.println(out);
System.out.println("\nTesting the tree on the " + testExamples.size() + " TEST examples...");
tester = new DecisionTreeTester(testExamples, masterAttributes, attributes, outputClasses);
tester.test(finalDecisionTree);
out = tester.printPerformanceMetrics();
System.out.println(out);
System.out.println("Successful completion of program");
}
