def test_KNN_adwin(test_path, package_path):
test_file = os.path.join(package_path,
'src/skmultiflow/data/datasets/sea_big.csv')
stream = FileStream(test_file, -1, 1)
stream.prepare_for_use()
learner = KNNAdwin(n_neighbors=8, leaf_size=40, max_window_size=2000)
cnt = 0
max_samples = 5000
predictions = []
correct_predictions = 0
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])
if y[0] == predictions[-1]:
correct_predictions += 1
learner.partial_fit(X, y)
cnt += 1
performance = correct_predictions / len(predictions)
expected_predictions = [
1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,
1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,
1
]
expected_correct_predictions = 40
expected_performance = 0.8163265306122449
assert np.alltrue(predictions == expected_predictions)
assert np.isclose(expected_performance, performance)
assert correct_predictions == expected_correct_predictions
def test_knn_adwin():
stream = ConceptDriftStream(stream=SEAGenerator(random_state=1),
drift_stream=SEAGenerator(
random_state=2, classification_function=2),
random_state=1,
position=250,
width=10)
stream.prepare_for_use()
learner = KNNAdwin(n_neighbors=8, leaf_size=40, max_window_size=200)
cnt = 0
max_samples = 1000
predictions = array('i')
correct_predictions = 0
wait_samples = 20
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
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1,
1
])
assert np.alltrue(predictions == expected_predictions)
expected_correct_predictions = 46
assert correct_predictions == expected_correct_predictions
learner.reset()
assert learner.window.n_samples == 0
expected_info = 'KNNAdwin(leaf_size=40, max_window_size=200, n_neighbors=8,\n' \
' nominal_attributes=None)'
assert learner.get_info() == expected_info
stream.restart()
X, y = stream.next_sample(max_samples)
learner.fit(X[:950], y[:950])
predictions = learner.predict(X[951:])
correct_predictions = sum(np.array(predictions) == y[951:])
expected_correct_predictions = 47
assert correct_predictions == expected_correct_predictions
assert type(learner.predict(X)) == np.ndarray
assert type(learner.predict_proba(X)) == np.ndarray
def demo():
""" _test_knn_adwin
This demo tests the KNNAdwin classifier on a file stream, which gives
instances coming from a SEA generator.
The test computes the performance of the KNNAdwin classifier as well as
the time to create the structure and classify max_samples (10000 by
default) instances.
"""
start = timer()
logging.basicConfig(format='%(message)s', level=logging.INFO)
# warnings.filterwarnings("ignore", ".*Passing 1d.*")
stream = FileStream('../data/datasets/sea_big.csv', -1, 1)
# stream = RandomRBFGeneratorDrift(change_speed=41.00, n_centroids=50, model_random_state=32523423,
# sample_seed=5435, n_classes=2, num_att=10, num_drift_centroids=50)
stream.prepare_for_use()
t = OneHotToCategorical([[10, 11, 12, 13],
[
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 51, 52, 53
]])
t2 = OneHotToCategorical([[10, 11, 12, 13],
[
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 51, 52, 53
]])
# knn = KNN(n_neighbors=8, max_window_size=2000, leaf_size=40)
knn = KNNAdwin(n_neighbors=8, leaf_size=40, max_window_size=2000)
# pipe = Pipeline([('one_hot_to_categorical', t), ('KNN', knn)])
compare = KNeighborsClassifier(n_neighbors=8,
algorithm='kd_tree',
leaf_size=40,
metric='euclidean')
# pipe2 = Pipeline([('one_hot_to_categorical', t2), ('KNN', compare)])
first = True
train = 200
if train > 0:
X, y = stream.next_sample(train)
# pipe.partial_fit(X, y, classes=stream.target_values)
# pipe.partial_fit(X, y, classes=stream.target_values)
# pipe2.fit(X, y)
knn.partial_fit(X, y, classes=stream.target_values)
compare.fit(X, y)
first = False
n_samples = 0
max_samples = 10000
my_corrects = 0
compare_corrects = 0
while n_samples < max_samples:
if n_samples % (max_samples / 20) == 0:
logging.info('%s%%', str((n_samples // (max_samples / 20) * 5)))
X, y = stream.next_sample()
# my_pred = pipe.predict(X)
my_pred = knn.predict(X)
# my_pred = [1]
if first:
# pipe.partial_fit(X, y, classes=stream.target_values)
# pipe.partial_fit(X, y, classes=stream.target_values)
knn.partial_fit(X, y, classes=stream.target_values)
first = False
else:
# pipe.partial_fit(X, y)
knn.partial_fit(X, y)
# compare_pred = pipe2.predict(X)
compare_pred = compare.predict(X)
if y[0] == my_pred[0]:
my_corrects += 1
if y[0] == compare_pred[0]:
compare_corrects += 1
n_samples += 1
end = timer()
print('Evaluation time: ' + str(end - start))
print(str(n_samples) + ' samples analyzed.')
print('My performance: ' + str(my_corrects / n_samples))
print('Compare performance: ' + str(compare_corrects / n_samples))