def test_hoeffding_tree_model_information():
stream = SEAGenerator(random_state=1, noise_percentage=0.05)
stream.prepare_for_use()
X, y = stream.next_sample(5000)
nominal_attr_idx = [x for x in range(5, stream.n_features)]
learner = HoeffdingTree(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_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 = HoeffdingTree(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_coverage():
# Cover memory management
stream = SEAGenerator(random_state=1, noise_percentage=0.05)
stream.prepare_for_use()
X, y = stream.next_sample(5000)
learner = HoeffdingTree(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)
stream.prepare_for_use()
X, y = stream.next_sample(1000)
learner = HoeffdingTree(leaf_prediction='mc',
nominal_attributes=[i for i in range(10)])
learner.partial_fit(X, y, classes=stream.target_values)
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('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 = '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)
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
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)
stream.prepare_for_use()
nominal_attr_idx = [x for x in range(5, stream.n_features)]
learner = HoeffdingTree(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 = "HoeffdingTree(binary_split=False, grace_period=200, leaf_prediction='nb',\n" \
" max_byte_size=33554432, memory_estimate_period=1000000,\n" \
" nb_threshold=0, no_preprune=False,\n" \
" nominal_attributes=[5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n" \
" remove_poor_atts=False, split_confidence=1e-07,\n" \
" split_criterion='info_gain', stop_mem_management=False,\n" \
" tie_threshold=0.05)"
assert learner.get_info() == expected_info
class VFDT(IncrementalClassifier):
def __init__(self,
grace_period=200,
split_confidence=0.5,
leaf_prediction='nba',
split_criterion='info_gain'):
super().__init__()
self.clf = HoeffdingTree(split_confidence=split_confidence,
grace_period=grace_period,
leaf_prediction=leaf_prediction,
split_criterion=split_criterion)
def partial_fit(self, one_row):
self.clf.partial_fit([one_row[0]], [one_row[1]])
def predict(self, x):
return self.clf.predict(x)
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)
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('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 = "HoeffdingTree(binary_split=False, grace_period=200, leaf_prediction='nba',\n" \
" max_byte_size=33554432, memory_estimate_period=1000000,\n" \
" nb_threshold=0, no_preprune=False,\n" \
" nominal_attributes=[5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n" \
" remove_poor_atts=False, split_confidence=1e-07,\n" \
" split_criterion='info_gain', stop_mem_management=False,\n" \
" tie_threshold=0.05)"
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'
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()
probas2 = []
ys = []
# limit = 150
for chunk in tqdm(range(n_chunks[n] - 1)):
X, y = stream.get_chunk()
proba = clf.predict_proba(X)[:, 1]
try:
proba2 = clf2.predict_proba(X)[:, 1]
except:
try:
proba2 = np.concatenate(proba2)
except:
pass
clf.partial_fit(X, y)
clf2.partial_fit(X, y)
probas.append(proba)
probas2.append(proba2)
ys.append(y)
# if chunk == limit:
# break
probas = np.array(probas)
probas = probas.reshape(-1)
probas2 = np.array(probas2)
probas2 = probas2.reshape(-1)
ys = np.array(ys)
ys = ys.reshape(-1)
ls = (np.linspace(0, 1, probas.shape[0]))
# print(ls.shape)
# print(probas.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# Define a tree for fitting the complete dataset and one for streaming.
ht_complete = HoeffdingTree()
ht_partial = HoeffdingTree()
# Fit the complete dataset.
ht_complete.fit(X_train, y_train)
ht_complete_score = ht_complete.score(X_test, y_test)
print(f'Score when fitting at once: {ht_complete_score}')
# Streaming samples one after another.
timer = False
j = 0
for i in range(len(X_train)):
ht_partial.partial_fit(X_train[i].reshape(1, -1), np.array([y_train[i]]))
res.append(ht_partial.score(X_test, y_test))
print(f'Score when streaming after {i} samples: {res[-1]}')
if res[-1] >= ht_complete_score - 0.01:
print(f'(Almost) full score reached! Continue for another {20 - j} samples.')
timer = True
if timer:
j += 1
if j == 20:
break
# Plot the scores after each sample.
plt.figure(figsize=(12, 6))
plt.plot([0, i], [ht_complete_score, ht_complete_score], '--', label='Hoeffding Tree built at once')
plt.plot(res, label='Incrementally built Hoeffding Tree')
plt.xlabel('Number of Samples', fontsize=15)