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 demo():
""" _test_streams
This demo tests if the streams are correctly generating samples.
:return:
"""
stream = FileStream("https://raw.githubusercontent.com/scikit-multiflow/streaming-datasets/"
"master/covtype.csv")
rbf_drift = RandomRBFGeneratorDrift(change_speed=41.00, n_centroids=50, model_seed=32523423, instance_seed=5435,
n_classes=2, n_features=10, num_drift_centroids=50)
sea = SEAGenerator()
print('1 instance:\n')
X,y = stream.next_sample()
print(X)
print(y)
X, y = sea.next_sample()
print(X)
print(y)
print('\n\n10 instances:\n')
X,y = stream.next_sample(10)
print(X)
print(y)
X, y = sea.next_sample(10)
print(X)
print(y)
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_accuracy():
# an ensemble of Adaptive Random Forests should perform at the very least 80% with 200 instances of SEAGenerator
n_samples_train = 200
n_samples_test = 200
gen = SEAGenerator(noise_percentage=0.0)
# gen.prepare_for_use()
arf = AdaptiveRandomForest()
desdd = DESDDMethod(arf)
X_train, y_train = gen.next_sample(n_samples_train)
X_test, y_test = gen.next_sample(n_samples_test)
desdd.partial_fit(X_train, y_train)
assert desdd.score(X_test, y_test) > 0.80
def demo():
""" _test_leverage_bagging
This demo tests the LeverageBagging classifier on a file stream, which gives
instances coming from a SEA generator.
The test computes the performance of the LeverageBagging classifier as well
as the time to create the structure and classify max_samples (2000 by default)
instances.
"""
logging.basicConfig(format='%(message)s', level=logging.INFO)
warnings.filterwarnings("ignore", ".*Passing 1d.*")
stream = SEAGenerator(1, noise_percentage=0.067, random_state=1)
stream.prepare_for_use()
clf = LeverageBagging(base_estimator=KNN(n_neighbors=8,
max_window_size=2000,
leaf_size=30),
n_estimators=1,
random_state=1)
sample_count = 0
correctly_classified = 0
max_samples = 2000
train_size = 200
first = True
if train_size > 0:
X, y = stream.next_sample(train_size)
clf.partial_fit(X, y, classes=stream.target_values)
first = False
logging.info('%s%%', 0.0)
while sample_count < max_samples:
if (sample_count + 1) % (max_samples / 20) == 0:
logging.info('%s%%',
str(((sample_count // (max_samples / 20) + 1) * 5)))
X, y = stream.next_sample(2)
my_pred = clf.predict(X)
if first:
clf.partial_fit(X, y, classes=stream.target_values)
first = False
else:
clf.partial_fit(X, y)
if my_pred is not None:
if y[0] == my_pred[0]:
correctly_classified += 1
sample_count += 1
print(str(sample_count) + ' samples analyzed.')
print('My performance: ' + str(correctly_classified / sample_count))
print(clf.get_info())
def test_accuracy():
# an ensemble of Naive Bayes should perform at the very least 85% with 200 instances of SEAGenerator
chunk_size = 100
n_samples_train = 1050
n_samples_test = 200
gen = SEAGenerator(noise_percentage=0.0)
# gen.prepare_for_use()
nb = NaiveBayes()
mde = MDEMethod(nb, chunk_size, KNORAU())
X_train, y_train = gen.next_sample(n_samples_train)
X_test, y_test = gen.next_sample(n_samples_test)
mde.partial_fit(X_train, y_train)
assert mde.score(X_test, y_test) > 0.85
def demo():
""" _test_oza_bagging_adwin
This demo tests the OzaBaggingADWINClassifier using KNNADWINClassifier as base estimator
on samples given by a SEAGenerator.
The test computes the performance of the OzaBaggingADWINClassifier as well
as the time to create the structure and classify max_samples (20000 by
default) instances.
