class test_Mondrian_Tree(unittest.TestCase):
# TODO: Run tests with inputing data with numpy arrays
def setUp(self):
self.d = 3
self.a = 1
self.linear_dims = [[0, 1]] * self.d
self.mt1 = Mondrian_Tree(self.linear_dims)
self.n_points = 100
self.n_labelled = 20
random.seed(123)
self.labels = [random.random() for i in range(self.n_labelled)]
self.labelled_indices = range(self.n_labelled)
self.data = []
random.seed(1)
for i in range(self.n_points):
point = []
for j in range(self.d):
point.append(random.random())
self.data.append(point)
###########################################
# Basic life time updating tests
def test_update_first_life_time_0p1(self):
temp = Mondrian_Tree([[0, 1]] * 5)
temp.update_life_time(0.1, set_seed=1)
self.assertEqual(temp._num_leaves, 3)
def test_update_first_life_time_0p5(self):
temp = Mondrian_Tree([[0, 1]] * 5)
temp.update_life_time(0.5, set_seed=1)
self.assertEqual(temp._num_leaves, 8)
def test_update_first_life_time_1(self):
temp = Mondrian_Tree([[0, 1]] * 5)
temp.update_life_time(1, set_seed=1)
self.assertEqual(temp._num_leaves, 36)
def test_update_life_time_tuned(self):
self.mt1.update_life_time(self.a * self.n_labelled**(1 /
(2 + self.d) - 1),
set_seed=1)
self.assertEqual(self.mt1._num_leaves, 2)
def test__test_point(self):
with self.assertRaises(ValueError):
self.mt1._test_point([])
self.mt1._test_point([1, 2, 3, 4, 5])
with self.assertRaises(TypeError):
self.mt1._test_point(1)
self.mt1._test_point({})
self.mt1._test_point('abc')
good_point = [1] * self.d
self.assertEqual(good_point, self.mt1._test_point(good_point))
self.assertEqual(good_point,
self.mt1._test_point(np.array(good_point)))
###########################################
# Testing input data
def test_input_data_empty_root(self):
self.mt1.input_data([], [], [])
self.assertEqual(self.mt1._num_points, 0)
def test_input_data_empty(self):
lbda = 1
self.mt1.update_life_time(lbda, set_seed=1)
self.mt1.input_data([], [], [])
self.assertEqual(self.mt1._num_points, 0)
def test_input_data_root_no_labels(self):
self.mt1.input_data([[0] * self.d], [], [])
self.assertEqual(self.mt1._num_points, 1)
self.assertEqual(self.mt1.labels, [None])
self.assertEqual(self.mt1._root.unlabelled_index, [0])
self.assertEqual(self.mt1._root.labelled_index, [])
def test_input_data_root_labels(self):
self.mt1.input_data([[0] * self.d], [0], [3.141])
self.assertEqual(self.mt1._num_points, 1)
self.assertEqual(self.mt1.labels, [3.141])
self.assertEqual(self.mt1._root.unlabelled_index, [])
self.assertEqual(self.mt1._root.labelled_index, [0])
def test_input_data(self):
lbda = 0.5
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.assertEqual(self.mt1._num_points, self.n_points)
self.assertEqual(self.mt1._num_labelled, self.n_labelled)
self.mt1.make_full_leaf_list()
for node in self.mt1._full_leaf_list:
# print(len(node.labelled_index), len(node.unlabelled_index))
linear_dims = node.linear_dims
for ind in node.labelled_index:
point = self.mt1.points[ind]
for dim in range(self.d):
# print(linear_dims[dim][0], point[dim], linear_dims[dim][1])
self.assertTrue(point[dim] >= linear_dims[dim][0])
self.assertTrue(point[dim] <= linear_dims[dim][1])
for ind in node.unlabelled_index:
point = self.mt1.points[ind]
for dim in range(self.d):
# print(linear_dims[dim][0], point[dim], linear_dims[dim][1])
self.assertTrue(point[dim] >= linear_dims[dim][0])
self.assertTrue(point[dim] <= linear_dims[dim][1])
def test_update_life_time_with_data(self):
lbda = 0.5
lbda2 = 1
