def test_clone():
stream = SEAGenerator(random_state=1)
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=[0, 1])
cnt += 1
cloned = clone(learner)
assert learner._observed_class_distribution != {} and cloned._observed_class_distribution == {}
class Bayes(IncrementalClassifier):
def __init__(self):
super().__init__()
self.clf = NaiveBayes()
def partial_fit(self, one_row):
self.clf.partial_fit([one_row[0]], [one_row[1]])
def predict(self, x):
return self.clf.predict(x)
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 main():
overall_kswin_tp = overall_kswin_tn = overall_kswin_fp = overall_kswin_fn = 0
overall_adwin_tp = overall_adwin_tn = overall_adwin_fp = overall_adwin_fn = 0
# mebwin_drifts = []
overall_k_swmebwin_tp = overall_k_swmebwin_tn = overall_k_swmebwin_fp = overall_k_swmebwin_fn = 0
overall_swmebwin_tp = overall_swmebwin_tn = overall_swmebwin_fp = overall_swmebwin_fn = 0
overall_eddm_tp = overall_eddm_tn = overall_eddm_fp = overall_eddm_fn = 0
overall_ddm_tp = overall_ddm_tn = overall_ddm_fp = overall_ddm_fn = 0
for stream in streams:
print(stream.name)
f = open('drifts.txt', 'a+')
f.write(f'**{stream.name}**\n\n')
f.close()
stream.prepare_for_use()
stream.next_sample()
# mebwin = MEBWIN(epsilon=0.1, sensitivity=0.98, w_size=100, stat_size=30)
adwin = []
kswin = []
ddm = DDM(min_num_instances=30)
eddm = EDDM()
data = []
labels = []
predictions = []
kswin_drifts = []
adwin_drifts = []
# mebwin_drifts = []
k_swmebwin_drifts = []
swmebwin_drifts = []
eddm_drifts = []
ddm_drifts = []
swmebwin = SWMEBWIN(classes=stream.target_values, w_size=80, epsilon=0.05)
# k_swmebwin = Kernel_SWMEBWIN(classes=stream.target_values, w_size=80, epsilon=0.05, gamma=10**10)
k_swmebwin = Kernel_SWMEBWIN(classes=stream.target_values, w_size=80, epsilon=0.05)
# gamma maybe 1.0 / stream.current_sample_x.shape[1]
RANGE = 1000000
DIM = 50
# - 2 because first drift is at 2000 not 1000 and last drift is not detectable
# COUNT_DRIFTS = RANGE / 1000 - 2
n_rand_dims = DIM - stream.current_sample_x.size
multiply = n_rand_dims // stream.current_sample_x.size
# partial fit -> pretrain
for _m in range(multiply):
current_sample_x = np.array([[]])
current_sample_x = np.concatenate(
(current_sample_x, stream.current_sample_x), axis=1)
bayes = NaiveBayes()
bayes.partial_fit(np.array(current_sample_x), list(stream.current_sample_y.ravel()))
for j in range(DIM):
adwin.append(ADWIN(delta=0.002))
kswin.append(KSWIN(w_size=300, stat_size=30, alpha=0.0001))
"""Add dims"""
for i in range(RANGE):
current_sample_x = np.array([[]])
for _m in range(multiply):
current_sample_x = np.concatenate(
(current_sample_x, stream.current_sample_x), axis=1)
data.append(current_sample_x.ravel())
labels.append(stream.current_sample_y.ravel()[0])
predictions.append(0 if bayes.predict(current_sample_x) == labels[i] else 1)
bayes.partial_fit(current_sample_x, list(stream.current_sample_y.ravel()))
stream.next_sample()
# MEBWIN
# start = time.time()
# for i in range(RANGE):
# mebwin.add_element(data[i])
#
# if mebwin.change_detected is True:
# mebwin_drifts.append(i)
#
# f = open('drifts.txt', 'a+')
# f.write(f'MEBWIN detected {len(mebwin_drifts)} drifts in {time.time() - start} {mebwin_drifts}\n\n')
# f.close()
# print(f'MEBWIN took {time.time() - start} sec and detected {len(mebwin_drifts)} drifts')
# Kernel SWMEBWIN
start = time.time()
for i in range(RANGE):
k_swmebwin.add_element(value=data[i], label=labels[i])
if k_swmebwin.change_detected is True:
k_swmebwin_drifts.append(i)
end = time.time() - start
f1, tp, fp, tn, fn = confusion_matrix_stats(k_swmebwin_drifts, RANGE)
overall_k_swmebwin_tp += tp
overall_k_swmebwin_tn += tn
overall_k_swmebwin_fp += fp
overall_k_swmebwin_fn += fn
print(f'F1-Score: {f1}')
print(f'{tp} true positives, {fp} false positives')
print(f'{tn} true negatives, {fn} false negatives')
f = open('drifts.txt', 'a+')
f.write(f'K-SWMEB detected {len(k_swmebwin_drifts)} drifts in {time.time() - start} {k_swmebwin_drifts}\n\n')
f.close()
print(f'K-SW-MEBWIN took {end} sec and detected {len(k_swmebwin_drifts)} drifts\n')
# SWMEBWIN
start = time.time()
for i in range(RANGE):
