def test_eddm():
"""
EDDM drift detection test.
The first half of the data contains a sequence corresponding to a normal distribution with mean 0 and sigma 0.1.
The second half corresponds to a normal distribution with mean 0.5 and sigma 0.1.
"""
eddm = EDDM()
np.random.seed(1)
mu, sigma = 0, 0.1 # mean and standard deviation
d_1 = np.random.normal(mu, sigma, 1000) > 0
mu, sigma = 0.5, 0.1 # mean and standard deviation
d_2 = np.random.normal(mu, sigma, 1000) > 0
data_stream = np.concatenate((d_1.astype(int), d_2.astype(int)))
expected_indices = [64, 1135]
detected_indices = []
for i in range(data_stream.size):
eddm.add_element(data_stream[i])
if eddm.detected_change():
detected_indices.append(i)
assert detected_indices == expected_indices
expected_info = "EDDM()"
assert eddm.get_info() == expected_info
def sim_eddm(input_stream, start_point=0):
eddm = EDDM()
change_point = []
detected_warning = []
for i in range(len(input_stream)):
eddm.add_element(input_stream[i])
if eddm.detected_warning_zone():
detected_warning.append(i + start_point)
if eddm.detected_change():
# plt.axvline(i, color='r', linestyle='dashed')
change_point.append(i + start_point)
# print('Change detected in data: ' + str(input_stream[i]) + ' - at index: ' + str(i)+'\n\n')
return detected_warning, change_point
def test_eddm(test_path):
"""
EDDM drift detection test.
The first half of the stream contains a sequence corresponding to a normal distribution of integers from 0 to 1.
From index 999 to 1999 the sequence is a normal distribution of integers from 0 to 7.
"""
eddm = EDDM()
test_file = os.path.join(test_path, 'drift_stream.npy')
data_stream = np.load(test_file)
expected_indices = [51, 129, 291, 337, 396, 456, 523, 581, 675, 730, 851]
detected_indices = []
for i in range(data_stream.size):
eddm.add_element(data_stream[i])
if eddm.detected_change():
detected_indices.append(i)
assert detected_indices == expected_indices
eddm = EDDM()
# Setting up the stream
stream = SineGenerator(classification_function=0,
random_state=112,
balance_classes=True,
has_noise=True)
# stream = ConceptDriftStream(random_state=123456, position=25000)
# Retrieving one sample
stream.generate_drift()
data = stream.next_sample(2000)
list_data = []
change_point = []
print(data[0].size)
j = 0
for i in range(data[0].size):
list_data.append(data[0].item(i))
eddm.add_element(data[0].item(i))
if eddm.detected_change():
change_point(j)
j = j + 1
plt.plot(list_data)
for i in change_point:
plt.axvline(i, color='black', linestyle='-', linewidth=0.1)
fig = plt.gcf()
fig.set_size_inches(20, 5.5)
plt.ylabel('value')
plt.xlabel('Time')
plt.show()