"""
logging.basicConfig(format='%(message)s', level=logging.INFO)
warnings.filterwarnings("ignore", ".*Passing 1d.*")
stream = SEAGenerator(1, noise_percentage=0.067, random_state=1)
clf = OzaBaggingADWINClassifier(base_estimator=KNNADWINClassifier(
n_neighbors=8, max_window_size=2000, leaf_size=30),
n_estimators=2,
random_state=1)
sample_count = 0
correctly_classified = 0
max_samples = 20000
train_size = 10
first = True
if train_size > 0:
X, y = stream.next_sample(train_size)
clf.partial_fit(X, y, classes=stream.target_values)
first = False
while sample_count < max_samples:
if sample_count % (max_samples / 20) == 0:
logging.info('%s%%', str((sample_count // (max_samples / 20) * 5)))
X, y = stream.next_sample()
my_pred = clf.predict(X)
if first:
clf.partial_fit(X, y, classes=stream.target_values)
first = False
else:
clf.partial_fit(X, y)
if my_pred is not None:
if y[0] == my_pred[0]:
correctly_classified += 1
sample_count += 1
print(str(sample_count) + ' samples analyzed.')
print('My performance: ' + str(correctly_classified / sample_count))
def test_clone():
stream = SEAGenerator(random_state=1)
stream.prepare_for_use()
learner = NaiveBayes()
cnt = 0
max_samples = 5000
y_pred = array('i')
X_batch = []
y_batch = []
y_proba = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# 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, classes=stream.target_values)
cnt += 1
cloned = clone(learner)
assert learner._observed_class_distribution != {} and cloned._observed_class_distribution == {}
def run(self):
producer = KafkaProducer(bootstrap_servers='localhost:9092')
stream = SEAGenerator()
# Send Signal to model server to extract persist model from pickle in local file
producer.send(
topic='testTopicAdmin',
value=b'../modelPersist/online_hoeffding_tree_persist.pkl',
key=b'extract')
time.sleep(10)
while not self.stop_event.is_set():
dummy_data, dummy_label = stream.next_sample()
print("Dummy Event Generated:", str(dummy_data))
dummy_data_and_label = np.concatenate((dummy_data, dummy_label),
axis=None)
producer.send(topic='testTopic',
value=dummy_data_and_label.tobytes(),
key=b'labeled')
time.sleep(1)
# Send signal to model server to persist model
producer.send(
topic='testTopicAdmin',
value=b'../modelPersist/online_hoeffding_tree_persist.pkl',
key=b'flush')
producer.close()
def create_sea_drift_dataset(n_samples_per_concept=200, concepts=[0, 1, 2, 3]):
X_stream = []
Y_stream = []
concept_drifts = []
t = 0
gen = SEAGenerator()
gen.prepare_for_use()
for _ in concepts:
if t != 0:
concept_drifts.append(t)
X, y = gen.next_sample(batch_size=n_samples_per_concept)
X_stream.append(X)
Y_stream.append(y)
gen.generate_drift()
t += n_samples_per_concept
return {
"data": (np.concatenate(X_stream, axis=0),
np.concatenate(Y_stream, axis=0).reshape(-1, 1)),
"drifts":
np.array(concept_drifts)
}
def test_evaluate_delayed_coverage(tmpdir):
from skmultiflow.data import SEAGenerator
from skmultiflow.bayes import NaiveBayes
max_samples = 1000
# Stream
data = SEAGenerator(random_state=1)
# Get X and y
X, y = data.next_sample(max_samples)
time = generate_random_dates(seed=1, samples=max_samples)
# Setup temporal stream
stream = TemporalDataStream(X, y, time, ordered=False)
# Learner
nb = NaiveBayes()
output_file = os.path.join(str(tmpdir), "prequential_delayed_summary.csv")
metrics = ['running_time', 'model_size']
evaluator = EvaluatePrequentialDelayed(max_samples=max_samples,
metrics=metrics,
data_points_for_classification=True,
output_file=output_file)
evaluator.evaluate(stream=stream, model=nb, model_names=['NB'])
def test_half_space_trees(test_path):
stream = SEAGenerator(classification_function=0,
noise_percentage=0.1,
random_state=1)
learner = HalfSpaceTrees(n_estimators=13,
size_limit=75,
anomaly_threshold=0.90,
depth=10,
random_state=5)
cnt = 0
max_samples = 5000
y_pred = array('i')
y_proba = []
wait_samples = 500
while cnt < max_samples:
X, y = stream.next_sample()
# Scale inputs between 0 and 1
X = X / 10
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, 0, 0, 0, 1, 0, 0, 1, 0])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'test_half_space_trees.npy')
expected_proba = np.load(test_file)
assert np.allclose(y_proba, expected_proba)
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_knn():
stream = SEAGenerator(random_state=1)
stream.prepare_for_use()
learner = KNN(n_neighbors=8, max_window_size=2000, leaf_size=40)
cnt = 0
max_samples = 5000
predictions = array('i')
correct_predictions = 0
wait_samples = 100
X_batch = []
y_batch = []
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
if y[0] == predictions[-1]:
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0,
