def testSimpleCase(self):
tree = DecisionTree()
tree.fit(simpleData)
for datum in simpleData:
self.assertEqual(datum[-1], tree.predict(datum))
tree.print()
def test(): X = [[1, 2, 0, 1, 0], [0, 1, 1, 0, 1], [1, 0, 0, 0, 1], [2, 1, 1, 0, 1], [1, 1, 0, 1, 1]] y = ['yes','yes','no','no','no'] decision_tree = DecisionTree(mode = 'C4.5') decision_tree.fit(X,y) res = decision_tree.predict(X) print res model_name = 'test.dt' decision_tree.saveModel(model_name) new_tree = DecisionTree() new_tree.loadModel(model_name) print new_tree.predict(X)
label2id = {'loss': 0, 'draw': 1, 'win': 2}
p2f = {'b': 0, 'x': 1, 'o': 2}
for line in fin:
line = line.strip().split(',')
label = label2id[line[-1]]
feature = np.array([p2f[p] for p in line[:-1]])
data.append((feature, label))
fin.close()
return data
data = read('connect-4.data')
x = np.array([d[0] for d in data])
y = np.array([d[1] for d in data])
#y = label_binarize(y, classes=list(range(3)))
kf = KFold(5, True)
all_f1 = []
for train_index, test_index in kf.split(x):
x_train, y_train, x_test, y_test = x[train_index], y[train_index], x[
test_index], y[test_index]
#x_train, x_test, y_train, y_test = train_test_split(x, y)
#print('training')
#model = OneVsRestClassifier(SVC(kernel='rbf'))
model = DecisionTree()
model.fit(x_train, y_train)
#print('testing')
y_pred = model.predict(x_test)
all_f1.append(f1_score(y_test, y_pred, average='macro'))
print(sum(all_f1) / 5)
def testComplexCase(self):
tree = DecisionTree()
tree.fit(complexData)
for datum in complexData:
self.assertEqual(datum[-1], tree.predict(datum))
# Overcast = YES
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Overcast, Temperature.Hot, Humidity.High, Wind.Weak]))
self.assertEqual(
Label.YES,
tree.predict([
Outlook.Overcast, Temperature.Cool, Humidity.Normal,
Wind.Strong
]))
# Sunny + Normal = YES
self.assertEqual(
Label.YES,
tree.predict([
Outlook.Sunny, Temperature.Cool, Humidity.Normal, Wind.Strong
]))
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Sunny, Temperature.Hot, Humidity.Normal, Wind.Weak]))
# Sunny + High = NO
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Sunny, Temperature.Cool, Humidity.High, Wind.Strong]))
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Sunny, Temperature.Hot, Humidity.High, Wind.Weak]))
# Rain + Weak = Yes
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Rain, Temperature.Cool, Humidity.Normal, Wind.Weak]))
self.assertEqual(
Label.YES,
tree.predict(
[Outlook.Rain, Temperature.Hot, Humidity.High, Wind.Weak]))
# Rain + Strong = No
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Rain, Temperature.Cool, Humidity.Normal,
Wind.Strong]))
self.assertEqual(
Label.NO,
tree.predict(
[Outlook.Rain, Temperature.Hot, Humidity.High, Wind.Strong]))
tree.print()