self.mt1.update_life_time(lbda, set_seed=100)
# print(self.mt1._num_points)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.update_life_time(lbda2, set_seed=100)
# print(self.mt1._num_points)
self.mt1.make_full_leaf_list()
for node in self.mt1._full_leaf_list:
# print(len(node.labelled_index), len(node.unlabelled_index))
linear_dims = node.linear_dims
for ind in node.labelled_index:
point = self.mt1.points[ind]
for dim in range(self.d):
# print(linear_dims[dim][0], point[dim], linear_dims[dim][1])
self.assertTrue(point[dim] >= linear_dims[dim][0])
self.assertTrue(point[dim] <= linear_dims[dim][1])
for ind in node.unlabelled_index:
point = self.mt1.points[ind]
for dim in range(self.d):
# print(linear_dims[dim][0], point[dim], linear_dims[dim][1])
self.assertTrue(point[dim] >= linear_dims[dim][0])
self.assertTrue(point[dim] <= linear_dims[dim][1])
def test_input_data_with_numpy(self):
lbda = 0.5
self.mt1.update_life_time(lbda, set_seed=100)
np_data = np.array(self.data)
# print(np_data)
np_labels = np.array(self.labels)
self.mt1.input_data(np_data, self.labelled_indices, np_labels)
self.assertEqual(self.mt1._num_points, self.n_points)
self.assertEqual(self.mt1._num_labelled, self.n_labelled)
self.mt1.make_full_leaf_list()
for node in self.mt1._full_leaf_list:
# print(len(node.labelled_index), len(node.unlabelled_index))
linear_dims = node.linear_dims
for ind in node.labelled_index:
point = self.mt1.points[ind]
for dim in range(self.d):
# print(linear_dims[dim][0], point[dim], linear_dims[dim][1])
self.assertTrue(point[dim] >= linear_dims[dim][0])
self.assertTrue(point[dim] <= linear_dims[dim][1])
for ind in node.unlabelled_index:
point = self.mt1.points[ind]
for dim in range(self.d):
# print(linear_dims[dim][0], point[dim], linear_dims[dim][1])
self.assertTrue(point[dim] >= linear_dims[dim][0])
self.assertTrue(point[dim] <= linear_dims[dim][1])
def test_label_point_root(self):
val = 1
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.label_point(self.n_labelled, val)
self.assertEqual(self.mt1.labels[self.n_labelled], val)
self.assertEqual(self.mt1._num_labelled, self.n_labelled + 1)
leaf = self.mt1._root.leaf_for_point(self.data[self.n_labelled])
self.assertTrue(self.n_labelled in leaf.labelled_index)
def test_label_point_empty(self):
with self.assertRaises(RuntimeError):
self.mt1.label_point(1, 1)
def test_label_point_too_big(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
with self.assertRaises(ValueError):
self.mt1.label_point(self.n_points + 1, 1)
def test_label_point(self):
val = 1
lbda = 0.5
seed = 1
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.label_point(self.n_labelled, val)
self.assertEqual(self.mt1.labels[self.n_labelled], val)
self.assertEqual(self.mt1._num_labelled, self.n_labelled + 1)
leaf = self.mt1._root.leaf_for_point(self.data[self.n_labelled])
# print(leaf.labelled_index)
self.assertTrue(self.n_labelled in leaf.labelled_index)
def test_add_data_point_bad(self):
with self.assertRaises(TypeError):
self.mt1.add_data_point(1)
def test_add_data_point_empty(self):
self.mt1.add_data_point([1] * self.d)
self.assertEqual([[1] * self.d], self.mt1.points)
self.assertEqual([None], self.mt1.labels)
self.assertEqual(self.mt1._root.unlabelled_index, [0])
self.assertEqual(self.mt1._root.labelled_index, [])
def test_add_data_point_empty_2(self):
self.mt1.add_data_point([1] * self.d, 2)
self.assertEqual([[1] * self.d], self.mt1.points)
self.assertEqual([2], self.mt1.labels)
self.assertEqual(self.mt1._root.unlabelled_index, [])
