def test_train(self):
print('start test test_split_data_and_attrs')
# def split_data_and_attrs(self, train_X, train_Y, attrs_ids, parent_entropy):
train_X = np.array([[1, 2, 3, 1], [1, 3, 5, 8], [1, 2, 7, 5]])
train_Y = np.array([0, 0, 1])
attrs_ids = range(4)
node = DecisionTreeNode(1, 3, set(train_Y))
left_train_X, left_train_Y, left_attrs_ids, right_train_X, right_train_Y, right_attrs_ids = \
node.split_data_and_attrs(train_X=train_X, train_Y=train_Y, attrs_ids=attrs_ids, parent_entropy=1)
print('case 1:')
node.train(train_X=train_X, train_Y=train_Y, attrs_ids=attrs_ids)
print('--------------')
def test_split_data_and_attrs(self): # pass
return
print('start test test_split_data_and_attrs')
# def split_data_and_attrs(self, train_X, train_Y, attrs_ids, parent_entropy):
train_X = np.array([[1, 2, 3], [1, 3, 5], [1, 2, 7]])
train_Y = np.array([0, 0, 1])
node = DecisionTreeNode(1, 3, set(train_Y))
left_train_X, left_train_Y, left_attrs_ids, right_train_X, right_train_Y, right_attrs_ids = \
node.split_data_and_attrs(train_X=train_X, train_Y=train_Y, attrs_ids=[0, 1, 2], parent_entropy=1)
print('case 1:')
print('left_train_X:')
print(left_train_X)
print(left_train_Y)
print('\nright_train_X')
print(right_train_X)
print(right_train_Y)
print('--------------')
print('case 2:')
train_X = np.array([[1, 1, 1], [1, 1, 0], [0, 0, 1], [1, 0, 0]])
train_Y = np.array([1, 1, 2, 2])
node = DecisionTreeNode(1, 3, set(train_Y))
left_train_X, left_train_Y, left_attrs_ids, right_train_X, right_train_Y, right_attrs_ids = \
node.split_data_and_attrs(train_X=train_X, train_Y=train_Y, attrs_ids=[0, 1, 2], parent_entropy=1)
print('\nleft_train_X:')
print(left_train_X)
print(left_train_Y)
print('\nright_train_X')
print(right_train_X)
print(right_train_Y)
print('\nleft_attrs_ids:')
print(left_attrs_ids)
print('\nright_attrs_ids')
print(right_attrs_ids)
print('--------------')
print('case 3:')
train_X = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]])
train_Y = np.array([1, 1, 1, 1])
node = DecisionTreeNode(1, 3, set(train_Y))
attrs_ids = [0, 2]
left_train_X, left_train_Y, left_attrs_ids, right_train_X, right_train_Y, right_attrs_ids = \
node.split_data_and_attrs(train_X=train_X, train_Y=train_Y, attrs_ids=attrs_ids, parent_entropy=1)
print('\nleft_train_X:')
print(left_train_X)
print(left_train_Y)
print('\nright_train_X')
print(right_train_X)
print(right_train_Y)
print('\nleft_attrs_ids:')
print(left_attrs_ids)
print('\nright_attrs_ids')
print(right_attrs_ids)
print('--------------')
print('case 4:')
train_X = np.array([[1, 0], [0, 1]])
train_Y = np.array([1, 1])
node = DecisionTreeNode(1, 3, set(train_Y))
attrs_ids = [0, 1]
left_train_X, left_train_Y, left_attrs_ids, right_train_X, right_train_Y, right_attrs_ids = \
node.split_data_and_attrs(train_X=train_X, train_Y=train_Y, attrs_ids=attrs_ids, parent_entropy=1)
print('\nleft_train_X:')
print(left_train_X)
print(left_train_Y)
print('\nright_train_X')
print(right_train_X)
print(right_train_Y)
print('\nleft_attrs_ids:')
print(left_attrs_ids)
print('\nright_attrs_ids')
print(right_attrs_ids)
print('--------------')
print('case 5:')
train_X = np.array([[1, 2, 3], [1, 3, 5], [1, 2, 7]])
train_Y = np.array([1, 0, 1])
node = DecisionTreeNode(1, 3, set(train_Y))
left_train_X, left_train_Y, left_attrs_ids, right_train_X, right_train_Y, right_attrs_ids = \
node.split_data_and_attrs(train_X=train_X, train_Y=train_Y, attrs_ids=[0, 1, 2], parent_entropy=1)
print('left_train_X:')
print(left_train_X)
print(left_train_Y)
print('\nright_train_X')
print(right_train_X)
print(right_train_Y)