def test_hoeffding_tree_categorical_features(test_path):
data_path = os.path.join(test_path, 'ht_categorical_features_testcase.npy')
stream = np.load(data_path)
# Removes the last two columns (regression targets)
stream = stream[:, :-2]
X, y = stream[:, :-1], stream[:, -1]
nominal_attr_idx = np.arange(7).tolist()
learner = HoeffdingTreeClassifier(nominal_attributes=nominal_attr_idx)
learner.partial_fit(X, y, classes=np.unique(y))
expected_description = "if Attribute 0 = -15.0:\n" \
" Leaf = Class 2 | {2: 350.0}\n" \
"if Attribute 0 = 0.0:\n" \
" Leaf = Class 0 | {0: 420.0, 1: 252.0}\n" \
"if Attribute 0 = 1.0:\n" \
" Leaf = Class 1 | {0: 312.0, 1: 332.0}\n" \
"if Attribute 0 = 2.0:\n" \
" Leaf = Class 1 | {0: 236.0, 1: 383.0}\n" \
"if Attribute 0 = 3.0:\n" \
" Leaf = Class 1 | {0: 168.0, 1: 459.0}\n" \
"if Attribute 0 = -30.0:\n" \
" Leaf = Class 3.0 | {3.0: 46.0, 4.0: 42.0}\n"
assert learner.get_model_description() == expected_description
def test_hoeffding_tree_model_information():
stream = SEAGenerator(random_state=1, noise_percentage=0.05)
X, y = stream.next_sample(5000)
nominal_attr_idx = [x for x in range(5, stream.n_features)]
learner = HoeffdingTreeClassifier(nominal_attributes=nominal_attr_idx)
learner.partial_fit(X, y, classes=stream.target_values)
expected_info = {
'Tree size (nodes)': 5,
'Tree size (leaves)': 3,
'Active learning nodes': 3,
'Tree depth': 2,
'Active leaf byte size estimate': 0.0,
'Inactive leaf byte size estimate': 0.0,
'Byte size estimate overhead': 1.0
}
observed_info = learner.get_model_measurements
for k in expected_info:
assert k in observed_info
assert expected_info[k] == observed_info[k]
expected_description = "if Attribute 0 <= 4.549969620513424:\n" \
" if Attribute 1 <= 5.440182925299016:\n" \
" Leaf = Class 0 | {0: 345.54817975126275, 1: 44.43855503614928}\n" \
" if Attribute 1 > 5.440182925299016:\n" \
" Leaf = Class 1 | {0: 54.451820248737235, 1: 268.5614449638507}\n" \
"if Attribute 0 > 4.549969620513424:\n" \
" Leaf = Class 1 | {0: 390.5845685762964, 1: 2372.3747376855454}\n" \
assert expected_description == learner.get_model_description()
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()