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_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)