def test_sam_knn(package_path):
test_file = os.path.join(package_path, 'src/skmultiflow/data/datasets/sea_big.csv')
stream = FileStream(test_file)
stream.prepare_for_use()
hyperParams = {'maxSize': 1000, 'nNeighbours': 5, 'knnWeights': 'distance', 'STMSizeAdaption': 'maxACCApprox',
'useLTM': False}
learner = SAMKNN(n_neighbors=hyperParams['nNeighbours'], max_window_size=hyperParams['maxSize'],
weighting=hyperParams['knnWeights'],
stm_size_option=hyperParams['STMSizeAdaption'], use_ltm=hyperParams['useLTM'])
cnt = 0
max_samples = 5000
predictions = array('d')
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])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('d', [1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0,
0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0,
1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0,
0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0,
0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0])
assert np.alltrue(predictions == expected_predictions)
def test_grid():
clfs = [
OzaBagging(base_estimator=KNN()),
OzaBaggingAdwin(base_estimator=KNN()),
AdaptiveRandomForest(),
SAMKNN()
]
cv = CrossValidation(clfs=clfs, max_samples=1000000, test_size=1)
cv.streams = [
ConceptDriftStream(
stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=3),
drift_stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=7),
random_state=None,
alpha=90.0, # angle of change grade 0 - 90
position=250000,
width=1),
ConceptDriftStream(
stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=3),
drift_stream=LEDGeneratorDrift(has_noise=False,
noise_percentage=0.0,
n_drift_features=7),
random_state=None,
alpha=90.0, # angle of change grade 0 - 90
position=250000,
width=50000)
]
cv.test()
cv.save_summary()
def test_sam_knn_coverage(package_path):
test_file = os.path.join(package_path,
'src/skmultiflow/data/datasets/sea_big.csv')
stream = FileStream(test_file)
stream.prepare_for_use()
hyperParams = {
'maxSize': 50,
'n_neighbors': 3,
'weighting': 'uniform',
'stm_size_option': 'maxACC',
'min_stm_size': 10,
'useLTM': True
}
learner = SAMKNN(n_neighbors=hyperParams['n_neighbors'],
max_window_size=hyperParams['maxSize'],
weighting=hyperParams['weighting'],
stm_size_option=hyperParams['stm_size_option'],
min_stm_size=hyperParams['min_stm_size'],
use_ltm=hyperParams['useLTM'])
cnt = 0
max_samples = 1000
predictions = array('i')
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])
learner.partial_fit(X, y)
cnt += 1
expected_predictions = array('i', [
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1,
1
])
assert np.alltrue(predictions == expected_predictions)
def evaluation():
classifiers = [
GLVQ(prototypes_per_class=4),
HoeffdingTree(),
HAT(),
KNN(),
SAMKNN(),
LeverageBagging(),
KNNAdwin(max_window_size=1000)
] # Array mit Klassifikationsalgorithmen die getestet werden sollen
cv = CrossValidation(clfs=classifiers, max_samples=1000000, test_size=1)
cv.streams = cv.init_standard_streams() + cv.init_real_world(
) + cv.init_reoccuring_streams(
) # initialisiert Stream Generatoren des Scikit-Multiflow Package
cv.test()
cv.save_summary()
def init_classifiers():
n_prototypes_per_class = 4
sigma = 4
rslvq = RSLVQ(prototypes_per_class=4, sigma=4)
arslvq = ARSLVQ(prototypes_per_class=n_prototypes_per_class,
sigma=sigma,
confidence=0.0001,
window_size=300)
oza = OzaBaggingAdwin(base_estimator=KNN())
adf = AdaptiveRandomForest()
samknn = SAMKNN()
hat = HAT()
clfs = [samknn]
names = ["SamKnn"]
# clfs = [rslvq]
# names = ["rslvq"]
return clfs, names
total_length = int(total_length)
for data in response.iter_content(chunk_size=4096):
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
sys.stdout.write("\r[%s%s]" % ('=' * done, ' ' * (50 - done)))
sys.stdout.flush()
data = np.load(file_name, allow_pickle=True)
return data
# data = download_data()
#If dataset file is already downloaded
data = np.load(file_name, allow_pickle=True)
sam = SAMKNN()
arf = HoeffdingAdaptiveTreeClassifier()
stream = DataStream(data[:, 1:], data[:, 0].astype(int))
stream.prepare_for_use()
evaluator = EvaluatePrequential(max_samples=10000,
max_time=1000,
show_plot=True,
metrics=['accuracy', 'kappa'])
evaluator.evaluate(stream=stream,
model=[sam, arf],
model_names=['Sam', 'RSLVQ'])
def test_grid():
clfs = [RRSLVQ(prototypes_per_class=4,sigma=8),RSLVQ(prototypes_per_class=4,sigma=8),HAT(),OzaBaggingAdwin(base_estimator=KNN()),AdaptiveRandomForest(),SAMKNN()]
cv = CrossValidation(clfs=clfs,max_samples=1000000,test_size=1)
cv.streams = cv.init_reoccuring_streams()
cv.test()
cv.save_summary()
print("here")
stream.prepare_for_use()
evluator = EvaluatePrequential(batch_size=10,
max_samples=10000,
show_plot=True,
metrics=['accuracy'])
evluator.evaluate(stream=stream, model=cls, model_names=detectors)
arslvq = ARSLVQ(prototypes_per_class=n_prototypes_per_class,
drift_detector="KS",
confidence=0.05,
sigma=sigma)
oza = OzaBaggingAdwin(base_estimator=KNN())
adf = AdaptiveRandomForest()
samknn = SAMKNN()
hat = HAT()
cls = [arslvq, oza, adf, samknn, hat]
detectors = ["ARSLVQ", "OzaAdwin", "ADF", "SamKNN", "HAT"]
s1 = MIXEDGenerator(classification_function=1,
random_state=112,
balance_classes=False)
s2 = MIXEDGenerator(classification_function=0,
random_state=112,
balance_classes=False)
stream = ReoccuringDriftStream(stream=s1,
drift_stream=s2,
random_state=None,
alpha=90.0,