1
])
assert np.alltrue(predictions == expected_predictions)
expected_correct_predictions = 49
assert correct_predictions == expected_correct_predictions
expected_info = 'KNN(leaf_size=40, max_window_size=2000, n_neighbors=8, nominal_attributes=None)'
assert learner.get_info() == expected_info
learner.reset()
assert learner.get_info() == expected_info
X_batch = np.array(X_batch)
y_batch = np.array(y_batch)
learner.fit(X_batch[:4500], y_batch[:4500], classes=[0, 1])
predictions = learner.predict(X_batch[4501:4550])
expected_predictions = array('i', [
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,
1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
0
])
assert np.alltrue(predictions == expected_predictions)
correct_predictions = sum(predictions == y_batch[4501:4550])
expected_correct_predictions = 49
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_perceptron(test_path):
stream = SEAGenerator(random_state=1)
learner = PerceptronMask(random_state=1)
cnt = 0
max_samples = 5000
y_pred = array('i')
X_batch = []
y_batch = []
y_proba = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# 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, classes=stream.target_values)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'data_perceptron_proba.npy')
y_proba_expected = np.load(test_file)
assert np.allclose(y_proba, y_proba_expected)
expected_info = "PerceptronMask(alpha=0.0001, class_weight=None, early_stopping=False, " \
"eta0=1.0, fit_intercept=True, max_iter=1000, n_iter_no_change=5, " \
"n_jobs=None, penalty=None, random_state=1, shuffle=True, tol=0.001, " \
"validation_fraction=0.1, verbose=0, warm_start=False)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
# Coverage tests
learner.reset()
if not sklearn_version.startswith("0.21"):
learner.fit(X=np.asarray(X_batch[:4500]),
y=np.asarray(y_batch[:4500], dtype=int))
else:
# Root cause of failure (TypeError: an integer is required) is in the fit() method
# in sklearn 0.21.0. This is a workaround until a fix is made available in sklearn
learner.partial_fit(X=np.asarray(X_batch[:4500]),
y=np.asarray(y_batch[:4500]),
classes=stream.target_values)
learner.predict(X=X_batch[4501:]) # Run for coverage
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_standardize():
stream = SEAGenerator(random_state=1)
learner = WeightedKNNClassifier(n_neighbors=8,
max_window_size=2000,
leaf_size=40,
standardize=True)
# # Test for Implementing moving average
example_features = np.array(
[np.array([[1, 2]]),
np.array([[2, 4]]),
np.array([[3, 9]])])
example_targets = np.array([[1], [1], [1]])
for i in range(len(example_features)):
learner.partial_fit(example_features[i], example_targets[i])
moving_average = learner.get_mean
assert type(moving_average) is np.ndarray
assert np.alltrue(moving_average == np.array([[2, 5]]))
moving_sd = learner.get_sd
assert type(moving_sd) is np.ndarray
assert np.alltrue(moving_sd.astype(int) == np.array([[0, 2]]))
stream = SEAGenerator(random_state=1)
learner = WeightedKNNClassifier(n_neighbors=8,
max_window_size=2000,
leaf_size=40,
standardize=True)
cnt = 0
max_samples = 5000
predictions = array('i')
correct_predictions = 0
wait_samples = 100
X_batch = []
y_batch = []
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# Test every n samples
if (cnt % wait_samples == 0) and (cnt != 0):
predictions.append(learner.predict(X)[0])
if y[0] == predictions[-1]:
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0,
1
])
assert np.alltrue(predictions == expected_predictions)
def test_ensemble_size():
# since each member of the ensemble is initialized when the number of instances reach the chunk size, the size of
# the ensemble should n_samples // chunk_size
chunk_size = 100
n_samples = 1050
gen = SEAGenerator()
# gen.prepare_for_use()
dynse = DYNSEMethod(NaiveBayes(), chunk_size, ModifiedRank())
X, y = gen.next_sample(n_samples)
dynse.partial_fit(X, y)
assert len(dynse.ensemble) == n_samples // chunk_size
def test_ensemble_size():
# since each member of the ensemble is initialized when the number of instances reach the chunk size, the size of
# the ensemble should n_samples // chunk_size
chunk_size = 100
n_samples = 1050
gen = SEAGenerator(balance_classes=True)
# gen.prepare_for_use()
mde = MDEMethod(NaiveBayes(), chunk_size, KNORAE(), alpha=0.0)
X, y = gen.next_sample(n_samples)
mde.partial_fit(X, y)
assert len(mde.ensemble) == n_samples // chunk_size
def test_perceptron(test_path):
stream = SEAGenerator(random_state=1)
stream.prepare_for_use()
learner = PerceptronMask(random_state=1)
cnt = 0
max_samples = 5000
y_pred = array('i')
X_batch = []