self.assertEqual(self.mt1._root.labelled_index, [0])
def test_add_data_point(self):
lbda = 0.5
seed = 1
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.add_data_point([1] * self.d)
self.assertEqual(self.data + [[1] * self.d], self.mt1.points)
self.assertEqual(
self.labels + [None] * (self.n_points - self.n_labelled) + [None],
self.mt1.labels)
leaf = self.mt1._root.leaf_for_point([1] * self.d)
self.assertEqual(leaf.unlabelled_index[-1], self.n_points)
self.assertEqual(len(self.mt1.points), self.mt1._num_points)
def test_add_data_point_labelled(self):
lbda = 0.5
seed = 1
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.add_data_point([1] * self.d, 1)
self.assertEqual(self.data + [[1] * self.d], self.mt1.points)
self.assertEqual(
self.labels + [None] * (self.n_points - self.n_labelled) + [1],
self.mt1.labels)
leaf = self.mt1._root.leaf_for_point([1] * self.d)
self.assertEqual(leaf.labelled_index[-1], self.n_points)
self.assertEqual(len(self.mt1.points), self.mt1._num_points)
self.assertEqual(self.n_labelled + 1, self.mt1._num_labelled)
###########################################
# Testing make leaf list
def test_make_full_leaf_list_root(self):
self.mt1.make_full_leaf_list()
self.assertEqual(len(self.mt1._full_leaf_list), 1)
def test_make_full_leaf_list(self):
lbda = 1
self.mt1.update_life_time(lbda, set_seed=1)
self.mt1.make_full_leaf_list()
# print(len(self.mt1._full_leaf_list))
# for node in self.mt1._full_leaf_list:
# print(node.leaf_id)
self.assertEqual(len(self.mt1._full_leaf_list), self.mt1._num_leaves)
# Testing calculating leaf variances
def test_make_full_leaf_var_list_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_var_list()
self.assertEqual(utils.unbiased_var(self.labels),
self.mt1._full_leaf_var_list[0])
def test_make_full_leaf_var_list_empty(self):
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_var_list()
self.assertEqual(0, self.mt1._full_leaf_var_list[0])
def test_make_full_leaf_var_list(self):
lbda = 0.5
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_var_list()
for i, node in enumerate(self.mt1._full_leaf_list):
node_labels = [self.mt1.labels[x] for x in node.labelled_index]
# print(node_labels)
if len(node_labels) != 0:
temp_mean = sum(node_labels) / len(node_labels)
# print(temp_mean)
temp_var = 1 / (len(node_labels) - 1) * sum(
[(x - temp_mean)**2 for x in node_labels])
self.assertTrue(
abs(self.mt1._full_leaf_var_list[i] - temp_var) < 1e-9)
else:
self.assertEqual(self.mt1._full_leaf_var_list[i], 0)
# Testing calculating leaf mean
def test_make_full_leaf_mean_list_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_mean_list()
self.assertEqual(
sum(self.labels) / self.n_labelled,
self.mt1._full_leaf_mean_list[0])
def test_make_full_leaf_mean_list_empty(self):
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_mean_list()
self.assertEqual(0, self.mt1._full_leaf_mean_list[0])
def test_make_full_leaf_mean_list(self):
lbda = 0.5
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_mean_list()
for i, node in enumerate(self.mt1._full_leaf_list):
node_labels = [self.mt1.labels[x] for x in node.labelled_index]
# print(node_labels)
if len(node_labels) != 0:
temp_mean = sum(node_labels) / len(node_labels)
# print(temp_mean)
self.assertTrue(
abs(self.mt1._full_leaf_mean_list[i] - temp_mean) < 1e-9)
else:
self.assertEqual(self.mt1._full_leaf_mean_list[i], 0)
# Testing calculating leaf marginal probabilities
def test_make_full_leaf_marginal_list_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_marginal_list()
