FHDDM¶
Fast Hoeffding Drift Detection Method.
FHDDM is a drift detection method based on the Hoeffding's inequality which uses the input average as estimator.
Input: x is an entry in a stream of bits, where 1 indicates error/failure and 0 represents correct/normal values.
For example, if a classifier's prediction \(y'\) is right or wrong w.r.t. the true target label \(y\):
-
0: Correct, \(y=y'\)
-
1: Error, \(y \neq y'\)
Implementation based on MOA.
Parameters¶
-
sliding_window_size
Type → int
Default →
100The minimum required number of analyzed samples so change can be detected.
-
confidence_level
Type → float
Default →
1e-06Confidence level used to determine the epsilon coefficient in Hoeffding’s inequality. The default value gives a 99\% of confidence level to the drift assessment.
-
short_window_size
Type → int | None
Default →
NoneThe size of the short window size that it is used in a Stacking version of FHDDM 2.
Attributes¶
-
drift_detected
Whether or not a drift is detected following the last update.
-
warning_detected
Whether or not a drift is detected following the last update.
Examples¶
import random
from river import drift
rng = random.Random(42)
fhddm = drift.binary.FHDDM()
fhddm_s = drift.binary.FHDDM(short_window_size = 20)
data_stream = rng.choices([0, 1], k=250)
data_stream = data_stream + rng.choices([0, 1], k=250, weights=[0.9, 0.1])
for i, x in enumerate(data_stream):
fhddm.update(x)
fhddm_s.update(x)
if fhddm.drift_detected or fhddm_s.drift_detected:
print(f"Change detected at index {i}")
Change detected at index 279
Change detected at index 315
Methods¶
update
Update the detector with a single boolean input.
Parameters
- x — 'bool'