y_batch = []
y_proba = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# 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, classes=stream.target_values)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'data_perceptron_proba.npy')
y_proba_expected = np.load(test_file)
assert np.allclose(y_proba, y_proba_expected)
expected_info = 'PerceptronMask: - penalty: None - alpha: 0.0001 - fit_intercept: True - max_iter: 1000 ' \
'- tol: 0.001 - shuffle: True - eta0: 1.0 - warm_start: False - class_weight: None - n_jobs: 1'
assert learner.get_info() == expected_info
# Coverage tests
learner.reset()
learner.fit(X=X_batch[:4500], y=y_batch[:4500])
y_pred = learner.predict(X=X_batch[4501:])
accuracy = accuracy_score(y_true=y_batch[4501:], y_pred=y_pred)
expected_accuracy = 0.8897795591182365
# assert np.isclose(expected_accuracy, accuracy) # Removed due to npn-replicable error in Travis build
assert 'estimator' == learner.get_class_type()
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_naive_bayes(test_path):
stream = SEAGenerator(random_state=1)
stream.prepare_for_use()
learner = NaiveBayes()
cnt = 0
max_samples = 5000
y_pred = array('i')
X_batch = []
y_batch = []
y_proba = []
wait_samples = 100
while cnt < max_samples:
X, y = stream.next_sample()
X_batch.append(X[0])
y_batch.append(y[0])
# 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, classes=stream.target_values)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,
1
])
assert np.alltrue(y_pred == expected_predictions)
test_file = os.path.join(test_path, 'data_naive_bayes_proba.npy')
y_proba_expected = np.load(test_file)
assert np.allclose(y_proba, y_proba_expected)
expected_info = 'NaiveBayes: nominal attributes: [] - '
assert learner.get_info() == expected_info
learner.reset()
learner.fit(X=np.array(X_batch[:4500]), y=np.array(y_batch[:4500]))
expected_score = 0.9378757515030061
assert np.isclose(
expected_score,
learner.score(X=np.array(X_batch[4501:]), y=np.array(y_batch[4501:])))
assert 'estimator' == learner.get_class_type()
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def test_online_csb2():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
stream.prepare_for_use()
nb = NaiveBayes()
learner = OnlineCSB2Classifier(base_estimator=nb,
n_estimators=3,
cost_positive=1,
cost_negative=0.9,
random_state=112)
first = True
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
expected_correct_predictions = 43
expected_performance = 0.8775510204081632
assert np.alltrue(predictions == expected_predictions)
assert np.isclose(expected_performance, performance)
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
expected_info = "OnlineCSB2Classifier(base_estimator=NaiveBayes(nominal_attributes=None), cost_negative=0.9, " \
"cost_positive=1, drift_detection=True, n_estimators=3, random_state=112)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
def test_online_rus_1():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
stream.prepare_for_use()
nb = NaiveBayes()
learner = OnlineRUSBoost(base_estimator=nb,
n_estimators=3,
sampling_rate=5,
algorithm=1,
random_state=112)
first = True
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
1
]
expected_correct_predictions = 33
expected_performance = 0.673469387755102
assert np.alltrue(predictions == expected_predictions)
assert np.isclose(expected_performance, performance)
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
expected_info = "OnlineRUSBoost(algorithm=1, base_estimator=NaiveBayes(nominal_attributes=None),\n" \
" drift_detection=True, n_estimators=3, random_state=112,\n" \
" sampling_rate=5)"
assert learner.get_info() == expected_info
def test_leverage_bagging():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
stream.prepare_for_use()
knn = KNNClassifier(n_neighbors=8, leaf_size=40, max_window_size=2000)
learner = LeverageBaggingClassifier(base_estimator=knn,
n_estimators=3,
random_state=112)
first = True
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
assert np.alltrue(predictions == expected_predictions)
expected_performance = 0.8571428571428571
assert np.isclose(expected_performance, performance)
expected_correct_predictions = 42
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
expected_info = "LeverageBaggingClassifier(base_estimator=KNNClassifier(leaf_size=40, max_window_size=2000, " \
"n_neighbors=8, nominal_attributes=None), delta=0.002, enable_code_matrix=False, " \
"leverage_algorithm='leveraging_bag', n_estimators=3, random_state=112, w=6)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
def demo():
""" _test_streams
This demo tests if the streams are correctly generating samples.