self.assertEqual(1, self.mt1._full_leaf_marginal_list[0])
def test_make_full_leaf_marginal_list_empty(self):
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_marginal_list()
self.assertEqual(0, self.mt1._full_leaf_marginal_list[0])
def test_make_full_leaf_marginal_list(self):
lbda = 0.5
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.make_full_leaf_list()
self.mt1.make_full_leaf_marginal_list()
for i, node in enumerate(self.mt1._full_leaf_list):
node_points = ([self.mt1.points[x] for x in node.labelled_index] +
[self.mt1.points[x] for x in node.unlabelled_index])
# print(node_labels)
temp_marginal = len(node_points) / self.mt1._num_points
self.assertTrue(
abs(self.mt1._full_leaf_marginal_list[i] -
temp_marginal) < 1e-9)
###########################################
# Testing using predict and get_point_in_same_leaf
def test_predict_and_get_point_in_same_leaf_bad_input(self):
with self.assertRaises(TypeError):
self.mt1.predict(1)
self.mt1.get_points_in_same_leaf(1)
def test_predict_and_get_point_in_same_leaf_bad_input_2(self):
with self.assertRaises(TypeError):
self.mt1.predict('abc')
self.mt1.get_points_in_same_leaf('abc')
def test_predict_and_get_point_in_same_leaf_bad_length(self):
with self.assertRaises(ValueError):
self.mt1.predict([])
self.mt1.get_points_in_same_leaf([])
def test_predict_empty(self):
with self.assertWarns(UserWarning):
self.mt1.predict([0.5] * self.d)
def test_predict_no_leaf_list(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_point = []
for j in range(self.d):
new_point.append(random.random())
pred = self.mt1.predict(new_point)
# print(pred)
node = self.mt1._root.leaf_for_point(new_point)
vals = [self.labels[x] for x in node.labelled_index]
# print(len(vals))
self.assertEqual(pred, sum(vals) / len(vals))
def test_predict_leaf_list(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_point = []
for j in range(self.d):
new_point.append(random.random())
pred = self.mt1.predict(new_point)
# print(pred)
node = self.mt1._root.leaf_for_point(new_point)
vals = [self.labels[x] for x in node.labelled_index]
# print(len(vals))
self.assertEqual(pred, sum(vals) / len(vals))
def test_predict_leaf_list_numpy(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_point = []
for j in range(self.d):
new_point.append(random.random())
pred = self.mt1.predict(new_point)
# print(pred)
node = self.mt1._root.leaf_for_point(np.array(new_point))
vals = [self.labels[x] for x in node.labelled_index]
# print(len(vals))
self.assertEqual(pred, sum(vals) / len(vals))
def test_predict_multi_values(self):
num_preds = 10
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_points = []
for i in range(num_preds):
new_point = []
for j in range(self.d):
new_point.append(random.random())
new_points.append(new_point)
preds = self.mt1.predict(new_points)
# print(pred)
check_preds = []
for i in range(num_preds):
node = self.mt1._root.leaf_for_point(np.array(new_points[i]))
vals = [self.labels[x] for x in node.labelled_index]
check_preds.append(sum(vals) / len(vals))
# print(len(vals))
self.assertAlmostEqual(preds, check_preds)
def test_get_point_in_same_leaf_labelled(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_point = []
for j in range(self.d):
new_point.append(random.random())
labelled_list = self.mt1.get_points_in_same_leaf(new_point)
node = self.mt1._root.leaf_for_point(new_point)
self.assertEqual(labelled_list, node.labelled_index)
