def test_extremely_fast_decision_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 = ExtremelyFastDecisionTreeClassifier(max_byte_size=30,
memory_estimate_period=100,
grace_period=10,
leaf_prediction='nba')
learner.partial_fit(X, y, classes=stream.target_values)
learner.reset()
# Cover nominal attribute observer
stream = RandomTreeGenerator(tree_random_state=23,
sample_random_state=12,
n_classes=2,
n_cat_features=2,
n_categories_per_cat_feature=4,
n_num_features=1,
max_tree_depth=30,
min_leaf_depth=10,
fraction_leaves_per_level=0.45)
stream.prepare_for_use()
X, y = stream.next_sample(5000)
learner = ExtremelyFastDecisionTreeClassifier(
leaf_prediction='nba', nominal_attributes=[i for i in range(1, 9)])
learner.partial_fit(X, y, classes=stream.target_values)
def test_extremely_fast_decision_tree_coverage():
# Cover memory management
max_size_kb = 20
stream = SEAGenerator(random_state=1, noise_percentage=0.05)
X, y = get_next_n_samples(stream, 5000)
# Unconstrained model has over 50 kB
learner = ExtremelyFastDecisionTreeClassifier(leaf_prediction='mc',
memory_estimate_period=200,
max_byte_size=max_size_kb *
2**10,
min_samples_reevaluate=2500)
learner.partial_fit(X, y, classes=[0, 1])
assert calculate_object_size(learner, 'kB') <= max_size_kb
learner.reset()
# Cover nominal attribute observer
stream = RandomTreeGenerator(tree_random_state=23,
sample_random_state=12,
n_classes=2,
n_cat_features=2,
n_categories_per_cat_feature=4,
n_num_features=1,
max_tree_depth=30,
min_leaf_depth=10,
fraction_leaves_per_level=0.45)
X, y = get_next_n_samples(stream, 5000)
learner = ExtremelyFastDecisionTreeClassifier(
leaf_prediction='nba', nominal_attributes=[i for i in range(1, 9)])
learner.partial_fit(X, y, classes=[0, 1])
def test_extremely_fast_decision_tree_nba(test_path):
stream = RandomTreeGenerator(tree_random_state=23,
sample_random_state=12,
n_classes=2,
n_cat_features=2,
n_categories_per_cat_feature=4,
n_num_features=1,
max_tree_depth=30,
min_leaf_depth=10,
fraction_leaves_per_level=0.45)
stream.prepare_for_use()
learner = ExtremelyFastDecisionTreeClassifier(
nominal_attributes=[i for i in range(1, 9)])
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', [
1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0
])
assert np.alltrue(predictions == expected_predictions)
test_file = os.path.join(test_path, 'test_hoeffding_anytime_tree.npy')
expected_proba = np.load(test_file)[:49, :]
assert np.allclose(proba_predictions, expected_proba)
expected_info = "ExtremelyFastDecisionTreeClassifier(binary_split=False, grace_period=200, " \
"leaf_prediction='nba', max_byte_size=33554432, memory_estimate_period=1000000, " \
"min_samples_reevaluate=20, nb_threshold=0, nominal_attributes=[1, 2, 3, 4, 5, 6, 7, 8], " \
"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 = 'ifAttribute1=0.0:ifAttribute3=0.0:Leaf=Class1|{0:260.0,1:287.0}' \
'ifAttribute3=1.0:Leaf=Class0|{0:163.0,1:117.0}ifAttribute1=1.0:Leaf=Class0|{0:718.0,1:495.0}'
assert (learner.get_model_description().replace("\n", " ").replace(
" ", "") == expected_model.replace(" ", ""))
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_extremely_fast_decision_tree_nb_gini(test_path):
stream = RandomTreeGenerator(tree_random_state=23,
sample_random_state=12,
n_classes=2,
n_cat_features=2,
n_categories_per_cat_feature=4,
n_num_features=1,
max_tree_depth=30,
min_leaf_depth=10,
fraction_leaves_per_level=0.45)
stream.prepare_for_use()
learner = ExtremelyFastDecisionTreeClassifier(
nominal_attributes=[i for i in range(1, 9)],
leaf_prediction='nb',
split_criterion='gini')
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', [
1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,
0
])
assert np.alltrue(predictions == expected_predictions)
expected_info = "ExtremelyFastDecisionTreeClassifier(binary_split=False, grace_period=200, " \
"leaf_prediction='nb', max_byte_size=33554432, memory_estimate_period=1000000, " \
"min_samples_reevaluate=20, nb_threshold=0, nominal_attributes=[1, 2, 3, 4, 5, 6, 7, 8], " \
"split_confidence=1e-07, split_criterion='gini', stop_mem_management=False, tie_threshold=0.05)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
ds = ConceptDriftStream(random_state=777, position=30000)
ds
# Output:
#ConceptDriftStream(alpha=0.0,
# drift_stream=AGRAWALGenerator(balance_classes=False,
# classification_function=2,
# perturbation=0.0,
# random_state=112),
# position=30000, random_state=777,
# stream=AGRAWALGenerator(balance_classes=False,
# classification_function=0,
# perturbation=0.0, random_state=112),
# width=1000)
# Instantiate the model object
model_hat = ExtremelyFastDecisionTreeClassifier()
# Prequential evaluation
eval1 = EvaluatePrequential(pretrain_size=400,
max_samples=300000,
batch_size=1,
n_wait=100,
max_time=2000,
show_plot=False,
metrics=['accuracy'])
eval1.evaluate(stream=ds, model=model_hat)
# Holdout evaluation
eval2 = EvaluateHoldout(max_samples=30000,
max_time=2000,