def test_hoeffding_tree(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)
stream.prepare_for_use()
nominal_attr_idx = [x for x in range(5, stream.n_features)]
learner = HoeffdingTree(nominal_attributes=nominal_attr_idx)
cnt = 0
max_samples = 5000
predictions = array('d')
proba_predictions = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if cnt % wait_samples == 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('d', [0.0, 0.0, 1.0, 3.0, 0.0, 0.0, 3.0, 0.0, 1.0, 1.0,
2.0, 0.0, 2.0, 1.0, 1.0, 2.0, 1.0, 3.0, 0.0, 1.0,
1.0, 1.0, 1.0, 0.0, 3.0, 1.0, 2.0, 1.0, 1.0, 3.0,
2.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 0.0, 1.0,
2.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 2.0])
test_file = os.path.join(test_path, 'test_hoeffding_tree.npz')
data = np.load(test_file)
expected_proba_predictions_0 = data["a"]
expected_proba_predictions_1 = data["b"]
assert np.alltrue(predictions == expected_predictions)
assert np.alltrue(proba_predictions == expected_proba_predictions_0) or \
np.alltrue(proba_predictions == expected_proba_predictions_1)
assert np.alltrue(predictions == expected_predictions)
expected_info = 'HoeffdingTree: max_byte_size: 33554432 - memory_estimate_period: 1000000 - grace_period: 200 ' \
'- split_criterion: info_gain - split_confidence: 1e-07 - tie_threshold: 0.05 ' \
'- binary_split: False - stop_mem_management: False - remove_poor_atts: False ' \
'- no_pre_prune: False - leaf_prediction: nba - nb_threshold: 0 - nominal_attributes: [5, 6, 7,' \
' 8, 9, 10, 11, 12, 13, 14] - '
assert learner.get_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'
expected_model_2 = 'Leaf = Class 1.0 | {1.0: 1745.0, 2.0: 978.0, 0.0: 1423.0, 3.0: 854.0}\n'
assert (learner.get_model_description() == expected_model_1) \
or (learner.get_model_description() == expected_model_2)
def test_hoeffding_tree():
stream = RandomTreeGenerator(tree_seed=23,
instance_seed=12,
n_classes=4,
n_nominal_attributes=2,
n_numerical_attributes=5,
n_values_per_nominal=5,
max_depth=6,
min_leaf_depth=3,
fraction_leaves_per_level=0.15)
stream.prepare_for_use()
nominal_attr_idx = [
x for x in range(15, len(stream.get_attributes_header()))
]
learner = HoeffdingTree(nominal_attributes=nominal_attr_idx)
cnt = 0
max_samples = 5000
predictions = array('d')
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_instance()
# Test every n samples
if cnt % wait_samples == 0:
predictions.append(learner.predict(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [
0.0, 3.0, 2.0, 1.0, 1.0, 2.0, 0.0, 2.0, 0.0, 3.0, 3.0, 0.0, 1.0, 0.0,
0.0, 1.0, 1.0, 1.0, 0.0, 3.0, 0.0, 1.0, 1.0, 0.0, 2.0, 2.0, 1.0, 2.0,
0.0, 0.0, 0.0, 2.0, 1.0, 0.0, 2.0, 0.0, 2.0, 2.0, 0.0, 1.0, 1.0, 3.0,
1.0, 0.0, 3.0, 0.0, 1.0, 1.0, 0.0, 0.0
])
assert np.alltrue(predictions == expected_predictions)
expected_info = 'HoeffdingTree: max_byte_size: 33554432 - memory_estimate_period: 1000000 - grace_period: 200 ' \
'- split_criterion: info_gain - split_confidence: 1e-07 - tie_threshold: 0.05 ' \
'- binary_split: False - stop_mem_management: False - remove_poor_atts: False ' \
'- no_pre_prune: False - leaf_prediction: nba - nb_threshold: 0 - nominal_attributes: [] - '
assert learner.get_info() == expected_info
expected_model_1 = 'Leaf = Class 1.0 | {0.0: 1384.0, 1.0: 1720.0, 2.0: 1005.0, 3.0: 891.0}\n'
expected_model_2 = 'Leaf = Class 1.0 | {1.0: 1720.0, 2.0: 1005.0, 0.0: 1384.0, 3.0: 891.0}\n'
assert (learner.get_model_description() == expected_model_1) \
or (learner.get_model_description() == expected_model_2)