def run(X, y, hyperParams):
""" run
Test function for SAMKNN, not integrated with evaluation modules.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix, coded as 64 bits.
y: numpy.array of size n_samples
The labels for all the samples in X coded as 8 bits.
hyperParams: dict
A dictionary containing the __init__ params for the SAMKNN.
"""
r, c = get_dimensions(X)
classifier = SAMKNN(n_neighbors=hyperParams['nNeighbours'],
max_window_size=hyperParams['maxSize'],
weighting=hyperParams['knnWeights'],
stm_size_option=hyperParams['STMSizeAdaption'],
use_ltm=hyperParams['useLTM'])
logging.info('applying model on dataset')
predicted_labels = []
true_labels = []
for i in range(r):
pred = classifier.predict(np.asarray([X[i]]))
predicted_labels.append(pred[0])
true_labels.append(y[i])
classifier = classifier.partial_fit(np.asarray([X[i]]), np.asarray([y[i]]), None)
if (i % (r // 20)) == 0:
logging.info(str((i // (r / 20))*5) + "%")
accuracy = accuracy_score(true_labels, predicted_labels)
logging.info('error rate %.2f%%' % (100-100*accuracy))
def test_sam_knn():
stream = SEAGenerator(random_state=1)
stream.prepare_for_use()
hyperParams = {'maxSize': 1000, 'nNeighbours': 5, 'knnWeights': 'distance', 'STMSizeAdaption': 'maxACCApprox',
'use_ltm': False}
learner = SAMKNN(n_neighbors=hyperParams['nNeighbours'], max_window_size=hyperParams['maxSize'],
weighting=hyperParams['knnWeights'],
stm_size_option=hyperParams['STMSizeAdaption'], use_ltm=hyperParams['use_ltm'])
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('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)
assert type(learner.predict(X)) == np.ndarray
with pytest.raises(NotImplementedError):
learner.predict_proba(X)
def test_sam_knn_coverage():
stream = SEAGenerator(random_state=1)
stream.prepare_for_use()
hyperParams = {'maxSize': 50,
'n_neighbors': 3,
'weighting': 'uniform',
'stm_size_option': 'maxACC',
'min_stm_size': 10,
'use_ltm': 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['use_ltm'])
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, 0, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0])
assert np.alltrue(predictions == expected_predictions)
expected_info = "SAMKNN(ltm_size=0.4, max_window_size=None, min_stm_size=10, n_neighbors=3,\n" \
" stm_size_option='maxACC', use_ltm=True, weighting='uniform')"
assert learner.get_info() == expected_info