DriftDetectionMethod
Description
DriftDetectionMethod model object represents an incremental
concept drift detector that uses the Drift Detection Method [1]. After creating the object,
you can use the detectdrift object
function to update the statistics and check for any drift in the concept data (for example,
failure rate, regression loss, and so on).
DriftDetectionMethod is suitable for incremental concept drift detection. For
drift detection on raw data, see detectdrift for
batch drift detection.
Creation
You can create DriftDetectionMethod by specifying the
DetectionMethod argument as "ddm" in the call to
incrementalConceptDriftDetector.
Properties
Object Functions
detectdrift | Update drift detector states and drift status with new data |
reset | Reset incremental concept drift detector |
Examples
Initiate the concept drift detector using the Drift Detection Method (DDM).
incCDDetector = incrementalConceptDriftDetector("ddm");Create a random stream such that for the first 1000 observations, failure rate is 0.1 and after 1000 observations, failure rate increases to 0.6.
rng(1234) % For reproducibility numObservations = 3000; switchPeriod = 1000; for i = 1:numObservations if i <= switchPeriod failurerate = 0.1; else failurerate = 0.6; end X(i) = rand()<failurerate; % Value 1 represents failure end
Preallocate variables for tracking drift status.
status = zeros(numObservations,1); statusname = strings(numObservations,1);
Continuously feed the data to the drift detector and perform incremental drift detection. At each iteration:
Update statistics of the drift detector and monitor for drift using the new data point with
detectdrift. (Note:detectdriftchecks for drift after the warm-up period.)Track and record the drift status for visualization purposes.
When a drift is detected, reset the incremental concept drift detector by using
reset.
for i = 1:numObservations incCDDetector = detectdrift(incCDDetector,X(i)); statusname(i) = string(incCDDetector.DriftStatus); if incCDDetector.DriftDetected status(i) = 2; incCDDetector = reset(incCDDetector); % If drift detected, reset the detector sprintf("Drift detected at Observation #%d. Detector reset.",i) elseif incCDDetector.WarningDetected status(i) = 1; else status(i) = 0; end end
ans = "Drift detected at Observation #1078. Detector reset."
After the change in the failure rate at observation number 1000, detectdrift detects the shift at observation number 1078.
Plot the drift status versus the observation number.
gscatter(1:numObservations,status,statusname,'gyr','*',4,'on',"Observation number","Drift status")
Initiate the concept drift detector using the Drift Detection Method (DDM).
incCDDetector = incrementalConceptDriftDetector("ddm",Alternative="less",WarmupPeriod=100);
Create a random stream such that for the first 1000 observations, failure rate is 0.4 and after 1000 failure rate decreases to 0.1.
rng(1234) % For reproducibility numObservations = 3000; switchPeriod = 1000; for i = 1:numObservations if i <= switchPeriod failurerate = 0.4; else failurerate = 0.125; end X(i) = rand()<failurerate; % Value 1 represents failure end
Preallocate variables for tracking drift status and the optimal mean and optimal standard deviation value.
optmean = zeros(numObservations,1); optstddev = zeros(numObservations,1); status = zeros(numObservations,1); statusname = strings(numObservations,1);
Continuously feed the data to the drift detector and monitor for any potential change. Record the drift status for visualization purposes.
for i = 1:numObservations incCDDetector = detectdrift(incCDDetector,X(i)); statusname(i) = string(incCDDetector.DriftStatus); optmean(i) = incCDDetector.OptimalMean; optstddev(i) = incCDDetector.OptimalStandardDeviation; if incCDDetector.DriftDetected status(i) = 2; incCDDetector = reset(incCDDetector); % If drift detected, reset the detector sprintf("Drift detected at Observation #%d. Detector reset.",i) elseif incCDDetector.WarningDetected status(i) = 1; else status(i) = 0; end end
ans = "Drift detected at Observation #1107. Detector reset."
After the change in the failure rate at observation number 1000, detectdrift detects the shift at observation number 1096.
Plot the change in the optimal mean and optimal standard deviation.
tiledlayout(2,1); ax1 = nexttile; plot(ax1,1:numObservations,optmean) ax2 = nexttile; plot(ax2,1:numObservations,optstddev)
Plot the drift status versus the observation number.
figure(); gscatter(1:numObservations,status,statusname,'gyr','*',4,'on',"Observation number","Drift status")
detectdrift concludes on a warning status for multiple observations before it decides on a drift.
References
[1] Gama, Joao, Pedro Medas, Gladys Castillo, and Pedro P. Rodrigues. “Learning with drift detection.“ In Brazilian symposium on artificial intelligence, pp. 286-295. Berlin, Heidelberg: Springer. 2004, September.
Version History
Introduced in R2022a