swmebwin.add_element(value=data[i], label=labels[i])
if swmebwin.change_detected is True:
swmebwin_drifts.append(i)
end = time.time() - start
f1, tp, fp, tn, fn = confusion_matrix_stats(swmebwin_drifts, RANGE)
overall_swmebwin_tp += tp
overall_swmebwin_tn += tn
overall_swmebwin_fp += fp
overall_swmebwin_fn += fn
print(f'F1-Score: {f1}')
print(f'{tp} true positives, {fp} false positives')
print(f'{tn} true negatives, {fn} false negatives')
f = open('drifts.txt', 'a+')
f.write(f'SWMEB detected {len(swmebwin_drifts)} drifts in {time.time() - start} {swmebwin_drifts}\n\n')
f.close()
print(f'SW-MEBWIN took {end} sec and detected {len(swmebwin_drifts)} drifts\n')
# ADWIN
start = time.time()
for i in range(RANGE):
adwin_detected = False
for j in range(data[i].size):
adwin[j].add_element(data[i][j])
if adwin[j].detected_change():
adwin_detected = True
if adwin_detected is True:
adwin_drifts.append(i)
end = time.time() - start
f1, tp, fp, tn, fn = confusion_matrix_stats(adwin_drifts, RANGE)
overall_adwin_tp += tp
overall_adwin_tn += tn
overall_adwin_fp += fp
overall_adwin_fn += fn
print(f'F1-Score: {f1}')
print(f'{tp} true positives, {fp} false positives')
print(f'{tn} true negatives, {fn} false negatives')
f = open('drifts.txt', 'a+')
f.write(f'ADWIN detected {len(adwin_drifts)} drifts in {time.time() - start} at {adwin_drifts}\n\n')
f.close()
print(f'ADWIN took {end} sec and detected {len(adwin_drifts)} drifts\n')
# KSWIN
start = time.time()
for i in range(RANGE):
kswin_detected = False
for j in range(data[i].size):
kswin[j].add_element(data[i][j])
if kswin[j].detected_change():
kswin_detected = True
if kswin_detected is True:
kswin_drifts.append(i)
end = time.time() - start
f1, tp, fp, tn, fn = confusion_matrix_stats(kswin_drifts, RANGE)
overall_kswin_tp += tp
overall_kswin_tn += tn
overall_kswin_fp += fp
overall_kswin_fn += fn
print(f'F1-Score: {f1}')
print(f'{tp} true positives, {fp} false positives')
print(f'{tn} true negatives, {fn} false negatives')
f = open('drifts.txt', 'a+')
f.write(f'KSWIN detected {len(kswin_drifts)} drifts in {time.time() - start} at {kswin_drifts}\n\n')
f.close()
print(f'KSWIN took {end} sec and detected {len(kswin_drifts)} drifts\n')
# EDDM
start = time.time()
for i in range(RANGE):
eddm_detected = False
eddm.add_element(predictions[i])
if eddm.detected_change():
eddm_detected = True
if eddm_detected is True:
eddm_drifts.append(i)
end = time.time() - start
f1, tp, fp, tn, fn = confusion_matrix_stats(eddm_drifts, RANGE)
overall_eddm_tp += tp
overall_eddm_tn += tn
overall_eddm_fp += fp
overall_eddm_fn += fn
print(f'F1-Score: {f1}')
print(f'{tp} true positives, {fp} false positives')
print(f'{tn} true negatives, {fn} false negatives')
f = open('drifts.txt', 'a+')
f.write(f'EDDM detected {len(eddm_drifts)} drifts in {time.time() - start} at {eddm_drifts}\n\n')
f.close()
print(f'EDDM took {end} sec and detected {len(eddm_drifts)} drifts\n')
# DDM
start = time.time()
for i in range(RANGE):
ddm_detected = False
ddm.add_element(predictions[i])
if ddm.detected_change():
ddm_detected = True
if ddm_detected is True:
ddm_drifts.append(i)
end = time.time() - start
f1, tp, fp, tn, fn = confusion_matrix_stats(ddm_drifts, RANGE)
overall_ddm_tp += tp
overall_ddm_tn += tn
overall_ddm_fp += fp
overall_ddm_fn += tn
print(f'F1-Score: {f1}')
print(f'{tp} true positives, {fp} false positives')
print(f'{tn} true negatives, {fn} false negatives')
f = open('drifts.txt', 'a+')
f.write(f'DDM detected {len(ddm_drifts)} drifts in {time.time() - start} at {ddm_drifts}\n\n')
f.close()
print(f'DDM took {end} sec and detected {len(ddm_drifts)} drifts\n')
# OVERALL STATISTICS
print(50 * '-')
print('K-SWMEBWIN\n')
print(f'Overall F1: {calc_f1(overall_k_swmebwin_tp, overall_k_swmebwin_fp, overall_k_swmebwin_tn, overall_k_swmebwin_fn)}')
print(f'{overall_k_swmebwin_tp} true positives, {overall_k_swmebwin_fp} false positives')
print(f'{overall_k_swmebwin_tn} true negatives, {overall_k_swmebwin_fn} false negatives')
print(50* '-')
print(50 * '-')
print('SWMEBWIN\n')
print(f'Overall F1: {calc_f1(overall_swmebwin_tp, overall_swmebwin_fp, overall_swmebwin_tn, overall_swmebwin_fn)}')
