HoeffdingDriftDetectionMethod
Incremental concept drift detector that utilizes Hoeffding's Bounds Drift Detection Method (HDDM)
Since R2022a
Description
HoeffdingDriftDetectionMethod model object represents an incremental
concept drift detector that uses the Hoeffding's Bounds nonparametric drift detection methods
based on moving averages (A-test) or exponentially weighted moving averages (W-test) [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).
HoeffdingDriftDetectionMethod is suitable for incremental concept drift detection. For
drift detection on raw data, see detectdrift for
batch drift detection.
Creation
You can create HoeffdingDriftDetectionMethod by specifying the
DetectionMethod argument as "hddma" or
"hddmw" 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
Create a random stream such that the observations come from a normal distribution with standard deviation 0.75, but the mean changes over time. First 1000 observations come from a distribution with mean 2, the next 1000 come from a distribution with mean 4, and the following 1000 come from a distribution with mean 7.
rng(1234) % For reproducibility numObservations = 3000; switchPeriod1 = 1000; switchPeriod2 = 2000; X = zeros([numObservations 1]); % Generate the data for i = 1:numObservations if i <= switchPeriod1 X(i) = normrnd(2,0.75); elseif i <= switchPeriod2 X(i) = normrnd(4,0.75); else X(i) = normrnd(7,0.75); end end
In an incremental drift detection application, access to data stream and model update would happen consecutively. One would not collect the data first and then feed into the model. However, for the purpose of clarification, this example demonstrates the simulation of data separately.
Specify the drift warm-up period as 50 observations and estimation period for the data input bounds as 100.
driftWarmupPeriod = 50; estimationPeriod = 100;
Initiate the incremental concept drift detector. Utilize the Hoeffding's bounds method with exponentially weighted moving average method (EWMA). Specify the input type and warm-up period.
incCDDetector = incrementalConceptDriftDetector("hddmw",InputType="continuous", ... WarmupPeriod=driftWarmupPeriod,EstimationPeriod=estimationPeriod)
incCDDetector =
HoeffdingDriftDetectionMethod
PreviousDriftStatus: 'Stable'
DriftStatus: 'Stable'
IsWarm: 0
NumTrainingObservations: 0
Alternative: 'greater'
InputType: 'continuous'
TestMethod: 'ewma'
Properties, Methods
incDDetector is a HoeffdingDriftDetectionMethod object. When you first create the object, properties such as DriftStatus, IsWarm, CutMean, and NumTrainingObservations are at their initial state. detectdrift updates them as you feed the data incrementally and monitor for drift.
Preallocate the batch size and the variables to record drift status and the mean the drift detector computes with each income of data.
status = zeros([numObservations 1]); statusname = strings([numObservations 1]); M = zeros([numObservations 1]);
Simulate the data stream of one observation at a time and perform incremental drift detection. At each iteration:
Monitor for drift using the new data with
detectdrift.Track and record the drift status and the statistics for visualization purposes.
When a drift is detected, reset the incremental concept drift detector by using the function
reset.
for i = 1:numObservations incCDDetector = detectdrift(incCDDetector,X(i)); M(i) = incCDDetector.Mean; if incCDDetector.DriftDetected status(i) = 2; statusname(i) = string(incCDDetector.DriftStatus); incCDDetector = reset(incCDDetector); % If drift detected, reset the detector sprintf("Drift detected at observation #%d. Detector reset.",i) elseif incCDDetector.WarningDetected status(i) = 1; statusname(i) = string(incCDDetector.DriftStatus); sprintf("Warning detected at observation #%d.",i) else status(i) = 0; statusname(i) = string(incCDDetector.DriftStatus); end end
ans = "Warning detected at observation #1024."
ans = "Warning detected at observation #1025."
ans = "Warning detected at observation #1026."
ans = "Warning detected at observation #1027."
ans = "Warning detected at observation #1028."
ans = "Warning detected at observation #1029."
ans = "Drift detected at observation #1030. Detector reset."
ans = "Warning detected at observation #2012."
ans = "Warning detected at observation #2013."
ans = "Warning detected at observation #2014."
ans = "Drift detected at observation #2015. Detector reset."
Plot the drift status versus the observation number.
gscatter(1:numObservations,status,statusname,'gyr','*',5,'on',"Number of observations","Drift status")
Plot the mean values versus the number of observations.
scatter(1:numObservations,M)
You can see the increase in the sample mean from the plot. The mean value becomes larger and the software eventually detects the drift in the data. Once a drift is detected, reset the incremental drift detector. This also resets the mean value. In the plot, the observations where the sample mean is zero correspond to the estimation periods. There is an estimation period at the beginning and then twice after the drift detector is reset following the detection of a drift.
References
[1] Frias-Blanco, Isvani, Jose del Campo-Ávila, Ramos-Jimenez Gonzalo, Rafael Morales-Bueno, Augustin Ortiz-Diaz, and Yaile Caballero-Mota. “Online and non-parametric drift detection methods based on Hoeffding's bounds.“ IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 3, pp.810-823. 2014.
Version History
Introduced in R2022a
See Also
incrementalConceptDriftDetector | DriftDetectionMethod | detectdrift | reset