def test_get_point_in_same_leaf_unlabelled(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_point = []
for j in range(self.d):
new_point.append(random.random())
unlabelled_list = self.mt1.get_points_in_same_leaf(
new_point, 'unlabelled')
node = self.mt1._root.leaf_for_point(new_point)
self.assertEqual(unlabelled_list, node.unlabelled_index)
def test_get_point_in_same_leaf_bad_list_name(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=100)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
random.seed(seed)
new_point = []
for j in range(self.d):
new_point.append(random.random())
with self.assertWarns(UserWarning):
labelled_list = self.mt1.get_points_in_same_leaf(
new_point, 'neither')
node = self.mt1._root.leaf_for_point(new_point)
self.assertEqual(labelled_list, node.labelled_index)
###########################################
# Testing theoretical bounds
def test_expected_split_bound(self):
reps = 100
tot = 0
lbda = 5
d = 3
for i in range(reps):
temp = Mondrian_Tree([[0, 1]] * d)
temp.update_life_time(lbda, set_seed=i)
tot += temp._num_leaves - 1
# print(tot/reps, ((1+lbda)*math.exp(1))**d)
self.assertTrue(tot / reps < ((1 + lbda) * math.exp(1))**d)
def test_expected_cell_diameter_bounds(self):
tot = 0
lbda = 5
self.mt1.update_life_time(lbda, set_seed=1)
self.mt1.make_full_leaf_list()
for node in self.mt1._full_leaf_list:
tot += node.calculate_cell_l2_diameter()**2
# print(tot/len(self.mt1._full_leaf_list), 4*self.d/lbda**2)
self.assertTrue(
tot / len(self.mt1._full_leaf_list) < 4 * self.d / lbda**2)
def test_probabilistic_cell_diameter_bounds(self):
lbda = 5
deltas = [0.1, 0.2, 0.3, 0.4, 0.5, 1, 1.5]
self.mt1.update_life_time(lbda, set_seed=1)
self.mt1.make_full_leaf_list()
# print(len(self.mt1._full_leaf_list))
for delta in deltas:
tot = 0
for node in self.mt1._full_leaf_list:
tot += int(node.calculate_cell_l2_diameter() > delta)
# print(tot/len(self.mt1._full_leaf_list),
# self.d * (1+ (lbda * delta)/math.sqrt(self.d))*
# math.exp(-lbda * delta/math.sqrt(self.d)))
self.assertTrue(tot / len(self.mt1._full_leaf_list) < self.d *
(1 + (lbda * delta) / math.sqrt(self.d)) *
math.exp(-lbda * delta / math.sqrt(self.d)))
def test_limit_number_of_leaves(self):
# n_values = [1,10,100,1000,10000,100000]
if False:
n_values = [1, 10, 100, 1000]
d_values = [1, 3, 5, 10, 20, 50]
seed = 1
for d_val in d_values:
test_tree = Mondrian_Tree([[0, 1]] * d_val)
print('d = {}'.format(d_val))
for n_val in n_values:
test_tree.update_life_time(n_val**(1 / (2 + d_val)) - 1,
set_seed=seed + n_val + d_val)
print(n_val, test_tree._num_leaves,
test_tree._num_leaves / n_val)
###########################################
# Testing data driven default values for prediction and active learning variance
def test_set_default_pred_global_mean_empty(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.make_full_leaf_list()
self.mt1.prediction_default_value = 1
self.mt1.set_default_pred_global_mean()
self.assertEqual(self.mt1.prediction_default_value, 0)
def test_set_default_pred_global_mean(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.set_default_pred_global_mean()
self.assertEqual(self.mt1.prediction_default_value,
sum(self.labels) / self.n_labelled)
def test_al_set_default_var_global_var_empty(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.make_full_leaf_list()
self.mt1.al_default_var = 1
self.mt1.al_set_default_var_global_var()
self.assertEqual(self.mt1.al_default_var, 0)
def test_al_set_default_var_global_var(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.make_full_leaf_list()
