def test_hoeffding_adaptive_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 = HoeffdingAdaptiveTreeClassifier(
nominal_attributes=nominal_attr_idx, random_state=1)
learner.partial_fit(X, y, classes=np.unique(y))
expected_description = "if Attribute 0 = -15.0:\n" \
" Leaf = Class 2 | {2: 475.0}\n" \
"if Attribute 0 = 0.0:\n" \
" Leaf = Class 0 | {0: 560.0, 1: 345.0}\n" \
"if Attribute 0 = 1.0:\n" \
" Leaf = Class 1 | {0: 416.0, 1: 464.0}\n" \
"if Attribute 0 = 2.0:\n" \
" Leaf = Class 1 | {0: 335.0, 1: 504.0}\n" \
"if Attribute 0 = 3.0:\n" \
" Leaf = Class 1 | {0: 244.0, 1: 644.0}\n" \
"if Attribute 0 = -30.0:\n" \
" Leaf = Class 3 | {3: 65.0, 4: 55.0}\n"
assert learner.get_model_description() == expected_description
def test_hoeffding_adaptive_tree_mc(test_path):
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1,
noise_percentage=0.05),
drift_stream=SEAGenerator(
random_state=2,
classification_function=2,
noise_percentage=0.05),
random_state=1,
position=250,
width=10)
learner = HoeffdingAdaptiveTreeClassifier(leaf_prediction='mc')
cnt = 0
max_samples = 1000
y_pred = array('i')
y_proba = []
wait_samples = 20
while cnt < max_samples:
X, y = stream.next_sample()
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
y_pred.append(learner.predict(X)[0])
y_proba.append(learner.predict_proba(X)[0])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_mc.npy')
data = np.load(test_file)
assert np.allclose(y_proba, data)
expected_info = "HoeffdingAdaptiveTreeClassifier(binary_split=False, bootstrap_sampling=True, grace_period=200, " \
"leaf_prediction='mc', max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, " \
"no_preprune=False, nominal_attributes=None, 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: 398.0, 1.0: 1000.0}\n'
assert (learner.get_model_description() == expected_model_1)
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
stream.restart()
X, y = stream.next_sample(5000)
learner = HoeffdingAdaptiveTreeClassifier(max_byte_size=30,
leaf_prediction='mc',
grace_period=10)
learner.partial_fit(X, y)
def test_hoeffding_adaptive_tree_alternate_tree():
stream = AGRAWALGenerator(random_state=7)
learner = HoeffdingAdaptiveTreeClassifier(random_state=1)
cnt = 0
change_point1 = 1500
change_point2 = 2500
change_point3 = 4000
max_samples = 5000
while cnt < max_samples:
X, y = stream.next_sample()
learner.partial_fit(X, y)
cnt += 1
if cnt > change_point1:
stream.generate_drift()
change_point1 = float('Inf')
expected_description = "if Attribute 2 <= 63.63636363636363:\n" \
" if Attribute 2 <= 39.54545454545455:\n" \
" Leaf = Class 0 | {0: 397.5023676194098}\n" \
" if Attribute 2 > 39.54545454545455:\n" \
" if Attribute 2 <= 58.81818181818181:\n" \
" Leaf = Class 1 | {1: 299.8923824199619}\n" \
" if Attribute 2 > 58.81818181818181:\n" \
" Leaf = Class 0 | {0: 54.0, 1: 20.107617580038095}\n" \
"if Attribute 2 > 63.63636363636363:\n" \
" Leaf = Class 0 | {0: 512.5755895049351}\n"
assert expected_description == learner.get_model_description()
if cnt > change_point2:
stream.generate_drift()
change_point2 = float('Inf')
expected_description = "if Attribute 8 <= 268547.7178694747:\n" \
" Leaf = Class 0 | {0: 446.18690518790413, 1: 80.6180778406834}\n" \
"if Attribute 8 > 268547.7178694747:\n" \
" Leaf = Class 1 | {0: 36.8130948120959, 1: 356.38192215931656}\n"
assert expected_description == learner.get_model_description()
if cnt > change_point3:
stream.generate_drift()
change_point3 = float('Inf')
expected_description = "Leaf = Class 0 | {0: 1083.0, 1: 2.0}\n"
assert expected_description == learner.get_model_description()