print(f'{overall_swmebwin_tp} true positives, {overall_swmebwin_fp} false positives')
print(f'{overall_swmebwin_tn} true negatives, {overall_swmebwin_fn} false negatives')
print(50* '-')
print(50 * '-')
print('KSWIN\n')
print(f'Overall F1: {calc_f1(overall_kswin_tp, overall_kswin_fp, overall_kswin_tn, overall_kswin_fn)}')
print(f'{overall_kswin_tp} true positives, {overall_kswin_fp} false positives')
print(f'{overall_kswin_tn} true negatives, {overall_kswin_fn} false negatives')
print(50* '-')
print(50 * '-')
print('ADWIN\n')
print(f'Overall F1: {calc_f1(overall_adwin_tp, overall_adwin_fp, overall_adwin_tn, overall_adwin_fn)}')
print(f'{overall_adwin_tp} true positives, {overall_adwin_fp} false positives')
print(f'{overall_adwin_tn} true negatives, {overall_adwin_fn} false negatives')
print(50* '-')
print(50 * '-')
print('DDM\n')
print(f'Overall F1: {calc_f1(overall_ddm_tp, overall_ddm_fp, overall_ddm_tn, overall_ddm_fn)}')
print(f'{overall_ddm_tp} true positives, {overall_ddm_fp} false positives')
print(f'{overall_ddm_tn} true negatives, {overall_ddm_fn} false negatives')
print(50* '-')
print(50 * '-')
print('EDDM\n')
print(f'Overall F1: {calc_f1(overall_eddm_tp, overall_eddm_fp, overall_eddm_tn, overall_eddm_fn)}')
print(f'{overall_eddm_tp} true positives, {overall_eddm_fp} false positives')
print(f'{overall_eddm_tn} true negatives, {overall_eddm_fn} false negatives')
print(50* '-')
def partial_fit(self, X, y=None, classes=None, weight=None):
"""
Fit the ensemble to a data chunk
Implement the basic Algorithm 1 as described in the paper
:param X: the training data (a data chunk S)
:param y: the training labels
:param classes: array-like, contains all possible labels,
if not provided, it will be derived from y
:param weight: array-like, instance weight
if not provided, uniform weights are assumed
:return: self
"""
# if the classes are not provided, we derive it from y
N, D = X.shape
class_count = None # avoid calling unique multiple times
if classes is None:
classes, class_count = np.unique(y, return_counts=True)
# (1) train classifier C' from X
# allows a wider variety of classifiers
# not a lot but still...
if self.base_learner == "bayes": # Naive Bayes
C_new = NaiveBayes()
else: # by default, set to Hoeffding Tree
C_new = HoeffdingTree()
C_new.partial_fit(X, y, classes=classes)
# (2) compute error rate/benefit of C_new via cross-validation on S
# MSE_r: compute the baseline error rate given by a random classifier
# a. class distribution learnt from the data
# use this improve the performance
if class_count is None:
_, class_count = np.unique(classes, return_counts=True)
class_dist = [class_count[i] / N for i, c in enumerate(classes)]
MSE_r = np.sum([class_dist[i] * ((1 - class_dist[i]) ** 2) for i, c in enumerate(classes)])
# b. assumption: uniform distribution
# p_c = 1/L
# MSE_r = L * (p_c * ((1 - p_c) ** 2))
# MSE_i: compute the error rate of C_new via cross-validation on X
# f_ic = the probability given by C_new that x is an instance of class c
MSE_i = self.compute_MSE(y, C_new.predict_proba(X), classes)
# (3) derive weight w_new for C_new using (8) or (9)
w_new = MSE_r - MSE_i
# create a new classifier with its associated weight,
# the unique labels of the data chunk it is trained on
clf_new = self.WeightedClassifier(clf=C_new, weight=w_new, chunk_labels=classes)
# (4) update the weights of each classifier in the ensemble
for i, clf in enumerate(self.models):
MSE_i = self.compute_MSE(y, clf.clf.predict_proba(X), clf.chunk_labels) # apply Ci on S to derive MSE_i
clf.weights = MSE_r - MSE_i # update wi based on (8) or (9)
# (5) C <- top K weighted classifiers in C U { C' }
# selecting top K models by dropping the worst model i.e. clf with smallest weight in C U { C' }
if len(self.models) < self.K:
# just push the new model in if there is still slots
hq.heappush(self.models, clf_new)
else:
# if the new model has a weight > that of the bottom classifier (worst one)
if clf_new.weight > self.models[0].weight:
hq.heappushpop(self.models, clf_new) # push the new classifier and remove the bottom one
# do nothing if the new model has a weight even lower than that of the worst classifier
return self
