def test_hoeffding_tree_coverage():
# Cover memory management
max_samples = 5000
max_size_kb = 50
stream = RandomTreeGenerator(tree_random_state=23,
sample_random_state=12,
n_classes=10,
n_cat_features=2,
n_num_features=5,
n_categories_per_cat_feature=5,
max_tree_depth=15,
min_leaf_depth=3,
fraction_leaves_per_level=0.15)
nominal_attr_idx = [x for x in range(5, stream.n_features)]
# Unconstrained model has over 72 kB
learner = HoeffdingTreeClassifier(nominal_attributes=nominal_attr_idx,
leaf_prediction='mc',
memory_estimate_period=100,
max_byte_size=max_size_kb * 2**10)
X, y = stream.next_sample(max_samples)
learner.partial_fit(X, y)
assert calculate_object_size(learner, 'kB') <= max_size_kb
learner.reset()
def test_hoeffding_tree_coverage():
# Cover memory management
stream = SEAGenerator(random_state=1, noise_percentage=0.05)
X, y = stream.next_sample(5000)
learner = HoeffdingTreeClassifier(max_byte_size=30, memory_estimate_period=100, grace_period=10, leaf_prediction='mc')
learner.partial_fit(X, y, classes=stream.target_values)
learner.reset()
# Cover nominal attribute observer
stream = RandomTreeGenerator(tree_random_state=1, sample_random_state=1, n_num_features=0,
n_categories_per_cat_feature=2)
X, y = stream.next_sample(1000)
learner = HoeffdingTreeClassifier(leaf_prediction='mc', nominal_attributes=[i for i in range(10)])
learner.partial_fit(X, y, classes=stream.target_values)