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_set_default_var_global_var()
self.assertEqual(self.mt1.al_default_var,
utils.unbiased_var(self.labels))
###########################################
# Testing active learning parts
def test_al_calculate_leaf_proportions_empty_root(self):
with self.assertWarns(UserWarning):
self.mt1.al_calculate_leaf_proportions()
self.assertEqual(self.mt1._al_proportions, [1])
def test_al_calculate_leaf_proportions_empty(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
with self.assertWarns(UserWarning):
self.mt1.al_calculate_leaf_proportions()
self.assertEqual(self.mt1._al_proportions,
[1 / self.mt1._num_leaves] * self.mt1._num_leaves)
def test_al_calculate_leaf_proportions_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.assertEqual(self.mt1._al_proportions, [1])
def test_al_calculate_leaf_proportions(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
temp_prop_list = []
for i, node in enumerate(self.mt1._full_leaf_list):
node_points = [x for x in node.labelled_index
] + [x for x in node.unlabelled_index]
# print(len(node_points))
# print(self.mt1._full_leaf_marginal_list[i])
if len(node_points) != 0:
temp_var = utils.unbiased_var(
[self.labels[x] for x in node.labelled_index])
# print(self.mt1._al_proportions[i])
# print(math.sqrt(temp_var * (len(node_labels)/self.n_points)))
temp_prop_list.append(
math.sqrt(temp_var * (len(node_points) / self.n_points)))
else:
self.assertEqual(self.mt1._al_proportions[i], 0)
normalizer = sum(temp_prop_list)
temp_prop_list = [x / normalizer for x in temp_prop_list]
# print(temp_prop_list)
for i, val in enumerate(temp_prop_list):
# print(val, self.mt1._al_proportions[i])
self.assertTrue(abs(self.mt1._al_proportions[i] - val) < 1e-9)
def test_al_calculate_leaf_number_new_labels_empty(self):
with self.assertRaises(ValueError):
self.mt1.al_calculate_leaf_number_new_labels(1)
def test_al_calculate_leaf_number_new_labels_too_many_labelled(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
with self.assertRaises(ValueError):
self.mt1.al_calculate_leaf_number_new_labels(1)
def test_al_calculate_leaf_number_new_labels_too_few_points(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
with self.assertRaises(ValueError):
self.mt1.al_calculate_leaf_number_new_labels(101)
def test_al_calculate_leaf_number_new_labels_bad_round_by(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
with self.assertRaises(ValueError):
self.mt1.al_calculate_leaf_number_new_labels(21, round_by='bad')
def test_al_calculate_leaf_number_new_labels_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_leaf_number_new_labels(21)
self.assertEqual(self.mt1._al_leaf_number_new_labels, [1])
def test_al_calculate_leaf_number_new_labels_single(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
with self.assertWarns(UserWarning):
self.mt1.al_calculate_leaf_number_new_labels(21)
# print(self.mt1._al_leaf_number_new_labels)
self.assertEqual(sum(self.mt1._al_leaf_number_new_labels), 1)
# for i, node in enumerate(self.mt1._full_leaf_list):
# curr_num = len(node.labelled_index)
# tot_num = curr_num + self.mt1._al_leaf_number_new_labels[i]
# print(self.mt1._al_leaf_number_new_labels[i],tot_num, self.mt1._al_proportions[i])
def test_al_calculate_leaf_number_new_labels_many(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_leaf_number_new_labels(40)
# print(self.mt1._al_leaf_number_new_labels)
self.assertEqual(sum(self.mt1._al_leaf_number_new_labels), 20)
