def test_hoeffding_tree_nb(test_path):
stream = RandomTreeGenerator(tree_random_state=23,
sample_random_state=12,
n_classes=4,
n_cat_features=2,
n_num_features=5,
n_categories_per_cat_feature=5,
max_tree_depth=6,
min_leaf_depth=3,
fraction_leaves_per_level=0.15)
nominal_attr_idx = [x for x in range(5, stream.n_features)]
learner = HoeffdingTreeClassifier(nominal_attributes=nominal_attr_idx,
leaf_prediction='nb')
cnt = 0
max_samples = 5000
predictions = array('i')
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
proba_predictions.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
0, 1, 3, 0, 0, 3, 0, 1, 1, 2, 0, 2, 1, 1, 2, 1, 3, 0, 1, 1, 1, 1, 0, 3,
1, 2, 1, 1, 3, 2, 1, 2, 2, 2, 1, 1, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 1, 3,
2
])
assert np.alltrue(predictions == expected_predictions)
expected_info = "HoeffdingTreeClassifier(binary_split=False, grace_period=200, leaf_prediction='nb', " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=[5, 6, 7, 8, 9, 10, 11, 12, 13, 14], remove_poor_atts=False, " \
"split_confidence=1e-07, split_criterion='info_gain', stop_mem_management=False, " \
"tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
def test_hoeffding_tree_nba(test_path):
stream = RandomTreeGenerator(tree_random_state=23, sample_random_state=12, n_classes=4, n_cat_features=2,
n_num_features=5, n_categories_per_cat_feature=5, max_tree_depth=6, min_leaf_depth=3,
fraction_leaves_per_level=0.15)
nominal_attr_idx = [x for x in range(5, stream.n_features)]
learner = HoeffdingTreeClassifier(nominal_attributes=nominal_attr_idx)
cnt = 0
max_samples = 5000
predictions = array('i')
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
proba_predictions.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [0, 1, 3, 0, 0, 3, 0, 1, 1, 2,
0, 2, 1, 1, 2, 1, 3, 0, 1, 1,
1, 1, 0, 3, 1, 2, 1, 1, 3, 2,
1, 2, 2, 2, 1, 1, 1, 0, 1, 2,
0, 2, 0, 0, 0, 0, 1, 3, 2])
test_file = os.path.join(test_path, 'test_hoeffding_tree.npy')
data = np.load(test_file)
assert np.alltrue(predictions == expected_predictions)
assert np.allclose(proba_predictions, data)
expected_info = "HoeffdingTreeClassifier(binary_split=False, grace_period=200, leaf_prediction='nba', " \
"max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, " \
"nominal_attributes=[5, 6, 7, 8, 9, 10, 11, 12, 13, 14], remove_poor_atts=False, " \
"split_confidence=1e-07, split_criterion='info_gain', stop_mem_management=False, " \
"tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
expected_model_1 = 'Leaf = Class 1.0 | {0.0: 1423.0, 1.0: 1745.0, 2.0: 978.0, 3.0: 854.0}\n'
assert (learner.get_model_description() == expected_model_1)
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
X, y = stream.next_sample(20000)
learner.split_criterion = 'hellinger'
learner.partial_fit(X, y)
expected_rules = 'Att (5) == 0.000 and Att (12) == 0.000 | class: 1\n' + \
'Att (5) == 0.000 and Att (12) == 1.000 | class: 1\n' + \
'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) <= 0.550 and Att (3) <= 0.730 | class: 0\n' +\
'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) <= 0.550 and Att (3) > 0.730 | class: 2\n' + \
'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) > 0.550 and Att (1) <= 0.800 | class: 0\n' + \
'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) > 0.550 and Att (1) > 0.800 and Att (14) == 0.000' \
' | class: 0\n' + \
'Att (5) == 1.000 and Att (13) == 0.000 and Att (1) > 0.550 and Att (1) > 0.800 and Att (14) == 1.000' \
' | class: 1\n' + \
'Att (5) == 1.000 and Att (13) == 1.000 and Att (3) <= 0.730 | class: 1\n' + \
'Att (5) == 1.000 and Att (13) == 1.000 and Att (3) > 0.730 | class: 0\n'
assert expected_rules == learner.get_rules_description()
