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")Result
Window Size = 300
GaussianNB: Mean acc within the window 300: 73.22457902031908
AdWin: Drift detection: 168 Mean acc within the window 300: 74.65794479341767