# for i, node in enumerate(self.mt1._full_leaf_list):
# curr_num = len(node.labelled_index)
# tot_num = curr_num + self.mt1._al_leaf_number_new_labels[i]
# print(self.mt1._al_leaf_number_new_labels[i],tot_num, self.mt1._al_proportions[i])
# def test_al_calculate_leaf_number_new_labels_incomplete(self):
# This is always a possible source of weird behaviour... Keep thinking about these heurisitcs
# self.assertTrue(False, 'Do more testing for this function. Both auto and visual. '
# 'And read the function code for more bugs.')
def test_al_calculate_point_probabilities_proportions_empty(self):
with self.assertWarns(UserWarning):
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_point_probabilities_proportions()
self.assertEqual(self.mt1._al_point_weights_proportional, [])
def test_al_calculate_point_probabilities_proportions_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_point_probabilities_proportions()
self.assertEqual(self.mt1._al_point_weights_proportional, [None] * 20 +
[1 / (self.n_points - self.n_labelled)] *
(self.n_points - self.n_labelled))
def test_al_calculate_point_probabilities_proportions(self):
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_point_probabilities_proportions()
for i, node in enumerate(self.mt1._full_leaf_list):
tot = 0
for ind in node.unlabelled_index:
tot += self.mt1._al_point_weights_proportional[ind]
self.assertTrue(abs(tot - self.mt1._al_proportions[i]) < 1e-9)
def test_al_calculate_point_probabilities_adjustment_empty(self):
with self.assertWarns(UserWarning):
self.mt1.al_calculate_leaf_proportions()
with self.assertRaises(ValueError):
self.mt1.al_calculate_point_probabilities_adjustment(1)
self.assertEqual(self.mt1._al_point_weights_adjustment, None)
def test_al_calculate_point_probabilities_adjustment_root(self):
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_point_probabilities_adjustment(21)
for val in self.mt1._al_point_weights_adjustment:
if val is not None:
self.assertAlmostEqual(val,
1 / (self.n_points - self.n_labelled))
def test_al_calculate_point_probabilities_adjustment(self):
# TODO: Try and improve this test. It passing is not very informative
lbda = 0.5
seed = 1
self.mt1.update_life_time(lbda, set_seed=seed)
self.mt1.input_data(self.data, self.labelled_indices, self.labels)
self.mt1.al_calculate_leaf_proportions()
self.mt1.al_calculate_point_probabilities_adjustment(21)
self.assertAlmostEqual(
sum([x for x in self.mt1._al_point_weights_adjustment if x]), 1)
# for val in self.mt1._al_point_weights_adjustment:
# if not None:
# print(val)
###########################################
# Checking reasonable performance of tree
def test_constant_value_performance_root(self):
self.mt1.input_data(self.data, self.labelled_indices,
[1] * self.n_labelled)
self.assertEqual(self.mt1.predict([0.5] * self.d), 1)
def test_constant_value_performance(self):
seed = 1
self.mt1.input_data(self.data, self.labelled_indices,
[1] * self.n_labelled)
self.mt1.update_life_time(0.5, set_seed=seed)
self.assertEqual(self.mt1.predict([0.5] * self.d), 1)
def test_OLS_noiseless_better_than_mean(self):
seed = 1
labs = []
for i in self.data[:self.n_labelled]:
labs.append(sum(i))
self.mt1.input_data(self.data, self.labelled_indices, labs)
self.mt1.update_life_time(0.5, set_seed=seed)
self.mt1.make_full_leaf_list()
# for i, node in enumerate(self.mt1._full_leaf_list):
# print(len(node.labelled_index))
test_labs = []
for i in self.data[self.n_labelled:]:
test_labs.append(sum(i))
test_mean = sum(test_labs) / len(test_labs)
SST = 0
SSE = 0
for point in self.data[self.n_labelled:]:
SST += (sum(point) - test_mean)**2
SSE += (sum(point) - self.mt1.predict(point))**2
# print(SST, SSE)
self.assertTrue(SST > SSE)
