def test_ddm():
"""
DDM 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.
"""
ddm = DDM()
# Data
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 = [103, 1060]
detected_indices = []
for i in range(data_stream.size):
ddm.update(data_stream[i])
if ddm.change_detected:
detected_indices.append(i)
assert detected_indices == expected_indices
# DDM
import numpy as np
from river.drift import DDM
np.random.seed(12345)
ddm = DDM()
# Simulate a data stream as a normal distribution of 1's and 0's
data_stream = np.random.randint(2, size=2000)
# Change the data distribution from index 999 to 1500, simulating an
# increase in error rate (1 indicates error)
data_stream[1000:1200] = [np.random.binomial(1, .8) for _ in range(200)]
# Update drift detector and verify if change is detected
for i, val in enumerate(data_stream):
in_drift, in_warning = ddm.update(val)
if in_drift:
print(f"Change detected at index {i}, input value: {val}")
# Change detected at index 1157, input value: 1