:return:
"""
stream = FileStream('../data/datasets/covtype.csv', -1, 1)
stream.prepare_for_use()
rbf_drift = RandomRBFGeneratorDrift(change_speed=41.00,
n_centroids=50,
model_seed=32523423,
instance_seed=5435,
n_classes=2,
n_features=10,
num_drift_centroids=50)
rbf_drift.prepare_for_use()
sea = SEAGenerator()
print('1 instance:\n')
X, y = stream.next_sample()
print(X)
print(y)
X, y = sea.next_sample()
print(X)
print(y)
print('\n\n10 instances:\n')
X, y = stream.next_sample(10)
print(X)
print(y)
X, y = sea.next_sample(10)
print(X)
print(y)
def test_additive_expert_ensemble_weakest():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
stream.prepare_for_use()
learner = AdditiveExpertEnsemble(3,
NaiveBayes(),
beta=0.5,
gamma=0.1,
pruning='weakest')
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
first = True
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
expected_correct_predictions = 45
expected_performance = 0.9183673469387755
assert np.alltrue(predictions == expected_predictions)
assert np.isclose(expected_performance, performance)
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
expected_info = "AdditiveExpertEnsemble(base_estimator=NaiveBayes(nominal_attributes=None),\n" \
" beta=0.5, gamma=0.1, n_estimators=3, pruning='weakest')"
assert learner.get_info() == expected_info
def test_oza_bagging_adwin():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
stream.prepare_for_use()
knn = KNN(n_neighbors=8, leaf_size=40, max_window_size=2000)
learner = OzaBaggingAdwin(base_estimator=knn,
n_estimators=3,
random_state=112)
first = True
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
assert np.alltrue(predictions == expected_predictions)
expected_performance = 0.8979591836734694
assert np.isclose(expected_performance, performance)
expected_correct_predictions = 44
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
expected_info = "OzaBaggingAdwin(base_estimator=KNN(leaf_size=40, max_window_size=2000,\n" \
" n_neighbors=8, nominal_attributes=None),\n" \
" n_estimators=3, random_state=112)"
assert learner.get_info() == expected_info
def test_oza_bagging():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
knn = KNNClassifier(n_neighbors=8, leaf_size=40, max_window_size=2000)
learner = OzaBaggingClassifier(base_estimator=knn,
n_estimators=3,
random_state=112)
first = True
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
assert np.alltrue(predictions == expected_predictions)
expected_performance = 0.8979591836734694
assert np.isclose(expected_performance, performance)
expected_correct_predictions = 44
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
expected_info = "OzaBaggingClassifier(base_estimator=KNNClassifier(leaf_size=40, " \
"max_window_size=2000, metric='euclidean', n_neighbors=8), " \
"n_estimators=3, random_state=112)"
info = " ".join([line.strip() for line in learner.get_info().split()])
assert info == expected_info
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 = stream.next_sample(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=stream.target_values)
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 = 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_dynamic_weighted_majority():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
learner = DynamicWeightedMajorityClassifier(3,
NaiveBayes(),
beta=0.5,
theta=0.01)
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
first = True
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
expected_correct_predictions = 44
expected_performance = 0.8979591836734694
assert np.alltrue(predictions == expected_predictions)
assert np.isclose(expected_performance, performance)
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
expected_info = 'DynamicWeightedMajorityClassifier(base_estimator=NaiveBayes(nominal_attributes=None),\n' \
' beta=0.5, n_estimators=3, period=50,\n' \
' theta=0.01)'
assert learner.get_info() == expected_info
def test_online_adac2():
stream = SEAGenerator(1, noise_percentage=0.067, random_state=112)
stream.prepare_for_use()
nb = NaiveBayes()
learner = OnlineAdaC2(base_estimator=nb,
n_estimators=3,
random_state=112,
cost_positive=1,
cost_negative=1)
first = True
cnt = 0
max_samples = 5000
predictions = []
wait_samples = 100
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
if first:
learner.partial_fit(X, y, classes=stream.target_values)
first = False
else:
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1,
1
]
expected_correct_predictions = 44
expected_performance = 0.8979591836734694
assert np.alltrue(predictions == expected_predictions)
assert np.isclose(expected_performance, performance)
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