def test_OLS_better_than_mean(self):
seed = 1
std = 0.1
labs = []
for i in self.data[:self.n_labelled]:
val = sum(i) + random.normalvariate(0, std)
# print(sum(i),val)
labs.append(val)
self.mt1.input_data(self.data, self.labelled_indices, labs)
self.mt1.update_life_time(0.5, set_seed=seed)
self.mt1.make_full_leaf_list()
# for i, node in enumerate(self.mt1._full_leaf_list):
# print(len(node.labelled_index))
test_labs = []
for i in self.data[self.n_labelled:]:
test_labs.append(sum(i) + random.normalvariate(0, std))
test_mean = sum(test_labs) / len(test_labs)
SST = 0
SSE = 0
for point in self.data[self.n_labelled:]:
SST += (sum(point) - test_mean)**2
SSE += (sum(point) - self.mt1.predict(point))**2
# print(SST, SSE)
self.assertTrue(SST > SSE)
MT = Mondrian_Tree([[0,1]]*p)
MT.update_life_time(n**(1/(2+p))-1, set_seed=seed)
# print(MT._num_leaves)
MT.input_data(np.concatenate((X_train, X_test),axis=0), range(1000), y_train)
MT.make_full_leaf_list()
MT.make_full_leaf_var_list()
used_leaf_counter = 0
for node in MT._full_leaf_list:
if len(node.labelled_index) != 0:
print(len(node.labelled_index), node.calculate_cell_l2_diameter())
# print('var = {}'.format(MT._full_leaf_var_list[node.full_leaf_list_pos]))
used_leaf_counter += 1
print('number of used leaves = {}'.format(used_leaf_counter))
MT.al_set_default_var_global_var()
# print(MT.al_default_var)
MT.set_default_pred_global_mean()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MT_preds = MT.predict(X_test)
MT_preds = np.array(MT_preds)
print(sum(1/X_test.shape[0]*(y_test - MT_preds)**2))
BT = DecisionTreeRegressor(random_state=seed, max_leaf_nodes = used_leaf_counter)
BT.fit(X_train, y_train)
BT_preds = BT.predict(X_test)
print(sum(1/X_test.shape[0]*(y_test - BT_preds)**2))
# print(i)
curr_num = len(node.labelled_index)
tot_num = curr_num + MT_al._al_leaf_number_new_labels[i]
# print(curr_num,tot_num, MT_al._al_proportions[i] * n_final,node.rounded_linear_dims(2))
num_new_points = MT_al._al_leaf_number_new_labels[i]
labels_to_add = node.pick_new_points(num_new_points,self_update = False, set_seed = tree_seed*i)
# print(labels_to_add)
new_labelled_points.extend(labels_to_add)
for ind in labels_to_add:
MT_al.label_point(ind, y[ind])
MT_al.set_default_pred_global_mean()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MT_al_preds = MT_al.predict(X_test)
MT_al_preds = np.array(MT_al_preds)
MT_al_MSE[n_final_ind] += sum(1/X_test.shape[0]*(y_test - MT_al_preds)**2)
# print('Done MT_al')
# MT_rn
# MT_rn = Mondrian_Tree([[0,1]]*p)
# MT_rn.update_life_time(n_final**(1/(2+p))-1, set_seed=tree_seed)
# print(MT._num_leaves)
MT_rn.input_data(X, range(n_final), y[:n_final])
MT_rn.set_default_pred_global_mean()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MT_rn_preds = MT_rn.predict(X_test)
print(curr_num, tot_num, MT._al_proportions[i] * n_final,
node.rounded_linear_dims(2))
num_new_points = MT._al_leaf_number_new_labels[i]
labels_to_add = node.pick_new_points(num_new_points,
self_update=False,
set_seed=seed * i)
# print(labels_to_add)
new_labelled_points.extend(labels_to_add)
for ind in labels_to_add:
MT.label_point(ind, y[ind])
MT.set_default_pred_global_mean()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MT_preds = MT.predict(X_test)
MT_preds = np.array(MT_preds)
print('MSE from AL = {}'.format(
sum(1 / X_test.shape[0] * (y_test - MT_preds)**2)))
MT2 = Mondrian_Tree([[0, 1]] * p)
MT2.update_life_time(n_final**(1 / (2 + p)) - 1, set_seed=seed)
# print(MT._num_leaves)
MT2.input_data(np.concatenate((X_train, X_test), axis=0), range(n_final),
y_train_rn)
MT2.set_default_pred_global_mean()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MT2_preds = MT2.predict(X_test)
MT2_preds = np.array(MT2_preds)