public void testInducedDecisionTreeClassifiesRestaurantDataSetCorrectly()
{
DecisionTreeLearner learner = new DecisionTreeLearner(
createInducedRestaurantDecisionTree(), "Unable to clasify");
int[] results = learner.test(DataSetFactory.getRestaurantDataSet());
Assert.AreEqual(12, results[0]);
Assert.AreEqual(0, results[1]);
}
public void testDefaultUsedWhenTrainingDataSetHasNoExamples()
{
// tests RecursionBaseCase#1
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionTreeLearner learner = new DecisionTreeLearner();
DataSet ds2 = ds.emptyDataSet();
Assert.AreEqual(0, ds2.size());
learner.train(ds2);
Assert.AreEqual("Unable To Classify", learner.predict(ds
.getExample(0)));
}
public void testStumpPredictionForDataSet()
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
List<String> unmatchedValues = new List<String>();
unmatchedValues.Add(NO);
DecisionTree tree = DecisionTree.getStumpFor(ds, "hungry", YES, YES,
unmatchedValues, "Unable to Classify");
DecisionTreeLearner learner = new DecisionTreeLearner(tree,
"Unable to Classify");
int[] result = learner.test(ds);
Assert.AreEqual(5, result[0]);
Assert.AreEqual(7, result[1]);
}
public void testClassificationReturnedWhenAllExamplesHaveTheSameClassification()
{
// tests RecursionBaseCase#2
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionTreeLearner learner = new DecisionTreeLearner();
DataSet ds2 = ds.emptyDataSet();
// all 3 examples have the same classification (willWait = yes)
ds2.add(ds.getExample(0));
ds2.add(ds.getExample(2));
ds2.add(ds.getExample(3));
learner.train(ds2);
Assert.AreEqual("Yes", learner.predict(ds.getExample(0)));
}
public void testMajorityReturnedWhenAttributesToExamineIsEmpty()
{
// tests RecursionBaseCase#2
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionTreeLearner learner = new DecisionTreeLearner();
DataSet ds2 = ds.emptyDataSet();
// 3 examples have classification = "yes" and one ,"no"
ds2.add(ds.getExample(0));
ds2.add(ds.getExample(1));// "no"
ds2.add(ds.getExample(2));
ds2.add(ds.getExample(3));
ds2.setSpecification(new MockDataSetSpecification("will_wait"));
learner.train(ds2);
Assert.AreEqual("Yes", learner.predict(ds.getExample(1)));
}
public void testInducedTreeClassifiesDataSetCorrectly()
{
DataSet ds = DataSetFactory.getRestaurantDataSet();
DecisionTreeLearner learner = new DecisionTreeLearner();
learner.train(ds);
int[] result = learner.test(ds);
Assert.AreEqual(12, result[0]);
Assert.AreEqual(0, result[1]);
}
