def test_k_folds_decision_tree(self):
folds = dt.generate_k_folds(self.dataset, 20)
for fold in folds:
tree = dt.DecisionTree()
features, classes = fold[1]
tree.fit(features, classes)
testf, testc = fold[0]
output = tree.classify(testf)
def test_decision_tree_all_data(self):
"""Test decision tree classifies all data correctly.
Asserts:
classification is 100% correct.
"""
tree = dt.DecisionTree()
tree.fit(self.train_features, self.train_classes)
output = tree.classify(self.train_features)
assert cmp(output, self.train_classes) == 0
def test_decision_tree_all_data(self):
"""Test decision tree classifies all data correctly.
Asserts:
classification is 100% correct.
"""
#tree = dt.RandomForest(4,10,0.8,0.8)
tree = dt.DecisionTree()
#print tree,"tree"
tree.fit(self.train_features, self.train_classes)
output = tree.classify(self.train_features)
for i in range(len(self.train_classes)):
if cmp(output[i], self.train_classes[i]) != 0:
print i, "not equal"
assert cmp(output, self.train_classes) == 0