AdWin: ADaptive sliding WINdow algorithm
Based on paper:
Bifet and R. Gavalda. 2007. Learning from Time-Changing Data with Adaptive Windowing
class concept_drift.adwin.AdWin(
delta=0.002, max_buckets=5, min_clock=32, min_win_len=10, min_sub_win_len=5
)| Parameters | |
|---|---|
| delta | Confidence value |
| max_buckets | Max number of buckets within one bucket row |
| min_clock | Min number of new data for starting to reduce window and detect change |
| min_window_len | Min window length for starting to reduce window and detect change |
| min_sub_window_len | Min sub-window length, which is split from whole window |
Methods
set_input(value)Set input value to the drift detector - ADWIN.
| Type | Input - Output |
|---|---|
| Parameters | value: Input value |
| Return | Boolean: Whether has drift |
Example
from concept_drift.adwin import AdWin
adwin = AdWin()
for i in range(1000):
if adwin.set_input(i):
print("Here is a drift")Page-Hinckley Test
Based on Chapter 5.1:
Gama, J., Sebastião, R., & Rodrigues, P. P. (2013). On evaluating stream learning algorithms
class concept_drift.page_hinkley.PageHinkley(
delta_=0.005, lambda_=50, alpha_=1 - 0.0001
)| Parameters | |
|---|---|
| delta_ | Magnitude of changes that are allowed |
| lambda_ | Difference threshold |
| alpha_ | Adaption variable for lambda adjustment |
Methods
set_input(value)Set input value to the drift detector - PageHinkley.
| Type | Input - Output |
|---|---|
| Parameters | value: Input value |
| Return | Boolean: Whether has drift |
Test
Used the elecNormNew dataset;
Used GaussianNB as based classification;
Used accuracy_score as input date for change detector;
Used the prequential evaluation;
GaussianNB:
Mean acc within the window 1000: 0.7289912189511405
Page-Hinkley:
Drift detection: 8
Mean acc within the window 1000: 0.7342849306584686
AdWin:
Drift detection: 139
Mean acc within the window 1000: 0.7496421003738032